South America Map

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-01-17

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot South America Map plugin. You can also use the Hiplot website to achieve no code ploting. For more information please see the following link:

https://hiplot.cn/basic/map-south-america?lang=en

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages: data.table; jsonlite; ggplot2; RColorBrewer

# Install packages
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("RColorBrewer", quietly = TRUE)) {
  install.packages("RColorBrewer")
}

# Load packages
library(data.table)
library(jsonlite)
library(ggplot2)
library(RColorBrewer)

sessioninfo::session_info("attached")

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31)
 os       Ubuntu 24.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-01-17
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.8.27 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package      * version date (UTC) lib source
 data.table   * 1.18.0  2025-12-24 [1] RSPM
 ggplot2      * 4.0.1   2025-11-14 [1] RSPM
 jsonlite     * 2.0.0   2025-03-27 [1] RSPM
 RColorBrewer * 1.1-3   2022-04-03 [1] RSPM

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.2/lib/R/site-library
 [3] /opt/R/4.5.2/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

Data Preparation

# Load data
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/map-south-america/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)
dt_map <- readRDS(url("https://download.hiplot.cn/ui/basic/map-south-america/sa.rds"))

# Convert data structure
dt_map$Value <- data$value[match(dt_map$ENG_NAME, data$region)]

# View data
head(data)

     region value
1 Argentina   198
2   Bolivia   568
3    Brazil   191
4     Chile   275
5  Colombia   901
6   Ecuador   604

Visualization

# South America Map
p <- ggplot(dt_map) +
  geom_polygon(aes(x = long, y = lat, group = group, fill = Value),
               alpha = 0.9, size = 0.5) +
  geom_path(aes(x = long, y = lat, group = group), color = "black", size = 0.2) +
  coord_fixed() +
  scale_fill_gradientn(
    colours = colorRampPalette(rev(brewer.pal(11,"RdYlBu")))(500),
    breaks = seq(min(data$value), max(data$value), 
                 round((max(data$value)-min(data$value))/7)),
    name = "Color Key",
    guide = guide_legend(
      direction = "vertical", keyheight = unit(1, units = "mm"),
      keywidth = unit(8, units = "mm"),
      title.position = "top", title.hjust = 0.5, label.hjust = 0.5,
      nrow = 1, byrow = T, reverse = F, label.position = "bottom")) +
  theme(text = element_text(color = "#3A3F4A"),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = "top",
        legend.text = element_text(size = 4 * 1.5, color = "black"),
        legend.title = element_text(size = 5 * 1.5, color = "black"),
        plot.title = element_text(
          face = "bold", size = 5 * 1.5, hjust = 0.5, 
          margin = margin(t = 4, b = 5), color = "black"),
        plot.background = element_rect(fill = "#FFFFFF", color = "#FFFFFF"),
        panel.background = element_rect(fill = "#FFFFFF", color = NA),
        legend.background = element_rect(fill = "#FFFFFF", color = NA),
        plot.margin = unit(c(1.5, 1.5, 1.5, 1.5), "cm")) +
  labs(x = NULL, y = NULL, title = "South America Map")

p