Skip to contents

Transform Structured Data Lists into a Tidy Data Frame for Plotting

structure_to_plot.Rd

This function takes a nested list of data (with up to three possible structures) and transforms it into a tidy data.frame suitable for visualization in LISSY. It supports three structures, typically outputs by weighted summary functions in lissyrtools such as: run_weighted_mean(), run_weighted_percentiles(), and run_weighted_count().

Usage

structure_to_plot(data_list)

Arguments

data_list

A named list containing the data to be transformed. The structure and naming conventions of this list determine how the data is processed.

  • 1st structure: List with country names as keys and named vectors of year-values. Expected when names(data_list) are country codes matching get_countries_lis() or get_countries_lws().

  • 2nd structure: List with ccyy abbreviations as keys, and named numeric/integer vectors, where names represent categories. Typically output from functions grouped by a categorical variable, e.g. "educ".

  • 3rd structure: List with ccyy abbreviations as keys, each containing sublists of named vectors. Represents more complex summaries with multiple grouping variables, percentiles, or shares.

Value

A tidy data.frame with the following columns (depending on input structure):

  • cname: Country name.

  • year: Year.

  • dname: Dataset country-year code in the ccyy form.

  • category: (2nd and 3rd structure) Grouping variable categories.

  • share / percentile / by_var: (3rd structure) Variable describing the vector names.

  • value: Numeric values from the input list.

Details

Depending on the summary statistics computed before, the function renames one of the columns in the 3rd structure based on pattern matching:

  • If the values contains "%" and "-", the column is renamed to share.

  • If the values contains "%", renamed to percentile.

  • Otherwise, renamed to by_var.

Examples

if (FALSE) { # \dontrun{

library(ggplot2)
library(lissyrtools)
library(RColorBrewer)
library(ggthemes)
library(purrr)
library(forcats)
library(dplyr)

data <- lissyrtools::lissyuse(data = c("es", "de"), vars = c("dhi", "educ", "pi11", "rural"), from = 2016)

# Example usage for 1st structure
weighted_means <- run_weighted_mean(data, "pi11")
df1 <- structure_to_plot(weighted_means)

# Example usage for 2nd structure
weighted_means_educ <- run_weighted_mean(data, "pi11", by = "educ")
df2 <- structure_to_plot(weighted_means_educ)

# Example usage for 3rd structure
weighted_percentiles <- run_weighted_percentiles(data, "pi11", by = "educ")
df3 <- structure_to_plot(weighted_percentiles)

# Example usage for 3rd structure but with shares
weighted_percentiles <- run_weighted_percentiles(data, "pi11", by = "educ", share = TRUE)
df3 <- structure_to_plot(weighted_percentiles)



# Chart example: Plotting education group shares over years by country
 

run_weighted_count(data, "educ", percent = TRUE, na.rm = TRUE) %>%
  structure_to_plot() %>%
  ggplot(aes(x = year, y = value, color = cname, group = interaction(cname, category))) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 0.6) +
  scale_color_stata() +
  labs(
    x = "Year",
    y = "Share of Education Group (%)",
    color = "Country"
  ) + 
  expand_limits(y = 0) + 
  facet_grid(~fct_relevel(category, 'low', 'medium', 'high')) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 25, hjust = 1))
  

# Another example: plotting weighted mean of 'dhi' over years by country
data %>%
  purrr::map(~ .x %>% filter(relation == 1000) %>% mutate(new_wgt = nhhmem * hwgt)) %>%
  apply_ppp_adjustment("dhi", "lis", "lisppp") %>%
  run_weighted_mean("dhi", "new_wgt") %>%
  structure_to_plot() %>%
  ggplot(aes(x = year, y = value, color = cname, group = cname)) +
  geom_point() +
  geom_line() +
  labs(
    title = "dhi trend",
    caption = "Source: Luxembourg Income Study"
  ) +
  scale_color_stata() +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 25, hjust = 1))
} # }