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More on sigr

If you’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 tip: Make Your Results Clear with sigr

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 with wrapr

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 %?% […]

R Tip: Give data.table a Try

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 […]

Speed up your R Work

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 […]