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R-snippets

by jvcasillas ST2/ST3

R snippets for Sublimetext

Labels snippets, R, statistics

Details

  • 2017.05.12.20.00.37
  • github.​com
  • github.​com
  • 2 years ago
  • 46 minutes ago
  • 6 years ago

Installs

  • Total 9K
  • Win 4K
  • OS X 3K
  • Linux 2K
Aug 18 Aug 17 Aug 16 Aug 15 Aug 14 Aug 13 Aug 12 Aug 11 Aug 10 Aug 9 Aug 8 Aug 7 Aug 6 Aug 5 Aug 4 Aug 3 Aug 2 Aug 1 Jul 31 Jul 30 Jul 29 Jul 28 Jul 27 Jul 26 Jul 25 Jul 24 Jul 23 Jul 22 Jul 21 Jul 20 Jul 19 Jul 18 Jul 17 Jul 16 Jul 15 Jul 14 Jul 13 Jul 12 Jul 11 Jul 10 Jul 9 Jul 8 Jul 7 Jul 6 Jul 5 Jul 4
Windows 1 3 4 4 0 0 1 1 2 3 6 1 2 2 0 1 4 2 3 1 3 2 0 0 2 4 1 2 1 0 1 7 3 3 3 1 3 0 2 5 3 1 0 1 4 1
OS X 1 2 1 0 1 1 0 0 1 0 0 1 2 0 0 0 0 2 0 1 0 1 0 0 0 2 1 1 0 1 0 0 0 3 1 0 0 0 5 2 1 0 0 1 1 0
Linux 1 1 1 1 1 1 1 0 1 0 1 3 1 3 0 2 0 0 1 1 2 0 0 0 0 1 1 2 0 1 0 0 1 2 2 1 1 3 2 0 2 1 0 0 0 3

Readme

Source
raw.​githubusercontent.​com

R-snippets

This package includes a selection of R snippets for Sublimetext that I find useful when using R through SublimeREPL

Check out the project page at http://www.jvcasillas.com/code/projects/R-snippets

Just type the trigger and hit the tab key. For example…

lm

Expands to…

# load lme4 for mixed models
library(lme4)

# random intercept and random slope model
modelName <- lmer(DV ~ fixedFactor1 +* fixedFactor2 + (1 + randomSlope|randomInt), data=df)
summary(modelName)

Main triggers

  • “plot”: templates for plotting in base R
  • “edit”: options useful for data cleansing and saving
  • “desc”: descriptive statistics of data
  • “ttest”: distinct types of t-test
  • “aov”: distinct analysis of variance models
  • “lm”: linear and logistic regression
  • “lmem”: linear mixed effects models

Extras

  • “subset”: make subsets of a DF
  • “read”: read/load/install data/packages into R
  • “save”: save plots, dfs, tables, etc.
  • “tikz”: template for creating R plots in LaTeX

Note All snippets have the following scopes:

source.r, text.html.markdown.knitr, text.tex.latex, text.html.markdown.rmarkdown

To add

  • knitr
  • dplyr
  • coursera