More iteration use cases that can come in handy

ENST/MRNE 222 Environmental Data Analysis and Visualization

Before we begin

Compare your responses to ae-12-iteration to the solutions file, now available on GitHub: https://github.com/MRNE222-sp25-activities/ae-12-iteraton/blob/main/ae-12-iteration_solutions.md

Recall last time…

We used purrr::map() to iteratively read in multiple .csv files:

list.files("data/no_sitenames", full.names = TRUE) |> 
  map(read_csv) |> 
  list_rbind()

You can use the map() family of functions to apply any function iteratively over your data

purrr::map() function

Usage:

map(.x, .f, ...)

• .x: The input object. Can be:

  • a vector, in which case the iteration is done over each entry of the vector
  • a list, where iteration is done over each element of the list
  • a data frame, where the iteration is done over the columns of the data frame

• .f: a function that is applied to each element of .x

purrr::map() function

• map() executes a function (.f) for each element of your input data (.x)
• A list is always returned -
• Since the first argument is always the data (.x), map() functions work well with piping (|>)

A trivial example to show you how map works

The data:

x <- c(3, 5, 9)

x

[1] 3 5 9

The function:

add10 <- function(x) x + 10

Usage: map(.x, .f, ...)

x <- c(3, 5, 9)

map() without piping

map(x, add10)

[[1]]
[1] 13

[[2]]
[1] 15

[[3]]
[1] 19

map() with piping

x |> 
  map(add10)

[[1]]
[1] 13

[[2]]
[1] 15

[[3]]
[1] 19

How to map() columns of a data frame

Usage: map(.x, .f, ...)

The add10() function will be applied to each column.

x <- tibble(a = c(1, 5, 8), 
            b = c(5, 8, 1), 
            c = c(6, 9, 2))
x

# A tibble: 3 × 3
      a     b     c
  <dbl> <dbl> <dbl>
1     1     5     6
2     5     8     9
3     8     1     2

The output is a list, where each element is the output of add10() applied to each column.

x |> 
  map(add10)

$a
[1] 11 15 18

$b
[1] 15 18 11

$c
[1] 16 19 12

map() elements of a list

Usage: map(.x, .f, ...)

The add10() function will be applied to each element of the list.

x <- list(a = 1:4,
          b = 4:7,
          c = 6:9)
x

$a
[1] 1 2 3 4

$b
[1] 4 5 6 7

$c
[1] 6 7 8 9

In the resulting list, each element is the output of add10() applied to each element of the list, x.

x |> 
  map(add10)

$a
[1] 11 12 13 14

$b
[1] 14 15 16 17

$c
[1] 16 17 18 19

Other map functions are available if you want the output to be something besides a list

Map to numeric vector:

x <- c(4, 7, 1, 6)

x |> 
  map_dbl(add10)

[1] 14 17 11 16

Map to character vector:

x <- c(4, 7, 1, 6)

x |> 
  map_chr(add10)

[1] "14.000000" "17.000000" "11.000000" "16.000000"

Other map functions are available if you want the output to be something besides a list

Map to dataframe:

map_dfr(c(4, 7, 1, 6), \(x){
  return(tibble(old_number = x,
                new_number = add10(x)))
})

# A tibble: 4 × 2
  old_number new_number
       <dbl>      <dbl>
1          4         14
2          7         17
3          1         11
4          6         16

Recall \(x) is and anonymous function–it does the same thing as function(x)

map() use case: Plot all variables in a dataframe for preliminary exploratory analysis

Plot all variables in mtcars for preliminary exploratory analysis

Have: mtcars dataset

mtcars

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Want: A density plot for each column (variable) to explore data distributions

map() over columns to iteratively create plots

• If map() is supplied with a dataframe, it will iterate over each column.
• We need to write a function that takes each column (aka variable) as the argument.

density_plot <- function(variable){
  ggplot(mtcars, aes(x = variable)) +
    geom_density()
}

plot_list <- mtcars |> 
  map(density_plot)

plot_list[[1]]

Make it more concise with an anonymous function

plot_list <- mtcars |> 
  map(\(col) ggplot(mtcars, aes(x = col)) +
        geom_density() +
        theme(text = element_text(size = 15)))

plot_list[[1]]

Use patchwork::wrap_plots() to view all plots in plot_list

wrap_plots(plot_list)

The problem is we don’t know what variables are mapped to the x-axes 😩

map2() when you need to use a function that takes two arguments

Use map2() when you need to use a function that takes two arguments

Usage:

map2(.x, .y, .f)

• .x, .y A pair of vectors, usually the same length

• .f A function that is takes .x and .y as arguments

Here, we need a function that takes column (variable) and label (lab) as arguments so we can properly label our x-axes

density_plot2 <- function(variable, lab){
  ggplot(mtcars, aes(x = variable)) +
    geom_density() +
        labs(x = lab) +
    theme(text = element_text(size = 15))
}

How do we get a vector of variable names?

• The names() function takes a data frame as input and outputs a vector of column names.
• The column names are the names of the variables we will be plotting!

labels <- names(mtcars) 

labels 

 [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
[11] "carb"

Use map2() to execute the density_plot2() function

plot_list <- mtcars |> 
  map2(.y = labels, .f = density_plot2)

• The mtcars data frame is piped down as the .x argument
• Since we are mapping, each column is supplied to our density_plot2() function as the variable argument
• .y is the labels vector we created above.
• .f is the density_plot2() function.
• map2 takes each element of .x and .y and iteratively applies the plotting function to each .x and .y combination.

Use map2() to execute the density_plot2() function

plot_list <- mtcars |> 
  map2(.y = labels, .f = density_plot2)

Now we have an informative x-axis label!

plot_list[[1]]

More map() use cases

Want: using mpg dataset, plot highway mpg vs displacement, with points highlighted for each manufacturer

mpg

# A tibble: 234 × 11
   manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
   <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
 1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
 2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
 3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
 4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
 5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
 6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
 7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
 8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
 9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
# ℹ 224 more rows

Want: using mpg dataset, plot highway mpg vs displacement, with points highlighted for each manufacturer

ggplot(mpg, aes(x = hwy, y = displ)) +
  geom_point() +
  gghighlight(manufacturer == "audi") +
  labs(title = "Audi")

We could facet, but with 15 manufacturers the panels are hard to read and we can’t use the gghighlight function

ggplot(mpg, aes(x = hwy, y = displ)) +
  geom_point() +
  facet_wrap(~manufacturer)

1. Create a function to make a plot for each manufacturer

mpg_plot <- function(mfr){
  ggplot(mpg, aes(x = hwy, y = displ)) +
  geom_point() +
  gghighlight(manufacturer == {{mfr}}) +
  labs(title = {{mfr}})
}

• The only thing that changes is manufacturer (mfr)
• Remember to “embrace” when you want R to use the value supplied by the mfr argument and not literally mfr

2. Create vector of manufacturers (makes) to iterate over

distinct() finds unique values of the manufacturer column in the mpg dataset. We need to use the unlist() function to make mfrs a one-dimensional vector.

makes <- mpg |> 
  distinct(manufacturer) |> 
  unlist()

makes

 manufacturer1  manufacturer2  manufacturer3  manufacturer4  manufacturer5 
        "audi"    "chevrolet"        "dodge"         "ford"        "honda" 
 manufacturer6  manufacturer7  manufacturer8  manufacturer9 manufacturer10 
     "hyundai"         "jeep"   "land rover"      "lincoln"      "mercury" 
manufacturer11 manufacturer12 manufacturer13 manufacturer14 manufacturer15 
      "nissan"      "pontiac"       "subaru"       "toyota"   "volkswagen" 

3. Use map() to execute the function iteratively over each manufacturer. Assign the output to a list called plot_list.

plot_list <- makes |> 
  map(mpg_plot)

plot_list[[3]]

wrap_plots(plot_list)

We can also split a data frame into a list by a grouping variable using group_split() and apply a function to each element of the list.

Here, let’s create a density plot of highway mileage for each class of cars in the mpg dataset

mpg

# A tibble: 234 × 11
   manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
   <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
 1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
 2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
 3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
 4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
 5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
 6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
 7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
 8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
 9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
# ℹ 224 more rows

1. Split mpg into a list of data frames

Each data frame in the list represents a different class

data_list <- mpg |> 
  group_split(class)

data_list[[1]]

# A tibble: 5 × 11
  manufacturer model    displ  year   cyl trans    drv     cty   hwy fl    class
  <chr>        <chr>    <dbl> <int> <int> <chr>    <chr> <int> <int> <chr> <chr>
1 chevrolet    corvette   5.7  1999     8 manual(… r        16    26 p     2sea…
2 chevrolet    corvette   5.7  1999     8 auto(l4) r        15    23 p     2sea…
3 chevrolet    corvette   6.2  2008     8 manual(… r        16    26 p     2sea…
4 chevrolet    corvette   6.2  2008     8 auto(s6) r        15    25 p     2sea…
5 chevrolet    corvette   7    2008     8 manual(… r        15    24 p     2sea…

2. Write a function that takes a data frame as the argument

mpg_plot <- function(data){
  ggplot(data, aes(x = hwy)) +
        geom_density() +
        labs(title = unique(data$class))
}

• To get class labels, use unique() function.
• The data frames are grouped by class, so each data frame in data_list will only have 1 unique class value.

3. map() the function over each data frame in data_list

plot_list <- data_list |> 
  map(mpg_plot)

plot_list[[1]]

Make it more efficient with an anonymous function

max_hwy <- max(mpg$hwy) # get max hwy value
min_hwy <- min(mpg$hwy) #get min hwy value

plot_list <- mpg |> 
  group_split(class) |> 
  map(\(data) ggplot(data, aes(x = hwy)) +
        geom_density() +
        labs(title = unique(data$class)) +
        xlim(min_hwy, max_hwy)) #scale x axes so each plot has same min and max

Check to make sure it worked

plot_list[[1]]

map() wrapup

• Think carefully about what you want to do:

  • What element(s) of your data do you want to iterate over?
  • Make sure the function you write takes this element as an argument
  • The output of map() is a list, but other map functions have different outputs

• There are many other use cases for map() functions!

  • Any time you find yourself copying and pasting the same code, write a function
  • If you are still copying and pasting the code to execute your function, you can probably map() it to automate the execution