Data manipulation

There are 85 countries from the first five waves in this dataset with about 255,000 observations. My outcome variable is based on a battery of questions from the WVS that ask if the respondent is a member of any of the following types of association: sports, arts, labor, politics, environmental, charity, women’s rights, human rights, or other. A respondent gets a “1” if he and she answers “yes” to any of the associational membership.

For the purpose of this app, I extract variables that are relevant, such as regions, country IDs, gender, educational attainment, and membership from the larger data set. Because the lowest unit of analysis is “country”, I calculate the country and regional averages of membership by topics of gender and educational attainment.

Reactive input

The reactive input function in shiny adjusts the data displayed based on the criteria selected. This allows many ways to dissect data. I utilize it to present data at the world, region, and country levels as well as by gender and education.

I create three tabs by topics. Users can choose “the world” or a region of interest or on the left, and using the reactive with selectInput, I create an object that makes the main panel display the corresponding data. I also use observe to add another reactive element, which displays the list of country after the region is selected.

For gender and educational attainment tabs, renderTable is perfect for displaying the information because it allows users to sort the data based on variables of interest. It also has other options, such as including a search box in the table, but I do not want extraneous features to clutter the table and a simple version is adequate in conveying the information.

server.R
 
  selectedData1 <- reactive({
    if (input$region == "the world") {
      highested_table[,-1]
    } else {
      region = input$region
      highested_table[(highested_table$region == region), -1]
    }
  })
 
  output$mytable1 = renderDataTable({
    selectedData1()
  }, options = list(lengthMenu = c(5,10), pageLength = 5, 
                    searching = FALSE)
  )
 
observe({
    region = input$region
    updateSelectInput(session, "country",
                      choices = levels(as.factor(as.character(wvs_c$country[wvs_c$region==region]))),       selected = levels(as.factor(as.character(wvs_c$country[wvs_c$region==region])))[1]
    )
  })
ui.R
 
 sidebarPanel(
      selectInput("region", "Select a region:",
                  list("All World"= "the world",
                       "North America & Western Europe"="region1",
                       "Central Europe"="region2",
                       "Asia"="region3",
                       "Latina America & Caribbean"="region4",
                       "Sub-Saharan Africa"="region5",
                       "Middle East & Northern Africa"="region6",
                       "Oceania"="region7"),
                  selected= "the World" )
 
      mainPanel(
        tabPanel('Gender', dataTableOutput('mytable'),
                 selectInput('country', 'Select a Country:', 
                             names(wvs_c$country), selected=names(wvs_c$country)[1]),
                 plotOutput("myplot")
        ),

Visualization

The maps provide a holistic view of world and regional comparisons. I choose the rworldmap package because it uses ISO3 as country identifier and I also use ISO3 in my own data, which makes merging of country level data and spatial polygons quite easy. Moreover, its default is a choropleth map by country, with which I only have to adjust the palette for styling.

#server.R
 
library(rworldmap)
wvs_c <- read.csv("/Users/peggyfan/Downloads/R_data/Developing_data_products/wvs_c")
wvs_c <- wvs_c[, -1]
 
colourPalette1 <-c("#F5A9A9", "#F6D8CE", "#F8ECE0", "#EFFBFB", "#E0F2F7", "#CEE3F6", "#A9BCF5")
world <- joinCountryData2Map(wvs_c
                             ,joinCode = "ISO3"
                             ,nameJoinColumn = "iso3"
                             ,mapResolution="li")

When “the world” is chosen, the map tab shows the mapping of the entire data set. The gender and educational attainment tabs show the regional breakdown of those two topics using ggplot.

Below the table, I embed another panel so users can choose a specific country (listed in alphabetical order) to view its gender and educational attainment breakdown in charts created also with ggplot.

For those who are interested in the tabular data displayed on the “gender” and “education attainment” tabs, they can download the data from the website for their own research purposes.

App address: https://peggyfan.shinyapps.io/shinyapps/