Foundations of
Data Science

Spring 2026 | Data 2 (399)
Jeffrey M. Girard | Lecture 04b

Roadmap

Transform data by row using arrange(), filter(), and distinct()
Transform data by column using mutate(), select(), rename(), and relocate()

Basic wrangling verbs

tidyverse provides tools for wrangling tibbles
- These functions are named after verbs
- So if you name your objects after nouns…
- …your code becomes easier to read

Noun(noun) ❌	Verb(noun) ✔️
`blender(fruit)`	`blend(fruit)`
`screwdriver(screw)`	`drive(screw)`
`boxcutter(box)`	`cut(box)`

Important: Saving changes

dplyr functions do not modify the original
- Running a command returns a copy of the result
- It does not change the original object!
- This is useful in teaching but less so in practice

To save changes, you must use assignment <-
- Print only: arrange(data, var)
- Save/update: data <- arrange(data, var)

Setup

library(tidyverse)
data("starwars", package = "dplyr")

glimpse(starwars)

Rows: 87
Columns: 14
$ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Or…
$ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180, 2…
$ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, 77.…
$ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "brown", N…
$ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "light", "…
$ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "blue",…
$ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.0, …
$ sex        <chr> "male", "none", "none", "male", "female", "male", "female",…
$ gender     <chr> "masculine", "masculine", "masculine", "masculine", "femini…
$ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", "T…
$ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human", "Huma…
$ films      <list> <"A New Hope", "The Empire Strikes Back", "Return of the J…
$ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp…
$ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",…

Rows

Row-focused verbs

Arrange
- Reorders the rows according to the values in specified columns

Filter
- Retains only the rows that satisfy your specified conditions

Distinct
- Removes duplicate rows to retain only unique combinations of values

Arrange

Arrange alphabetically from A to Z

# Sort the rows by the name variable (ascending)
arrange(starwars, name)

# A tibble: 87 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Ackbar       180    83 none       brown mot… orange          41   male  mascu…
2 Adi Gall…    184    50 none       dark       blue            NA   fema… femin…
3 Anakin S…    188    84 blond      fair       blue            41.9 male  mascu…
4 Arvel Cr…     NA    NA brown      fair       brown           NA   male  mascu…
5 Ayla Sec…    178    55 none       blue       hazel           48   fema… femin…
6 BB8           NA    NA none       none       black           NA   none  mascu…
7 Bail Pre…    191    NA black      tan        brown           67   male  mascu…
8 Barriss …    166    50 black      yellow     blue            40   fema… femin…
# ℹ 79 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Arrange alphabetically from Z to A

# Sort the rows by the name variable (descending)
arrange(starwars, desc(name))

# A tibble: 87 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Zam Wese…    168    55 blonde     fair, gre… yellow            NA fema… femin…
2 Yoda          66    17 white      green      brown            896 male  mascu…
3 Yarael P…    264    NA none       white      yellow            NA male  mascu…
4 Wilhuff …    180    NA auburn, g… fair       blue              64 male  mascu…
5 Wicket S…     88    20 brown      brown      brown              8 male  mascu…
6 Wedge An…    170    77 brown      fair       hazel             21 male  mascu…
7 Watto        137    NA black      blue, grey yellow            NA male  mascu…
8 Wat Tamb…    193    48 none       green, gr… unknown           NA male  mascu…
# ℹ 79 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Arrange numerically

# Sort the rows by height from shortest to tallest
arrange(starwars, height)

# A tibble: 87 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Yoda          66    17 white      green      brown            896 male  mascu…
2 Ratts Ty…     79    15 none       grey, blue unknown           NA male  mascu…
3 Wicket S…     88    20 brown      brown      brown              8 male  mascu…
4 Dud Bolt      94    45 none       blue, grey yellow            NA male  mascu…
5 R2-D2         96    32 <NA>       white, bl… red               33 none  mascu…
6 R4-P17        96    NA none       silver, r… red, blue         NA none  femin…
7 R5-D4         97    32 <NA>       white, red red               NA none  mascu…
8 Sebulba      112    40 none       grey, red  orange            NA male  mascu…
# ℹ 79 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Arrange by multiple variables

# Sort by multiple variables to break ties
arrange(starwars, hair_color, mass)

# A tibble: 87 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Mon Moth…    150  NA   auburn     fair       blue            48   fema… femin…
2 Wilhuff …    180  NA   auburn, g… fair       blue            64   male  mascu…
3 Obi-Wan …    182  77   auburn, w… fair       blue-gray       57   male  mascu…
4 Barriss …    166  50   black      yellow     blue            40   fema… femin…
5 Luminara…    170  56.2 black      yellow     blue            58   fema… femin…
6 Boba Fett    183  78.2 black      fair       brown           31.5 male  mascu…
7 Lando Ca…    177  79   black      dark       brown           31   male  mascu…
8 Jango Fe…    183  79   black      tan        brown           66   male  mascu…
# ℹ 79 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Filter

Filter by exact match

# Retain only rows where the species is exactly "Droid"
filter(starwars, species == "Droid")

# A tibble: 6 × 14
  name   height  mass hair_color skin_color  eye_color birth_year sex   gender  
  <chr>   <int> <dbl> <chr>      <chr>       <chr>          <dbl> <chr> <chr>   
1 C-3PO     167    75 <NA>       gold        yellow           112 none  masculi…
2 R2-D2      96    32 <NA>       white, blue red               33 none  masculi…
3 R5-D4      97    32 <NA>       white, red  red               NA none  masculi…
4 IG-88     200   140 none       metal       red               15 none  masculi…
5 R4-P17     96    NA none       silver, red red, blue         NA none  feminine
6 BB8        NA    NA none       none        black             NA none  masculi…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Filter by numeric condition

# Retain only characters with a body mass greater than or equal to 100
filter(starwars, mass >= 100)

# A tibble: 10 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
2 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
3 Chewbacca    228   112 brown      unknown    blue           200   male  mascu…
4 Jabba De…    175  1358 <NA>       green-tan… orange         600   herm… mascu…
5 Jek Tono…    180   110 brown      fair       blue            NA   <NA>  <NA>  
6 IG-88        200   140 none       metal      red             15   none  mascu…
7 Bossk        190   113 none       green      red             53   male  mascu…
8 Dexter J…    198   102 none       brown      yellow          NA   male  mascu…
# ℹ 2 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Filter by multiple conditions (AND)

# Retain "Droid" characters AND those with mass < 50
filter(starwars, species == "Droid", mass < 50)

# A tibble: 2 × 14
  name  height  mass hair_color skin_color  eye_color birth_year sex   gender   
  <chr>  <int> <dbl> <chr>      <chr>       <chr>          <dbl> <chr> <chr>    
1 R2-D2     96    32 <NA>       white, blue red               33 none  masculine
2 R5-D4     97    32 <NA>       white, red  red               NA none  masculine
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Filter by multiple conditions (OR)

# Retain characters that have sex equal to male OR none
filter(starwars, sex == "male" | sex == "none")

# A tibble: 66 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu…
2 C-3PO        167    75 <NA>       gold       yellow         112   none  mascu…
3 R2-D2         96    32 <NA>       white, bl… red             33   none  mascu…
4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
5 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
6 R5-D4         97    32 <NA>       white, red red             NA   none  mascu…
7 Biggs Da…    183    84 black      light      brown           24   male  mascu…
8 Obi-Wan …    182    77 auburn, w… fair       blue-gray       57   male  mascu…
# ℹ 58 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Distinct

Find distinct values

# Find the unique homeworlds listed in the dataset
distinct(starwars, homeworld)

# A tibble: 49 × 1
  homeworld
  <chr>    
1 Tatooine 
2 Naboo    
3 Alderaan 
4 Stewjon  
5 Eriadu   
6 Kashyyyk 
7 Corellia 
8 Rodia    
# ℹ 41 more rows

Find distinct combinations

# Find unique combinations of species and sex
distinct(starwars, species, sex)

# A tibble: 41 × 2
  species        sex           
  <chr>          <chr>         
1 Human          male          
2 Droid          none          
3 Human          female        
4 Wookiee        male          
5 Rodian         male          
6 Hutt           hermaphroditic
7 <NA>           <NA>          
8 Yoda's species male          
# ℹ 33 more rows

Columns

Column-focused verbs

Select
- Retains specific columns and drops the rest

Mutate
- Creates new columns or modifies existing ones

Rename
- Changes the names of specific columns

Relocate
- Changes the positions of specific columns

Select

Select specific columns

# Retain only the name, height, and mass columns
select(starwars, name, height, mass)

# A tibble: 87 × 3
  name               height  mass
  <chr>               <int> <dbl>
1 Luke Skywalker        172    77
2 C-3PO                 167    75
3 R2-D2                  96    32
4 Darth Vader           202   136
5 Leia Organa           150    49
6 Owen Lars             178   120
7 Beru Whitesun Lars    165    75
8 R5-D4                  97    32
# ℹ 79 more rows

Drop specific columns

# Retain all columns EXCEPT films, vehicles, and starships
select(starwars, -films, -vehicles, -starships)

# A tibble: 87 × 11
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu…
2 C-3PO        167    75 <NA>       gold       yellow         112   none  mascu…
3 R2-D2         96    32 <NA>       white, bl… red             33   none  mascu…
4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
5 Leia Org…    150    49 brown      light      brown           19   fema… femin…
6 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
7 Beru Whi…    165    75 brown      light      blue            47   fema… femin…
8 R5-D4         97    32 <NA>       white, red red             NA   none  mascu…
# ℹ 79 more rows
# ℹ 2 more variables: homeworld <chr>, species <chr>

Select all between two columns

# Retain all columns between height and gender
select(starwars, height:gender)

# A tibble: 87 × 8
  height  mass hair_color  skin_color  eye_color birth_year sex    gender   
   <int> <dbl> <chr>       <chr>       <chr>          <dbl> <chr>  <chr>    
1    172    77 blond       fair        blue            19   male   masculine
2    167    75 <NA>        gold        yellow         112   none   masculine
3     96    32 <NA>        white, blue red             33   none   masculine
4    202   136 none        white       yellow          41.9 male   masculine
5    150    49 brown       light       brown           19   female feminine 
6    178   120 brown, grey light       blue            52   male   masculine
7    165    75 brown       light       blue            47   female feminine 
8     97    32 <NA>        white, red  red             NA   none   masculine
# ℹ 79 more rows

Select using helpers

# Retain only columns that end with the phrase "color"
select(starwars, ends_with("color"))

# A tibble: 87 × 3
  hair_color  skin_color  eye_color
  <chr>       <chr>       <chr>    
1 blond       fair        blue     
2 <NA>        gold        yellow   
3 <NA>        white, blue red      
4 none        white       yellow   
5 brown       light       brown    
6 brown, grey light       blue     
7 brown       light       blue     
8 <NA>        white, red  red      
# ℹ 79 more rows

See also: starts_with(), contains(), matches(), and where()

Mutate

Mutate new columns

# Create a new column and add all the way to the right
mutate(starwars, height_m = height / 100)

# A tibble: 87 × 15
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu…
2 C-3PO        167    75 <NA>       gold       yellow         112   none  mascu…
3 R2-D2         96    32 <NA>       white, bl… red             33   none  mascu…
4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
5 Leia Org…    150    49 brown      light      brown           19   fema… femin…
6 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
7 Beru Whi…    165    75 brown      light      blue            47   fema… femin…
8 R5-D4         97    32 <NA>       white, red red             NA   none  mascu…
# ℹ 79 more rows
# ℹ 6 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>, height_m <dbl>

Mutate existing columns

# Modify the height variable "in place"
mutate(starwars, height = height * 10)

# A tibble: 87 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <dbl> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Luke Sky…   1720    77 blond      fair       blue            19   male  mascu…
2 C-3PO       1670    75 <NA>       gold       yellow         112   none  mascu…
3 R2-D2        960    32 <NA>       white, bl… red             33   none  mascu…
4 Darth Va…   2020   136 none       white      yellow          41.9 male  mascu…
5 Leia Org…   1500    49 brown      light      brown           19   fema… femin…
6 Owen Lars   1780   120 brown, gr… light      blue            52   male  mascu…
7 Beru Whi…   1650    75 brown      light      blue            47   fema… femin…
8 R5-D4        970    32 <NA>       white, red red             NA   none  mascu…
# ℹ 79 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Rename and Relocate

Rename columns

# Change the name of 'homeworld' to 'planet' (new = old)
rename(starwars, planet = homeworld)

# A tibble: 87 × 14
  name      height  mass hair_color skin_color eye_color birth_year sex   gender
  <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu…
2 C-3PO        167    75 <NA>       gold       yellow         112   none  mascu…
3 R2-D2         96    32 <NA>       white, bl… red             33   none  mascu…
4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
5 Leia Org…    150    49 brown      light      brown           19   fema… femin…
6 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
7 Beru Whi…    165    75 brown      light      blue            47   fema… femin…
8 R5-D4         97    32 <NA>       white, red red             NA   none  mascu…
# ℹ 79 more rows
# ℹ 5 more variables: planet <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>

Relocate columns

# Move the 'species' column to the very first position
relocate(starwars, species, .before = name)

# A tibble: 87 × 14
  species name     height  mass hair_color skin_color eye_color birth_year sex  
  <chr>   <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>
1 Human   Luke Sk…    172    77 blond      fair       blue            19   male 
2 Droid   C-3PO       167    75 <NA>       gold       yellow         112   none 
3 Droid   R2-D2        96    32 <NA>       white, bl… red             33   none 
4 Human   Darth V…    202   136 none       white      yellow          41.9 male 
5 Human   Leia Or…    150    49 brown      light      brown           19   fema…
6 Human   Owen La…    178   120 brown, gr… light      blue            52   male 
7 Human   Beru Wh…    165    75 brown      light      blue            47   fema…
8 Droid   R5-D4        97    32 <NA>       white, red red             NA   none 
# ℹ 79 more rows
# ℹ 5 more variables: gender <chr>, homeworld <chr>, films <list>,
#   vehicles <list>, starships <list>

Can also use .after and position numbers, e.g., .after = 1

Foundations of Data Science

Roadmap

Basic wrangling verbs

Important: Saving changes

Setup

Rows

Row-focused verbs

Arrange

Arrange alphabetically from A to Z

Arrange alphabetically from Z to A

Arrange numerically

Arrange by multiple variables

Filter

Filter by exact match

Filter by numeric condition

Filter by multiple conditions (AND)

Filter by multiple conditions (OR)

Distinct

Find distinct values

Find distinct combinations

Columns

Column-focused verbs

Select

Select specific columns

Drop specific columns

Select all between two columns

Select using helpers

Mutate

Mutate new columns

Mutate existing columns

Rename and Relocate

Rename columns

Relocate columns

Foundations of
Data Science