August 2021

August 1

Sometimes you may wish to insert a textual table in a {ggplot2} plot the same way you insert text labels at a specified position.

The {geom_table} function from {ggpp} 📦 offers just the geometric layer! 🙌https://t.co/bQdaxiMffR#rstats #DataScience pic.twitter.com/6raba4JIDV

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

August 2

If you are used to {dplyr}'s pipeable syntax (using %>%) and wish to continue using it when you switch to {data.table}, the {dt} helper function from {tidytable} 📦 can help! 👩‍🔧https://t.co/YzFfw2meLF#rstats #DataScience pic.twitter.com/3SQqnjj3Bz

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

August 3

If you are used to reading code with syntax highlighting, you may wish the same when code is printed in the terminal/console.

The {prettycode} function from the eponymous 📦 does exactly this! 🎨https://t.co/dF1WYPJSSc#rstats #DataScience pic.twitter.com/4fK9FjnTOu

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

August 4

Compared to CSS, Sass is a more flexible HTML styling framework. As it becomes ubiquitous, you may wish to learn how Sass code you are reading translates to familiar CSS.

The {sass} function from the eponymous 📦 does this 🔄https://t.co/FR8UlmQyTY#rstats #DataScience pic.twitter.com/3ZgYl0E6j3

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

August 5

A dot plot is a good visualization to represent the distribution of labeled numeric data.

The {ggdotplotstats} function from {ggstatsplot} 📦 easily produces these charts (with additional descriptive and inferential statistics) 🍒https://t.co/OLcqX643Vx#rstats #DataScience pic.twitter.com/GZ3huWi9qU

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

August 6

If you know C and wish to understand the implementation of R data structures at C-level as S-expressions or SEXPs (https://t.co/pCGK6tlgNB), the {sxp} function from {lobstr} 📦 can be helpful! ⛏️https://t.co/pb7um2e1RR#rstats #DataScience pic.twitter.com/NYrdqEJqJW

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

August 7

For teaching or for quality assessment, we may sometimes wish to visualize residuals of a model to check the goodness of fit.

The {stat_fit_deviations} function from {ggpmisc} 📦 makes this easy in {ggplot2} 🙌https://t.co/YezhmuyXgP#rstats #DataScience pic.twitter.com/M6o4MAcnIH

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

August 8

If we need to write an "impure" function that modifies R landscape (e.g. changes working directory), we'd do it so that behavior of other R functions doesn't change.

The {local_} function family in {withr} 📦 can help to do so! 🥡https://t.co/CgklVPJp7n#rstats #DataScience pic.twitter.com/rrpbojxoev

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

August 9

Gelman and Hill (2007) propose a binned residual plot to check the under-or over-fitting of binomial logistic regression models. 0⃣1⃣

The {binned_residuals} function from {performance} 📦easily produces such visualization 🔍https://t.co/gAAjFNIf92#rstats #DataScience pic.twitter.com/cKw8CxpJfC

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

August 10

Even after doing everything you can in R, sometimes you may want to further improve the performance of your code by rewriting it in C++.

The {cpp_function} from {cpp11} 📦 provides a convenient way to do so 👏https://t.co/5fdrx9KPc6#rstats #DataScience pic.twitter.com/IbKeeuZdjb

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

August 11

For common statistical tests, we may wish to extract not only the details of inferential statistics but also effect size estimates.

Functions, like {two_sample_test}, from {statsExpressions} 📦 provide a tidy way to do so 🙌https://t.co/MJ1avaR6WJ#rstats #DataScience pic.twitter.com/zKFUetTeXR

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

August 12

Sometimes data analysis may require finding the first non-missing value (to replace it with another value, to create a new variable, etc.) across vectors or columns.

The {coalesce} function from {dplyr} 📦 comes in handy here 🔍https://t.co/Q7EPEAmFZC#rstats #DataScience pic.twitter.com/Upqhjd5eJY

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

August 13

Unified Modeling Language provides a way to visualize the design of a system, and nomnoml (https://t.co/X9yAhs166L) is online tool to draw UML diagrams.

The {nomnoml} function from eponymous 📦 renders UML diagrams from R itself! 📝https://t.co/CBZgudgltr#rstats #DataScience pic.twitter.com/KmksKDgQPz

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

August 14

Sometimes you might need to analyze the scanned text data present in a PDF.

The {pdf_ocr_text} function from {pdftools} 📦 can extract such text ⛏https://t.co/cnoqBhZ0Ys#rstats #DataScience pic.twitter.com/1LMVHfGgsL

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

August 15

Although {rmarkdown} 📦 makes writing reports easy, it lacks automatic numbering of and cross-referencing (for figures, tables, sections, etc) features.

The 2nd variant of function family in {bookdown} 📦 provides these features ✍️https://t.co/EKFeVqQJb1#rstats #DataScience pic.twitter.com/RcIAtscT0K

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

August 16

The {tmap} 📦 provides API to create (static and interactive) thematic maps with a syntax resembling that of {ggplot2}.

The {qtm} function provides a convenient short-hand to draw such maps quickly! 🗺️https://t.co/WDYN5ofasw#rstats #DataScience pic.twitter.com/PZk4thnd4q

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

August 17

Sometimes we need to convert a deeply nested list into a rectangular dataframe. But in doing so, we may not wish to bring to the top all list elements.

The {hoist} function from {tidyr} 📦 can help with such transformations 🛠️https://t.co/yrhHtF2YVl#rstats #DataScience pic.twitter.com/m5TvEse0bm

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

August 18

Sometimes we want to draw text in a {ggplot2} plot so that it fits inside a defined area without manually fiddling around with font size.

The {geom_fit_text} function from {ggfittext} 📦 resizes text to fit inside a defined box 🗜️https://t.co/GiaXx9QEgb#rstats #DataScience pic.twitter.com/krnvdRqIWz

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

August 19

Posterior predictive check for a regression model uses simulated data under the fitted model to check for any discrepancies from observed data.

The {pp_check} function from {performance} 📦 carries out and visualizes this check 🔍https://t.co/qk7dnlHyP5#rstats #DataScience pic.twitter.com/R95Evuhhdp

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

August 20

If you use {dplyr} 📦, you may wish to avoid the {group_by} + {ungroup} workflow for grouped analysis if you just need to carry out a single operation.

The {with_groups} function provides an alternative for such contexts ✂️https://t.co/FiEeP2loxm#rstats #DataScience pic.twitter.com/XANjLS22ki

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

August 21

Sometimes you may wish to display results from a statistical test in a graphics you have prepared.

The {statsExpressions} 📦 functions, like {corr_test}, provide the necessary expressions 👏https://t.co/ERUsOJKliZ#rstats #DataScience pic.twitter.com/4T3awwOSFQ

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

August 22

For some reason, you may need to prepare a calendar in R and you might prefer to do so in the {ggplot2} framework.

The {calendR} function from the eponymous 📦 provides customizable monthly or yearly calendars 📅https://t.co/KPx4ozhjrV#rstats #DataScience pic.twitter.com/C1dM0FSllv

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

August 23

The {mlr3} project is a modern and powerful object-oriented framework for doing machine learning (ML) in R.

The {autoplot} function from {mlr3viz} 📦 provides a model-agnostic general-purpose tool for visualizing ML algorithms! 📊📈https://t.co/ky8yhLtYyG#rstats #DataScience pic.twitter.com/YBu2rLz44K

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

August 24

Instead of using third-party softwares, you may sometimes wish to edit (join, convert, etc.) images from within R itself.

The {image_} function family from {magick} provide such helpers 📷🎞https://t.co/ald6jelSWG#rstats #DataScience pic.twitter.com/y4H67cTPnY

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

August 25

For correlation hypotheses, in addition to the joint distribution in a scatterplot, one may also wish to visualize the marginal distributions for each variable.

This is easy to do using the {_side} function family from {ggside} 📦 !https://t.co/vQYV4ELEaG#rstats #DataScience pic.twitter.com/VSBpVqtO6K

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

August 26

The UpSet plot is an efficient alternative to the Venn Diagram for visualizing a complex intersections of multiple sets.

The {scale_x_upset} function from {ggupset} 📦 provides a {ggplot2} scale to create UpSet plots easily 🧱https://t.co/5rwIgAhzxa#rstats #DataScience pic.twitter.com/wk352FtXb0

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

August 27

Sometimes (e.g. for meta-analysis) we may need to either extract effect size from a test statistic or convert the available effect size to a more suitable one.

The {to} function family from {effectsize} can do such conversions 🔄https://t.co/EO6hb9NkER#rstats #DataScience pic.twitter.com/2ItAfBPwps

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

August 28

A correlation matrix is a nifty visualization for displaying relationships between multiple variables.

The {ggcorrmat} function from {ggstatsplot} 📦 creates such matrices with significance testing and other descriptive details ↔︎️https://t.co/rbwEs6qVON#rstats #DataScience pic.twitter.com/EDPanyNE0x

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

August 29

For a given generic or class, sometimes we may be interested to systematically list and investigate the available S3/S4 methods.

The {methods} function family in {sloop} 📦 provides the needed helpers 📝https://t.co/EV7AVaSHPF#rstats #DataScience pic.twitter.com/XxFb3Es3pB

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

August 30

When we are interested in studying group differences, sometimes we may wish to annotate our visualizations with results from significance testing.

The {geom_signif} from {ggsignif} 📦 provides just the geometric layer! ⚖️https://t.co/O9XM5nHdgf#rstats #DataScience pic.twitter.com/iZhilHeGO3

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

August 31

YAML language is ubiquitous, esp. in configuration files, and sometime we may need to convert R object to a YAML object.

The {as_yml} function from {ymlthis} 📦 makes this conversion easy 🔄https://t.co/GSTD3slB0k#rstats #DataScience pic.twitter.com/YeVunUZoR8

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