Find and visualize best and worst items using boxplot - r

Question

I am a dataset of jokes Dataset 2 (jester_dataset_2.zip) from the Jester project and I would like to divide the jokes into groups of jokes with similar rating and visualize the results appropriately.
The data look like this
> str(tabulka)
'data.frame': 1761439 obs. of 3 variables:
$ User : int 1 1 1 1 1 1 1 1 1 1 ...
$ Joke : int 5 7 8 13 15 16 17 18 19 20 ...
$ Rating: num 0.219 -9.281 -9.281 -6.781 0.875 ...
Here is a subset of Dataset 2.
> head(tabulka)
User Joke Rating
1 1 5 0.219
2 1 7 -9.281
3 1 8 -9.281
4 1 13 -6.781
5 1 15 0.875
6 1 16 -9.656
I found out I can't use ANOVA since the homogenity is not the same. Hence I am using Kruskal–Wallis method from agricolae package in R.
KWtest <- with ( tabulka , kruskal ( Rating , Joke ))
Here are the groups.
> head(KWtest$groups)
trt means M
1 53 1085099 a
2 105 1083264 a
3 89 1077435 ab
4 129 1072706 b
5 35 1070016 bc
6 32 1062102 c
The thing is I don't know how to visualize the joke groups appropriately. I am using boxplot to show the confidence intervals for each joke.
barvy <- c ("yellow", "grey")
boxplot (Rating ~ Joke, data = tabulka,
col = barvy,
xlab = "Joke",
ylab = "Rating",
ylim=c(-7,7))
It would be nice to somehow color each box (each joke) with an appropriate color according to the color given by the KW test.
How could I do that? Or is there some better way to find the best and the worst jokes in the dataset?

Answer 1

Interesting question per se. It's easy to color each bar according to the group the joke belongs to. However, I think it is just a intermediate solution, there must be better visualization for these data. So, certainly not the best one, but there is my version:
library(tidyverse)
# download data (jokes, part 1) to temporaty file, and unzip
tmp <- tempfile()
download.file("http://eigentaste.berkeley.edu/dataset/jester_dataset_1_1.zip", tmp)
tmp <- unzip(tmp)
# read data from temp
vtipy <- readxl::read_excel(tmp, col_names = F, na = '99')
# clean data
vtipy <- vtipy %>%
mutate(user = 1:n()) %>%
gather(key = 'joke', value = 'rating', -c('..1', 'user')) %>%
rename(n = '..1', ) %>%
filter(!is.na(rating)) %>%
mutate(joke = as.character(as.numeric(gsub('\\.+', '', joke)) - 1)) %>%
select(user, n, joke, rating)
# your code
KWtest <- with(vtipy, agricolae::kruskal(rating, joke))
# join groups from KWtest to original data, clean and plot
KWtest$groups %>%
rownames_to_column('joke') %>%
select(joke, groups) %>%
right_join(vtipy, by = 'joke') %>%
mutate(joke = stringi::stri_pad_left(joke, 3, '0')) %>%
ggplot(aes(x = joke, y = rating, fill = groups)) +
geom_boxplot(show.legend = F) +
scale_x_discrete(breaks = stringi::stri_pad_left(c(1, seq(5, 100, by = 5)), 3, '0')) +
ggthemes::theme_tufte() +
labs(x = 'Joke', y = 'Rating')