library(tidytext)
library(wordcloud)
library(knitr)
library(kableExtra)

How to make wordclouds in R!

First, download stop words (such as and, the, of) to filter out of the dataset

data("stop_words")

Then we will will unnest tokens and count the occurences of each word in each decade.

tokens <- democracy_aid %>%
  select(description, year) %>% 
  mutate(decade = substr(year, 1, 3)) %>% 
  mutate(decade = paste0(decade, "0s")) %>% 
  group_by(decade) %>% 
  unnest_tokens(word, activity_description) %>% 
  count(word, sort = TRUE) %>% 
  ungroup() %>% 
  anti_join(stop_words) 

nums <- tokens %>% filter(str_detect(word, "^[0-9]")) %>% select(word) %>% unique()

tokens %<>%
  anti_join(nums, by = "word") 

And with the kable() function we can make a HTML table that I copy and paste to this blog. Below I rewrite the HTML to change the headings

tokens %>% 
    group_by(decade) %>% 
    top_n(n = 10,
          wt = n)  %>%
    arrange(decade, desc(n)) %>%
    arrange(desc(n)) %>%
    knitr::kable("html")

decade	word	n
2010s	rights	4541
2010s	local	3981
2010s	youth	3778
2010s	promote	3679
2010s	democratic	3618
2010s	public	3444
2010s	national	3060
2010s	political	3020
2010s	human	3009
2010s	organization	2711
2000s	rights	2548
2000s	human	1745
2000s	local	1544
2000s	conduct	1381
2000s	political	1257
2000s	training	1217
2000s	promote	1142
2000s	public	1121
2000s	democratic	1071
2000s	national	988

Create a vector of colors:

my_colors <- c("#0450b4", "#046dc8", "#1184a7","#15a2a2", "#6fb1a0", 
               "#b4418e", "#d94a8c", "#ea515f", "#fe7434", "#fea802")

tokens %<>% 
  mutate(word = ifelse(grepl("democr", word), "democracy", 
                ifelse(grepl("politi", word), "politics", 
                ifelse(grepl("institut", word), "institution", 
                ifelse(grepl("govern", word), "government", 
                ifelse(grepl("organiz", word), "organization", 
                ifelse(grepl("elect", word), "election", word))))))) 

wordcloud(tokens$word, tokens$n, random.order = FALSE, max.words = 50, colors = my_colors)

2010s Decade	Word	Count
2010s	rights	4541
2010s	local	3981
2010s	youth	3778
2010s	promote	3679
2010s	democratic	3618
2010s	public	3444
2010s	national	3060
2010s	political	3020
2010s	human	3009
2010s	organization	2711
2000s Decade	Word	Count
2000s	rights	2548
2000s	human	1745
2000s	local	1544
2000s	conduct	1381
2000s	political	1257
2000s	training	1217
2000s	promote	1142
2000s	public	1121
2000s	democratic	1071
2000s	national	988

And if we compare civic versus politically-oriented aid, we can see that more money goes towards projects that have political or electoral aims rather than civic or civil society education goals

tokens %>% 
  group_by(year) %>% 
  top_n(n = 20,
        wt = n) %>% 
  mutate(word = case_when(word == "party" ~ "political",
                          word == "parties" ~ "political",
                          word == "election" ~ "political",
                          word == "electoral" ~ "political",
                          word == "civil" ~ "civic", 
                          word == "civic" ~ "civic",
                          word == "social" ~ "civic",
                          word == "education" ~ "civic",
                          word == "society" ~ "civic", 
                          TRUE ~ as.character(word))) %>% 
  filter(word == "political" | word == "civic") %>% 
  ggplot(aes(x = year, y = n, group = word)) + 
  geom_line(aes(color = word ), size = 2.5,alpha = 0.6)  +
  geom_point(aes(color = word ), fill = "white", 
             shape = 21, size = 3, stroke = 2) +
  bbplot::bbc_style() + 
  scale_x_discrete(limits = c(2001:2019)) +
  theme(axis.text.x= element_text(size = 15,
                                  angle = 45)) +
  scale_color_discrete(name = "Aid type", labels = c("Civic grants", "Political grants"))