# Iteration

library(tidyverse)
library(rcis)
library(palmerpenguins)
library(modeldata)

set.seed(1234)
theme_set(theme_minimal())


Run the code below in your console to download this exercise as a set of R scripts.

usethis::use_course("cis-ds/vectors-and-iteration")


## Writing for loops

Functions are one method of reducing duplication in your code. Another tool for reducing duplication is iteration, which lets you do the same thing to multiple inputs.

## Example for loop

df <- tibble(
a = rnorm(10),
b = rnorm(10),
c = rnorm(10),
d = rnorm(10)
)


Let’s say we want to compute the median for each column.

median(df$a)  ## [1] -0.5555419  median(df$b)

## [1] -0.4941011

median(df$c)  ## [1] -0.4656169  median(df$d)

## [1] -0.605349


Boo! We’ve copied-and-pasted median() three times. We don’t want to do that. Instead, we can use a for loop:

output <- vector(mode = "double", length = ncol(df))
for (i in seq_along(df)) {
output[[i]] <- median(df[[i]])
}
output

## [1] -0.5555419 -0.4941011 -0.4656169 -0.6053490


Let’s review the three components of every for loop.

## Output

output <- vector("double", length = ncol(df))

Before you start a loop, you need to create an empty vector to store the output of the loop. Preallocating space for your output is much more efficient than creating space as you move through the loop. The vector() function allows you to create an empty vector of any type. The first argument mode defines the type of vector (“logical”, “integer”, “double”, “character”, etc.) and the second argument length defines the length of the vector.

Numeric vectors are initialized to 0, logical vectors are initialized to FALSE, character vectors are initialized to "", and list elements to NULL.

vector(mode = "double", length = ncol(df))

## [1] 0 0 0 0

vector(mode = "logical", length = ncol(df))

## [1] FALSE FALSE FALSE FALSE

vector(mode = "character", length = ncol(df))

## [1] "" "" "" ""

vector(mode = "list", length = ncol(df))

## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL
##
## [[4]]
## NULL


## Sequence

i in seq_along(df)

This component determines what to loop over. During each iteration through the for loop, a new value will be assigned to i based on the the defined sequence. Here, the sequence is seq_along(df) which creates a numeric vector for a sequence of numbers beginning at 1 and continuing until it reaches the length of df (the length here is the number of columns in df).

seq_along(df)

## [1] 1 2 3 4


## Body

output[[i]] <- median(df[[i]])

This is the code that actually performs the desired calculations. It runs multiple times for every value of i. We use [[ notation to reference each column of df and store it in the appropriate element in output.

## Why we preallocate space for the output

If you don’t preallocate space for the output, each time the for loop iterates, it makes a copy of the output and appends the new value at the end. Copying data takes time and memory. If the output is preallocated space, the loop simply fills in the slots with the correct values.

Consider the following task: duplicate the data frame palmerpenguins::penguins 100 times and bind them together into a single data frame. We can accomplish the latter task using bind_rows(), and use a for loop to create 100 copies of penguins. What is the difference if we preallocate space for the output as opposed to just copying and extending the data frame each time?

# no preallocation
penguins_no_preall <- tibble()

for(i in 1:100){
penguins_no_preall <- bind_rows(penguins_no_preall, penguins)
}

# with preallocation using a list
penguins_preall <- vector(mode = "list", length = 100)

for(i in 1:100){
penguins_preall[[i]] <- penguins
}

penguins_preall <- bind_rows(penguins_preall)


Let’s compare the time it takes to complete each of these loops by replicating each example 100 times and measuring how long it takes for the expression to evaluate.

## Warning in microbenchmark(No preallocation = {: less accurate nanosecond times
## to avoid potential integer overflows


Here, preallocating space for each data frame prior to binding together cuts the computation time by a factor of 10.

## Exercise: write a for loop

### Mean of columns in car_prices

Write a for loop that calculates the arithmetic mean for every column in modeldata::car_prices.

data("car_prices", package = "modeldata")
car_prices

## # A tibble: 804 × 18
##     Price Mileage Cylin…¹ Doors Cruise Sound Leather Buick Cadil…² Chevy Pontiac
##     <dbl>   <int>   <int> <int>  <int> <int>   <int> <int>   <int> <int>   <int>
##  1 22661.   20105       6     4      1     0       0     1       0     0       0
##  2 21725.   13457       6     2      1     1       0     0       0     1       0
##  3 29143.   31655       4     2      1     1       1     0       0     0       0
##  4 30732.   22479       4     2      1     0       0     0       0     0       0
##  5 33359.   17590       4     2      1     1       1     0       0     0       0
##  6 30315.   23635       4     2      1     0       0     0       0     0       0
##  7 33382.   17381       4     2      1     1       1     0       0     0       0
##  8 30251.   27558       4     2      1     0       1     0       0     0       0
##  9 30167.   25049       4     2      1     0       0     0       0     0       0
## 10 27060.   17319       4     4      1     0       1     0       0     0       0
## # … with 794 more rows, 7 more variables: Saab <int>, Saturn <int>,
## #   convertible <int>, coupe <int>, hatchback <int>, sedan <int>, wagon <int>,
## #   and abbreviated variable names ¹​Cylinder, ²​Cadillac
## # ℹ Use print(n = ...) to see more rows, and colnames() to see all variable names


Before you write the for loop, identify the three components you need:

• Output
• Sequence
• Body
Click for the solution

• Output - a numeric vector of length 18
• Sequence - i in seq_along(car_prices)
• Body - calculate the mean() of the $i$th column, store the new value as the $i$th element of the vector output
# preallocate space for the output
output <- vector("numeric", ncol(car_prices))

# initialize the loop along all the columns of car_prices
for (i in seq_along(car_prices)) {
# calculate the mean value for the i-th column
output[[i]] <- mean(car_prices[[i]], na.rm = TRUE)
}

output

##  [1] 2.134314e+04 1.983193e+04 5.268657e+00 3.527363e+00 7.524876e-01
##  [6] 6.791045e-01 7.238806e-01 9.950249e-02 9.950249e-02 3.980100e-01
## [11] 1.865672e-01 1.417910e-01 7.462687e-02 6.218905e-02 1.741294e-01
## [16] 7.462687e-02 6.094527e-01 7.960199e-02


### Maximum value in each column of car_prices

Write a for loop that calculates the maximum value in each column of car_prices.

Before you write the for loop, identify the three components you need:

• Output
• Sequence
• Body
Click for the solution

• Output - a vector of length 18
• Sequence - i in seq_along(car_prices)
• Body - get the maximum value of the $i$th column of the data frame car_prices, store the new value as the $i$th element of the list output
# preallocate space for the output
output <- vector("numeric", ncol(car_prices))

# initialize the loop along all the columns of car_prices
for (i in seq_along(car_prices)) {
# calculate the max value for the i-th column
output[i] <- max(car_prices[[i]])
}

output

##  [1] 70755.47 50387.00     8.00     4.00     1.00     1.00     1.00     1.00
##  [9]     1.00     1.00     1.00     1.00     1.00     1.00     1.00     1.00
## [17]     1.00     1.00


To preserve the name attributes from car_prices, use the names() function to extract the names of each column in car_prices and apply them as the names to the output vector:

# get the names of the columns in car_prices
names(car_prices)

##  [1] "Price"       "Mileage"     "Cylinder"    "Doors"       "Cruise"
##  [6] "Sound"       "Leather"     "Buick"       "Cadillac"    "Chevy"
## [11] "Pontiac"     "Saab"        "Saturn"      "convertible" "coupe"
## [16] "hatchback"   "sedan"       "wagon"

# assign the names of the car_prices columns to output
names(output) <- names(car_prices)
output

##       Price     Mileage    Cylinder       Doors      Cruise       Sound
##    70755.47    50387.00        8.00        4.00        1.00        1.00
##     Leather       Buick    Cadillac       Chevy     Pontiac        Saab
##        1.00        1.00        1.00        1.00        1.00        1.00
##      Saturn convertible       coupe   hatchback       sedan       wagon
##        1.00        1.00        1.00        1.00        1.00        1.00


## Map functions

You will frequently need to iterate over vectors or data frames, perform an operation on each element, and save the results somewhere. for loops are not the devil. At first, they may seem more intuitive to use because you are explicitly identifying each component of the process. However the downside is that they focus on a lot of non-essential stuff. You have to track the value on which you are iterating, you need to explicitly create a vector to store the output, you have to assign the output of each iteration to the appropriate element in the output vector, etc.

tidyverse is all about focusing on verbs, not nouns. That is, focus on the operation being performed (e.g. mean(), median(), max()), not all the extra code needed to make the operation work. The purrr library provides a family of functions that mirrors for loops. They:

• Loop over a vector
• Do something to each element
• Save the results

There is one function for each type of output:

• map() makes a list.
• map_lgl() makes a logical vector.
• map_int() makes an integer vector.
• map_dbl() makes a double vector.
• map_chr() makes a character vector.
map_dbl(df, mean)

##          a          b          c          d
## -0.3831574 -0.1181707 -0.3879468 -0.7661931

map_dbl(df, median)

##          a          b          c          d
## -0.5555419 -0.4941011 -0.4656169 -0.6053490

map_dbl(df, sd)

##         a         b         c         d
## 0.9957875 1.0673376 0.6660013 0.8942458


Just like all of our functions in the tidyverse, the first argument is always the data object, and the second argument is the function to be applied. Additional arguments for the function to be applied can be specified like this:

map_dbl(df, mean, na.rm = TRUE)

##          a          b          c          d
## -0.3831574 -0.1181707 -0.3879468 -0.7661931


Or data can be piped:

df %>%
map_dbl(mean, na.rm = TRUE)

##          a          b          c          d
## -0.3831574 -0.1181707 -0.3879468 -0.7661931


## Exercise: rewrite our for loops using a map() function

### Mean of columns in car_prices

Write a map() function that calculates the arithmetic mean for every column in car_prices.

car_prices

## # A tibble: 804 × 18
##     Price Mileage Cylin…¹ Doors Cruise Sound Leather Buick Cadil…² Chevy Pontiac
##     <dbl>   <int>   <int> <int>  <int> <int>   <int> <int>   <int> <int>   <int>
##  1 22661.   20105       6     4      1     0       0     1       0     0       0
##  2 21725.   13457       6     2      1     1       0     0       0     1       0
##  3 29143.   31655       4     2      1     1       1     0       0     0       0
##  4 30732.   22479       4     2      1     0       0     0       0     0       0
##  5 33359.   17590       4     2      1     1       1     0       0     0       0
##  6 30315.   23635       4     2      1     0       0     0       0     0       0
##  7 33382.   17381       4     2      1     1       1     0       0     0       0
##  8 30251.   27558       4     2      1     0       1     0       0     0       0
##  9 30167.   25049       4     2      1     0       0     0       0     0       0
## 10 27060.   17319       4     4      1     0       1     0       0     0       0
## # … with 794 more rows, 7 more variables: Saab <int>, Saturn <int>,
## #   convertible <int>, coupe <int>, hatchback <int>, sedan <int>, wagon <int>,
## #   and abbreviated variable names ¹​Cylinder, ²​Cadillac
## # ℹ Use print(n = ...) to see more rows, and colnames() to see all variable names

Click for the solution

map_dbl(car_prices, mean)

##        Price      Mileage     Cylinder        Doors       Cruise        Sound
## 2.134314e+04 1.983193e+04 5.268657e+00 3.527363e+00 7.524876e-01 6.791045e-01
##      Leather        Buick     Cadillac        Chevy      Pontiac         Saab
## 7.238806e-01 9.950249e-02 9.950249e-02 3.980100e-01 1.865672e-01 1.417910e-01
##       Saturn  convertible        coupe    hatchback        sedan        wagon
## 7.462687e-02 6.218905e-02 1.741294e-01 7.462687e-02 6.094527e-01 7.960199e-02


### Maximum value in each column of car_prices

Write a map() function that calculates the maximum value in each column of car_prices.

Click for the solution

map_dbl(car_prices, max)

##       Price     Mileage    Cylinder       Doors      Cruise       Sound
##    70755.47    50387.00        8.00        4.00        1.00        1.00
##     Leather       Buick    Cadillac       Chevy     Pontiac        Saab
##        1.00        1.00        1.00        1.00        1.00        1.00
##      Saturn convertible       coupe   hatchback       sedan       wagon
##        1.00        1.00        1.00        1.00        1.00        1.00


## across()

When working with data frames, it’s often useful to perform the same operation on multiple columns. For instance, calculating the average value of each column in car_prices. If we want to calculate the average of a single column, it would be pretty simple to do so just by using tidyverse functions:

car_prices %>%
summarize(Price = mean(Price))

## # A tibble: 1 × 1
##    Price
##    <dbl>
## 1 21343.


If we want to calculate the mean for all of the columns, we would have to duplicate this code many times over:

car_prices %>%
summarize(
Price = mean(Price),
Mileage = mean(Mileage),
Cylinder = mean(Cylinder),
Doors = mean(Doors),
Cruise = mean(Cruise),
Sound = mean(Sound),
Leather = mean(Leather),
Buick = mean(Buick),
Chevy = mean(Chevy),
Pontiac = mean(Pontiac),
Saab = mean(Saab),
Saturn = mean(Saturn),
convertible = mean(convertible),
coupe = mean(coupe),
hatchback = mean(hatchback),
sedan = mean(sedan),
wagon = mean(wagon)
)

## # A tibble: 1 × 18
##    Price Mileage Cylin…¹ Doors Cruise Sound Leather  Buick Cadil…² Chevy Pontiac
##    <dbl>   <dbl>   <dbl> <dbl>  <dbl> <dbl>   <dbl>  <dbl>   <dbl> <dbl>   <dbl>
## 1 21343.  19832.    5.27  3.53  0.752 0.679   0.724 0.0995  0.0995 0.398   0.187
## # … with 7 more variables: Saab <dbl>, Saturn <dbl>, convertible <dbl>,
## #   coupe <dbl>, hatchback <dbl>, sedan <dbl>, wagon <dbl>, and abbreviated
## #   variable names ¹​Cylinder, ²​Cadillac
## # ℹ Use colnames() to see all variable names


But this process is very repetitive and prone to mistakes - I cannot tell you how many typos I originally had in this code when I first wrote it.

We’ve seen how to use loops and map() functions to solve this task - let’s check out one final method: the across() function.

across() makes it easy to apply the same transformation to multiple columns, allowing you to use select() semantics inside summarize() and mutate(), and other dplyr verbs (or functions).

across() has two primary arguments:

• The first argument, .cols, selects the columns you want to operate on. It uses tidy selection (like select()) so you can pick variables by position, name, and type.
• The second argument, .fns, is a function or list of functions to apply to each column. This can also be a purrr style formula (or list of formulas) like ~ .x / 2.

Here are a couple of examples of across() in conjunction with its favorite verb, summarize():

## Summarize

### summarize(), across(), and everything()

To apply a function to each column in a tibble, use across() in conjunction with everything(). everything() is a selection helper that selects all the variables in a data frame. It should be the first argument in across().

car_prices %>%
summarize(across(.cols = everything(), .fns = mean))

## # A tibble: 1 × 18
##    Price Mileage Cylin…¹ Doors Cruise Sound Leather  Buick Cadil…² Chevy Pontiac
##    <dbl>   <dbl>   <dbl> <dbl>  <dbl> <dbl>   <dbl>  <dbl>   <dbl> <dbl>   <dbl>
## 1 21343.  19832.    5.27  3.53  0.752 0.679   0.724 0.0995  0.0995 0.398   0.187
## # … with 7 more variables: Saab <dbl>, Saturn <dbl>, convertible <dbl>,
## #   coupe <dbl>, hatchback <dbl>, sedan <dbl>, wagon <dbl>, and abbreviated
## #   variable names ¹​Cylinder, ²​Cadillac
## # ℹ Use colnames() to see all variable names


If you want to apply multiple summaries, you store the functions in a list():

car_prices %>%
summarize(across(everything(), .fns = list(min, max)))

## # A tibble: 1 × 36
##   Price_1 Price_2 Mileage_1 Mileage_2 Cylinder_1 Cylin…¹ Doors_1 Doors_2 Cruis…²
##     <dbl>   <dbl>     <int>     <int>      <int>   <int>   <int>   <int>   <int>
## 1   8639.  70755.       266     50387          4       8       2       4       0
## # … with 27 more variables: Cruise_2 <int>, Sound_1 <int>, Sound_2 <int>,
## #   Leather_1 <int>, Leather_2 <int>, Buick_1 <int>, Buick_2 <int>,
## #   Pontiac_1 <int>, Pontiac_2 <int>, Saab_1 <int>, Saab_2 <int>,
## #   Saturn_1 <int>, Saturn_2 <int>, convertible_1 <int>, convertible_2 <int>,
## #   coupe_1 <int>, coupe_2 <int>, hatchback_1 <int>, hatchback_2 <int>,
## #   sedan_1 <int>, sedan_2 <int>, wagon_1 <int>, wagon_2 <int>, and …
## # ℹ Use colnames() to see all variable names


To clearly distinguish each summarized variable, we can name them in the list:

car_prices %>%
summarize(across(everything(), .fns = list(min = min, max = max)))

## # A tibble: 1 × 36
##   Price_min Price_max Mileage_…¹ Milea…² Cylin…³ Cylin…⁴ Doors…⁵ Doors…⁶ Cruis…⁷
##       <dbl>     <dbl>      <int>   <int>   <int>   <int>   <int>   <int>   <int>
## 1     8639.    70755.        266   50387       4       8       2       4       0
## # … with 27 more variables: Cruise_max <int>, Sound_min <int>, Sound_max <int>,
## #   Leather_min <int>, Leather_max <int>, Buick_min <int>, Buick_max <int>,
## #   Pontiac_min <int>, Pontiac_max <int>, Saab_min <int>, Saab_max <int>,
## #   Saturn_min <int>, Saturn_max <int>, convertible_min <int>,
## #   convertible_max <int>, coupe_min <int>, coupe_max <int>,
## #   hatchback_min <int>, hatchback_max <int>, sedan_min <int>, …
## # ℹ Use colnames() to see all variable names


Because across() is usually used in combination with summarise() and mutate(), it does not select grouping variables in order to avoid accidentally modifying them:

car_prices %>%
group_by(Cylinder) %>%
summarize(across(everything(), .fns = mean))

## # A tibble: 3 × 18
##   Cylinder  Price Mileage Doors Cruise Sound Leather Buick Cadil…¹ Chevy Pontiac
##      <int>  <dbl>   <dbl> <dbl>  <dbl> <dbl>   <dbl> <dbl>   <dbl> <dbl>   <dbl>
## 1        4 17863.  20108.  3.44  0.599 0.698   0.746 0      0      0.457   0.127
## 2        6 20081.  19564.  3.74  0.868 0.706   0.606 0.258  0.0645 0.387   0.258
## 3        8 38968.  19575.  3.2   1     0.52    1     0      0.6    0.2     0.2
## # … with 7 more variables: Saab <dbl>, Saturn <dbl>, convertible <dbl>,
## #   coupe <dbl>, hatchback <dbl>, sedan <dbl>, wagon <dbl>, and abbreviated
## # ℹ Use colnames() to see all variable names


### summarize() and across()

As mentioned earlier, across() allows you to pick variables by position and name:

# pick by name
worldbank %>%
summarize(across(.cols = life_exp, .fns = mean))

## # A tibble: 1 × 1
##   life_exp
##      <dbl>
## 1     76.6

# by position
worldbank %>%
summarize(across(.cols = 12, .fns = mean))

## # A tibble: 1 × 1
##   life_exp
##      <dbl>
## 1     76.6


By default, the newly created columns have the shortest names needed to uniquely identify the output.

worldbank %>%
summarize(across(.cols = life_exp, .fns = list(min, max)))

## # A tibble: 1 × 2
##   life_exp_1 life_exp_2
##        <dbl>      <dbl>
## 1       67.3       82.6

worldbank %>%
summarize(across(.cols = c(life_exp, pop_growth), .fns = min))

## # A tibble: 1 × 2
##   life_exp pop_growth
##      <dbl>      <dbl>
## 1     67.3      0.479

worldbank %>%
summarize(across(.cols = -life_exp, .fns = list(min, max)))

## # A tibble: 1 × 26
##   iso3c_1 iso3c_2 date_1 date_2 iso2c_1 iso2c_2 country_1 country_2
##   <chr>   <chr>   <chr>  <chr>  <chr>   <chr>   <chr>     <chr>
## 1 ARG     USA     2005   2017   AR      US      Argentina United States
## # … with 18 more variables: perc_energy_fosfuel_1 <dbl>,
## #   perc_energy_fosfuel_2 <dbl>, rnd_gdpshare_1 <dbl>, rnd_gdpshare_2 <dbl>,
## #   real_netinc_percap_1 <dbl>, real_netinc_percap_2 <dbl>, gdp_capita_1 <dbl>,
## #   gdp_capita_2 <dbl>, top10perc_incshare_1 <dbl>, top10perc_incshare_2 <dbl>,
## #   employment_ratio_1 <dbl>, employment_ratio_2 <dbl>, pop_growth_1 <dbl>,
## #   pop_growth_2 <dbl>, pop_1 <dbl>, pop_2 <dbl>


### summarize(), across(), and where()

To pick variables to summarize based on type, use across() in conjunction with where(). where() is another selection helper that allows you to pick variables based on a predicate function like is.numeric() or is.character(). For example, if you want to apply a numeric summary function only to numeric columns:

worldbank %>%
group_by(country) %>%
summarize(across(.cols = where(is.numeric), .fns = mean, na.rm = TRUE))

## # A tibble: 6 × 11
##   country        perc_…¹ rnd_g…² percg…³ real_…⁴ gdp_c…⁵ top10…⁶ emplo…⁷ life_…⁸
##   <chr>            <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 Argentina         89.1  0.501     17.5   8560.  10648.    31.6    55.4    75.4
## 2 China             87.6  1.67      48.3   3661.   5397.    30.8    69.8    74.7
## 3 Indonesia         65.3  0.0841    30.5   2041.   2881.    31.2    62.5    69.5
## 4 Norway            58.9  1.60      37.2  70775.  85622.    21.9    67.3    81.3
## 5 United Kingdom    86.3  1.68      13.5  34542.  43416.    26.2    58.7    80.4
## 6 United States     84.2  2.69      17.6  42824.  51285.    30.1    60.2    78.4
## # … with 2 more variables: pop_growth <dbl>, pop <dbl>, and abbreviated
## #   variable names ¹​perc_energy_fosfuel, ²​rnd_gdpshare,
## #   ⁶​top10perc_incshare, ⁷​employment_ratio, ⁸​life_exp
## # ℹ Use colnames() to see all variable names


(Note that na.rm = TRUE is passed on to mean() in the same way as in purrr::map().)

across() also allows you to create compound selections. For example, you can now transform all numeric columns whose name begins with “perc”:

worldbank %>%
group_by(country) %>%
summarize(across(
.cols = where(is.numeric) & starts_with("perc"),
.fn = mean, na.rm = TRUE
))

## # A tibble: 6 × 3
##   <chr>                        <dbl>                     <dbl>
## 1 Argentina                     89.1                      17.5
## 2 China                         87.6                      48.3
## 3 Indonesia                     65.3                      30.5
## 4 Norway                        58.9                      37.2
## 5 United Kingdom                86.3                      13.5
## 6 United States                 84.2                      17.6


## Mutate

Combinations of mutate() and across() work in a similar way to their summarize equivalents.

car_prices %>%
mutate(across(.cols = Price:Doors, .fns = log10))

## # A tibble: 804 × 18
##    Price Mileage Cylinder Doors Cruise Sound Leather Buick Cadil…¹ Chevy Pontiac
##    <dbl>   <dbl>    <dbl> <dbl>  <int> <int>   <int> <int>   <int> <int>   <int>
##  1  4.36    4.30    0.778 0.602      1     0       0     1       0     0       0
##  2  4.34    4.13    0.778 0.301      1     1       0     0       0     1       0
##  3  4.46    4.50    0.602 0.301      1     1       1     0       0     0       0
##  4  4.49    4.35    0.602 0.301      1     0       0     0       0     0       0
##  5  4.52    4.25    0.602 0.301      1     1       1     0       0     0       0
##  6  4.48    4.37    0.602 0.301      1     0       0     0       0     0       0
##  7  4.52    4.24    0.602 0.301      1     1       1     0       0     0       0
##  8  4.48    4.44    0.602 0.301      1     0       1     0       0     0       0
##  9  4.48    4.40    0.602 0.301      1     0       0     0       0     0       0
## 10  4.43    4.24    0.602 0.602      1     0       1     0       0     0       0
## # … with 794 more rows, 7 more variables: Saab <int>, Saturn <int>,
## #   convertible <int>, coupe <int>, hatchback <int>, sedan <int>, wagon <int>,
## #   and abbreviated variable name ¹​Cadillac
## # ℹ Use print(n = ...) to see more rows, and colnames() to see all variable names


## Filter

We cannot directly use across() in filter() because we need an extra step to combine the results. To that end, filter() has two special purpose companion functions:

• if_any() keeps the rows where the predicate is true for at least one selected column:

worldbank %>%
filter(if_any(everything(), ~ !is.na(.x)))

## # A tibble: 78 × 14
##    iso3c date  iso2c country   perc_en…¹ rnd_g…² percg…³ real_…⁴ gdp_c…⁵ top10…⁶
##    <chr> <chr> <chr> <chr>         <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 ARG   2005  AR    Argentina      89.1   0.379    15.5   6198.   5110.    35
##  2 ARG   2006  AR    Argentina      88.7   0.400    22.1   7388.   5919.    33.9
##  3 ARG   2007  AR    Argentina      89.2   0.402    22.8   8182.   7245.    33.8
##  4 ARG   2008  AR    Argentina      90.7   0.421    21.6   8576.   9021.    32.5
##  5 ARG   2009  AR    Argentina      89.6   0.519    18.9   7904.   8225.    31.4
##  6 ARG   2010  AR    Argentina      89.5   0.518    17.9   8803.  10386.    32
##  7 ARG   2011  AR    Argentina      88.9   0.537    17.9   9528.  12849.    31
##  8 ARG   2012  AR    Argentina      89.0   0.609    16.5   9301.  13083.    29.7
##  9 ARG   2013  AR    Argentina      89.0   0.612    15.3   9367.  13080.    29.4
## 10 ARG   2014  AR    Argentina      87.7   0.613    16.1   8903.  12335.    29.9
## # … with 68 more rows, 4 more variables: employment_ratio <dbl>,
## #   life_exp <dbl>, pop_growth <dbl>, pop <dbl>, and abbreviated variable names
## #   ⁴​real_netinc_percap, ⁵​gdp_capita, ⁶​top10perc_incshare
## # ℹ Use print(n = ...) to see more rows, and colnames() to see all variable names

• if_all() keeps the rows where the predicate is true for all selected columns:

worldbank %>%
filter(if_all(everything(), ~ !is.na(.x)))

## # A tibble: 42 × 14
##    iso3c date  iso2c country   perc_en…¹ rnd_g…² percg…³ real_…⁴ gdp_c…⁵ top10…⁶
##    <chr> <chr> <chr> <chr>         <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 ARG   2005  AR    Argentina      89.1   0.379    15.5   6198.   5110.    35
##  2 ARG   2006  AR    Argentina      88.7   0.400    22.1   7388.   5919.    33.9
##  3 ARG   2007  AR    Argentina      89.2   0.402    22.8   8182.   7245.    33.8
##  4 ARG   2008  AR    Argentina      90.7   0.421    21.6   8576.   9021.    32.5
##  5 ARG   2009  AR    Argentina      89.6   0.519    18.9   7904.   8225.    31.4
##  6 ARG   2010  AR    Argentina      89.5   0.518    17.9   8803.  10386.    32
##  7 ARG   2011  AR    Argentina      88.9   0.537    17.9   9528.  12849.    31
##  8 ARG   2012  AR    Argentina      89.0   0.609    16.5   9301.  13083.    29.7
##  9 ARG   2013  AR    Argentina      89.0   0.612    15.3   9367.  13080.    29.4
## 10 ARG   2014  AR    Argentina      87.7   0.613    16.1   8903.  12335.    29.9
## # … with 32 more rows, 4 more variables: employment_ratio <dbl>,
## #   life_exp <dbl>, pop_growth <dbl>, pop <dbl>, and abbreviated variable names
## #   ⁴​real_netinc_percap, ⁵​gdp_capita, ⁶​top10perc_incshare
## # ℹ Use print(n = ...) to see more rows, and colnames() to see all variable names


## Exercise: iterate over columns using across()

### Rescale numeric variables

Use across() to rescale all numeric variables in rcis::worldbank to range between 0 and 1. You can reuse the function from R for Data Science.

rescale01 <- function(x) {
rng <- range(x, na.rm = TRUE)
(x - rng[1]) / (rng[2] - rng[1])
}

Click for the solution

worldbank %>%
mutate(across(.cols = where(is.numeric), .fns = rescale01))

## # A tibble: 78 × 14
##    iso3c date  iso2c country   perc_en…¹ rnd_g…² percg…³ real_…⁴ gdp_c…⁵ top10…⁶
##    <chr> <chr> <chr> <chr>         <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 ARG   2005  AR    Argentina     0.955   0.108  0.0944  0.0938  0.0378   1
##  2 ARG   2006  AR    Argentina     0.944   0.116  0.262   0.110   0.0458   0.924
##  3 ARG   2007  AR    Argentina     0.960   0.116  0.279   0.121   0.0589   0.917
##  4 ARG   2008  AR    Argentina     1       0.124  0.249   0.126   0.0763   0.828
##  5 ARG   2009  AR    Argentina     0.971   0.159  0.180   0.117   0.0685   0.752
##  6 ARG   2010  AR    Argentina     0.968   0.159  0.156   0.129   0.0897   0.793
##  7 ARG   2011  AR    Argentina     0.950   0.166  0.154   0.139   0.114    0.724
##  8 ARG   2012  AR    Argentina     0.954   0.192  0.120   0.136   0.116    0.634
##  9 ARG   2013  AR    Argentina     0.953   0.193  0.0907  0.137   0.116    0.614
## 10 ARG   2014  AR    Argentina     0.918   0.194  0.110   0.130   0.109    0.648
## # … with 68 more rows, 4 more variables: employment_ratio <dbl>,
## #   life_exp <dbl>, pop_growth <dbl>, pop <dbl>, and abbreviated variable names
## #   ⁴​real_netinc_percap, ⁵​gdp_capita, ⁶​top10perc_incshare
## # ℹ Use print(n = ...) to see more rows, and colnames() to see all variable names


## Session Info

sessioninfo::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.2.1 (2022-06-23)
##  os       macOS Monterey 12.3
##  system   aarch64, darwin20
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2022-08-30
##  pandoc   2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package        * version    date (UTC) lib source
##  assertthat       0.2.1      2019-03-21 [2] CRAN (R 4.2.0)
##  backports        1.4.1      2021-12-13 [2] CRAN (R 4.2.0)
##  blogdown         1.10       2022-05-10 [2] CRAN (R 4.2.0)
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##  broom            1.0.0      2022-07-01 [2] CRAN (R 4.2.0)
##  bslib            0.4.0      2022-07-16 [2] CRAN (R 4.2.0)
##  cachem           1.0.6      2021-08-19 [2] CRAN (R 4.2.0)
##  cellranger       1.1.0      2016-07-27 [2] CRAN (R 4.2.0)
##  cli              3.3.0      2022-04-25 [2] CRAN (R 4.2.0)
##  codetools        0.2-18     2020-11-04 [2] CRAN (R 4.2.1)
##  colorspace       2.0-3      2022-02-21 [2] CRAN (R 4.2.0)
##  crayon           1.5.1      2022-03-26 [2] CRAN (R 4.2.0)
##  DBI              1.1.3      2022-06-18 [2] CRAN (R 4.2.0)
##  dbplyr           2.2.1      2022-06-27 [2] CRAN (R 4.2.0)
##  digest           0.6.29     2021-12-01 [2] CRAN (R 4.2.0)
##  dplyr          * 1.0.9      2022-04-28 [2] CRAN (R 4.2.0)
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##  fastmap          1.1.0      2021-01-25 [2] CRAN (R 4.2.0)
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##  fs               1.5.2      2021-12-08 [2] CRAN (R 4.2.0)
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##  ggplot2        * 3.3.6      2022-05-03 [2] CRAN (R 4.2.0)
##  glue             1.6.2      2022-02-24 [2] CRAN (R 4.2.0)
##  googledrive      2.0.0      2021-07-08 [2] CRAN (R 4.2.0)
##  googlesheets4    1.0.0      2021-07-21 [2] CRAN (R 4.2.0)
##  gtable           0.3.0      2019-03-25 [2] CRAN (R 4.2.0)
##  haven            2.5.0      2022-04-15 [2] CRAN (R 4.2.0)
##  here             1.0.1      2020-12-13 [2] CRAN (R 4.2.0)
##  hms              1.1.1      2021-09-26 [2] CRAN (R 4.2.0)
##  htmltools        0.5.3      2022-07-18 [2] CRAN (R 4.2.0)
##  httr             1.4.3      2022-05-04 [2] CRAN (R 4.2.0)
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##  jsonlite         1.8.0      2022-02-22 [2] CRAN (R 4.2.0)
##  knitr            1.39       2022-04-26 [2] CRAN (R 4.2.0)
##  lifecycle        1.0.1      2021-09-24 [2] CRAN (R 4.2.0)
##  lubridate        1.8.0      2021-10-07 [2] CRAN (R 4.2.0)
##  magrittr         2.0.3      2022-03-30 [2] CRAN (R 4.2.0)
##  microbenchmark * 1.4.9      2021-11-09 [2] CRAN (R 4.2.0)
##  modeldata      * 1.0.0      2022-07-01 [2] CRAN (R 4.2.0)
##  modelr           0.1.8      2020-05-19 [2] CRAN (R 4.2.0)
##  munsell          0.5.0      2018-06-12 [2] CRAN (R 4.2.0)
##  palmerpenguins * 0.1.0      2020-07-23 [2] CRAN (R 4.2.0)
##  pillar           1.8.0      2022-07-18 [2] CRAN (R 4.2.0)
##  pkgconfig        2.0.3      2019-09-22 [2] CRAN (R 4.2.0)
##  purrr          * 0.3.4      2020-04-17 [2] CRAN (R 4.2.0)
##  R6               2.5.1      2021-08-19 [2] CRAN (R 4.2.0)
##  rcfss          * 0.2.5      2022-08-04 [2] local
##  rcis           * 0.2.5      2022-08-08 [2] local
##  readr          * 2.1.2      2022-01-30 [2] CRAN (R 4.2.0)
##  readxl           1.4.0      2022-03-28 [2] CRAN (R 4.2.0)
##  reprex           2.0.1.9000 2022-08-10 [1] Github (tidyverse/reprex@6d3ad07)
##  rlang            1.0.4      2022-07-12 [2] CRAN (R 4.2.0)
##  rmarkdown        2.14       2022-04-25 [2] CRAN (R 4.2.0)
##  rprojroot        2.0.3      2022-04-02 [2] CRAN (R 4.2.0)
##  rstudioapi       0.13       2020-11-12 [2] CRAN (R 4.2.0)
##  rvest            1.0.2      2021-10-16 [2] CRAN (R 4.2.0)
##  sass             0.4.2      2022-07-16 [2] CRAN (R 4.2.0)
##  scales           1.2.0      2022-04-13 [2] CRAN (R 4.2.0)
##  sessioninfo      1.2.2      2021-12-06 [2] CRAN (R 4.2.0)
##  stringi          1.7.8      2022-07-11 [2] CRAN (R 4.2.0)
##  stringr        * 1.4.0      2019-02-10 [2] CRAN (R 4.2.0)
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##  tidyr          * 1.2.0      2022-02-01 [2] CRAN (R 4.2.0)
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##  vctrs            0.4.1      2022-04-13 [2] CRAN (R 4.2.0)
##  withr            2.5.0      2022-03-03 [2] CRAN (R 4.2.0)
##  xfun             0.31       2022-05-10 [1] CRAN (R 4.2.0)
##  xml2             1.3.3      2021-11-30 [2] CRAN (R 4.2.0)
##  yaml             2.3.5      2022-02-21 [2] CRAN (R 4.2.0)
##
##  [1] /Users/soltoffbc/Library/R/arm64/4.2/library
##  [2] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
##
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