Demonstration
We will continue with Worksheet A-2.
library(tidyverse)
library(gapminder)
gapminder## # A tibble: 1,704 x 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.
## # … with 1,694 more rowsDemo 1
Compute a weighted mean of life expectancy for different years, weighting by population.
gapminder %>%
group_by(year) %>%
mutate(wmean = sum(lifeExp * pop) / sum(pop))## # A tibble: 1,704 x 7
## # Groups: year [12]
## country continent year lifeExp pop gdpPercap wmean
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 48.9
## 2 Afghanistan Asia 1957 30.3 9240934 821. 52.1
## 3 Afghanistan Asia 1962 32.0 10267083 853. 52.3
## 4 Afghanistan Asia 1967 34.0 11537966 836. 57.0
## 5 Afghanistan Asia 1972 36.1 13079460 740. 59.5
## 6 Afghanistan Asia 1977 38.4 14880372 786. 61.2
## 7 Afghanistan Asia 1982 39.9 12881816 978. 62.9
## 8 Afghanistan Asia 1987 40.8 13867957 852. 64.4
## 9 Afghanistan Asia 1992 41.7 16317921 649. 65.6
## 10 Afghanistan Asia 1997 41.8 22227415 635. 66.8
## # … with 1,694 more rowsWhat if I wanted those values repeated for each line?
Demo 2
For each continent and year, how many countries have a “low” life expectancy? Let’s say life expectancy is “low” if it’s less than the median most recent life expectancy for each continent.
v <- c(4, 7, 2, 4, 5, 9)
v[v < 6]## [1] 4 2 4 5v < 6## [1] TRUE FALSE TRUE TRUE TRUE FALSEv[1:2]## [1] 4 7gapminder %>%
group_by(continent) %>%
mutate(median = median(lifeExp[year == 2007])) %>%
filter(lifeExp < median) %>%
count(continent, year)## # A tibble: 60 x 3
## # Groups: continent [5]
## continent year n
## <fct> <int> <int>
## 1 Africa 1952 52
## 2 Africa 1957 50
## 3 Africa 1962 50
## 4 Africa 1967 47
## 5 Africa 1972 41
## 6 Africa 1977 38
## 7 Africa 1982 29
## 8 Africa 1987 28
## 9 Africa 1992 27
## 10 Africa 1997 26
## # … with 50 more rows # group_by(continent, year) %>%
# summarise(n = n())
gapminder %>%
mutate(asia = continent == "Asia")## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap asia
## <fct> <fct> <int> <dbl> <int> <dbl> <lgl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. TRUE
## 2 Afghanistan Asia 1957 30.3 9240934 821. TRUE
## 3 Afghanistan Asia 1962 32.0 10267083 853. TRUE
## 4 Afghanistan Asia 1967 34.0 11537966 836. TRUE
## 5 Afghanistan Asia 1972 36.1 13079460 740. TRUE
## 6 Afghanistan Asia 1977 38.4 14880372 786. TRUE
## 7 Afghanistan Asia 1982 39.9 12881816 978. TRUE
## 8 Afghanistan Asia 1987 40.8 13867957 852. TRUE
## 9 Afghanistan Asia 1992 41.7 16317921 649. TRUE
## 10 Afghanistan Asia 1997 41.8 22227415 635. TRUE
## # … with 1,694 more rowsDemo 3
Get the maximum and minimum gdpPercap for all continents.
gapminder %>%
group_by(continent) %>%
summarise(max_g = max(gdpPercap),
min_g = min(gdpPercap)) %>%
group_by(continent == "Asia") %>%
summarise(max2 = max(max_g))## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## `continent == "Asia"` max2
## <lgl> <dbl>
## 1 FALSE 49357.
## 2 TRUE 113523.Demo 4
Filter gapminder to all entries that have experienced a drop in life expectancy. Hint: dplyr::lag().
v <- 1:4
v## [1] 1 2 3 4dplyr::lag(v)## [1] NA 1 2 3gapminder %>%
group_by(country) %>%
arrange(year) %>%
mutate(inc = lifeExp - lag(lifeExp)) %>%
filter(inc < 0)## # A tibble: 102 x 7
## # Groups: country [52]
## country continent year lifeExp pop gdpPercap inc
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 China Asia 1962 44.5 665770000 488. -6.05
## 2 Cambodia Asia 1972 40.3 7450606 422. -5.10
## 3 Czech Republic Europe 1972 70.3 9862158 13108. -0.0900
## 4 Netherlands Europe 1972 73.8 13329874 18795. -0.0700
## 5 Slovak Republic Europe 1972 70.4 4593433 9674. -0.63
## 6 Bulgaria Europe 1977 70.8 8797022 7612. -0.09
## 7 Cambodia Asia 1977 31.2 6978607 525. -9.10
## 8 El Salvador Americas 1977 56.7 4282586 5139. -1.51
## 9 Poland Europe 1977 70.7 34621254 9508. -0.180
## 10 Uganda Africa 1977 50.4 11457758 844. -0.666
## # … with 92 more rowsDemo 5
Filter gapminder to contain six rows: the rows with the three largest GDP per capita, and the rows with the three smallest GDP per capita. Don’t make any excerpts.
gap1 <- filter(gapminder, continent == "Europe")
gap2 <- summarise(gap1, mean_gdp = mean(gdpPercap))
gap2## # A tibble: 1 x 1
## mean_gdp
## <dbl>
## 1 14469.nrow(gapminder)## [1] 1704gapminder %>%
arrange(gdpPercap) %>%
slice(c(1 , 2 , 3, 1702, 1703 ,1704)
#c(1:3, (n() - 2):n())
)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Congo, Dem. Rep. Africa 2002 45.0 55379852 241.
## 2 Congo, Dem. Rep. Africa 2007 46.5 64606759 278.
## 3 Lesotho Africa 1952 42.1 748747 299.
## 4 Kuwait Asia 1952 55.6 160000 108382.
## 5 Kuwait Asia 1972 67.7 841934 109348.
## 6 Kuwait Asia 1957 58.0 212846 113523.c(1:3, (1704 - 2):1704)## [1] 1 2 3 1702 1703 1704c(1:3, 1702:1704)## [1] 1 2 3 1702 1703 1704gapminder %>%
group_by(continent) %>%
mutate(n_function = n())## # A tibble: 1,704 x 7
## # Groups: continent [5]
## country continent year lifeExp pop gdpPercap n_function
## <fct> <fct> <int> <dbl> <int> <dbl> <int>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 396
## 2 Afghanistan Asia 1957 30.3 9240934 821. 396
## 3 Afghanistan Asia 1962 32.0 10267083 853. 396
## 4 Afghanistan Asia 1967 34.0 11537966 836. 396
## 5 Afghanistan Asia 1972 36.1 13079460 740. 396
## 6 Afghanistan Asia 1977 38.4 14880372 786. 396
## 7 Afghanistan Asia 1982 39.9 12881816 978. 396
## 8 Afghanistan Asia 1987 40.8 13867957 852. 396
## 9 Afghanistan Asia 1992 41.7 16317921 649. 396
## 10 Afghanistan Asia 1997 41.8 22227415 635. 396
## # … with 1,694 more rowsgapminder %>%
mutate(decade = floor(year / 10)) %>%
group_by(country, continent, decade) %>%
summarise(across(where(is.numeric), mean)) %>%
select(-year)## `summarise()` regrouping output by 'country', 'continent' (override with `.groups` argument)## # A tibble: 852 x 6
## # Groups: country, continent [142]
## country continent decade lifeExp pop gdpPercap
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 195 29.6 8833134. 800.
## 2 Afghanistan Asia 196 33.0 10902524. 845.
## 3 Afghanistan Asia 197 37.3 13979916 763.
## 4 Afghanistan Asia 198 40.3 13374886. 915.
## 5 Afghanistan Asia 199 41.7 19272668 642.
## 6 Afghanistan Asia 200 43.0 28579164 851.
## 7 Albania Europe 195 57.3 1379601 1772.
## 8 Albania Europe 196 65.5 1856098. 2537.
## 9 Albania Europe 197 68.3 2386301 3423.
## 10 Albania Europe 198 71.2 2927709 3685.
## # … with 842 more rows