I am trying to create a map using the Gapminder package in R.
I have a basic map working, using the choroplethr package. But creating the map requires some data manipulation and the map and legend aren't particularly pretty looking. For example:
library(gapminder)
library(dplyr)
library(choroplethr)
# load data
data(gapminder, package = "gapminder")
# set up data for plot
plotdata <- gapminder %>%
filter(year == 2007) %>%
rename(
region = country,
value = lifeExp
) %>%
mutate(region = tolower(region)) %>%
mutate(region = recode(region,
"united states" = "united states of america",
"congo, dem. rep." = "democratic republic of the congo",
"congo, rep." = "republic of congo",
"korea, dem. rep." = "south korea",
"korea. rep." = "north korea",
"tanzania" = "united republic of tanzania",
"serbia" = "republic of serbia",
"slovak republic" = "slovakia",
"yemen, rep." = "yemen"
))
# make plot
country_choropleth(plotdata, legend = "life expectancy")
I was wondering if there are any other packages that could allow me to plot a map of the life expectancy using the Gapminder data from the Gapminder package?
Preferably, I would like to keep the data manipulation as minimal as possible (I need to demonstrate it to teenagers)... although I realise this may not be possible. Is there an easier way to plot this data on a map?
Any suggestions would be greatly appreciated.
Related
I am working on a project where I need to make a map for the top global streaming music. And I have got the map with song's streamings in different location.
Here is my map
Here is my code, Below are the background info and some data cleaning:
library("dplyr")
library("stringr")
library("tidyverse")
library("scales")
library("ggplot2")
library("maps")
library("leaflet")
# load the .csv into R studio, you can do this 1 of 2 ways
#read.csv("the name of the .csv you downloaded from kaggle")
#spotiify_origional <- read.csv("charts.csv")
spotiify_origional <- read.csv("https://raw.githubusercontent.com/info201a-au2022/project-group-1-section-aa/main/data/charts.csv")
# filters down the data
# removes the track id, explicit, and duration columns
spotify_modify <- spotiify_origional %>%
select(name, country, date, position, streams, artists, genres = artist_genres)
#returns all the data just from 2022
#this is the data set you should you on the project
spotify_2022 <- spotify_modify %>%
filter(date >= "2022-01-01") %>%
arrange(date) %>%
group_by(date)
spotify_2022$streams <- as.numeric(spotify_2022$streams)
View(spotify_2022)
spotify_2022_global <- spotify_modify %>%
filter(date >= "2022-01-01") %>%
filter(country == "global") %>%
arrange(date) %>%
group_by(date)
View(spotify_2022_global)
# use write.csv() to turn the new dataset into a .csv file
#write.csv(Your DataFrame,"Path to export the DataFrame\\File Name.csv", row.names = FALSE)
#write.csv(spotify_2022_global, "/Users/oliviasapp/Documents/info201/project-group-1-section-aa/data/spotify_2022.csv" , row.names = FALSE)
# top 5 most popular songs globally
top_5 <- spotify_2022_global[order(spotify_2022_global$streams, decreasing = TRUE), ]
top_5 <- top_5[1:5, ]
top_5$streams <- as.numeric(top_5$streams)
View(top_5)
# Pepas, Blank Space, I'm Tired, Yonaguni, and Heather
# were the most streamed song of the year according to top_5
Here is the map part:
# makes the map template
world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
geom_polygon(fill="lightgray", colour = "white")
# a new data frame that has all the abrevated country codes and the country names
abrevations <- read.csv("https://pkgstore.datahub.io/core/country-list/data_csv/data/d7c9d7cfb42cb69f4422dec222dbbaa8/data_csv.csv")
#abrevations <- read.csv("wikipedia-iso-country-codes.csv")
# shortens abreviations to only include names and 2 char codes
abrevations <- abrevations %>%
select(region = Name, Code)
abrevations$region <- str_replace(abrevations$region, "United States", "USA")
abrevations$region <- str_replace(abrevations$region, "Libyan Arab Jamahiriya", "Libya")
abrevations$region <- str_replace(abrevations$region, "Côte d'Ivoire", "Ivory Coast")
abrevations$region <- str_replace(abrevations$region, "Tanzania, United Republic of", "Tanzania")
abrevations$region <- str_replace(abrevations$region, "Republic of Democratic Republic of the Congo", "Democratic Republic of the Congo")
abrevations$region <- str_replace(abrevations$region, "Congo", "Republic of Congo")
abrevations$region <- str_replace(abrevations$region, "Republic of Republic of Republic of Congo", "Republic of Congo")
abrevations$region <- str_replace(abrevations$region, "South Sudan", "Sudan")
abrevations$region <- str_replace(abrevations$region, "Syrian Arab Republic", "Syria")
abrevations$region <- str_replace(abrevations$region, "Korea, Democratic People's Republic of", "North Korea")
abrevations$region <- str_replace(abrevations$region, "Korea, Republic of (South Korea)", "South Korea")
abrevations$region <- str_replace(abrevations$region, "Lao People's Democratic Republic", "Laos")
abrevations$region <- str_replace(abrevations$region, "United Kingdom", "UK")
abrevations$region <- str_replace(abrevations$region, "Moldova, Republic of", "Moldova")
abrevations$region <- str_replace(abrevations$region, "Macedonia, the former Yugoslav Republic of", "North Macedonia")
# makes a list off all the countries where the song was popular this year
get_song_streams <- function(song_name) {
song_streams <- spotify_2022 %>%
select(name, date, country, streams) %>%
filter(name == song_name) %>%
filter(country != "global") %>%
group_by(country) %>%
summarize(streams = sum(streams)) %>%
rename(Code = country)
song_streams$Code <- toupper(song_streams$Code) #capatalizes country codes
# data frame that join's abrevations with the modified countries that listened to a song
# if a country listened to Peopas they get a 1, if not they get a 0
abrevs <- left_join(abrevations, song_streams, by = "Code") %>%
replace(is.na(.), 0)
# fixes some of the names of the countries in abrevs so they match the countries in world_map
# dataframe that will go into the map
top_country.map <- left_join(world_map, abrevs, by = "region")
return(top_country.map)
}
# gets rid of grid lines
blank_theme <- theme_bw() +
theme(
axis.line = element_blank(), # remove axis lines
axis.text = element_blank(), # remove axis labels
axis.ticks = element_blank(), # remove axis ticks
axis.title = element_blank(), # remove axis titles
plot.background = element_blank(), # remove gray background
panel.grid.major = element_blank(), # remove major grid lines
panel.grid.minor = element_blank(), # remove minor grid lines
panel.border = element_blank() # remove border around plot
)
plot_song_map <- function(song_name){
# map of the world. Yellow countries listened to Blenk Space, blue countries did not
# grey countries means we have no data
plot<- ggplot(get_song_streams(song_name), aes(map_id = region, fill = streams))+
geom_map(map = get_song_streams(song_name), color = "white")+
expand_limits(x = get_song_streams(song_name)$long,
y = get_song_streams(song_name)$lat)+
ggtitle(paste("How popular was the song", song_name, "in each country?")) +
scale_fill_continuous(type = "viridis", labels = comma) +
labs(fill = "Streams") +
blank_theme
return(plot)
}
leaflet(plot_song_map("Blank Space"))
plot_song_map("Blank Space")
plot_song_map("I’m Tired (with Zendaya) - Bonus Track")
plot_song_map("Yonaguni")
I wonder how can I make this map interactive? So I can upload it to Shiny later on. I tried to use leaflet but it returns a blank.
Thank you so much in advance! Any comments or suggestions will help!
I created a new field where I intend to populate some data(continents) from some vectors, based on data of an existing column(country). There are several datasets involved in the analysis and the code worked perfectly on only the last table, but the other tables which are as much of importance failed to fill in continents to the required column.
meats_long <- meats_long %>%
add_column(Territory = NA)
Africa <- c("Cote d'Ivoire", "Madagascar", "South Africa", "Ghana", "Morocco", "Ethiopia", "Kenya","Egypt")
NorthAmerica <- c("Mexico","Panama","Jamaica", "Dominican Republic","Nicaragua","Honduras","Costa Rica","Guatemala","Canada","El Salvador")
Europe <- c("Iceland","Belgium-Luxembourg","Ukraine","Latvia","Sweden","Greece","Portugal","Russia","Poland","Denmark","Norway","Switzerland","Austria","Germany","Netherlands","Spain","France","Italy","Ireland","United Kingdom")
Asia <- c("Taiwan","Turkey", "Pakistan","Japan","China","Vietnam", "Hong Kong","Thailand","India","Indonesia","Philippines","Korea, South","Malaysia","Israel","Sri Lanka","Pakistan")
Oceania <- c("Australia","Other Pacific Islands, NEC")
SouthAmerica <- c("Uruguay","Bolivia","Colombia","Argentina","Ecuador","Peru","New Zealand", "Brazil","Chile")
Others <- c("Rest of world")
meats_long <- meats_long %>%
mutate(Territory = case_when(Source %in% Africa ~ "Africa",
Source %in% NorthAmerica ~ "North America",
Source %in% Europe ~ "Europe",
Source %in% Asia ~ "Asia",
Source %in% Oceania ~ "Oceania",
Source %in% SouthAmerica ~ "South America",
Source %in% Others ~ "Rest of world"))
I have written the following code:
library(ozmaps)
oz(states = TRUE, coast = TRUE,
ylim = NULL, add = FALSE, ar = 1, eps = 0.25)
That generates:
I wonder how to add the names of the states on the map? If the whole process can be done by "map" package, it is fine as well.
Thanks.
I'm not familiar with package ozmaps. I could not find data for the state names and latitude and longitude values for state centroids within the package.
A quick internet search produced some representative state data from https://www.distancelatlong.com/distancecalculator/country/australia/
You can adjust this data or find a better source, so this should be a start.
library(tibble)
library(ggplot2)
library(ozmaps)
ggplot(ozmap_states)+
geom_sf()+
geom_text(data = oz_states, aes(long, lat, label = state))+
theme_void()
data
oz_states <- tribble(
~state, ~lat, ~long,
"Australian Capital Territory", -35.3546004, 149.2113468,
"New South Wales", -33.42004148, 151.3000048,
"Northern Territory", -13.81617348, 131.816698,
"Queensland", -26.67998777, 153.0500272,
"South Australia", -34.28293455, 140.6000378,
"Tasmania", -40.83292234, 145.1166613,
"Victoria", -37.73119953, 142.0234135,
"Western Australia", -33.58287392, 120.0333345
)
Created on 2021-03-29 by the reprex package (v1.0.0)
I am trying to map a bunch of countries with the following code.
require(mapdata)
# get the names
cc <- map('world', names = TRUE, plot = FALSE)
take <- unlist(sapply(c("iran", "malaysia", "saudi arabia", "united arab emirates", "kuwait", "bahrain", "indonesia", "qatar", "sudan", "pakistan", "bangladesh", "turkey", "egypt", "united kingdom", "jordan", "brunei", "sri lanka", "oman", "yemen", "lebanon", "kenya"), grep, tolower(cc), value = TRUE))
suppressWarnings(map())
suppressWarnings(map('world', regions=take, fill=TRUE, col='red', add = TRUE))
I get what I want, however, the Pdf version has the following warning printed on page
#### # maps v3.1: updated 'world': all lakes moved to separate new ### # 'lakes' database. Type '?world' or 'news(package="maps")'. #
I use suppressWarnings, but the warning is still printed. How can I hide this when I knit the document with knitr?
I would like to display the polygon of Canada on a leaflet map.
# create map
library(leaflet)
m = leaflet() %>% addTiles()
m
I was able to find the polygon for Canada: http://www.gadm.org/country.
I chose the SpatialPolygonsDataFrame format for R, but there are other formats available (such as Shapefile)
# load object in R
load("country_polygons/CAN_adm0.RData")
pol_can <- gadm
How can I display the shape on the map? I assume I have to leverage the sp package but I could not figure out how to do so.
Many thanks in advance for your help!
You can pass a SpatialPolygons* object to the addPolygons function as per Section 2.2 of the docs here.
For example (note that the following includes a ~11.4 MB download):
library(sp)
download.file('http://biogeo.ucdavis.edu/data/gadm2/R/CAN_adm0.RData', f <- tempfile())
load(f)
leaflet() %>% addTiles() %>% addPolygons(data=gadm, weight=2)
Note that GADM data can also be downloaded with the getData function in the raster package:
library(raster)
can <- getData('GADM', country='VAT', level=0)
EDIT
In response to the comments, I really like the lightweight polygon datasets that Natural Earth provides. Here's an example where I download the 1:50,000,000 countries shapefile (Admin 0) from Natural Earth, subset it to the current members of the Commonwealth of Nations, and plot those. The zipped shapefile is under 1 MB.
library(rgdal)
library(leaflet)
download.file(file.path('http://www.naturalearthdata.com/http/',
'www.naturalearthdata.com/download/50m/cultural',
'ne_50m_admin_0_countries.zip'),
f <- tempfile())
unzip(f, exdir=tempdir())
world <- readOGR(tempdir(), 'ne_50m_admin_0_countries', encoding='UTF-8')
commonwealth <- c("Antigua and Barb.", "Australia", "Bahamas", "Bangladesh",
"Barbados", "Belize", "Botswana", "Brunei", "Cameroon", "Canada", "Cyprus",
"Dominica", "Fiji", "Ghana", "Grenada", "Guyana", "India", "Jamaica", "Kenya",
"Kiribati", "Lesotho", "Malawi", "Malaysia", "Maldives", "Malta", "Mauritius",
"Mozambique", "Namibia", "Nauru", "New Zealand", "Nigeria", "Pakistan", "Papua
New Guinea", "Rwanda", "St. Kitts and Nevis", "Saint Lucia", "St. Vin. and
Gren.", "Samoa", "Seychelles", "Sierra Leone", "Singapore", "Solomon Is.",
"South Africa", "Sri Lanka", "Swaziland", "Tanzania", "Tonga", "Trinidad and
Tobago", "Tuvalu", "Uganda", "United Kingdom", "Vanuatu", "Zamibia")
leaflet() %>% addTiles() %>%
addPolygons(data=subset(world, name %in% commonwealth), weight=2)