ID 543: Day 2
Introduction to R
Homework review
Today’s goals
- Learn how to select variables with
select() - Learn how to subset data with
filter() - Learn how to use the pipe (
%>%or|>) to chain functions together
Selecting the variables you want
- We’ve made tons of new variables!
- You don’t want to keep them all!
- Luckily there’s an easy way to select the variables you want:
select()!
# A tibble: 1,205 × 5
id income eyesight sex region
<dbl> <dbl> <dbl> <dbl> <dbl>
1 3 22390 1 2 1
2 6 35000 2 1 1
3 8 7227 2 2 1
4 16 48000 3 2 1
5 18 4510 3 1 3
6 20 50000 2 2 1
7 27 20000 1 2 1
8 49 23900 1 2 1
9 57 23289 2 2 1
10 67 35000 1 1 1
# ℹ 1,195 more rowsselect() syntax
- Like
mutate(), the first argument is the dataset you want to select from - Then you can just list the variables you want!
- Or you can list the variables you don’t want, preceded by a negative sign (
-)
select() syntax
There are also a lot of “helpers”!
var1:var10(consecutively placed variables)all_of()/any_of()starts_with()ends_with()contains()matches()(like contains, but for regular expressions)num_range()(for patterns likex01,x02, …)
everything()where(is.factor)(or anything else)
Tip
Like the fct_() functions, you don’t need to memorize all these! Just know they exist and you can look them up when you need them.
all_of()
Notice that the variable names we used in select() weren’t in quotation marks.
Let’s say you have a vector of column names that you want. Then you can use all_of() to choose them.
# A tibble: 1,205 × 3
age_bir nsibs region
<dbl> <dbl> <dbl>
1 19 3 1
2 30 1 1
3 17 7 1
4 31 3 1
5 19 2 3
6 30 2 1
7 27 1 1
8 24 6 1
9 21 1 1
10 36 1 1
# ℹ 1,195 more rowsIf you don’t want an error if they don’t exist, use any_of().
starts_with, ends_with
You can also use starts_with() and ends_with() to select variables that start or end with a certain string.
What variables are in new_subset?
Note
Recall that nlsy has variables “id”, “glasses”, “eyesight”, “sleep_wkdy”, “sleep_wknd”, “nsibs”, “race_eth”, “sex”, “region”, “income”, “age_bir”, “eyesight_cat”, “glasses_cat”, “race_eth_cat”, “sex_cat”.
starts_with, ends_with
# A tibble: 1,205 × 3
sleep_wkdy sleep_wknd id
<dbl> <dbl> <dbl>
1 5 7 3
2 6 7 6
3 7 9 8
4 6 7 16
5 10 10 18
6 7 8 20
7 8 8 27
8 8 8 49
9 7 8 57
10 8 8 67
# ℹ 1,195 more rowsNote
Variables won’t be repeated even if they meet multiple criteria!
starts_with, ends_with
Use & in between multiple criteria if they have to meet all of them
where()
where() is a helper that lets you select variables based on their properties
# A tibble: 1,205 × 4
eyesight_cat glasses_cat race_eth_cat sex_cat
<fct> <fct> <fct> <fct>
1 Excellent Doesn't wear glasses Non-Black, Non-Hispanic Female
2 Very Good Wears glasses/contacts Non-Black, Non-Hispanic Male
3 Very Good Doesn't wear glasses Non-Black, Non-Hispanic Female
4 Good Wears glasses/contacts Non-Black, Non-Hispanic Female
5 Good Doesn't wear glasses Non-Black, Non-Hispanic Male
6 Very Good Wears glasses/contacts Non-Black, Non-Hispanic Female
7 Excellent Doesn't wear glasses Non-Black, Non-Hispanic Female
8 Excellent Wears glasses/contacts Non-Black, Non-Hispanic Female
9 Very Good Wears glasses/contacts Non-Black, Non-Hispanic Female
10 Excellent Doesn't wear glasses Non-Black, Non-Hispanic Male
# ℹ 1,195 more rowsThink back to how we named our factor variables. What’s another way we could have selected them?
Rearranging variables
You can use these helpers to rearrange variables
# A tibble: 1,205 × 15
id eyesight_cat glasses_cat race_eth_cat sex_cat glasses eyesight
<dbl> <fct> <fct> <fct> <fct> <dbl> <dbl>
1 3 Excellent Doesn't wear glasses Non-Black, … Female 0 1
2 6 Very Good Wears glasses/conta… Non-Black, … Male 1 2
3 8 Very Good Doesn't wear glasses Non-Black, … Female 0 2
4 16 Good Wears glasses/conta… Non-Black, … Female 1 3
5 18 Good Doesn't wear glasses Non-Black, … Male 0 3
6 20 Very Good Wears glasses/conta… Non-Black, … Female 1 2
7 27 Excellent Doesn't wear glasses Non-Black, … Female 0 1
8 49 Excellent Wears glasses/conta… Non-Black, … Female 1 1
9 57 Very Good Wears glasses/conta… Non-Black, … Female 1 2
10 67 Excellent Doesn't wear glasses Non-Black, … Male 0 1
# ℹ 1,195 more rows
# ℹ 8 more variables: sleep_wkdy <dbl>, sleep_wknd <dbl>, nsibs <dbl>,
# race_eth <dbl>, sex <dbl>, region <dbl>, income <dbl>, age_bir <dbl>Tip
If you end with everything(), it’s basically saying “and everything else” in its original order.
Exercises
Subsetting data
We usually don’t do an analysis in an entire dataset. We usually apply some eligibility criteria to find the people who we will analyze. One function we can use to do that in R is filter().
filter() syntax
- Like the other functions, we give
filter()the dataset first, then we give it a series of criteria that we want to subset our data on. - As with
case_when(), these criteria should be questions withTRUE/FALSEanswers. We’ll keep all those rows for which the answer isTRUE. - If there are multiple criteria, we can connect them with
&or just by separating with commas, and we’ll get back only the rows that answerTRUEto all of them.
Another new function: nrow()
nrow() tells you how many rows are in a dataset
Of course, you can look in the environment pane, print a tibble, or use a function like glimpse(), but with nrow() you can get the number of rows in a dataset programatically
Tip
There’s also ncol() for columns, or dim() for both!
Logicals in R
When we used case_when(), we got TRUE/FALSE answers when we asked whether a variable was > or < some number, for example.
When we want to know if something is
- equal:
== - not equal:
!= - greater than or equal to:
>= - less than or equal to:
<=
We also can ask about multiple conditions with & (and) and | (or).
Behind the scenes of filter()
We are pulling out everyone for whom the statement evaluates to TRUE:
[1] TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE
[13] FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE
[25] TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
[37] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE
[49] FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE
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[757] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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[781] TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
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[649] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[673] FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
[685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[721] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[733] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[745] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[757] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[781] TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE
[793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[805] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[829] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
[841] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[853] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
[865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
[877] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[889] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[901] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[913] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[925] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[949] FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
[961] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[973] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
[997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[1009] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
[1045] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1057] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
[1069] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
[1081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[1093] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[1105] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE
[1117] TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
[1129] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE
[1141] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[1153] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[1165] FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
[1177] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
[1189] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1201] FALSE FALSE FALSE FALSE FALSEOr statements
To get the extreme values of eyesight (1 and 5), we would do something like:
# A tibble: 2 × 2
eyesight n
<dbl> <int>
1 1 474
2 5 19We could of course do the same thing with a factor variable:
Exercises
Multiple “or” possibilities
Often we have a number of options for one variable that would meet our eligibility criteria. R’s special %in% function comes in handy here:
If the variable’s value is any one of those values, it will return TRUE.
Note
This is the same as saying region_cat == "South" | region_cat == "West" | region_cat == "Northeast"
More %in%
This is just a regular R function that works outside of the filter() function, of course!
This is just the same as writing:
Opposite of %in%
We can’t say “not in” with the syntax %!in% or something like that. We have to put the ! before the question to make it the opposite of what it otherwise would be
Other questions
R offers a number of shortcuts to use when determining whether values meet certain criteria:
is.na(): is it a missing value?is.finite()/is.infinite(): when you might have infinite values in your datais.factor(): asks whether some variable is a factor
You can find lots of these if you tab-complete is. or is_ (the latter are tidyverse versions). Most you will never find a use for!
Putting it all together
We can of course filter() on multiple variables at once:
my_data <- filter(nlsy,
age_bir < 20,
sex != 1,
nsibs %in% c(1, 2, 3),
!is.na(sleep_wkdy))
summary(select(my_data, age_bir, sex, nsibs, sleep_wkdy)) age_bir sex nsibs sleep_wkdy
Min. :14.00 Min. :2 Min. :1.000 Min. : 2.000
1st Qu.:17.00 1st Qu.:2 1st Qu.:1.750 1st Qu.: 6.000
Median :18.00 Median :2 Median :2.000 Median : 7.000
Mean :17.42 Mean :2 Mean :2.163 Mean : 6.452
3rd Qu.:19.00 3rd Qu.:2 3rd Qu.:3.000 3rd Qu.: 7.000
Max. :19.00 Max. :2 Max. :3.000 Max. :10.000 Putting it all together
Spot the difference?
oth_dat <- filter(nlsy,
(age_bir < 20) &
(sex != 1 | nsibs %in% c(1, 2, 3)) &
!is.na(sleep_wkdy))
summary(select(oth_dat, age_bir, sex, nsibs, sleep_wkdy)) age_bir sex nsibs sleep_wkdy
Min. :13.00 Min. :1.000 Min. : 0.000 Min. : 0.000
1st Qu.:16.00 1st Qu.:2.000 1st Qu.: 2.000 1st Qu.: 6.000
Median :17.00 Median :2.000 Median : 4.000 Median : 7.000
Mean :17.24 Mean :1.929 Mean : 4.539 Mean : 6.687
3rd Qu.:18.00 3rd Qu.:2.000 3rd Qu.: 6.000 3rd Qu.: 8.000
Max. :19.00 Max. :2.000 Max. :16.000 Max. :13.000 Exercises
Putting it together
What does this sequence of code do?
nlsy2 <- mutate(nlsy,
only = case_when(
nsibs == 0 ~ "yes",
.default = "no"))
nlsy3 <- select(nlsy2, id, contains("sleep"), only)
only_kids <- filter(nlsy3, only == "yes")
only_kids# A tibble: 30 × 4
id sleep_wkdy sleep_wknd only
<dbl> <dbl> <dbl> <chr>
1 458 7 8 yes
2 653 6 7 yes
3 1101 7 8 yes
4 1166 5 6 yes
5 2163 7 8 yes
6 2442 7 9 yes
7 2545 8 8 yes
8 3036 5 8 yes
9 3194 7 7 yes
10 3538 5 5 yes
# ℹ 20 more rowsPutting it together
What does this sequence of code do?
nlsy2 <- filter(nlsy, age_bir < 20)
nlsy3 <- select(nlsy2, id, contains("cat"), age_bir)
nlsy_final <- mutate(nlsy3,
age_bir_decade = case_when(
age_bir < 20 ~ "<20",
age_bir < 30 ~ "20-29",
age_bir < 40 ~ "30-39",
age_bir < 50 ~ "40-49",
age_bir >= 50 ~ "50+"
),
age_bir_decade = fct_relevel(age_bir_decade,
"<20", "20-29", "30-39", "40-49", "50+")
)Exercises
Sequence of functions
In any data management and/or analysis task, we perform a series of functions to the data until we get some object we want.
Sometimes this can be hard to read/keep track of.
The pipe
If you have experience with unix programming, you may be familiar with the version of the pipe there: |.
Starting with R version 4.1, R has its own pipe: |>
The original pipe function in R %>% has been part of the tidyverse for a while and is originally from the magrittr package, named after René Magritte
We use the pipe to chain together steps
It’s like a recipe for our dataset.1 
Instead of successive command lines
or all-in-one
It’s like reading a story (or nursery rhyme!)
A natural order of operations
Using pipes with functions we already know
nlsy2 <- mutate(nlsy, only = case_when(
nsibs == 0 ~ "yes",
.default = "no"))
nlsy3 <- select(nlsy2,
id, contains("sleep"), only)
only_kids <- filter(nlsy3, only == "yes")
only_kids# A tibble: 30 × 4
id sleep_wkdy sleep_wknd only
<dbl> <dbl> <dbl> <chr>
1 458 7 8 yes
2 653 6 7 yes
3 1101 7 8 yes
4 1166 5 6 yes
5 2163 7 8 yes
6 2442 7 9 yes
7 2545 8 8 yes
8 3036 5 8 yes
9 3194 7 7 yes
10 3538 5 5 yes
# ℹ 20 more rowsonly_kids <- nlsy |>
mutate(only = case_when(
nsibs == 0 ~ "yes",
TRUE ~ "no")) |>
select(id, contains("sleep"), only) |>
filter(only == "yes")
only_kids# A tibble: 30 × 4
id sleep_wkdy sleep_wknd only
<dbl> <dbl> <dbl> <chr>
1 458 7 8 yes
2 653 6 7 yes
3 1101 7 8 yes
4 1166 5 6 yes
5 2163 7 8 yes
6 2442 7 9 yes
7 2545 8 8 yes
8 3036 5 8 yes
9 3194 7 7 yes
10 3538 5 5 yes
# ℹ 20 more rowsPipes fill first argument of the next function with what is being piped in
Usage
mutate(.data, ...)
select(.data, ...)
filter(.data, ...)
Pipes fill first argument of the next function with what is being piped in
Pipes fill first argument of the next function with what is being piped in
Exercises
Today’s summary
select()lets you choose the variables you wantfilter()lets you choose the rows you wantmutate()lets you create new variables- the pipe (
|>or%>%) lets you chain these functions together
Additional functions
starts_with(),ends_with(),contains(),matches(),num_range(),all_of(),any_of(),everything(),where(): helpers forselect()is.na(),is.finite(),is.infinite(),is.factor(): helpers forfilter()%in%: for multiple==or!=possibilities
ID 543: Day 2 Introduction to R