The script visualizes the popularity trends of "R" and "Python" programming languages in Chile.

programming-languages-github.R

library(tidyverse)

# URL for the dataset
url <- "https://raw.githubusercontent.com/github/innovationgraph/main/data/languages.csv"

# Read the dataset
data <- read_csv(url)

# Perform data manipulation and aggregation
p <- data |>
  mutate(periodo = paste0(year, " Q", quarter),
         year_quarter = zoo::as.yearqtr(periodo)) |>
  filter(iso2_code == "CL") |>
  group_by(year_quarter, language) |>
  summarise(num_pushers = sum(num_pushers)) |>
  mutate(ranking = rank(-num_pushers))

# Create and customize the plot
p |>
  filter(language %in% c("R", "Python")) |>
  ggplot(aes(year_quarter, ranking, color = language)) +
  geom_line(linewidth = 3, alpha = 0.5) +
  geom_point(size = 6) +
  labs(title = "Ranking of programming languages in Chile",
       x = "Year",
       y = "Ranking",
       color = "Language",
       caption = "Source: Github") +
  scale_y_reverse(breaks = 30:1, labels = 30:1) +
  scale_color_manual(values = c("R" = "#00509d", "Python" = "#fdc500")) +
  theme_minimal() +
  theme(plot.title = element_text(margin = margin(b = 20), size = 20),
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20))