## R Tip: Use seqi() For Indexes

R Tip: use seqi() for indexing. R‘s “1:0 trap” is a mal-feature that confuses newcomers and is a reliable source of bugs. This note will show how to use seqi() to write more reliable code and document intent.

R Tip: use seqi() for indexing. R‘s “1:0 trap” is a mal-feature that confuses newcomers and is a reliable source of bugs. This note will show how to use seqi() to write more reliable code and document intent.

If youâ€™ve read our previous R Tip on using sigr with linear models, you might have noticed that the lm() summary object does in fact carry the R-squared and F statistics, both in the printed form: model_lm <- lm(formula = Petal.Length ~ Sepal.Length, data = iris) (smod_lm <- summary(model_lm)) ## […]

R is designed to make working with statistical models fast, succinct, and reliable. For instance building a model is a one-liner: model <- lm(Petal.Length ~ Sepal.Length, data = iris) And producing a detailed diagnostic summary of the model is also a one-liner: summary(model) # Call: # lm(formula = Petal.Length ~ […]

coalesce is a classic useful SQL operator that picks the first non-NULL value in a sequence of values. We thought we would share a nice version of it for picking non-NA R with convenient operator infix notation wrapr::coalesce(). Here is a short example of it in action: library("wrapr") NA %?% […]

If your R or dplyr work is taking what you consider to be a too long (seconds instead of instant, or minutes instead of seconds, or hours instead of minutes, or a day instead of an hour) then try data.table. For some tasks data.table is routinely faster than alternatives at […]

R tip : how to pass a formula to lm(). Often when modeling in R one wants to build up a formula outside of the modeling call. This allows the set of columns being used to be passed around as a vector of strings, and treated as data. Being able […]

Today’s R tip is: put your values in columns.

R tip: consider using radix sort.

Introduction In this note we will show how to speed up work in R by partitioning data and process-level parallelization. We will show the technique with three different R packages: rqdatatable, data.table, and dplyr. The methods shown will also work with base-R and other packages. For each of the above […]

We are pleased to announce that seplyr version 0.5.8 is now available on CRAN. seplyr is an R package that provides a thin wrapper around elements of the dplyr package and (now with version 0.5.8) the tidyr package. The intent is to give the part time R user the ability […]