Introduction to R

Data-based Storytelling

Authors

Affiliations

Daniel Winkler

Institute for Retailing & Data Science

Stephan Fally

Department Marketing

The Basics

Why R?

Well established in business and scientific computing
Very powerful language
- Express any operation in terms of
- Often complex functions are already implemented
Very good package manager and ecosystem
- We will use many tools created by companies, universities, and other R community members
Very good for reproducibility
- Code documents the process
- Should run the same on my and your machine
- Should be easily adaptable to changing data
Open source
- All packages can be inspected
- Free to install and use on any computer
Developed (partly) and hosted at WU 😋

What do we need?

The R interpreter
- The program that “interprets” R code and runs it
- Very bare-bones, essentially just a text field
- Does not store code files for reproducibility

print("Hello, WU!")

[1] "Hello, WU!"

paste0("One plus two is: ", 1 + 2)

[1] "One plus two is: 3"

c <- data.frame(p = seq(0, 2 * pi, by = 0.001))
c$h_x <- 16 * sin(c$p)^3
c$h_y <- 13 * cos(c$p) - 5 * cos(2 * c$p) - 2 * cos(3 * c$p) - cos(4 * c$p)
plot(c$h_x, c$h_y, type = "l", main = "I <3 R", frame = F, xlab = NA, ylab = NA)

An Integrated Development Environment (IDE)
- Makes writing and storing R code easier (more fun!)
- Three options compatible with this course:
  - R Studio Desktop (Recommended)
    - Focused on R
    - Easiest option
  - VS Code
    - Recommended if you (plan to) use other languages (Python, C++, Julia, etc.)
    - Needs extension for R but works well
  - JupyterLab
    - “Notebooks” for R, Python, and Julia
    - Output generated directly under code “cells”

If you already know R

My favorite R package

Download the presentation template my_favorite_r_package.qmd and the bibliography file data_literacy.bib from Canvas
Select a package that was useful to you in the past
Prepare a short presentation about the package
- Include examples of how to use the package
- Show in which situations that is useful

Basic Workflow (R Studio)

Ctrl + Enter to run current line of code (any cursor position)

Basic Workflow (VS Code)

Ctrl + Enter to run current line of code (any cursor position)

R Syntax: comments, assignment

# This is a comment
print("Hi") # also a comment

[1] "Hi"

## Assignment of varibale names
x <- 1
x

[1] 1

## Missing values
NA

[1] NA

## Vectors
y <- c(1, 2, 3, NA)
y

[1]  1  2  3 NA

Functions

## built-in
sum(y)

[1] NA

sum(y, na.rm = FALSE)

[1] NA

sum(y, na.rm = TRUE)

[1] 6

## User functions
a_plus_b <- function(a, b = 1) {
    return(a + b)
}
a_plus_b(y)

[1]  2  3  4 NA

a_plus_b(y, 2)

[1]  3  4  5 NA

a_plus_b(b = 2, a = y)

[1]  3  4  5 NA

## Functions provided by packages
## Installation
#install.packages("ineq")
ineq::Gini(y)

[1] 0.2222222

## or
library(ineq)
Gini(y)

[1] 0.2222222

## Help 
?Gini

R Syntax: indexing, logic

y[1]

[1] 1

y[-1]

[1]  2  3 NA

y[2:3]

[1] 2 3

y[c(1, 3, 4)]

[1]  1  3 NA

set.seed(1)
x <- y / 2 + rnorm(length(y))
cbind(y, x)

      y          x
[1,]  1 -0.1264538
[2,]  2  1.1836433
[3,]  3  0.6643714
[4,] NA         NA

y > 2

[1] FALSE FALSE  TRUE    NA

y > 2 & x > 0

[1] FALSE FALSE  TRUE    NA

y > 2 | x > 0

[1] FALSE  TRUE  TRUE    NA

y[y > 2 | x > 0]

[1]  2  3 NA

R Syntax: loops, ranges

## 'elem' is a temporary variable
for (elem in y) {
    print(paste("Current y value is:", elem))
}

[1] "Current y value is: 1"
[1] "Current y value is: 2"
[1] "Current y value is: 3"
[1] "Current y value is: NA"

## 'seq_along' returns a vector which indexes the argument
for (i in seq_along(y)) {
    print(paste("Current y value is:", y[i]))
}

[1] "Current y value is: 1"
[1] "Current y value is: 2"
[1] "Current y value is: 3"
[1] "Current y value is: NA"

## set.seed guarantees the same random numbers every time
set.seed(1)
total <- 0
while (total < 1) {
    ## runif generates random numbers between 0 and 1
    total <- total + runif(1)
    print(paste("Current total value is:", total))
}

[1] "Current total value is: 0.2655086631421"
[1] "Current total value is: 0.63763256277889"
[1] "Current total value is: 1.21048592613079"

## ranges
1:3

[1] 1 2 3

10:3

[1] 10  9  8  7  6  5  4  3

seq(3, 11, by = 2)

[1]  3  5  7  9 11

R Syntax: conditional logic

z <- -2:3
for (x in z) {
  print(paste("x =", x))
    if (x > 0) {
        print("x is positive")
    } else if (x > 2) {
        print("x is greater than 2")
    } else if (x < 0) {
        print("x is negative")
    } else if (x == 0) {
        print("x is zero")
    }
}

[1] "x = -2"
[1] "x is negative"
[1] "x = -1"
[1] "x is negative"
[1] "x = 0"
[1] "x is zero"
[1] "x = 1"
[1] "x is positive"
[1] "x = 2"
[1] "x is positive"
[1] "x = 3"
[1] "x is positive"

z[z <= 0]

[1] -2 -1  0

z[z >= 0]

[1] 0 1 2 3

z[z != 0]

[1] -2 -1  1  2  3

z[! z < 0]

[1] 0 1 2 3

Exercise

Write your own function

The function should take two arguments a and b
First, check if a and b have the same number of elements (see ?length)
- If they have a different number of elements, return NA
Iterate over the elements of a and b and check which vector’s element is larger (or if they are equal)
If they are equal print the index of the element and “equal”
If the element in a is larger print the index of the elemnt and “a larger”
If the element in b is larger print the index of the elemnt and “b larger”

Example 1

a <- c(1, 2, 3)
b <- c(1, 2, 3, 4)
cat("a is:", a, "\n")

a is: 1 2 3

cat("b is:", b, "\n")

b is: 1 2 3 4

cat("Result:\n")

Result:

NA

[1] NA

Example 2

a <- c(1, 2, 3)
b <- c(0, 2, 4)
cat("a is:", a, "\n")

a is: 1 2 3

cat("b is:", b, "\n")

b is: 0 2 4

cat("Result:\n")

Result:

print("1 a larger")

[1] "1 a larger"

print("2 equal")

[1] "2 equal"

print("3 b larger")

[1] "3 b larger"

Rectangular data frames: creation and access

data <- data.frame(x = -1:1, y = 3:1, z = c("a", "b", NA))
data

   x y    z
1 -1 3    a
2  0 2    b
3  1 1 <NA>

class(data)

[1] "data.frame"

## Variable access
data$x

[1] -1  0  1

data$x + data$y

[1] 2 2 2

row_summaries <- with(data, 
  data.frame(
    rsum = x + y,
    rdiff = x - y
  ))
row_summaries

  rsum rdiff
1    2    -4
2    2    -2
3    2     0

Rectangular data frames: overview

str(data)

'data.frame':   3 obs. of  3 variables:
 $ x: int  -1 0 1
 $ y: int  3 2 1
 $ z: chr  "a" "b" NA

summary(data)

       x              y            z            
 Min.   :-1.0   Min.   :1.0   Length:3          
 1st Qu.:-0.5   1st Qu.:1.5   Class :character  
 Median : 0.0   Median :2.0   Mode  :character  
 Mean   : 0.0   Mean   :2.0                     
 3rd Qu.: 0.5   3rd Qu.:2.5                     
 Max.   : 1.0   Max.   :3.0

head(data)

   x y    z
1 -1 3    a
2  0 2    b
3  1 1 <NA>

Rectangular data frames: Indexing

## 2D structure of data
## Empty argument means "all"#| 
data[, c("x", "y")]

data[1:3, c("x", "y")]

data[1, ]

   x y z
1 -1 3 a

data[c(1, 3), c("x", "z")]

   x    z
1 -1    a
3  1 <NA>

data[data$x < 3,]

   x y    z
1 -1 3    a
2  0 2    b
3  1 1 <NA>

Rectangular data: adding and removing variables

## new data has to have the same number of elements
data$a <- 2 * data$x
data

   x y    z  a
1 -1 3    a -2
2  0 2    b  0
3  1 1 <NA>  2

data$b <- c("one", "two", "three")
data

   x y    z  a     b
1 -1 3    a -2   one
2  0 2    b  0   two
3  1 1 <NA>  2 three

data$x <- NULL
data

  y    z  a     b
1 3    a -2   one
2 2    b  0   two
3 1 <NA>  2 three

data$a <- log(data$a)

Warning in log(data$a): NaNs produced

data

  y    z         a     b
1 3    a       NaN   one
2 2    b      -Inf   two
3 1 <NA> 0.6931472 three

data$b[data$b == "two"] <- "TWO!"
data$z[is.na(data$z)] <- "c"
data$a[is.nan(data$a)] <- 0
data

  y z         a     b
1 3 a 0.0000000   one
2 2 b      -Inf  TWO!
3 1 c 0.6931472 three

Exercise

Generate your own data

Look at the helpfiles of rnorm, runif, and ifelse
Create a data.frame with 10 rows and variables x, generated using runif and y, generated using rnorm
Add variable z which takes the value 1 if x is larger than y and 0 otherwise
Create a second data.frame that holds the rows of the original one for which z == 1 is TRUE.
Remove column z from the second data.frame
What happens if you try to create a data.frame when x and y have a differnent number of elements?
What happens if you run the code you wrote for this exercise again (and again)?
How can you ensure that each run yields the same results?

Reading data

Please download & unzip the folder found in “data” in Canvas
We will first use the “penguins” folder which includes the penguins_raw data set in multiple file formats

## CSV
penguins_raw <- readr::read_csv("data/penguins/penguins_raw.csv")

New names:
• `` -> `...1`

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Rows: 345 Columns: 18
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (10): ...1, studyName, Species, Region, Island, Stage, Individual ID, C...
dbl   (7): Sample Number, Culmen Length (mm), Culmen Depth (mm), Flipper Len...
date  (1): Date Egg

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(penguins_raw, 2)

# A tibble: 2 × 18
  ...1     studyName `Sample Number` Species Region Island Stage `Individual ID`
  <chr>    <chr>               <dbl> <chr>   <chr>  <chr>  <chr> <chr>          
1 # this … <NA>                   NA <NA>    <NA>   <NA>   <NA>  <NA>           
2 1        PAL0708                 1 Adelie… Anvers Torge… Adul… N1A1           
# ℹ 10 more variables: `Clutch Completion` <chr>, `Date Egg` <date>,
#   `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>,
#   `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>,
#   `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>

. . .

penguins_raw[1,1]

# A tibble: 1 × 1
  ...1                                              
  <chr>                                             
1 # this is a comment making the data harder to read

Fix the data

The second row (after the column names) in the penguins_raw.csv file is a comment
Look at the help file for the readr::read_csv function (?readr::read_csv)
How can we ignore the comment row?

Reading data: solution

## CSV
penguins_raw <- readr::read_csv(
  "data/penguins/penguins_raw.csv",
  comment = "#")

New names:
Rows: 344 Columns: 18
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(9): studyName, Species, Region, Island, Stage, Individual ID, Clutch C... dbl
(8): ...1, Sample Number, Culmen Length (mm), Culmen Depth (mm), Flippe... date
(1): Date Egg
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`

head(penguins_raw, 2)

# A tibble: 2 × 18
   ...1 studyName `Sample Number` Species    Region Island Stage `Individual ID`
  <dbl> <chr>               <dbl> <chr>      <chr>  <chr>  <chr> <chr>          
1     1 PAL0708                 1 Adelie Pe… Anvers Torge… Adul… N1A1           
2     2 PAL0708                 2 Adelie Pe… Anvers Torge… Adul… N1A2           
# ℹ 10 more variables: `Clutch Completion` <chr>, `Date Egg` <date>,
#   `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>,
#   `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>,
#   `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>

penguins_raw[1,1]

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

str(penguins_raw)

spc_tbl_ [344 × 18] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ ...1               : num [1:344] 1 2 3 4 5 6 7 8 9 10 ...
 $ studyName          : chr [1:344] "PAL0708" "PAL0708" "PAL0708" "PAL0708" ...
 $ Sample Number      : num [1:344] 1 2 3 4 5 6 7 8 9 10 ...
 $ Species            : chr [1:344] "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" ...
 $ Region             : chr [1:344] "Anvers" "Anvers" "Anvers" "Anvers" ...
 $ Island             : chr [1:344] "Torgersen" "Torgersen" "Torgersen" "Torgersen" ...
 $ Stage              : chr [1:344] "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" ...
 $ Individual ID      : chr [1:344] "N1A1" "N1A2" "N2A1" "N2A2" ...
 $ Clutch Completion  : chr [1:344] "Yes" "Yes" "Yes" "Yes" ...
 $ Date Egg           : Date[1:344], format: "2007-11-11" "2007-11-11" ...
 $ Culmen Length (mm) : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
 $ Culmen Depth (mm)  : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
 $ Flipper Length (mm): num [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
 $ Body Mass (g)      : num [1:344] 3750 3800 3250 NA 3450 ...
 $ Sex                : chr [1:344] "MALE" "FEMALE" "FEMALE" NA ...
 $ Delta 15 N (o/oo)  : num [1:344] NA 8.95 8.37 NA 8.77 ...
 $ Delta 13 C (o/oo)  : num [1:344] NA -24.7 -25.3 NA -25.3 ...
 $ Comments           : chr [1:344] "Not enough blood for isotopes." NA NA "Adult not sampled." ...
 - attr(*, "spec")=
  .. cols(
  ..   ...1 = col_double(),
  ..   studyName = col_character(),
  ..   `Sample Number` = col_double(),
  ..   Species = col_character(),
  ..   Region = col_character(),
  ..   Island = col_character(),
  ..   Stage = col_character(),
  ..   `Individual ID` = col_character(),
  ..   `Clutch Completion` = col_character(),
  ..   `Date Egg` = col_date(format = ""),
  ..   `Culmen Length (mm)` = col_double(),
  ..   `Culmen Depth (mm)` = col_double(),
  ..   `Flipper Length (mm)` = col_double(),
  ..   `Body Mass (g)` = col_double(),
  ..   Sex = col_character(),
  ..   `Delta 15 N (o/oo)` = col_double(),
  ..   `Delta 13 C (o/oo)` = col_double(),
  ..   Comments = col_character()
  .. )
 - attr(*, "problems")=<externalptr>

Reading data: other file formats

The readxl package provides functions for reading Excel files

penguins_raw <- readxl::read_excel("data/penguins/penguins_raw.xlsx")

New names:
• `` -> `...1`

head(penguins_raw, 2)

# A tibble: 2 × 18
  ...1  studyName `Sample Number` Species    Region Island Stage `Individual ID`
  <chr> <chr>               <dbl> <chr>      <chr>  <chr>  <chr> <chr>          
1 1     PAL0708                 1 Adelie Pe… Anvers Torge… Adul… N1A1           
2 2     PAL0708                 2 Adelie Pe… Anvers Torge… Adul… N1A2           
# ℹ 10 more variables: `Clutch Completion` <chr>, `Date Egg` <dttm>,
#   `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>,
#   `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>,
#   `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>

## Read a subset 
penguins_subset <- readxl::read_excel("data/penguins/penguins_raw.xlsx", sheet = "Sheet1", range = "B1:O345")
head(penguins_subset, 2)

# A tibble: 2 × 14
  studyName `Sample Number` Species          Region Island Stage `Individual ID`
  <chr>               <dbl> <chr>            <chr>  <chr>  <chr> <chr>          
1 PAL0708                 1 Adelie Penguin … Anvers Torge… Adul… N1A1           
2 PAL0708                 2 Adelie Penguin … Anvers Torge… Adul… N1A2           
# ℹ 7 more variables: `Clutch Completion` <chr>, `Date Egg` <dttm>,
#   `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>,
#   `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>

Reading data: other file formats

The haven package provides functions for reading SPSS, Stata, and SAS files
It looks like SPSS does not support spaces in column names so this is slightly different

penguins_raw <- haven::read_sav("data/penguins/penguins_raw.sav")
head(penguins_raw, 2)

# A tibble: 2 × 17
  study_name sample_number species             region island stage individual_id
  <chr>              <dbl> <chr>               <chr>  <chr>  <chr> <chr>        
1 PAL0708                1 Adelie Penguin (Py… Anvers Torge… Adul… N1A1         
2 PAL0708                2 Adelie Penguin (Py… Anvers Torge… Adul… N1A2         
# ℹ 10 more variables: clutch_completion <chr>, date_egg <date>,
#   culmen_length_mm <dbl>, culmen_depth_mm <dbl>, flipper_length_mm <dbl>,
#   body_mass_g <dbl>, sex <chr>, delta_15_n_o_oo <dbl>, delta_13_c_o_oo <dbl>,
#   comments <chr>

Reading big data: the `arrow` package

The arrow package provides functions for reading Parquet and Feather files
Optimized formats used in many data science projects
Provides facility to read from “object storage” (e.g., Amazon S3)

The major benefits of object storage are the virtually unlimited scalability and the lower cost of storing large volumes of data for use cases such as data lakes, cloud native applications, analytics, log files, and machine learning (ML). ¹

Rule of thumb:
- use parquet for large files and long term storage
  - optimized file size
- use feather for optimized reading and short term storage
  - memory layout the same as in the process

Reading big data: the `arrow` package

penguins_raw <- arrow::read_parquet("data/penguins/penguins_raw.parquet")
penguins_raw <- arrow::read_feather("data/penguins/penguins_raw.feather")
head(penguins_raw, 2)

# A tibble: 2 × 17
  studyName `Sample Number` Species          Region Island Stage `Individual ID`
  <chr>               <dbl> <chr>            <chr>  <chr>  <chr> <chr>          
1 PAL0708                 1 Adelie Penguin … Anvers Torge… Adul… N1A1           
2 PAL0708                 2 Adelie Penguin … Anvers Torge… Adul… N1A2           
# ℹ 10 more variables: `Clutch Completion` <chr>, `Date Egg` <date>,
#   `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>,
#   `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>,
#   `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>

Support for complex data structures

penguin_species_island <- arrow::read_parquet('data/penguins/penguin_species_nested.parquet')
head(penguin_species_island, 2)

# A tibble: 2 × 2
  Island                                 data
  <chr>     <list<tbl_df<Species:character>>>
1 Torgersen                          [52 × 1]
2 Biscoe                            [168 × 1]

head(tidyr::unnest(penguin_species_island), 2)

Warning: `cols` is now required when using `unnest()`.
ℹ Please use `cols = c(data)`.

# A tibble: 2 × 2
  Island    Species                            
  <chr>     <chr>                              
1 Torgersen Adelie Penguin (Pygoscelis adeliae)
2 Torgersen Adelie Penguin (Pygoscelis adeliae)

Benchmarks

library(microbenchmark)
microbenchmark(
  csv = readr::read_csv("data/penguins/penguins_raw.csv", 
   show_col_types = FALSE, name_repair = 'minimal'),
  parquet = arrow::read_parquet("data/penguins/penguins_raw.parquet"),
  feather = arrow::read_feather("data/penguins/penguins_raw.feather")
)

Warning in microbenchmark(csv =
readr::read_csv("data/penguins/penguins_raw.csv", : less accurate nanosecond
times to avoid potential integer overflows

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Unit: milliseconds
    expr     min       lq     mean   median       uq       max neval cld
     csv 7.57926 7.868412 8.300206 7.994467 8.246842 11.847032   100 a  
 parquet 1.71585 1.867591 1.974616 1.937373 2.024211  4.510369   100  b 
 feather 1.33168 1.431679 1.559638 1.479936 1.540678  4.132226   100   c

Data types

The most important types of data are:

Data type	Description
Numeric	Approximations of the real numbers, \(\normalsize\mathbb{R}\) (e.g., mileage a car gets: 23.6, 20.9, etc.)
Integer	Whole numbers, \(\normalsize\mathbb{Z}\) (e.g., number of sales: 7, 0, 120, 63, etc.)
Character	Text data (strings, e.g., product names)
Factor	Categorical data for classification (e.g., product groups)
Logical	TRUE, FALSE
Date	Date variables (e.g., sales dates: 21-06-2015, 06-21-15, 21-Jun-2015, etc.)

Variables can be converted from one type to another using the appropriate functions (e.g., as.numeric(), as.integer(), as.character(), as.factor(), as.logical(), as.Date()).

Let’s clean up the penguins!

str(penguins_raw)

tibble [344 × 17] (S3: tbl_df/tbl/data.frame)
 $ studyName          : chr [1:344] "PAL0708" "PAL0708" "PAL0708" "PAL0708" ...
 $ Sample Number      : num [1:344] 1 2 3 4 5 6 7 8 9 10 ...
 $ Species            : chr [1:344] "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" ...
 $ Region             : chr [1:344] "Anvers" "Anvers" "Anvers" "Anvers" ...
 $ Island             : chr [1:344] "Torgersen" "Torgersen" "Torgersen" "Torgersen" ...
 $ Stage              : chr [1:344] "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" ...
 $ Individual ID      : chr [1:344] "N1A1" "N1A2" "N2A1" "N2A2" ...
 $ Clutch Completion  : chr [1:344] "Yes" "Yes" "Yes" "Yes" ...
 $ Date Egg           : Date[1:344], format: "2007-11-11" "2007-11-11" ...
 $ Culmen Length (mm) : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
 $ Culmen Depth (mm)  : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
 $ Flipper Length (mm): num [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
 $ Body Mass (g)      : num [1:344] 3750 3800 3250 NA 3450 ...
 $ Sex                : chr [1:344] "MALE" "FEMALE" "FEMALE" NA ...
 $ Delta 15 N (o/oo)  : num [1:344] NA 8.95 8.37 NA 8.77 ...
 $ Delta 13 C (o/oo)  : num [1:344] NA -24.7 -25.3 NA -25.3 ...
 $ Comments           : chr [1:344] "Not enough blood for isotopes." NA NA "Adult not sampled." ...
 - attr(*, "spec")=
  .. cols(
  ..   studyName = col_character(),
  ..   `Sample Number` = col_double(),
  ..   Species = col_character(),
  ..   Region = col_character(),
  ..   Island = col_character(),
  ..   Stage = col_character(),
  ..   `Individual ID` = col_character(),
  ..   `Clutch Completion` = col_character(),
  ..   `Date Egg` = col_date(format = ""),
  ..   `Culmen Length (mm)` = col_double(),
  ..   `Culmen Depth (mm)` = col_double(),
  ..   `Flipper Length (mm)` = col_double(),
  ..   `Body Mass (g)` = col_double(),
  ..   Sex = col_character(),
  ..   `Delta 15 N (o/oo)` = col_double(),
  ..   `Delta 13 C (o/oo)` = col_double(),
  ..   Comments = col_character()
  .. )

Clean the column names

penguins <- janitor::clean_names(penguins_raw)
str(penguins)

tibble [344 × 17] (S3: tbl_df/tbl/data.frame)
 $ study_name       : chr [1:344] "PAL0708" "PAL0708" "PAL0708" "PAL0708" ...
 $ sample_number    : num [1:344] 1 2 3 4 5 6 7 8 9 10 ...
 $ species          : chr [1:344] "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" ...
 $ region           : chr [1:344] "Anvers" "Anvers" "Anvers" "Anvers" ...
 $ island           : chr [1:344] "Torgersen" "Torgersen" "Torgersen" "Torgersen" ...
 $ stage            : chr [1:344] "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" ...
 $ individual_id    : chr [1:344] "N1A1" "N1A2" "N2A1" "N2A2" ...
 $ clutch_completion: chr [1:344] "Yes" "Yes" "Yes" "Yes" ...
 $ date_egg         : Date[1:344], format: "2007-11-11" "2007-11-11" ...
 $ culmen_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
 $ culmen_depth_mm  : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
 $ flipper_length_mm: num [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
 $ body_mass_g      : num [1:344] 3750 3800 3250 NA 3450 ...
 $ sex              : chr [1:344] "MALE" "FEMALE" "FEMALE" NA ...
 $ delta_15_n_o_oo  : num [1:344] NA 8.95 8.37 NA 8.77 ...
 $ delta_13_c_o_oo  : num [1:344] NA -24.7 -25.3 NA -25.3 ...
 $ comments         : chr [1:344] "Not enough blood for isotopes." NA NA "Adult not sampled." ...
 - attr(*, "spec")=
  .. cols(
  ..   studyName = col_character(),
  ..   `Sample Number` = col_double(),
  ..   Species = col_character(),
  ..   Region = col_character(),
  ..   Island = col_character(),
  ..   Stage = col_character(),
  ..   `Individual ID` = col_character(),
  ..   `Clutch Completion` = col_character(),
  ..   `Date Egg` = col_date(format = ""),
  ..   `Culmen Length (mm)` = col_double(),
  ..   `Culmen Depth (mm)` = col_double(),
  ..   `Flipper Length (mm)` = col_double(),
  ..   `Body Mass (g)` = col_double(),
  ..   Sex = col_character(),
  ..   `Delta 15 N (o/oo)` = col_double(),
  ..   `Delta 13 C (o/oo)` = col_double(),
  ..   Comments = col_character()
  .. )

Data-preprocessing: syntax

|> is the “pipe” operator
- It takes the result of the left side and passes it to the right side as the first argument
- Very useful when “chaining” multiple operations

penguins |>
  head(2)

# A tibble: 2 × 17
  study_name sample_number species             region island stage individual_id
  <chr>              <dbl> <chr>               <chr>  <chr>  <chr> <chr>        
1 PAL0708                1 Adelie Penguin (Py… Anvers Torge… Adul… N1A1         
2 PAL0708                2 Adelie Penguin (Py… Anvers Torge… Adul… N1A2         
# ℹ 10 more variables: clutch_completion <chr>, date_egg <date>,
#   culmen_length_mm <dbl>, culmen_depth_mm <dbl>, flipper_length_mm <dbl>,
#   body_mass_g <dbl>, sex <chr>, delta_15_n_o_oo <dbl>, delta_13_c_o_oo <dbl>,
#   comments <chr>

head(penguins, 2)

# A tibble: 2 × 17
  study_name sample_number species             region island stage individual_id
  <chr>              <dbl> <chr>               <chr>  <chr>  <chr> <chr>        
1 PAL0708                1 Adelie Penguin (Py… Anvers Torge… Adul… N1A1         
2 PAL0708                2 Adelie Penguin (Py… Anvers Torge… Adul… N1A2         
# ℹ 10 more variables: clutch_completion <chr>, date_egg <date>,
#   culmen_length_mm <dbl>, culmen_depth_mm <dbl>, flipper_length_mm <dbl>,
#   body_mass_g <dbl>, sex <chr>, delta_15_n_o_oo <dbl>, delta_13_c_o_oo <dbl>,
#   comments <chr>

Data preprocessing: mutation

Pkg: dplyr provides function for data.frame manipulation
Pkg: stringr provides functions to manipulate strings (characters)
fn: mutate takes each row and applies a function to create a new (or overwrite a) column
fn: select selects columns

library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(stringr)
penguins_subset <- penguins |>
  mutate(
    species = str_split(species, " ", n = 2, simplify = TRUE)[,1],
    is_adult = str_detect(str_to_lower(stage), "adult"),
    is_female = str_detect(str_to_lower(sex), "female"),
    sex = str_to_lower(sex)) |>
  select(species, island, sex, is_adult,  culmen_length_mm, culmen_depth_mm, is_female)
penguins_subset |> head(2)

# A tibble: 2 × 7
  species island    sex    is_adult culmen_length_mm culmen_depth_mm is_female
  <chr>   <chr>     <chr>  <lgl>               <dbl>           <dbl> <lgl>    
1 Adelie  Torgersen male   TRUE                 39.1            18.7 FALSE    
2 Adelie  Torgersen female TRUE                 39.5            17.4 TRUE

Data preprocessing: multiple columns

#penguins_subset <- 
penguins_subset <- penguins_subset |>
  mutate(
    across(starts_with('culmen'), \(x) x / 10),
    across(species:sex, as.factor),
    across(c('is_adult', 'is_female'), as.numeric)
    ) |>
  mutate_if(is.numeric,
    list(scaled = \(x) (x - mean(x, na.rm=TRUE)) / sd(x, na.rm=TRUE))
  ) |>
  rename_with(
    \(name) str_replace(name, "mm", "cm"),
    starts_with('culmen'))
penguins_subset |> select(-starts_with('is')) |> str()

tibble [344 × 6] (S3: tbl_df/tbl/data.frame)
 $ species                : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ sex                    : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
 $ culmen_length_cm       : num [1:344] 3.91 3.95 4.03 NA 3.67 3.93 3.89 3.92 3.41 4.2 ...
 $ culmen_depth_cm        : num [1:344] 1.87 1.74 1.8 NA 1.93 2.06 1.78 1.96 1.81 2.02 ...
 $ culmen_length_cm_scaled: num [1:344] -0.883 -0.81 -0.663 NA -1.323 ...
 $ culmen_depth_cm_scaled : num [1:344] 0.784 0.126 0.43 NA 1.088 ...
 - attr(*, "spec")=
  .. cols(
  ..   studyName = col_character(),
  ..   `Sample Number` = col_double(),
  ..   Species = col_character(),
  ..   Region = col_character(),
  ..   Island = col_character(),
  ..   Stage = col_character(),
  ..   `Individual ID` = col_character(),
  ..   `Clutch Completion` = col_character(),
  ..   `Date Egg` = col_date(format = ""),
  ..   `Culmen Length (mm)` = col_double(),
  ..   `Culmen Depth (mm)` = col_double(),
  ..   `Flipper Length (mm)` = col_double(),
  ..   `Body Mass (g)` = col_double(),
  ..   Sex = col_character(),
  ..   `Delta 15 N (o/oo)` = col_double(),
  ..   `Delta 13 C (o/oo)` = col_double(),
  ..   Comments = col_character()
  .. )

Hint: figuring out what is going on

Split the problem into smaller pieces

\(name) str_replace(name, "mm", "cm")

\(name) str_replace(name, "mm", "cm")

str_replace("ammm", "mm", "cm")

[1] "acmm"

Check help files

?str_replace

Check typeof() and class()

typeof(\(name) str_replace(name, "mm", "cm"))

[1] "closure"

\(\rightarrow\) closures are functions

See if you can produce some outcome on the reduced problem

my_function <- \(name) str_replace(name, "mm", "cm")
my_function('here are some mms')

[1] "here are some cms"

Hint: read the source code

This is only useful if a function is pure R code

str_replace

function (string, pattern, replacement) 
{
    if (!missing(replacement) && is_replacement_fun(replacement)) {
        replacement <- as_function(replacement)
        return(str_transform(string, pattern, replacement))
    }
    check_lengths(string, pattern, replacement)
    switch(type(pattern), empty = no_empty(), bound = no_boundary(), 
        fixed = stri_replace_first_fixed(string, pattern, replacement, 
            opts_fixed = opts(pattern)), coll = stri_replace_first_coll(string, 
            pattern, replacement, opts_collator = opts(pattern)), 
        regex = stri_replace_first_regex(string, pattern, fix_replacement(replacement), 
            opts_regex = opts(pattern)))
}
<bytecode: 0x110e89a00>
<environment: namespace:stringr>

Aside: functions and variable names

Common mistake that leads to cryptic error:

means <- c(4,5,6)
mean[1]

Error in mean[1]: object of type 'closure' is not subsettable

Reducing rows: filtering

Create different subsets of data
“Filter in” (not out) \(\rightarrow\) TRUE rows remain

adelies <- penguins_subset |>
  filter(species == "Adelie")
unique(adelies$species)

[1] Adelie
Levels: Adelie Chinstrap Gentoo

female_adelies <- penguins_subset |>
  filter(species == "Adelie", is_female == 1)
female_adelies |> select(species, sex) |> summary()

      species       sex    
 Adelie   :73   female:73  
 Chinstrap: 0   male  : 0  
 Gentoo   : 0

Reducing rows: summarizing

Calculate any appropriate summary for a variable

library(tidyr)
penguins |>
  drop_na(body_mass_g) |>
  summarize(avg_weight = mean(body_mass_g))

# A tibble: 1 × 1
  avg_weight
       <dbl>
1      4202.

Calculate the summary for each group

penguins_summary <- penguins_subset |>
  drop_na(culmen_length_cm) |>
  group_by(species, sex) |>
  summarize(avg_clength = mean(culmen_length_cm))

`summarise()` has grouped output by 'species'. You can override using the
`.groups` argument.

penguins_summary

# A tibble: 8 × 3
# Groups:   species [3]
  species   sex    avg_clength
  <fct>     <fct>        <dbl>
1 Adelie    female        3.73
2 Adelie    male          4.04
3 Adelie    <NA>          3.78
4 Chinstrap female        4.66
5 Chinstrap male          5.11
6 Gentoo    female        4.56
7 Gentoo    male          4.95
8 Gentoo    <NA>          4.56

Pivot tables

penguins_summary |>
  pivot_wider(names_from = species, values_from = avg_clength)

# A tibble: 3 × 4
  sex    Adelie Chinstrap Gentoo
  <fct>   <dbl>     <dbl>  <dbl>
1 female   3.73      4.66   4.56
2 male     4.04      5.11   4.95
3 <NA>     3.78     NA      4.56

penguins_wide <- penguins_subset |>
  drop_na(culmen_length_cm) |>
  select(culmen_length_cm, species, sex) |>
  pivot_wider(values_from = culmen_length_cm, names_from = species, values_fn = mean) |>
  arrange(sex) |>
  select(sex, Adelie, Chinstrap, Gentoo)
penguins_wide

# A tibble: 3 × 4
  sex    Adelie Chinstrap Gentoo
  <fct>   <dbl>     <dbl>  <dbl>
1 female   3.73      4.66   4.56
2 male     4.04      5.11   4.95
3 <NA>     3.78     NA      4.56

pivot_longer(penguins_wide, cols = -sex, names_to = "species", values_to = "avg_clength")

# A tibble: 9 × 3
  sex    species   avg_clength
  <fct>  <chr>           <dbl>
1 female Adelie           3.73
2 female Chinstrap        4.66
3 female Gentoo           4.56
4 male   Adelie           4.04
5 male   Chinstrap        5.11
6 male   Gentoo           4.95
7 <NA>   Adelie           3.78
8 <NA>   Chinstrap       NA   
9 <NA>   Gentoo           4.56

Exercise

Become the Ornithologist

Read the penguins_raw.feather file
Remove all whitespace and special characters from the column names
Calculate the body mass for each penguin in kg
Create a pivot-table with the median (?median) body mass for each species on each island
- the island names should be in the first column
- the species names should be the remaining columns
Repeat the analysis but only for female penguins

Become the music manager

Read the top10_charts.csv in chart_data
What is the range of dates in this dataset? (Hint: ?min, ?max)
What is the top region in terms of streams overall? (Hint: ?slice_max)
Create a pivot-table of the total streams (in this dataset) within a region on a given day (1st column day, remaining columns region names, values total streams)

Merging data I

Often we have two separate datasets with corresponding groups of rows
- Streams, trackID in top10_charts.csv and Song metadata, trackID in top10_meta.csv
- purchaseid, customerid in noahs-orders.csv and productid, purchaseid in noahs-orders_items.csv and customerid, customer metadata in noahs-customers.csv
Combine data using joins

charts <- readr::read_csv("data/chart_data/top10_charts.csv")

Rows: 7320 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): trackID, region, isrc
dbl  (3): rank, streams, dayNumber
date (1): day

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

songs <- readr::read_csv("data/chart_data/top10_meta.csv")

Rows: 347 Columns: 29
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (12): trackID, trackName, artistName, artistIds, isrc, primary_artistNa...
dbl  (16): explicit, trackPopularity, n_available_markets, danceability, ene...
date  (1): releaseDate

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

str(charts, give.attr=FALSE)

spc_tbl_ [7,320 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ trackID  : chr [1:7320] "012iHyRvQQquWQGUTYvDxy" "017PF4Q3l4DBUiWoXk4OWT" "017PF4Q3l4DBUiWoXk4OWT" "017PF4Q3l4DBUiWoXk4OWT" ...
 $ rank     : num [1:7320] 7 6 7 7 7 8 9 9 10 10 ...
 $ streams  : num [1:7320] 26234 4276985 3688979 3255639 3478044 ...
 $ day      : Date[1:7320], format: "2020-08-14" "2020-03-27" ...
 $ dayNumber: num [1:7320] 1318 1178 1179 1180 1181 ...
 $ region   : chr [1:7320] "at" "global" "global" "global" ...
 $ isrc     : chr [1:7320] "DEUM72004523" "GBAHT1901303" "GBAHT1901303" "GBAHT1901303" ...

str(songs, give.attr=FALSE)

spc_tbl_ [347 × 29] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ trackID              : chr [1:347] "012iHyRvQQquWQGUTYvDxy" "017PF4Q3l4DBUiWoXk4OWT" "01I9AEz658sQnQzCL3K3QG" "033if6Adj8fwBYsQzHOfQ8" ...
 $ trackName            : chr [1:347] "Fall Auf" "Break My Heart" "HOES UP G'S DOWN" "100k Cash" ...
 $ artistName           : chr [1:347] "Cro feat. badchieff" "Dua Lipa" "Shirin David" "Capital Bra feat. Samra" ...
 $ artistIds            : chr [1:347] "3utZ2yeQk0Z3BCOBWP7Vlu,6GoNVmYCl0yUm4pEp80vn6" "6M2wZ9GZgrQXHCFfjv46we" "0JBdTCGs111JKKYfLqOEBa" "4WZGDpNwrC0vNQyl9QzF7d,6h1s4i4XKIYv4ErDelLDN0" ...
 $ isrc                 : chr [1:347] "DEUM72004523" "GBAHT1901303" "DECE72000379" "DECE72000176" ...
 $ explicit             : num [1:347] 0 0 1 0 0 0 0 1 0 0 ...
 $ trackPopularity      : num [1:347] 64 83 75 71 69 82 70 74 89 79 ...
 $ primary_artistName   : chr [1:347] "Cro" "Dua Lipa" "Shirin David" "Capital Bra" ...
 $ primary_artistID     : chr [1:347] "3utZ2yeQk0Z3BCOBWP7Vlu" "6M2wZ9GZgrQXHCFfjv46we" "0JBdTCGs111JKKYfLqOEBa" "4WZGDpNwrC0vNQyl9QzF7d" ...
 $ artistIDs            : chr [1:347] "3utZ2yeQk0Z3BCOBWP7Vlu,6GoNVmYCl0yUm4pEp80vn6" "6M2wZ9GZgrQXHCFfjv46we" "0JBdTCGs111JKKYfLqOEBa" "4WZGDpNwrC0vNQyl9QzF7d,6h1s4i4XKIYv4ErDelLDN0" ...
 $ albumName            : chr [1:347] "Fall Auf" "Future Nostalgia" "HOES UP G'S DOWN" "100k Cash" ...
 $ albumID              : chr [1:347] "1qdHQo41Vkkgs8HtMk5b96" "7fJJK56U9fHixgO0HQkhtI" "15Njx2PcwnNsI65fnbM7Pw" "5cqwoGrjFr3VKYZ9ZC0eL2" ...
 $ available_markets    : chr [1:347] "AD, AE, AL, AR, AT, AU, BA, BE, BG, BH, BO, BR, BY, CA, CH, CL, CO, CR, CY, CZ, DE, DK, DO, DZ, EC, EE, EG, ES,"| __truncated__ "AD, AE, AR, AU, BE, BG, BH, BO, BR, CA, CL, CO, CR, CY, CZ, DK, DO, DZ, EC, EE, EG, ES, FI, FR, GB, GR, GT, HK,"| __truncated__ "AD, AE, AR, AT, AU, BE, BG, BH, BO, BR, CA, CH, CL, CO, CR, CY, CZ, DE, DK, DO, DZ, EC, EE, EG, ES, FI, FR, GB,"| __truncated__ "AD, AE, AR, AT, AU, BE, BG, BH, BO, BR, CA, CH, CL, CO, CR, CY, CZ, DE, DK, DO, DZ, EC, EE, EG, ES, FI, FR, GB,"| __truncated__ ...
 $ n_available_markets  : num [1:347] 92 76 79 79 87 79 3 79 92 92 ...
 $ releaseDate          : Date[1:347], format: "2020-08-13" "2020-03-27" ...
 $ releaseDate_precision: chr [1:347] "day" "day" "day" "day" ...
 $ danceability         : num [1:347] 0.5 0.73 0.73 0.701 0.84 0.795 0.814 0.774 0.641 0.571 ...
 $ energy               : num [1:347] 0.743 0.729 0.777 0.714 0.648 0.607 0.794 0.805 0.324 0.693 ...
 $ key                  : num [1:347] 2 4 1 10 10 7 7 11 11 6 ...
 $ loudness             : num [1:347] -6.65 -3.43 -6.38 -5.91 -5.54 ...
 $ mode                 : num [1:347] 1 0 0 1 0 1 1 0 1 0 ...
 $ speechiness          : num [1:347] 0.0373 0.0886 0.29 0.524 0.0489 0.23 0.0887 0.302 0.0299 0.0545 ...
 $ acousticness         : num [1:347] 0.307 0.167 0.0455 0.289 0.101 0.128 0.119 0.0509 0.698 0.0054 ...
 $ instrumentalness     : num [1:347] 0.00 1.39e-06 1.10e-03 0.00 1.00e-04 1.90e-01 9.00e-04 0.00 0.00 0.00 ...
 $ liveness             : num [1:347] 0.133 0.349 0.0759 0.0883 0.0996 0.111 0.348 0.149 0.328 0.173 ...
 $ valence              : num [1:347] 0.332 0.467 0.578 0.604 0.431 0.25 0.647 0.261 0.273 0.393 ...
 $ tempo                : num [1:347] 166.3 113 177.9 86.9 103 ...
 $ duration_ms          : num [1:347] 191827 221820 130307 173353 124690 ...
 $ time_signature       : num [1:347] 4 4 4 4 4 4 4 4 4 4 ...

Merging data II

The name of the join determines which “ids” are kept
left_join keeps all rows that have an id in the left dataset
inner_join only keeps rows with ids in both datasets

data1 <- data.frame(group = c('a', 'a', 'b','c'), value = c(1,2,3,4)) # missing group 'd'
data2 <- data.frame(group2 = c('a', 'c', 'd'), value2 = factor(c("abc", "def", "ghi"))) # missing group 'b'

left_join(data1, data2, by = c("group" = "group2"))

  group value value2
1     a     1    abc
2     a     2    abc
3     b     3   <NA>
4     c     4    def

right_join(data1, data2, by = c("group" = "group2"))

  group value value2
1     a     1    abc
2     a     2    abc
3     c     4    def
4     d    NA    ghi

inner_join(data1, data2, by = c("group" = "group2"))

  group value value2
1     a     1    abc
2     a     2    abc
3     c     4    def

Special joins

full_join returns all rows from both datasets
semi_join returns only the columns of the left dataset and filters rows with id in the right dataset
anti_join keeps only rows that do not have an id in the right table

full_join(data1, data2, by = c("group" = "group2"))

  group value value2
1     a     1    abc
2     a     2    abc
3     b     3   <NA>
4     c     4    def
5     d    NA    ghi

semi_join(data1, data2, by = c("group" = "group2"))

  group value
1     a     1
2     a     2
3     c     4

filter(data1, group %in% data2$group2)

  group value
1     a     1
2     a     2
3     c     4

anti_join(data1, data2, by = c("group" = "group2"))

  group value
1     b     3

Exercise

BE the music manager

Who are the top 10 artists in terms of total global streams?
What is their most succesful song?
For how many songs are do we not have meta data?
How many songs are there in the data? (Hint: ?n_distinct, or ?length, ?unique)
Save the combined data as top10_all.parquet

References

Links

Bibliography

Footnotes

https://aws.amazon.com/what-is/object-storage/↩︎

The Basics

Why R?

What do we need?

If you already know R

Basic Workflow (R Studio)

Basic Workflow (VS Code)

R Syntax: comments, assignment

Functions

R Syntax: indexing, logic

R Syntax: loops, ranges

R Syntax: conditional logic

Exercise

Example 1

Example 2

Rectangular data frames: creation and access

Rectangular data frames: overview

Rectangular data frames: Indexing

Rectangular data: adding and removing variables

Exercise

Reading data

Reading data: solution

Reading data: other file formats

Reading data: other file formats

Reading big data: the arrow package

Reading big data: the arrow package

Benchmarks

Data types

Let’s clean up the penguins!

Data-preprocessing: syntax

Data preprocessing: mutation

Data preprocessing: multiple columns

Hint: figuring out what is going on

Hint: read the source code

Aside: functions and variable names

Reducing rows: filtering

Reducing rows: summarizing

Pivot tables

Exercise

Merging data I

Merging data II

Special joins

Exercise

References

Footnotes

Reading big data: the `arrow` package

Reading big data: the `arrow` package