4  Data wrangling

Code

# Create references.json file based on the citations in this script
# make sure you have 'bibliography: references.json' in the YAML
rbbt::bbt_update_bib("wrangling.qmd")

Code

## play sound if error encountered
### from: https://sejohnston.com/2015/02/24/make-r-beep-when-r-markdown-finishes-or-when-it-fails/
options(error = function(){    # Beep on error
  beepr::beep(sound = "wilhelm")
  Sys.sleep(2) # 
  }
 )
## and when knitting is complete
.Last <- function() {          # Beep on exiting session
  beepr::beep(sound = "ping")
  Sys.sleep(6) # allow to play for 6 seconds
  }

5 ‘wrangle’ defined

/ˈraŋɡl/

noun

a dispute or argument, typically one that is long and complicated. “an insurance wrangle is holding up compensation payments”

verb

1. have a long, complicated dispute or argument. “the bureaucrats continue wrangling over the fine print”

2. NORTH AMERICAN round up, herd, or take charge of (livestock). “the horses were wrangled early”

5.1 Wrangler

Jeep Wrangler

Wrangler Jeans

Cowboys

6 Data Wrangling

• data wrangling = tidying + transforming
• an often long, arduous stage of analysis

Tidy

• re-shaping
  • e.g., from wide to long data
• outcome:
  • each column = a variable
  • each row = an observation

Transform

• filtering
• creating new variables based on observations (e.g., reaction times)
• computing summary statistics (e.g., means)

Image source: Wickham et al. (n.d.) (all rights reserved)

6.1 Why tidy data?

• helps future you
  • and collaborators
• facilitates sharing your data and code (Laurinavichyute et al., 2022)
• in short: facilitates reproducibility!

6.2 What does tidy data look like?

Three rules (Wickham et al., n.d.):

1. Each variable is a column, each column is a variable
2. Each observation is a row, each row is an observation
3. Each value is a cell, each cell is a single value

Image source: Wickham et al. (n.d.) (all rights reserved)

• N.B., how you define a variable or observation is relative to what you want to do
  • for now, let’s consider a single trial per participant as an observation

7 the tidyverse

• a collection of R packages for tidy data
• you need to load a package at the beginning of every session
  • today we will mostly use functions from the dplyr package
    • if you load the tidyverse you don’t need to also load dplyr

# load tidyverse
library(tidyverse)

package versions

• you can check the package version with:

packageVersion("tidyverse")

[1] '2.0.0'

• need to update?

# update a single package
install.packages("tidyverse")

• what about your other packages?

# which packages need updating?
old.packages()
# update all old packages
update.packages()

7.1 the magritrr pipe %>%

• takes the object before it and feeds it into the next command
  • the pipe could be read as “and then”
  • N.B., there’s a new pipe in town! The R native |> (Ctrl/Cmd+Shift+M)

# take data frame and then...
iris %>%
  # print the head
  head()

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Image source: magrittr documentation (all rights reserved)

7.2 load our data

# load lifetime data
readr::read_csv(here::here("data/data_lifetime_pilot.csv")) 

# A tibble: 4,431 × 28
   RECORDING_SESSION_LABEL TRIAL_INDEX EYE_USED IA_DWELL_TIME
   <chr>                         <dbl> <chr>            <dbl>
 1 px3                               1 RIGHT                0
 2 px3                               2 RIGHT                0
 3 px3                               3 RIGHT                0
 4 px3                               3 RIGHT                0
 5 px3                               3 RIGHT                0
 6 px3                               3 RIGHT                0
 7 px3                               3 RIGHT                0
 8 px3                               3 RIGHT                0
 9 px3                               4 RIGHT                0
10 px3                               5 RIGHT                0
# ℹ 4,421 more rows
# ℹ 24 more variables: IA_FIRST_FIXATION_DURATION <dbl>,
#   IA_FIRST_RUN_DWELL_TIME <dbl>, IA_FIXATION_COUNT <dbl>, IA_ID <dbl>,
#   IA_LABEL <chr>, IA_REGRESSION_IN <dbl>, IA_REGRESSION_IN_COUNT <dbl>,
#   IA_REGRESSION_OUT <dbl>, IA_REGRESSION_OUT_COUNT <dbl>,
#   IA_REGRESSION_PATH_DURATION <dbl>, KeyPress <dbl>, rt <dbl>, bio <chr>,
#   critical <chr>, gender <chr>, item_id <dbl>, list <dbl>, match <chr>, …

• was anything added to the Environment pane (top right box in RStudio)?

7.3 variable assignment with <-

• object_name <- code_output_to_be_saved_as_object_name

# load lifetime data and store it under df_lifetime
df_lifetime <- readr::read_csv(here::here("data/data_lifetime_pilot.csv"), 
                               # for special characters
                               locale = readr::locale(encoding = "latin1") 
                               ) 

• you should now see the object df_lifetime in the Environment pane

A note on annotation

• annotate as you go: provide useful comments to describe your code (# comment)
• you always have at least one collaborator: future you!
  • comments

First we load required libraries.

# load libraries
library(tidyverse) # for e.g., wrangling and plotting
library(here) # for file-paths relative to project folder

8 Tidyverse verbs

• verbs are functions from the tidyverse package
• for data tidying and transforming we’ll mostly use verbs from the dplyr package, which is part of the tidyverse
• check out RLadies Freiburg to see a YouTube video that covers most of these verbs

8.1 rename()

• one of the first things you’ll often want to do is rename some variables
• let’s start by re-naming some of our variables
  • e.g., RECORDING_SESSION_LABEL is a long way of saying ‘participant’

# rename variables
df_lifetime <- df_lifetime %>% # make df_lifetime from df_lifetime BUT THEN
  rename("px" = RECORDING_SESSION_LABEL, # rename a variable and (comma = 'and')
         "trial" = TRIAL_INDEX) # another variable

8.1.1 Exercise

Change the following names:

• EYE_USED to eye
• IA_DWELL_TIME to tt
• IA_FIRST_FIXATION_DURATION to ff
• IA_FIXATION_COUNT to fix_count
• IA_FIRST_RUN_DWELL_TIME to fp
• IA_ID to region_n
• IA_LABEL to region_text

• IA_REGRESSION_IN to reg_in
• IA_REGRESSION_IN_COUNT to reg_in_count
• IA_REGRESSION_OUT to reg_out
• IA_REGRESSION_OUT_COUNT to reg_out_count
• IA_REGRESSION_PATH_DURATION to rpd
• name_vital_status to lifetime

# the names should then look like this:
names(df_lifetime)

 [1] "px"            "trial"         "eye"           "tt"           
 [5] "ff"            "fp"            "fix_count"     "region_n"     
 [9] "region_text"   "reg_in"        "reg_in_count"  "reg_out"      
[13] "reg_out_count" "rpd"           "KeyPress"      "rt"           
[17] "bio"           "critical"      "gender"        "item_id"      
[21] "list"          "match"         "condition"     "name"         
[25] "lifetime"      "tense"         "type"          "yes_press"    

8.2 relocate

• the second step thing you might want to do is reorder your variables so the most important/relevant are near the beginning and ordered logically
  • let’s order our continuous reading time variables from ‘earliest’ to ‘latest’ measure

df_lifetime <- df_lifetime %>%
  relocate(ff,fp,rpd,tt, .after="eye") %>% 
  relocate(region_n, region_text, .after="trial")

names(df_lifetime[1:10])

 [1] "px"          "trial"       "region_n"    "region_text" "eye"        
 [6] "ff"          "fp"          "rpd"         "tt"          "fix_count"  

8.3 mutate()

Make some change

• new columns

df_lifetime <- df_lifetime %>%
  mutate(new_column = "new")

• change existing column

df_lifetime <- df_lifetime %>%
  mutate(new_column = px,
         trial = trial + 5)

• but let’s undo that…

df_lifetime <- df_lifetime %>%
  mutate(trial = trial - 5)

8.3.1 if_else()

• can be used inside mutate()
  • change values based on some logical condition
  • can be used to change an existing column, or create a new one
• ifelse(condition, output_if_true, output_if_false)

df_lifetime <- df_lifetime %>%
  mutate(new_column = if_else(name=="Aaliyah","name is Aaliyah","name is not Aaliyah"))

8.3.2 case_when()

• can be used inside mutate()
  • change values based on multiple logical conditions
  • can be used to change an existing column, or create a new one
• case_when(condition & other_condition | other_condition ~ output, TRUE ~ output_otherwise)
  • if you don’t include TRUE ~ output then NAs will created

df_lifetime <- df_lifetime %>%
  mutate(newer_column = case_when(
    name=="Aaliyah" & trial > 104 ~ "Aaliyah 2nd half",
    name=="Beyoncé" & (px == "px01" | px == "px04") ~ "Beyoncé px04 or px06",
    TRUE ~ "otherwise"))

8.3.3 Exercise

1. Create a new variable accept that checks whether the button pressed (KeyPress) equals the button that corresponds to an acceptance (yes_press)
   • if KeyPress and yes_press are the same, accept should be 1. If not, accept should be 0
   • hint: you will need if_else() or case_when()

2. Create a new variable accuracy where:
   • if match is yes and accept is 1, accuracy is 1
   • if match is no and accept is 0, accuracy is 1
   • if match is yes and accept is 0, accuracy is 0
   • if match is no and accept is 1, accuracy is 0

• the means and summaries should look like this:

mean(df_lifetime$accept)

[1] 0.6068608

summary(as_factor(df_lifetime$accept))

   0    1 
1742 2689 

mean(df_lifetime$accuracy)

[1] 0.6267208

summary(as_factor(df_lifetime$accuracy))

   0    1 
1654 2777 

8.3.4 Extra exercise

3. Create a new variable region, that has the following values based on region_n

• region_n 1 is region verb-1
• region_n 2 is region verb
• region_n 3 is region verb+1
• region_n 4 is region verb+2
• region_n 5 is region verb+3
• region_n 6 is region verb+4

summary(as_factor(df_lifetime$region))

filler verb-1   verb verb+1 verb+2 verb+3 verb+4 
  1024    639    639    639    639    639    212 

4. Now relocate our new variables so that:

• region is before region_n
• KeyPress is after yes_press

names(df_lifetime)

 [1] "px"            "trial"         "region"        "region_n"     
 [5] "region_text"   "eye"           "ff"            "fp"           
 [9] "rpd"           "tt"            "fix_count"     "reg_in"       
[13] "reg_in_count"  "reg_out"       "reg_out_count" "rt"           
[17] "bio"           "critical"      "gender"        "item_id"      
[21] "list"          "match"         "condition"     "name"         
[25] "lifetime"      "tense"         "type"          "yes_press"    
[29] "KeyPress"      "new_column"    "newer_column"  "accept"       
[33] "accuracy"     

8.4 group_by() and ungroup()

Group data by certain variable(s)

• then perform some mutation
• then ungroup the data

df_lifetime <- df_lifetime |>
  group_by(px) |>
  mutate(px_accuracy = mean(accuracy)) %>%
  ungroup()

round(
  range(df_lifetime$px_accuracy),
  2)

[1] 0.26 0.90

8.5 select()

• keep only certain column(s)

df_lifetime %>%
  select(px)

# A tibble: 4,431 × 1
   px   
   <chr>
 1 px3  
 2 px3  
 3 px3  
 4 px3  
 5 px3  
 6 px3  
 7 px3  
 8 px3  
 9 px3  
10 px3  
# ℹ 4,421 more rows

df_lifetime %>%
  select(px, trial)

# A tibble: 4,431 × 2
   px    trial
   <chr> <dbl>
 1 px3       1
 2 px3       2
 3 px3       3
 4 px3       3
 5 px3       3
 6 px3       3
 7 px3       3
 8 px3       3
 9 px3       4
10 px3       5
# ℹ 4,421 more rows

select()

• or remove certain columns

df_lifetime %>%
  select(-px, -trial)

# A tibble: 4,431 × 32
   region region_n region_text    eye      ff    fp   rpd    tt fix_count reg_in
   <chr>     <dbl> <chr>          <chr> <dbl> <dbl> <dbl> <dbl>     <dbl>  <dbl>
 1 filler        1 He owned innu… RIGHT     0     0     0     0         0      0
 2 filler        1 She is a moth… RIGHT     0     0     0     0         0      0
 3 verb-1        1 She            RIGHT     0     0     0     0         0      0
 4 verb          2 will perform   RIGHT     0     0     0     0         0      0
 5 verb+1        3 in prestigiou… RIGHT     0     0     0     0         0      0
 6 verb+2        4 in the future, RIGHT     0     0     0     0         0      0
 7 verb+3        5 as reported i… RIGHT     0     0     0     0         0      0
 8 verb+4        6 as reported i… RIGHT     0     0     0     0         0      0
 9 filler        1 He interviewe… RIGHT     0     0     0     0         0      0
10 verb-1        1 She            RIGHT     0     0     0     0         0      0
# ℹ 4,421 more rows
# ℹ 22 more variables: reg_in_count <dbl>, reg_out <dbl>, reg_out_count <dbl>,
#   rt <dbl>, bio <chr>, critical <chr>, gender <chr>, item_id <dbl>,
#   list <dbl>, match <chr>, condition <chr>, name <chr>, lifetime <chr>,
#   tense <chr>, type <chr>, yes_press <dbl>, KeyPress <dbl>, new_column <chr>,
#   newer_column <chr>, accept <dbl>, accuracy <dbl>, px_accuracy <dbl>

Select criteria

You can also use criteria for select:

• select(starts_with("x")) select columns that start with a character string
• select(ends_with("x")) select columns that end with a character string
• select(contains("x")) select columns that contain a character string
• select(num_range("prefix",10:20)) select columns with a prefix followed by a range of values

8.5.1 Exercise

Remove the example variables we created with mutate:

• new_column and newer_column

# should look like this after
names(df_lifetime)

 [1] "px"            "trial"         "region"        "region_n"     
 [5] "region_text"   "eye"           "ff"            "fp"           
 [9] "rpd"           "tt"            "fix_count"     "reg_in"       
[13] "reg_in_count"  "reg_out"       "reg_out_count" "rt"           
[17] "bio"           "critical"      "gender"        "item_id"      
[21] "list"          "match"         "condition"     "name"         
[25] "lifetime"      "tense"         "type"          "yes_press"    
[29] "KeyPress"      "accept"        "accuracy"      "px_accuracy"  

8.6 filter()

• select certain rows based on certain criteria (==, !=, >, <, |)
  • N.B. when testing logical conditions == is needed

df_lifetime %>%
  filter(trial == 1)

# A tibble: 8 × 32
  px    trial region region_n region_text          eye      ff    fp   rpd    tt
  <chr> <dbl> <chr>     <dbl> <chr>                <chr> <dbl> <dbl> <dbl> <dbl>
1 px3       1 filler        1 He owned innumerabl… RIGHT     0     0     0     0
2 px5       1 filler        1 She is a mother of … RIGHT   145  1603  1603  1603
3 px6       1 filler        1 He is a father of t… RIGHT   147  1224  1224  1224
4 px2       1 filler        1 She made innumerabl… RIGHT    84  1829  1829  1829
5 px7       1 filler        1 In the '70s, he own… RIGHT   138  2456  2456  2456
6 px1       1 filler        1 Beloved morning sho… RIGHT   160  1708  1708  1708
7 px8       1 filler        1 She was a mother of… RIGHT   220   806   806   806
8 px4       1 filler        1 In the '70s, he own… LEFT    171  3557  3557  3557
# ℹ 22 more variables: fix_count <dbl>, reg_in <dbl>, reg_in_count <dbl>,
#   reg_out <dbl>, reg_out_count <dbl>, rt <dbl>, bio <chr>, critical <chr>,
#   gender <chr>, item_id <dbl>, list <dbl>, match <chr>, condition <chr>,
#   name <chr>, lifetime <chr>, tense <chr>, type <chr>, yes_press <dbl>,
#   KeyPress <dbl>, accept <dbl>, accuracy <dbl>, px_accuracy <dbl>

8.7 filter()

What are these code chunks doing?

df_lifetime %>%
  filter(px_accuracy > .5)

df_lifetime %>%
  filter(px == "px3")

df_lifetime %>%
  filter(px == "px3" | trial == "3")

df_lifetime %>%
  filter(px == "px3" & trial != "3")

Logical operators

• symbols used to describe a logical condition

• == is idential (1 == 1)

• != is not identical (1 != 2)

• > is greater than (2 > 1)

• < is less than (1 < 2)

• & and also (for multiple conditions)

• | or (for multiple conditions)

8.7.1 Exercise

1. Create a new dataframe df_crit that includes only critical trials
2. Create a new dataframe df_fill that includes only filler trials

• Tip: trial type is stored in the column type

df_crit |> select(type) |> head()

# A tibble: 6 × 1
  type    
  <chr>   
1 critical
2 critical
3 critical
4 critical
5 critical
6 critical

df_fill |> select(type) |> head()

# A tibble: 6 × 1
  type  
  <chr> 
1 filler
2 filler
3 filler
4 filler
5 filler
6 filler

8.8 distinct()

• like filter(), but for distinct values of a variable
  • “select rows with distinct values for some row(s)”

df_crit %>%
  distinct(px)

# A tibble: 8 × 1
  px   
  <chr>
1 px3  
2 px5  
3 px6  
4 px2  
5 px7  
6 px1  
7 px8  
8 px4  

df_crit %>%
  distinct(px, name)

# A tibble: 639 × 2
   px    name          
   <chr> <chr>         
 1 px3   Edith Piaf    
 2 px3   Aaliyah       
 3 px3   David Beckham 
 4 px3   Jana Novotna  
 5 px3   Grace Kelly   
 6 px3   Nigella Lawson
 7 px3   Coco Chanel   
 8 px3   Ben Kingsley  
 9 px3   Jim Carrey    
10 px3   Judy Garland  
# ℹ 629 more rows

df_crit %>%
  distinct(px, name,
           .keep_all=T)

# A tibble: 639 × 32
   px    trial region region_n region_text eye      ff    fp   rpd    tt
   <chr> <dbl> <chr>     <dbl> <chr>       <chr> <dbl> <dbl> <dbl> <dbl>
 1 px3       3 verb-1        1 She         RIGHT     0     0     0     0
 2 px3       5 verb-1        1 She         RIGHT     0     0     0     0
 3 px3       8 verb-1        1 He          RIGHT     0     0     0     0
 4 px3      10 verb-1        1 She         RIGHT     0     0     0     0
 5 px3      13 verb-1        1 She         RIGHT     0     0     0     0
 6 px3      16 verb-1        1 She         RIGHT     0     0     0     0
 7 px3      18 verb-1        1 She         RIGHT     0     0     0     0
 8 px3      21 verb-1        1 He          RIGHT     0     0     0     0
 9 px3      23 verb-1        1 He          RIGHT     0     0     0     0
10 px3      26 verb-1        1 She         RIGHT     0     0     0     0
# ℹ 629 more rows
# ℹ 22 more variables: fix_count <dbl>, reg_in <dbl>, reg_in_count <dbl>,
#   reg_out <dbl>, reg_out_count <dbl>, rt <dbl>, bio <chr>, critical <chr>,
#   gender <chr>, item_id <dbl>, list <dbl>, match <chr>, condition <chr>,
#   name <chr>, lifetime <chr>, tense <chr>, type <chr>, yes_press <dbl>,
#   KeyPress <dbl>, accept <dbl>, accuracy <dbl>, px_accuracy <dbl>

8.9 arrange()

• sort column(s) in ascending or descending order
  • this is really just for ease of reading

# default: ascending order (A-Z)
df_crit %>%
  distinct(px, trial, name, condition) %>%
  arrange(px, trial)

# A tibble: 639 × 4
   px    trial name            condition
   <chr> <dbl> <chr>           <chr>    
 1 px1       3 Amy Winehouse   deadPP   
 2 px1       5 John Wayne      deadPP   
 3 px1       8 Abraham Lincoln deadPP   
 4 px1      10 Helen Mirren    livingSF 
 5 px1      13 Paul McCartney  livingSF 
 6 px1      16 Ariana Grande   livingPP 
 7 px1      18 Kate Middleton  livingSF 
 8 px1      21 Johan Cruyff    deadSF   
 9 px1      23 Marilyn Monroe  deadPP   
10 px1      26 Biggie Smalls   deadSF   
# ℹ 629 more rows

# descending order (Z-A)
df_crit %>%
  distinct(px, trial, name, condition) %>%
  arrange(desc(px), trial)

# A tibble: 639 × 4
   px    trial name            condition
   <chr> <dbl> <chr>           <chr>    
 1 px8       3 Whitney Houston deadPP   
 2 px8       5 Elton John      livingSF 
 3 px8       8 Jackie Chan     livingPP 
 4 px8      10 Romy Schneider  deadPP   
 5 px8      13 James Cameron   livingSF 
 6 px8      16 Ella Fitzgerald deadSF   
 7 px8      18 Kathryn Hepburn deadPP   
 8 px8      21 Kate Middleton  livingPP 
 9 px8      23 Janis Joplin    deadPP   
10 px8      26 Serena Williams livingSF 
# ℹ 629 more rows

8.10 separate()

• create new columns from a single column

df_crit<- df_crit %>%
  separate(name,
           sep=" ",
           into = c("First","Last"),
           remove = F, # don't remove original column (name)
           extra = "merge") # if extra chunks, combine in 'Last' (von der...)

• opposite: unite()

9 Reshape data

• this is the major step of data tidying

  • make each column a variable
  • make each row an observation
  • make each cell a data point

• what variable and observation mean will depend on what you want to do, and will change at different steps of your analyses

• you typically want long data

  • but our dataset isn’t as long as it could be

9.1 pivot_longer()

• takes wide data and makes it longer
  • converts headers of columns into values of a new column
  • combines the values of those columns into a new condensed column
• takes a few arguments:
  • cols: which columns do we want to combine into a single column?
  • names_to: what should we call the new column containing the previous column names?
  • values_to: what should we call the new column containing the values from the previous columns?

pivot_longer()

df_lifetime %>% 
  select(px,trial,region,ff,fp,rpd,tt,rt,type,accept,condition) %>%
  filter(type=="critical",region=="verb",px!="px3") %>%
  pivot_longer(
  cols = c(ff,fp,rpd,tt,rt), # columns to make long 
  names_to = "measure", # new column name for headers
  values_to = "time" # new column name for values
)

# A tibble: 2,795 × 8
   px    trial region type     accept condition measure  time
   <chr> <dbl> <chr>  <chr>     <dbl> <chr>     <chr>   <dbl>
 1 px5       3 verb   critical      1 livingPP  ff        175
 2 px5       3 verb   critical      1 livingPP  fp        175
 3 px5       3 verb   critical      1 livingPP  rpd       175
 4 px5       3 verb   critical      1 livingPP  tt        321
 5 px5       3 verb   critical      1 livingPP  rt       4736
 6 px5       5 verb   critical      1 livingPP  ff        207
 7 px5       5 verb   critical      1 livingPP  fp        413
 8 px5       5 verb   critical      1 livingPP  rpd       413
 9 px5       5 verb   critical      1 livingPP  tt        413
10 px5       5 verb   critical      1 livingPP  rt       4622
# ℹ 2,785 more rows

Source: PsyTeachR

9.2 pivot_wider()

• takes long data and makes it wider
• takes a few arguments:
  • id_cols: identifying columns
  • names_from: what should we call the new column containing the previous column names?
  • names_prefix:
  • values_from: new column values

pivot_wider()

df_lifetime %>% 
  select(px,trial,region,ff,fp,rpd,tt,rt,type,accept,condition) %>%
  filter(type=="critical",px!="px3") %>%
  pivot_wider(
  id_cols = c(px,trial), # columns to make long 
  names_from = region, # new column name for headers
  names_prefix = "reg_", # new column name for values
  values_from = tt
)

# A tibble: 559 × 8
   px    trial `reg_verb-1` reg_verb `reg_verb+1` `reg_verb+2` `reg_verb+3`
   <chr> <dbl>        <dbl>    <dbl>        <dbl>        <dbl>        <dbl>
 1 px5       3          190      321         1723          672          575
 2 px5       5            0      413          476          279         2441
 3 px5       8          246     1892          967          450          981
 4 px5      10            0      601          932          243          702
 5 px5      13            0      407         1115            0            0
 6 px5      16            0     1010         1502          337         1426
 7 px5      18          238      389         1415          359          584
 8 px5      21            0      376          584          475         2015
 9 px5      23          231      215          717          184          255
10 px5      26          125      347          400          317          981
# ℹ 549 more rows
# ℹ 1 more variable: `reg_verb+4` <dbl>

Source: PsyTeachR

10 Save your tidy data

• once your data is nice and tidy, save it with a new filename
  • this way you always have the same starting point for your data exploration/analyses

# run this manually!
write.csv(df_lifetime, here::here("data/tidy_data_lifetime_pilot.csv"),row.names=FALSE)

11 Summary

• we saw that the equation for a straight line boils down to its intercept and slope

• we fit our first linear model with a categorical predictor

• next, we’ll look at a case with more than one predictor: multiple regression

Important terms

wrangle	have a long dispute
data wrangling	tidying and transforming your data
tidy data	data where each column is a variable and each row is an observation
the tidyverse	a group of packages for tidy data
dplyr	a package within the tidyverse for data wrangling
pipe operator (%>% or |>)	operational function, passes the result of one function/argument to the next
logical operators	compare values of two arguments: &, |, ==, !=, >, <

Important functions

read_csv()	read-in a csv as a tibble (from readr package)
rename()	rename variables
relocate()	move variables
mutate()	change or create new variables
if_else()	condition for `mutate()`
case_when()	handle multiple conditions for `mutate()`
group_by()	group by a certain variable
select()	keep (or exclude) certain variables
filter()	keep (or exclude) rows based on some criteria
distinct()	keep rows with distinct value of given variable(s)
arrange()	sort variable(s) in ascending or descending order
separate()	split a variable into multiple variables
pivot_longer()	make wide data longer
pivot_wider()	make long data wider

12 Session Info

Show code

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.2.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.2    
 [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1   
 [9] ggplot2_3.4.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] utf8_1.2.3        generics_0.1.3    renv_0.17.3       stringi_1.7.12   
 [5] hms_1.1.3         digest_0.6.33     magrittr_2.0.3    evaluate_0.21    
 [9] grid_4.3.0        timechange_0.2.0  fastmap_1.1.1     rprojroot_2.0.3  
[13] jsonlite_1.8.7    httr_1.4.6        fansi_1.0.4       scales_1.2.1     
[17] cli_3.6.1         rlang_1.1.1       crayon_1.5.2      bit64_4.0.5      
[21] munsell_0.5.0     withr_2.5.0       yaml_2.3.7        tools_4.3.0      
[25] parallel_4.3.0    tzdb_0.4.0        colorspace_2.1-0  here_1.0.1       
[29] curl_5.0.1        vctrs_0.6.3       R6_2.5.1          magick_2.7.4     
[33] lifecycle_1.0.3   fs_1.6.2          htmlwidgets_1.6.2 bit_4.0.5        
[37] vroom_1.6.3       rbbt_0.0.0.9000   pkgconfig_2.0.3   pillar_1.9.0     
[41] gtable_0.3.3      Rcpp_1.0.11       glue_1.6.2        xfun_0.39        
[45] tidyselect_1.2.0  rstudioapi_0.15.0 knitr_1.43        htmltools_0.5.5  
[49] rmarkdown_2.23    compiler_4.3.0   

References

Laurinavichyute, A., Yadav, H., & Vasishth, S. (2022). Share the code, not just the data: A case study of the reproducibility of articles published in the Journal of Memory and Language under the open data policy. Journal of Memory and Language, 125, 12.

Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (n.d.). R for Data Science (2nd ed.). https://r4ds.hadley.nz/