Second series in exploratory data visualizations on World Happiness Report 2022 using the {leaflet} packages in R.
Similar to the previous
post we will plot an interactive map using the
{leaflet} package in R with the world happiness report data
2022.
With the effects of COVID-19 finally getting subdued via mass vaccination drives and other precautionary measures taken by the governments around the world, the happiness data of 2022 shows light on how different countries are doing in the recovery stage.
Like last time, Finland is again ranked first among 149 countries with an overall score of 7.82 and Afghanistan ranked the last with a score of 2.40.
Lets plot an interactive world map with happiness score as a
variable, where greater scores indicates happier countries and vice
versa. We will be using the {leaflet} package in R for
plotting the world map.
First lets download and import the data into R. You can download the
.csv file using this link.
Then we have to download the world map data which comes as a
.shp file.
Run the codes given below to download the .shp file and
load the .csv file required to plot the map. I will be
reusing the codes from the previous
post. For0 plotting the interactive map, we will be using the
{sf} package for reading the .shp file and the
{leaflet} package for plotting the map.
library(readr)
library(dplyr)
# loading the .csv file which was downloaded
# please change the location to where your .csv file is kept
hap_pre <- read.csv("/home/jeweljohnson/Work/datasets/2022.csv")
# renaming column names of ease of use
colnames(hap_pre)[1] <- "rank"
colnames(hap_pre)[2] <- "country"
colnames(hap_pre)[3] <- "score"
hap_pre <- hap_pre[-147,]
# the score values are separated by commas
# let us change that to dots
hap_pre$score <- scan(text=hap_pre$score, dec=",", sep=".")
# selecting country and score columns
hap <- hap_pre %>% select(rank,country,score)
# downloading and loading the .shp file
# please change the 'destfile' location to where your zip file is located
download.file("http://thematicmapping.org/downloads/TM_WORLD_BORDERS_SIMPL-0.3.zip" , destfile="shp/world_shape_file.zip")
# unzip the file into a directory. You can do it with R (as below).
# the directory of my choice was folder named 'shp'
unzip("shp/world_shape_file.zip", exdir = "shp/")
# Read the shape file with 'sf'
#install.packages("sf")
library(sf)
world_spdf <- st_read(paste0(getwd(),"/shp/TM_WORLD_BORDERS_SIMPL-0.3.shp"), stringsAsFactors = FALSE)
Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source
`/home/jeweljohnson/Work/R_distill_github/jeweljohnson.github.io/_posts/2022-05-16-the-world-happiness-report-2022/shp/TM_WORLD_BORDERS_SIMPL-0.3.shp'
using driver `ESRI Shapefile'
Simple feature collection with 246 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
Geodetic CRS: WGS 84# checking which country names are a mismatch between map data and the downloaded dataset
# this is an important check as we have to join the happiness dataset and .shp file with country names
anti_join(hap, world_spdf, by = c("country" = "NAME"))
rank country score
1 6 Luxembourg* 7.404
2 18 Czechia 6.920
3 26 Taiwan Province of China 6.512
4 32 Kosovo 6.455
5 39 Guatemala* 6.262
6 50 Kuwait* 6.106
7 59 South Korea 5.935
8 62 Moldova 5.857
9 65 Belarus* 5.821
10 77 Vietnam 5.485
11 78 Turkmenistan* 5.474
12 79 North Cyprus* 5.467
13 81 Hong Kong S.A.R. of China 5.425
14 86 Libya* 5.330
15 88 Ivory Coast 5.235
16 89 North Macedonia 5.199
17 92 Azerbaijan* 5.173
18 93 Gambia* 5.164
19 95 Laos 5.140
20 97 Liberia* 5.122
21 104 Niger* 5.003
22 110 Iran 4.888
23 116 Comoros* 4.609
24 122 Palestinian Territories* 4.483
25 125 Eswatini, Kingdom of* 4.396
26 126 Myanmar 4.394
27 128 Madagascar* 4.339
28 130 Chad* 4.251
29 132 Yemen* 4.197
30 133 Mauritania* 4.153
31 139 Tanzania 3.702
32 141 Lesotho* 3.512
33 142 Botswana* 3.471
34 143 Rwanda* 3.268# correcting country names, note that some countries are not available in the .shp file
correct_names <- c("Luxembourg*" = "Luxembourg",
"Czechia" = "Czech Republic",
"Taiwan Province of China" = "Taiwan",
"Guatemala*" = "Guatemala",
"Kuwait*" = "Kuwait",
"South Korea" = "Korea, Republic of",
"Moldova" = "Republic of Moldova",
"Belarus*" = "Belarus",
"Vietnam" = "Viet Nam",
"Turkmenistan*" = "Turkmenistan",
"Hong Kong S.A.R. of China"= "Hong Kong",
"Libya*" = "Libyan Arab Jamahiriya",
"Ivory Coast" = "Cote d'Ivoire",
"North Macedonia" = "The former Yugoslav Republic of Macedonia",
"Azerbaijan*" = "Azerbaijan",
"Gambia*" = "Gambia",
"Liberia*" = "Liberia",
"Laos" = "Lao People's Democratic Republic",
"Niger*" = "Niger",
"Iran" = "Iran (Islamic Republic of)",
"Comoros*" = "Comoros",
"Palestinian Territories*" = "Palestine",
"Eswatini, Kingdom of*" = "Swaziland",
"Myanmar" = "Burma",
"Madagascar*" = "Madagascar",
"Chad*" = "Chad",
"Yemen*" = "Yemen",
"Mauritania*" = "Mauritania",
"Tanzania" = "United Republic of Tanzania",
"Lesotho*" = "Lesotho",
"Botswana*" = "Botswana",
"Rwanda*" = "Rwanda")
# recoding country names
hap2 <- hap %>% mutate(country = recode(country, !!!correct_names))
# joining .shp file and the happiness data
world_hap <- left_join(world_spdf, hap2, by = c("NAME" = "country"))
#install.packages("leaflet")
library(leaflet)
library(viridis)
# making colour palette for filling
fill_col <- colorNumeric(palette="magma", domain=world_hap$score, na.color="transparent")
# Prepare the text for tooltips:
text <- paste(
"Country: ", world_hap$NAME,"<br/>",
"Score: ", world_hap$score, "<br/>",
"Rank: ", world_hap$rank,
sep="") %>%
lapply(htmltools::HTML)
# plotting interactive map
leaflet(world_hap) %>%
addTiles() %>%
setView( lat=10, lng=0 , zoom=2) %>%
addPolygons(
fillColor = ~fill_col(score),
stroke=TRUE,
fillOpacity = 0.9,
color= "grey",
weight=0.3,
label = text,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "13px",
direction = "auto"
)
) %>%
addLegend( pal=fill_col, values=~score, opacity=0.7, title = "Score", position = "bottomleft" )
In continuation with my previous post, this post showcases an interactive map for the latest happiness index data for 2022.
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Joe Cheng, Bhaskar Karambelkar and Yihui Xie (2021). leaflet: Create Interactive Web Maps with the JavaScript ‘Leaflet’ Library. R package version 2.0.4.1. https://CRAN.R-project.org/package=leaflet
Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009
Tutorial on plotting interactive maps in R.
Source
for .csv file of World Happiness Score of countries 2022.
Compiled by Mathurin Aché in Kaggle.com
[1] "2022-05-17 18:14:32 IST"If you see mistakes or want to suggest changes, please create an issue on the source repository.
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/jeweljohnsonj/jeweljohnson.github.io, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Johnson (2022, May 16). One-carat Blog: The World Happiness Report 2022. Retrieved from https://jeweljohnsonj.github.io/jeweljohnson.github.io/posts/2022-05-16-the-world-happiness-report-2022/
BibTeX citation
@misc{johnson2022the,
author = {Johnson, Jewel},
title = {One-carat Blog: The World Happiness Report 2022},
url = {https://jeweljohnsonj.github.io/jeweljohnson.github.io/posts/2022-05-16-the-world-happiness-report-2022/},
year = {2022}
}