Fear of WaPo Using Bad Pie Charts Has Increased Since Last Year

By hrbrmstr

library(tidyr)

library(ggplot2)

library(ggthemes)

library(scales)

library(dplyr)
# Easiest way to transcribe the PDF table

# The slope calculation will enable us to color the lines/points based on up/down

dat  data_frame(`2014-11-01`=c(0.11, 0.22, 0.35, 0.31, 0.01),

                  `2015-12-01`=c(0.17, 0.30, 0.30, 0.23, 0.00),

                  slope=factor(sign(`2014-11-01` - `2015-12-01`)),

                  fear_level=c("Very worried", "Somewhat worried", "Not too worried",

                               "Not at all", "Don't know/refused"))
# Transform that into something we can use

dat  gather(dat, month, value, -fear_level, -slope)
# We need real dates for the X-axis manipulation

dat  mutate(dat, month=as.Date(as.character(month)))
# Since 2 categories have the same ending value, we need to

# take care of that (this is one of a few "gotchas" in slopegraph preparation)

end_lab  dat %>%

  filter(month==as.Date("2015-12-01")) %>%

  group_by(value) %>%

  summarise(lab=sprintf("%s", paste(fear_level, collapse=", ")))
gg  ggplot(dat)
# line

gg  gg + geom_line(aes(x=month, y=value, color=slope, group=fear_level), size=1)

# points

gg  gg + geom_point(aes(x=month, y=value, fill=slope, group=fear_level),

                      color="white", shape=21, size=2.5)
# left labels

gg  gg + geom_text(data=filter(dat, month==as.Date("2014-11-01")),

                     aes(x=month, y=value, label=sprintf("%s — %s  ", fear_level, percent(value))),

                     hjust=1, size=3)

# right  ...read more
Source:: r-bloggers.com