The Most Underrated R packages: 2020 Edition | by Alessandro Arrigo | Towards Data Science

In my experience as an R user, I’ve come across a lot of different packages and curated lists. Some are in my bookmarks like the great awesome-R list, or the monthly “best of” list curated by R studio. If you don’t know them, go check them out asap.
In this post, I’d like to show you something else. These are the results of late-night GitHub/Reddit browsing, and cool stuff shared by colleagues.
Some of these packages are really unique, others are just fun to use and real underdogs among the data scientist/statistician I’ve worked with.
BRRR and beepr: Have you ever wanted to know — and celebrate — when your simulations are finally done running in R? Have you ever been so proud of pulling off a tricky bit of code that you wanted Flavor Flav to yell “yeaaahhhh, boi!!” as soon as it successfully completes?
calendR: Ready to print monthly and yearly calendars made with ggplot2.
checkpoint: It makes it possible to install package versions from a specific date in the past as if you had a CRAN time machine.
DataEditR: DataEditR is a lightweight package to interactively view, enter or edit data in R.
Drake: It analyzes your workflow, skips steps with up-to-date results, and orchestrates the rest with optional distributed computing. In the end, drake provides evidence that your results match the underlying code and data, which increases your ability to trust your research
flow: Visualize as flow diagrams the logic of functions, expressions or scripts and ease debugging.
generativeart: Beautiful math-inspired abstract art.
here: The goal of the here package is to enable easy file referencing. In contrast to using setwd(), which is fragile and dependent on the way you organize your files, here uses the top-level directory of a project to easily build paths to files.
installr: It allows users to update R and all installed packages with just one command.
mailR: Send Email from inside R.
plumber: An R package that converts your existing R code to a web API.
pushoverr: Send push notifications from R to mobile devices or the desktop.
statcheck: statcheck is a free, open-source R package that can be used to automatically extract statistical null-hypothesis significant testing (NHST) results from articles and recompute the p-values based on the reported test statistic and degrees of freedom to detect possible inconsistencies.
usethis: usethis is a workflow package: it automates repetitive tasks that arise during project setup and development, both for R packages and non-package projects.
bayesplot: An R package providing an extensive library of plotting functions for use after fitting Bayesian models (typically with MCMC). The plots created by bayesplot are ggplot objects, which means that after a plot is created it can be further customized using various functions from the ggplot2 package.
cowplot: Awesome for aligning graphs to grids.
Esquisse: Basically creates a drag & drop GUI for ggplot, so you don’t have to code the majority of the plots.
hrbrthemes: Additional Themes and Theme Components for ‘ggplot2’.
gganatogram: Create anatograms using ggplot2. Yeah, for real.
ggannotate: ggannotate is a point-and-click tool to help you put your annotations exactly where you want them to go.
golem: The package makes creating production-ready shiny apps child’s play.
patchwork: The goal of patchwork is to make it ridiculously simple to combine separate ggplots into the same graphic.
Rayshader: 3D Plots that doesn’t suck.
see: Easy blackboard background for ggplots.
sjplots: Collection of plotting and table output functions for data visualization.
tmap: Astonishing thematic maps in R.
vaporwave: A E S T H E T I C S’ time. Beautify your plots like it’s still the 80's.
visreg: for displaying the results of a fitted model in terms of how a predictor variable x is estimated to affect an outcome y.
wesanderson: A Wes Anderson color palette for R
janitor: A lot of cool function to clean data, go check their example on the github link.
sqldf: data management using SQL syntax. That’s the way to go if you need to load data bigger than your machine can handle. You can filter it and load on R just that selection.
naniar: All you need for Missing Data.
Tidylog: It provides feedback about basic dplyr operations. Great for long pipe chains.
validate: A great package to check if your data obeys predefined rules (to be used with errorlocate by the same author). Also by the same author, go check deductive and dcmodify.
DataExplorer: Great functions for exploratory analysis.
dlookr: Several custom functions for data quality diagnosis and EDA in a compact form.
DHARMa: an interesting R package for residual diagnostics of GLMMs.
finalfit: The finalfit package provides functions that help you quickly create elegant final results tables and plots when modelling in R. These can easily be exported as Word documents, PDFs, or html files. Example below.
explanatory = c("age.factor", "sex.factor",   "obstruct.factor", "perfor.factor")dependent = 'mort_5yr'colon_s %>%  finalfit(dependent, explanatory, metrics=TRUE) -> t2knitr::kable(t2[[1]], row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))knitr::kable(t2[[2]], row.names=FALSE, col.names="")
LongCatEDA: Useful to visualize longitudinal changes in categorical variables.
jtools: This package consists of a series of functions created by the author (Jacob) to automate otherwise tedious research tasks. At this juncture, the unifying theme is the more efficient presentation of regression analyses.
modelbased: It’s a lightweight package helping with model-based estimations, used in the computation of marginal means, contrast analysis and predictions.
performance: The primary goal of the performance package is to provide utilities for computing indices of model quality and goodness of fit. This includes measures like r-squared (R2), root mean squared error (RMSE) or intraclass correlation coefficient (ICC) , but also functions to check (mixed) models for overdispersion, zero-inflation, convergence or singularity.
skimr: A frictionless, pipeable approach to dealing with summary statistics.
speedglm: Fast glm for big data.
syuzhet: Easy sentiment analysis in R.
I hope you found something useful or fun for your work. Let me know in the comment section if you have something cool to add to the list!