8: Functions and Lists

BSTA 526: R Programming for Health Data Science

Meike Niederhausen, PhD & Jessica Minnier, PhD

OHSU-PSU School of Public Health

2026-02-26

1 Welcome to R Programming: Part 8!

Today we will learn more about customizing tables, adding and reordering factors, and ggplot.

Before you get started:

Remember to save this notebook under a new name, such as part_08_b526_YOURNAME.qmd.

• Load the packages in the setup code chunk

1.1 Learning Objectives

• Learn how to write and use custom functions
• Learn about lists as a general purpose data structure
• Learn and utilize list properties
• Access list elements using $ and [[]]
• Understand the difference between homogeneous and heterogeneous lists
• Use purrr::pluck() to access list elements
• Understand how data.frames() are list-like

1.2 Load data sets

# load various data sets
data(penguins)

smoke_complete <- read_csv(here::here("part8", "data", "clinical.csv"))
lusc_data <- read_csv(here::here("part8", "data", "tumor", "LUSC.csv"))
meso_data <- read_csv(here::here("part8", "data", "tumor", "MESO.csv"))
prad_data <- read_csv(here::here("part8", "data", "tumor", "PRAD.csv"))
read_data <- read_csv(here::here("part8", "data", "tumor", "READ.csv"))

2 Motivation

Today: building blocks for truly automating your data wrangling or analysis work.

This is the true power of learning how to program!

2.1 Custom Functions & Lists

At first these two sections will seem disjointed:

• Functions
  • Create your own functions
  • Use for data wrangling or analyses or visualization, anything
• Lists
  • An object type in R that can be very useful
  • Spoiler alert: data frames can actually behave like a list

• Next week we will use them together with “Functional Programming Tools”
  • such as purrr::map() type functions to iterate a function
  • over a list or over rows of a data frame, for example.

2.2 Custom Functions

Please read the Functions chapter in R4DS for some background! (Chapter 25)

We know what a function is in R, for instance, these are “built-in” functions in R

mean(1:10)

[1] 5.5

toupper("abc")

[1] "ABC"

But what if we want to create our own custom functions?

We have actually done this before! When we used across():

penguins %>% summarize(across(.cols = ends_with("mm"),
                              .fns = ~ mean(.x, na.rm = TRUE))) # this is a custom function, in a way

# A tibble: 1 × 3
  bill_length_mm bill_depth_mm flipper_length_mm
           <dbl>         <dbl>             <dbl>
1           43.9          17.2              201.

2.3 Motivating Example

• Here is another example for motivating today’s work.

• One dataset
• Many datasets
• Custom function
• With lists

• Let’s say we want some summary measures from our data,
  • specifically the means of days to death, age at diagnosis, and
  • the correlation of those two variables.

lusc_data %>% summarize(
  mean_cig = mean(days_to_death, na.rm = TRUE),
  mean_age = mean(age_at_diagnosis, na.rm = TRUE),
  cor_cig_age = cor(days_to_death, age_at_diagnosis, 
                    use = "complete.obs"))

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     872.   24754.     -0.0299

• Now, we have read in multiple data sets, and want to calculate this for each data set.
• We can do this separately, with copy and paste and
  • hope we don’t make a mistake, and
  • also keep track of all our outputs somehow:

lusc_data %>% summarize(
  mean_cig = mean(days_to_death, na.rm = TRUE),
  mean_age = mean(age_at_diagnosis, na.rm = TRUE),
  cor_cig_age = cor(days_to_death, age_at_diagnosis, 
                    use = "complete.obs"))

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     872.   24754.     -0.0299

meso_data %>% summarize(
  mean_cig = mean(days_to_death, na.rm = TRUE),
  mean_age = mean(age_at_diagnosis, na.rm = TRUE),
  cor_cig_age = cor(days_to_death, age_at_diagnosis, 
                    use = "complete.obs"))

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     584.   23188.      -0.206

prad_data %>% summarize(
  mean_cig = mean(days_to_death, na.rm = TRUE),
  mean_age = mean(age_at_diagnosis, na.rm = TRUE),
  cor_cig_age = cor(days_to_death, age_at_diagnosis, 
                    use = "complete.obs"))

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     1579   22480.       0.152

read_data %>% summarize(
  mean_cig = mean(days_to_death, na.rm = TRUE),
  mean_age = mean(age_at_diagnosis, na.rm = TRUE),
  cor_cig_age = cor(days_to_death, age_at_diagnosis, 
                    use = "complete.obs"))

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     786.   23705.      -0.288

• Or, we can create our own function to do this!
• We will go through the parts of a function below,
  • but this is what it would look like:

# custom function
myfun <- function(mydata){
  mydata %>% summarize(
    mean_cig = mean(days_to_death, na.rm = TRUE),
    mean_age = mean(age_at_diagnosis, na.rm = TRUE),
    cor_cig_age = cor(days_to_death, age_at_diagnosis, 
                      use = "complete.obs"))
}

# apply custom function to each of the datasets 
# (note that the output is not being saved below)
myfun(lusc_data)

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     872.   24754.     -0.0299

myfun(meso_data)

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     584.   23188.      -0.206

myfun(prad_data)

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     1579   22480.       0.152

myfun(read_data)

# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     786.   23705.      -0.288

• Later we will see that we can make a list of data frames, and
  • use purrr::map() to iterate over this list and
  • obtain all of the outputs at once.
• This is a motivating example, it will take some time to get to this point!

list_of_data <- list("LUSC" = lusc_data, 
                     "MESO" = meso_data, 
                     "PRAD" = prad_data, 
                     "READ" = read_data)

• Return a list of outputs
• Return a data frame of outputs:

# returns a list of outputs
map(list_of_data, myfun)

$LUSC
# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     872.   24754.     -0.0299

$MESO
# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     584.   23188.      -0.206

$PRAD
# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     1579   22480.       0.152

$READ
# A tibble: 1 × 3
  mean_cig mean_age cor_cig_age
     <dbl>    <dbl>       <dbl>
1     786.   23705.      -0.288

# returns a data frame of outputs
map_df(list_of_data, myfun, .id = "cancer_type")

# A tibble: 4 × 4
  cancer_type mean_cig mean_age cor_cig_age
  <chr>          <dbl>    <dbl>       <dbl>
1 LUSC            872.   24754.     -0.0299
2 MESO            584.   23188.     -0.206 
3 PRAD           1579    22480.      0.152 
4 READ            786.   23705.     -0.288 

We’ll start with learning about how to create our own functions.

3 Functions: Don’t Repeat Yourself (DRY)

3.1 Motivation

• If you do something in R once and have to do it again,
  • it’s probably best to think of how to do it well once,
  • rather than cutting and pasting again.
• Part of doing this is by using and writing functions.
  • We can take code that we might have to write 10 times in a row, and
  • package it in a way that we can use it multiple times.
• For the example above,
  • we have some computations we want to perform on specific variables in a data set, and
  • we have multiple data sets with those variables we need to do this with.
  • A function can be used to perform these computations.

3.2 Advantages over using copy-and-paste

From R for Data Science:

Writing a function has four big advantages over using copy-and-paste:

1. You can give a function an evocative name that makes your code easier to understand.
2. As requirements change, you only need to update code in one place, instead of many.
3. You eliminate the chance of making incidental mistakes when you copy and paste (i.e. updating a variable name in one place, but not in another).
4. It makes it easier to reuse work from project-to-project, increasing your productivity over time.

3.3 Creating a function

Let’s learn about how we define a function.

f <- function(<arguments>) {
  ## Do something interesting or useful
  ## Code
  ## Return an output

}

Things to ask yourself:

1. What do you want your function to do?
2. What will the output of the function be?
3. What kind of object do you want the output to be?

3.4 Example of creating a function

square_number <- function(number) {
  output_number <- number * number 
  return(output_number)
}

square_number(4)

[1] 16

• Name & arguments
• The {
• Stuff between the {....}
• return()
• Function without return()

• We define our function by
  • giving it a name, square_number, and
  • defining its arguments within the parentheses of function().
• Our example function only has one argument, number:

square_number <- function(number) {

• What about the {?
  • It defines the beginning of a code block -
    • a code block defines the code that will run when we use the function.

• Between the {....} we calculate the square of our number and
  • assign it to output_number.
• The next line uses return() to return a value from our function.
• Then we close the code block with a }.

• The return value of a function is the last expression in the function body to be evaluated.
  • i.e. return() should appear in the last line of the function.
• Note that you don’t have to use return()
  • but it makes things clearer once your functions get complicated, and
  • especially if you are returning multiple outputs.

• The function below without `return() does the same as above.

If you don’t specify the return object with return(), R will assume you meant to return the last line of code inside the function that returns a value.

square_number_again <- function(number) {
  number * number
}

square_number_again(4)

[1] 16

• However, be careful! Notice that the code below doesn’t work.
• Why? Because nothing was returned,
  • the last line does not return a value but saves an object.
• This is why it’s best to just use return()
  • so you can be sure you’re returning what you want.

square_number_again <- function(number) {
  mynumsq <- number * number
}

square_number_again(4)

# note the lack of output when running this function!

3.5 Mini-challenge (on your own)

• Make a function called cube_number that returns the cube of a number.

• Test it out and make sure it works.

• Hint: you can find the cube of a number using num^3.

cube_number <- ___

3.6 Arguments

• We also can input multiple arguments to a function, which can be of different types:

• Example 1
• Example 2
• Default arguments

abs_fun <- function(x, i) {
  res <- abs(x - mean(x)) ^ i
  return(res)
}

• What is the output of this function?
• What type of data object is the output?

abs_fun(x = 1:10, i = 4)

 [1] 410.0625 150.0625  39.0625   5.0625   0.0625   0.0625   5.0625  39.0625
 [9] 150.0625 410.0625

mypet <- function(name, pet) {
  mystring <- paste(name,"'s ", pet, sep = "")
  mystring <- toupper(mystring)
  return(mystring)
}

• You can name arguments, or use them in order, just like built-in R functions

# not naming arguments and hoping they are ordered correctly:
mypet("meike", "cat")

[1] "MEIKE'S CAT"

# oops
mypet("cat", "meike")

[1] "CAT'S MEIKE"

# naming arguments:
mypet(name = "meike",
      pet = "cat")

[1] "MEIKE'S CAT"

# naming arguments not in order:
mypet(pet = "cat", 
      name = "meike")

[1] "MEIKE'S CAT"

• What if we only give one argument?

mypet("meike")

• We can also set default arguments.
• If we define an argument inside function(),
  • that is the default value and it no longer needs to be specified.

mypet <- function(name, pet = "Mouse") {
  mystring <- paste(name,"'s ", pet, sep = "")
  mystring <- toupper(mystring)
  return(mystring)
}

mypet("meike", "cat")

[1] "MEIKE'S CAT"

mypet("meike")

[1] "MEIKE'S MOUSE"

3.7 Save the output of the function

• We can save the output of a function just as we do with built-in functions:

petstring <- mypet(name = "meike", pet = "cat")
petstring

[1] "MEIKE'S CAT"

• We can also manipulate the output, since it is an object:

tolower(petstring)

[1] "meike's cat"

str_replace_all(string = petstring, 
                pattern = "CAT", 
                replacement = "DOG")

[1] "MEIKE'S DOG"

3.8 Challenge 1 (10 min)

1. Create a function that calculates the mean of a numeric vector, removing NA’s. Make sure to test that it works. What happens when you input a character vector?

2. Create a function that takes a data frame and calculates the mean of all numeric variables using your custom mean function above. Try this with the penguins data set.

3.9 What does the function name return?

• Type/run cor in the console (no parentheses).
  • What do you see?
• Also try a name of a function you created, such as square_number.

# note: what yo see in the console is different than in the html output
square_number

<srcref: file "" chars 1:18 to 4:1>

3.10 Example: standardizing values in a vector

• Below we create our own function to standardize values in a vector:
  • subtract the mean of the values and
  • divide by the standard deviation of the values

# argument is a numeric vector
standardize_fun <- function(vec) {
  vec2 <- (vec - mean(vec, na.rm = TRUE)) / sd(vec, na.rm = TRUE)
  return(vec2) # returns another numeric vector
}

# test 1
tmpvec <- 1:10
standardize_fun(tmpvec)

 [1] -1.4863011 -1.1560120 -0.8257228 -0.4954337 -0.1651446  0.1651446
 [7]  0.4954337  0.8257228  1.1560120  1.4863011

# test 2
tmpvec <- c(-3, -2, -1, 0, 1, 2, 3)
standardize_fun(tmpvec)

[1] -1.3887301 -0.9258201 -0.4629100  0.0000000  0.4629100  0.9258201  1.3887301

• Usually we create functions to do more complex tasks than simple number or text manipulation.
  • Often the input argument is a data frame or tibble.

3.11 Example & Mini challenge: counting categories in a variable from a data frame

1. What are the input and output of the function below?
2. What requirements do we need for the input data frame?

• Function code
• Apply the function
• Comments

count_categories <- function(df){
  counts <- df %>%
    count(disease)
  
  return(counts)
}

• Apply the function to different data sets:

# these data are loaded in the first section above

count_categories(smoke_complete)

# A tibble: 15 × 2
   disease     n
   <chr>   <int>
 1 BLCA      412
 2 BRCA     1098
 3 CESC      307
 4 COAD      461
 5 GBM       617
 6 LGG       516
 7 LUSC     1008
 8 MESO       87
 9 PRAD      500
10 READ      172
11 SKCM      470
12 STAD      443
13 THYM      124
14 UCEC      560
15 UCS        57

count_categories(lusc_data)

# A tibble: 1 × 2
  disease     n
  <chr>   <int>
1 LUSC      504

count_categories(meso_data)

# A tibble: 1 × 2
  disease     n
  <chr>   <int>
1 MESO       87

• Why does this not work?

count_categories(penguins)

• This function might be useful if you needed to summarize the diseases across a large number of files.

• One thing you might notice is that disease is “hard coded”
  • which means it always counts the column with that exact name.
• There is a way to make this more flexible
  • (i.e. take an argument that specifies the column name)
  • using something called tidyeval, which we will learn about next time.

3.12 Example: read in data from a file path

• If you find yourself reading in many data sets the same way,
• you might create your own custom function to do this the way you like.

# custom function to read in a file (specified with argument path)
# output is a data frame

load_files <- function(path){
  out_frame <- readxl::read_excel(path, na="NA", sheet=1)
  out_frame <- janitor::clean_names(out_frame) %>%
    mutate(path_name = path)
  return(out_frame)
}

• Run the function:

smoke1 <- load_files(path = here::here("part8", "data", "smoke_1.xlsx"))
glimpse(smoke1)            # notice last column

Rows: 549
Columns: 6
$ primary_diagnosis <chr> "C34.1", "C34.1", "C34.3", "C34.1", "C34.1", "C34.3"…
$ tumor_stage       <chr> "stage ia", "stage ib", "stage ib", "stage ia", "sta…
$ age_at_diagnosis  <dbl> 24477, 26615, 28171, 27154, 23370, 19025, 26938, 284…
$ vital_status      <chr> "dead", "dead", "dead", "alive", "alive", "dead", "d…
$ morphology        <chr> "8070/3", "8070/3", "8070/3", "8083/3", "8070/3", "8…
$ path_name         <chr> "/Users/niederha/Library/CloudStorage/OneDrive-Orego…

• You don’t have to include “path =”

smoke2 <- load_files(here::here("part8", "data", "smoke_2.xlsx"))
glimpse(smoke2)            # notice last column

Rows: 603
Columns: 6
$ primary_diagnosis <chr> "C67.9", "C67.9", "C67.9", "C67.9", "C67.9", "C67.9"…
$ tumor_stage       <chr> "stage iv", "stage ii", "stage iii", "stage iv", "st…
$ age_at_diagnosis  <dbl> 17682, 19776, 23631, 26546, 24534, 25134, 26527, 243…
$ vital_status      <chr> "alive", "alive", "alive", "dead", "dead", "dead", "…
$ morphology        <chr> "8120/3", "8120/3", "8120/3", "8120/3", "8120/3", "8…
$ path_name         <chr> "/Users/niederha/Library/CloudStorage/OneDrive-Orego…

3.13 Using custom functions within mutate() and across()

• Within mutate()
• Within mutate() and across()

• We can use custom functions within mutate():

# standardize_fun is a function we created above

# using inside mutate without across:
smoke1 %>% 
  mutate(age_std = standardize_fun(age_at_diagnosis)) %>% 
  # selecting just a few columns to see new column:
  select(primary_diagnosis, age_at_diagnosis, age_std)

# A tibble: 549 × 3
   primary_diagnosis age_at_diagnosis age_std
   <chr>                        <dbl>   <dbl>
 1 C34.1                        24477 -0.0933
 2 C34.1                        26615  0.574 
 3 C34.3                        28171  1.06  
 4 C34.1                        27154  0.743 
 5 C34.1                        23370 -0.439 
 6 C34.3                        19025 -1.80  
 7 C34.3                        26938  0.675 
 8 C34.1                        28430  1.14  
 9 C34.1                        30435  1.77  
10 C34.9                        24019 -0.236 
# ℹ 539 more rows

• When using across() within the mutate(), we can use the custom function
• Note that the code below replaces values in all numeric columns with standardized transformations of the values

smoke1 %>% 
  mutate(across(.cols = where(is.numeric),
                .fns = standardize_fun))

# A tibble: 549 × 6
   primary_diagnosis tumor_stage age_at_diagnosis vital_status morphology
   <chr>             <chr>                  <dbl> <chr>        <chr>     
 1 C34.1             stage ia             -0.0933 dead         8070/3    
 2 C34.1             stage ib              0.574  dead         8070/3    
 3 C34.3             stage ib              1.06   dead         8070/3    
 4 C34.1             stage ia              0.743  alive        8083/3    
 5 C34.1             stage iiia           -0.439  alive        8070/3    
 6 C34.3             stage ib             -1.80   dead         8070/3    
 7 C34.3             stage iv              0.675  dead         8070/3    
 8 C34.1             stage ib              1.14   dead         8070/3    
 9 C34.1             stage iib             1.77   dead         8070/3    
10 C34.9             stage iv             -0.236  dead         8070/3    
# ℹ 539 more rows
# ℹ 1 more variable: path_name <chr>

# we don't need the .fns = ~ standarize_fun(.x) 
# because it is a name of a function and we aren't changing arguments

3.14 Creating a function inside across()

• We have seen examples where we created a function inside across()
• This is a bit messier but does the same thing
• We have to use .x as our function argument placeholder/name.

smoke1 %>% 
  mutate(across(.cols = where(is.numeric),
                .fns = ~ (.x - mean(.x, na.rm = TRUE)) / sd(.x, na.rm = TRUE)
                ))

# A tibble: 549 × 6
   primary_diagnosis tumor_stage age_at_diagnosis vital_status morphology
   <chr>             <chr>                  <dbl> <chr>        <chr>     
 1 C34.1             stage ia             -0.0933 dead         8070/3    
 2 C34.1             stage ib              0.574  dead         8070/3    
 3 C34.3             stage ib              1.06   dead         8070/3    
 4 C34.1             stage ia              0.743  alive        8083/3    
 5 C34.1             stage iiia           -0.439  alive        8070/3    
 6 C34.3             stage ib             -1.80   dead         8070/3    
 7 C34.3             stage iv              0.675  dead         8070/3    
 8 C34.1             stage ib              1.14   dead         8070/3    
 9 C34.1             stage iib             1.77   dead         8070/3    
10 C34.9             stage iv             -0.236  dead         8070/3    
# ℹ 539 more rows
# ℹ 1 more variable: path_name <chr>

• You might think of the function like the one created in the code below,
  • which is the same as what we specified above,
  • just with .x instead of vec, and no return()

standardize_fun <- function(.x) {
  (.x - mean(.x, na.rm = TRUE)) / sd(.x, na.rm = TRUE)
}

3.15 Using custom functions within summarize()

• Below we create a function and use it within summarize().
  • Input: a numeric vector (such as a numeric column of a dataset)
  • Output: it divides the mean of the vector by the standard deviation of the vector
• This custom function is a “summarizing” function that returns one value,
  • not a vector of multiple values.

mean_over_sd <- function(vec) {
  mean(vec, na.rm = TRUE) / sd(vec, na.rm = TRUE)
}

• Use the custom function within summarize():

penguins %>% 
  group_by(species) %>%
  summarize(std = mean_over_sd(bill_length_mm))

# A tibble: 3 × 2
  species     std
  <fct>     <dbl>
1 Adelie     14.6
2 Chinstrap  14.6
3 Gentoo     15.4

• Use the custom function within summarize() and across():

penguins %>% 
  group_by(species) %>%
  summarize(across(
    .cols = where(is.numeric), 
    .fns = mean_over_sd))

# A tibble: 3 × 6
  species   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
  <fct>              <dbl>         <dbl>             <dbl>       <dbl> <dbl>
1 Adelie              14.6          15.1              29.0        8.07 2443.
2 Chinstrap           14.6          16.2              27.5        9.71 2326.
3 Gentoo              15.4          15.3              33.5       10.1  2535.

3.16 Challenge 2 (15 min)

1. Use the above functions standardize_fun() and mean_over_sd() with the LUSC data to standardize the numeric vectors age_at_diagnosis and cigarettes_per_day. Calculate mean/sd for those vectors as well.

2. Create a custom function that converts mm to cm, and apply the function within a mutate() and across() on all the columns in penguins data that end with “mm”.

3.17 Checking for user errors with if()

• Motivation
• if(){ stop() } code
• Do you need to check for all errors?

• What happens when someone tries square_number("two")?
• Since “two” is a character, you can’t multiply it.

# run this yourself, eval: false so we can knit
square_number("two")

• We can let the the user to know that this isn’t what the function is expecting for its input.

• We can use if(){ } as a way to check if the input value number is actually numeric.
• if() statements check the condition within the parentheses,
  • and then run the code in the { } (curly brackets) if the condition is true.

# run this yourself, eval: false so we can knit

square_number_improved <- function(number) {
  # check for whether number is numeric:
  if(!is.numeric(number)) { 
    # stop gives the error and exits the function:
    stop("Your input was not a number")
  }
  
  output_number <- number * number 
  
  return(output_number)
  
}

square_number_improved(2)

square_number_improved("two")

• It depends on who uses your functions.
• If the functions are just for you and your lab, as long as everyone understands what goes into the functions, you probably don’t need to anticipate all kinds of errors, just a few major ones.
• It’s when you are releasing your software as a freely available package that you should think about doing extensive testing for errors, especially the functions you anticipate will be used by most people who use your software.

3.18 Challenge 3 (10 minutes)

• Instructions
• Code to “functionize”
• Code chunk for your solutions

• Create a custom function that does the same data cleaning steps as the code below for one dataset, so that you don’t repeat the same code 3 times. Put your solution in the code chunk below this, labeled challenge1solns.
• Your custom function should essentially be a copy of the code that does these steps:
  • Loads the time point data from an .xlsx file,
  • cleans the data,
  • renames any necessary columns, and
  • then pivots the data into a long format.
• The function should have two arguments:
  • the name of the excel file and
  • the name of the sheet
• The function should output a long dataset
• Bind the data at the end of the code chunk after running the function three times.
• If you have time, add in an error check that when the sheet number is not between 2 and 4, returns an error message to say “Incorrect sheet value for time point data.”

# Load data
mouse_tp1 <- read_excel(here::here("part8", "data", "mouse_biomarker.xlsx"), 
                        sheet = 2, 
                        na = c("","/"),
                        n_max = 34, # CAREFUL WITH THIS
                        .name_repair = janitor::make_clean_names) %>%
  remove_empty(which = c("rows","cols"))

mouse_tp2 <- read_excel(here::here("part8", "data", "mouse_biomarker.xlsx"), 
                        sheet = 3, 
                        na = c("","/"),
                        .name_repair = janitor::make_clean_names) %>%
  remove_empty(which = c("rows","cols"))

mouse_tp3 <- read_excel(here::here("part8", "data", "mouse_biomarker.xlsx"), 
                        sheet = 4, 
                        na = c("","/"),
                        .name_repair = janitor::make_clean_names) %>%
  remove_empty(which = c("rows","cols"))

# rename columns (do this to all data, just in case)
colnames(mouse_tp2) <- str_replace(colnames(mouse_tp2), "_ng", "_pg")

mouse_tp1 <- mouse_tp1 %>%
  rename(mirna1 = mirna_1,
         mirna2 = mi_rna_2,
         preference_obj1 = preference,
         preference_obj2 = x_4) %>%
  filter(!is.na(sid)) %>%
  mutate(
    preference_obj1 = as.numeric(preference_obj1),
    preference_obj2 = as.numeric(preference_obj2)
  ) 

mouse_tp2 <- mouse_tp2 %>%
  rename(mirna1 = mirna_1,
         mirna2 = mi_rna_2,
         preference_obj1 = preference,
         preference_obj2 = x_4) %>%
  filter(!is.na(sid)) %>%
  mutate(
    preference_obj1 = as.numeric(preference_obj1),
    preference_obj2 = as.numeric(preference_obj2)
  )  

mouse_tp3 <- mouse_tp3 %>%
  rename(mirna1 = mirna_1,
         mirna2 = mi_rna_2,
         preference_obj1 = preference,
         preference_obj2 = x_4) %>%
  filter(!is.na(sid)) %>%
  mutate(
    preference_obj1 = as.numeric(preference_obj1),
    preference_obj2 = as.numeric(preference_obj2)
  ) 

# Pivot data
mouse_tp1 <- mouse_tp1 %>%
  pivot_longer(cols = starts_with("normalized"),
               names_to = "biomarker_type",
               values_to = "biomarker_value")
mouse_tp2 <- mouse_tp2 %>%
  pivot_longer(cols = starts_with("normalized"),
               names_to = "biomarker_type",
               values_to = "biomarker_value")
mouse_tp3 <- mouse_tp3 %>%
  pivot_longer(cols = starts_with("normalized"),
               names_to = "biomarker_type",
               values_to = "biomarker_value")

# Note: the code below is not a part of your custom funcion

# Bind data
mouse_tp <- bind_rows("tp1" = mouse_tp1, 
                      "tp2" = mouse_tp2,
                      "tp3" = mouse_tp3,
                      .id = "time")

# create your function here

# implement your function 3 times

# bind the outputted data frames

4 Lists

4.1 Lists, what are they?

• Lists are a general purpose data structure in R.
• They are very flexible, in that they have slots.
• Unlike vectors, a list can be heterogeneous.
  • Each slot can contain elements of different types, such as vectors, matrices, data frames, or even other lists!
• Lists allow you to organize and store diverse types of data in a single object.

4.2 List basics

• Make a list
• List properties
• Accessing list elements

• We’ll start with a making a list and understand different properties about it.

my_list <- list(cat_names = c("Morris", "Julia"), 
                hedgehog_names = "Spiny", 
                dog_names = c("Rover", "Spot"))

my_list

$cat_names
[1] "Morris" "Julia" 

$hedgehog_names
[1] "Spiny"

$dog_names
[1] "Rover" "Spot" 

• Lists have a length():

length(my_list)

[1] 3

# note that there are no dimensions though:
dim(my_list)

NULL

• glimpse() a list

glimpse(my_list)

List of 3
 $ cat_names     : chr [1:2] "Morris" "Julia"
 $ hedgehog_names: chr "Spiny"
 $ dog_names     : chr [1:2] "Rover" "Spot"

• If the lists elements are named, you can ask for their names:

names(my_list)

[1] "cat_names"      "hedgehog_names" "dog_names"     

• Accessing with $
• Accessing with [[]]

• We can access a single element in the list using $ and its name,
  • similar to how we access data frame columns:

names(my_list)

[1] "cat_names"      "hedgehog_names" "dog_names"     

my_list$cat_names

[1] "Morris" "Julia" 

my_list$hedgehog_names

[1] "Spiny"

my_list$dog_names

[1] "Rover" "Spot" 

• We can also access an element using the [[]] (double brackets) accessor

my_list[[1]]

[1] "Morris" "Julia" 

my_list[[2]]

[1] "Spiny"

my_list[[3]]

[1] "Rover" "Spot" 

# what happens when you run this?
# my_list[[4]]

• If a list’s slots are named, they can be accessed with their name:

my_list[["hedgehog_names"]]

[1] "Spiny"

# what is the type/structure of the result?
str(my_list[["hedgehog_names"]])

 chr "Spiny"

str(my_list[["dog_names"]])

 chr [1:2] "Rover" "Spot"

4.3 Difference between [] and [[]]

• Beware of the difference between [] and [[]].
• The [] accessor returns a list of length 1, not the element in it:

my_list <- list(cat_names = c("Morris", "Julia"), 
                hedgehog_names = "Spiny", 
                dog_names = c("Rover", "Spot"))

my_list["hedgehog_names"]

$hedgehog_names
[1] "Spiny"

str(my_list["hedgehog_names"])

List of 1
 $ hedgehog_names: chr "Spiny"

• Compare to [[]]:

my_list[["hedgehog_names"]]

[1] "Spiny"

In most cases, you want to use [[]].

4.4 Vectors vs. lists, and [] vs. [[]]

• Vectors
  • vec[1:3] returns a vector of length 3
  • vec[1] returns a vector of length 1
  • [[]] cannot be used with vectors

my_vec <- c("Morris", "Julia", "Spiny", "Rover", "Spot")

my_vec[1:3]

[1] "Morris" "Julia"  "Spiny" 

my_vec[1]

[1] "Morris"

• Lists
  • list[1:3] returns a list of length 3
  • list[1] returns a list of length 1
  • list[[1]] returns the first element of a list
    • this could be a number (vector of length 1), a vector, another list, a data frame, a ggplot, whatever!

my_list[1:3]

$cat_names
[1] "Morris" "Julia" 

$hedgehog_names
[1] "Spiny"

$dog_names
[1] "Rover" "Spot" 

my_list[1]

$cat_names
[1] "Morris" "Julia" 

my_list[[1]]

[1] "Morris" "Julia" 

From the help manual of [[:

“The most important distinction between [, [[ and $ is that the [ can select more than one element whereas the other two select a single element.”

4.5 What does my_list[[1]][1] do?

my_list[[1]][1]

[1] "Morris"

• Let’s break it up step-by-step to better understand what it does:

# save the first element of the list as tmpout
tmpout <-  my_list[[1]]
str(tmpout)

 chr [1:2] "Morris" "Julia"

# now take the first element of tmpout
tmpout[1]

[1] "Morris"

• The first part, my_list[[1]], returns the first element of my_list
  • which is a vector of length two (c("Morris", "Julia"))
• The second part, [1], returns the first element of that vector “Morris”:

str(my_list[[1]][1])

 chr "Morris"

• If you try to pull out an element that doesn’t exist, you get a strange variety of results, so be careful!!

# run in R on your computer to see the errors
my_list[[1]][3]

my_list[[6]]

my_list[["dogg_names"]] # check your spelling!!!

4.6 Mini-challenge (your turn)

• Find the length of the cat_names element in my_list. Then, find the length of hedgehog_names.

my_list <- list(cat_names = c("Morris", "Julia"), 
                hedgehog_names = "Spiny", 
                dog_names = c("Rover", "Spot"))

4.7 purrr::pluck()

• The purrr package has an additional way to extract list elements: purrr::pluck().
• This is equivalent to using [[]], but always returns NULL when the element does not exist.
• pluck() can be a handy way to extract something from a list without dealing with the [[]] and [] confusion.

my_list <- list(cat_names = c("Morris", "Julia"), 
                hedgehog_names = "Spiny", 
                dog_names = c("Rover", "Spot"))

my_list %>%
  pluck("cat_names")

[1] "Morris" "Julia" 

# go deeper in a list
my_list %>%
  pluck("cat_names", 2)

[1] "Julia"

• pluck() outputs a vector, just like my_list[["cat_names"]]:

my_list %>%
  pluck("cat_names") %>%
  str()

 chr [1:2] "Morris" "Julia"

• pluck() outputs NULL instead of an error:

my_list %>% 
  pluck("dogggg_names")

NULL

my_list %>% 
  pluck(10)

NULL

4.8 Mini-challenge (your turn)

• Run the code below. What does it output?

Did it output a data.frame, or a vector?

penguins %>%
  pluck("species")

  [1] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
  [8] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [15] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [22] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [29] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [36] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [43] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [50] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [57] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [64] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [71] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [78] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [85] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [92] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [99] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[106] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[113] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[120] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[127] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[134] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[141] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[148] Adelie    Adelie    Adelie    Adelie    Adelie    Gentoo    Gentoo   
[155] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[162] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[169] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[176] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[183] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[190] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[197] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[204] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[211] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[218] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[225] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[232] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[239] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[246] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[253] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[260] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[267] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[274] Gentoo    Gentoo    Gentoo    Chinstrap Chinstrap Chinstrap Chinstrap
[281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[344] Chinstrap
Levels: Adelie Chinstrap Gentoo

4.9 Adding things to Lists

• The slots in a list are not fixed.
• We can take an already made list and add a new slot to it using <-:

my_list <- list(cat_names = c("Morris", "Julia"), 
                hedgehog_names = "Spiny", 
                dog_names = c("Rover", "Spot"))

my_list$raccoon_names <- c("Rocky")
my_list

$cat_names
[1] "Morris" "Julia" 

$hedgehog_names
[1] "Spiny"

$dog_names
[1] "Rover" "Spot" 

$raccoon_names
[1] "Rocky"

my_list$cat_ages <- c(2, 5)
my_list

$cat_names
[1] "Morris" "Julia" 

$hedgehog_names
[1] "Spiny"

$dog_names
[1] "Rover" "Spot" 

$raccoon_names
[1] "Rocky"

$cat_ages
[1] 2 5

• This is a little bit like adding a column to a data frame (with mutate() or add_column() or even $).

• We can just keep adding!

• This also works numerically:

# another list, this time not named, but named 
my_list <- list(hello = c("hello", "world"),
                c("a","b","c"),
                stuff = "stuff")
# notice only two elements have names, this is allowed
my_list

$hello
[1] "hello" "world"

[[2]]
[1] "a" "b" "c"

$stuff
[1] "stuff"

names(my_list)

[1] "hello" ""      "stuff"

my_list[[4]] <- c("new","stuff")
my_list

$hello
[1] "hello" "world"

[[2]]
[1] "a" "b" "c"

$stuff
[1] "stuff"

[[4]]
[1] "new"   "stuff"

my_list[[6]] <- "Does this work?"

# What happened to the fifth element?
my_list

$hello
[1] "hello" "world"

[[2]]
[1] "a" "b" "c"

$stuff
[1] "stuff"

[[4]]
[1] "new"   "stuff"

[[5]]
NULL

[[6]]
[1] "Does this work?"

names(my_list)

[1] "hello" ""      "stuff" ""      ""      ""     

4.10 homogeneous versus heterogeneous lists

• Difference
• Example 1: lm() output
• Example 3

• When we automate a repetitive process, we usually assume that the lists that we create are homogeneous.
  • That is, the data type of the list element is the same for each slot in the list.
  • In our case, usually the data.type will be a data.frame.
• However, lists can also be heterogenous as we can see in the list above when we added cat_ages element.
• A common pattern is to return a heterogenous list from a function.
  • For example, the lm() function actually returns a list.

• Run model
• Output is a list
• Access the coefficients
• Object type of coefficients

• Run a linear regression model with lm()

# outcome is on left side of ~
# predictor/independent variable/covariate  is on right side of ~
# specify data in data argument
output <- lm(body_mass_g ~ bill_length_mm, data = penguins)

# summary() produces summaries about the model results
sum_output <- summary(output)

sum_output

Call:
lm(formula = body_mass_g ~ bill_length_mm, data = penguins)

Residuals:
     Min       1Q   Median       3Q      Max 
-1762.08  -446.98    32.59   462.31  1636.86 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     362.307    283.345   1.279    0.202    
bill_length_mm   87.415      6.402  13.654   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 645.4 on 340 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.3542,    Adjusted R-squared:  0.3523 
F-statistic: 186.4 on 1 and 340 DF,  p-value: < 2.2e-16

• The summary regression output is a list:

names(sum_output)

 [1] "call"          "terms"         "residuals"     "coefficients" 
 [5] "aliased"       "sigma"         "df"            "r.squared"    
 [9] "adj.r.squared" "fstatistic"    "cov.unscaled"  "na.action"    

str(sum_output)

List of 12
 $ call         : language lm(formula = body_mass_g ~ bill_length_mm, data = penguins)
 $ terms        :Classes 'terms', 'formula'  language body_mass_g ~ bill_length_mm
  .. ..- attr(*, "variables")= language list(body_mass_g, bill_length_mm)
  .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:2] "body_mass_g" "bill_length_mm"
  .. .. .. ..$ : chr "bill_length_mm"
  .. ..- attr(*, "term.labels")= chr "bill_length_mm"
  .. ..- attr(*, "order")= int 1
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. ..- attr(*, "predvars")= language list(body_mass_g, bill_length_mm)
  .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. ..- attr(*, "names")= chr [1:2] "body_mass_g" "bill_length_mm"
 $ residuals    : Named num [1:342] -30.2 -15.2 -635.1 -120.4 -147.7 ...
  ..- attr(*, "names")= chr [1:342] "1" "2" "3" "5" ...
 $ coefficients : num [1:2, 1:4] 362.31 87.42 283.35 6.4 1.28 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "(Intercept)" "bill_length_mm"
  .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
 $ aliased      : Named logi [1:2] FALSE FALSE
  ..- attr(*, "names")= chr [1:2] "(Intercept)" "bill_length_mm"
 $ sigma        : num 645
 $ df           : int [1:3] 2 340 2
 $ r.squared    : num 0.354
 $ adj.r.squared: num 0.352
 $ fstatistic   : Named num [1:3] 186 1 340
  ..- attr(*, "names")= chr [1:3] "value" "numdf" "dendf"
 $ cov.unscaled : num [1:2, 1:2] 1.93e-01 -4.32e-03 -4.32e-03 9.84e-05
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "(Intercept)" "bill_length_mm"
  .. ..$ : chr [1:2] "(Intercept)" "bill_length_mm"
 $ na.action    : 'omit' Named int [1:2] 4 272
  ..- attr(*, "names")= chr [1:2] "4" "272"
 - attr(*, "class")= chr "summary.lm"

• There are different ways to access the coefficients element of the list:

sum_output$coefficients

                Estimate Std. Error   t value     Pr(>|t|)
(Intercept)    362.30672 283.345233  1.278676 2.018833e-01
bill_length_mm  87.41528   6.401985 13.654401 3.808283e-34

sum_output[["coefficients"]]

                Estimate Std. Error   t value     Pr(>|t|)
(Intercept)    362.30672 283.345233  1.278676 2.018833e-01
bill_length_mm  87.41528   6.401985 13.654401 3.808283e-34

sum_output %>% pluck(coefficients)

                Estimate Std. Error   t value     Pr(>|t|)
(Intercept)    362.30672 283.345233  1.278676 2.018833e-01
bill_length_mm  87.41528   6.401985 13.654401 3.808283e-34

• Note this is a matrix, not a data frame!

str(sum_output[["coefficients"]])

 num [1:2, 1:4] 362.31 87.42 283.35 6.4 1.28 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:2] "(Intercept)" "bill_length_mm"
  ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"

• What if we use []?

# [] returns a list:
sum_output["coefficients"]

$coefficients
                Estimate Std. Error   t value     Pr(>|t|)
(Intercept)    362.30672 283.345233  1.278676 2.018833e-01
bill_length_mm  87.41528   6.401985 13.654401 3.808283e-34

str(sum_output["coefficients"])

List of 1
 $ coefficients: num [1:2, 1:4] 362.31 87.42 283.35 6.4 1.28 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "(Intercept)" "bill_length_mm"
  .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"

• Use [] to access multiple elements of the list:

sum_output[c("call","coefficients")]

$call
lm(formula = body_mass_g ~ bill_length_mm, data = penguins)

$coefficients
                Estimate Std. Error   t value     Pr(>|t|)
(Intercept)    362.30672 283.345233  1.278676 2.018833e-01
bill_length_mm  87.41528   6.401985 13.654401 3.808283e-34

# a list of length 2
str(sum_output[c("call","coefficients")])

List of 2
 $ call        : language lm(formula = body_mass_g ~ bill_length_mm, data = penguins)
 $ coefficients: num [1:2, 1:4] 362.31 87.42 283.35 6.4 1.28 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "(Intercept)" "bill_length_mm"
  .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"

• Remember you cannot use [[]] with more than one name:

sum_output[[c("call","coefficients")]]

NULL

• This function runs a linear model using the penguins data and saves some of the regression output as a heterogeneous list

run_penguin_model <- function(myisland = "Torgersen") {
  
  subset_penguins <- penguins %>%
    filter(island == myisland)
  
  output <- summary(lm(body_mass_g ~ bill_length_mm, data=subset_penguins))
  
  out_list <- list(
    coefficients = output$coefficients, 
    call = output$call, 
    island = myisland)
  
  return(out_list)
}

output_model <- run_penguin_model("Torgersen")

output_model

$coefficients
                Estimate Std. Error  t value    Pr(>|t|)
(Intercept)    1144.5433   734.4877 1.558288 0.125601038
bill_length_mm   65.7706    18.8012 3.498212 0.001006702

$call
lm(formula = body_mass_g ~ bill_length_mm, data = subset_penguins)

$island
[1] "Torgersen"

4.11 List of lists

• Lists can be heterogeneous, and since they can contain anything, they can also contain other lists!

my_info_list <- list(
  name = "Patty",
  pets = c("Cat", "Dog","Parrot","Kangaroo"),
  address = list(number = 273,
                 street = "N Main St",
                 city = "Portland",
                 state = "Oregon")
)

my_info_list

$name
[1] "Patty"

$pets
[1] "Cat"      "Dog"      "Parrot"   "Kangaroo"

$address
$address$number
[1] 273

$address$street
[1] "N Main St"

$address$city
[1] "Portland"

$address$state
[1] "Oregon"

• We can pull out elements from the list inside a list

my_info_list[["address"]][["street"]]

[1] "N Main St"

my_info_list %>% pluck("address", "street")

[1] "N Main St"

4.12 View a list

• Try using View() with my_list, my_info_list, and output from the linear model above.
• Rstudio tries to give you an interactive viewing experience for each element.
• While in the View screen, click on the icon at the very right of the screen, what does that do?

# run this in R

View(my_list)

View(my_info_list)

View(output)

4.13 data.frames are list-like

• You can access columns in a data.frame using $.

• This is because a data.frame is a special instance of a list.

• Hence, you can use all of the above accessors to manipulate variables in a data.frame.

• Load the mtcars data frame (it’s nice and small)

data(mtcars)
glimpse(mtcars)

Rows: 32
Columns: 11
$ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,…
$ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,…
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16…
$ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,…
$ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18…
$ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
$ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,…
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,…
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…

• What is length doing here? how does it compare to dim?

length(mtcars)

[1] 11

• mtcars is also a kind of list - we can use $, [[]], and pluck():

mtcars$mpg

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

mtcars[[1]]

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

str(mtcars[[1]])

 num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...

mtcars[["mpg"]]

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

mtcars %>% pluck("mpg")

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

mtcars %>% pull("mpg")

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

• Compare the output above to that below when using a single []:

mtcars[1]

                     mpg
Mazda RX4           21.0
Mazda RX4 Wag       21.0
Datsun 710          22.8
Hornet 4 Drive      21.4
Hornet Sportabout   18.7
Valiant             18.1
Duster 360          14.3
Merc 240D           24.4
Merc 230            22.8
Merc 280            19.2
Merc 280C           17.8
Merc 450SE          16.4
Merc 450SL          17.3
Merc 450SLC         15.2
Cadillac Fleetwood  10.4
Lincoln Continental 10.4
Chrysler Imperial   14.7
Fiat 128            32.4
Honda Civic         30.4
Toyota Corolla      33.9
Toyota Corona       21.5
Dodge Challenger    15.5
AMC Javelin         15.2
Camaro Z28          13.3
Pontiac Firebird    19.2
Fiat X1-9           27.3
Porsche 914-2       26.0
Lotus Europa        30.4
Ford Pantera L      15.8
Ferrari Dino        19.7
Maserati Bora       15.0
Volvo 142E          21.4

str(mtcars[1])

'data.frame':   32 obs. of  1 variable:
 $ mpg: num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...

• A common pattern in Base-R for filtering that you should be aware of is using the $ operator for filtering on rows. - The tidyverse simplifies this with filter() and that’s what I encourage you to use,
  • but this pattern is often used with objects in the Bioconductor package (or in other people’s code ;))

mtcars[mtcars$cyl == 8, ]

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    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
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
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

• This is the tidyverse version of the above “base-R” code

mtcars %>% 
  filter(cyl == 8)

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    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
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
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

4.14 Recap of lists

• These are some basic operations with lists in R.
• Lists are powerful for organizing and manipulating complex data structures in a flexible way.

• Creating a List:
• Accessing Elements:
• Adding Elements:
• List Attributes:
• Nested Lists:

• You can create a list using the list() function.
• Elements within the list can be of different types:

# Example list
my_list <- list(
  numeric_vector = c(1, 2, 3),
  character_vector = c("a", "b", "c"),
  matrix = matrix(1:4, nrow = 2),
  data_frame = data.frame(name = c("John", "Jane"), age = c(25, 30))
)

my_list

$numeric_vector
[1] 1 2 3

$character_vector
[1] "a" "b" "c"

$matrix
     [,1] [,2]
[1,]    1    3
[2,]    2    4

$data_frame
  name age
1 John  25
2 Jane  30

• You can access elements in a list using
  • double square brackets [[]]
  • or the dollar sign $ for named elements:

# Accessing elements
(numeric_vector <- my_list[[1]])

[1] 1 2 3

(character_vector <- my_list$character_vector)

[1] "a" "b" "c"

• You can add elements to a list
  • using the c() function or
  • by assigning a new element to a specific index or
  • using the familiar $:

# Adding elements
my_list <- c(my_list, new_element = "This is a new element")

my_list[[6]] <- "Another new element"
my_list$new3 <- "Yet another new element"

my_list

$numeric_vector
[1] 1 2 3

$character_vector
[1] "a" "b" "c"

$matrix
     [,1] [,2]
[1,]    1    3
[2,]    2    4

$data_frame
  name age
1 John  25
2 Jane  30

$new_element
[1] "This is a new element"

[[6]]
[1] "Another new element"

$new3
[1] "Yet another new element"

• Lists can have attributes, such as names and dimensions.
• You can assign names to list elements:

names(my_list)

[1] "numeric_vector"   "character_vector" "matrix"           "data_frame"      
[5] "new_element"      ""                 "new3"            

# Assigning names to list elements
names(my_list) <- c("numeric", "character", "matrix", "dataframe", "new1", "new2", "new3")

names(my_list)

[1] "numeric"   "character" "matrix"    "dataframe" "new1"      "new2"     
[7] "new3"     

• Lists can be nested, meaning you can have lists within lists:

# Nested list
nested_list <- list(
  sublist1 = list(a = 1, b = 2),
  sublist2 = list(c = "apple", d = "banana")
)

nested_list

$sublist1
$sublist1$a
[1] 1

$sublist1$b
[1] 2


$sublist2
$sublist2$c
[1] "apple"

$sublist2$d
[1] "banana"

5 Next time

Next class we will focus on iteration using functions, lists, and purrr::map().

6 Post Class Survey

Please fill out the post-class survey.

Your responses are anonymous in that I separate your names from the survey answers before compiling/reading.

You may want to review previous years’ feedback here.

7 Acknowledgements

• Part 8 is based on the BSTA 505 Winter 2023 course, taught by Jessica Minnier.
  • I made modifications to update the material from RMarkdown to Quarto, and streamlined/edited content for slides.
• Minnier’s Acknowledgements:
  • Written by Jessica Minnier and Ted Laderas.
  • Some material inspired by Kelly Bodwin’s Adventures in R.