Lecture 6 demo code

We will need the following packages:

library(tidyverse)

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

## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1

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

library(gapminder)
library(datasets)

Filtering (slide 3)

Using matrix operations to subset a tibble.

gapminder[gapminder$country == "Afghanistan" & gapminder$year > 1979,]

## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1982    39.9 12881816      978.
## 2 Afghanistan Asia       1987    40.8 13867957      852.
## 3 Afghanistan Asia       1992    41.7 16317921      649.
## 4 Afghanistan Asia       1997    41.8 22227415      635.
## 5 Afghanistan Asia       2002    42.1 25268405      727.
## 6 Afghanistan Asia       2007    43.8 31889923      975.

Selecting (slide 5)

gapminder[, c("country", "year", "lifeExp", "pop")]

## # A tibble: 1,704 × 4
##    country      year lifeExp      pop
##    <fct>       <int>   <dbl>    <int>
##  1 Afghanistan  1952    28.8  8425333
##  2 Afghanistan  1957    30.3  9240934
##  3 Afghanistan  1962    32.0 10267083
##  4 Afghanistan  1967    34.0 11537966
##  5 Afghanistan  1972    36.1 13079460
##  6 Afghanistan  1977    38.4 14880372
##  7 Afghanistan  1982    39.9 12881816
##  8 Afghanistan  1987    40.8 13867957
##  9 Afghanistan  1992    41.7 16317921
## 10 Afghanistan  1997    41.8 22227415
## # … with 1,694 more rows

Combining select and filter (slide 7)

Matrix approach

gapminder[gapminder$country == "Rwanda" &
            gapminder$year > 1960 & gapminder$year < 1970,
          c("country", "year", "lifeExp")]

## # A tibble: 2 × 3
##   country  year lifeExp
##   <fct>   <int>   <dbl>
## 1 Rwanda   1962    43  
## 2 Rwanda   1967    44.1

dplyr approach

select(filter(gapminder, country == "Rwanda", year > 1960, year < 1970),
       country, year, lifeExp)

## # A tibble: 2 × 3
##   country  year lifeExp
##   <fct>   <int>   <dbl>
## 1 Rwanda   1962    43  
## 2 Rwanda   1967    44.1

The Pipe (slide 13)

gapminder |> 
  filter(country == "Chile") |>
  select(continent, year, lifeExp) |>
  head(3)

## # A tibble: 3 × 3
##   continent  year lifeExp
##   <fct>     <int>   <dbl>
## 1 Americas   1952    54.7
## 2 Americas   1957    56.1
## 3 Americas   1962    57.9

More practice (slide 16)

gapminder |>
  filter(gdpPercap < 300, year < 2007) |>
  select(country, year, gdpPercap)

## # A tibble: 3 × 3
##   country           year gdpPercap
##   <fct>            <int>     <dbl>
## 1 Congo, Dem. Rep.  2002      241.
## 2 Guinea-Bissau     1952      300.
## 3 Lesotho           1952      299.