dplyr ExerciseOptional, but recommended startup:
Change the file output to both html and md documents (not notebook).
knit the document.
Stage and commit the rmd, and knitted documents.
dplyr syntaxLoad the gapminder and tidyverse packages. Hint: suppressPackageStartupMessages()! - This loads dplyr, too.
# load your packages here:
library(gapminder)
library(tidyverse)select() (8 min)year, lifeExp, country from the gapminder data, in that order.# select = choose columns
select(gapminder, year, lifeExp, country)## # A tibble: 1,704 x 3
## year lifeExp country
## <int> <dbl> <fct>
## 1 1952 28.8 Afghanistan
## 2 1957 30.3 Afghanistan
## 3 1962 32.0 Afghanistan
## 4 1967 34.0 Afghanistan
## 5 1972 36.1 Afghanistan
## 6 1977 38.4 Afghanistan
## 7 1982 39.9 Afghanistan
## 8 1987 40.8 Afghanistan
## 9 1992 41.7 Afghanistan
## 10 1997 41.8 Afghanistan
## # … with 1,694 more rowscountry to lifeExp.# This will work:
select(gapminder, country, continent, year, lifeExp)## # A tibble: 1,704 x 4
## country continent year lifeExp
## <fct> <fct> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8
## 2 Afghanistan Asia 1957 30.3
## 3 Afghanistan Asia 1962 32.0
## 4 Afghanistan Asia 1967 34.0
## 5 Afghanistan Asia 1972 36.1
## 6 Afghanistan Asia 1977 38.4
## 7 Afghanistan Asia 1982 39.9
## 8 Afghanistan Asia 1987 40.8
## 9 Afghanistan Asia 1992 41.7
## 10 Afghanistan Asia 1997 41.8
## # … with 1,694 more rows# Better way:
# use colon-- start:end column names
select(gapminder, country:lifeExp)## # A tibble: 1,704 x 4
## country continent year lifeExp
## <fct> <fct> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8
## 2 Afghanistan Asia 1957 30.3
## 3 Afghanistan Asia 1962 32.0
## 4 Afghanistan Asia 1967 34.0
## 5 Afghanistan Asia 1972 36.1
## 6 Afghanistan Asia 1977 38.4
## 7 Afghanistan Asia 1982 39.9
## 8 Afghanistan Asia 1987 40.8
## 9 Afghanistan Asia 1992 41.7
## 10 Afghanistan Asia 1997 41.8
## # … with 1,694 more rowslifeExp.# use - to denote exclusion
# select() takes column names directly
# do not need to put them in a vector
select(gapminder, -lifeExp)## # A tibble: 1,704 x 5
## country continent year pop gdpPercap
## <fct> <fct> <int> <int> <dbl>
## 1 Afghanistan Asia 1952 8425333 779.
## 2 Afghanistan Asia 1957 9240934 821.
## 3 Afghanistan Asia 1962 10267083 853.
## 4 Afghanistan Asia 1967 11537966 836.
## 5 Afghanistan Asia 1972 13079460 740.
## 6 Afghanistan Asia 1977 14880372 786.
## 7 Afghanistan Asia 1982 12881816 978.
## 8 Afghanistan Asia 1987 13867957 852.
## 9 Afghanistan Asia 1992 16317921 649.
## 10 Afghanistan Asia 1997 22227415 635.
## # … with 1,694 more rowscontinent first. Hint: use the everything() function.# everything() knows not to include 'continent' since it was already listed
# can think of everything() as everything ELSE
select(gapminder, continent, everything())## # A tibble: 1,704 x 6
## continent country year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Asia Afghanistan 1952 28.8 8425333 779.
## 2 Asia Afghanistan 1957 30.3 9240934 821.
## 3 Asia Afghanistan 1962 32.0 10267083 853.
## 4 Asia Afghanistan 1967 34.0 11537966 836.
## 5 Asia Afghanistan 1972 36.1 13079460 740.
## 6 Asia Afghanistan 1977 38.4 14880372 786.
## 7 Asia Afghanistan 1982 39.9 12881816 978.
## 8 Asia Afghanistan 1987 40.8 13867957 852.
## 9 Asia Afghanistan 1992 41.7 16317921 649.
## 10 Asia Afghanistan 1997 41.8 22227415 635.
## # … with 1,694 more rowscontinent to cont.# compare
# select has reordered continent to the front AND renamed
select(gapminder, cont = continent, everything())## # A tibble: 1,704 x 6
## cont country year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Asia Afghanistan 1952 28.8 8425333 779.
## 2 Asia Afghanistan 1957 30.3 9240934 821.
## 3 Asia Afghanistan 1962 32.0 10267083 853.
## 4 Asia Afghanistan 1967 34.0 11537966 836.
## 5 Asia Afghanistan 1972 36.1 13079460 740.
## 6 Asia Afghanistan 1977 38.4 14880372 786.
## 7 Asia Afghanistan 1982 39.9 12881816 978.
## 8 Asia Afghanistan 1987 40.8 13867957 852.
## 9 Asia Afghanistan 1992 41.7 16317921 649.
## 10 Asia Afghanistan 1997 41.8 22227415 635.
## # … with 1,694 more rows# rename has only changed the name, kept the same column positioning
rename(gapminder, cont = continent)## # A tibble: 1,704 x 6
## country cont 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 rowsarrange() (8 min)# arrange by year (increasing)
arrange(gapminder, year)## # 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 Albania Europe 1952 55.2 1282697 1601.
## 3 Algeria Africa 1952 43.1 9279525 2449.
## 4 Angola Africa 1952 30.0 4232095 3521.
## 5 Argentina Americas 1952 62.5 17876956 5911.
## 6 Australia Oceania 1952 69.1 8691212 10040.
## 7 Austria Europe 1952 66.8 6927772 6137.
## 8 Bahrain Asia 1952 50.9 120447 9867.
## 9 Bangladesh Asia 1952 37.5 46886859 684.
## 10 Belgium Europe 1952 68 8730405 8343.
## # … with 1,694 more rows# use desc to use descending order
arrange(gapminder, desc(year))## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Albania Europe 2007 76.4 3600523 5937.
## 3 Algeria Africa 2007 72.3 33333216 6223.
## 4 Angola Africa 2007 42.7 12420476 4797.
## 5 Argentina Americas 2007 75.3 40301927 12779.
## 6 Australia Oceania 2007 81.2 20434176 34435.
## 7 Austria Europe 2007 79.8 8199783 36126.
## 8 Bahrain Asia 2007 75.6 708573 29796.
## 9 Bangladesh Asia 2007 64.1 150448339 1391.
## 10 Belgium Europe 2007 79.4 10392226 33693.
## # … with 1,694 more rows# arrange by year then life expectancy (input order translates into arrange order)
arrange(gapminder, year, lifeExp)## # 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 Gambia Africa 1952 30 284320 485.
## 3 Angola Africa 1952 30.0 4232095 3521.
## 4 Sierra Leone Africa 1952 30.3 2143249 880.
## 5 Mozambique Africa 1952 31.3 6446316 469.
## 6 Burkina Faso Africa 1952 32.0 4469979 543.
## 7 Guinea-Bissau Africa 1952 32.5 580653 300.
## 8 Yemen, Rep. Asia 1952 32.5 4963829 782.
## 9 Somalia Africa 1952 33.0 2526994 1136.
## 10 Guinea Africa 1952 33.6 2664249 510.
## # … with 1,694 more rows%>% (8 min)Note: think of %>% as the word “then”!
Demonstration:
Here I want to combine select() Task 1 with arrange() Task 3.
This is how I could do it by nesting the two function calls:
# Nesting function calls can be hard to read
arrange(select(gapminder, year, lifeExp, country), year, lifeExp)Now using with pipes:
# alter the below to include 2 "pipes"
# shortcut for pipe: CMD + SHIFT + M
# %>% takes the output of the previous function as the FIRST argument of the next function
# think of the %>% as the | in bash
gapminder %>%
select(year, lifeExp, country) %>%
arrange(year,lifeExp)## # A tibble: 1,704 x 3
## year lifeExp country
## <int> <dbl> <fct>
## 1 1952 28.8 Afghanistan
## 2 1952 30 Gambia
## 3 1952 30.0 Angola
## 4 1952 30.3 Sierra Leone
## 5 1952 31.3 Mozambique
## 6 1952 32.0 Burkina Faso
## 7 1952 32.5 Guinea-Bissau
## 8 1952 32.5 Yemen, Rep.
## 9 1952 33.0 Somalia
## 10 1952 33.6 Guinea
## # … with 1,694 more rowsReturn to guide at section 6.7.
filter() (10 min)# filter = choose rows that match a certain criteria
gapminder %>%
filter(pop > 100000000)## # A tibble: 77 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Bangladesh Asia 1987 52.8 103764241 752.
## 2 Bangladesh Asia 1992 56.0 113704579 838.
## 3 Bangladesh Asia 1997 59.4 123315288 973.
## 4 Bangladesh Asia 2002 62.0 135656790 1136.
## 5 Bangladesh Asia 2007 64.1 150448339 1391.
## 6 Brazil Americas 1972 59.5 100840058 4986.
## 7 Brazil Americas 1977 61.5 114313951 6660.
## 8 Brazil Americas 1982 63.3 128962939 7031.
## 9 Brazil Americas 1987 65.2 142938076 7807.
## 10 Brazil Americas 1992 67.1 155975974 6950.
## # … with 67 more rows# <- shortcut: ALT + - (alt + dash)
# learn the difference between using & (and) and | (or)
gapminder %>%
filter(pop > 10**8) %>%
filter(continent == "Asia")## # A tibble: 52 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Bangladesh Asia 1987 52.8 103764241 752.
## 2 Bangladesh Asia 1992 56.0 113704579 838.
## 3 Bangladesh Asia 1997 59.4 123315288 973.
## 4 Bangladesh Asia 2002 62.0 135656790 1136.
## 5 Bangladesh Asia 2007 64.1 150448339 1391.
## 6 China Asia 1952 44 556263527 400.
## 7 China Asia 1957 50.5 637408000 576.
## 8 China Asia 1962 44.5 665770000 488.
## 9 China Asia 1967 58.4 754550000 613.
## 10 China Asia 1972 63.1 862030000 677.
## # … with 42 more rowsgapminder %>%
filter(pop > 10**8 & continent == "Asia")## # A tibble: 52 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Bangladesh Asia 1987 52.8 103764241 752.
## 2 Bangladesh Asia 1992 56.0 113704579 838.
## 3 Bangladesh Asia 1997 59.4 123315288 973.
## 4 Bangladesh Asia 2002 62.0 135656790 1136.
## 5 Bangladesh Asia 2007 64.1 150448339 1391.
## 6 China Asia 1952 44 556263527 400.
## 7 China Asia 1957 50.5 637408000 576.
## 8 China Asia 1962 44.5 665770000 488.
## 9 China Asia 1967 58.4 754550000 613.
## 10 China Asia 1972 63.1 862030000 677.
## # … with 42 more rowsgapminder %>%
filter(pop > 10**8,
continent == "Asia")## # A tibble: 52 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Bangladesh Asia 1987 52.8 103764241 752.
## 2 Bangladesh Asia 1992 56.0 113704579 838.
## 3 Bangladesh Asia 1997 59.4 123315288 973.
## 4 Bangladesh Asia 2002 62.0 135656790 1136.
## 5 Bangladesh Asia 2007 64.1 150448339 1391.
## 6 China Asia 1952 44 556263527 400.
## 7 China Asia 1957 50.5 637408000 576.
## 8 China Asia 1962 44.5 665770000 488.
## 9 China Asia 1967 58.4 754550000 613.
## 10 China Asia 1972 63.1 862030000 677.
## # … with 42 more rows# use OR in this case since each row can only have one value for country
gapminder %>%
filter (country == "Brazil" | country == "China")## # A tibble: 24 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Brazil Americas 1952 50.9 56602560 2109.
## 2 Brazil Americas 1957 53.3 65551171 2487.
## 3 Brazil Americas 1962 55.7 76039390 3337.
## 4 Brazil Americas 1967 57.6 88049823 3430.
## 5 Brazil Americas 1972 59.5 100840058 4986.
## 6 Brazil Americas 1977 61.5 114313951 6660.
## 7 Brazil Americas 1982 63.3 128962939 7031.
## 8 Brazil Americas 1987 65.2 142938076 7807.
## 9 Brazil Americas 1992 67.1 155975974 6950.
## 10 Brazil Americas 1997 69.4 168546719 7958.
## # … with 14 more rowsmutate() (10 min)Let’s get:
gdpBill), rounded to two decimals.# round(number, num_decimal_places)
# mutate to make a new column with calculated values
gapminder %>%
mutate(gdbBill = round(gdpPercap*pop/(10**8),2))## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap gdbBill
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 65.7
## 2 Afghanistan Asia 1957 30.3 9240934 821. 75.8
## 3 Afghanistan Asia 1962 32.0 10267083 853. 87.6
## 4 Afghanistan Asia 1967 34.0 11537966 836. 96.5
## 5 Afghanistan Asia 1972 36.1 13079460 740. 96.8
## 6 Afghanistan Asia 1977 38.4 14880372 786. 117.
## 7 Afghanistan Asia 1982 39.9 12881816 978. 126.
## 8 Afghanistan Asia 1987 40.8 13867957 852. 118.
## 9 Afghanistan Asia 1992 41.7 16317921 649. 106.
## 10 Afghanistan Asia 1997 41.8 22227415 635. 141.
## # … with 1,694 more rowsgapminder %>%
mutate(gdpBill = (gdpPercap*pop/(10**8)) %>% round(2))## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap gdpBill
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 65.7
## 2 Afghanistan Asia 1957 30.3 9240934 821. 75.8
## 3 Afghanistan Asia 1962 32.0 10267083 853. 87.6
## 4 Afghanistan Asia 1967 34.0 11537966 836. 96.5
## 5 Afghanistan Asia 1972 36.1 13079460 740. 96.8
## 6 Afghanistan Asia 1977 38.4 14880372 786. 117.
## 7 Afghanistan Asia 1982 39.9 12881816 978. 126.
## 8 Afghanistan Asia 1987 40.8 13867957 852. 118.
## 9 Afghanistan Asia 1992 41.7 16317921 649. 106.
## 10 Afghanistan Asia 1997 41.8 22227415 635. 141.
## # … with 1,694 more rowsNotice the backwards compatibility! No need for loops!
Try the same thing, but with transmute (drops all other variables).
# transmute drops all other columns except new one, unless one is specified
# transmute(# columns to keep (optional), new_calculated_column)
gapminder %>%
transmute(country,gdbBill = round(gdpPercap*pop/(10**8),2))## # A tibble: 1,704 x 2
## country gdbBill
## <fct> <dbl>
## 1 Afghanistan 65.7
## 2 Afghanistan 75.8
## 3 Afghanistan 87.6
## 4 Afghanistan 96.5
## 5 Afghanistan 96.8
## 6 Afghanistan 117.
## 7 Afghanistan 126.
## 8 Afghanistan 118.
## 9 Afghanistan 106.
## 10 Afghanistan 141.
## # … with 1,694 more rowsThe if_else function is useful for changing certain elements in a data frame.
Example: Suppose Canada’s 1952 life expectancy was mistakenly entered as 68.8 in the data frame, but is actually 70. Fix it using if_else and mutate.
# if the country is equal to Canada and the year is 1952, life Expectancy is equal to 70, otherwise, keep lifeExpectancy as is
gapminder %>%
mutate(lifeExp = if_else(country == "Canada" & year == 1952, 70, lifeExp))## # 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 rows# To double check that it worked:
gapminder %>%
mutate(lifeExp = if_else(country == "Canada" & year == 1952, 70, lifeExp)) %>%
filter(country == "Canada",year == 1952)## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Canada Americas 1952 70 14785584 11367.Your turn: Make a new column called cc that pastes the country name followed by the continent, separated by a comma. (Hint: use the paste function with the sep=", " argument).
# paste( words to be pasted together separated by comma, sep = delimiter)
gapminder %>%
mutate(cc = paste(country,continent, sep=", "))## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap cc
## <fct> <fct> <int> <dbl> <int> <dbl> <chr>
## 1 Afghanistan Asia 1952 28.8 8425333 779. Afghanistan, Asia
## 2 Afghanistan Asia 1957 30.3 9240934 821. Afghanistan, Asia
## 3 Afghanistan Asia 1962 32.0 10267083 853. Afghanistan, Asia
## 4 Afghanistan Asia 1967 34.0 11537966 836. Afghanistan, Asia
## 5 Afghanistan Asia 1972 36.1 13079460 740. Afghanistan, Asia
## 6 Afghanistan Asia 1977 38.4 14880372 786. Afghanistan, Asia
## 7 Afghanistan Asia 1982 39.9 12881816 978. Afghanistan, Asia
## 8 Afghanistan Asia 1987 40.8 13867957 852. Afghanistan, Asia
## 9 Afghanistan Asia 1992 41.7 16317921 649. Afghanistan, Asia
## 10 Afghanistan Asia 1997 41.8 22227415 635. Afghanistan, Asia
## # … with 1,694 more rowsThese functions we’ve seen are called vectorized functions.