July 2021

July 1

If you share code plus data, it's a good idea to adopt a few defensive techniques to ensure that the data are what the code expects.

The {assert_that} function from {assertthat} 📦 provides just the tool! 🥋https://t.co/kQT74S0AQX#rstats #DataScience pic.twitter.com/ScTmUxQxnr

— R Function A Day (@rfunctionaday) July 1, 2021

July 2

Either for aesthetic or for highlighting purposes, you may sometimes wish to draw borders around legend keys in {ggplot2} plots. 🖼️

The {keybox} from {ggfun} 📦 does exactly this, easily and flexibly 🙌https://t.co/dBAjEqKzY2#rstats #DataScience pic.twitter.com/mvFxIeInyj

— R Function A Day (@rfunctionaday) July 2, 2021

July 3

If you don't use RMarkdown and copy-paste software output to report statistics, you'd want to check that no errors were made in the process.

The {statcheck} function from eponymous 📦 does this (for single or multiple files)! 🔍https://t.co/HdW6CKKPO8#rstats #DataScience pic.twitter.com/tgC5ysvz6T

— R Function A Day (@rfunctionaday) July 3, 2021

July 4

Correctly specifying a distribution family for regression model can improve estimate accuracy. But what if we’re unsure?

The {check_distribution} function from {performance} 📦 uses Random Forest to help you reconsider the choice ⚠️https://t.co/qD7cQvNLLz#rstats #DataScience pic.twitter.com/MLhkCT2cwh

— R Function A Day (@rfunctionaday) July 4, 2021

July 5

While scraping web data in R, sometimes we may wish that the extracted text layout mimics its browser/HTML behavior (e.g. ignore whitespace).

The {html_text2} function from {rvest} 📦 helps with exactly this! 🙇‍♀️https://t.co/QJwe3nXx43#rstats #DataScience pic.twitter.com/T7NU9MYKqL

— R Function A Day (@rfunctionaday) July 5, 2021

July 6

Simple slopes analyses can help understand interaction effects in linear regression.

The {sim_slopes} function from {interactions} 📦 provides an easy way to both run and visualize this analysis for 2-way or 3-way interactions! 🎚️https://t.co/4Xvc4FMrEr#rstats #DataScience pic.twitter.com/70AFS2QQcF

— R Function A Day (@rfunctionaday) July 6, 2021

July 7

A histogram is a good visualization to represent the distribution of numeric data. 📊

The {gghistostats} function from {ggstatsplot} 📦 provides ready-made histograms (with additional descriptive and inferential statistics) 🍒https://t.co/zSmT4MBGjX#rstats #DataScience pic.twitter.com/xMFwXedQSO

— R Function A Day (@rfunctionaday) July 7, 2021

July 8

If you want to use non-standard fonts or characters, getting graphics devices to work with them can be a pain.

The {showtext_auto} function from {showtext} 📦 supports a large collection of font formats and graphics devices! 🙌https://t.co/bt3SzZfyhI#rstats #DataScience pic.twitter.com/bYmyfZEfhL

— R Function A Day (@rfunctionaday) July 8, 2021

July 9

Sometimes we may wish to provide descriptive labels for colors we are using, but may not know how to label them.

The {name} function from {ColorNameR} 📦 produces color labels in multiple languages and colorspaces! 🎨https://t.co/RVkqjq7Ksz#rstats #DataScience pic.twitter.com/MmD1MWPHF3

— R Function A Day (@rfunctionaday) July 9, 2021

July 10

Sometimes we may wish to check all relevant assumptions for a regression model in one go. 🔍

The {check_model} function from {performance} 📦 does exactly this and also provides elegant visualizations with helpful pointers ✅📊https://t.co/4SIIL0u9Jn#rstats #DataScience pic.twitter.com/EMmye4qAZk

— R Function A Day (@rfunctionaday) July 10, 2021

July 11

If you are writing manuscripts in RMarkdown, you may wish to auto-generate citations for all R packages used in the document. ✍️

The {write_bib} function from {knitr} 📦 does exactly this! 📄https://t.co/JYux01tB7h#rstats #DataScience pic.twitter.com/YvOiaPeYfY

— R Function A Day (@rfunctionaday) July 11, 2021

July 12

While plotting time series data, we may wish to plot several subseries corresponding to the periods of interest (seasons, months, etc.).

The {ggfreqplot} function from {ggfortify} 📦 makes this task effortless! 📈📉 https://t.co/rRmcQbKv4U#rstats #DataScience pic.twitter.com/Vic17LXkM9

— R Function A Day (@rfunctionaday) July 12, 2021

July 13

If 📦s being used happen to have a function with an identical name, using that function may fail.

Aside from :: qualifier, {conflicted_prefer} function from {conflicted} 📦 can solve this conflict by prioritizing one function 🥇https://t.co/n96Mt8kHx3#rstats #DataScience pic.twitter.com/V9jZwxADoM

— R Function A Day (@rfunctionaday) July 13, 2021

July 14

Exploratory data analysis often involves specifying and comparing multiple regression models.

The {compare_parameters} function from {parameters} 📦 provides dot-and-whisker plots to display and compare regression estimates! 🥇🥈🥉https://t.co/vmuGta5W9i#rstats #DataScience pic.twitter.com/Xyve8SMk1Q

— R Function A Day (@rfunctionaday) July 14, 2021

July 15

Either computing environment or good practice recommendations may compel you to check for file paths, character encoding, etc. in your script. 🔍

The {is_} function family from {xfun} 📦 provides tools to run such checks easily ✅https://t.co/LdRTIKXOCA#rstats #DataSciencee pic.twitter.com/YCtgZEufae

— R Function A Day (@rfunctionaday) July 15, 2021

July 16

Sometimes we may wish to assess the polarity (positive, negative, neutral) of text data.

The {sentiment} function from {sentimentr} 📦 provides a convenient and flexible way to approximate the sentiment of the text by sentence ➕➖https://t.co/mMpxUxXyVP#rstats #DataScience pic.twitter.com/9oXGUwVTnf

— R Function A Day (@rfunctionaday) July 16, 2021

July 17

During exploratory phase, we may wish to visualize and model data quickly and thoroughly. ➿

The {ggwithinstats} function from {ggstatsplot} 📦 does this for one-way repeated measures designs via plots with statistical details 📊https://t.co/lOT7qa37z8#rstats #DataScience pic.twitter.com/UxoUQr1cvB

— R Function A Day (@rfunctionaday) July 17, 2021

July 18

To keep related data together, you might sometimes create dataframes with columns that themselves contain dataframes. 🧳

Since working with them can be a pain, the {unpack} function from {tidyr} 📦 helps you "unpack" them! 🔨https://t.co/rt0ekSiMUg#rstats #DataScience pic.twitter.com/m2O8OQRDgF

— R Function A Day (@rfunctionaday) July 18, 2021

July 19

If you use {dplyr} 📦 for data analysis, you may sometimes wish to carry out statistical analysis on a grouped data frame.

If the statistical function requires the whole dataframe, the {cur_data} function provides just the tool! 🗄️https://t.co/IemOGQqqrH#rstats #DataScience pic.twitter.com/MzEnBq0tp0

— R Function A Day (@rfunctionaday) July 19, 2021

July 20

Visualizing variable distribution via violin plot is easy in {ggplot2}, but we may wish to avoid redundant mirroring of the density plot.

The {stat_density_ridges} function from {ggridges} 📦 provides just the geometric layer! 🙌https://t.co/x6yAh0hHW9#rstats #DataScience pic.twitter.com/V3JhOamnFb

— R Function A Day (@rfunctionaday) July 20, 2021

July 21

During analysis, model selection may involve the specification of multiple models and formally testing if they are different.

The {test_performance} function from {performance} 📦 performs and summarizes indices from these tests 🥇https://t.co/Qd25p55XPq#rstats #DataScience pic.twitter.com/EvmEOVXNRn

— R Function A Day (@rfunctionaday) July 21, 2021

July 22

While visualizing data across a combination of variables, {facet_wrap} in {ggplot2} creates small multiples. But what if the variables are nested?

The {facet_nested_wrap} function from {ggh4x} 📦 handles exactly such designs! 📊🙌https://t.co/WVp154GvPA#rstats #DataScience pic.twitter.com/P6ZjOUF9eV

— R Function A Day (@rfunctionaday) July 22, 2021

July 23

Sometimes you may wish to do something in R and can't think of any package that might be helpful.

In such cases, the {findPackage} function from {packagefinder} 📦 can search and return relevant CRAN packages given the keywords 🔍https://t.co/0IiwJtafTq#rstats #DataScience pic.twitter.com/n946xE4cAS

— R Function A Day (@rfunctionaday) July 23, 2021

July 24

Sometimes you may wish to write SQL queries (for practice?) without access to a database. 😩

In such cases, you can use the {dbwritetable} function from {DBI} 📦 to copy a dataframe to a database table, and then write queries! ✍️🙌https://t.co/Gu3HwjrR0S#rstats #DataScience pic.twitter.com/z6IZnW0o1o

— R Function A Day (@rfunctionaday) July 24, 2021

July 25

Simulating parameter draws can sometimes be a (computationally faster) alternative to bootstrapping. ⏲️

The {simulate_parameters} function from {parameters} 📦 can run and visualize such simulations for various regression models 🎲https://t.co/SHdA3JANeq#rstats #DataScience pic.twitter.com/sRe2BhPKjW

— R Function A Day (@rfunctionaday) July 25, 2021

July 26

In case you are looking for an alternate, "operator" way to access object attributes in R, you can use the infix attribute accessor (%@%) from {rlang} 📦!️https://t.co/C0T97lOtWV#rstats #DataScience pic.twitter.com/zAYw4JCo5i

— R Function A Day (@rfunctionaday) July 26, 2021

July 27

Sometimes you need to plot data from different geographical entities into a grid and may wish to preserve the original geographical orientation of the entities.

The {facet_geo} function from {geofacet} 📦 produces such a grid! 🗺️https://t.co/IUIQ1B7rUw#rstats #DataScience pic.twitter.com/MamwLytO4F

— R Function A Day (@rfunctionaday) July 27, 2021

July 28

Operating on multiple columns in a row-wise manner is fairly straightforward in {dplyr} 📦.

In this workflow, the {c_across} function allows you to use the tidy selection syntax to select columns to operate on 🔍https://t.co/gmFEid0WDK#rstats #DataScience pic.twitter.com/8uMiwmcxtq

— R Function A Day (@rfunctionaday) July 28, 2021

July 29

Either out of curiosity or to improve its performance, sometimes you may want to time your R code.

The {tic}/{toc} function from {tictoc} 📦 provides just the tool ⏲️https://t.co/yCu9ArIdBd#rstats #DataScience pic.twitter.com/PyHJCSm2NG

— R Function A Day (@rfunctionaday) July 29, 2021

July 30

To ensure reproducibility of R script, you may wish it to download needed package versions on a certain date.

The {groundhog.library} function from {groundhog} 📦 creates a local library with the needed package versions 📑https://t.co/bOhOXRzfMZ#rstats #DataScience pic.twitter.com/1pLfn8uU4q

— R Function A Day (@rfunctionaday) July 30, 2021

July 31

ROC curves provide a convenient way to compare responses and predictions of a binomial model.

The {performance_roc} function from {performance} 📦 computes AUC metric and visualizes ROC curves for a collection of models 🥇🥈🥉https://t.co/ZKty2kA5Br#rstats #DataScience pic.twitter.com/LYJgfvZHIF

— R Function A Day (@rfunctionaday) July 31, 2021