Description

The data visualization found here comes from World Mapper, and the original data comes from The World Bank. The purpose of this data visualization is to express Alcohol consumption levels in various countries or regions around the world, adjusted for absolute alcohol content, that was drunk there in 2016 (by population age 15 and older) by using a map.

Original


The proportion of worldwide alcohol, adjusted for absolute alcohol content, that was drunk there in 2016 (by population age 15 and older)


Objective and Target Audience

  • Objective

The main Objective of this data visualization is to show the level of alcohol consumption in different countries or regions around the world in 2016. Because of differences in culture, habits, and geographic location, alcohol consumption in different regions of the world varies greatly. It can provide researchers or marketers with different alcohol consumption levels and geographic distribution information to help them study the relevant research or the formulation of alcoholic product sales strategies.

  • Target Audience

The audience of this data visualization is mainly alcoholic beverage marketers (especially international alcoholic beverage traders), and they can learn which regions are the sales destinations. At the same time, public health related departments and research institutions are also the audience, because alcohol intake will have varying degrees of impact on public health, happiness index, traffic and other issues.

Issues The visualisation chosen had the following three main issues:

  • Issue 1: Misleading figure area

Probably because in order to focus on artistic performance, countries or regions here use abstract area sizes to express differences in alcohol consumption levels. Therefore, it is difficult to accurately compare the exact differences between different countries or regions. For example, it is difficult to judge the consumption levels in North Asia (because of color and graphics issues, it is very resistant to judge specific countries or regions) and Central Europe.

  • Issue 2: Color confusion

Although the colors in the introduction map of the source website of the data visualization indicate different countries or regions, there is a lack of labeling in this picture, and the shape of the map has been abstracted. Also the colors of some areas are too similar to the colors of neighboring areas. Therefore, it is difficult to judge countries or regions by color alone. For example, in North America, colors are even used to distinguish different regions, but it is difficult to distinguish the differences. And if you don’t pay attention, you can easily ignore Central Asia, or treat it as North Asia.

  • Issue 3: Lack Labels

There is a lack of labeling. In addition to the aforementioned color labeling that is not displayed in the gragh, there is also a lack of labeling how much area represents the alcohol consumption level.

Therefore, it is impossible to intuitively observe the alcohol consumption levels of different countries or regions.

Reference

  1. Miguel de Cervantes, Don Quixote de la Mancha (2016). Alcohol Consumption, website: https://worldmapper.org/maps/alcohol-consumption/

  2. Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age) (2016). website: https://data.worldbank.org/indicator/SH.ALC.PCAP.LI?end=2016&name_desc=false&start=2016&view=map

Code

The following code was used to fix the issues identified in the original.

library(rworldmap)
library(tidyverse)
library(classInt)
library(RColorBrewer)

# load data and data preprocessing
alcohol_records <- readLines("API_SH.ALC.PCAP.LI_DS2_en_csv_v2_1351226.csv")[-(1:4)]

alcohol_data <- read.csv(text=alcohol_records, header = TRUE) %>% select(c(1,2,61)) %>% na.omit()

# data visualisation
### process the data in map by iso3 country code
mapped_data <- joinCountryData2Map(alcohol_data, joinCode = "ISO3", 
                                   nameJoinColumn = "Country.Code")


### get countries' name 
country_coord <- data.frame(coordinates(mapped_data),stringsAsFactors=F)

### creating map
par(mai=c(0,0,0.4,0),xaxs="i",yaxs="i")

map <- mapCountryData(mapped_data, nameColumnToPlot = "X2016", addLegend = F,
                      colourPalette = brewer.pal(7,'Blues'),
                      mapTitle = "Total alcohol consumption per capita (liters of pure alcohol)")

### add countries' name
text(x=country_coord$X1,y=country_coord$X2,labels=row.names(country_coord), cex=0.5)

### add legend
do.call(addMapLegend, c(map,
                        legendLabels="all",
                        legendWidth=0.5,
                        legendIntervals="data"))

Data Reference

Reconstruction

The following figure shows the processed data visualization. First of all, I use the color gradient to indicate the amount of consumption first. The darker color represents the greater the consumption, and a label is added below the image to mark the level of consumption.

Then the name of each country is added to the map to facilitate the understanding of the countries corresponding to different consumption.

The revised data visualization more clearly shows the differences in alcohol consumption levels in different countries or regions, and clearly shows the relationship between alcohol consumption levels and geographic locations.