One point of convenience is that there is often a direct correspondence between a tidyverse command and its base R equivalent.
These generally follow a tidyverse::snake_case vs base::period.case rule. E.g. Compare:
tidyverse
base
?readr::read_csv
?utils::read.csv
?dplyr::if_else
?base::ifelse
?tibble::tibble
?base::data.frame
Etcetera.
If you call up the above examples, you’ll see that the tidyverse alternative typically offers some enhancements or other useful options (and sometimes restrictions) over its base counterpart.
Remember: There are (almost) always multiple ways to achieve a single goal in R.
Tidyverse packages
Let’s load the tidyverse meta-package and check the output.
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
We see that we have actually loaded a number of packages (which could also be loaded individually): ggplot2, tibble, dplyr, etc.
We can also see information about the package versions and some namespace conflicts.
Tidyverse packages (cont.)
The tidyverse actually comes with a lot more packages than those that are just loaded automatically.
These are the workhorse packages for cleaning and wrangling data.
Meet the pipe %>%
The pipe operator %>% is a key feature of the tidyverse. It allows you to chain together multiple operations in a way that is both readable and efficient.
There are five key dplyr verbs that you need to learn.
filter: Filter (i.e. subset) rows based on their values.
arrange: Arrange (i.e. reorder) rows based on their values.
select: Select (i.e. subset) columns by their names:
mutate: Create new columns.
summarise: Collapse multiple rows into a single summary value1.
Let’s practice these commands together using the starwars data frame that comes pre-packaged with dplyr.
filter
We can chain multiple filter commands with the pipe (%>%), or just separate them within a single filter command using commas.
starwars %>%filter( species =="Human", height >=190 )
# A tibble: 4 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Darth Va… 202 136 none white yellow 41.9 male mascu…
2 Qui-Gon … 193 89 brown fair blue 92 male mascu…
3 Dooku 193 80 white fair brown 102 male mascu…
4 Bail Pre… 191 NA black tan brown 67 male mascu…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
filter (cont.)
Regular expressions work well too.
starwars %>%filter(grepl("Skywalker", name))
# A tibble: 3 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Luke Sky… 172 77 blond fair blue 19 male mascu…
2 Anakin S… 188 84 blond fair blue 41.9 male mascu…
3 Shmi Sky… 163 NA black fair brown 72 fema… femin…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
filter (cont.)
A very common filter use case is identifying (or removing) missing data cases.
starwars %>%filter(is.na(height))
# A tibble: 6 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Arvel Cr… NA NA brown fair brown NA male mascu…
2 Finn NA NA black dark dark NA male mascu…
3 Rey NA NA brown light hazel NA fema… femin…
4 Poe Dame… NA NA brown light brown NA male mascu…
5 BB8 NA NA none none black NA none mascu…
6 Captain … NA NA none none unknown NA fema… femin…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
To remove missing observations, simply use negation: filter(!is.na(height)). Try this yourself.
arrange
The arrange function is used to reorder rows based on the values of one or more columns.
starwars %>%arrange(birth_year)
# A tibble: 87 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Wicket … 88 20 brown brown brown 8 male mascu…
2 IG-88 200 140 none metal red 15 none mascu…
3 Luke Sk… 172 77 blond fair blue 19 male mascu…
4 Leia Or… 150 49 brown light brown 19 fema… femin…
5 Wedge A… 170 77 brown fair hazel 21 male mascu…
6 Plo Koon 188 80 none orange black 22 male mascu…
7 Biggs D… 183 84 black light brown 24 male mascu…
8 Han Solo 180 80 brown fair brown 29 male mascu…
9 Lando C… 177 79 black dark brown 31 male mascu…
10 Boba Fe… 183 78.2 black fair brown 31.5 male mascu…
# ℹ 77 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
Note
Arranging on a character-based column (i.e. strings) will sort alphabetically.
arrange (cont.)
We can also arrange items in descending order using arrange(desc()).
starwars %>%arrange(desc(birth_year))
# A tibble: 87 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Yoda 66 17 white green brown 896 male mascu…
2 Jabba D… 175 1358 <NA> green-tan… orange 600 herm… mascu…
3 Chewbac… 228 112 brown unknown blue 200 male mascu…
4 C-3PO 167 75 <NA> gold yellow 112 none mascu…
5 Dooku 193 80 white fair brown 102 male mascu…
6 Qui-Gon… 193 89 brown fair blue 92 male mascu…
7 Ki-Adi-… 198 82 white pale yellow 92 male mascu…
8 Finis V… 170 NA blond fair blue 91 male mascu…
9 Palpati… 170 75 grey pale yellow 82 male mascu…
10 Cliegg … 183 NA brown fair blue 82 male mascu…
# ℹ 77 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
select
Use commas to select multiple columns out of a data frame. (You can also use “first:last” for consecutive columns). Deselect a column with “-”.
# A tibble: 87 × 5
name mass hair_color skin_color species
<chr> <dbl> <chr> <chr> <chr>
1 Luke Skywalker 77 blond fair Human
2 C-3PO 75 <NA> gold Droid
3 R2-D2 32 <NA> white, blue Droid
4 Darth Vader 136 none white Human
5 Leia Organa 49 brown light Human
6 Owen Lars 120 brown, grey light Human
7 Beru Whitesun Lars 75 brown light Human
8 R5-D4 32 <NA> white, red Droid
9 Biggs Darklighter 84 black light Human
10 Obi-Wan Kenobi 77 auburn, white fair Human
# ℹ 77 more rows
select (cont.)
You can also rename some (or all) of your selected variables in place.
# A tibble: 87 × 3
alias crib sex
<chr> <chr> <chr>
1 Luke Skywalker Tatooine masculine
2 C-3PO Tatooine masculine
3 R2-D2 Naboo masculine
4 Darth Vader Tatooine masculine
5 Leia Organa Alderaan feminine
6 Owen Lars Tatooine masculine
7 Beru Whitesun Lars Tatooine feminine
8 R5-D4 Tatooine masculine
9 Biggs Darklighter Tatooine masculine
10 Obi-Wan Kenobi Stewjon masculine
# ℹ 77 more rows
If you just want to rename columns without subsetting them, you can use rename. Try this now by replacing select(...) in the above code chunk with rename(...).
select (cont.)
The select(contains(PATTERN)) option provides a nice shortcut in relevant cases.
starwars %>%select(name, contains("color"))
# A tibble: 87 × 4
name hair_color skin_color eye_color
<chr> <chr> <chr> <chr>
1 Luke Skywalker blond fair blue
2 C-3PO <NA> gold yellow
3 R2-D2 <NA> white, blue red
4 Darth Vader none white yellow
5 Leia Organa brown light brown
6 Owen Lars brown, grey light blue
7 Beru Whitesun Lars brown light blue
8 R5-D4 <NA> white, red red
9 Biggs Darklighter black light brown
10 Obi-Wan Kenobi auburn, white fair blue-gray
# ℹ 77 more rows
select (cont.)
The select(..., everything()) option is another useful shortcut if you only want to bring some variable(s) to the “front” of a data frame.
# A tibble: 5 × 14
species homeworld name height mass hair_color skin_color eye_color
<chr> <chr> <chr> <int> <dbl> <chr> <chr> <chr>
1 Human Tatooine Luke Skywalker 172 77 blond fair blue
2 Droid Tatooine C-3PO 167 75 <NA> gold yellow
3 Droid Naboo R2-D2 96 32 <NA> white, blue red
4 Human Tatooine Darth Vader 202 136 none white yellow
5 Human Alderaan Leia Organa 150 49 brown light brown
# ℹ 6 more variables: birth_year <dbl>, sex <chr>, gender <chr>, films <list>,
# vehicles <list>, starships <list>
Note
relocate function is a more flexible alternative to select(..., everything()).
mutate
You can create new columns from scratch, or (more commonly) as transformations of existing columns.
starwars %>%select(name, birth_year) %>%mutate(dog_years = birth_year *7) %>%mutate(comment =paste(name, "is", dog_years, "in dog years."))
# A tibble: 87 × 4
name birth_year dog_years comment
<chr> <dbl> <dbl> <chr>
1 Luke Skywalker 19 133 Luke Skywalker is 133 in dog years.
2 C-3PO 112 784 C-3PO is 784 in dog years.
3 R2-D2 33 231 R2-D2 is 231 in dog years.
4 Darth Vader 41.9 293. Darth Vader is 293.3 in dog years.
5 Leia Organa 19 133 Leia Organa is 133 in dog years.
6 Owen Lars 52 364 Owen Lars is 364 in dog years.
7 Beru Whitesun Lars 47 329 Beru Whitesun Lars is 329 in dog yea…
8 R5-D4 NA NA R5-D4 is NA in dog years.
9 Biggs Darklighter 24 168 Biggs Darklighter is 168 in dog year…
10 Obi-Wan Kenobi 57 399 Obi-Wan Kenobi is 399 in dog years.
# ℹ 77 more rows
mutate (cont.)
mutate is order aware. So you can chain multiple mutates in a single call.
starwars %>%select(name, birth_year) %>%mutate(dog_years = birth_year *7, ## Separate with a commacomment =paste0(name, " is ", dog_years, " in dog years.") )
# A tibble: 87 × 4
name birth_year dog_years comment
<chr> <dbl> <dbl> <chr>
1 Luke Skywalker 19 133 Luke Skywalker is 133 in dog years.
2 C-3PO 112 784 C-3PO is 784 in dog years.
3 R2-D2 33 231 R2-D2 is 231 in dog years.
4 Darth Vader 41.9 293. Darth Vader is 293.3 in dog years.
5 Leia Organa 19 133 Leia Organa is 133 in dog years.
6 Owen Lars 52 364 Owen Lars is 364 in dog years.
7 Beru Whitesun Lars 47 329 Beru Whitesun Lars is 329 in dog yea…
8 R5-D4 NA NA R5-D4 is NA in dog years.
9 Biggs Darklighter 24 168 Biggs Darklighter is 168 in dog year…
10 Obi-Wan Kenobi 57 399 Obi-Wan Kenobi is 399 in dog years.
# ℹ 77 more rows
mutate (cont.)
Boolean, logical and conditional operators all work well with mutate too.
starwars %>%select(name, height) %>%filter(name %in%c("Luke Skywalker", "Anakin Skywalker")) %>%mutate(tall1 = height >180) %>%mutate(tall2 =ifelse(height >180, "Tall", "Short")) ## Same effect, but can choose labels
# A tibble: 2 × 4
name height tall1 tall2
<chr> <int> <lgl> <chr>
1 Luke Skywalker 172 FALSE Short
2 Anakin Skywalker 188 TRUE Tall
mutate (cont.)
Lastly, combining mutate with the across allows you to easily work on a subset of variables. For example:
Note that including na.rm = TRUE is usually a good idea with summarise functions. Otherwise, any missing value will propogate to the summarised value too.
## Probably not what we wantstarwars %>%summarise(mean_height =mean(height))
# A tibble: 1 × 1
mean_height
<dbl>
1 NA
## Much betterstarwars %>%summarise(mean_height =mean(height, na.rm =TRUE))
# A tibble: 1 × 1
mean_height
<dbl>
1 175.
summarise (cont.)
The same across-based workflow that we saw with mutate a few slides back also works with summarise. For example:
# A tibble: 5 × 4
species height mass birth_year
<chr> <dbl> <dbl> <dbl>
1 Aleena 79 15 NaN
2 Besalisk 198 102 NaN
3 Cerean 198 82 92
4 Chagrian 196 NaN NaN
5 Clawdite 168 55 NaN
Other dplyr goodies
group_by and ungroup: For (un)grouping.
Particularly useful with the summarise and mutate commands, as we’ve already seen.
slice: Subset rows by position rather than filtering by values.
E.g. starwars %>% slice(c(1, 5))
pull: Extract a column from as a data frame as a vector or scalar.
E.g. starwars %>% filter(gender=="female") %>% pull(height)
count and distinct: Number and isolate unique observations.
E.g. starwars %>% count(species), or starwars %>% distinct(species)
You could also use a combination of mutate, group_by, and n(), e.g. starwars %>% group_by(species) %>% mutate(num = n()).
Joins
One of the mainstays of the dplyr package is merging data with the family join operations.
inner_join(df1, df2)
left_join(df1, df2)
right_join(df1, df2)
full_join(df1, df2)
semi_join(df1, df2)
anti_join(df1, df2)
For the simple examples that I’m going to show here, we’ll need some data sets that come bundled with the nycflights13 package.
Load it now and then inspect these data frames in your own console.
library(nycflights13)flights planes
Joins (cont.)
Let’s perform a left join on the flights and planes datasets.
Note that dplyr made a reasonable guess about which columns to join on (i.e. columns that share the same name). It also told us its choices:
## Joining, by = c("year", "tailnum")
However, there’s an obvious problem here: the variable “year” does not have a consistent meaning across our joining datasets!
In one it refers to the year of flight, in the other it refers to year of construction.
Luckily, there’s an easy way to avoid this problem.
See if you can figure it out before turning to the next slide.
Try ?dplyr::join.
Joins (cont.)
(continued from previous slide)
You just need to be more explicit in your join call by using the by = argument.
You can also rename any ambiguous columns to avoid confusion.
left_join( flights, planes %>%rename(year_built = year), ## Not necessary w/ below line, but helpfulby ="tailnum"## Be specific about the joining column ) %>%select(year, month, day, dep_time, arr_time, carrier, flight, tailnum, year_built, type, model) %>%head(3) ## Just to save vertical space on the slide
# A tibble: 3 × 11
year month day dep_time arr_time carrier flight tailnum year_built type
<int> <int> <int> <int> <int> <chr> <int> <chr> <int> <chr>
1 2013 1 1 517 830 UA 1545 N14228 1999 Fixed w…
2 2013 1 1 533 850 UA 1714 N24211 1998 Fixed w…
3 2013 1 1 542 923 AA 1141 N619AA 1990 Fixed w…
# ℹ 1 more variable: model <chr>
Joins (cont.)
(continued from previous slide)
Last thing I’ll mention for now; note what happens if we again specify the join column… but don’t rename the ambiguous “year” column in at least one of the given data frames.
left_join( flights, planes, ## Not renaming "year" to "year_built" this timeby ="tailnum" ) %>%select(contains("year"), month, day, dep_time, arr_time, carrier, flight, tailnum, type, model) %>%head(3)
# A tibble: 3 × 11
year.x year.y month day dep_time arr_time carrier flight tailnum type model
<int> <int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <chr>
1 2013 1999 1 1 517 830 UA 1545 N14228 Fixe… 737-…
2 2013 1998 1 1 533 850 UA 1714 N24211 Fixe… 737-…
3 2013 1990 1 1 542 923 AA 1141 N619AA Fixe… 757-…
Make sure you know what “year.x” and “year.y” are. Again, it pays to be specific.
tidyr
Key tidyr verbs
pivot_longer: Pivot wide data into long format (i.e. “melt”).
pivot_wider: Pivot long data into wide format (i.e. “cast”).
separate: Separate (i.e. split) one column into multiple columns.
unite: Unite (i.e. combine) multiple columns into one.
pivot_longer
stocks <-data.frame( ## Could use "tibble" instead of "data.frame" if you prefertime =as.Date('2009-01-01') +0:1,X =rnorm(2, 0, 1),Y =rnorm(2, 0, 2),Z =rnorm(2, 0, 4) )stocks
time X Y Z
1 2009-01-01 0.9013962 1.2001963 2.571433
2 2009-01-02 -1.4042349 0.3960372 -1.042896
# A tibble: 6 × 3
time stock price
<date> <chr> <dbl>
1 2009-01-01 X 0.901
2 2009-01-01 Y 1.20
3 2009-01-01 Z 2.57
4 2009-01-02 X -1.40
5 2009-01-02 Y 0.396
6 2009-01-02 Z -1.04
pivot_longer (cont.)
Let’s quickly save the “tidy” (i.e. long) stocks data frame for use on the next slide.
## Write out the argument names this time: i.e. "names_to=" and "values_to="tidy_stocks <- stocks %>%pivot_longer(-time, names_to="stock", values_to="price")
first_name last_name
1 Adam Smith
2 Paul Samuelson
3 Milton Friedman
This command is pretty smart. But to avoid ambiguity, you can also specify the separation character with separate(..., sep=".").
separate (cont.)
A related function is separate_rows, for splitting up cells that contain multiple fields or observations (a frustratingly common occurence with survey data).
jobs <-data.frame(name =c("Jack", "Jill"),occupation =c("Homemaker", "Philosopher, Philanthropist, Troublemaker") ) jobs
name occupation
1 Jack Homemaker
2 Jill Philosopher, Philanthropist, Troublemaker
## Now split out Jill's various occupations into different rowsjobs %>%separate_rows(occupation)
# A tibble: 4 × 2
name occupation
<chr> <chr>
1 Jack Homemaker
2 Jill Philosopher
3 Jill Philanthropist
4 Jill Troublemaker
unite (cont.)
gdp <-tibble(yr =rep(2016, times =4),mnth =rep(1, times =4),dy =1:4,gdp =rnorm(4, mean =100, sd =2) )gdp
# A tibble: 4 × 4
yr mnth dy gdp
<dbl> <dbl> <int> <dbl>
1 2016 1 1 98.9
2 2016 1 2 101.
3 2016 1 3 99.1
4 2016 1 4 101.
## Combine "yr", "mnth", and "dy" into one "date" columngdp %>%unite(date, c("yr", "mnth", "dy"), sep ="-")
# A tibble: 4 × 2
date gdp
<chr> <dbl>
1 2016-1-1 98.9
2 2016-1-2 101.
3 2016-1-3 99.1
4 2016-1-4 101.
unite (cont.)
Note that unite will automatically create a character variable. You can see this better if we convert it to a tibble.
gdp_u <- gdp %>%unite(date, c("yr", "mnth", "dy"), sep ="-") %>%as_tibble()gdp_u
# A tibble: 4 × 2
date gdp
<chr> <dbl>
1 2016-1-1 98.9
2 2016-1-2 101.
3 2016-1-3 99.1
4 2016-1-4 101.
If you want to convert it to something else (e.g. date or numeric) then you will need to modify it using mutate. See the next slide for an example, using the lubridate package’s super helpful date conversion functions.
# A tibble: 4 × 2
side height
<chr> <chr>
1 left bottom
2 left top
3 right bottom
4 right top
See ?expand and ?complete for more specialised functions that allow you to fill in (implicit) missing data or variable combinations in existing data frames.
