clauswilke

library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
library(colorspace)

# data to analyze
data <- select(MASS::fgl, -type, -RI, -Si)

# cluster

clauswilke

library(ggplot2) # requires 2.3.0
library(purrr)

make_plot <- function(frame) {
  ggplot(mtcars, aes(mpg, hp, color = factor(cyl))) +
    geom_point() +
    scale_color_brewer(
      palette = 2, type = "qual", name = "cyl",
      guide = guide_legend(
        direction = "horizontal",

clauswilke

library(purrr)
library(ggplot2)

# function that does the work
make_partial_plots <- function(p) {
  g <- ggplot_build(p)
  n <- length(g$plot$layers)
  
  blank_out <- function(i) {
    g1 <- g

clauswilke

library(rlang)

# Functions with names ending in `_impl` take quoted expressions as input.
# This removes the need for constant quoting and unquoting
simplify_sum_impl <- function(e1, e2) {
  if (is_syntactic_literal(e1) & is_syntactic_literal(e2)) {
    return(eval_bare(e1 + e2))
  }
  
  if (e1 == 0) {

clauswilke

library(tidyverse) # code uses development version of ggplot2 for viridis scales; otherwise, viridis package would be needed

df_internet <- read.csv("internet.csv")

# sort countries by internet users in final year
internet_summary <- df_internet %>%
  group_by(country) %>%
  summarize(last = users[n()]) %>%
  arrange(last)

clauswilke

country,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011
Abkhazia,,,,,,,,,,,,,,,,,,,,,,
Afghanistan,0,,,,,,,,,,,0.004188346,0.004092114,0.079875078,0.097163516,1.130397829,1.947423469,1.751202161,1.688485448,3.246305573,3.654114396,4.580669921
Akrotiri and Dhekelia,,,,,,,,,,,,,,,,,,,,,,
Albania,0,,,,,0.011168695,0.032196828,0.048593919,0.06502737,0.081437045,0.114097347,0.325798377,0.390081273,0.971900415,2.420387798,6.043890864,9.609991316,15.03611541,23.86,41.2,45,49
Algeria,0,,,,0.000360674,0.001768954,0.001738533,0.010268463,0.020238555,0.199523843,0.491705679,0.646114017,1.59164126,2.195359731,4.634475088,5.843942092,7.375984956,9.451190626,10.18,11.23,12.5,14
American Samoa,0,,,,,,,,,,,,,,,,,,,,,
Andorra,0,,,,,,1.526601023,3.050175385,6.886209218,7.635686143,10.53883561,,11.26046872,13.54641288,26.83795439,37.60576622,48.936847,70.87,70.04,78.53,81,81
Angola,0,,,,,,0.000775929,0.005673746,0.018453724,0.071964087,0.105045562,0.136013867,0.27037

clauswilke

# original version
library(DAAG)
library(ggplot2)
library(ggridges)
ggplot(ais, aes(x=ht, y=sport, point_size=pcBfat, point_color=sex, group=sport)) +
  geom_density_ridges(jittered_points=TRUE, scale = .8, rel_min_height = .01, fill = "gray90",
                      points_scaling_range = c(.1, .8)) +
  scale_y_discrete(expand = c(.01, 0)) +
  scale_x_continuous(expand = c(0, 0), name = "height [cm]") +
  scale_point_size_continuous(range = c(0.1, 5), name = "% bodyfat") +