lvmisc is a package with miscellaneous R functions, including basic
data computation/manipulation, easy plotting and tools for working with
statistical models objects. You can learn more about the methods for
working with models in vignette("working_with_models").
Installation
You can install the released version of lvmisc from CRAN with:
install.packages("lvmisc")And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("verasls/lvmisc")
Getting started
Some of what you can do with lvmisc.
library(lvmisc) library(dplyr) # Compute body mass index (BMI) and categorize it starwars %>% select(name, birth_year, mass, height) %>% mutate( BMI = bmi(mass, height / 100), BMI_category = bmi_cat(BMI) ) #> # A tibble: 87 × 6 #> name birth_year mass height BMI BMI_category #> <chr> <dbl> <dbl> <int> <dbl> <fct> #> 1 Luke Skywalker 19 77 172 26.0 Overweight #> 2 C-3PO 112 75 167 26.9 Overweight #> 3 R2-D2 33 32 96 34.7 Obesity class I #> 4 Darth Vader 41.9 136 202 33.3 Obesity class I #> 5 Leia Organa 19 49 150 21.8 Normal weight #> 6 Owen Lars 52 120 178 37.9 Obesity class II #> 7 Beru Whitesun lars 47 75 165 27.5 Overweight #> 8 R5-D4 NA 32 97 34.0 Obesity class I #> 9 Biggs Darklighter 24 84 183 25.1 Overweight #> 10 Obi-Wan Kenobi 57 77 182 23.2 Normal weight #> # … with 77 more rows # Divide numerical variables in quantiles divide_by_quantile(mtcars$wt, 4) #> [1] 2 2 1 2 3 3 3 2 2 3 3 4 4 4 4 4 4 1 1 1 1 3 3 4 4 1 1 1 2 2 3 2 #> Levels: 1 2 3 4 # Center and scale variables by group center_variable(iris$Petal.Width, by = iris$Species, scale = TRUE) #> [1] -0.046 -0.046 -0.046 -0.046 -0.046 0.154 0.054 -0.046 -0.046 -0.146 #> [11] -0.046 -0.046 -0.146 -0.146 -0.046 0.154 0.154 0.054 0.054 0.054 #> [21] -0.046 0.154 -0.046 0.254 -0.046 -0.046 0.154 -0.046 -0.046 -0.046 #> [31] -0.046 0.154 -0.146 -0.046 -0.046 -0.046 -0.046 -0.146 -0.046 -0.046 #> [41] 0.054 0.054 -0.046 0.354 0.154 0.054 -0.046 -0.046 -0.046 -0.046 #> [51] 0.074 0.174 0.174 -0.026 0.174 -0.026 0.274 -0.326 -0.026 0.074 #> [61] -0.326 0.174 -0.326 0.074 -0.026 0.074 0.174 -0.326 0.174 -0.226 #> [71] 0.474 -0.026 0.174 -0.126 -0.026 0.074 0.074 0.374 0.174 -0.326 #> [81] -0.226 -0.326 -0.126 0.274 0.174 0.274 0.174 -0.026 -0.026 -0.026 #> [91] -0.126 0.074 -0.126 -0.326 -0.026 -0.126 -0.026 -0.026 -0.226 -0.026 #> [101] 0.474 -0.126 0.074 -0.226 0.174 0.074 -0.326 -0.226 -0.226 0.474 #> [111] -0.026 -0.126 0.074 -0.026 0.374 0.274 -0.226 0.174 0.274 -0.526 #> [121] 0.274 -0.026 -0.026 -0.226 0.074 -0.226 -0.226 -0.226 0.074 -0.426 #> [131] -0.126 -0.026 0.174 -0.526 -0.626 0.274 0.374 -0.226 -0.226 0.074 #> [141] 0.374 0.274 -0.126 0.274 0.474 0.274 -0.126 -0.026 0.274 -0.226 # Quick and easy plotting with {ggplot} plot_scatter(mtcars, disp, mpg, color = factor(cyl))
# Work with statistical model objects m <- lm(disp ~ mpg + hp + cyl + mpg:cyl, mtcars) accuracy(m) #> AIC BIC R2 R2_adj MAE MAPE RMSE #> 1 344.64 353.43 0.87 0.85 34.9 15.73% 43.75 plot_model(m)
