bimodal

popol.R

require(tidyverse)
require(tidyr)
require(moments)

nrespondants <- c(223, 41, 63, 86, 64, 251, 82, 73, 74, 17, 64, 168, 43, 47, 64, 35, 135, 28, 15, 43, 10, 43, 366, 45, 73, 50, 26, 121, 25, 38, 44, 5, 46, 260, 36, 54, 49, 31, 63, 26, 30, 29, 3, 44)

score <- rep(0:10, 4)

wave <- sort(rep(1:4, 11))

tibble(wave = wave, score = score, nrespondants = nrespondants) %>% uncount(nrespondants) %>% group_by(wave) %>% summarise(k = kurtosis(score), y = skewness(score), beta = (y^2 + 1) /k) %>% ggplot(aes(x = wave, y = beta)) + geom_point(size = 3) + ylim(0, 0.7)
ggsave("polpol.jpg")

npolice <- c(68, 24, 45, 89, 50, 264, 110, 117, 126, 40, 107, 143, 23, 42, 61, 27, 119, 21, 49, 49, 116, 79, 360, 46, 66, 50, 30, 82, 19, 41, 45, 22, 76, 301, 35, 44, 31, 18, 40, 8, 17, 44, 16, 63)

tibble(wave = wave, score = score, npolice = npolice) %>% uncount(npolice) %>% group_by(wave) %>% summarise(k = kurtosis(score), y = skewness(score), beta = (y^2 + 1) /k) %>% ggplot(aes(x = wave, y = beta)) + geom_point(size = 3) + ylim(0, 1)

ggsave("policepolpol.jpg")