[ { "date_posted": "2023-06-06", "project_name": "Energy Data", "project_source": [ "https://github.com/owid/energy-data/tree/master", "https://github.com/owid/energy-data", "https://ourworldindata.org/about", "https://ourworldindata.org/explorers/energy" ], "description": "The data this week comes from Our World in Data'sEnergy Data Explorer. Complete dataset available viahttps://github.com/owid/energy-data. The complete Energy dataset is a collection of key metrics maintained by Our World in Data. It is updated regularly and includes data on energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix and other relevant metrics. This data has been collected, aggregated, and documented by Hannah Ritchie, Pablo Rosado, Edouard Mathieu, Max Roser. Our World in Datamakes data and research on the world's largest problems understandable and accessible. Data cleaning was done byOur World in Data", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-06", "data_dictionary": [ { "variable": [ "country", "year", "iso_code", "population", "gdp", "biofuel_cons_change_pct", "biofuel_cons_change_twh", "biofuel_cons_per_capita", "biofuel_consumption", "biofuel_elec_per_capita", "biofuel_electricity", "biofuel_share_elec", "biofuel_share_energy", "carbon_intensity_elec", "coal_cons_change_pct", "coal_cons_change_twh", "coal_cons_per_capita", "coal_consumption", "coal_elec_per_capita", "coal_electricity", "coal_prod_change_pct", "coal_prod_change_twh", "coal_prod_per_capita", "coal_production", "coal_share_elec", "coal_share_energy", "electricity_demand", "electricity_generation", "electricity_share_energy", "energy_cons_change_pct", "energy_cons_change_twh", "energy_per_capita", "energy_per_gdp", "fossil_cons_change_pct", "fossil_cons_change_twh", "fossil_elec_per_capita", "fossil_electricity", "fossil_energy_per_capita", "fossil_fuel_consumption", "fossil_share_elec", "fossil_share_energy", "gas_cons_change_pct", "gas_cons_change_twh", "gas_consumption", "gas_elec_per_capita", "gas_electricity", "gas_energy_per_capita", "gas_prod_change_pct", "gas_prod_change_twh", "gas_prod_per_capita", "gas_production", "gas_share_elec", "gas_share_energy", "greenhouse_gas_emissions", "hydro_cons_change_pct", "hydro_cons_change_twh", "hydro_consumption", "hydro_elec_per_capita", "hydro_electricity", "hydro_energy_per_capita", "hydro_share_elec", "hydro_share_energy", "low_carbon_cons_change_pct", "low_carbon_cons_change_twh", "low_carbon_consumption", "low_carbon_elec_per_capita", "low_carbon_electricity", "low_carbon_energy_per_capita", "low_carbon_share_elec", "low_carbon_share_energy", "net_elec_imports", "net_elec_imports_share_demand", "nuclear_cons_change_pct", "nuclear_cons_change_twh", "nuclear_consumption", "nuclear_elec_per_capita", "nuclear_electricity", "nuclear_energy_per_capita", "nuclear_share_elec", "nuclear_share_energy", "oil_cons_change_pct", "oil_cons_change_twh", "oil_consumption", "oil_elec_per_capita", "oil_electricity", "oil_energy_per_capita", "oil_prod_change_pct", "oil_prod_change_twh", "oil_prod_per_capita", "oil_production", "oil_share_elec", "oil_share_energy", "other_renewable_consumption", "other_renewable_electricity", "other_renewable_exc_biofuel_electricity", "other_renewables_cons_change_pct", "other_renewables_cons_change_twh", "other_renewables_elec_per_capita", "other_renewables_elec_per_capita_exc_biofuel", "other_renewables_energy_per_capita", "other_renewables_share_elec", "other_renewables_share_elec_exc_biofuel", "other_renewables_share_energy", "per_capita_electricity", "primary_energy_consumption", "renewables_cons_change_pct", "renewables_cons_change_twh", "renewables_consumption", "renewables_elec_per_capita", "renewables_electricity", "renewables_energy_per_capita", "renewables_share_elec", "renewables_share_energy", "solar_cons_change_pct", "solar_cons_change_twh", "solar_consumption", "solar_elec_per_capita", "solar_electricity", "solar_energy_per_capita", "solar_share_elec", "solar_share_energy", "wind_cons_change_pct", "wind_cons_change_twh", "wind_consumption", "wind_elec_per_capita", "wind_electricity", "wind_energy_per_capita", "wind_share_elec", "wind_share_energy" ], "class": [ "character", "double", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "Geographic location", "Year of observation", "ISO 3166-1 alpha-3 three-letter country codes", "Population", "Total real gross domestic product, inflation-adjusted", "Annual percentage change in biofuel consumption", "Annual change in biofuel consumption, measured in terawatt-hours", "Per capita primary energy consumption from biofuels, measured in kilowatt-hours", "Primary energy consumption from biofuels, measured in terawatt-hours", "Per capita electricity generation from biofuels, measured in kilowatt-hours", "Electricity generation from biofuels, measured in terawatt-hours", "Share of electricity generation that comes from biofuels", "Share of primary energy consumption that comes from biofuels", "Carbon intensity of electricity production, measured in grams of carbon dioxide emitted per kilowatt-hour", "Annual percentage change in coal consumption", "Annual change in coal consumption, measured in terawatt-hours", "Per capita primary energy consumption from coal, measured in kilowatt-hours", "Primary energy consumption from coal, measured in terawatt-hours", "Per capita electricity generation from coal, measured in kilowatt-hours", "Electricity generation from coal, measured in terawatt-hours", "Annual percentage change in coal production", "Annual change in coal production, measured in terawatt-hours", "Per capita coal production, measured in kilowatt-hours", "Coal production, measured in terawatt-hours", "Share of electricity generation that comes from coal", "hare of primary energy consumption that comes from coal", "Electricity demand, measured in terawatt-hours", "Electricity generation, measured in terawatt-hours", "Electricity generation as a share of primary energy", "Annual percentage change in primary energy consumption", "Annual change in primary energy consumption, measured in terawatt-hours", "Primary energy consumption per capita, measured in kilowatt-hours", "Energy consumption per unit of GDP. This is measured in kilowatt-hours per 2011 international-$", "Annual percentage change in fossil fuel consumption", "Annual change in fossil fuel consumption, measured in terawatt-hours", "Per capita electricity generation from fossil fuels, measured in kilowatt-hours. This is the sum of electricity generated from coal, oil and gas.", "Electricity generation from fossil fuels, measured in terawatt-hours. This is the sum of electricity generation from coal, oil and gas.", "Per capita fossil fuel consumption, measured in kilowatt-hours. This is the sum of primary energy from coal, oil and gas.", "Fossil fuel consumption, measured in terawatt-hours. This is the sum of primary energy from coal, oil and gas.", "Share of electricity generation that comes from fossil fuels (coal, oil and gas combined)", "Share of primary energy consumption that comes from fossil fuels", "Annual percentage change in gas consumption", "Annual change in gas consumption, measured in terawatt-hours", "Primary energy consumption from gas, measured in terawatt-hours", "Per capita electricity generation from gas, measured in kilowatt-hours", "Electricity generation from gas, measured in terawatt-hours", "Per capita primary energy consumption from gas, measured in kilowatt-hours", "Annual percentage change in gas production", "Annual change in gas production, measured in terawatt-hours", "Per capita gas production, measured in kilowatt-hours", "Gas production, measured in terawatt-hours", "Share of electricity generation that comes from gas", "Share of primary energy consumption that comes from gas", "Greenhouse-gas emissions produced in the generation of electricity, measured in million tonnes of CO2 equivalent", "Annual percentage change in hydropower consumption", "Annual change in hydropower consumption, measured in terawatt-hours", "Primary energy consumption from hydropower, measured in terawatt-hours", "Per capita electricity generation from hydropower, measured in kilowatt-hours", "Electricity generation from hydropower, measured in terawatt-hours", "Per capita primary energy consumption from hydropower, measured in kilowatt-hours", "Share of electricity generation that comes from hydropower", "Share of primary energy consumption that comes from hydropower", "Annual percentage change in low-carbon energy consumption", "Annual change in low-carbon energy consumption, measured in terawatt-hours", "Primary energy consumption from low-carbon sources, measured in terawatt-hours", "Per capita electricity generation from low-carbon sources, measured in kilowatt-hours", "Electricity generation from low-carbon sources, measured in terawatt-hours. This is the sum of electricity generation from renewables and nuclear power", "Per capita primary energy consumption from low-carbon sources, measured in kilowatt-hours", "Share of electricity generation that comes from low-carbon sources. This is the sum of electricity from renewables and nuclear", "Share of primary energy consumption that comes from low-carbon sources. This is the sum of primary energy from renewables and nuclear", "Net electricity imports, measured in terawatt-hours", "Net electricity imports as a share of electricity demand", "Annual percentage change in nuclear consumption", "Annual change in nuclear consumption, measured in terawatt-hours", "Primary energy consumption from nuclear power, measured in terawatt-hours", "Per capita electricity generation from nuclear power, measured in kilowatt-hours", "Electricity generation from nuclear power, measured in terawatt-hours", "Per capita primary energy consumption from nuclear, measured in kilowatt-hours", "Share of electricity generation that comes from nuclear power", "Share of primary energy consumption that comes from nuclear power", "Annual percentage change in oil consumption", "Annual change in oil consumption, measured in terawatt-hours", "Primary energy consumption from oil, measured in terawatt-hours", "Per capita electricity generation from oil, measured in kilowatt-hours", "Electricity generation from oil, measured in terawatt-hours", "Per capita primary energy consumption from oil, measured in kilowatt-hours", "Annual percentage change in oil production", "Annual change in oil production, measured in terawatt-hours", "Per capita oil production, measured in kilowatt-hours", "Oil production, measured in terawatt-hours", "Share of electricity generation that comes from oil", "Share of primary energy consumption that comes from oil", "Primary energy consumption from other renewables, measured in terawatt-hours", "Electricity generation from other renewable sources including biofuels, measured in terawatt-hours", "Electricity generation from other renewable sources excluding biofuels, measured in terawatt-hours", "Annual percentage change in energy consumption from other renewables", "Annual change in other renewable consumption, measured in terawatt-hours", "Per capita electricity generation from other renewables including biofuels, measured in kilowatt-hours", "Per capita electricity generation from other renewables excluding biofuels, measured in kilowatt-hours", "Per capita primary energy consumption from other renewables, measured in kilowatt-hours", "Share of electricity generation that comes from other renewables including biofuels", "Share of electricity generation that comes from other renewables excluding biofuels", "Share of primary energy consumption that comes from other renewables", "Electricity generation per capita, measured in kilowatt-hours", "Primary energy consumption, measured in terawatt-hours", "Annual percentage change in renewable energy consumption", "Annual change in renewable energy consumption, measured in terawatt-hours", "Primary energy consumption from renewables, measured in terawatt-hours", "Per capita electricity generation from renewables, measured in kilowatt-hours", "Electricity generation from renewables, measured in terawatt-hours", "Per capita primary energy consumption from renewables, measured in kilowatt-hours", "Share of electricity generation that comes from renewables", "Share of primary energy consumption that comes from renewables", "Annual percentage change in solar consumption", "Annual change in solar consumption, measured in terawatt-hours", "Primary energy consumption from solar, measured in terawatt-hours", "Per capita electricity generation from solar, measured in kilowatt-hours", "Electricity generation from solar, measured in terawatt-hours", "Per capita primary energy consumption from solar, measured in kilowatt-hours", "Share of electricity generation that comes from solar", "Share of primary energy consumption that comes from solar", "Annual percentage change in wind consumption", "Annual change in wind consumption", "Primary energy consumption from wind, measured in terawatt-hours", "Per capita electricity generation from wind, measured in kilowatt-hours", "Electricity generation from wind, measured in terawatt-hours", "Per capita primary energy consumption from wind, measured in kilowatt-hours", "Share of electricity generation that comes from wind", "Share of primary energy consumption that comes from wind" ] } ], "data": { "file_name": [ "owid-energy.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-06/owid-energy.csv" ] } }, { "date_posted": "2023-02-14", "project_name": "Hollywood Age Gaps", "project_source": [ "https://www.data-is-plural.com/archive/2018-02-07-edition/", "https://tidytues.day/2021/2021-03-09", "https://hollywoodagegap.com/" ], "description": "The data this week comes fromHollywood Age GapviaData Is Plural. An informational site showing the age gap between movie love interests. The data follows certain rules: The two (or more) actors play actual love interests (not just friends, coworkers, or some other non-romantic type of relationship) The youngest of the two actors is at least 17 years old Not animated characters We previously provided a dataset about theBechdel Test. It might be interesting to see whether there is any correlation between these datasets! The Bechdel Test dataset also included additional information about the films that were used in that dataset. Note: The age gaps dataset includes \"gender\" columns, which always contain the values \"man\" or \"woman\". These values appear to indicate how thecharactersin each film identify. Some of these values do not match how theactoridentifies. We apologize if any characters are misgendered in the data!", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-14", "data_dictionary": [ { "variable": [ "movie_name", "release_year", "director", "age_difference", "couple_number", "actor_1_name", "actor_2_name", "character_1_gender", "character_2_gender", "actor_1_birthdate", "actor_2_birthdate", "actor_1_age", "actor_2_age" ], "class": [ "character", "integer", "character", "integer", "integer", "character", "character", "character", "character", "date", "date", "integer", "integer" ], "description": [ "Name of the film", "Release year", "Director of the film", "Age difference between the characters in whole years", "An identifier for the couple in case multiple couples are listed for this film", "The name of the older actor in this couple", "The name of the younger actor in this couple", "The gender of the older character, as identified by the person who submitted the data for this couple", "The gender of the younger character, as identified by the person who submitted the data for this couple", "The birthdate of the older member of the couple", "The birthdate of the younger member of the couple", "The age of the older actor when the film was released", "The age of the younger actor when the film was released" ] } ], "data": { "file_name": [ "age_gaps.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-14/age_gaps.csv" ] } }, { "date_posted": "2023-09-12", "project_name": "The Global Human Day", "project_source": [ "https://www.pnas.org/doi/10.1073/pnas.2219564120#sec-2", "https://www.pnas.org/doi/10.1073/pnas.2219564120#supplementary-materials", "https://www.humanchronome.org/", "https://zenodo.org/record/8040631", "https://www.data-is-plural.com/archive/2023-07-12-edition/" ], "description": "The data this week comes from theThe Human Chronome Projectan initiative based at McGill University in Montreal, from their paperThe global human day in PNASand theassociated dataset on Zenodo. The daily activities of ≈8 billion people occupy exactly 24 h per day, placing a strict physical limit on what changes can be achieved in the world. These activities form the basis of human behavior, and because of the global integration of societies and economies, many of these activities interact across national borders. This project estimates how all humans spend their time using a generalized, physical outcome–based categorization that facilitates the integration of data from hundreds of diverse datasets. See theirsupplementary materialsfor details about their methods and additional visualizations. TheZenodo datasetincludes the input data and scripts used to create the datasets used in the paper. The datasets are from the outputData file \"all_countries.csv\", \"global_human_day.csv\", \"global_economic_activity.csv\" and inputData \"country_regions.csv\". The outputData files are aggregated output data from data collected, created from the scripts in the 'scripts' directory. h/tData is Plural 2023-07-13 newsletterfor the dataset.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-12", "data_dictionary": [ { "variable": [ "Category", "Subcategory", "country_iso3", "region_code", "population", "hoursPerDayCombined", "uncertaintyCombined" ], "class": [ "character", "character", "character", "character", "double", "double", "double" ], "description": [ "M24 categories", "M24 subcategories", "Country code in iso3", "Region code", "Population", "Hours per day combined for the country", "Uncertainty combined. Uncertainty is in units variance." ] }, { "variable": [ "region_code", "region_name", "country_name", "M49_code", "country_iso2", "country_iso3", "alt_country_name", "alt_country_name1", "alt_country_name2", "alt_country_name3", "alt_country_name4", "alt_country_name5", "alt_country_name6", "other_code1", "other_code2" ], "class": [ "character", "character", "character", "double", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character" ], "description": [ "Region code", "Region name", "Country name", "M49 code", "Country code in iso2", "Country code in iso3", "Alternative country name", "Alternative country name 1", "Alternative country name 2", "Alternative country name 3", "Alternative country name 4", "Alternative country name 5", "Alternative country name 6", "Other country code 1", "Other country code 2" ] }, { "variable": [ "Subcategory", "hoursPerDay", "uncertainty" ], "class": [ "character", "double", "double" ], "description": [ "M24 subcategory", "Hours per day for all countries", "Uncertainty in units variance." ] }, { "variable": [ "Subcategory", "hoursPerDay", "uncertainty" ], "class": [ "character", "double", "double" ], "description": [ "M24 subcategory", "Hours per day for all countries.", "Uncertainty in units variance." ] } ], "data": { "file_name": [ "all_countries.csv", "country_regions.csv", "global_economic_activity.csv", "global_human_day.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-12/all_countries.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-12/country_regions.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-12/global_economic_activity.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-12/global_human_day.csv" ] } }, { "date_posted": "2023-09-26", "project_name": "Roy Kent F**k count", "project_source": [ "https://deepshamenghani.github.io/posit_plotly_crosstalk/#/title-slide", "https://en.wikipedia.org/wiki/Ted_Lasso", "https://github.com/deepshamenghani/richmondway", "https://ted-lasso.fandom.com/wiki/Roy_Kent" ], "description": "For Deepsha Menghani's talk onData Viz animation and interactivity in Quarto, she watched each episode of Ted Lasso at 2X speed and diligently noted down every F*bomb and gesture reference, and then made it into therichmondway R package! What is Ted Lasso and who is Roy Kent? Ted Lassois a TV show that \"follows Ted Lasso, an American college football coach who is hired to coach an English soccer team with the secret intention that his inexperience will lead it to failure, but whose folksy, optimistic leadership proves unexpectedly successful.\" Roy Kentis one of the main characters who goes from captain of AFC Richmond to one of the coaches. Particularly in early seasons, he's a man of few words, but many of them are f**k, expressed in various moods - mad, sad, happy, amused, loving, surprised, thoughtful, and joyous. This dataset includes the number, percentage, and context of f**k used in the show for each episode. Source Created by Deepsha Menghani by watching the show and counting the number of F-cks used in sentences and as gestures. The data was collected and created as an R package by Deepsha Menghani.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-26", "data_dictionary": [ { "variable": [ "Character", "Episode_order", "Season", "Episode", "Season_Episode", "F_count_RK", "F_count_total", "cum_rk_season", "cum_total_season", "cum_rk_overall", "cum_total_overall", "F_score", "F_perc", "Dating_flag", "Coaching_flag", "Imdb_rating" ], "class": [ "character", "double", "double", "double", "character", "double", "double", "double", "double", "double", "double", "double", "double", "character", "character", "double" ], "description": [ "Character single value - Roy Kent", "The order of the episodes from the first to the last", "The season 1, 2 or 3 associated with the count", "The episode within the season associated with the count", "Season and episode as a combined variable", "Roy Kent's F-ck count in that season and episode", "Total F-ck count by all characters combined including Roy Kent in that season and episode", "Roy Kent's cumulative F-ck count within that season", "Cumulative total F-ck count by all characters combined including Roy Kent within that season", "Roy Kent's cumulative F-ck count across all episodes and seasons until that episode", "Cumulative total F-ck count by all characters combined including Roy Kent across all episodes and seasons until that episode", "Roy Kent's F-count divided by the total F-count in the episode", "F-score as percentage", "Flag of yes or no for whether during the episode Roy Kent was dating the characted Keeley", "Flag of yes or no for whether during the episode Roy Kent was coaching the team", "Imdb rating of that episode" ] } ], "data": { "file_name": [ "richmondway.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-09-26/richmondway.csv" ] } }, { "date_posted": "2023-04-04", "project_name": "Premier League Match Data 2021-2022", "project_source": [ "https://www.kaggle.com/datasets/evangower/premier-league-match-data", "https://theathletic.com/3459766/2022/07/29/liverpool-manchester-city-premier-league-fouls-yellow-card/", "https://github.com/evangower", "https://www.kaggle.com/code/evangower/who-wins-the-epl-if-games-end-at-half-time/" ], "description": "The data this week comes from thePremier League Match Data 2021-2022viaEvan Goweron Kaggle. You can explore match day statistics of every game and every team during the 2021-22 season of the English Premier League Data. Data includes teams playing, date, referee, and stats for home and away side such as fouls, shots, cards, and more! Also included is a dataset of the weekly rankings for the season. The data was collected from the official website of the Premier League. Evan then cleaned the data using google sheets. Evan did an analysis ofWho wins the EPL if games end at half time?and there'san article from the Athleticabout fouls conceded per yellow card article. No data cleaning", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-04", "data_dictionary": [ { "variable": [ "Date", "HomeTeam", "AwayTeam", "FTHG", "FTAG", "FTR", "HTHG", "HTAG", "HTR", "Referee", "HS", "AS", "HST", "AST", "HF", "AF", "HC", "AC", "HY", "AY", "HR", "AR" ], "class": [ "character", "character", "character", "double", "double", "character", "double", "double", "character", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "The date when the match was played", "The home team", "The away team", "Full time home goals", "Full time away goals", "Full time result", "Halftime home goals", "Halftime away goals", "Halftime results", "Referee of the match", "Number of shots taken by the home team", "Number of shots taken by the away team", "Number of shots on target by the home team", "Number of shots on target by the away team", "Number of fouls by the home team", "Number of fouls by the away team", "Number of corners taken by the home team", "Number of corners taken by the away team", "Number of yellow cards received by the home team", "Number of yellow cards received by the away team", "Number of red cards received by the home team", "Number of red cards received by the away team" ] } ], "data": { "file_name": [ "soccer21-22.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-04/soccer21-22.csv" ] } }, { "date_posted": "2023-11-28", "project_name": "Doctor Who Episodes", "project_source": [ "https://en.wikipedia.org/wiki/List_of_Doctor_Who_episodes_(2005%E2%80%93present)", "https://github.com/KittJonathan/datardis/tree/main/misc", "https://cran.r-project.org/package=datardis", "https://github.com/KittJonathan/datardis" ], "description": "Doctor Who is an extremely long-running British television program. The show was revived in 2005, and has proven very popular since then. To celebrate this year's 60th anniversary of Doctor Who, we have three datasets. The data this week comes from Wikipedia's [List of Doctor Who episodes](https://en.wikipedia.org/wiki/List_of_Doctor_Who_episodes_(2005%E2%80%93present)via the{datardis} packagebyJonathan Kitt. Thank you to Jonathan for compiling and sharing this data! As of 2023-11-24, the data only includes episodes from the \"revived\" era. For an added challenge, consider submitting a pull request to the {datardis} package to update thedata-extraction scriptsto also fetch the \"classic\" era data! Clean data from the{datardis} package.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-28", "data_dictionary": [ { "variable": [ "era", "season_number", "serial_title", "story_number", "episode_number", "episode_title", "type", "first_aired", "production_code", "uk_viewers", "rating", "duration" ], "class": [ "character", "double", "character", "character", "double", "character", "character", "double", "character", "double", "double", "double" ], "description": [ "Whether the episode is in the \\\"classic\\\" or \\\"revived\\\" era. All data in this dataset is within the \\\"revived\\\" era.", "The season number within the era. Note that some episodes are outside of a season.", "Serial title if available", "Story number", "Episode number in season", "Episode title", "\\\"episode\\\" or \\\"special\\\"", "Date the episode first aired in the U.K.", "Episode's production code if available", "Number of U.K. viewers (millions)", "Episode's rating", "Episode's duration in minutes" ] }, { "variable": [ "story_number", "director" ], "class": [ "character", "character" ], "description": [ "Story number", "Episode's director" ] }, { "variable": [ "story_number", "writer" ], "class": [ "character", "character" ], "description": [ "Story number", "Episode's writer" ] } ], "data": { "file_name": [ "drwho_directors.csv", "drwho_episodes.csv", "drwho_writers.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-28/drwho_directors.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-28/drwho_episodes.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-28/drwho_writers.csv" ] } }, { "date_posted": "2023-04-25", "project_name": "London Marathon", "project_source": [ "https://nrennie.rbind.io/blog/web-scraping-rvest-london-marathon/", "https://github.com/nrennie/LondonMarathon" ], "description": "The data this week comes from Nicola Rennie'sLondonMarathon R package. This is an R package containing two data sets scraped from Wikipedia (1 November 2022) on London Marathon winners, and some general data. How the dataset was created, and some analysis, is described in Nicola's post\"Scraping London Marathon data with {rvest}\". Thank you for putting this dataset together @nrennie! No data cleaning", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-25", "data_dictionary": [ { "variable": [ "Category", "Year", "Athlete", "Nationality", "Time" ], "class": [ "character", "double", "character", "character", "double" ], "description": [ "Category of race", "Year", "Name of the winner", "Nationality of the winner", "Winning time" ] }, { "variable": [ "Date", "Year", "Applicants", "Accepted", "Starters", "Finishers", "Raised", "Official charity" ], "class": [ "double", "double", "double", "double", "double", "double", "double", "character" ], "description": [ "Date of the race", "Year", "Number of people who applied", "Number of people accepted", "Number of people who started", "Number of people who finished", "Amount raised for charity (£ millions)", "Official charity" ] } ], "data": { "file_name": [ "london_marathon.csv", "winners.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-25/london_marathon.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-25/winners.csv" ] } }, { "date_posted": "2023-10-24", "project_name": "Patient Risk Profiles", "project_source": [ "https://rinpharma.com/", "https://github.com/jreps" ], "description": "The virtualR/Pharma Conferenceis happening this week! To celebrate, we're exploring Patient Risk Profiles. Thank you toJenna Repsfor preparing this week's data! This dataset contains 100 simulated patient's medical history features and the predicted 1-year risk of 14 outcomes based on each patient's medical history features. The predictions used real logistic regression models developed on a large real world healthcare dataset. Note: We didnotclean the column names this week. This data looks more like the sort of data you're likely to encounter in the wild, so we thought it would be good practice to work with it as-is. Clean data provided by the R/Pharma team!", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-10-24", "data_dictionary": [ { "variable": [ "personId", "age group: 10 - 14", "age group: 15 - 19", "age group: 20 - 24", "age group: 65 - 69", "age group: 40 - 44", "age group: 45 - 49", "age group: 55 - 59", "age group: 85 - 89", "age group: 75 - 79", "age group: 5 - 9", "age group: 25 - 29", "age group: 0 - 4", "age group: 70 - 74", "age group: 50 - 54", "age group: 60 - 64", "age group: 35 - 39", "age group: 30 - 34", "age group: 80 - 84", "age group: 90 - 94", "Sex = FEMALE", "sex = MALE", "Acetaminophen exposures in prior year", "Occurrence of Alcoholism in prior year", "Anemia in prior year", "Angina events in prior year", "ANTIEPILEPTICS in prior year", "Occurrence of Anxiety in prior year", "Osteoarthritis in prior year", "Aspirin exposures in prior year", "Occurrence of Asthma in prior year", "Atrial Fibrillation, incident in prior year", "HORMONAL CONTRACEPTIVES in prior year", "Any cancer (excl. prostate cancer and benign cancer) in prior year", "Acute Kidney Injury (AKI) in prior year", "Chronic kidney disease or end stage renal disease in prior year", "Heart failure in prior year", "Chronic obstructive pulmonary disease (COPD) in prior year", "Coronary artery disease (CAD) in prior year", "Major depressive disorder, with NO occurrence of certain psychiatric disorder in prior year", "Type 1 diabetes and no prior specific non-T1DM diabetes in prior year", "Type 2 Diabetes Mellitus (DM), with no type 1 or secondary DM in prior year", "Deep Vein Thrombosis (DVT) in prior year", "Dyspnea in prior year", "Edema in prior year", "Gastroesophageal reflux disease in prior year", "Acute gastrointestinal (GI) bleeding in prior year", "Heart valve disorder in prior year", "Chronic hepatitis in prior year", "Hyperlipidemia in prior year", "Hypertension in prior year", "Hypothyroidism in prior year", "Inflammatory Bowel Disease in prior year", "Low back pain in prior year", "Occurrence of neuropathy in prior year", "Obesity in prior year", "Opioids in prior year", "Osteoporosis in prior year", "Peripheral vascular disease in prior year", "Pneumonia in prior year", "Psychotic disorder in prior year", "Acute Respiratory failure in prior year", "Rheumatoid Arthritis in prior year", "Seizure in prior year", "Sepsis in prior year", "Skin ulcer in prior year", "Sleep apnea in prior year", "Smoking in prior year", "STEROIDS in prior year", "Hemorrhagic stroke in an inpatient setting in prior year", "Non-hemorrhagic Stroke in an inpatient setting in prior year", "Urinary tract infectious disease in prior year", "Antibiotics Carbapenems in prior year", "Antibiotics Aminoglycosides in prior year", "Antibiotics Cephalosporins in prior year", "Antibiotics Fluoroquinolones in prior year", "Antibiotics Glycopeptides and lipoglycopeptides in prior year", "Antibiotics Macrolides in prior year", "Antibiotics Monobactams in prior year", "Antibiotics Oxazolidinones in prior year", "Antibiotics Penicillins in prior year", "Antibiotics Polypeptides in prior year", "Antibiotics Rifamycins in prior year", "Antibiotics Sulfonamides in prior year", "Antibiotics Streptogramins in prior year", "Antibiotics Tetracyclines in prior year", "predicted risk of Pulmonary Embolism", "predicted risk of Sudden Hearing Loss, No congenital anomaly or middle or inner ear conditions", "predicted risk of Restless Leg Syndrome", "predicted risk of Sudden Vision Loss, with no eye pathology causes", "predicted risk of Muscle weakness or injury", "predicted risk of Ankylosing Spondylitis", "predicted risk of Autoimmune hepatitis", "predicted risk of Multiple Sclerosis", "predicted risk of Acute pancreatitis, with No chronic or hereditary or common causes of pancreatitis", "predicted risk of Ulcerative colitis", "predicted risk of Migraine", "predicted risk of Dementia", "predicted risk of Treatment resistant depression (TRD)", "predicted risk of Parkinson's disease, inpatient or with 2nd diagnosis" ], "class": [ "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric" ], "description": [ "A unique identifier for the simulated patient", "A binary column where 1 means the patient is aged between 10-14 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 15-19 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 20-24 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 65-69 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 40-44 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 45-49 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 55-59 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 85-89 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 75-79 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 5-9 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 25-29 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 0-4 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 70-74 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 50-54 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 60-64 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 35-39 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 30-34 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 80-84 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient is aged between 90-94 (inclusive) and 0 means the patient is not in that age group", "A binary column where 1 means the patient has a female sex", "A binary column where 1 means the patient has a male sex", "A binary column where 1 means the patient had a record for acetaminophen in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for alcoholism in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for anemia in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for angina in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for a drug in the category ANTIEPILEPTICS in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for anxiety in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for osteoarthritis in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for aspirin in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for asthma in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for atrial fibrillation in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for a drug in the category hormonal contraceptives in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for cancer excluding prostate and benign cancers in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for acute kidney injury in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for chronic kidney disease in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for heart failure in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for chronic obstructive pulmonary disease in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for coronary artery disease in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for major depressive disorder and no certain psychiatric disorders in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for type 1 diabetes in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for type 2 diabetes in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for deep vein thrombosis in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for dyspnea in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for edema in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for gastroesophageal reflux in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for acute gastrointestinal bleeding in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for heart valve disorder in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for chronic hepatitis in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for hyperlipidemia in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for hypertension in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for hypothyroidism in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for inflammatory bowel disease in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for low back pain in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for neuropathy in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for obesity in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an opioid in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for osteoporosis in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for peripheral vascular disease in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for pneumonia in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for a psychotic disorder in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for acute respiratory failure in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for rheumatoid arthritis in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for a seizure in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for sepsis in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for a skin ulcer in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for sleep apnea in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for smoking in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for any steroid in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for hemorrhagic stroke in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for non-hemorrhagic stroke in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for urinary tract infection in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class carbapenems in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class aminoglycosides in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class cephalosporins in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class Fluoroquinolones in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class glycopeptides and lipoglycopeptides in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class macrolides in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class monobactams in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class oxazolidinones in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class penicillins in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class polypeptides in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class carbapenems in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class sulfonamides in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class streptogramins in the prior year and 0 means they did not", "A binary column where 1 means the patient had a record for an antibiotic in the class tetracyclines in the prior year and 0 means they did not", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having pulmonary embolism given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having sudden hearing loss given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having restless leg syndrome given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having sudden vision loss given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having muscle weakness or injury given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having ankylosing spondylitis given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having autoimmune hepatitis given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having multiple sclerosis given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having acute pancreatitis given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having ulcerative colitis given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having a migraine given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having dementia given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having treatment resistant depression given their features (0 = 0% and 1= 100%)", "A value between 0 and 1 corresponding to the patient's predicted 1-year risk of having Parkinson's disease given their features (0 = 0% and 1= 100%)" ] } ], "data": { "file_name": [ "patient_risk_profiles.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-10-24/patient_risk_profiles.csv" ] } }, { "date_posted": "2023-01-17", "project_name": "Art History", "project_source": [ "https://research.repository.duke.edu/concern/datasets/q811kk70n?locale=en", "https://github.com/hollandstam1/thesis", "https://saralemus7.github.io/arthistory/", "https://github.com/saralemus7/arthistory" ], "description": "The data this week comes from thearthistory data package This dataset contains data that was used for Holland Stam's thesis work, titledQuantifying art historical narratives. The data was collected to assess the demographic representation of artists through editions of Janson's History of Art and Gardner's Art Through the Ages, two of the most popular art history textbooks used in the American education system. In this package specifically, both artist-level and work-level data was collected along with variables regarding the artists' demographics and numeric metrics for describing how much space they or their work took up in each edition of each textbook. This package contains three datasets: Acknowledging arthistory Citation Lemus S, Stam H (2022). arthistory: Art History Textbook Data.https://github.com/saralemus7/arthistory,https://saralemus7.github.io/arthistory/. Examples of analyses are included inHolland Stam's thesisin Quarto files. No data cleaning", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-17", "data_dictionary": [ { "variable": [ "artist_name", "edition_number", "year", "artist_nationality", "artist_nationality_other", "artist_gender", "artist_race", "artist_ethnicity", "book", "space_ratio_per_page_total", "artist_unique_id", "moma_count_to_year", "whitney_count_to_year", "artist_race_nwi" ], "class": [ "character", "double", "double", "character", "character", "character", "character", "character", "character", "double", "double", "double", "double", "character" ], "description": [ "The name of each artist", "The edition number of the textbook from either Janson's History or Art or Gardner's Art Through the Ages.", "The year of publication for a given edition of Janson or Gardner.", "The nationality of a given artist.", "The nationality of the artist. Of the total count of artists through all editions of Janson's History of Art and Gardner's Art Through the Ages, 77.32% account for French, Spanish, British, American and German. Therefore, the categorical strings of this variable are French, Spanish, British, American, German and Other", "The gender of the artist", "The race of the artist", "The ethnicity of the artist", "Which book, either Janson or Gardner the particular artist at that particular time was included.", "The area in centimeters squared of both the text and the figure of a particular artist in a given edition of Janson's History of Art divided by the area in centimeters squared of a single page of the respective edition. This variable is continuous.", "The unique identifying number assigned to artists across books is denoted in alphabetical order. This variable is discrete.", "The total count of exhibitions ever held by the Museum of Modern Art (MoMA) of a particular artist at a given year of publication. This variable is discrete.", "The count of exhibitions held by The Whitney of a particular artist at a particular moment of time, as highlighted by year. This variable in discrete.", "The non-white indicator for artist race, meaning if an artist's race is denoted as either white or non-white." ] } ], "data": { "file_name": [ "artists.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-17/artists.csv" ] } }, { "date_posted": "2023-02-28", "project_name": "African Language Sentiment", "project_source": [ "https://r4ds.io/join", "https://arxiv.org/pdf/2302.08956.pdf", "https://github.com/shmuhammad2004", "https://github.com/afrisenti-semeval/afrisent-semeval-2023" ], "description": "The data this week comes fromAfriSenti: Sentiment Analysis dataset for 14 African languagesvia@shmuhammad2004(the corresponding author on theassociated paper, and an active member of theR4DS Online Learning Community Slack). This repository contains data for the SemEval 2023 Shared Task 12: Sentiment Analysis in African Languages (AfriSenti-SemEval). The source repository also includes sentiment lexicons for several languages.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-28", "data_dictionary": [ { "variable": [ "language_iso_code", "tweet", "label", "intended_use" ], "class": [ "character", "character", "character", "character" ], "description": [ "The unique code used to identify the language", "The text content of a tweet", "A sentiment label of positive, negative, or neutral assigned by a native speaker of that language", "Whether the data came from the dev, test, or train set for that language" ] }, { "variable": [ "language_iso_code", "language" ], "class": [ "character", "character" ], "description": [ "The unique code used to identify the language", "The name of the language" ] }, { "variable": [ "language_iso_code", "script" ], "class": [ "character", "character" ], "description": [ "The unique code used to identify the language", "The script used to write the language" ] }, { "variable": [ "language_iso_code", "country" ], "class": [ "character", "character" ], "description": [ "The unique code used to identify the language", "A country in which the language is spoken" ] }, { "variable": [ "country", "region" ], "class": [ "character", "character" ], "description": [ "A country in which the language is spoken", "The region of Africa in which that country is categorized. Note that Mozambique is categorized as \\\"East Africa\\\", \\\"Southern Africa\\\", and \\\"Southeastern Africa\\\"" ] } ], "data": { "file_name": [ "afrisenti.csv", "country_regions.csv", "language_countries.csv", "language_scripts.csv", "languages.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-28/afrisenti.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-28/country_regions.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-28/language_countries.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-28/language_scripts.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-28/languages.csv" ] } }, { "date_posted": "2023-07-04", "project_name": "Historical Markers", "project_source": [ "http://www.geonames.org/", "https://www.hmdb.org/geolists.asp?c=United%20States%20of%20America", "https://www.hmdb.org/stats.asp", "https://www.hmdb.org/", "https://github.com/rfordatascience/tidytuesday/issues/574#issuecomment-1601050053" ], "description": "The data this week comes from theHistorical Marker Database USA Index. Learn more about the markers on theHMDb.org site, which includes a number of articles, includingDatabase Counts and Statistics. We included a dataset of places that donothave entries in the Historical Markers Database. You might try to combine that with information fromgeonames.org(code: HSTS) to find markers that need to be submitted. Thanks toJesus M. Castagnettofor the geonames tip!", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-04", "data_dictionary": [ { "variable": [ "marker_id", "marker_no", "title", "subtitle", "addl_subtitle", "year_erected", "erected_by", "latitude_minus_s", "longitude_minus_w", "street_address", "city_or_town", "section_or_quarter", "county_or_parish", "state_or_prov", "location", "missing", "link" ], "class": [ "double", "character", "character", "character", "character", "integer", "character", "double", "double", "character", "character", "character", "character", "character", "character", "character", "character" ], "description": [ "Unique ID for this marker in the HMdb.", "Number of this marker in the state numbering scheme.", "Main title of the marker.", "Subtitle of the marker, if any.", "Additional subtitle text.", "The year in which the marker was erected.", "The organization which erected the marker.", "The latitude of the marker.", "The longitude of the marker.", "The street address of the marker, if available.", "The city, town, etc in which the marker is located.", "The section of the city, town, etc, when available.", "The county, parish, or similar designation in which the marker appears.", "The state, province, territory, etc in which the marker appears.", "A description of the marker's location.", "Whether the marker is \\\"Reported missing\\\" or \\\"Confirmed missing\\\". NA values indicate that the marker has neither been reported missing nor confirmed as missing.", "The HMDb link to the marker. Links include additional details, such as photos and topic lists to which this marker belongs." ] }, { "variable": [ "county", "state" ], "class": [ "character", "character" ], "description": [ "County or equivalent.", "State or territory." ] } ], "data": { "file_name": [ "historical_markers.csv", "no_markers.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-04/historical_markers.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-04/no_markers.csv" ] } }, { "date_posted": "2023-07-25", "project_name": "Scurvy", "project_source": [ "https://github.com/higgi13425/medicaldata/tree/master/data-raw", "https://htmlpreview.github.io/?https://github.com/higgi13425/medicaldata/blob/master/man/description_docs/scurvy_desc.html", "https://higgi13425.github.io/medicaldata/" ], "description": "The data this week comes from themedicaldata R package. This is a data package from Peter Higgins, with 19 medical datasets for teaching Reproducible Medical Research with R. We're using thescurvy dataset. Source: This data set is from a study published in 1757 in A Treatise on the Scurvy in Three Parts, by James Lind. This data set contains 12 participants with scurvy. In 1757, it was not known that scurvy is a manifestation of vitamin C deficiency. A variety of remedies had been anecdotally reported, but Lind was the first to test different regimens of acidic substances (including citrus fruits) against each other in a randomized, controlled trial. 6 distinct therapies were tested in 12 seamen with symptomatic scurvy, who were selected for similar severity. Six days of therapy were provided, and endpoints were reported in the text at the end of 6 days. These include rotting of the gums, skin sores, weakness of the knees, and lassitude, which are described in terms of severity. These have been translated into Likert scales from 0(none) to 3(severe). A dichotomous endpoint, fitness for duty, was also reported. Scurvy was a common affliction of seamen on long voyages, leading to mouth sores, skin lesions, weakness of the knees, and lassitude. Scurvy could be fatal on long voyages. James Lind reported the treatment of 12 seamen with scurvy in 1757, in _A Treatise on the Scurvy in Three Parts). This 476 page bloviation can be found scanned to the Google Books website A Treatise on the Scurvy. Pages 149-153 are a rare gem among what can be generously described as 400+ pages of evidence-free blathering, and these 4 pages may represent the first report of a controlled clinical trial. Lind was the ship’s surgeon on board the HMS Salisbury, and had a number of scurvy-affected seamen at his disposal. Many remedies had been described and advocated for, with no more than anecdotal evidence. On May 20, 1747, Lind decided to try the 6 therapies on the Salisbury in a comparative study in 12 affected seamen. He selected 12 with roughly similar severity, with notable skin and mouth sores, weakness of the knees, and significant lassitude, making them unfit for duty. They each received the standard shipboard diet of gruel and mutton broth, supplemented with occasional biscuits and puddings. Each treatment was a dietary supplement (including citrus fruits) or a medicinal. This data frame was reconstructed from Lind’s account as recorded on these 4 pages, with his estimates of severity translated to a 4 point Likert scale (0-3) for each of the symptoms he described at his chosen endpoint on day 6. A somewhat fanciful study_id variable was added, along with detailed descriptions of the dosing schedule of each treatment. Of note, there is some dispute about whether this was truly the first clinical trial, or whether it actually happened, as there are no contemporaneous corroborating accounts. See link about the historical debate. Lind reported that the seamen treated with 2 lemons and an orange daily did best, followed by those treated with cider. Those treated with elixir of vitriol only had improvement in mouth sores. One imagines that acidic substances (like dilute sulfuric acid, vinegar, cider, and citrus fruits) might have been rather painful on these mouth sores. Unfortunately, the burial of the 4 valuable pages of data in 476 pages of noise, a publication delay of 10 years, and Lind’s half-hearted conclusions (he was focused on acidity), meant that it took until 1795 before the British Navy mandated daily limes for seamen. The first column was removed from the scurvy.csv file available athttps://github.com/higgi13425/medicaldata/tree/master/data-raw.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-25", "data_dictionary": [ { "variable": [ "study_id", "treatment", "dosing_regimen_for_scurvy", "gum_rot_d6", "skin_sores_d6", "weakness_of_the_knees_d6", "lassitude_d6", "fit_for_duty_d6" ], "class": [ "double", "character", "character", "character", "character", "character", "character", "character" ], "description": [ "Participant ID", "Treatment; cider, dilute_sulfuric_acid, vinegar, sea_water, citrus, purgative_mixture", "Dosing Regimen; 1 quart per day; 25 drops of elixir of vitriol, three times a day; two spoonfuls, three times daily; half pint daily; two lemons and an orange daily; a nutmeg-sized paste of garlic, mustard seed, horseradish, balsam of Peru, and gum myrrh three times a day", "Gum Rot on Day 6; 0_none, 1_mild, 2_moderate, 3_severe", "Skin Sores on Day 6; 0_none, 1_mild, 2_moderate, 3_severe", "Weakness of the Knees on Day 6; 0_none, 1_mild, 2_moderate, 3_severe", "Lassitude on Day 6; 0_none, 1_mild, 2_moderate, 3_severe", "Fit for Duty on Day 6; 0_no, 1_yes" ] } ], "data": { "file_name": [ "scurvy.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-25/scurvy.csv" ] } }, { "date_posted": "2023-06-20", "project_name": "UFO Sightings Redux", "project_source": [ "https://github.com/jonthegeek/apis/", "https://github.com/jonthegeek/apis/blob/main/01_ufo-enrich.qmd", "https://nuforc.org/webreports/ndxshape.html", "https://github.com/jonthegeek/apis/blob/main/01_ufo-data.qmd", "https://tidytues.day/2019/2019-06-25", "https://sunrise-sunset.org/" ], "description": "The data this week comes from theNational UFO Reporting Center,cleanedandenrichedwith data fromsunrise-sunset.orgbyJon Harmon. If this dataset looks familiar, that's because weused a version of it back in 2019. The new version adds the last several years of data, adds information about time-of-day, and cleans up some errors in the original dataset. We'd love to see visualizations describing the differences between the 2019 dataset and this new dataset! See Jon Harmon'scleaningandenrichingscripts for most of the (extensive) cleaning.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-20", "data_dictionary": [ { "variable": [ "reported_date_time", "reported_date_time_utc", "posted_date", "city", "state", "country_code", "shape", "reported_duration", "duration_seconds", "summary", "has_images", "day_part" ], "class": [ "datetime", "datetime", "datetime", "character", "character", "character", "character", "character", "double", "character", "logical", "character" ], "description": [ "The time and date of the sighting, as it appears in the original NUFORC data.", "The time and date of the sighting, normalized to UTC.", "The date when the sighting was posted to NUFORC.", "The city of the sighting. Some of these have been cleaned from the original data.", "The state, province, or similar division of the sighting.", "The 2-letter country code of the sighting, normalized from the original data.", "The reported shape of the craft.", "The reported duration of the event, in the reporter's words.", "The duration normalized to seconds using regex.", "The reported summary of the event.", "Whether the sighting has images available on NUFORC.", "The approximate part of the day in which the sighting took place, based on the reported date and time, the place, and data from sunrise-sunset.org. Latitude and longitude were rounded to the 10s digit, and the date was rounded to the week, to match against time points such as \\\"nautical twilight\\\", \\\"sunrise\\\", and \\\"sunset.\\\"" ] }, { "variable": [ "city", "alternate_city_names", "state", "country", "country_code", "latitude", "longitude", "timezone", "population", "elevation_m" ], "class": [ "character", "character", "character", "character", "character", "double", "double", "character", "double", "double" ], "description": [ "Unique cities in which sightings took place.", "Comma-separated other names for the city.", "The state, province, or similar division of the sighting.", "The name of the country.", "The 2-letter country code of the sighting.", "The latitude for this city, from geonames.org.", "The longitude for this city, from geonames.org.", "The timezone for this city, from geonames.org.", "The population for this city, from geonames.org.", "The elevation in meters for this city, from geonames.org." ] }, { "variable": [ "rounded_lat", "rounded_long", "rounded_date", "astronomical_twilight_begin", "nautical_twilight_begin", "civil_twilight_begin", "sunrise", "solar_noon", "sunset", "civil_twilight_end", "nautical_twilight_end", "astronomical_twilight_end" ], "class": [ "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "Latitudes rounded to the tens digit.", "Longitudes rounded to the tens digit.", "Dates rounded to the nearest week.", "The UTC time of day when astronomical twilight began on this date in this location. Astronomical twilight begins when the sun is 18 degrees below the horizon before sunrise.", "The UTC time of day when nautical twilight began on this date (or the next date) in this location. Nautical twilight begins when the sun is 12 degrees below the horizon before sunrise.", "The UTC time of day when civil twilight began on this date (or the next date) in this location. Civil twilight begins when the sun is 6 degrees below the horizon before sunrise.", "The UTC time of day when the sun rose on this date (or the next date) in this location.", "The UTC time of day when the sun was at its zenith on this date (or the next date) in this location.", "The UTC time of day when the sun set on this date (or the next date) in this location.", "The UTC time of day when civil twilight ended on this date (or the next date) in this location. Civil twilight ends when the sun is 6 degrees below the horizon after sunset.", "The UTC time of day when nautical twilight ended on this date (or the next date) in this location. Nautical twilight ends when the sun is 12 degrees below the horizon after sunset.", "The UTC time of day when astronomical twilight ended on this date (or the next date) in this location. Astronomical twilight ends when the sun is 18 degrees below the horizon after sunset." ] } ], "data": { "file_name": [ "day_parts_map.csv", "places.csv", "ufo_sightings.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-20/day_parts_map.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-20/places.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-20/ufo_sightings.csv" ] } }, { "date_posted": "2023-02-21", "project_name": "Bob Ross Paintings", "project_source": [ "https://www.twoinchbrush.com/all-paintings", "https://github.com/jwilber/Bob_Ross_Paintings/blob/master/data/bob_ross_paintings.csv", "https://github.com/jwilber/Bob_Ross_Paintings/tree/master/data/paintings", "https://tidytues.day/2019/2019-08-06", "https://github.com/frankiethull/BobRossColors" ], "description": "The data this week comes from Jared Wilber's data onBob Ross Paintingsvia @frankiethullBob Ross Colors data package. This is data from thepaintings of Bob Rossfeatured in the TV Show 'The Joy of Painting'. @frankiethull created an R data package{BobRossColors}with information on the palettes that leveraged imgpalr to mine divergent and qualitative colors from each painting image. In addition, unique Bob Ross named colors are in the package as well. In the github repository of the dataset, there are alsopngs of the paintingsthemselves! You might also want to check out ourprevious Bob Ross dataset from 2019-08-06to see if there are correlations between named objects and named colors!", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-21", "data_dictionary": [ { "variable": [ "painting_index", "img_src", "painting_title", "season", "episode", "num_colors", "youtube_src", "colors", "color_hex", "Black_Gesso", "Bright_Red", "Burnt_Umber", "Cadmium_Yellow", "Dark_Sienna", "Indian_Red", "Indian_Yellow", "Liquid_Black", "Liquid_Clear", "Midnight_Black", "Phthalo_Blue", "Phthalo_Green", "Prussian_Blue", "Sap_Green", "Titanium_White", "Van_Dyke_Brown", "Yellow_Ochre", "Alizarin_Crimson" ], "class": [ "double", "character", "character", "double", "double", "double", "character", "character", "character", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical", "logical" ], "description": [ "Painting number as enumerated in collection.", "Url path to image.", "Title of the painting.", "Season of 'The Joy of Painting' in which the painting was featured.", "Episode of 'The Joy of Painting' in which the painting was featured.", "Number of unique colors used in the painting.", "Youtube video of episode featuring the painting.", "List of colors used in the painting.", "List of colors (hexadecimal code) used in the painting.", "Black_Gesso used", "Bright_Red used", "Burnt_Umber used", "Cadmium_Yellow used", "Dark_Sienna used", "Indian_Red used", "Indian_Yellow used", "Liquid_Black used", "Liquid_Clear used", "Midnight_Black used", "Phthalo_Blue used", "Phthalo_Green used", "Prussian_Blue used", "Sap_Green used", "Titanium_White used", "Van_Dyke_Brown used", "Yellow_Ochre used", "Alizarin_Crimson used" ] } ], "data": { "file_name": [ "bob_ross.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-21/bob_ross.csv" ] } }, { "date_posted": "2024-01-09", "project_name": "Canadian Hockey Player Birth Months", "project_source": [ "https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1310041501&pickMembers%5B0%5D=3.1&cubeTimeFrame.startYear=1991&cubeTimeFrame.endYear=2022&referencePeriods=19910101%2C20220101", "https://api-web.nhle.com/v1/", "https://api.nhle.com/stats/rest/en/team", "https://jlaw.netlify.app/2023/12/04/are-birth-dates-still-destiny-for-canadian-nhl-players/", "/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-09/Mastodon", "https://www.amazon.com/Outliers-Story-Success-Malcolm-Gladwell/dp/0316017930" ], "description": "If you're a Canadian hockey player, happy birth month! That's more likely to be correct this time of year than in the Fall! The dataset this week comes fromStatistics Canada, theNHL team list endpoint, and theNHL API. The dataset was inspired by the blogAre Birth Dates Still Destiny for Canadian NHL Players?by JLaw (viahttps://universeodon.com/@jlaw/111522860812359901)! In the first chapterMalcolm Gladwell’s Outliershe discusses how in Canadian Junior Hockey there is a higher likelihood for players to be born in the first quarter of the year. Because these kids are older within their year they make all the important teams at a young age which gets them better resources for skill development and so on. While it seems clear that more players are born in the first few months of the year, what isn’t explored is whether or not this would be expected. Maybe more people in Canada in general are born earlier in the year. I will explore whether Gladwell’s result is expected as well as whether this is still true in today’s NHL for Canadian-born players. Can you reproduce JLaw's results? What else can you find in the NHL player data?", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-09", "data_dictionary": [ { "variable": [ "year", "month", "births" ], "class": [ "integer", "integer", "integer" ], "description": [ "birth year", "birth month", "number of live births in Canada in that year and month" ] }, { "variable": [ "player_id", "first_name", "last_name", "birth_date", "birth_city", "birth_country", "birth_state_province", "birth_year", "birth_month" ], "class": [ "double", "character", "character", "date", "character", "character", "character", "integer", "integer" ], "description": [ "unique id of this player (note: 2 players are listed twice with slightly different data)", "first name", "last name", "birth date", "birth city", "3-letter code for the birth country", "birth state or province, if applicable", "birth year", "birth month" ] }, { "variable": [ "team_code", "season", "position_type", "player_id", "headshot", "first_name", "last_name", "sweater_number", "position_code", "shoots_catches", "height_in_inches", "weight_in_pounds", "height_in_centimeters", "weight_in_kilograms", "birth_date", "birth_city", "birth_country", "birth_state_province" ], "class": [ "character", "integer", "character", "double", "character", "character", "character", "double", "character", "character", "integer", "integer", "integer", "integer", "date", "character", "character", "character" ], "description": [ "unique 3-letter code for this team", "season, as YYYYYYYY", "\\\"defensemen\\\", \\\"forwards\\\", or \\\"goalies\\\"", "unique id of this player", "headshot url for this player-season", "first name", "last name", "sweater number", "position code (C = center, D = defense, G = goal, L = left wing, R = right wing)", "hand preferred by this player for shooting and catching (L, R, or NA)", "height in inches at the start of this season", "weight in pounds at the start of this season", "height in centimeters at the start of this season", "weight in kilograms at the start of this season", "birth date", "birth city", "3-letter code for the birth country", "birth state or province, if applicable" ] }, { "variable": [ "team_code", "full_name" ], "class": [ "character", "character" ], "description": [ "unique 3-letter code for this team", "full name of this team" ] } ], "data": { "file_name": [ "canada_births_1991_2022.csv", "nhl_player_births.csv", "nhl_rosters.csv", "nhl_teams.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-09/canada_births_1991_2022.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-09/nhl_player_births.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-09/nhl_rosters.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-09/nhl_teams.csv" ] } }, { "date_posted": "2023-06-13", "project_name": "SAFI survey data", "project_source": [ "https://datacarpentry.org/socialsci-workshop/data/", "https://figshare.com/articles/dataset/SAFI_Survey_Results/6262019", "https://datacarpentry.org/socialsci-workshop/" ], "description": "The data this week comes from theSAFI (Studying African Farmer-Led Irrigation) survey, a subset of the data used in theData Carpentry Social Sciences workshop. So, if you're looking how to learn how to work with this data, lessons are already available! Data is available throughFigshare. CITATION: Woodhouse, Philip; Veldwisch, Gert Jan; Brockington, Daniel; Komakech, Hans C.; Manjichi, Angela; Venot, Jean-Philippe (2018): SAFI Survey Results. doi:10.6084/m9.figshare.6262019.v1 SAFI (Studying African Farmer-Led Irrigation) is a currently running project which is looking at farming and irrigation methods. This is survey data relating to households and agriculture in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017 using forms downloaded to Android Smartphones. The survey forms were created using the ODK (Open Data Kit) software via an Excel spreadsheet. The collected data is then sent back to a central server. The server can be used to download the collected data in both JSON and CSV formats. This is a teaching version of the collected data that we will be using. It is not the full dataset. The survey covered such things as; household features (e.g. construction materials used, number of household members), agricultural practices (e.g. water usage), assets (e.g. number and types of livestock) and details about the household members. The basic teaching dataset used in these lessons is a subset of the JSON dataset that has been converted into CSV format. Data was cleaned for the Data Carpentry Social Science lessons. Information available on theirSAFI Teaching Dataset page.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-13", "data_dictionary": [ { "variable": [ "key_ID", "village", "interview_date", "no_membrs", "years_liv", "respondent_wall_type", "rooms", "memb_assoc", "affect_conflicts", "liv_count", "items_owned", "no_meals", "months_lack_food", "instanceID" ], "class": [ "integer", "character", "character", "integer", "integer", "character", "integer", "character", "character", "integer", "character", "integer", "character", "character" ], "description": [ "Added to provide a unique Id for each observation. (The InstanceID field does this as well but it is not as convenient to use)", "Village name", "Date of interview", "Number of members in the household", "Number of years living in this village or a neighboring village", "Type of walls the house has", "Number of rooms in the main house used for sleeping", "Are you a member of an irrigation association?", "Have you been affected by conflicts with other irrigators in the area?", "Livestock count", "Items owned by the household", "How many meals do people in your household normally eat in a day?", "Indicate which months, In the last 12 months have you faced a situation when you did not have enough food to feed the household?", "Unique identifier for the form data submission" ] } ], "data": { "file_name": [ "safi_data.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-13/safi_data.csv" ] } }, { "date_posted": "2023-05-16", "project_name": "Tornados", "project_source": [ "https://github.com/rfordatascience/tidytuesday/issues/549", "https://www.kaggle.com/code/evangower/diving-into-us-tornado-data", "https://www.spc.noaa.gov/wcm/#data" ], "description": "The data this week comes from NOAA's National Weather Service Storm Prediction CenterSevere Weather Maps, Graphics, and Data Page. Thank you toEvan Gowerfor the suggestion! Evaninvestigateda version of this dataset on Kaggle.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-16", "data_dictionary": [ { "variable": [ "om", "yr", "mo", "dy", "date", "time", "tz", "datetime_utc", "st", "stf", "mag", "inj", "fat", "loss", "slat", "slon", "elat", "elon", "len", "wid", "ns", "sn", "f1", "f2", "f3", "f4", "fc" ], "class": [ "integer", "integer", "integer", "integer", "date", "time", "character", "datetime", "character", "integer", "integer", "integer", "integer", "double", "double", "double", "double", "double", "double", "double", "integer", "integer", "integer", "integer", "integer", "integer", "logical" ], "description": [ "Tornado number. Effectively an ID for this tornado in this year.", "Year, 1950-2022.", "Month, 1-12.", "Day of the month, 1-31.", "Date.", "Time.", "Canonical tz database timezone.", "Date and time normalized to UTC.", "Two-letter postal abbreviation for the state (DC = Washington, DC; PR = Puerto Rico; VI = Virgin Islands).", "State FIPS (Federal Information Processing Standards) number.", "Magnitude on the F scale (EF beginning in 2007). Some of these values are estimated (see fc).", "Number of injuries. When summing for state totals, use sn == 1 (see below).", "Number of fatalities. When summing for state totals, use sn == 1 (see below).", "Estimated property loss information in dollars. Prior to 1996, values were grouped into ranges. The reported number for such years is the maximum of its range.", "Starting latitude in decimal degrees.", "Starting longitude in decimal degrees.", "Ending latitude in decimal degrees.", "Ending longitude in decimal degrees.", "Length in miles.", "Width in yards.", "Number of states affected by this tornado. 1, 2, or 3.", "State number for this row. 1 means the row contains the entire track information for this state, 0 means there is at least one more entry for this state for this tornado (om + yr).", "FIPS code for the 1st county.", "FIPS code for the 2nd county.", "FIPS code for the 3rd county.", "FIPS code for the 4th county.", "Was the mag column estimated?" ] } ], "data": { "file_name": [ "tornados.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-16/tornados.csv" ] } }, { "date_posted": "2023-01-10", "project_name": "Project FeederWatch", "project_source": [ "https://www.frontiersin.org/articles/10.3389/fevo.2021.619682/full", "https://feederwatch.org/explore/", "https://feederwatch.org/explore/raw-dataset-requests/", "https://drive.google.com/file/d/1kHmx2XhA2MJtEyTNMpwqTQEnoa9M7Il2/view?usp=sharing" ], "description": "The data this week comes from theProject FeederWatch. FeederWatch is a November-April survey of birds that visit backyards, nature centers, community areas, and other locales in North America. Citizen scientists could birds in areas with plantings, habitat, water, or food that attracts birds. The schedule is completely flexible. People count birds as long as they like on days of their choosing, then enter their counts online. This allows anyone to track what is happening to birds around your home and to contribute to a continental data-set of bird distribution and abundance. FeederWatch data show which bird species visit feeders at thousands of locations across the continent every winter. The data also indicate how many individuals of each species are seen. This information can be used to measure changes in the winter ranges and abundances of bird species over time. A subset of the 2021 data is included for this TidyTuesday, but data available through 1988 is available for download onFeederWatch Raw Dataset Downloads page Project FeederWatch is operated by the Cornell Lab of Ornithology and Birds Canada. Since 2016, Project FeederWatch has been sponsored by Wild Bird Unlimited. Acknowledging FeederWatch. The Cornell Lab of Ornithology and Birds Canada are committed to making data gathered through our citizen science programs freely accessible to students, journalists, and the general public.\" \"This unique dataset is completely dependent on the efforts of our network of volunteer participants. We ask that all data analysts give credit to the thousands of participants who have made FeederWatch possible, as well as to Birds Canada and the Cornell Lab of Ornithology for developing and managing the program.\" Examples of analysesare included with the raw data and there is a section toExplorethe data. More details on analyzing this dataset: Over 30 Years of Standardized Bird Counts at Supplementary Feeding Stations in North America: A Citizen Science Data Report for Project FeederWatchby David N. Bonter and Emma I. Greig TheProject FeederWatch Data Dictionaryexplains all fields and codes used in the database and is essential for understanding the dataset.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-10", "data_dictionary": [ { "variable": [ "loc_id", "latitude", "longitude", "subnational1_code", "entry_technique", "sub_id", "obs_id", "Month", "Day", "Year", "PROJ_PERIOD_ID", "species_code", "how_many", "valid", "reviewed", "day1_am", "day1_pm", "day2_am", "day2_pm", "effort_hrs_atleast", "snow_dep_atleast", "Data_Entry_Method" ], "class": [ "character", "double", "double", "character", "character", "character", "character", "double", "double", "double", "character", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "character" ], "description": [ "Unique identifier for each survey site", "Latitude in decimal degrees for each survey site", "Longitude in decimal degrees for each survey site", "Country abbreviation and State or Province abbreviation of each survey site. Note that the files may contain some \\\"XX\\\" locations. These are sites that were incorrectly placed by the user (e.g., site plotted in the ocean.)", "Variable indicating method of site localization", "Unique identifier for each checklist", "Unique identifier for each observation of a species", "Month of 1st day of two-day observation period", "Day of 1st day of two-day observation period", "Year of 1st day of two-day observation period", "Calendar year of end of FeederWatch season", "Bird species observed, stored as 6-letter species codes", "Maximum number of individuals seen at one time during observation period", "Validity of each observation based on flagging system", "Review state of each observation based on flagging system", "Variable indicating if observer watched during morning of count Day 1", "Variable indicating if observer watched during afternoon of count Day 1", "Variable indicating if observer watched during morning of count Day 2", "Variable indicating if observer watched during afternoon of count Day 2", "Participant estimate of survey time for each checklist", "Participant estimate of minimum snow depth during a checklist", "Data entry method for each checklist (e.g., web, mobile app or paper form)" ] }, { "variable": [ "loc_id", "proj_period_id", "yard_type_pavement", "yard_type_garden", "yard_type_landsca", "yard_type_woods", "yard_type_desert", "hab_dcid_woods", "hab_evgr_woods", "hab_mixed_woods", "hab_orchard", "hab_park", "hab_water_fresh", "hab_water_salt", "hab_residential", "hab_industrial", "hab_agricultural", "hab_desert_scrub", "hab_young_woods", "hab_swamp", "hab_marsh", "evgr_trees_atleast", "evgr_shrbs_atleast", "dcid_trees_atleast", "dcid_shrbs_atleast", "fru_trees_atleast", "cacti_atleast", "brsh_piles_atleast", "water_srcs_atleast", "bird_baths_atleast", "nearby_feeders", "squirrels", "cats", "dogs", "humans", "housing_density", "fed_yr_round", "fed_in_jan", "fed_in_feb", "fed_in_mar", "fed_in_apr", "fed_in_may", "fed_in_jun", "fed_in_jul", "fed_in_aug", "fed_in_sep", "fed_in_oct", "fed_in_nov", "fed_in_dec", "numfeeders_suet", "numfeeders_ground", "numfeeders_hanging", "numfeeders_platfrm", "numfeeders_humming", "numfeeders_water", "numfeeders_thistle", "numfeeders_fruit", "numfeeders_hopper", "numfeeders_tube", "numfeeders_other", "population_atleast", "count_area_size_sq_m_atleast" ], "class": [ "character", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "loc_id", "proj_period_id", "yard_type_pavement", "yard_type_garden", "yard_type_landsca", "yard_type_woods", "yard_type_desert", "habitat type decidious woods", "habitat type evergreen woods", "habitat type mixed woods", "habitat type orchard", "habitat type park", "habitat type fresh water", "habitat type salt water", "habitat type residential", "habitat type industrial", "habitat type agricultural", "habitat type desert_scrub", "habitat type young_woods", "habitat type swamp", "habitat type marsh", "minimum number of trees or shrubs in the count area - evergreen trees", "minimum number of trees or shrubs in the count area - evergreen shrubs", "minimum number of trees or shrubs in the count area - deciduous trees", "minimum number of trees or shrubs in the count area - deciduous srubs", "minimum number of trees or shrubs in the count area - fruit trees", "minimum number of trees or shrubs in the count area - cacti", "minimum number of brush piles located within the count area", "minimum number of water sources located within the count area", "minimum number of bird baths located within the count area", "presence or absense of feeders", "do squirrels take food from feeders at least 3 times per week?", "are cats active within 30 m of the feeders for at least 30 minutes 3 days per week?", "are dogs active within 30 m of the feeders for at least 30 minutes 3 days per week?", "are humans active within 30 m of the feeders for at least 30 minutes 3 days per week?", "participant estimated housing density of neighborhood", "fed_yr_round", "fed_in_jan", "fed_in_feb", "fed_in_mar", "fed_in_apr", "fed_in_may", "fed_in_jun", "fed_in_jul", "fed_in_aug", "fed_in_sep", "fed_in_oct", "fed_in_nov", "fed_in_dec", "numfeeders suet", "numfeeders ground", "numfeeders hanging", "numfeeders platfrm", "numfeeders hummingbird", "numfeeders water dispensers", "numfeeders thistle", "numfeeders fruit", "numfeeders hopper", "numfeeders tube", "numfeeders other", "participant estimated population of city or town", "participant estimated area of survey site" ] } ], "data": { "file_name": [ "PFW_2021_public.csv", "PFW_count_site_data_public_2021.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-10/PFW_2021_public.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-10/PFW_count_site_data_public_2021.csv" ] } }, { "date_posted": "2023-01-24", "project_name": "Alone", "project_source": [ "https://gradientdescending.com/alone-r-package-datasets-from-the-survival-tv-series/", "https://gradientdescending.com/", "https://github.com/doehm/alone", "https://www.history.com/shows/alone" ], "description": "The data this week comes from theAlone data packageby Dan Oehm. This dataset contains data from the TV seriesAlonecollected and shared byDan Oehm. As described in Oehm's blog post](https://gradientdescending.com/alone-r-package-datasets-from-the-survival-tv-series/), in the survival TV series ‘Alone,' 10 survivalists are dropped in an extremely remote area and must fend for themselves. They aim to last 100 days in the Artic winter, living off the land through their survival skills, endurance, and mental fortitude. This package contains four datasets: Acknowledging the Alone dataset Dan Oehm: Examples of analyses are included inDan Oehm's blog post. No data cleaning", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-24", "data_dictionary": [ { "variable": [ "season", "name", "age", "gender", "city", "state", "country", "result", "days_lasted", "medically_evacuated", "reason_tapped_out", "reason_category", "team", "day_linked_up", "profession", "url" ], "class": [ "double", "character", "double", "character", "character", "character", "character", "double", "double", "logical", "character", "character", "character", "double", "character", "character" ], "description": [ "The season number", "Name of the survivalist", "Age of the survivalist", "Gender", "City", "State", "Country", "Place survivalist finished in the season", "The number of days lasted in the game before tapping out or winning", "If the survivalist was medically evacuated from the game", "The reason the survivalist tapped out of the game. NA means they were the winner. Reason being that technically if they won they never tapped out.", "A simplified category of the reason for tapping out", "The team they were associated with (only for season 4)", "Day the team members linked up (only for season 4)", "Profession", "URL of cast page on the history channel website. Prefix URL with https://www.history.com/shows/alone/cast" ] }, { "variable": [ "version", "season", "name", "item_number", "item_detailed", "item" ], "class": [ "character", "double", "character", "double", "character", "character" ], "description": [ "Country code for the version of the show", "The season number", "Name of the survivalist", "Item number", "Detailed loadout item description", "Loadout item. Simplified for aggregation" ] }, { "variable": [ "version", "season", "episode_number_overall", "episode", "title", "air_date", "viewers", "quote", "author", "imdb_rating", "n_ratings" ], "class": [ "character", "double", "double", "double", "character", "double", "double", "character", "character", "double", "double" ], "description": [ "Country code for the version of the show", "The season number", "Episode number across seasons", "Episode", "Episode title", "Date the episode originally aired", "Number of viewers in the US (millions)", "The beginning quote", "Author of the beginning quote", "IMDb rating of the episode", "Number of ratings given for the episode" ] }, { "variable": [ "version", "season", "location", "country", "n_survivors", "lat", "lon", "date_drop_off" ], "class": [ "character", "double", "character", "character", "double", "double", "double", "double" ], "description": [ "Country code for the version of the show", "The season number", "Location", "Country", "Number of survivalists in the season. In season 4 there were 7 teams of 2.", "Latitude", "Longitude", "The date the survivalists were dropped off" ] } ], "data": { "file_name": [ "episodes.csv", "loadouts.csv", "seasons.csv", "survivalists.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-24/episodes.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-24/loadouts.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-24/seasons.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-01-24/survivalists.csv" ] } }, { "date_posted": "2023-06-27", "project_name": "US Populated Places", "project_source": [ "https://github.com/jonthegeek/apis/blob/main/01_ufo-data.qmd", "https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/GeographicNames/", "https://github.com/jonthegeek/apis/blob/main/01_ufo-enrich.qmd", "https://www.usgs.gov/us-board-on-geographic-names/download-gnis-data" ], "description": "While we embark on a road trip for summer vacation, the data this week comes from theNational Map Staged Products Directoryfrom theUS Board of Geographic Names. Note: Quite a lot of more data is available from the GNIS. See the cleaning script for clues for downloading the additional data. See Jon Harmon'scleaningandenrichingscripts for most of the (extensive) cleaning.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-27", "data_dictionary": [ { "variable": [ "feature_id", "feature_name", "state_name", "county_name", "county_numeric", "date_created", "Geographic Names Information System.", "date_edited", "edited.", "prim_lat_dec", "prim_long_dec" ], "class": [ "double", "character", "character", "character", "double", "date", "", "date", "", "double", "double" ], "description": [ "Permanent, unique feature record identifier.", "Official feature name.", "The name of the state containing the primary coordinates.", "The name of the county containing the primary coordinates.", "The 3-digit code for the county containing the primary coordinates.", "The date the record was initially entered into the", "", "The date any attribute of an existing record was", "", "The latitude of the official feature location. Note that some values are unknown.", "The longitude of the official feature location. Note that some values are unknown." ] }, { "variable": [ "feature_id", "description", "history" ], "class": [ "double", "character", "character" ], "description": [ "Permanent, unique feature record identifier.", "Characteristics or information about a feature or the feature data", "Refers to the name origin, and/or cultural history of a feature." ] } ], "data": { "file_name": [ "us_place_history.csv", "us_place_names.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-27/us_place_history.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-06-27/us_place_names.csv" ] } }, { "date_posted": "2023-04-18", "project_name": "Neolithic Founder Crops", "project_source": [ "https://link.springer.com/article/10.1007/s00334-023-00917-1", "https://fosstodon.org/@joeroe@archaeo.social/110186477750041419", "https://github.com/joeroe", "https://github.com/joeroe/SWAsiaNeolithicFounderCrops/" ], "description": "The data this week comes fromThe \"Neolithic Founder Crops\"\" in Southwest Asia: Research Compendium.\"Revisiting the concept of the 'Neolithic Founder Crops' in southwest Asia\"is an open-access research paper that uses the data. Thank you for sharing your research,@joeroe! According to thesocial media thread about this dataset: Eight 'founder crops' — emmer wheat, einkorn wheat, barley, lentil, pea, chickpea, bitter vetch, and flax — have long been thought to have been the bedrock of #Neolithic economies. ... We found that Neolithic economies were much more diverse than previously thought, incorporating dozens of species of cereals, legumes, small-seeded grasses, brassicas, pseudocereals, sedges, flowering plants, trees, and shrubs. Free-threshing wheat, grass pea, faba bean, and ‘new' glume wheat were especially widely cultivated. Read the thread for context about this data!", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-18", "data_dictionary": [ { "variable": [ "source", "source_id", "source_site_name", "site_name", "latitude", "longitude", "phase", "phase_description", "phase_code", "age_start", "age_end", "taxon_source", "n", "prop", "reference", "taxon_detail", "taxon", "genus", "family", "category", "founder_crop", "edibility", "grass_type", "legume_type" ], "class": [ "character", "character", "character", "character", "double", "double", "character", "character", "character", "double", "double", "character", "double", "double", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character" ], "description": [ "the source database", "id of this record in the source database", "name of the site in the source database", "standardized site name", "latitude", "longitude", "phase", "phase_description", "phase_code", "oldest date for the record, in years before 1950 CE (years BP)", "most recent date for the record, in years before 1950 CE (years BP)", "taxonomy as stated in the course database", "number of individuals in the sample", "proportion of this sample that contains this crop", "papers describing this data", "canonical name for this taxonomic group", "taxonomic details for this sample; this and the previous column may have been swapped in the source", "genus", "family", "broad category for this sample", "traditional founder crop to which this sample belongs", "parts of the plant that are edible, if any", "for grasses, the category for this sample", "for legumes, the category for this sample" ] } ], "data": { "file_name": [ "founder_crops.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-18/founder_crops.csv" ] } }, { "date_posted": "2023-02-07", "project_name": "Big Tech Stock Prices", "project_source": [ "https://github.com/rfordatascience/tidytuesday/issues/509", "https://www.morningstar.com/articles/1129535/5-charts-on-big-tech-stocks-collapse", "https://www.kaggle.com/datasets/evangower/big-tech-stock-prices" ], "description": "The data this week comes from Yahoo Finance viaKaggle(byEvan Gower). This dataset consists of the daily stock prices and volume of 14 different tech companies, including Apple (AAPL), Amazon (AMZN), Alphabet (GOOGL), and Meta Platforms (META) and more! A number of articles have examined the collapse of \"Big Tech\" stock prices, includingthis article from morningstar.com. Note: Allstock_symbols have 3271 prices, except META (2688) and TSLA (3148) because they were not publicly traded for part of the period examined.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-07", "data_dictionary": [ { "variable": [ "stock_symbol", "date", "open", "high", "low", "close", "adj_close", "volume" ], "class": [ "character", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "stock_symbol", "date", "The price at market open.", "The highest price for that day.", "The lowest price for that day.", "The price at market close, adjusted for splits.", "The closing price after adjustments for all applicable splits and dividend distributions. Data is adjusted using appropriate split and dividend multipliers, adhering to Center for Research in Security Prices (CRSP) standards.", "The number of shares traded on that day." ] }, { "variable": [ "stock_symbol", "company" ], "class": [ "character", "character" ], "description": [ "stock_symbol", "Full name of the company." ] } ], "data": { "file_name": [ "big_tech_companies.csv", "big_tech_stock_prices.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-07/big_tech_companies.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-02-07/big_tech_stock_prices.csv" ] } }, { "date_posted": "2023-03-07", "project_name": "Numbats in Australia", "project_source": [ "/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-07/data/numbats.csv", "https://www.ala.org.au", "https://github.com/numbats/numbats-tidytuesday", "https://bie.ala.org.au/species/https://biodiversity.org.au/afd/taxa/6c72d199-f0f1-44d3-8197-224a2f7cff5f" ], "description": "The data this week comes from theAtlas of Living Australia. Thanks to Di Cook forpreparing this week's dataset! ThisNumbat page at the Atlas of Living Australiatalks about these endangered species in greater detail. Acsvfile of numbat sightings is provided. The code to refresh the data is below. Questions that would be interesting to answer are:", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-07", "data_dictionary": [ { "variable": [ "decimalLatitude", "decimalLongitude", "eventDate", "scientificName", "taxonConceptID", "recordID", "dataResourceName", "year", "month", "wday", "hour", "day", "dryandra", "prcp", "tmax", "tmin" ], "class": [ "double", "double", "datetime", "factor", "factor", "character", "factor", "integer", "factor", "factor", "integer", "date", "logical", "double", "double", "double" ], "description": [ "decimalLatitude", "decimalLongitude", "eventDate", "Either \\\"Myrmecobius fasciatus\\\" or \\\"Myrmecobius fasciatus rufus\\\"", "The URL for this (sub)species", "recordID", "dataResourceName", "The 4-digit year of the event (when available)", "The 3-letter month abbreviation of the event (when available)", "The 3-letter weekday abbreviation of the event (when available)", "The hour of the event (when available)", "The date of the event (when available)", "whether the observation was in Dryandra Woodland", "Precipitation on that day in Dryandra Woodland (when relevant), in millimeters", "Maximum temperature on that day in Dryandra Woodland (when relevant), in degrees Celsius", "Minimum temperature on that day in Dryandra Woodland (when relevant), in degrees Celsius" ] } ], "data": { "file_name": [ "numbats.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-07/numbats.csv" ] } }, { "date_posted": "2024-02-06", "project_name": "A few world heritage sites", "project_source": [ "https://100.datavizproject.com/", "https://whc.unesco.org/en/list" ], "description": "This week we're exploring a very small subset ofUNESCO World Heritage Sites. The1 dataset, 100 visualizations projectused this dataset to explore different ways of visualizing a simple dataset. This is your chance to try that out too! Try recreating some of their plots, or make your own. You can add to your data visualization code toolbox by creating types of visualizations you could use with other datasets, or getting inspiration from others. Share your favorite ones! Data from1 dataset, 100 visualizations. No cleaning.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-02-06", "data_dictionary": [ { "variable": [ "country", "2004", "2022" ], "class": [ "character", "integer", "integer" ], "description": [ "Country", "Number of UNESCO World Heritage sites in 2004", "Number of UNESCO World Heritage sites in 2022" ] } ], "data": { "file_name": [ "heritage.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-02-06/heritage.csv" ] } }, { "date_posted": "2024-01-30", "project_name": "Groundhog predictions", "project_source": [ "https://groundhog-day.com/predictions", "https://groundhog-day.com" ], "description": "Happy Groundhog Day! This week we're exploring Groundhog Day Predictions fromgroundhog-day.com! See if you can find a better way to present the annual data than theirtable of predictions by year! For anyone not familiar with the Groundhog Day tradition, if the groundhog sees its shadow and goes back into its burrow, that is a prediction of six more weeks of winter. Otherwise spring will come early. We attempted to provide weather data to accompany this dataset, but so far we've been unsuccessful. Watch for a follow-up dataset in the future! Note: \"Oil Springs Ollie\" (groundhog #55) has been succeeded by \"Heaven's Wildlife Harvey\" (groundhog #70).", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-30", "data_dictionary": [ { "variable": [ "id", "slug", "shortname", "name", "city", "region", "country", "latitude", "longitude", "source", "current_prediction", "is_groundhog", "type", "active", "description", "image", "predictions_count" ], "class": [ "integer", "character", "character", "character", "character", "character", "character", "double", "double", "character", "character", "logical", "character", "logical", "character", "character", "integer" ], "description": [ "A numeric id for this groundhog.", "The name of the groundhog, in simplified kebab case.", "A short version of the name of the groundhog or groundhog substitute.", "The full name of the groundhog or groundhog substitute.", "The city in which the prediction takes place.", "The state or province of the prediction.", "The country of the prediction (USA or Canada).", "The latitude of the city.", "The longitude of the city.", "A url with information about this groundhog.", "A url with information about the most recent prediction.", "A logical value indicating whether this predictor is a living groundhog.", "A short description of the type of animal or other thing that is said to make the prediction.", "A logical value indicating whether this predictor is active (as of 2023).", "A free-text description of the predictor.", "A URL with an image of the predictor.", "The number of predictions available for this predictor." ] }, { "variable": [ "id", "year", "shadow", "details" ], "class": [ "integer", "integer", "logical", "character" ], "description": [ "A numeric id for this groundhog.", "The year of the prediction.", "Whether the groundhog saw its shadow, and thus predicts 6 more weeks of winter.", "Free text with more information about this prediction." ] } ], "data": { "file_name": [ "groundhogs.csv", "predictions.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-30/groundhogs.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-30/predictions.csv" ] } }, { "date_posted": "2023-05-02", "project_name": "The Portal Project", "project_source": [ "https://www.weecology.org/", "https://weecology.github.io/portalr/", "https://portal.weecology.org/", "https://datacarpentry.org/ecology-workshop/", "https://www.data-retriever.org/" ], "description": "The data this week comes from thePortal Project. This is a long-term ecological research site studying the dynamics of desert rodents, plants, ants and weather in Arizona. The Portal Project is a long-term ecological study being conducted near Portal, AZ. Since 1977, the site has been used to study the interactions among rodents, ants and plants and their respective responses to climate. To study the interactions among organisms, they experimentally manipulate access to 24 study plots. This study has produced over 100 scientific papers and is one of the longest running ecological studies in the U.S. TheWeecology research groupmonitors rodents, plants, ants, and weather. All data from the Portal Project are made openly available in near real-time so that they can provide the maximum benefit to scientific research and outreach. The core dataset is managed using an automated living data workflow run using GitHub and Continuous Analysis. This dataset focuses on the rodent data. Full data is available through these resources: The Portal Project data can also be accessed through the Data Retriever, a package manager for data. Data Retriever A teaching focused version of the dataset is also maintained with some of the complexities of the data removed to make it easy to use for computational training purposes. This dataset serves as the core dataset for theData Carpentry Ecologymaterial and has been downloaded almost 50,000 times. Thanks to @ethanwhite for the data cleaning script. This script downloads the data using the{portalr}package. It filters for the species and plot data, and years greater than 1977.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-02", "data_dictionary": [ { "variable": [ "plot", "treatment" ], "class": [ "double", "character" ], "description": [ "Plot number", "Treatment type" ] }, { "variable": [ "species", "scientificname", "taxa", "commonname", "censustarget", "unidentified", "rodent", "granivore", "minhfl", "meanhfl", "maxhfl", "minwgt", "meanwgt", "maxwgt", "juvwgt" ], "class": [ "character", "character", "character", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "Species", "Scientific Name", "Taxa", "Common Name", "Target species (0 or 1)", "Unidentified (0 or 1)", "Rodent (0 or 1)", "Granivore (0 or 1)", "Minimum hindfoot length", "Mean hindfoot length", "Maximum hindfoot length", "Minimum weight", "Mean weight", "Maximum weight", "Juvenile weight" ] }, { "variable": [ "censusdate", "month", "day", "year", "treatment", "plot", "stake", "species", "sex", "reprod", "age", "testes", "vagina", "pregnant", "nipples", "lactation", "hfl", "wgt", "tag", "note2", "ltag", "note3" ], "class": [ "double", "double", "double", "double", "character", "double", "double", "character", "character", "character", "character", "character", "character", "character", "character", "character", "double", "double", "character", "character", "character", "character" ], "description": [ "Census date", "Month", "Day", "Year", "Treatment type", "Plot number", "Stake number", "Species code", "Sex", "Reproductive condition", "Age", "Testes (Scrotal, Recent, or Minor)", "Vagina (Swollen, Plugged, or Both)", "Pregnant", "Nipples (Enlarged, Swollen, or Both)", "Lactating", "Hindfoot length", "Weight", "Primary individual identifier", "Newly tagged individual for 'tag'", "Secondary tag information when ear tags were used in both ears", "Newly tagged individual for 'ltag'" ] } ], "data": { "file_name": [ "plots.csv", "species.csv", "surveys.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-02/plots.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-02/species.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-02/surveys.csv" ] } }, { "date_posted": "2023-11-07", "project_name": "US House Election Results", "project_source": [ "https://electionlab.mit.edu/", "https://electionlab.mit.edu/articles/new-report-how-we-voted-2022", "https://docs.posit.co/ide/user/ide/guide/tools/copilot.html", "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IG0UN2" ], "description": "It's election day in the United States! To celebrate, the data this week comes from theMIT Election Data and Science Lab(MEDSL). Hat tip this week to theRStudio GitHub Copilot integration, which suggested the MEDSL. From the MEDSL's reportNew Report: How We Voted in 2022: The Survey of the Performance of American Elections (SPAE) provides information about how Americans experienced voting in the most recent federal election. The survey has been conducted after federal elections since 2008, and is the only public opinion project in the country that is dedicated explicitly to understanding how voters themselves experience the election process. We're specifically providing data on House elections from 1976-2022. Check out theMEDSL websitefor additional datasets and tools. Be sure to cite the MEDSL in your work: Clean data and dictionary downloaded from theHarvard Dataverse", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-07", "data_dictionary": [ { "variable": [ "year", "state", "state_po", "state_fips", "state_cen", "state_ic", "office", "district", "stage", "runoff", "special", "candidate", "party", "writein", "mode", "candidatevotes", "totalvotes", "unofficial", "version", "fusion_ticket" ], "class": [ "double", "character", "character", "double", "double", "double", "character", "character", "character", "logical", "logical", "character", "character", "logical", "character", "double", "double", "logical", "double", "logical" ], "description": [ "year in which election was held", "state name", "U.S. postal code state abbreviation", "State FIPS code", "U.S. Census state code", "ICPSR state code", "U.S. House (constant)", "district number. At-large districts are coded as 0 (zero)", "electoral stage (gen = general elections, pri = primary elections)", "runoff election", "special election", "name of the candidate as it appears in the House Clerk report", "party of the candidate (always entirely lowercase) (Parties are as they appear in the House Clerk report. In states that allow candidates to appear on multiple party lines, separate vote totals are indicated for each party. Therefore, for analysis that involves candidate totals, it will be necessary to aggregate across all party lines within a district. For analysis that focuses on two-party vote totals, it will be necessary to account for major party candidates who receive votes under multiple party labels. Minnesota party labels are given as they appear on the Minnesota ballots. Future versions of this file will include codes for candidates who are endorsed by major parties, regardless of the party label under which they receive votes.)", "vote totals associated with write-in candidates", "mode of voting; states with data that doesn't break down returns by mode are marked as \\\"total\\\"", "votes received by this candidate for this particular party", "total number of votes cast for this election", "TRUE/FALSE indicator for unofficial result (to be updated later); this appears only for 2018 data in some cases", "date when this dataset was finalized", "A TRUE/FALSE indicator as to whether the given candidate is running on a fusion party ticket, which will in turn mean that a candidate will appear multiple times, but by different parties, for a given election. States with fusion tickets include Connecticut, New Jersey, New York, and South Carolina." ] } ], "data": { "file_name": [ "house.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-07/house.csv" ] } }, { "date_posted": "2024-01-16", "project_name": "US Polling Places 2012-2020", "project_source": [ "https://github.com/PublicI/us-polling-places", "https://publicintegrity.org/", "https://github.com/kelseygonzalez", "https://publicintegrity.org/politics/elections/ballotboxbarriers/data-release-sheds-light-on-past-polling-place-changes/" ], "description": "This week we're honoring the legacy of Martin Luther King Jr. by exploring the US Polling Places. Thedatasetcomes fromThe Center for Public Integrity. You can read more about the data and how it was collected in their September 2020 article\"National data release sheds light on past polling place changes\". Thank youKelsey E Gonzalezfor the dataset suggestion back in 2020! Note: Some states do not have data in this dataset. Several states (Colorado, Hawaii, Oregon, Washington and Utah) vote primarily by mail and have little or no data in this colletion, and others were not available for other reasons. For states with data for multiple elections, how have polling location counts per county changed over time?", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-16", "data_dictionary": [ { "variable": [ "election_date", "state", "county_name", "jurisdiction", "jurisdiction_type", "precinct_id", "precinct_name", "polling_place_id", "location_type", "name", "address", "notes", "source", "source_date", "source_notes" ], "class": [ "date", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character", "date", "character" ], "description": [ "date of the election as YYYY-MM-DD", "2-letter abbreviation of the state", "county name, if available", "jurisdiction, if available", "type of jurisdiction, if available; one of \\\"county\\\", \\\"borough\\\", \\\"town\\\", \\\"municipality\\\", \\\"city\\\", \\\"parish\\\", or \\\"county_municipality\\\"", "unique ID of the precinct, if available", "name of the precinct, if available", "unique ID of the polling place, if available", "type of polling location, if available; one of \\\"early_vote\\\", \\\"early_vote_site\\\", \\\"election_day\\\", \\\"polling_location\\\", \\\"polling_place\\\", or \\\"vote_center\\\"", "name of the polling place, if available", "address of the polling place, if available", "optional notes about the polling place", "source of the polling place data; one of \\\"ORR\\\", \\\"VIP\\\", \\\"website\\\", or \\\"scraper\\\"", "date that the source was compiled", "optional notes about the source" ] } ], "data": { "file_name": [ "polling_places.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-16/polling_places.csv" ] } }, { "date_posted": "2023-04-11", "project_name": "US Egg Production", "project_source": [ "https://osf.io/z2gxn/", "https://samaramendez.github.io/", "https://thehumaneleague.org/article/E008R01-us-egg-production-data" ], "description": "The data this week comes fromThe Humane League's US Egg Production dataset bySamara Mendez. Dataset and code is available for this project on OSF atUS Egg Production Data Set. This dataset tracks the supply of cage-free eggs in the United States from December 2007 to February 2021. For TidyTuesday we've used data through February 2021, but the full dataset, with data through the present, is available in theOSF project. In this project, they synthesize an analysis-ready data set that tracks cage-free hens and the supply of cage-free eggs relative to the overall numbers of hens and table eggs in the United States. The data set is based on reports produced by the United States Department of Agriculture (USDA), which are published weekly or monthly. They supplement these data with definitions and a taxonomy of egg products drawn from USDA and industry publications. The data include flock size (both absolute and relative) and egg production of cage-free hens as well as all table-egg-laying hens in the US, collected to understand the impact of the industry's cage-free transition on hens. Data coverage ranges from December 2007 to February 2021. This data was already cleaned for the report. Raw data is also available atUS Egg Production Dataset.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-11", "data_dictionary": [ { "variable": [ "observed_month", "prod_type", "prod_process", "n_hens", "n_eggs", "source" ], "class": [ "double", "character", "character", "double", "double", "character" ], "description": [ "Month in which report observations are collected,Dates are recorded in ISO 8601 format YYYY-MM-DD", "type of egg product: hatching, table eggs", "type of production process and housing: cage-free (organic), cage-free (non-organic), all. The value 'all' includes cage-free and conventional housing.", "number of hens produced by hens for a given month-type-process combo", "number of eggs producing eggs for a given month-type-process combo", "Original USDA report from which data are sourced. Values correspond to titles of PDF reports. Date of report is included in title." ] }, { "variable": [ "observed_month", "percent_hens", "percent_eggs", "source" ], "class": [ "double", "double", "double", "character" ], "description": [ "Month in which report observations are collected,Dates are recorded in ISO 8601 format YYYY-MM-DD", "observed or computed percentage of cage-free hens relative to all table-egg-laying hens", "computed percentage of cage-free eggs relative to all table eggs,This variable is not available for data sourced from the Egg Markets Overview report", "Original USDA report from which data are sourced. Values correspond to titles of PDF reports. Date of report is included in title." ] } ], "data": { "file_name": [ "cage-free-percentages.csv", "egg-production.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-11/cage-free-percentages.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-04-11/egg-production.csv" ] } }, { "date_posted": "2023-11-21", "project_name": "R-Ladies Chapter Events", "project_source": [ "https://github.com/rladies/meetup_archive", "https://youtu.be/EstytFNjrWc", "https://rladies.org/", "https://github.com/Fgazzelloni", "https://github.com/rfordatascience/tidytuesday/issues/632", "https://github.com/Fgazzelloni/RLadies-Chapters-Making-Talks-Work-for-Diverse-Audiences/tree/main" ], "description": "R-Ladies Global is an inspiring story of community, empowerment, and diversity in the field of data science. Founded by Gabriela de Queiroz, R-Ladies began as a grassroots movement with a simple mission: to promote gender diversity in the R programming community and provide a welcoming space for women and gender minorities to learn, collaborate, and excel in data science.R-Ladies Global Website The data this week comes fromFederica Gazzelloni'spresentationonR-Ladies Chapters: Making talks work for diverse audienceswith data from therladies meetup-archive. Cleaning script from @Fgazzelloni inher tidytuesday github issue.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-21", "data_dictionary": [ { "variable": [ "id", "chapter", "title", "date", "location", "year" ], "class": [ "double", "character", "character", "double", "character", "double" ], "description": [ "event id", "r-ladies chapter name", "event title", "event date", "event location if online or in person", "event year" ] } ], "data": { "file_name": [ "rladies_chapters.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-11-21/rladies_chapters.csv" ] } }, { "date_posted": "2023-07-11", "project_name": "Global surface temperatures", "project_source": [ "https://data.giss.nasa.gov/gistemp/", "https://rpubs.com/BrianBartling/GISTEMP", "https://data.giss.nasa.gov/gistemp/faq/" ], "description": "The data this week comes from theNASA GISS Surface Temperature Analysis (GISTEMP v4). This datasets are tables of global and hemispheric monthly means and zonal annual means. They combine land-surface, air and sea-surface water temperature anomalies (Land-Ocean Temperature Index, L-OTI). The values in the tables are deviations from the corresponding 1951-1980 means. The GISS Surface Temperature Analysis version 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in their publications Hansen et al. (2010) and Lenssen et al. (2019). These updated files incorporate reports for the previous month and also late reports and corrections for earlier months. When comparing seasonal temperatures, it is convenient to use “meteorological seasons” based on temperature and defined as groupings of whole months. Thus, Dec-Jan-Feb (DJF) is the Northern Hemisphere meteorological winter, Mar-Apr-May (MAM) is N.H. meteorological spring, Jun-Jul-Aug (JJA) is N.H. meteorological summer and Sep-Oct-Nov (SON) is N.H. meteorological autumn. String these four seasons together and you have the meteorological year that begins on Dec. 1 and ends on Nov. 30 (D-N). The full year is Jan to Dec (J-D).Brian Bartling An analysis and more information on the data can be found in Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522. There's also more detail and answers to commonly asked in questions intheir FAQ. Citation: GISTEMP Team, 2023: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies. Dataset accessed 2023-07-09 athttps://data.giss.nasa.gov/gistemp/. Missing data was indicated by***. Replaced***with an empty cell, so these would be NAs.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-11", "data_dictionary": [ { "variable": [ "Year", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "J-D", "D-N", "DJF", "MAM", "JJA", "SON" ], "class": [ "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "Year", "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December", "January-December", "Decemeber-November", "December-January-February", "March-April-May", "June-July-August", "September-October-November" ] }, { "variable": [ "Year", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "J-D", "D-N", "DJF", "MAM", "JJA", "SON" ], "class": [ "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "Year", "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December", "January-December", "Decemeber-November", "December-January-February", "March-April-May", "June-July-August", "September-October-November" ] }, { "variable": [ "Year", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "J-D", "D-N", "DJF", "MAM", "JJA", "SON" ], "class": [ "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "Year", "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December", "January-December", "Decemeber-November", "December-January-February", "March-April-May", "June-July-August", "September-October-November" ] }, { "variable": [ "Year", "Glob", "NHem", "SHem", "24N-90N", "24S-24N", "90S-24S", "64N-90N", "44N-64N", "24N-44N", "EQU-24N", "24S-EQU", "44S-24S", "64S-44S", "90S-64S" ], "class": [ "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "Year", "Global", "Northern Hemisphere", "Southern Hemisphere", "24N-90N lattitude", "24S-24N lattitude", "90S-24S lattitude", "64N-90N lattitude", "44N-64N lattitude", "24N-44N lattitude", "EQU-24N lattitude", "24S-EQU lattitude", "44S-24S lattitude", "64S-44S lattitude", "90S-64S lattitude" ] } ], "data": { "file_name": [ "global_temps.csv", "nh_temps.csv", "sh_temps.csv", "zonann_temps.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-11/global_temps.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-11/nh_temps.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-11/sh_temps.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-11/zonann_temps.csv" ] } }, { "date_posted": "2024-02-20", "project_name": "R Consortium ISC Grants", "project_source": [ "https://www.r-consortium.org/blog/2024/02/08/r-consortium-infrastructure-steering-committee-isc-grant-program-accepting-proposals-starting-march-1st" ], "description": "The R Consortium Infrastructure Steering Committee (ISC) Grant Program will accept proposals again between March 1 and April 1, 2024 (and then again in the fall). This initiative is a cornerstone of our commitment to bolstering and enhancing the R Ecosystem. We fund projects contributing to the R community’s technical and social infrastructures. Learn more intheir blog post announcing this round of grants. The R Consortium ISC has been awarding grants since 2016. This week's data is an exploration of past grant recipients. Are there any keywords that stand out in the titles or summaries of awarded grants? Have the funded amounts changed over time?", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-02-20", "data_dictionary": [ { "variable": [ "year", "group", "title", "funded", "proposed_by", "summary", "website" ], "class": [ "integer", "integer", "character", "integer", "character", "character", "character" ], "description": [ "The year in which the grant was awarded.", "Whether the grant was awarded in the spring cycle (1) or the fall cycle (2).", "The title of the project.", "The dollar amount funded for the project.", "The name of the person who requested the grant.", "A description of the project.", "The website associated with the project, if available." ] } ], "data": { "file_name": [ "isc_grants.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-02-20/isc_grants.csv" ] } }, { "date_posted": "2023-08-15", "project_name": "Spam E-mail", "project_source": [ "https://vincentarelbundock.github.io/Rdatasets/index.html", "https://archive.ics.uci.edu/dataset/94/spambase", "https://search.r-project.org/CRAN/refmans/kernlab/html/spam.html", "https://vincentarelbundock.github.io/Rdatasets/doc/DAAG/spam7.html" ], "description": "The data this week comes from Vincent Arel-Bundock's Rdatasets package(https://vincentarelbundock.github.io/Rdatasets/index.html). Rdatasets is a collection of 2246 datasets which were originally distributed alongside the statistical software environment R and some of its add-on packages. The goal is to make these data more broadly accessible for teaching and statistical software development. We're working with thespam emaildataset. This is a subset of thespam e-mail database. This is a dataset collected at Hewlett-Packard Labs by Mark Hopkins, Erik Reeber, George Forman, and Jaap Suermondt and shared with theUCI Machine Learning Repository. The dataset classifies 4601 e-mails as spam or non-spam, with additional variables indicating the frequency of certain words and characters in the e-mail. First column was removed.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-08-15", "data_dictionary": [ { "variable": [ "crl.tot", "dollar", "bang", "money", "n000", "make", "yesno" ], "class": [ "double", "double", "double", "double", "double", "double", "character" ], "description": [ "Total length of uninterrupted sequences of capitals", "Occurrences of the dollar sign, as percent of total number of characters", "Occurrences of ‘!’, as percent of total number of characters", "Occurrences of ‘money’, as percent of total number of characters", "Occurrences of the string ‘000’, as percent of total number of words", "Occurrences of ‘make’, as a percent of total number of words", "Outcome variable, a factor with levels 'n' not spam, 'y' spam" ] } ], "data": { "file_name": [ "spam.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-08-15/spam.csv" ] } }, { "date_posted": "2023-12-26", "project_name": "R Package Structure", "project_source": [ "https://r-pkgs.org/", "https://cran.r-project.org/package=pkgstats", "https://mpadge.github.io/pkgstats-analyses/articles/pkgstats.html", "https://zenodo.org/records/7414296", "https://cran.r-project.org/" ], "description": "Happy Boxing Day! While you're dealing with your physical packages, we're looking into R packages! The dataset this week comes from\"Historical Trends in R Package Structure and Interdependency on CRAN\"by Mark Padgham and Noam Ross. In that paper, they use the{pkgstats}R package to analyze the structure of R packages over time, using an archive of all packages onCRANas of 2022-11-22. We've provided csv versions of two ofthe datasets from that paper. The paper focuses on package characteristics over time. It might be interesting to look at the distribution of similar features (such as lines of code) across packages. If you're unfamiliar with some of the terminology in this dataset, you might find theR Packagesbook by Hadley Wickham and Jennifer Bryan helpful. If you would like to dive deeper, you can download the larger dataset with this code: The authors provided mostly [clean data](https://zenodo.org/records/7414296. We chose one of their datasets, lightly cleaned the data, and saved it as a CSV. We also split theexternal_callsdata into two files, one for calls to functions in other files in the same package (internal_calls.csv) and one for calls to functions in other packages (external_calls.csv).", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-26", "data_dictionary": [ { "variable": [ "package", "version", "date", "license", "files_R", "files_src", "files_inst", "files_vignettes", "files_tests", "loc_R", "loc_src", "loc_inst", "loc_vignettes", "loc_tests", "blank_lines_R", "blank_lines_src", "blank_lines_inst", "blank_lines_vignettes", "blank_lines_tests", "comment_lines_R", "comment_lines_src", "comment_lines_inst", "comment_lines_vignettes", "comment_lines_tests", "rel_space", "rel_space_R", "rel_space_src", "rel_space_inst", "rel_space_vignettes", "rel_space_tests", "indentation", "nexpr", "num_vignettes", "num_demos", "num_data_files", "data_size_total", "data_size_median", "translations", "urls", "bugs", "desc_n_aut", "desc_n_ctb", "desc_n_fnd", "desc_n_rev", "desc_n_ths", "desc_n_trl", "depends", "imports", "suggests", "enhances", "linking_to", "n_fns_r", "n_fns_r_exported", "n_fns_r_not_exported", "n_fns_src", "n_fns_per_file_r", "n_fns_per_file_src", "npars_exported_mn", "npars_exported_md", "loc_per_fn_r_mn", "loc_per_fn_r_md", "loc_per_fn_r_exp_mn", "loc_per_fn_r_exp_md", "loc_per_fn_r_not_exp_mn", "loc_per_fn_r_not_exp_md", "loc_per_fn_src_mn", "loc_per_fn_src_md", "languages", "doclines_per_fn_exp_mn", "doclines_per_fn_exp_md", "doclines_per_fn_not_exp_mn", "doclines_per_fn_not_exp_md", "doclines_per_fn_src_mn", "doclines_per_fn_src_md", "docchars_per_par_exp_mn", "docchars_per_par_exp_md", "n_edges", "n_edges_r", "n_edges_src", "n_clusters", "centrality_dir_mn", "centrality_dir_md", "centrality_dir_mn_no0", "centrality_dir_md_no0", "centrality_undir_mn", "centrality_undir_md", "centrality_undir_mn_no0", "centrality_undir_md_no0", "num_terminal_edges_dir", "num_terminal_edges_undir", "node_degree_mn", "node_degree_md", "node_degree_max", "cpl_instability_pkg" ], "class": [ "character", "character", "double", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "character", "character", "character", "double", "double", "double", "double", "double", "double", "character", "character", "character", "character", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "character", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double" ], "description": [ "The name of the pacakge", "The package version", "The release date of that version of the package", "License information", "Number of files in the /R directory, where numbers are recursively counted in all sub-directories", "Number of files in the /src directory, where numbers are recursively counted in all sub-directories", "Number of files in the /inst/include directory, where numbers are recursively counted in all sub-directories", "Number of files in the /vignettes directory, where numbers are recursively counted in all sub-directories", "Number of files in the /tests directory, where numbers are recursively counted in all sub-directories", "Total lines of code across all files in the /R directory", "Total lines of code across all files in the /src directory", "Total lines of code across all files in the /inst/include directory", "Total lines of code across all files in the /vignettes directory", "Total lines of code across all files in the /tests directory", "Total numbers of blank lines across all files in the /R directory", "Total numbers of blank lines across all files in the /src directory", "Total numbers of blank lines across all files in the /inst directory", "Total numbers of blank lines across all files in the /vignettes directory", "Total numbers of blank lines across all files in the /tests directory", "Total numbers of comment lines across all files in the /R directory", "Total numbers of comment lines across all files in the /src directory", "Total numbers of comment lines across all files in the /inst directory", "Total numbers of comment lines across all files in the /vignettes directory", "Total numbers of comment lines across all files in the /tests directory", "Measure of relative white space across all files in the /R, /src, and /inst directories", "Measure of relative white space across all files in the /R directory", "Measure of relative white space across all files in the /src directory", "Measure of relative white space across all files in the /inst directory", "Measure of relative white space across all files in the /vignettes directory", "Measure of relative white space across all files in the /tests directory", "The number of spaces used to indent code, with values of -1 indicating indentation with tab characters", "The median number of nested expression per line of code, counting only those lines which have any expressions", "Number of vignettes", "Number of demos", "Number of data files", "Total size of all package data", "Median size of package data files", "List of translations where package includes translations files, given as a comma-separated list of (spoken) language codes", "Package URL(s)", "URL for BugReports", "Number of contributors with role of author", "Number of contributors with role of contributor", "Number of contributors with role of funder", "Number of contributors with role of reviewer", "Number of contributors with role of thesis advisor", "Number of contributors with role of translator (relating to translation between computer and not spoken languages)", "Comma-separated character entries for all depends packages", "Comma-separated character entries for all imports packages", "Comma-separated character entries for all suggests packages", "Comma-separated character entries for all enhances packages", "Comma-separated character entries for all linking_to packages", "Numbers of functions in R", "Numbers of exported R functions", "Numbers of non-exported R functions", "Number of functions (or objects) in other computer languages, including functions in both src and inst/include directories", "Number of functions (or objects) per individual file in /R", "Number of functions (or objects) per individual file in source directories other than /R", "Mean number of parameters per exported R function", "Median number of parameters per exported R function", "Mean lines of code per function in /R", "Median lines of code per function in /R", "Mean lines of code per exported function in /R", "Median lines of code per exported function in /R", "Mean lines of code per non-exported function in /R", "Median lines of code per non-exported function in /R", "Mean lines of code per in other source directories", "Median lines of code per in other source directories", "languages", "Mean lines of documentation per exported function in /R", "Median lines of documentation per exported function in /R", "Mean lines of documentation per non-exported function in /R", "Median lines of documentation per non-exported function in /R", "Mean lines of code per in other source directories", "Median lines of code per in other source directories", "Mean number of documentation characters per parameter of exported R functions", "Median number of documentation characters per parameter of exported R functions", "Number of edges connecting functions (and other objects) across all languages in package code", "Number of edges connecting R functions (and other objects)", "Number of edges connecting functions (and other objects) in other languages", "Number of distinct clusters in package network", "Mean centrality of all network edges, calculated from directed representation of network", "Median centrality of all network edges, calculated from directed representation of network", "Mean centrality of all network edges, calculated from directed representation of network, excluding edges with centrality of zero", "Median centrality of all network edges, calculated from directed representation of network, excluding edges with centrality of zero", "Mean centrality of all network edges, calculated from undirected representation of network", "Median centrality of all network edges, calculated from undirected representation of network", "Mean centrality of all network edges, calculated from undirected representation of network, excluding edges with centrality of zero", "Median centrality of all network edges, calculated from undirected representation of network, excluding edges with centrality of zero", "Numbers of terminal edges, calculated from directed representation of network", "num_terminal_edges_undir, calculated from undirected representation of network", "Mean node degree", "Median node degree", "Maximum node degree", "Coupling instability, a measure of the extent to which packages depend on external functionality without other packages in turn depending on them" ] }, { "variable": [ "package_from", "package_to", "n_total", "n_unique" ], "class": [ "character", "character", "double", "double" ], "description": [ "The package that makes the call", "The package that the source package calls", "The total number of calls from package_from to package_to", "The number of unique calls from package_from to package_to" ] }, { "variable": [ "package", "n_total", "n_unique" ], "class": [ "character", "double", "double" ], "description": [ "The package being evaluated", "The total number of calls from functions in one file to functions in another file within the package", "The number of unique calls from functions in one file to functions in another file within the package" ] } ], "data": { "file_name": [ "cran_20221122.csv", "external_calls.csv", "internal_calls.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-26/cran_20221122.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-26/external_calls.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-26/internal_calls.csv" ] } }, { "date_posted": "2023-03-28", "project_name": "Time Zones", "project_source": [ "https://twitter.com/dvaughan32/status/1639281179433074692", "https://tzdb.r-lib.org/", "https://data.iana.org/time-zones/tz-link.html", "https://www.timeanddate.com/time/dst/", "https://www.timeanddate.com/time/time-zones-interesting.html#:~:text=Lord%20Howe%20Island%3A%20UTC%20%2B10%3A30%20/%20%2B11%3A00", "https://clock.r-lib.org/" ], "description": "The data this week comes from theIANA tz databasevia the {clock} and {tzdb} packages. Special thanks toDavis Vaughan for the assist in preparing this data! Many websites operate using the data in the IANA tz database.\"What Is Daylight Saving Time\"from timeanddate.com is a good place to start to find interesting information about time zones, such as the strange case ofLord Howe Island, Australia. Changes in the conversion of a given time zone to UTC (for example for daylight savings or because the definition of the time zone changed). Descriptions of time zones from theIANA time zone database. Countries (or other place names) that overlap with each time zone. Names of countries and other places.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-28", "data_dictionary": [ { "variable": [ "zone", "begin", "end", "offset", "dst", "abbreviation" ], "class": [ "character", "character", "character", "double", "logical", "character" ], "description": [ "The name of the time zone.", "When this definition went into effect, in UTC. Tip: convert to a datetime using lubridate::as_datetime().", "When this definition ended (and the next definition went into effect), in UTC. Tip: convert to a datetime using lubridate::as_datetime().", "The offset of this time zone from UTC, in seconds.", "Whether daylight savings time is active within this definition.", "The time zone abbreviation in use throughout this begin to end range." ] }, { "variable": [ "zone", "latitude", "longitude", "comments" ], "class": [ "character", "double", "double", "character" ], "description": [ "The name of the time zone.", "Latitude of the time zone's \\\"principal location.\\\"", "Longitude of the time zone's \\\"principal location.\\\"", "Comments from the tzdb definition file." ] }, { "variable": [ "zone", "country_code" ], "class": [ "character", "character" ], "description": [ "The name of the time zone.", "The ISO 3166-1 alpha-2 2-character country code." ] }, { "variable": [ "country_code", "place_name" ], "class": [ "character", "character" ], "description": [ "The ISO 3166-1 alpha-2 2-character country code.", "The usual English name for the coded region, chosen so that alphabetic sorting of subsets produces helpful lists. This is not the same as the English name in the ISO 3166 tables." ] } ], "data": { "file_name": [ "countries.csv", "timezone_countries.csv", "timezones.csv", "transitions.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-28/countries.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-28/timezone_countries.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-28/timezones.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-03-28/transitions.csv" ] } }, { "date_posted": "2023-12-19", "project_name": "Holiday Episodes", "project_source": [ "https://developer.imdb.com/non-commercial-datasets/", "https://tvtropes.org/pmwiki/pmwiki.php/Main/HolidayEpisode", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-12/readme.md" ], "description": "Happy holidays! Last week we explored\"holiday\" movies. This week we're exploring \"holiday\" TV episodes: individual episodes of TV shows with \"holiday\", \"Christmas\", \"Hanukkah\", or \"Kwanzaa\" (or variants thereof) in their title! The data this week again comes from theInternet Movie Database. We don't have an article using exactly this dataset, but you might get inspiration from the TVTropesHoliday Episodearticle.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-19", "data_dictionary": [ { "variable": [ "tconst", "parent_tconst", "season_number", "episode_number", "primary_title", "original_title", "year", "runtime_minutes", "genres", "simple_title", "average_rating", "num_votes", "parent_title_type", "parent_primary_title", "parent_original_title", "parent_start_year", "parent_end_year", "parent_runtime_minutes", "parent_genres", "parent_simple_title", "parent_average_rating", "parent_num_votes", "christmas", "hanukkah", "kwanzaa", "holiday" ], "class": [ "character", "character", "double", "double", "character", "character", "double", "double", "character", "character", "double", "double", "character", "character", "character", "double", "double", "double", "character", "character", "double", "double", "logical", "logical", "logical", "logical" ], "description": [ "alphanumeric unique identifier of the individual episode", "alphanumeric unique identifier of the parent TV series", "season number the episode belongs to", "episode number of the tconst in the TV series", "the more popular title / the title used by the filmmakers on promotional materials at the point of release", "original title, in the original language", "the release year of a title", "primary runtime of the title, in minutes", "includes up to three genres associated with the title (comma-delimited)", "the title in lowercase, with punctuation removed, for easier filtering and grouping", "weighted average of all the individual user ratings on IMDb", "number of votes the title has received on IMDb (titles with fewer than 10 votes were not included in this dataset)", "the type/format of the title (\\\"tvMiniSeries\\\" or \\\"tvSeries\\\"", "the more popular title / the title used by the filmmakers on promotional materials at the point of release (for the parent TV series)", "original title, in the original language (for the parent TV series)", "the series start year", "the series end year", "primary runtime of the TV series, in minutes", "includes up to three genres associated with the TV series", "the title in lowercase, with punctuation removed, for easier filtering and grouping (for the parent TV series)", "weighted average of all the individual user ratings on IMDb (for the parent TV series)", "number of votes the title has received on IMDb (for the parent TV series; titles with fewer than 10 votes were not included in this dataset)", "whether the episode title includes \\\"christmas\\\", \\\"xmas\\\", \\\"x mas\\\", etc", "whether the episode title includes \\\"hanukkah\\\", \\\"chanukah\\\", etc", "whether the episode title includes \\\"kwanzaa\\\"", "whether the episode title includes the word \\\"holiday\\\"" ] }, { "variable": [ "tconst", "genres" ], "class": [ "character", "character" ], "description": [ "alphanumeric unique identifier of the episode title", "genres associated with the episode, one row per genre" ] } ], "data": { "file_name": [ "holiday_episode_genres.csv", "holiday_episodes.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-19/holiday_episode_genres.csv", "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-19/holiday_episodes.csv" ] } }, { "date_posted": "2023-10-31", "project_name": "Horror Legends", "project_source": [ "https://www.snopes.com/" ], "description": "Happy Halloween! To celebrate, this week we're exploring horror legends fromSnopes.com! Since urban legends are often a means of expressing our fears about the dangers that ripple just beneath the surface of our seemingly calm and untroubled world, it should come as no surprise that horror legends are one of urban folklore's richest veins. We worry about the terrible accidents we're powerless to prevent, and we fear anonymous killers who choose victims at random. We cannot protect ourselves from the venomous animals who slither undetected into the places where we work, play, and shop, nor can we stop the onslaught of insects who invade our homes and our bodies. We're repulsed by the contaminants that may lurk in our food. We're afraid of foreigners and foreign places. We fear for our childrens' safety in a world full of drugs, kidnappers, and poisons. We never know what gruesome discovery may be waiting around the next corner. And even if we somehow escape all of these horrors, our own vanities may do us in. You might want to dig into the details of the articles this week -- particularly if the rating is \"mixture\". Each observation includes the URL to that article, which you can open directly from R with theutils::browseURL()function.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-10-31", "data_dictionary": [ { "variable": [ "title", "url", "rating", "subtitle", "author", "published", "claim" ], "class": [ "character", "character", "character", "character", "character", "Date", "character" ], "description": [ "The title of this article.", "The url for this article.", "Whether the claim was found to be \\\"false\\\", \\\"true\\\", or somewhere in-between.", "A subtitle for this article.", "The researcher who investigated this claim.", "The date when the article first appeared on Snopes.", "The claim being investigated." ] } ], "data": { "file_name": [ "horror_articles.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-10-31/horror_articles.csv" ] } }, { "date_posted": "2023-05-30", "project_name": "Verified Oldest People", "project_source": [ "https://en.wikipedia.org/wiki/List_of_the_verified_oldest_people", "https://github.com/frankiethull/centenarians" ], "description": "The data this week comes from theWikipedia List of the verified oldest peopleviafrankiethull on GitHub. Thank you for the submission, Frank! These are lists of the 100 known verified oldest people sorted in descending order by age in years and days. The oldest person ever whose age has been independently verified is Jeanne Calment (1875–1997) of France, who lived to the age of 122 years and 164 days. The oldest verified man ever is Jiroemon Kimura (1897–2013) of Japan, who lived to the age of 116 years and 54 days. The oldest known living person is Maria Branyas of Spain, aged 116 years, 85 days. The oldest known living man is Juan Vicente Pérez of Venezuela, aged 114 years, 1 day. The 100 oldest women have, on average, lived several years longer than the 100 oldest men. No data cleaning. See thesource GitHub repofor details.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-30", "data_dictionary": [ { "variable": [ "rank", "name", "birth_date", "death_date", "age", "place_of_death_or_residence", "gender", "still_alive" ], "class": [ "integer", "character", "date", "date", "double", "character", "character", "character" ], "description": [ "This person's overall rank by age.", "The full name of this person.", "This person's birth date.", "This person's death date (or NA if still alive).", "The person's age, either on the day of their death or on the day when the dataset was extracted on 2023-05-25.", "Where the person lives now or where they were when they died.", "Most likely actually the sex assigned to the person at birth (the source article does not specify).", "Either \\\"alive\\\" if the person was still alive at the time when the article as referenced, or \\\"deceased\\\" if the person was no longer alive." ] } ], "data": { "file_name": [ "centenarians.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-30/centenarians.csv" ] } }, { "date_posted": "2023-07-18", "project_name": "GPT detectors", "project_source": [ "https://arxiv.org/abs/2304.02819", "https://github.com/simonpcouch/detectors/" ], "description": "The data this week comes from Simon Couch'sdetectors R package. containing predictions from various GPT detectors. The data is based on the pre-print: GPT Detectors Are Biased Against Non-Native English Writers. Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, James Zou. arXiv:2304.02819 The study authors carried out a series of experiments passing a number of essays to different GPT detection models. Juxtaposing detector predictions for papers written by native and non-native English writers, the authors argue that GPT detectors disproportionately classify real writing from non-native English writers as AI-generated. csv file was generated from the detectors tibble in the 'detectors' R package.", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-18", "data_dictionary": [ { "variable": [ "kind", ".pred_AI", ".pred_class", "detector", "native", "name", "model", "document_id", "prompt" ], "class": [ "character", "double", "character", "character", "character", "character", "character", "double", "character" ], "description": [ "Whether the essay was written by a \\\"Human\\\" or \\\"AI\\\".", "The class probability from the GPT detector that the inputted text was written by AI.", "The uncalibrated class prediction, encoded as if_else(.pred_AI > .5, \\\"AI\\\", \\\"Human\\\")", "The name of the detector used to generate the predictions.", "For essays written by humans, whether the essay was written by a native English writer or not. These categorizations are coarse; values of \\\"Yes\\\" may actually be written by people who do not write with English natively. NA indicates that the text was not written by a human.", "A label for the experiment that the predictions were generated from.", "For essays that were written by AI, the name of the model that generated the essay.", "A unique identifier for the supplied essay. Some essays were supplied to multiple detectors. Note that some essays are AI-revised derivatives of others.", "For essays that were written by AI, a descriptor for the form of \\\"prompt engineering\\\" passed to the model." ] } ], "data": { "file_name": [ "detectors.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-07-18/detectors.csv" ] } }, { "date_posted": "2023-10-10", "project_name": "Haunted Places in the United States", "project_source": [ "https://query.data.world/s/glc736mqf4dxrqe6nbsamblqndemb6?dws=00000", "https://data.world/timothyrenner/haunted-places", "https://geocompx.org/post/2023/rgdal-retirement/index.html", "https://github.com/timothyrenner/shadowlands-haunted-places", "https://www.theshadowlands.net/places/" ], "description": "Halloween is coming soon, so we're exploring a spooky dataset: a compilation of Haunted Places in the United States. The dataset wascompiled by Tim Renner, usingThe Shadowlands Haunted Places Index, andshared on data.world. We're also using this dataset as a reminder thatseveral R packages for spatial data are heading to the graveyard next week. Don't be tricked by their demise! Data downloaded directly fromhttps://query.data.world/s/glc736mqf4dxrqe6nbsamblqndemb6?dws=00000", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-10-10", "data_dictionary": [ { "variable": [ "city", "country", "description", "location", "state", "state_abbrev", "longitude", "latitude", "city_longitude", "city_latitude" ], "class": [ "character", "character", "character", "character", "character", "character", "double", "double", "double", "double" ], "description": [ "The city where the place is located.", "The country where the place is located (always \\\"United States\\\")", "A text description of the place. The amount of detail in these descriptions is highly variable.", "A title for the haunted place.", "The US state where the place is located.", "The two-letter abbreviation for the state.", "Longitude of the place.", "Latitude of the place.", "Longitude of the city center.", "Latitude of the city center." ] } ], "data": { "file_name": [ "haunted_places.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-10-10/haunted_places.csv" ] } }, { "date_posted": "2024-01-23", "project_name": "Educational attainment of young people in English towns", "project_source": [ "https://github.com/acarpignani", "https://www.ons.gov.uk/peoplepopulationandcommunity/educationandchildcare/articles/whydochildrenandyoungpeopleinsmallertownsdobetteracademicallythanthoseinlargertowns/2023-07-25", "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/educationandchildcare/datasets/educationalattainmentofyoungpeopleinenglishtownsdata/200708201819/youngpeoplesattainmentintownsreferencetable1.xlsx", "https://www.ons.gov.uk/" ], "description": "Thedatasetthis week comes fromThe UK Office for National Statistics. It was explored in the July 2023 article\"Why do children and young people in smaller towns do better academically than those in larger towns?\". Thank youAndrea Carpignanifor the dataset suggestion. The article this week contains several plots, one of which is interactive. Can you reproduce them? Can you find anything in the data that isn't explored in the article?", "data_source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-23", "data_dictionary": [ { "variable": [ "town11cd", "town11nm", "population_2011", "size_flag", "rgn11nm", "coastal", "coastal_detailed", "ttwa11cd", "ttwa11nm", "ttwa_classification", "job_density_flag", "income_flag", "university_flag", "level4qual_residents35_64_2011", "ks4_2012_2013_counts", "key_stage_2_attainment_school_year_2007_to_2008", "key_stage_4_attainment_school_year_2012_to_2013", "level_2_at_age_18", "level_3_at_age_18", "activity_at_age_19_full_time_higher_education", "activity_at_age_19_sustained_further_education", "activity_at_age_19_appprenticeships", "activity_at_age_19_employment_with_earnings_above_0", "activity_at_age_19_employment_with_earnings_above_10_000", "activity_at_age_19_out_of_work", "highest_level_qualification_achieved_by_age_22_less_than_level_1", "highest_level_qualification_achieved_by_age_22_level_1_to_level_2", "highest_level_qualification_achieved_by_age_22_level_3_to_level_5", "highest_level_qualification_achieved_by_age_22_level_6_or_above", "highest_level_qualification_achieved_b_age_22_average_score", "education_score" ], "class": [ "character", "character", "numeric", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric" ], "description": [ "Town/city geography code (2011)", "Town/city geography name (2011)", "Measure of the usual resident population in the town/city", "Size category of the built-up area or built-up area subdivision based on resident population (from Census 2011)", "English region name", "Variable used to describe towns as coastal or non-coastal", "Coastal towns split by size and by seaside towns and other coastal (non-seaside) towns", "Travel-to-work area code (Census 2011 version)", "Travel-to-work area name (Census 2011 version)", "Travel to work area classification", "Variable used to describe towns as working, residential or mixed.", "Variable used to describe towns as lower income deprivation, mid income deprivation or higher income deprivatio", "Variable used to describe whether the town/city has a university", "Proportion of the town/city residents aged 35-64 with a Level 4 qualification or above.", "Count of pupils in the town/city in the 2012/13 Key stage 4 cohort", "Proportion of pupils that achieved level 4 or above (expected level) in key stage 2 in English and Maths in the 2007 to 2008 school year", "Proportion of pupils that achieved 5 GCSE or more, including English and Maths, with grades A*-C in the 2012 to 2013 school year", "Proportion of the town/city's 2012/13 key stage 4 cohort that achieved level 2 qualifications at the age 18.", "Proportion of the town/city's 2012/13 key stage 4 cohort that achieved level 3 qualifications at the age 18.", "Proportion of the town/city's 2012/13 key stage 4 cohort in full time higher education at the age 19.", "Proportion of the town/city's 2012/13 key stage 4 cohort in sustained further education at the age 19.", "Proportion of the town/city's 2012/13 key stage 4 cohort in an apprenticeship at the age 19.", "Proportion of the town/city's 2012/13 key stage 4 cohort in sustained employment at the age 19.", "Proportion of the town/city's 2012/13 key stage 4 cohort in sustained employment earning £10,000 or above at the age 19.", "Proportion of the town/city's 2012/13 key stage 4 cohort claiming out-of-work benefits at the age 19.", "Proportion of the town/city's 2012/13 key stage 4 cohort with less than a Level 1 qualification at age 22.", "Proportion of the town/city's 2012/13 key stage 4 cohort with a level 1 or level 2 qualification at age 22.", "Proportion of the town/city's 2012/13 key stage 4 cohort with level 3, level 4 or level 5 qualification at age 22.", "Proportion of the town/city's 2012/13 key stage 4 cohort with level 6 or above qualification at age 22.", "Town/city highest qualification average score based on highest levels of qualifications achieved of the 2012/13 KS4 cohort.", "Town/city education score based on attainment levels of the 2012/13 Key stage 4 cohort." ] } ], "data": { "file_name": [ "english_education.csv" ], "file_url": [ "https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-01-23/english_education.csv" ] } } ]