Chinastark/bird_dataset huggingface dataset:
Database: shooting
Question: From the cases where the subject are male, list the case number and the location and subject status.
Evidence: male refers to gender = 'M'
SQL: SELECT T1.case_number, T1.location, T1.subject_statuses FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T2.gender = 'M'
Database: shooting
Question: For case(s) where officer 'Evenden, George' is in charged, state the case number and the grand jury disposition?
Evidence: officer 'Evenden, George' refers to full_name = 'Evenden, George'
SQL: SELECT T1.case_number, T1.grand_jury_disposition FROM incidents AS T1 INNER JOIN officers AS T2 ON T1.case_number = T2.case_number WHERE T2.first_name = 'George' AND T2.last_name = 'Evenden'
Database: shooting
Question: For case number '134472-2015', list the last name of the officers involved and state the subject statuses.
SQL: SELECT T2.last_name, T1.subject_statuses FROM incidents AS T1 INNER JOIN officers AS T2 ON T1.case_number = T2.case_number WHERE T1.case_number = '134472-2015'
Database: shooting
Question: From the cases where the subject were deceased, list the subject's last name, gender, race and case number.
Evidence: subject were deceased refers to subject_statuses = 'Deceased'
SQL: SELECT T2.last_name, T2.gender, T2.race, T2.case_number FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T1.subject_statuses = 'Deceased'
Database: shooting
Question: What is the percentage of subject who are female used the Vehicle as weapon?
Evidence: female refers to gender = 'F'; use Vehicle as weapon refers to subject_weapon = 'Vehicle'; percentage = divide(count(case_number where subject_weapon = 'Vehicle'), count(case_number)) where gender = 'F' * 100%
SQL: SELECT CAST(SUM(T1.subject_weapon = 'Vehicle') AS REAL) * 100 / COUNT(T1.case_number) FROM incidents T1 INNER JOIN subjects T2 ON T1.case_number = T2.case_number WHERE T2.gender = 'F'
Database: shooting
Question: From the 'Injured' statuses of the subject, what is the ratio of weapons used are knife against handgun?
Evidence: the 'Shoot and Miss' status refers to subject_statuses = 'Shoot and Miss'; knife refers to subject_weapon = 'knife'; toy handgun refers to subject_weapon = 'toy handgun'; ratio = divide(count(case_number where subject_weapon = 'knife'), count(case_number where subject_weapon = 'toy handgun')) where subject_statuses = 'Shoot and Miss'
SQL: SELECT CAST(SUM(T1.subject_weapon = 'Knife') AS REAL) * 100 / SUM(T1.subject_weapon = 'Handgun') FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T1.subject_statuses = 'Injured'
Database: shooting
Question: List all cases from the year 2012 in which the subject was deceased
Evidence: case refers to case_number; year 2012 refers to date between '2012-01-01' and '2012-12-31'; subject was deceased refers to subject_statuses = 'Deceased'
SQL: SELECT case_number FROM incidents WHERE STRFTIME('%Y', date) > '2011' AND subject_statuses = 'Deceased'
Database: shooting
Question: Of all male officers, what percentage are black officers?
Evidence: male refers to gender = 'M'; black refers to race = 'B'; percentage = divide(count(officers where race = 'B'), count(officers)) where gender = 'M' * 100%
SQL: SELECT CAST(SUM(race = 'B') AS REAL) * 100 / COUNT(case_number) FROM officers WHERE gender = 'M'
Database: shooting
Question: How many incidents in which the subject's weapon was a vehicle were investigated by a female officer?
Evidence: subject's weapon was a vehicle refers to subject_weapon = 'Vehicle'; female refers to gender = 'F'
SQL: SELECT COUNT(T1.case_number) FROM incidents AS T1 INNER JOIN officers AS T2 ON T1.case_number = T2.case_number WHERE T1.subject_weapon = 'Vehicle' AND T2.gender = 'F'
Database: shooting
Question: In how many cases where the subject was a female was the subject's status listed as Deceased?
Evidence:  female refers to gender = 'F'; subject's status listed as Deceased refers to subject_statuses = 'Deceased'
SQL: SELECT COUNT(T1.case_number) FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T2.gender = 'F' AND T1.subject_statuses = 'Deceased'
Database: shooting
Question: Of the black officers, how many of them investigated cases between the years 2010 and 2015?
Evidence: black refers to race = 'B'; between the years 2010 and 2015 refers to date between '2010-01-01' and '2015-12-31'; case refers to case_number
SQL: SELECT COUNT(T1.case_number) FROM officers AS T1 INNER JOIN incidents AS T2 ON T2.case_number = T1.case_number WHERE T1.race = 'B' AND T2.date BETWEEN '2010-01-01' AND '2015-12-31'
Database: shooting
Question: How many instances were found in June 2015?
Evidence: in June 2015 refers to date between '2015-06-01' and '2015-06-30'; record number refers to case_number
SQL: SELECT COUNT(case_number) FROM incidents WHERE date BETWEEN '2015-06-01' AND '2015-06-30'
Database: shooting
Question: How many people were injured between 2006 and 2014 as a result of a handgun?
Evidence: injured refers to subject_statuses = 'injured'; between 2006 and 2014 refers to date between '2006-01-01' and '2013-12-31'; handgun refers to subject_weapon = 'handgun'; where the incidents took place refers to location
SQL: SELECT COUNT(location) FROM incidents WHERE subject_weapon = 'Handgun' AND subject_statuses = 'Injured' AND date BETWEEN '2006-01-01' AND '2013-12-31'
Database: shooting
Question: What is the most common type of weapon that causes death?
Evidence: the most common type of weapon refers to max(count(subject_weapon)); causes death refers to subject_statuses = 'Deceased'
SQL: SELECT subject_weapon FROM incidents WHERE subject_statuses = 'Deceased' GROUP BY subject_weapon ORDER BY COUNT(case_number) DESC LIMIT 1
Database: shooting
Question: What is the proportion of white males and females in the police force?
Evidence: white refers to race = 'W'; male refers to gender = 'M'; female refers to gender = 'F'; proportion of white males = divide(count(officers where race = 'W' and gender = 'M'), count(officers)) * 100%; proportion of white females = divide(count(officers where race = 'W' and gender = 'F'), count(officers)) * 100%
SQL: SELECT CAST(SUM(gender = 'M') AS REAL) / SUM(gender = 'F') FROM officers WHERE race = 'W'
Database: shooting
Question: How many more black female victims than white female victims were discovered?
Evidence: black refers to race = 'B'; female refers to gender = 'F'; white refers to race = 'W'; result = subtract(count(victims where race = 'B'), count(victims where race = 'W')) where gender = 'F'
SQL: SELECT SUM(race = 'B') - SUM(race = 'W') FROM subjects WHERE gender = 'F'
Database: shooting
Question: What percentage of deaths were caused by rifles?
Evidence: rifle refers to subject_weapon = 'rifles'; death refers to subject_statuses = 'Deceased'; percentage = divide(count(incidents where subject_weapon = 'rifles'), count(incidents)) where subject_statuses = 'Deceased' * 100%
SQL: SELECT CAST(SUM(subject_statuses = 'Deceased') AS REAL) * 100 / COUNT(case_number) FROM incidents WHERE subject_weapon = 'Rifle'
Database: shooting
Question: Which type of weapon was used to attack the victim in the record number 031347-2015? What is the victim's race and gender?
Evidence: type of weapon refers to subject_weapon; record number 031347-2015 refers to case_number = '031347-2015'
SQL: SELECT T1.subject_weapon, T2.race, T2.gender FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T1.case_number = '031347-2015'
Database: shooting
Question: Which near-death incident did a policeman by the name of Ruben Fredirick look into? What is the victim in this incident's race and gender?
Evidence: near-death refers to subject_statuses = 'Deceased Injured'; incident refers to case_number; Ruben Fredirick refers to full_name = 'Ruben Fredirick'
SQL: SELECT T1.case_number, T3.race, T3.gender FROM incidents AS T1 INNER JOIN officers AS T2 ON T1.case_number = T2.case_number INNER JOIN subjects AS T3 ON T1.case_number = T3.case_number WHERE T2.first_name = 'Fredirick' AND T2.last_name = 'Ruben'
Database: shooting
Question: What proportion of male police officers looked into events where people were injured?
Evidence: male refers to gender = 'M'; people were injured refers to subject_statuses = 'Injured'; proportion = divide(count(case_number where gender = 'M'), count(case_number)) where subject_statuses = 'Injured' * 100%
SQL: SELECT CAST(SUM(T2.gender = 'M') AS REAL) * 100 / COUNT(T1.case_number) FROM incidents T1 INNER JOIN officers T2 ON T1.case_number = T2.case_number WHERE T1.subject_statuses = 'Injured'
Database: genes
Question: For the genes that are located in the plasma membrane, please list their number of chromosomes.
SQL: SELECT T1.Chromosome FROM Genes AS T1 INNER JOIN Classification AS T2 ON T1.GeneID = T2.GeneID WHERE T2.Localization = 'plasma membrane'
Database: genes
Question: How many non-essential genes are located in the nucleus?
SQL: SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Classification AS T2 ON T1.GeneID = T2.GeneID WHERE T2.Localization = 'nucleus' AND T1.Essential = 'Non-Essential'
Database: genes
Question: Among the genes with nucleic acid metabolism defects, how many of them can be found in the vacuole?
SQL: SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Classification AS T2 ON T1.GeneID = T2.GeneID WHERE T2.Localization = 'vacuole' AND T1.Phenotype = 'Nucleic acid metabolism defects'
Database: genes
Question: Please list the location of the genes that have the most chromosomes.
SQL: SELECT T2.Localization FROM Genes AS T1 INNER JOIN Classification AS T2 ON T1.GeneID = T2.GeneID ORDER BY T1.Chromosome DESC LIMIT 1
Database: genes
Question: Among the pairs of genes that are both located in the nucleus, what is the highest expression correlation score?
SQL: SELECT T2.Expression_Corr FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 INNER JOIN Genes AS T3 ON T3.GeneID = T2.GeneID2 WHERE T1.Localization = 'nucleus' AND T3.Localization = 'nucleus' ORDER BY T2.Expression_Corr DESC LIMIT 1
Database: genes
Question: What are the functions of the pair of genes that have the lowest expression correlation score?a
SQL: SELECT T1.Function FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 ORDER BY T2.Expression_Corr ASC LIMIT 1
Database: genes
Question: Among the pairs of genes that are not from the class of motorproteins, how many of them are negatively correlated?
Evidence: If Expression_Corr < 0, it means the negatively correlated
SQL: SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 AND T1.Class = 'Motorproteins'
Database: genes
Question: For the pairs of genes with one having 8 chromosomes and the other having 6 chromosomes, what is the highest expression correlation score?
SQL: SELECT T2.Expression_Corr FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Chromosome = 6 OR T1.Chromosome = 8 ORDER BY T2.Expression_Corr DESC LIMIT 1
Database: genes
Question: Please list the motif of the genes that are located in the cytoplasm and have 7 chromosomes.
SQL: SELECT T2.GeneID1, T2.GeneID2 FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Localization = 'cytoplasm' AND T1.Chromosome = 7
Database: genes
Question: For the non-essential genes whose functions are transcription, how many of them are not located in the cytoplasm?
SQL: SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Localization != 'cytoplasm' AND T1.Function = 'TRANSCRIPTION' AND T1.Essential = 'NON-Essential'
Database: genes
Question: How many pairs of positively correlated genes are both non-essential?
Evidence: If Expression_Corr > 0, it means the expression correlation is positive
SQL: SELECT COUNT(T2.GeneID2) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr > 0 AND T1.Essential = 'Non-Essential'
Database: genes
Question: If a pair of genes is positively correlated, what is the possibility of it being composed of two genes both with over 10 chromosomes?
Evidence: Positively correlated means Expression_Corr > 0; Possibility = count(the pairs of genes with both chromosomes over 20) / count(pairs of genes that are positively correlated)
SQL: SELECT CAST(SUM(IIF(T1.Chromosome > 10 AND T3.Chromosome > 10, 1, 0)) AS REAL) * 100 / COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 INNER JOIN Genes AS T3 ON T3.GeneID = T2.GeneID2 WHERE T2.Expression_Corr > 0
Database: genes
Question: For the pairs of genes both from the class ATPases, what is the average expression correlation score?
SQL: SELECT AVG(T2.Expression_Corr) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Class = 'ATPases'
Database: genes
Question: Lists all genes by identifier number located in the cytoplasm and whose function is metabolism.
SQL: SELECT DISTINCT GeneID FROM Genes WHERE Localization = 'cytoplasm' AND Function = 'METABOLISM'
Database: genes
Question: How many different genes do we have if we add those located in the plasma and in the nucleus?
SQL: SELECT COUNT(GeneID) FROM Classification WHERE Localization IN ('plasma', 'nucleus')
Database: genes
Question: What kind of expression correlation occurs in physical type interacting gene pairs and what percentage of these are negatively correlated?
Evidence: If the Expression_Corr value is negative then it's negatively correlated. Percentage of Negative Correlation = count(negative Expression_Corr physical type) / count(Expression_Corr physical type) * 100%
SQL: SELECT Expression_Corr FROM Interactions WHERE Type = 'Physical' UNION ALL SELECT CAST(SUM(Expression_Corr < 0) AS REAL) * 100 / COUNT(*) FROM Interactions WHERE Type = 'Physical'
Database: genes
Question: What percentage of genes located in the cytoskeleton are of unknown class? And of these, how many are not conditional phenotypes?
Evidence: Percentage = count(genes located in the cytoskeleton unknown class) / count(genes located in the cytoskeleton) * 100%
SQL: SELECT SUM(Localization = 'cytoskeleton' AND Phenotype = 'Conditional phenotypes') , CAST(SUM(Localization = 'cytoskeleton') AS REAL) * 100 / COUNT(GeneID) FROM Genes;
Database: genes
Question: What type of interactions occurs in genes whose function is cellular transport and transport medicine and are classified as non-essential?
SQL: SELECT T2.Type FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Function = 'TRANSCRIPTION' AND T1.Essential = 'Non-Essential'
Database: genes
Question: List all genes whose interaction is with genes located in the nucleus in which it is positively correlated.
Evidence: If the Expression_Corr value is positive then it's positively correlated
SQL: SELECT T1.GeneID FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr > 0 AND T1.Localization = 'nucleus'
Database: genes
Question: Taking all the essential genes of the transcription factors class located in the nucleus as a reference, how many of them carry out a genetic-type interaction with another gene? List them.
SQL: SELECT T2.GeneID1 FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Localization = 'nucleus' AND T1.Class = 'Transcription factors' AND T1.Essential = 'Essential' AND T2.Expression_Corr != 0
Database: genes
Question: Of all the nonessential genes that are not of the motorprotein class and whose phenotype is cell cycle defects, how many do not have a physical type of interaction?
SQL: SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Type != 'Physical' AND T1.Phenotype = 'Cell cycle defects' AND T1.Class != 'Motorproteins' AND T1.Essential = 'Non-Essential'
Database: genes
Question: Of the genes whose phenotype and motif are nucleic acid metabolism defects, PS00107, what percentage perform positive interaction with another gene?
Evidence: Percentage of no interaction = [count(nucleic acid metabolism, PS00107, no interaction) / count(nucleic acid metabolism, PS00107) * 100%
SQL: SELECT CAST(SUM(IIF(T2.Expression_Corr > 0, 1, 0)) AS REAL) * 100 / COUNT(T2.GeneID1) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Phenotype = 'Nucleic acid metabolism defects' AND T1.Motif = 'PS00107'
Database: genes
Question: Which negatively correlated, genetically interacting genes are non-essential? What percentage do they represent with respect to those that are essential?
Evidence: If the Expression_Corr value is negative then it's negatively correlated; Percentage of Essensity = [count(negatively correlated, genetical interaction, non-essential) / count(negatively correlated, genetical interaction, non-essential+negatively correlated, genetical interaction, essential)] * 100%
SQL: SELECT CAST(COUNT(T1.GeneID) AS REAL) * 100 / ( SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 ) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 AND T1.Essential = 'Non-Essential'
Database: app_store
Question: How many apps were last updated in January of 2018? Please write one translated review with positive sentiment for each app, if there's any.
Evidence: updated in January of 2018 refers to Last Updated BETWEEN 'January 1, 2018' and 'January 31, 2018';
SQL: SELECT DISTINCT Translated_Review FROM user_reviews WHERE App IN ( SELECT App FROM playstore WHERE `Last Updated` BETWEEN 'January 1, 2018' AND 'January 31, 2018' ) AND Sentiment = 'Positive'
Database: app_store
Question: How many users mildly likes the 7 Minute Workout app and when was it last updated?
Evidence: mildly likes the app refers to Sentiment_Polarity> = 0 and Sentiment_Polarity<0.5;
SQL: SELECT COUNT(T2.Sentiment_Polarity), T1."Last Updated" FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = '7 Minute Workout' AND T2.Sentiment_Polarity BETWEEN 0 AND 0.5
Database: app_store
Question: How many users holds neutral attitude towards the HTC Weather app? Indicate the app's rating on the Google Play Store.
Evidence: user holds neutral attitude refers to Sentiment = 'Neutral';
SQL: SELECT COUNT(T1.Rating), T1.Rating FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'HTC Weather' AND T2.Sentiment = 'Neutral'
Database: app_store
Question: What is the name and category of the app with the highest amount of -1 sentiment polarity score?
Evidence: highest amount of -1 sentiment polarity score refers to MAX(Count(Sentiment_Polarity = 1.0))
SQL: SELECT DISTINCT T1.App, T1.Category FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment_Polarity = '-1.0'
Database: app_store
Question: What is the average sentiment polarity score of the Cooking Fever app? Indicate the age group that the app is targeted at.
Evidence: average sentiment polarity score = AVG(Sentiment_Polarity); age group the app is target at refers to Content Rating;
SQL: SELECT AVG(T2.Sentiment_Polarity), T1."Content Rating" FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Cooking Fever'
Database: app_store
Question: What is the lowest sentiment polarity score of the Basketball Stars app for people who dislikes the app pretty much and how many downloads does it have?
Evidence: lowest sentiment polarity score refers to MIN(Sentiment_Polarity); user dislike the app pretty much refers to Sentiment_Polarity<-0.5; number of downloads it has refers to installs;
SQL: SELECT MIN(T2.Sentiment_Polarity), T1.Installs FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Basketball Stars'
Database: app_store
Question: For the Akinator app, how many reviews have sentiment subjectivity of no more than 0.5 and what is its current version?
Evidence: Sentiment_Subjectivity<0.5; current version refers to Current Ver;
SQL: SELECT COUNT(T2.Sentiment_Subjectivity), T1."Current Ver" FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Akinator' AND T2.Sentiment_Subjectivity < 0.5
Database: app_store
Question: How many apps have rating of 5?
Evidence: FALSE;
SQL: SELECT COUNT(App) FROM playstore WHERE Rating = 5
Database: app_store
Question: What are the top 5 installed free apps?
Evidence: free app refers to price = 0; most installed app refers to MAX(Installs);
SQL: SELECT App FROM playstore WHERE Price = 0 ORDER BY CAST(REPLACE(REPLACE(Installs, ',', ''), '+', '') AS INTEGER) DESC LIMIT 5
Database: app_store
Question: Name the top 10 most reviewed apps.
Evidence: most reviewed app refers to MAX(Reviews);
SQL: SELECT DISTINCT App FROM playstore ORDER BY Reviews DESC LIMIT 10
Database: app_store
Question: How many of the users hold neutral attitude on "10 Best Foods for You" app and what category is this app?
Evidence: neutral attitude refers to Sentiment = 'Neutral';
SQL: SELECT COUNT(T2.App), T1.Category FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = '10 Best Foods for You' AND T2.Sentiment = 'Neutral'
Database: app_store
Question: What are the apps that users pretty like this app and how many installs amount of these apps?
Evidence: users pretty much likes the app refers to Sentiment_Polarity = 'Positive';
SQL: SELECT DISTINCT T1.App, T1.Installs FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment_Polarity > 0
Database: app_store
Question: List apps whose rating is 3.9 and state the translated review of each app.
Evidence: lowest rating refers to Rating = 1;
SQL: SELECT T1.App, T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Rating = 3.9
Database: app_store
Question: How many apps that are only compatible with Android ver 8.0 and above? List down the users' sentiment of these apps.
Evidence: compatible with android refers to Android Ver; Android Ver" = '8.0 and up';
SQL: SELECT DISTINCT Sentiment FROM user_reviews WHERE App IN ( SELECT App FROM playstore WHERE `Android Ver` = '8.0 and up' )
Database: app_store
Question: Which apps have multiple genres and what is the total sentiment subjectivity of these apps?
Evidence: multiple genres refers to COUNT(Genres>1; total sentiment subjectivity = Sum(Sentiment_Subjectivity);
SQL: SELECT SUM(T2.Sentiment_Subjectivity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Genres > 1
Database: app_store
Question: Which apps have not been updated since year 2015 and what kind of sentiment users hold on it?
Evidence: since year 2015 refers to "Last Updated"<'January 1, 2015';
SQL: SELECT DISTINCT App, Sentiment FROM user_reviews WHERE App IN ( SELECT App FROM playstore WHERE CAST(SUBSTR('Last Updated', -4, 4) AS INTEGER) < 2015 )
Database: app_store
Question: What is the total installs of apps with content rating of adults only 18+ and what are the translated reviews of it?
Evidence: total installs = SUM(Installs);
SQL: SELECT SUM(T1.Installs), T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1."Content Rating" = 'Adults only 18+'
Database: app_store
Question: Which of the app is the best selling app and what is the sentiments polarity of it?
Evidence: best selling app = MAX(MULTIPLY(Price, Installs));
SQL: SELECT T1.App, T2.Sentiment_Polarity FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App ORDER BY T1.Price * CAST(REPLACE(REPLACE(Installs, ',', ''), '+', '') AS INTEGER) DESC LIMIT 1
Database: app_store
Question: What is the average rating of comic category apps? How many users hold positive attitude towards this app?
Evidence: average rating = AVG(Rating where Category = 'COMICS'); number of users who hold a positive attitude towards the app refers to SUM(Sentiment = 'Positive');
SQL: SELECT AVG(T1.Rating) , COUNT(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE NULL END) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Category = 'COMICS'