Gapminder Exploration

Function #1: `mean()`

Calculate the average life expectancy of all countries in gapminder:

mean(gapminder$lifeExp)

## [1] 59.47444

Calculate the average life expectancy of Canada:

mean(gapminder[which(gapminder$country == 
                         "Canada"),]$lifeExp)

## [1] 74.90275

Function #1: `mean()`

Calculate the continental average for each numeric data column:

header <- c("Life Expectancy", "Population", "GDP per Capita")
temp <- data.frame()
for (continent in unique(gapminder$continent)) {
    current <- c(
        mean(gapminder[gapminder$continent==continent,]$lifeExp, 
             na.rm = TRUE), 
        mean(gapminder[gapminder$continent==continent,]$pop, 
             na.rm = TRUE),
        mean(gapminder[gapminder$continent==continent,]$gdpPercap, 
             na.rm=TRUE))
    temp <- rbind(temp,current)
}
names(temp) <- header
rownames(temp) <- unique(gapminder$continent)

temp

##          Life Expectancy Population GDP per Capita
## Asia            60.06490   77038722       7902.150
## Europe          71.90369   17169765      14469.476
## Africa          48.86533    9916003       2193.755
## Americas        64.65874   24504795       7136.110
## Oceania         74.32621    8874672      18621.609

Function #2: `max()` and `which()`

Extract the country (or countries) with the highest life expectancy:

gapminder[which(gapminder$lifeExp == 
                    max(gapminder$lifeExp)),]

## # A tibble: 1 x 6
##   country continent  year lifeExp       pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Japan   Asia       2007    82.6 127467972    31656.

Function #3: `min()` and `which()`

Extract the country (or countries) with the lowest GDP per capita:

gapminder[which(gapminder$gdpPercap == 
                    min(gapminder$gdpPercap)),]

## # A tibble: 1 x 6
##   country          continent  year lifeExp      pop gdpPercap
##   <fct>            <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Congo, Dem. Rep. Africa     2002    45.0 55379852      241.

Function #4: `length()` and `unique()`

Extract the number of unique countries in gapminder:

length(unique(gapminder$country))

## [1] 142

Function #5: `summary()`

Get a summary of gapminder:

summary(gapminder)

##         country        continent        year         lifeExp     
##  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
##  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
##  Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
##  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
##  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
##  Australia  :  12                  Max.   :2007   Max.   :82.60  
##  (Other)    :1632                                                
##       pop              gdpPercap       
##  Min.   :6.001e+04   Min.   :   241.2  
##  1st Qu.:2.794e+06   1st Qu.:  1202.1  
##  Median :7.024e+06   Median :  3531.8  
##  Mean   :2.960e+07   Mean   :  7215.3  
##  3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
##  Max.   :1.319e+09   Max.   :113523.1  
##

Function #1: mean()

Function #1: mean()

Function #2: max() and which()

Function #3: min() and which()

Function #4: length() and unique()

Function #5: summary()

Function #1: `mean()`

Function #1: `mean()`

Function #2: `max()` and `which()`

Function #3: `min()` and `which()`

Function #4: `length()` and `unique()`

Function #5: `summary()`