Effects on Happiness Ladder Scores of Healthy Life Expectancy

Is life expectancy proportional to the happiness ladder score?

This study explores through the World Happiness Report to see if life expectancy affects happiness ladder scores. I downloaded a csv file from Kaggle. This study includes several variables: WHR (stands for World Happiness Report), top10bottom10, top5bottom5, and top5bottom5le (stands for top 5 & bottom 5 countries’ healthy life expectancy). I also changed all the heads to lowercase and removed spaces through simple instructions in organizing the dataset. To briefly review the terms used in this dataset, the happiness ladder score is that the rankings of national happiness are based on a Cantril ladder survey.

Load the libraries and read the “World Happiness Report 2021” dataset

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ tibble  3.1.2     ✓ purrr   0.3.4
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

setwd("~/Downloads")                                   # set working directory
WHR <- read_csv("world-happiness-report-2021.csv")     # read csv file and rename to WHR

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   `Country name` = col_character(),
##   `Regional indicator` = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

head(WHR)                                              # show top 6 rows from the data

## # A tibble: 6 x 20
##   `Country name` `Regional indica… `Ladder score` `Standard error … upperwhisker
##   <chr>          <chr>                      <dbl>             <dbl>        <dbl>
## 1 Finland        Western Europe              7.84             0.032         7.90
## 2 Denmark        Western Europe              7.62             0.035         7.69
## 3 Switzerland    Western Europe              7.57             0.036         7.64
## 4 Iceland        Western Europe              7.55             0.059         7.67
## 5 Netherlands    Western Europe              7.46             0.027         7.52
## 6 Norway         Western Europe              7.39             0.035         7.46
## # … with 15 more variables: lowerwhisker <dbl>, Logged GDP per capita <dbl>,
## #   Social.support <dbl>, Healthy life expectancy <dbl>,
## #   Freedom to make life choices <dbl>, Generosity <dbl>,
## #   Perceptions of corruption <dbl>, Ladder score in Dystopia <dbl>,
## #   Explained by: Log GDP per capita <dbl>, Explained by: Social support <dbl>,
## #   Explained by: Healthy life expectancy <dbl>,
## #   Explained by: Freedom to make life choices <dbl>,
## #   Explained by: Generosity <dbl>,
## #   Explained by: Perceptions of corruption <dbl>, Dystopia + residual <dbl>

Clean up the data:

Make all headers lowercase and remove spaces.

names(WHR) <- tolower(names(WHR))                     # make all headers lowercase
names(WHR) <- gsub(" ","",names(WHR))                 # remove spaces
str(WHR)                                              # display the internal structure

## spec_tbl_df [149 × 20] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ countryname                         : chr [1:149] "Finland" "Denmark" "Switzerland" "Iceland" ...
##  $ regionalindicator                   : chr [1:149] "Western Europe" "Western Europe" "Western Europe" "Western Europe" ...
##  $ ladderscore                         : num [1:149] 7.84 7.62 7.57 7.55 7.46 ...
##  $ standarderrorofladderscore          : num [1:149] 0.032 0.035 0.036 0.059 0.027 0.035 0.036 0.037 0.04 0.036 ...
##  $ upperwhisker                        : num [1:149] 7.9 7.69 7.64 7.67 7.52 ...
##  $ lowerwhisker                        : num [1:149] 7.78 7.55 7.5 7.44 7.41 ...
##  $ loggedgdppercapita                  : num [1:149] 10.8 10.9 11.1 10.9 10.9 ...
##  $ social.support                      : num [1:149] 0.954 0.954 0.942 0.983 0.942 0.954 0.934 0.908 0.948 0.934 ...
##  $ healthylifeexpectancy               : num [1:149] 72 72.7 74.4 73 72.4 73.3 72.7 72.6 73.4 73.3 ...
##  $ freedomtomakelifechoices            : num [1:149] 0.949 0.946 0.919 0.955 0.913 0.96 0.945 0.907 0.929 0.908 ...
##  $ generosity                          : num [1:149] -0.098 0.03 0.025 0.16 0.175 0.093 0.086 -0.034 0.134 0.042 ...
##  $ perceptionsofcorruption             : num [1:149] 0.186 0.179 0.292 0.673 0.338 0.27 0.237 0.386 0.242 0.481 ...
##  $ ladderscoreindystopia               : num [1:149] 2.43 2.43 2.43 2.43 2.43 2.43 2.43 2.43 2.43 2.43 ...
##  $ explainedby:loggdppercapita         : num [1:149] 1.45 1.5 1.57 1.48 1.5 ...
##  $ explainedby:socialsupport           : num [1:149] 1.11 1.11 1.08 1.17 1.08 ...
##  $ explainedby:healthylifeexpectancy   : num [1:149] 0.741 0.763 0.816 0.772 0.753 0.782 0.763 0.76 0.785 0.782 ...
##  $ explainedby:freedomtomakelifechoices: num [1:149] 0.691 0.686 0.653 0.698 0.647 0.703 0.685 0.639 0.665 0.64 ...
##  $ explainedby:generosity              : num [1:149] 0.124 0.208 0.204 0.293 0.302 0.249 0.244 0.166 0.276 0.215 ...
##  $ explainedby:perceptionsofcorruption : num [1:149] 0.481 0.485 0.413 0.17 0.384 0.427 0.448 0.353 0.445 0.292 ...
##  $ dystopia+residual                   : num [1:149] 3.25 2.87 2.84 2.97 2.8 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   `Country name` = col_character(),
##   ..   `Regional indicator` = col_character(),
##   ..   `Ladder score` = col_double(),
##   ..   `Standard error of ladder score` = col_double(),
##   ..   upperwhisker = col_double(),
##   ..   lowerwhisker = col_double(),
##   ..   `Logged GDP per capita` = col_double(),
##   ..   Social.support = col_double(),
##   ..   `Healthy life expectancy` = col_double(),
##   ..   `Freedom to make life choices` = col_double(),
##   ..   Generosity = col_double(),
##   ..   `Perceptions of corruption` = col_double(),
##   ..   `Ladder score in Dystopia` = col_double(),
##   ..   `Explained by: Log GDP per capita` = col_double(),
##   ..   `Explained by: Social support` = col_double(),
##   ..   `Explained by: Healthy life expectancy` = col_double(),
##   ..   `Explained by: Freedom to make life choices` = col_double(),
##   ..   `Explained by: Generosity` = col_double(),
##   ..   `Explained by: Perceptions of corruption` = col_double(),
##   ..   `Dystopia + residual` = col_double()
##   .. )

Let’s pick the top 10 countries and the bottom 10 countries in the Happiness Ladder Score.

top10bottom10 <- WHR %>%
  filter(countryname =="Finland" | countryname =="Denmark" | countryname =="Switzerland" | countryname =="Iceland" | countryname =="Netherlands" | countryname =="Norway" | countryname =="Sweden" | countryname =="Luxembourg" | countryname =="New Zealand" | countryname =="Austria" | countryname =="Afghanistan" | countryname =="Zimbabwe" | countryname =="Rwanda" | countryname =="Botswana" | countryname =="Lesotho" | countryname =="Malawi" | countryname =="Haiti" | countryname =="Tanzania" | countryname =="Yemen" | countryname =="Burundi") %>%
  ggplot() +
  geom_bar(aes(x= countryname, y= ladderscore, fill = countryname),     # create a bargraph
  position = "dodge", stat = "identity") +
  labs(ylab = "Ladder score",
    title = "Top 10 vs Bottom 10 World Happiness Report in 2021",
    fill = "Country")
top10bottom10                                                           # pirnt

So many countries.

We do not see which country is which country, and it is difficult to analysis. At first glance, the ladder score of the top 10 countries is similar, and the ladder score of the bottom 10 countries is similar, so I will reset the top 5 and bottom 5. And I also change the position of the y-axis and x-axis because the country names are too long.

top5bottom5 <- WHR %>%
  filter(countryname =="Finland" | countryname =="Denmark" | countryname =="Switzerland" | countryname =="Iceland" | countryname =="Netherlands" | countryname =="Afghanistan" | countryname =="Zimbabwe" | countryname =="Rwanda" | countryname =="Botswana" | countryname =="Lesotho") %>%
  ggplot() +
  geom_bar(aes(x= ladderscore , y=countryname , fill = countryname),       # create a graph
  position = "dodge", stat = "identity") +
  labs(ylab = "Country",
    title = "Top 5 vs Bottom 5 World Happiness Report in 2021",
    fill = "Country")
top5bottom5                                                                # print

The graph is much cleaner.

Now, let’s compare the life expectancy of these ten countries.

top5bottom5le <- WHR %>%
  filter(countryname =="Finland" | countryname =="Denmark" | countryname =="Switzerland" | countryname =="Iceland" | countryname =="Netherlands" | countryname =="Afghanistan" | countryname =="Zimbabwe" | countryname =="Rwanda" | countryname =="Botswana" | countryname =="Lesotho") %>%
  ggplot() +
  geom_bar(aes(x=healthylifeexpectancy , y= countryname, fill =ladderscore ),   # create a graph
  position = "dodge", stat = "identity") +
  labs(ylab = "Country",
    title = "Effects of life expectancy on ladder scores",
    fill = "Happiness Ladder Score")
top5bottom5le                                                                   # print

Does life expectancy affect ladder scores? Yes, as you can see in this graph, light blue indicates countries with high happiness ladder scores and dark blue indicates countries with low happiness ladder scores. The graph clearly shows that higher happiness ladder scores also result in higher life expectancy (over 75 years old), and lower happiness ladder scores also lower life expectancy (less than 60 years old). In conclusion, happiness ladder scores and healthy life expectancy appear to be proportional.

In fact, I wanted to map the world through this dataset and show the happiness ladder score on the map, but I failed many times. It was successful to draw a map of the world, but it was not easy to mark each country’s happiness ladder score on top of it. I hope I can succeed at the end of DATA110 class.

Thank you. :)