app.R

library(dash)
# library(dashBootstrapComponents)
library(dashHtmlComponents)
library(ggplot2)
library(plotly)
library(tidyr)
library(dplyr)
library(tidyverse)

app <- Dash$new(external_stylesheets = dbcThemes$BOOTSTRAP)

df <- readr::read_csv(here::here("data", "raw", "world-data-gapminder_raw.csv"))
card4_df <- readr::read_csv(here::here("data", "raw", "combined-data-for-GDP-per-cap.csv"))

df[is.na(df)] <- 0

df_year_region <- df %>%
  group_by(year, region) %>%
  summarise(sum_co2_per_capita = sum(co2_per_capita))

df_subregion_year <- df %>%
  group_by(sub_region, year) %>%
  summarise(mean_life_expectancy = mean(life_expectancy))

marks_card2 <- list(
  "1918" = "1918",
  "1928" = "1928",
  "1938" = "1938",
  "1948" = "1948",
  "1958" = "1958",
  "1968" = "1968",
  "1978" = "1978",
  "1988" = "1988",
  "1998" = "1998",
  "2008" = "2008",
  "2018" = "2018"
)

years_card4 <- list(
  "1960" = "1960",
  "1968" = "1968",
  "1978" = "1978",
  "1988" = "1988",
  "1998" = "1998",
  "2008" = "2008",
  "2018" = "2018"
)
regions_card4 <- list(
  list(label = "All", value = ""),
  list(label = "Asia", value = "Asia"),
  list(label = "Europe", value = "Europe"),
  list(label = "Africa", value = "Africa"),
  list(label = "Americas", value = "Americas"),
  list(label = "Oceania", value = "Oceania")
)


plot <- function(year_range, filter) {
  if (filter == "") {
    df_by_year <- df %>%
      filter(year >= year_range[1] & year < year_range[2]) %>%
      group_by(year) %>%
      summarize(children_per_woman = mean(children_per_woman))
    p <- ggplot(
      df_by_year,
      aes(
        y = children_per_woman,
        x = year
      )
    ) +
      geom_line() +
      labs(y = "Children per woman", x = "Year") +
      ggtitle("Average Number of Children") +
      theme(plot.title = element_text(hjust = 0.5))
  } else {
    df_by_year <- df %>%
      filter(year >= year_range[1] & year < year_range[2]) %>%
      group_by(!!sym(filter), year) %>%
      summarize(children_per_woman = mean(children_per_woman))

    p <- ggplot(
      df_by_year,
      aes(
        y = children_per_woman,
        x = year
      )
    ) +
      geom_line(aes(color = !!sym(filter))) +
      labs(y = "Children per woman", x = "Year") +
      ggtitle("Average Number of Children") +
      theme(plot.title = element_text(hjust = 0.5))
  }
  return(p)
}


app$layout(
  div(
    list(
      dbcRow(
        className = "text-center bg-warning",
        list(
          h1("Gapminder Challenge"),
          p(
            paste(
              "Take the challenges below to see how you understand global issues such as healthcare and finance development."
            )
          ),
          p(
            paste(
              "Test yourselves now!"
            )
          )
        )
      ),
      dbcRow(
        list(
          dbcCol(dbcCard(
            className = "m-3 p-3",
            list(
              dccGraph(id = "bar_chart"),
              htmlLabel("Select year:"),
              dccSlider(
                id = "slider",
                min = 1914,
                max = 2014,
                step = 10,
                marks = list(
                  "1914" = "1914",
                  "1924" = "1924",
                  "1934" = "1934",
                  "1944" = "1944",
                  "1954" = "1954",
                  "1964" = "1964",
                  "1974" = "1974",
                  "1984" = "1984",
                  "1994" = "1994",
                  "2004" = "2004",
                  "2014" = "2014"
                ),
                value = 1914,
              ),
              htmlLabel("Select regions:"),
              dccDropdown(
                id = "dropdown",
                options = c(
                  "Americas",
                  "Asia",
                  "Europe",
                  "Oceania",
                  "Africa"
                ),
                value = c("Americas", "Oceania", "Africa"),
                multi = TRUE
              )
            )
          ),
          md = 6
          ),
          dbcCol(dbcCard(className = "m-3 p-3", div(
            list(
              dccGraph(id = "plot_area"),
              htmlLabel("Zoom in years: "),
              dccRangeSlider(
                min = 1918, max = 2018, step = 1 - 10, value = list(1918, 2018), id = "year_range_slider",
                marks = marks_card2
              ),
              htmlLabel("See breakdown number by: "),
              dccDropdown(
                options = list(
                  list(label = "All", value = ""),
                  list(label = "Income Group", value = "income_group"),
                  list(label = "Region", value = "region")
                ),
                value = "", clearable = FALSE, id = "filter_dropdown"
              )
            )
          )), md = 6)
        )
      ),
      dbcRow(
        list(
          dbcCol(dbcCard(
            className = "m-3 p-3",
            list(
              dccGraph(id = "line_chart_3"),
              htmlLabel("Zoom in Years: "),
              dccRangeSlider(
                id = "slider_3",
                min = 1918,
                max = 2018,
                step = 10,
                marks = list(
                  "1918" = "1918",
                  "1928" = "1928",
                  "1938" = "1938",
                  "1948" = "1948",
                  "1958" = "1958",
                  "1968" = "1968",
                  "1978" = "1978",
                  "1988" = "1988",
                  "1998" = "1998",
                  "2008" = "2008",
                  "2018" = "2018"
                ),
                value = list(1938, 2018)
              ),
              htmlLabel("Filter by  Sub-Region: "),
              dccDropdown(
                id = "dropdown_3",
                options = unique(df_subregion_year$sub_region),
                value = unique(df_subregion_year$sub_region)[15],
                multi = TRUE
              )
            )
          ), md = 6),
          dbcCol(dbcCard(
            className = "m-3 p-3",
            list(
              dccGraph(id = "plot-area"),
              htmlLabel("Zoom in Years: "),
              dccRangeSlider(
                id = "year_slider",
                min = 1960,
                max = 2018,
                step = 1,
                value = list(1960, 2018),
                marks = years_card4,
                tooltip = "placement"
              ),
              htmlLabel("Filter by Geographic Region: "),
              dccDropdown(
                id = "region_dropdown",
                options = regions_card4,
                value = "",
                multi = TRUE
              )
            )
          ), md = 6)
        )
      )
    )
  )
)


app$callback(
  output("bar_chart", "figure"),
  list(
    input("slider", "value"),
    input("dropdown", "value")
  ),
  function(selected_year, selected_regions) {
    p <- ggplot(df_year_region %>%
      filter(year == selected_year, region %in% selected_regions)) +
      aes(
        y = reorder(region, sum_co2_per_capita, ),
        x = sum_co2_per_capita, fill = region
      ) +
      geom_bar(stat = "identity") +
      labs(x = "CO2 emissions per capita", y = "Region")
    ggplotly(p) %>% layout()
  }
)

app$callback(
  output("plot_area", "figure"),
  list(
    input("year_range_slider", "value"),
    input("filter_dropdown", "value")
  ),
  function(year_range, filter) {
    return(ggplotly(plot(year_range, filter)))
  }
)

# card 3
app$callback(
  output("line_chart_3", "figure"),
  list(
    input("slider_3", "value"),
    input("dropdown_3", "value")
  ),
  function(selected_year, selected_regions) {
    title_text <- paste0(
      "Mean Life Expectancy from ",
      as.character(selected_year[1]),
      " to ",
      as.character(selected_year[2])
    )
    p <- ggplot(df_subregion_year %>%
      filter(
        year >= selected_year[1], year <= selected_year[2],
        sub_region %in% selected_regions
      )) +
      aes(
        y = mean_life_expectancy,
        x = year, color = sub_region
      ) +
      geom_line() +
      labs(x = "Year", y = "Mean Life Expectancy") +
      theme_bw(base_size = 13) +
      ggtitle(title_text)
    ggplotly(p) %>% layout()
  }
)

# callback for card 4
app$callback(
  output("plot-area", "figure"),
  list(
    input("year_slider", "value"),
    input("region_dropdown", "value")
  ),
  function(year_range, region_filter) {
    df_sub <- card4_df %>%
      filter(year >= year_range[1] & year <= year_range[2])

    if ("" %in% region_filter) {
      p <- df_sub %>%
        group_by(year) %>%
        summarise(
          income = sum(`GDP total`),
          pop = sum(population)
        ) %>%
        mutate(income_per_capita = income / pop) %>%
        ggplot(aes(x = year, y = income_per_capita)) +
        geom_line() +
        labs(
          x = "Year",
          y = "Income Per Capita (in 2017 US$)",
          title = "Income Per Capita Has Been Rising"
        ) +
        ggthemes::scale_color_tableau()
    } else {
      df_sub <- df_sub %>%
        filter(region %in% region_filter)
      p <- df_sub %>%
        group_by(year, region) %>%
        summarise(
          income = sum(`GDP total`),
          pop = sum(population)
        ) %>%
        mutate(income_per_capita = income / pop) %>%
        ggplot(aes(x = year, y = income_per_capita, color = region)) +
        geom_line() +
        labs(
          x = "Year",
          y = "Income Per Capita (in 2017 US$)",
          title = "Income Per Capita Has Been Rising"
        ) +
        ggthemes::scale_color_tableau()
    }
    ggplotly(p)
  }
)

app$run_server(host = "0.0.0.0")