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()!
nlsy_subset <- select(nlsy, id, income, eyesight, sex, region)
nlsy_subset
# 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 rows

select() 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(nlsy_subset, -c(id, region))
# A tibble: 1,205 × 3
   income eyesight   sex
    <dbl>    <dbl> <dbl>
 1  22390        1     2
 2  35000        2     1
 3   7227        2     2
 4  48000        3     2
 5   4510        3     1
 6  50000        2     2
 7  20000        1     2
 8  23900        1     2
 9  23289        2     2
10  35000        1     1
# ℹ 1,195 more rows

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 like x01, 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.

cols_I_want <- c("age_bir", "nsibs", "region")
select(nlsy, all_of(cols_I_want))
# 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 rows

If 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.

new_subset <- select(nlsy, starts_with("sleep"), ends_with("d"))

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

new_subset <- select(nlsy, starts_with("sleep"), ends_with("d"))
new_subset
# 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 rows

Note

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

select(nlsy, starts_with("sleep") & ends_with("d"))
# A tibble: 1,205 × 1
   sleep_wknd
        <dbl>
 1          7
 2          7
 3          9
 4          7
 5         10
 6          8
 7          8
 8          8
 9          8
10          8
# ℹ 1,195 more rows

where()

where() is a helper that lets you select variables based on their properties

select(nlsy, where(is.factor))
# 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 rows

Think 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

select(nlsy, id, where(is.factor), where(is.numeric), everything())
# 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().

wear_glasses <- filter(nlsy, glasses == 1)

nrow(wear_glasses)
[1] 624
summary(wear_glasses$glasses)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      1       1       1       1       1       1

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 with TRUE/FALSE answers. We’ll keep all those rows for which the answer is TRUE.
  • 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 answer TRUE to all of them.
no_glasses <- filter(nlsy, glasses == 0)
nrow(no_glasses)
[1] 581
glasses_great_eyes <- filter(nlsy, glasses == 0, eyesight == 1)
nrow(glasses_great_eyes)
[1] 220
yesno_glasses <- filter(nlsy, glasses == 0, glasses == 1)
nrow(yesno_glasses)
[1] 0

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:

nlsy$glasses == 0
   [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
  [61] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
  [73] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
  [85] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [97] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
 [109]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
 [121]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [133]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [145]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
 [157] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [169] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [181] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [193] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [205] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [217] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
 [229]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [241] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [253] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [265] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [277]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
 [289]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [301] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [313]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [325] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
 [337]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [349] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
 [361]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [373] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [385] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
 [397]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
 [409] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
 [421]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [433]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
 [445]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [457]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [469] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [481] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [493]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [505] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
 [517]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [529]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
 [541]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [553] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [565] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
 [577] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
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 [625] FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
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 [649] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
 [661] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
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 [685] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
 [697]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [709]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
 [721]  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [733]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
 [745] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
 [757]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [769] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
 [781]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE
 [793] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [805]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
 [817]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
 [829]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE
 [841] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [853]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [865]  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [877]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [889] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [901] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
 [913] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
 [925]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [937]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [949]  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [961] FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE
 [973] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
 [985] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [997]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
[1009]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
[1021] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
[1033] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
[1045] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
[1057] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
[1069] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
[1081] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
[1093] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
[1105]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
[1117]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
[1129] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
[1141]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
[1153]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
[1165] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
[1177]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
[1189]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
[1201]  TRUE  TRUE  TRUE FALSE  TRUE
nlsy$eyesight == 1
   [1]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
  [13]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [25]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [37] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
  [49] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
  [61]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
  [73]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
  [85] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
  [97]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
 [109] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [121]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [133] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [145]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [157] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
 [169]  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
 [181] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [193]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [205] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
 [217] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [229] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
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 [253]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [265] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [277]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [289]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [301]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [313] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
 [325] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
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 [349]  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [361] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [385] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [397]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [409]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [421] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
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 [457] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [469] FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [481]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [493]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [505] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [517] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [529]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [541] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [553]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE
 [565]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
 [577] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
 [589]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [601]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [613]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
 [625] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [637] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [649] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
 [661] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
 [673] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
 [685] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
 [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
 [709] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
 [721]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [733] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [745] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [757] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [769] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [781]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [793]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [805] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [817]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [829] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
 [841] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [853] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [865] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [877] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [889]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
 [901]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [913] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
 [925]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
 [937] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
 [949] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [961] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [973]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
 [985] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [997] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
[1009]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[1033]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
[1045] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
[1057] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
[1069]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
[1081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[1093] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
[1105] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
[1117]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
[1129] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
[1141] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE
[1153] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
[1165]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
[1177] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
[1189] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
[1201] FALSE FALSE FALSE FALSE FALSE
nlsy$glasses == 0 & nlsy$eyesight == 1
   [1]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
  [13] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [25]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [37] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
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  [73] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [85] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [121]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [133] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [145]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [157] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
 [169] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [181] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
 [193] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [217] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
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 [325] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
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 [349] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
 [361] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
 [385] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
 [397]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [421] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [433] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [469] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [481] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [493]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [505] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [517] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [529]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [541] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [553] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [565] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [601]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [625] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
 [637] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [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 FALSE

Or statements

To get the extreme values of eyesight (1 and 5), we would do something like:

extreme_eyes <- filter(nlsy, eyesight == 1 | 
                         eyesight == 5)
count(extreme_eyes, eyesight)
# A tibble: 2 × 2
  eyesight     n
     <dbl> <int>
1        1   474
2        5    19

We could of course do the same thing with a factor variable:

nlsy <- mutate(nlsy, region_cat = factor(region, labels = c("Northeast", "North Central", "South", "West")))
some_regions <- filter(nlsy, region_cat == "Northeast" | 
                         region_cat == "South")
count(some_regions, region_cat)
# A tibble: 2 × 2
  region_cat     n
  <fct>      <int>
1 Northeast    206
2 South        411

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:

more_regions <- filter(nlsy, region_cat %in% 
                         c("South", "West", "Northeast"))

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!

7 %in% c(4, 6, 7, 10)
[1] TRUE
5 %in% c(4, 6, 7, 10)
[1] FALSE

This is just the same as writing:

7 == 4 | 7 == 6 | 7 == 7 | 7 == 10
[1] TRUE

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

!7 %in% c(4, 6, 7, 10)
[1] FALSE
!5 %in% c(4, 6, 7, 10)
[1] TRUE
northcentralers <- filter(nlsy, 
                          !region_cat %in% 
                            c("South", "West", "Northeast"))
count(northcentralers, region_cat)
# A tibble: 1 × 2
  region_cat        n
  <fct>         <int>
1 North Central   333

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 data
  • is.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 rows

Putting 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

nlsy2 <- mutate(nlsy, 
                only = case_when(nsibs == 0 ~ "yes", 
                                 .default = "no"))
nlsy3 <- select(nlsy2, id, contains("sleep"), only)
only_kids <- filter(nlsy3, only == "yes")

or all-in-one

only_kids <- filter(select(mutate(nlsy, 
only = case_when(nsibs == 0 ~ "yes",
.default = "no")), id,
contains("sleep"), only), 
only == "yes")

It’s like reading a story (or nursery rhyme!)

foo_foo <- little_bunny()
bop_on(
  scoop_up(
    hop_through(foo_foo, forest),
    field_mouse),
  head)

vs

foo_foo |> 
  hop_through(forest) |> 
  scoop_up(field_mouse) |> 
  bop_on(head)

A natural order of operations

leave_house(
  get_dressed(
    get_out_of_bed(
      wake_up(me))))
me <- wake_up(me)
me <- get_out_of_bed(me)
me <- get_dressed(me)
me <- leave_house(me)
me  |> 
  wake_up() |> 
  get_out_of_bed() |> 
  get_dressed() |> 
  leave_house()

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 rows
only_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 rows

Pipes fill first argument of the next function with what is being piped in

help(mutate)
help(select)
help(filter)

Usage

mutate(.data, ...)

select(.data, ...)

filter(.data, ...)

Pipes fill first argument of the next function with what is being piped in

nlsy2 <- mutate(nlsy, 
         only = case_when(
          nsibs == 0 ~ "yes",
          .default = "no"))
only_kids <- nlsy  |> 
  mutate(only = case_when(
    nsibs == 0 ~ "yes",
    .default = "no"))

Pipes fill first argument of the next function with what is being piped in

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 <- nlsy  |> 
  mutate(only = case_when(
     nsibs == 0 ~ "yes",
     .default = "no")) |> 
  select(id,
         contains("sleep"),
         only)  |> 
  filter(only == "yes")

Exercises

Today’s summary

  • select() lets you choose the variables you want
  • filter() lets you choose the rows you want
  • mutate() 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 for select()
  • is.na(), is.finite(), is.infinite(), is.factor(): helpers for filter()
  • %in%: for multiple == or != possibilities