DATA 110 Assignment 6

Life Expectancy of The Big 4 Countries

Handling the data

Load the libraries and dataset

library(dslabs) #contains gapminder
library(dplyr) #manipulating data

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(highcharter) #making interactive charts

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 

Highcharts (www.highcharts.com) is a Highsoft software product which is

not free for commercial and Governmental use

Attaching package: 'highcharter'

The following object is masked from 'package:dslabs':

    stars

library(RColorBrewer) #palette

data(gapminder) #load data

Observe gapminder variables

str(gapminder) #observed 9 variables

'data.frame':   10545 obs. of  9 variables:
 $ country         : Factor w/ 185 levels "Albania","Algeria",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ year            : int  1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
 $ infant_mortality: num  115.4 148.2 208 NA 59.9 ...
 $ life_expectancy : num  62.9 47.5 36 63 65.4 ...
 $ fertility       : num  6.19 7.65 7.32 4.43 3.11 4.55 4.82 3.45 2.7 5.57 ...
 $ population      : num  1636054 11124892 5270844 54681 20619075 ...
 $ gdp             : num  NA 1.38e+10 NA NA 1.08e+11 ...
 $ continent       : Factor w/ 5 levels "Africa","Americas",..: 4 1 1 2 2 3 2 5 4 3 ...
 $ region          : Factor w/ 22 levels "Australia and New Zealand",..: 19 11 10 2 15 21 2 1 22 21 ...

Filter data

#observe only the "big 4"

big4 <- gapminder |>
  filter(country %in% c("China","Germany", "Japan", "United States")) |>
  arrange(year)

#head(big4)

Exploring life expectancy

Interactive line chart

#Variable for non-default color
cols <- brewer.pal(8,"Set2")

#Line chart type
highchart () |>
  hc_add_series(data = big4,
                type = "line",
                hcaes(x = year,
                      y = life_expectancy, 
                      group = country)) |>
  hc_add_theme(hc_theme_ggplot2(size = 4)) |> #changed default theme using "Help"
  hc_colors(cols) |> #Referenced Highcharter in R
  hc_title(text = "Life Expectancy of The Big 4") |>
  hc_caption(text = "Data Source: DS Labs", align = "right") |>
  hc_xAxis(title = list(text="Year")) |>
  hc_yAxis(title = list(text="Life expectancy (age)")) |>
  hc_legend(align = "right", verticalAlign = "top",
            layout = "vertical")

Conclusion

Visualization

In this visualization I used the gapminder dataset from DS Labs. My graph represents the changes in life expectancy of China, Germany, Japan, and the United States as time passes. I executed this by filtering the original gapminder data into only representing the life expectancy of a few countries with the largest economies in the world, named by the variable “Big4”. In observing the big four, highchart() was the best for representing life expectancy overtime. From analyzing my interactive line chart, I found that there wasn’t a great variation in life expectancy, except for an obvious economic boom experienced by China.

Technical Challenges and Future Studies

I have few technical challenges when using highchart() such as incorporating the title, caption, and theme. However, I did primarily struggle with choosing how to represent the data. Contrary to class notes, I could not use an area chart as observing the changes in life expectancy would be difficult as there was no great variation in values among the big four. If I were to continue exploring this data, I would like to analyze the correlation between a countries gross domestic product (GDP) and life expectancy.