An interactive exploration of cross-sectional data

Using the package ExPanDaR to generate interactive web applications

knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE
)
library(tidyverse)
library(ExPanDaR)      # explore panel data interactively   
library(gapminder)     # panel data for GDP and Health 

# Change the presentation of decimal numbers to 4 and avoid scientific notation
options(prompt="R> ", digits=3, scipen=999)

Suggested Citation:

Mendez Carlos (2020). An interactive exploration of cross-sectional data: Using the package ExPanDaR to generate interactive web applications. R Studio/RPubs. Available at https://rpubs.com/quarcs-lab/explore-cross-section-interactively

This work is licensed under the Creative Commons Attribution-Share Alike 4.0 International License.

1 Load the data

Let us use the gapminder data set:

Gapminder Data: Life Expectancy and GDP per capita for 184 countries from 1952 to 2007

Documentation of the gapminder dataset

gapminder

2 Transform the data

• Take the log of GDP

• Re-scale the population variable

gapminder <- gapminder %>% 
  mutate(
    logGDPpc = log(gdpPercap),
    pop = pop / 1e6
  )

3 Select cross-sectional sample:

• Only use the year 2007

• The order of the variables is important

gapmainder2007 <- gapminder %>% 
  filter(year == 2007) %>% 
  select(country, logGDPpc, lifeExp, pop, gdpPercap, continent)

gapmainder2007

4 Import dataframe of definitions

df_definitions <- read_csv("df_def.csv") 
df_definitions

5 Interactive exploration

• With the default configuration

# Run it in the console
ExPanD(
  gapmainder2007,
  df_def = df_definitions,
  export_nb_option = TRUE,
  title = "Cross-sectional exploration",
  abstract = paste("The relationship between life expectancy and GDP per capita")
  )

• With a customized configuration

First you customize the exploration, export configuration file, and included in the new set up of the exploration.

config_list_fromRDS <- readRDS("ExPanD_gapminder2007.RDS")

# Run it in the console
ExPanD(
  gapmainder2007,
  df_def = df_definitions,
  export_nb_option = TRUE,
  config_list = config_list_fromRDS,
  title = "Cross-sectional exploration",
  abstract = paste("The relationship between life expectancy and GDP per capita")
  )

5.1 Shiny app

https://carlosmendez777.shinyapps.io/short-project-explore-cross-section-interactively

6 Research Tasks

• Evaluate the relationship between life expectancy and log of GDP per capita for each continent. How does this relatioship change across continents?

• Evaluate the relationship between life expectancy and log of GDP per capita in the years 1952, 1972, and 1992. How does this relationship change over time?

• Create an interactive web application of your analysis.

7 References

• https://joachim-gassen.github.io/ExPanDaR/
• https://cran.r-project.org/web/packages/ExPanDaR/index.html

END