michaeldorman

// US area polygon
var pol = ee.Geometry.Polygon([ [ [ -80.919630492025277, 30.699135009797704 ], [ -80.871245451372644, 30.336014265533166 ], [ -80.762754341973988, 29.906426353226227 ], [ -80.504997201528766, 29.330160083879278 ], [ -80.083106700408266, 28.673401576509857 ], [ -80.070233511479728, 28.651960676800226 ], [ -80.058638153313169, 28.629988680787292 ], [ -80.04835227119456, 28.607547474440747 ], [ -80.039403769248054, 28.584700211077717 ], [ -80.031816742625793, 28.561511131077758 ], [ -80.025611421049575, 28.538045379128882 ], [ -80.020804123732901, 28.514368819546309 ], [ -80.0174072256752, 28.490547850204102 ], [ -80.01542913528462, 28.466649215616201 ], [ -80.0148742832517, 28.442739819698442 ], [ -80.015743122564871, 28.418886538736928 ], [ -80.018032139528088, 28.395156035079435 ], [ -80.021733875613705, 28.371614572058188 ], [ -80.026836959956768, 28.348327830641338 ], [ -80.033326152273503, 28.32536072829911 ], [ -80.041182395964384, 28.302777240558235 ], [ -80.050382881141516, 28.2806402

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