Country Comparison

The gapminder dataset has data on life expectancy, population, and GDP per capita for 142 countries from 1952 to 2007. To get a glimpse of the dataframe, namely to see the variable names, variable types, etc., we use the glimpse function. We also want to have a look at the first 20 rows of data.

glimpse(gapminder)

## Rows: 1,704
## Columns: 6
## $ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …

head(gapminder, 20) 

## # A tibble: 20 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## 11 Afghanistan Asia       2002    42.1 25268405      727.
## 12 Afghanistan Asia       2007    43.8 31889923      975.
## 13 Albania     Europe     1952    55.2  1282697     1601.
## 14 Albania     Europe     1957    59.3  1476505     1942.
## 15 Albania     Europe     1962    64.8  1728137     2313.
## 16 Albania     Europe     1967    66.2  1984060     2760.
## 17 Albania     Europe     1972    67.7  2263554     3313.
## 18 Albania     Europe     1977    68.9  2509048     3533.
## 19 Albania     Europe     1982    70.4  2780097     3631.
## 20 Albania     Europe     1987    72    3075321     3739.

# look at the first 20 rows of the dataframe

To produce two graphs of how life expectancy has changed over the years for the country and the continent I come from.

country_data <- gapminder %>% 
            filter(country == "China") 

continent_data <- gapminder %>% 
            filter(continent == "Asia")

First, create a plot of life expectancy over time for the single country I chose. Map year on the x-axis, and lifeExp on the y-axis.

 plot1 <- ggplot(data = country_data, mapping = aes(x = year, y = lifeExp))+
   geom_point() +
   geom_smooth(se = FALSE)+
   NULL 

 plot1

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Next we need to add a title. Create a new plot, or extend plot1, using the labs() function to add an informative title to the plot.

 plot1<- plot1 +
   labs(title = "China_life expextancy over time",
       x = "year",
       y = "lifeExp") +
   NULL


 plot1

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Secondly, produce a plot for all countries in the continent I come from.

 ggplot(continent_data, mapping = aes(x =year  , y = lifeExp, colour=country , group = country))+
   geom_point()+ 
   geom_smooth(se = FALSE) +
   NULL

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Finally, using the original gapminder data, produce a life expectancy over time graph, grouped (or faceted) by continent. We will remove all legends, adding the theme(legend.position="none") in the end of our ggplot.

 ggplot(data = gapminder , mapping = aes(x = year , y = lifeExp , colour=continent ))+
   geom_point() + 
   geom_smooth(se = FALSE) +
   facet_wrap(~continent) +
   theme(legend.position="none") + #remove all legends
   NULL

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'