noamross

library(tidyverse)
library(gganimate)
library(mgcv)
library(mvtnorm)

# Fit a GAM to the data
mod <- gam(hp ~ s(mpg), data=mtcars, method="REML")

# Get the linear prediction matrix
newdat = data.frame(

thomasp85

library(gganimate) # thomasp85/gganimate
library(cartogram)
library(geogrid) # Need github version jbaileyh/geogrid
library(rnaturalearth)
library(sf)
library(scico)

us <- ne_states('united states of america', returnclass = 'sf')
us <- us[!us$woe_name %in% c('Alaska', 'Hawaii'), ]
us <- st_transform(us, '+proj=eqdc +lat_0=39 +lon_0=-96 +lat_1=33 +lat_2=45 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs')

rushgeo

#!/bin/sh
# Simplified template based on a script from Jonathan Heiss

# create batchtools.lsf.tmpl in the working directory - this template used for child jobs
cat <<'TEMPLATE' > batchtools.lsf.tmpl
## Default resources can be set in your .batchtools.conf.R by defining the variable
## 'default.resources' as a named list.

#BSUB-J <%= job.name %>                             # Name of the job
#BSUB-o <%= log.file %>                             # Output is sent to logfile, stdout + stderr by default

thomasp85

library(ggplot2)
library(gganimate)
library(sf)
earth <- sf::st_as_sf(rnaturalearth::countries110)
views <- data.frame(rbind(
  st_bbox(earth[earth$name == 'Denmark',]),
  st_bbox(earth[earth$name == 'Australia',])
))
p <- ggplot() + 
  geom_sf(data = earth, fill = 'white') + 

halhen

# Download elevation tif from eg http://www.eea.europa.eu/data-and-maps/data/digital-elevation-model-of-europe
# First, convert elevation tif to a space delimited xyz (lng lat elevation) file
#  $ gdal_translate -of XYZ elevation3x3.tif /tmp/file.xyz

df <- read_delim('/tmp/file.xyz', delim=' ', col_names=FALSE)

df %>%
  mutate(X3 = na_if(X3, 0)) %>%
  ggplot(aes(X1, -X2 + 20 * X3/max(X3, na.rm=TRUE), group=X2)) +
    geom_line(size=0.05) +

thomasp85

library(tweenr) # Available on CRAN
library(ggforce) # Install from thomasp85/ggforce
library(gganimate) # Install from dgrtwo/gganimate
set.seed(2)
x <- sample(9,20, prob=c(1,2,3,4,5,4,3,2,1), replace=T)
df <- data.frame(x = x, y = 15)
dfs <- list(df)
for(i in seq_len(nrow(df))) {
    dftemp <- tail(dfs, 1)
    dftemp[[1]]$y[i] <- sum(dftemp[[1]]$x[seq_len(i)] == dftemp[[1]]$x[i])

rafapereirabr

This gist shows in two steps how to tilt and stack maps using ggplot2 in order to create an image like this one: [![enter image description here][1]][1]

Let's load the necessary libraries and data to use a reproducible example:

# load libraries
  library(rgeos)
  library(UScensus2000tract)
  library(ggplot2)

fitnr

hadley

Notes:

• I've tried to break up in to separate pieces, but it's not always possible: e.g. knowledge of data structures and subsetting are tidy intertwined.

• Level of Bloom's taxonomy listed in square brackets, e.g. http://bit.ly/15gqPEx. Few categories currently assess components higher in the taxonomy.

Programming R curriculum

Data structures

brentp

Power Calculations for Methylation Study

We have methylation data at 480K sites from the illumina 450 methylation chip for 96 controls and 97 asthmatics from the ICAC project.

NOTE

see this site: http://www.ats.ucla.edu/stat/r/dae/t_test_power2.htm