structure_to_plot: Transform Structured Data Lists into a Tidy Data Frame for...

In JosepER/lissyrtools: Tools for LISSY Jobs

Transform Structured Data Lists into a Tidy Data Frame for Plotting

Description

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.

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.

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.

Examples

## Not run: 

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

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 %>%
  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))

## End(Not run)

JosepER/lissyrtools documentation built on June 12, 2025, 12:11 p.m.