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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
  • 1 year ago
  • 55 minutes ago
  • 5 years ago

Installs

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

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