Extra code from Data Matters 2022

Day1-1-inclusiveness-geom_col.R

# Showing how to replicate geom_bar() with a combination of
# count() and geom_col

inclusiveness_index %>% count(Continent) %>%
  rename("count" = "n") %>%
  ggplot() +
  geom_col(mapping=aes(x=Continent, y=count))

Day1-2-labs_scales-palettes.R

# Showing how to use a Color Brewer palette instead of manual color values.

color_scatter +
  labels +
  scale_color_brewer(palette = "Set2")

Day2-1-gapminder_adv_techniques.R

# This version uses a logical test to color the dots. It also shows
# using scientific notation to indicate large numbers.

ggplot(gap, aes(x=gdp,y=life.exp)) +
  geom_point(aes(size=pop, color=pop<200000000)) +
  geom_smooth(method = "lm", se=FALSE, color="grey50") +
  labs(
    title="Averages across all years of the traditional Gapminder dataset",
    x="Average GDP per capita (log 10)",
    y="Average life expectany at birth",
    size="Average total population",
    color="Region"
  ) +
  theme_bw() +
  scale_x_log10(breaks=c(1000,10000)) +
  scale_size_continuous(breaks=c(75e5,75e6,75e7), 
                        labels=c("7.5 million", 
                                 "75 million", 
                                 "750 million")) +
  geom_text(aes(label=country), 
            data=gap %>% dplyr::filter(pop > 200000000))

Day2-GroupApp-app.R

# This is the Shiny app we all worked on together as a group.
# For the code to work, it should be placed in the Day 2 folder
# in a subdirectory - you can call that new subdirectory anything you want.
#
# -------------------------
#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#

library(shiny)
library(tidyverse)

got <- read_csv("../../data/got_ratings.csv")

inclusiveness_index <- read_excel('../../data/inclusiveness_index/global_data_for_website_2020.xlsx',
                                  na="9999") %>%
  rename_with(~ str_remove_all(.x, "[(|)]") %>% str_replace_all("[-| ]", ".")) %>%
  dplyr::filter(Continent != "Antarctica") %>% 
  mutate(Continent = Continent %>% as_factor() %>% fct_infreq(),
         Index.categories.2020 = Index.categories.2020 %>% as_factor() %>% 
           fct_relevel(c("High", "Medium-High", "Medium", "Medium-Low", "Low", "No data")))

incl_bar_data <- inclusiveness_index %>%
  count(Continent, Index.categories.2020)


palette_drop_down <- selectInput(
  inputId = "got_palette", 
  label = "Select a color palette", 
  choices = c("Rainbow", "Purple-Green", "Yellow-Orange-Red"))

# Define UI for application that draws a histogram
ui <- navbarPage(
  
  # overall page title
  title = "Group-Designed Shiny App",
  
  # First tab
  tabPanel(
    "Game of Thrones Heatmap", 
    sidebarLayout(
      sidebarPanel(
        palette_drop_down
      ),
      mainPanel(
        fluidRow(
          column(
            width=6,
            plotOutput(outputId = "got_heatmap")
            ),
          column(
            width=6,
            plotOutput(outputId = "got_scatter")
            )
        )
      )
   )       
  ),
  
  # Second tab
  tabPanel(
    "Inclusiveness Categorical Heatmap", 
    plotOutput(outputId = "incl_heatmap")
  ),
  
  # Third tab
  tabPanel("tab 3", "Tab 3 Contents")
)

# Define server logic required to draw a histogram
server <- function(input, output) {

    pal <- reactive({
      case_when(
        input$got_palette == "Rainbow" ~ "Spectral",
        input$got_palette == "Purple-Green" ~ "PRGn",
        input$got_palette == "Yellow-Orange-Red" ~ "YlOrRd",
        TRUE ~ "Blues"
      )
    })
  
    output$got_heatmap <- renderPlot({
      
      ggplot(got, aes(x=moral, y=physical)) +
        geom_bin2d(bins=8) +
        scale_fill_distiller(palette=pal(), direction=1)
      
    })
    
    output$got_scatter <- renderPlot({
      
      ggplot(got, aes(x=moral, y=physical, color = physical)) +
        geom_point()  +
        scale_color_distiller(palette=pal(), direction=1)
      
    })
    
    output$incl_heatmap <- renderPlot({
      
      ggplot(incl_bar_data, aes(x=Continent, 
                                y=Index.categories.2020,
                                fill=n)) +
        geom_tile()
      
    })
}

# Run the application 
shinyApp(ui = ui, server = server)

Day2-IntroToShiny-app.R

# This is the default Shiny web app with a few modifications.
#
# -------------------------
#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#

library(shiny)
library(tidyverse)

# Define UI for application that draws a histogram
ui <- fluidPage(

    # Application title
    titlePanel("Old Faithful Geyser Data"),

    # Sidebar with a slider input for number of bins 
    sidebarLayout(
        sidebarPanel(
            sliderInput(inputId = "bins",
                        label = "No. of bins:",
                        min = 1,
                        max = 51,
                        value = 25)
        ),

        # Show a plot of the generated distribution
        mainPanel(
           plotOutput(outputId = "distPlot")
        )
    )
)

# Define server logic required to draw a histogram
server <- function(input, output) {

    output$distPlot <- renderPlot({
      
      ggplot(faithful, aes(x=waiting)) +
        geom_histogram(bins = input$bins) +
        labs(
          x = 'Waiting time to next eruption (in mins)',
          title = 'Histogram of waiting times'
        )
  
    })
}

# Run the application 
shinyApp(ui = ui, server = server)