In worldbank/pipr: Client for the Poverty and Inequality Platform ('PIP') API

NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.height = 7, 
  fig.width = 9,
  purl = NOT_CRAN
)

library(pipr)
library(ggplot2)
library(tidyr)
library(ggthemes)
library(forcats)
library(scales)
library(dplyr)
library(purrr)

Global poverty trends 1990-2015

df <- get_wb() %>%
  filter(year > 1989, region_code == "WLD") %>%
  mutate(
    pop_in_poverty = round(pop_in_poverty / 1000000, 0),
    headcount = round(headcount, 3)
  )

headcount_col <- "#E69F00"

ggplot(df, aes(x = year)) +
  geom_text(aes(label = headcount * 100, y = headcount), vjust = 1, nudge_y = -0.02, color = headcount_col) +
  geom_text(aes(label = pop_in_poverty, y = pop_in_poverty / 5000), vjust = 0, nudge_y = 0.02) +
  geom_line(aes(y = headcount), color = headcount_col) +
  geom_line(aes(y = pop_in_poverty / 5000)) +
  geom_point(aes(y = headcount), color = headcount_col) +
  geom_point(aes(y = pop_in_poverty / 5000)) +
  scale_y_continuous(
    labels = scales::percent,
    limits = c(0, 0.5),
    breaks = c(0, 0.1, 0.2, 0.3, 0.4),
    sec.axis = sec_axis(~.*5000, name = "Number of poor (million)",
                        breaks = c(0, 500, 1000, 1500, 2000))) +
  labs(
    y = "Poverty rate (%)",
    x = ""
  ) +
  theme_classic()

Number of poor by region

df <- get_wb() %>%
  filter(year > 1989 & year < 2019) %>%
  mutate(
    pop_in_poverty = round(pop_in_poverty / 1000000, 0),
    headcount = round(headcount, 3)
  )

regions <- df %>%
  filter(!region_code %in% c("WLD", "AFE", "AFW")) %>%
  mutate(
    region_name = fct_relevel(region_name,
                               c("Other high Income",
                                 "Europe and Central Asia",
                                 "Middle East and North Africa",
                                 "Latin America and the Caribbean",
                                 "East Asia and Pacific",
                                 "South Asia",
                                 "Sub-Saharan Africa"
                                 ))
  )

world <- df %>%
  filter(region_code == "WLD")

ggplot(regions, aes(y = pop_in_poverty, x = year, fill = region_name)) +
  geom_area() +
  scale_y_continuous(
    limits = c(0, 2000),
    breaks = c(0, 500, 1000, 1500, 2000)
  ) +
  scale_fill_tableau(palette = "Tableau 10") +
  labs(
    y = "Number of poor (million)",
    x = ""
  ) +
  theme_classic() +
  theme(
    legend.position = "bottom"
  ) +
  geom_line(data = world, size = rel(1.5), alpha =.5, linetype = "longdash")

Gini Indices for Argentina, Ghana and Thailand

df <- get_stats(country = c("CHL", "GHA", "TGO"),
                reporting_level = "all") %>%
  filter(year > 1989) %>%
  select(country_name:is_interpolated, gini)

ggplot(df, aes(x = year, y = gini, color = country_name)) +
  geom_line() +
  geom_point(data = df[df$is_interpolated == 0, ]) +
  scale_y_continuous(
    limits = c(0.35, 0.55),
    breaks = c(0.35, 0.40, 0.45, 0.50, 0.55)
  ) +
  scale_color_colorblind() +
  labs(
    y = "Gini Index",
    x = ""
  ) +
  theme_classic() +
  theme(
    legend.position = "bottom"
  )

Distribution of Income in Sub-Saharan Africa and East Asia and Pacific over time

poverty_lines <- c(2.15, 3.65, 6.85)#, 15)
df <- map_dfr(poverty_lines, get_wb, year = "all")

out <- df %>%
  filter(year >= 1990,
         region_code %in% c("SSA", "EAP")) %>%
  select(poverty_line, region_code, region_name, year, headcount) %>%
  mutate(
    poverty_line = round(poverty_line * 100, 1),
    headcount = headcount * 100
  ) %>%
  pivot_wider(names_from = poverty_line,
              names_prefix = "headcount",
              values_from = headcount) %>%
  mutate(
    percentage_0 = headcount215,
    percentage_1 = headcount365 - headcount215,
    percentage_2 = headcount685 - headcount365,
    percentage_3 = 100 - headcount685
    #percentage_3 = headcount1500 - headcount685,
    #percentage_4 = 100 - headcount1500
  ) %>%
  select(region_code, region_name, year, starts_with("percentage_")) %>%
  pivot_longer(cols = starts_with("percentage_"), 
               names_to = "income_category", 
               values_to = "percentage") %>%
  mutate(
    income_category = recode(income_category,
                             percentage_0 = "Poor IPL (<$2.15)",
                             percentage_1 = "Poor LMI ($2.15-$3.65)",
                             percentage_2 = "Poor UMI ($3.65-$6.85)",
                             percentage_3 = "Non-poor (>$6.85)"),
                             #percentage_4 = "Middle class (>$15)"),
    income_category = as_factor(income_category),
    income_category = fct_relevel(income_category, rev)
  )

ggplot(out[out$region_code == "EAP",], aes(x = year, y = percentage, fill = income_category)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = round(percentage, 1)), 
            position = position_stack(0.5),
            size = rel(2.9)) +
  scale_fill_manual(values = c("#e6a14a", "#859a6a", "#ad6e72", "#5d7a96")) + #"#a7b6ba", 
  scale_y_continuous(breaks = c(0, 20, 40, 60, 80, 100)) +
  scale_x_continuous(breaks = unique(out$year)) +
  labs(
    title = "Distribution of income in East Asia and Pacific over time",
    y = "Population share in each income category (%)",
    x = ""
  ) +
  coord_cartesian(ylim = c(0, 105), expand = FALSE) +
  guides(fill = guide_legend(reverse = TRUE)) +
  theme_classic(base_size = 14) +
  theme(plot.title = element_text(face = "bold",
                                  size = rel(1.2)),
        axis.text.x = element_text(angle = 45,
                                   margin = margin(t = 10)), 
        axis.line.y = element_blank(),
        axis.line.x = element_line(colour = "black"),
        axis.ticks = element_blank(),
        panel.grid.major.y = element_line(colour = "#f0f0f0"),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.key.size= unit(0.5, "cm"),
        legend.margin = unit(0, "cm"),
        legend.title = element_blank(),
        plot.margin = unit(c(10,5,5,5),"mm"),
        strip.background=element_rect(colour = "#f0f0f0",fill = "#f0f0f0"),
        strip.text = element_text(face = "bold")
  )

ggplot(out[out$region_code == "SSA",], aes(x = year, y = percentage, fill = income_category)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = round(percentage, 1)), 
            position = position_stack(0.5),
            size = rel(2.9)) +
  scale_fill_manual(values = c("#e6a14a", "#859a6a", "#ad6e72", "#5d7a96")) + # "#a7b6ba", 
  scale_y_continuous(breaks = c(0, 20, 40, 60, 80, 100)) +
  scale_x_continuous(breaks = unique(out$year)) +
  labs(
    title = "Distribution of income in Sub-Saharan Africa over time\n",
    y = "Population share in each income category (%)",
    x = ""
  ) +
  coord_cartesian(ylim = c(0, 105), expand = FALSE) +
  guides(fill = guide_legend(reverse = TRUE)) +
  theme_classic(base_size = 14) +
  theme(plot.title = element_text(face = "bold",
                                  size = rel(1.2)),
        axis.text.x = element_text(angle = 45,
                                   margin = margin(t = 10)), 
        axis.line.y = element_blank(),
        axis.line.x = element_line(colour="black"),
        axis.ticks = element_blank(),
        panel.grid.major.y = element_line(colour="#f0f0f0"),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.key.size= unit(0.5, "cm"),
        legend.margin = unit(0, "cm"),
        legend.title = element_blank(),
        plot.margin=unit(c(10,5,5,5),"mm"),
        strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
        strip.text = element_text(face="bold")
  )