dfalster

##' Modifies 'data' by adding new values supplied in newDataFileName
##'
##' newDataFileName is expected to have columns 
##' c(lookupVariable,lookupValue,newVariable,newValue,source)
##' 
##' Within the column 'newVariable', replace values that
##' match 'lookupValue' within column 'lookupVariable' with the value
##' newValue'.  If 'lookupVariable' is NA, then replace *all* elements
##' of 'newVariable' with the value 'newValue'.
##'

hrbrmstr

library(rgeos)
library(rgdal) # needs gdal > 1.11.0
library(ggplot2)

# map theme
devtools::source_gist("https://gist.github.com/hrbrmstr/33baa3a79c5cfef0f6df")

map = readOGR("readme-swiss.json", "cantons")
map_df <- fortify(map)

hrbrmstr

  library(rgeos)
  library(rgdal) # needs gdal > 1.11.0
  library(ggplot2)
  
  # map theme
  devtools::source_gist("https://gist.github.com/hrbrmstr/33baa3a79c5cfef0f6df")
  
  # grab each of the layers
  
  limites = readOGR("division.json", "limites")

hrbrmstr

library(rgeos)
library(rgdal) # needs gdal > 1.11.0
library(ggplot2)

# map theme
devtools::source_gist("https://gist.github.com/hrbrmstr/33baa3a79c5cfef0f6df")

# topojson from the bl.ocks example
map = readOGR("us.json", "counties")

hrbrmstr

library(rgeos)
library(rgdal) # needs gdal > 1.11.0
library(ggplot2)

# map theme
devtools::source_gist("https://gist.github.com/hrbrmstr/33baa3a79c5cfef0f6df")

# use the topojson from the bl.ocks example
map = readOGR("us.json", "counties")

abresler

library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
	read.table() %>% data.frame %>% tbl_df -> data
names(data) <- c("month", "day", "year", "temp")

data %>%
	group_by(year, month) %>%

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

ibartomeus

#SevillaR talk

#The problem:

time <- c(2000:2015)
abundance <- rnorm(16, 150, 50) #poison??
plot(abundance ~ time, t = "l")


#can I detect a trend?

pteetor

#
# This is a toy example of splitting an R application
# into three distinct parts:
#
#   - Loading the data
#   - Calculating the analytics
#   - Rendering an RMarkdown document
#
# Note: The RMarkdown doc appears in a companion file, toyDoc.Rmd.
#

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',