Unlocking the power of data visualization with R

library(tidyverse)
tuesdata <- tidytuesdayR::tt_load(2023, week = 12)

languages <- tuesdata$languages

write_csv(languages,"languages.csv")

languages%>%head

languages%>%names

df <- languages%>%
  arrange(appeared)%>%
  select(pldb_id,appeared,type,language_rank,number_of_users)
df

ggplot(df,aes(appeared,language_rank))+
  geom_point()

df %>%
  filter(appeared>1900)%>%
ggplot(aes(appeared,language_rank))+
  geom_point()+
  scale_y_reverse()

df %>%
  count(type,sort = TRUE)%>%
  mutate(pct=round(n/sum(n)*100,2))

df %>%
  arrange(-appeared)%>%
  filter(between(appeared,2021,2023))%>%#count(type)
  mutate(appeared=as.factor(appeared))

df %>%
  arrange(-appeared)%>%
  filter(between(appeared,2021,2023))%>%#count(type)
  mutate(appeared=as.factor(appeared))%>%
  ggplot(aes(appeared,number_of_users,fill=type))+
  geom_col()+
  labs(title="New Language tools")

df%>%
  group_by(appeared)%>%
  reframe(n_languages=n(),pldb_id,type,avg=mean(number_of_users))%>%
  filter(between(appeared,2000,2023)) %>%
  ggplot(aes(appeared,n_languages))+
  geom_point()+
  geom_line()+
  geom_segment(aes(x=appeared,xend=appeared,y=0,yend=n_languages,
                   color=n_languages),
               size=6)+
  geom_text(aes(label=type),check_overlap = TRUE,vjust=-0.5)