mrdwab

cSplit <- function(indt, splitCols, sep = ",", direction = "wide", 
                   makeEqual = NULL, fixed = TRUE, drop = TRUE, 
                   stripWhite = FALSE) {
  message("`cSplit` is now part of the 'splitstackshape' package (V1.4.0)")
  ## requires data.table >= 1.8.11
  require(data.table)
  if (!is.data.table(indt)) setDT(indt)
  if (is.numeric(splitCols)) splitCols <- names(indt)[splitCols]
  if (any(!vapply(indt[, splitCols, with = FALSE],
                  is.character, logical(1L)))) {

hadley

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply

bryangoodrich

# Twitter Topic Modeling Using R
# Author: Bryan Goodrich
# Date Created: February 13, 2015
# Last Modified: April 3, 2015
# 
# Use twitteR API to query Twitter, parse the search result, and
# perform a series of topic models for identifying potentially
# useful topics from your query content. This has applications for
# social media, research, or general curiosity
#

hrbrmstr

library(rgdal)
library(rgeos)
library(ggplot2)
library(readr)
library(tidyr)
library(dplyr)
library(grid)

us <- readOGR("us_states_hexgrid.geojson", "OGRGeoJSON")

Pakillo

As far as I know Rstudio does not count words or characters at the moment, which would be useful particularly when writing Rmarkdown.

This is a quick shortcut using word_count and character_count functions from qdap package. See below for two wrapper functions that simplify their use.

library("qdap")

Just select and copy the text to the clipboard and then run in the console:

hrbrmstr

jeroen

library(magick)
library(magrittr)

# Convert to 'ico' format
image_read("https://www.r-project.org/logo/Rlogo.png") %>%
	image_scale("32x32!") %>% # remove the "!" after 32x32 to scale proportionally
	image_write("favicon.ico", format = "ico")


# Favicon can also be png/gif

halhen

library(tidyverse)

# data from https://www.kaggle.com/bls/american-time-use-survey

df.resp <- read_csv('../data/atus/atusresp.csv')
df.act <- read_csv('../data/atus/atusact.csv', col_types=cols(tustarttim = col_character(), tustoptime = col_character())) 
df.sum <- read_csv('../data/atus/atussum.csv')

df.tmp <- df.act %>%
  mutate(activity = case_when(trtier2p == 1301 ~ 'Exercise',

jimhester

library(gh)
library(tidyverse)
library("magick")
library(glue)

dir.create("data")

repos <- gh::gh("/orgs/:org/repos", org = "tidyverse", .limit = Inf) %>% map_chr("name")

urls <- repos %>%

walkerke

library(idbr) 
library(ggplot2)
library(gganimate)
library(tweenr)
library(countrycode)
library(dplyr)

idb_api_key("Your API key goes here")

africa_fips <- countrycode(c('Nigeria', 'Uganda', 'Tanzania', 'Ghana'),