- Data come in various formats (long, wide) and data type (xlsx, ods, json, csv, sav)
- No format is suitable for every goal
- Fluency in data wrangling gives you a lot of power.
- Make data format suitable to use: e.g. for statistical models (correlations, linear regression), functions, data viz, summary table
- Calculate new variables, filter or combine data
- Reveal information
- Summarise information
- (also creating counterbalanced, randomised stimulus lists)
Goals of data wrangling
Collection of R packages for data science that share:
- common data philosophies
- grammar
- data structures
- best practice
- designed to work together
# Installs 19 packages
install.packages("tidyverse")
# Loads 6 packages library(tidyverse)
Tidy data
- Each variable must have its own column.
- Each observation must have its own row.
- Each value have its own cell.
Why?
- Placing variables in columns takes advantage of R’s vectorised nature (faster processing, more compact code).
- Consistent data structure allows easier learning because tidyverse functions have similar underpinning principles.
tidyverse: verbs
Functions that do specific things to our data.
Must know:
read_csv,write_csv,glimpse,select,filter,mutate,group_by/ungroup,summarise,pivot_wider/_longerAlso important:
count,pull,slice,across,recode,unique,n,where,everything,~and.,map,starts_with,ends_with,contains,separate,unite,transmute,_join,bind_rows/_colsThere are more but these are the most important ones.
tbls (tibbles)
tidyverseis operating with tibbles- Type of data structure
- Easier to read in console
# Imports data as data frame
data_as_frame <- read.csv("path_to_data/data.csv")
# Imports data as tibble
data_as_tibble <- read_csv("path_to_data/data.csv")
.csv: comma separated filereadrpackage: e.g.read_csv,read_delim,read_tsv
For other data formats:
havenpackage: e.g.read_dta,read_sav,read_sasreadxlpackage: e.g.read_excel,read_xls,read_xlsx
# Summarise data structure in base R str(data_as_frame)
# Summarise data structure in tidyverse glimpse(data_as_tibble)
Open exercise script 1
tidyverse functions
Functions follow the principle
function_name(data_name, argument)
where argument specifies which variable / condition etc. the function has to operate on.
# Picking out variables select(data, variable) # Subsetting data filter(data, variable > 100) # Change / add variables mutate(data, sqr = variable^2) # Aggregate data summarise(data, avg = mean(variable))
Selecting variables
Extracts variables you’re interested in.
blomkvist <- read_csv("data/blomkvist.csv")glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10… $ sex <chr> "male", "female", "female", "… $ age <dbl> 84, 37, 62, 85, 73, 65, 30, 4… $ medicine <dbl> 8, 1, 0, 4, 5, 0, 0, 0, 11, 0… $ meds_cat <chr> "a lot", "little", "none", "f… $ smoker <chr> "former", "no", "yes", "forme… $ rt_hand_d <dbl> 702, 471, 639, 708, 607, 542,… $ rt_hand_nd <dbl> 780, 497, 638, 639, 652, 499,… $ rt_foot_d <dbl> 1009, 738, 878, 902, 923, 687… $ rt_foot_nd <dbl> 963, 692, 786, 1374, 805, 600…
Selecting variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, id, sex, age)
# A tibble: 267 × 3
id sex age
<dbl> <chr> <dbl>
1 1 male 84
2 2 female 37
3 3 female 62
4 4 female 85
5 5 male 73
# ℹ 262 more rowsSelecting variables: select range
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, id:age)
# A tibble: 267 × 3
id sex age
<dbl> <chr> <dbl>
1 1 male 84
2 2 female 37
3 3 female 62
4 4 female 85
5 5 male 73
# ℹ 262 more rowsSelecting variables: index
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, 1, 2, 3)
# A tibble: 267 × 3
id sex age
<dbl> <chr> <dbl>
1 1 male 84
2 2 female 37
3 3 female 62
4 4 female 85
5 5 male 73
# ℹ 262 more rowsSelecting variables: index range
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, 1:3)
# A tibble: 267 × 3
id sex age
<dbl> <chr> <dbl>
1 1 male 84
2 2 female 37
3 3 female 62
4 4 female 85
5 5 male 73
# ℹ 262 more rowsSelecting variables: rename
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, id, sex, rt = rt_hand_d)
# A tibble: 267 × 3
id sex rt
<dbl> <chr> <dbl>
1 1 male 702.
2 2 female 471.
3 3 female 639.
4 4 female 708
5 5 male 607.
# ℹ 262 more rowsSelecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, id, starts_with("rt_"))
# A tibble: 267 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 702. 780. 1009 963.
2 2 471. 497 738. 692.
3 3 639. 638 878 786
4 4 708 639. 902. 1374.
5 5 607. 652 923 805
# ℹ 262 more rowsSelecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, ends_with("d"))
???
Selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, id, ends_with("d"))
# A tibble: 267 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 702. 780. 1009 963.
2 2 471. 497 738. 692.
3 3 639. 638 878 786
4 4 708 639. 902. 1374.
5 5 607. 652 923 805
# ℹ 262 more rowsSelecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, id, contains("hand"))
???
Selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, id, contains("hand"))
# A tibble: 267 × 3
id rt_hand_d rt_hand_nd
<dbl> <dbl> <dbl>
1 1 702. 780.
2 2 471. 497
3 3 639. 638
4 4 708 639.
5 5 607. 652
# ℹ 262 more rows(Un-)selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, -contains("hand"))
# A tibble: 267 × 8
id sex age medicine meds_cat smoker rt_foot_d rt_foot_nd
<dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
1 1 male 84 8 a lot former 1009 963.
2 2 female 37 1 little no 738. 692.
3 3 female 62 0 none yes 878 786
4 4 female 85 4 few former 902. 1374.
5 5 male 73 5 a lot former 923 805
# ℹ 262 more rows(Un-)selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, -ends_with("_d"))
???
(Un-)selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, -ends_with("_d"))
# A tibble: 267 × 8
id sex age medicine meds_cat smoker rt_hand_nd rt_foot_nd
<dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
1 1 male 84 8 a lot former 780. 963.
2 2 female 37 1 little no 497 692.
3 3 female 62 0 none yes 638 786
4 4 female 85 4 few former 639. 1374.
5 5 male 73 5 a lot former 652 805
# ℹ 262 more rows(Un-)selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, -sex:-smoker)
???
(Un-)selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, -sex:-smoker)
# A tibble: 267 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 702. 780. 1009 963.
2 2 471. 497 738. 692.
3 3 639. 638 878 786
4 4 708 639. 902. 1374.
5 5 607. 652 923 805
# ℹ 262 more rowsSelecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, where(is.character))
# A tibble: 267 × 3 sex meds_cat smoker <chr> <chr> <chr> 1 male a lot former 2 female little no 3 female none yes 4 female few former 5 male a lot former # ℹ 262 more rows
Selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, where(is.numeric))
???
Selecting multiple variables
Extracts variables you’re interested in.
glimpse(blomkvist)
Rows: 267 Columns: 10 $ id <dbl> 1, 2, 3, … $ sex <chr> "male", "… $ age <dbl> 84, 37, 6… $ medicine <dbl> 8, 1, 0, … $ meds_cat <chr> "a lot", … $ smoker <chr> "former",… $ rt_hand_d <dbl> 702, 471,… $ rt_hand_nd <dbl> 780, 497,… $ rt_foot_d <dbl> 1009, 738… $ rt_foot_nd <dbl> 963, 692,…
select(blomkvist, where(is.numeric))
# A tibble: 267 × 7
id age medicine rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 84 8 702. 780. 1009 963.
2 2 37 1 471. 497 738. 692.
3 3 62 0 639. 638 878 786
4 4 85 4 708 639. 902. 1374.
5 5 73 5 607. 652 923 805
# ℹ 262 more rows
Continue with exercise 2
Filtering data
Select variables of interest
blomkvist_rt <- select(blomkvist, id, smoker, age, rt = rt_hand_d)
Subsetting data by selecting rows that meet one condition or more.
filter(data, condition)
Continuous variables
filter(blomkvist_rt, rt >= 708)
# A tibble: 64 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 4 former 85 708
2 9 former 83 737.
3 12 no 88 889
4 13 yes 62 884.
5 14 former 88 832.
# ℹ 59 more rowsfilter(blomkvist_rt, rt > 708)
# A tibble: 62 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 9 former 83 737.
2 12 no 88 889
3 13 yes 62 884.
4 14 former 88 832.
5 17 former 80 930
# ℹ 57 more rowsContinuous variables
filter(blomkvist_rt, rt > 708, rt < 900)
# A tibble: 38 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 9 former 83 737.
2 12 no 88 889
3 13 yes 62 884.
4 14 former 88 832.
5 39 no 88 727
# ℹ 33 more rows
Continuous variables
filter(blomkvist_rt, rt > 708 | rt < 900)
# A tibble: 266 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 2 no 37 471.
3 3 yes 62 639.
4 4 former 85 708
5 5 former 73 607.
# ℹ 261 more rows
# which is a logical tautology really
filter(blomkvist_rt, rt < 708 | rt > 900)
# A tibble: 226 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 2 no 37 471.
3 3 yes 62 639.
4 5 former 73 607.
5 6 no 65 542.
# ℹ 221 more rowsContinuous variables
filter(blomkvist_rt, rt > mean(rt, na.rm = TRUE))
# A tibble: 100 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 4 former 85 708
3 9 former 83 737.
4 12 no 88 889
5 13 yes 62 884.
# ℹ 95 more rowsmean(blomkvist_rt$rt, na.rm = TRUE)
[1] 639
NB. What’s na.rm = TRUE?
# Data with missing values y <- c(100, 1150, 200, 43, NA, 15)
mean(y)
[1] NA
mean(y, na.rm = TRUE)
[1] 302
sd(y, na.rm = TRUE)
[1] 480
Categorical variables
filter(blomkvist_rt, smoker == "yes")
# A tibble: 26 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 3 yes 62 639.
2 10 yes 58 550.
3 13 yes 62 884.
4 24 yes 57 612.
5 28 yes 59 586.
# ℹ 21 more rowsunique(blomkvist_rt$smoker)
[1] "former" "no" "yes" NA
Categorical variables
filter(blomkvist_rt, smoker != "yes")
# A tibble: 240 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 2 no 37 471.
3 4 former 85 708
4 5 former 73 607.
5 6 no 65 542.
# ℹ 235 more rowsunique(blomkvist_rt$smoker)
[1] "former" "no" "yes" NA
Categorical variables
filter(blomkvist_rt, smoker %in% c("yes", "former"))
# A tibble: 103 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 3 yes 62 639.
3 4 former 85 708
4 5 former 73 607.
5 8 former 49 509.
# ℹ 98 more rowsfilter(blomkvist_rt, !(smoker %in% c("yes", "former")))
# A tibble: 164 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 2 no 37 471.
2 6 no 65 542.
3 7 no 30 571.
4 11 no 25 548
5 12 no 88 889
# ℹ 159 more rowsMissing data
unique(blomkvist_rt$smoker)
[1] "former" "no" "yes" NA
filter(blomkvist_rt, smoker == "NA") # ooops!!!
# A tibble: 0 × 4 # ℹ 4 variables: id <dbl>, smoker <chr>, age <dbl>, rt <dbl>
filter(blomkvist_rt, smoker == NA) # double ooops!!!
# A tibble: 0 × 4 # ℹ 4 variables: id <dbl>, smoker <chr>, age <dbl>, rt <dbl>
Missing data
filter(blomkvist_rt, is.na(smoker))
# A tibble: 1 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 149 <NA> 34 479.filter(blomkvist_rt, !is.na(smoker))
# A tibble: 266 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 2 no 37 471.
3 3 yes 62 639.
4 4 former 85 708
5 5 former 73 607.
# ℹ 261 more rows
Continue with exercise 3
Mutating data
Adding new variables to the data or changing existing ones.
mutate(blomkvist_rt, rt_2 = rt * rt)
# A tibble: 267 × 5
id smoker age rt rt_2
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 former 84 702. 492336.
2 2 no 37 471. 221527.
3 3 yes 62 639. 407895.
4 4 former 85 708 501264
5 5 former 73 607. 368854.
# ℹ 262 more rowsmutate(blomkvist_rt, rt_2 = rt^2)
# A tibble: 267 × 5
id smoker age rt rt_2
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 former 84 702. 492336.
2 2 no 37 471. 221527.
3 3 yes 62 639. 407895.
4 4 former 85 708 501264
5 5 former 73 607. 368854.
# ℹ 262 more rowsMutating data
mutate(blomkvist_rt, log_rt = log(rt))
# A tibble: 267 × 5
id smoker age rt log_rt
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 former 84 702. 6.55
2 2 no 37 471. 6.15
3 3 yes 62 639. 6.46
4 4 former 85 708 6.56
5 5 former 73 607. 6.41
# ℹ 262 more rows
Mutating data
mutate(blomkvist_rt, is_slow = rt > 700)
# A tibble: 267 × 5
id smoker age rt is_slow
<dbl> <chr> <dbl> <dbl> <lgl>
1 1 former 84 702. TRUE
2 2 no 37 471. FALSE
3 3 yes 62 639. FALSE
4 4 former 85 708 TRUE
5 5 former 73 607. FALSE
# ℹ 262 more rows
Mutating data
mutate(blomkvist_rt, mean_rt = mean(rt, na.rm = TRUE))
# A tibble: 267 × 5
id smoker age rt mean_rt
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 former 84 702. 639.
2 2 no 37 471. 639.
3 3 yes 62 639. 639.
4 4 former 85 708 639.
5 5 former 73 607. 639.
# ℹ 262 more rows
Mutating data
mutate(blomkvist_rt, mean_rt = mean(rt, na.rm = TRUE),
is_slow = rt > mean_rt)
# A tibble: 267 × 6
id smoker age rt mean_rt is_slow
<dbl> <chr> <dbl> <dbl> <dbl> <lgl>
1 1 former 84 702. 639. TRUE
2 2 no 37 471. 639. FALSE
3 3 yes 62 639. 639. FALSE
4 4 former 85 708 639. TRUE
5 5 former 73 607. 639. FALSE
# ℹ 262 more rowsmutate(blomkvist_rt, # or both in one go
is_slow = rt > mean(rt, na.rm = TRUE))Mutating data: recode()
mutate(blomkvist_rt, smoker_recoded = recode(smoker, former = "former smoker",
yes = "smoker",
no = "non-smoker"))
# A tibble: 267 × 5
id smoker age rt smoker_recoded
<dbl> <chr> <dbl> <dbl> <chr>
1 1 former 84 702. former smoker
2 2 no 37 471. non-smoker
3 3 yes 62 639. smoker
4 4 former 85 708 former smoker
5 5 former 73 607. former smoker
# ℹ 262 more rows
Mutating data
Continue with exercise 4
Grouping data with group_by()
Perform an action (function) for each level the grouping variable individually.
blomkvist_grouped <- group_by(blomkvist_rt, smoker) # Group by smoker blomkvist_grouped
# A tibble: 267 × 4
# Groups: smoker [4]
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 2 no 37 471.
3 3 yes 62 639.
4 4 former 85 708
5 5 former 73 607.
# ℹ 262 more rows
# How many groups and what are they?
Summarise (grouped) data
… using descriptive tools.
summarise(blomkvist_rt, # not grouped
mean_rt = mean(rt, na.rm = TRUE),
N = n())
# A tibble: 1 × 2
mean_rt N
<dbl> <int>
1 639. 267summarise(blomkvist_grouped, # grouped data
mean_rt = mean(rt, na.rm = TRUE),
N = n())
# A tibble: 4 × 3 smoker mean_rt N <chr> <dbl> <int> 1 former 653. 77 2 no 635. 163 3 yes 633. 26 4 <NA> 479. 1
Summarise data
Summarising data using descriptive tools.
summarise(blomkvist_rt, # not grouped
mean_rt = mean(rt, na.rm = TRUE),
sd_rt = sd(rt, na.rm = TRUE),
min_rt = min(rt, na.rm = TRUE),
max_rt = max(rt, na.rm = TRUE),
N = n())
# A tibble: 1 × 5
mean_rt sd_rt min_rt max_rt N
<dbl> <dbl> <dbl> <dbl> <int>
1 639. 190. 379. 1552. 267summarise(blomkvist_grouped, # grouped data
mean_rt = mean(rt, na.rm = TRUE),
sd_rt = sd(rt, na.rm = TRUE),
min_rt = min(rt, na.rm = TRUE),
max_rt = max(rt, na.rm = TRUE),
N = n())
# A tibble: 4 × 6 smoker mean_rt sd_rt min_rt max_rt N <chr> <dbl> <dbl> <dbl> <dbl> <int> 1 former 653. 152. 411 1111. 77 2 no 635. 203. 379. 1552. 163 3 yes 633. 217. 427. 1527 26 4 <NA> 479. NA 479. 479. 1
Mutate grouped data
mutate(blomkvist_rt, # not grouped
mean_rt = mean(rt, na.rm = TRUE))
# A tibble: 267 × 5
id smoker age rt mean_rt
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 former 84 702. 639.
2 2 no 37 471. 639.
3 3 yes 62 639. 639.
4 4 former 85 708 639.
5 5 former 73 607. 639.
# ℹ 262 more rowsmutate(blomkvist_grouped, # grouped data
mean_rt = mean(rt, na.rm = TRUE))
# A tibble: 267 × 5
# Groups: smoker [4]
id smoker age rt mean_rt
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 former 84 702. 653.
2 2 no 37 471. 635.
3 3 yes 62 639. 633.
4 4 former 85 708 653.
5 5 former 73 607. 653.
# ℹ 262 more rows
# What's the grouping variable? # What's the difference?
Never forget to ungroup your data
otherwise you will keep performing by-group operations.
blomkvist_grouped # :(
# A tibble: 267 × 4
# Groups: smoker [4]
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 2 no 37 471.
3 3 yes 62 639.
4 4 former 85 708
5 5 former 73 607.
# ℹ 262 more rowsungroup(blomkvist_grouped) # :)
# A tibble: 267 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702.
2 2 no 37 471.
3 3 yes 62 639.
4 4 former 85 708
5 5 former 73 607.
# ℹ 262 more rows
# Spot the difference!
Grouping data
- Continue with exercise 5
Pivoting data: tidy data
Called, a long data format.
Pivoting data: wide format
blomkvist_wide <- select(blomkvist, id, starts_with("rt_"))
blomkvist_wide
# A tibble: 267 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 702. 780. 1009 963.
2 2 471. 497 738. 692.
3 3 639. 638 878 786
4 4 708 639. 902. 1374.
5 5 607. 652 923 805
# ℹ 262 more rows
RT is distributed across 4 columns: useful for certain analyses but isn’t tidy.
Pivoting data to long format
has never been easier
blomkvist_wide
# A tibble: 267 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 702. 780. 1009 963.
2 2 471. 497 738. 692.
3 3 639. 638 878 786
4 4 708 639. 902. 1374.
5 5 607. 652 923 805
# ℹ 262 more rowspivot_longer(blomkvist_wide, cols = starts_with("rt_"))
# A tibble: 1,068 × 3
id name value
<dbl> <chr> <dbl>
1 1 rt_hand_d 702.
2 1 rt_hand_nd 780.
3 1 rt_foot_d 1009
4 1 rt_foot_nd 963.
5 2 rt_hand_d 471.
# ℹ 1,063 more rowsPivoting data to long format
pivot_longer(blomkvist_wide, cols = -id) # shorthand
# A tibble: 1,068 × 3
id name value
<dbl> <chr> <dbl>
1 1 rt_hand_d 702.
2 1 rt_hand_nd 780.
3 1 rt_foot_d 1009
4 1 rt_foot_nd 963.
5 2 rt_hand_d 471.
# ℹ 1,063 more rowspivot_longer(blomkvist_wide,
cols = -id,
names_to = "variable",
values_to = "rt")
# A tibble: 1,068 × 3
id variable rt
<dbl> <chr> <dbl>
1 1 rt_hand_d 702.
2 1 rt_hand_nd 780.
3 1 rt_foot_d 1009
4 1 rt_foot_nd 963.
5 2 rt_hand_d 471.
# ℹ 1,063 more rowsPivoting data to long format
pivot_longer(blomkvist_wide,
cols = -id,
names_to = "variable",
values_to = "rt")
# A tibble: 1,068 × 3
id variable rt
<dbl> <chr> <dbl>
1 1 rt_hand_d 702.
2 1 rt_hand_nd 780.
3 1 rt_foot_d 1009
4 1 rt_foot_nd 963.
5 2 rt_hand_d 471.
# ℹ 1,063 more rowspivot_longer(blomkvist_wide,
cols = -id,
names_to = c(".value",
"response_by",
"dominant"),
names_sep = "_")
# A tibble: 1,068 × 4
id response_by dominant rt
<dbl> <chr> <chr> <dbl>
1 1 hand d 702.
2 1 hand nd 780.
3 1 foot d 1009
4 1 foot nd 963.
5 2 hand d 471.
# ℹ 1,063 more rowsPivoting data back to wide format
bk_long <- pivot_longer(blomkvist_wide,
cols = -id,
names_to = c(".value", "response_by", "dominant"),
names_sep = "_")
bk_long
# A tibble: 1,068 × 4
id response_by dominant rt
<dbl> <chr> <chr> <dbl>
1 1 hand d 702.
2 1 hand nd 780.
3 1 foot d 1009
4 1 foot nd 963.
5 2 hand d 471.
# ℹ 1,063 more rows
Pivoting data back to wide format
bk_long
# A tibble: 1,068 × 4
id response_by dominant rt
<dbl> <chr> <chr> <dbl>
1 1 hand d 702.
2 1 hand nd 780.
3 1 foot d 1009
4 1 foot nd 963.
5 2 hand d 471.
# ℹ 1,063 more rowspivot_wider(bk_long,
names_from = response_by,
values_from = rt)
# A tibble: 534 × 4
id dominant hand foot
<dbl> <chr> <dbl> <dbl>
1 1 d 702. 1009
2 1 nd 780. 963.
3 2 d 471. 738.
4 2 nd 497 692.
5 3 d 639. 878
# ℹ 529 more rowsPivoting data back to wide format
bk_long
# A tibble: 1,068 × 4
id response_by dominant rt
<dbl> <chr> <chr> <dbl>
1 1 hand d 702.
2 1 hand nd 780.
3 1 foot d 1009
4 1 foot nd 963.
5 2 hand d 471.
# ℹ 1,063 more rowspivot_wider(bk_long,
names_from = c(response_by, dominant),
values_from = rt)
# A tibble: 267 × 5
id hand_d hand_nd foot_d foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 702. 780. 1009 963.
2 2 471. 497 738. 692.
3 3 639. 638 878 786
4 4 708 639. 902. 1374.
5 5 607. 652 923 805
# ℹ 262 more rowsPivoting data back to wide format
bk_long
# A tibble: 1,068 × 4
id response_by dominant rt
<dbl> <chr> <chr> <dbl>
1 1 hand d 702.
2 1 hand nd 780.
3 1 foot d 1009
4 1 foot nd 963.
5 2 hand d 471.
# ℹ 1,063 more rowspivot_wider(bk_long,
names_from = c(response_by, dominant),
values_from = rt,
names_prefix = "rt_")
# A tibble: 267 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 702. 780. 1009 963.
2 2 471. 497 738. 692.
3 3 639. 638 878 786
4 4 708 639. 902. 1374.
5 5 607. 652 923 805
# ℹ 262 more rowsPivoting data
Continue with exercise 6
Congratulations!
Congratulations!
… you have worked through a large range of data visualisation and wrangling tools that will give you more flexibility to get the most out of your data.
- Use the slides to remind yourself how things work, repeat exercises, look at my advanced materials.
- Keep using
tidyverseand don’t fall back into old habits (e.g. spreadsheets). - Explore new functions for data processing and visualisation.
- R for Data Science by Wickham and Grolemund (2016)
- Doing Data Science in R, chapter 3 by Andrews (2021)
- Consider using R-Markdown; e.g. some work in progress by myself
Advanced data wrangling (homework)
The pipe: %>%
%>%moves or “pipes” the result forward into the next functionf(x)is the same asx %>% f()- Short-cut:
Ctrl+Shift+M
select(data, myvar1, myvar2) # or data %>% select(myvar1, myvar2)
*assumes first argument is data
The pipe: %>%
# Instead of
data_1 <- first_step(data)
data_2 <- second_step(data_2)
data_3 <- third_step(data_3)
data_4 <- fourth_step(data_4)
# or
fourth_step(
third_step(
second_step(
first_step(data)
)
)
)# Just do data %>% first_step() %>% second_step() %>% third_step() %>% fourth_step()
The pipe: %>%
# A tibble: 2 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 181 1552. 1072. 1068 1196.
2 305 1527 1416. 1713. 2471.
bk <- read_csv("data/blomkvist.csv")
bk_rts <- select(bk, id, starts_with("rt_"))
bk_rts_flt <- filter(bk_rts, rt_hand_d > 1500)
bk_rts_flt# A tibble: 2 × 5
id rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl> <dbl>
1 181 1552. 1072. 1068 1196.
2 305 1527 1416. 1713. 2471.
read_csv("data/blomkvist.csv") %>%
select(id, starts_with("rt_")) %>%
filter(rt_hand_d > 1500)The pipe: %>%
Continue with exercise 7
Combine data: overview
# Combine two datasets side-by-side bind_cols(data_1, data_2) # Stacking two data sets bind_rows(data_1, data_2) # Keep all rows of data_1 and add data_2 left_join(data_1, data_2) # Keep all rows of data_2 and add data_1 right_join(data_1, data_2) # Include all rows of both data sets full_join(data_1, data_2) # Include data that is present in both data sets inner_join(data_1, data_2)
Combine data: bind_cols()
bk_id_age <- select(blomkvist, id, age) bk_id_age
# A tibble: 267 × 2
id age
<dbl> <dbl>
1 1 84
2 2 37
3 3 62
4 4 85
5 5 73
# ℹ 262 more rowsbk_med_smoke <- select(blomkvist, medicine, smoker) bk_med_smoke
# A tibble: 267 × 2
medicine smoker
<dbl> <chr>
1 8 former
2 1 no
3 0 yes
4 4 former
5 5 former
# ℹ 262 more rowsCombine data: bind_cols()
# Combine two datasets side-by-side bind_cols(bk_id_age, bk_med_smoke)
# A tibble: 267 × 4
id age medicine smoker
<dbl> <dbl> <dbl> <chr>
1 1 84 8 former
2 2 37 1 no
3 3 62 0 yes
4 4 85 4 former
5 5 73 5 former
# ℹ 262 more rows
Combine data: bind_rows()
bk_former_smokers <- filter(blomkvist, smoker == "former") bk_smokers <- filter(blomkvist, smoker == "yes")
Combine data: bind_rows()
# Stacking two data sets bk_smoking <- bind_rows(bk_former_smokers, bk_smokers)
slice_head(bk_smoking, n = 1)
# A tibble: 1 × 10
id sex age medicine meds_cat smoker rt_hand_d rt_hand_nd rt_foot_d
<dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 1 male 84 8 a lot former 702. 780. 1009
# ℹ 1 more variable: rt_foot_nd <dbl>
slice_tail(bk_smoking, n = 1)
# A tibble: 1 × 10
id sex age medicine meds_cat smoker rt_hand_d rt_hand_nd rt_foot_d
<dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 342 male 60 3 few yes 708 686 909
# ℹ 1 more variable: rt_foot_nd <dbl>
This also works when columns are not in the same order as long as the names and types match.
Create two example data sets
bk_smoker <- select(blomkvist, smoker, age) bk_smoker_o40 <- filter(bk_smoker, age > 40)
bk_sex <- select(blomkvist, sex, age) bk_sex_u50 <- filter(bk_sex, age < 50)
Create two example data sets
bk_smoker_o40
# A tibble: 195 × 2 smoker age <chr> <dbl> 1 former 84 2 yes 62 3 former 85 4 former 73 5 no 65 # ℹ 190 more rows
bk_sex_u50
# A tibble: 99 × 2 sex age <chr> <dbl> 1 female 37 2 female 30 3 female 49 4 female 25 5 male 27 # ℹ 94 more rows
Combine data: left_join()
# Keep all rows of bk_sex_u50
bk_joined <- left_join(bk_sex_u50,
bk_smoker_o40, by = "age")
bk_joined
# A tibble: 173 × 3 sex age smoker <chr> <dbl> <chr> 1 female 37 <NA> 2 female 30 <NA> 3 female 49 former 4 female 49 former 5 female 49 no # ℹ 168 more rows
range(bk_joined$age)
[1] 20 49
Combine data: right_join()
# Keep all rows of bk_smoker_o40
bk_joined <- right_join(bk_sex_u50,
bk_smoker_o40, by = "age")
bk_joined
# A tibble: 269 × 3 sex age smoker <chr> <dbl> <chr> 1 female 49 former 2 female 49 former 3 female 49 no 4 female 49 no 5 female 49 former # ℹ 264 more rows
range(bk_joined$age)
[1] 41 96
Combine data: full_join()
# Include all rows of both data sets
bk_joined <- full_join(bk_sex_u50,
bk_smoker_o40, by = "age")
bk_joined
# A tibble: 341 × 3 sex age smoker <chr> <dbl> <chr> 1 female 37 <NA> 2 female 30 <NA> 3 female 49 former 4 female 49 former 5 female 49 no # ℹ 336 more rows
range(bk_joined$age)
[1] 20 96
Combine data: inner_join()
# Include only info that is present in both data sets
bk_joined <- inner_join(bk_sex_u50,
bk_smoker_o40, by = "age")
bk_joined
# A tibble: 101 × 3 sex age smoker <chr> <dbl> <chr> 1 female 49 former 2 female 49 former 3 female 49 no 4 female 49 no 5 female 49 former # ℹ 96 more rows
range(bk_joined$age)
[1] 41 49
Continue with exercise 8
Mutate data with across()
Create a new column.
mutate(blomkvist_rt, log_rt = log(rt))
# A tibble: 267 × 5
id smoker age rt log_rt
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 former 84 702. 6.55
2 2 no 37 471. 6.15
3 3 yes 62 639. 6.46
4 4 former 85 708 6.56
5 5 former 73 607. 6.41
# ℹ 262 more rowsReplace with transformed variable.
mutate(blomkvist_rt, across(rt, log))
# A tibble: 267 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 6.55
2 2 no 37 6.15
3 3 yes 62 6.46
4 4 former 85 6.56
5 5 former 73 6.41
# ℹ 262 more rows
First argument of across will be used as first argument of function (log) which is supplied as second argument to across.
Mutate data with across()
# Instead of
mutate(blomkvist, rt_hand_d = log(rt_hand_d),
rt_hand_nd = log(rt_hand_nd))
# Do mutate(blomkvist, across(c(rt_hand_d, rt_hand_nd), log))
# A tibble: 264 × 9
id sex age meds_cat smoker rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 male 84 a lot former 6.55 6.66 1009 963.
2 2 female 37 little no 6.15 6.21 738. 692.
3 3 female 62 none yes 6.46 6.46 878 786
4 4 female 85 few former 6.56 6.46 902. 1374.
5 5 male 73 a lot former 6.41 6.48 923 805
# ℹ 259 more rows
Mutate data with across()
mutate(blomkvist, across(where(is.numeric), log))
# A tibble: 264 × 9
id sex age meds_cat smoker rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 0 male 4.43 a lot former 6.55 6.66 6.92 6.87
2 0.693 female 3.61 little no 6.15 6.21 6.60 6.54
3 1.10 female 4.13 none yes 6.46 6.46 6.78 6.67
4 1.39 female 4.44 few former 6.56 6.46 6.80 7.23
5 1.61 male 4.29 a lot former 6.41 6.48 6.83 6.69
# ℹ 259 more rows
# Not ideal here: see `id` column
Mutate data with across()
mutate(blomkvist, across(starts_with("rt_"), log))
Rows: 264 Columns: 9 $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, … $ sex <chr> "male", "female", "female", "female", "male", "male", "fema… $ age <dbl> 84, 37, 62, 85, 73, 65, 30, 49, 83, 58, 25, 88, 62, 27, 60,… $ meds_cat <chr> "a lot", "little", "none", "few", "a lot", "none", "none", … $ smoker <chr> "former", "no", "yes", "former", "former", "no", "no", "for… $ rt_hand_d <dbl> 6.6, 6.2, 6.5, 6.6, 6.4, 6.3, 6.3, 6.2, 6.6, 6.3, 6.3, 6.8,… $ rt_hand_nd <dbl> 6.7, 6.2, 6.5, 6.5, 6.5, 6.2, 6.3, 6.3, 6.8, 6.3, 6.2, 6.6,… $ rt_foot_d <dbl> 6.9, 6.6, 6.8, 6.8, 6.8, 6.5, 6.7, 6.6, 6.6, 6.4, 6.5, 6.7,… $ rt_foot_nd <dbl> 6.9, 6.5, 6.7, 7.2, 6.7, 6.4, 6.6, 6.7, 6.7, 6.7, 6.6, 6.8,…
Mutate data with across()
mutate(blomkvist, across(starts_with("rt_"), log, .names = "log_{.col}"))
Rows: 264 Columns: 13 $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, … $ sex <chr> "male", "female", "female", "female", "male", "male", "… $ age <dbl> 84, 37, 62, 85, 73, 65, 30, 49, 83, 58, 25, 88, 62, 27,… $ meds_cat <chr> "a lot", "little", "none", "few", "a lot", "none", "non… $ smoker <chr> "former", "no", "yes", "former", "former", "no", "no", … $ rt_hand_d <dbl> 702, 471, 639, 708, 607, 542, 571, 509, 737, 550, 548, … $ rt_hand_nd <dbl> 780, 497, 638, 639, 652, 499, 527, 547, 865, 569, 507, … $ rt_foot_d <dbl> 1009, 738, 878, 902, 923, 687, 778, 743, 750, 629, 653,… $ rt_foot_nd <dbl> 963, 692, 786, 1374, 805, 600, 750, 797, 797, 800, 718,… $ log_rt_hand_d <dbl> 6.6, 6.2, 6.5, 6.6, 6.4, 6.3, 6.3, 6.2, 6.6, 6.3, 6.3, … $ log_rt_hand_nd <dbl> 6.7, 6.2, 6.5, 6.5, 6.5, 6.2, 6.3, 6.3, 6.8, 6.3, 6.2, … $ log_rt_foot_d <dbl> 6.9, 6.6, 6.8, 6.8, 6.8, 6.5, 6.7, 6.6, 6.6, 6.4, 6.5, … $ log_rt_foot_nd <dbl> 6.9, 6.5, 6.7, 7.2, 6.7, 6.4, 6.6, 6.7, 6.7, 6.7, 6.6, …
Mutate data with across()
mutate(blomkvist, across(starts_with("rt_"), list(lg = log, sqr = ~.^2), .names = "{.fn}_{.col}"))
Rows: 264 Columns: 17 $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, … $ sex <chr> "male", "female", "female", "female", "male", "male", "… $ age <dbl> 84, 37, 62, 85, 73, 65, 30, 49, 83, 58, 25, 88, 62, 27,… $ meds_cat <chr> "a lot", "little", "none", "few", "a lot", "none", "non… $ smoker <chr> "former", "no", "yes", "former", "former", "no", "no", … $ rt_hand_d <dbl> 702, 471, 639, 708, 607, 542, 571, 509, 737, 550, 548, … $ rt_hand_nd <dbl> 780, 497, 638, 639, 652, 499, 527, 547, 865, 569, 507, … $ rt_foot_d <dbl> 1009, 738, 878, 902, 923, 687, 778, 743, 750, 629, 653,… $ rt_foot_nd <dbl> 963, 692, 786, 1374, 805, 600, 750, 797, 797, 800, 718,… $ lg_rt_hand_d <dbl> 6.6, 6.2, 6.5, 6.6, 6.4, 6.3, 6.3, 6.2, 6.6, 6.3, 6.3, … $ sqr_rt_hand_d <dbl> 492336, 221527, 407895, 501264, 368854, 293403, 325660,… $ lg_rt_hand_nd <dbl> 6.7, 6.2, 6.5, 6.5, 6.5, 6.2, 6.3, 6.3, 6.8, 6.3, 6.2, … $ sqr_rt_hand_nd <dbl> 608920, 247009, 407044, 407895, 425104, 248668, 278080,… $ lg_rt_foot_d <dbl> 6.9, 6.6, 6.8, 6.8, 6.8, 6.5, 6.7, 6.6, 6.6, 6.4, 6.5, … $ sqr_rt_foot_d <dbl> 1018081, 544152, 770884, 814205, 851929, 471511, 605803… $ lg_rt_foot_nd <dbl> 6.9, 6.5, 6.7, 7.2, 6.7, 6.4, 6.6, 6.7, 6.7, 6.7, 6.6, … $ sqr_rt_foot_nd <dbl> 926727, 479325, 617796, 1886960, 648025, 359600, 562000…
What’s “~.”?
mutate(blomkvist_rt, across(rt, round, 0))
# A tibble: 267 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702
2 2 no 37 471
3 3 yes 62 639
4 4 former 85 708
5 5 former 73 607
# ℹ 262 more rowsmutate(blomkvist_rt, across(rt, ~round(., 0)))
# A tibble: 267 × 4
id smoker age rt
<dbl> <chr> <dbl> <dbl>
1 1 former 84 702
2 2 no 37 471
3 3 yes 62 639
4 4 former 85 708
5 5 former 73 607
# ℹ 262 more rowsWhat’s “~.”?
- “
~”: we want to make the position of the argument in function explicit. - “
.”: the location of the argument. - Always possible but necessary for operator functions (
>,==,^,+) and when argument is not in first position of supplied function.
mutate(blomkvist_rt,
across(rt, ~round(. , 0)), # optional
across(age, ~.^2), # operator
across(rt, ~paste("RT is", .))) # position
# A tibble: 267 × 4
id smoker age rt
<dbl> <chr> <dbl> <chr>
1 1 former 7056 RT is 702
2 2 no 1369 RT is 471
3 3 yes 3844 RT is 639
4 4 former 7225 RT is 708
5 5 former 5329 RT is 607
# ℹ 262 more rowsFilter data with across()
filter(blomkvist, rt_hand_d > 1000, rt_hand_nd > 1000, rt_foot_d > 1000, rt_foot_nd > 100)
# A tibble: 9 × 9
id sex age meds_cat smoker rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 70 female 85 a lot no 1102. 1352. 1458 1335.
2 96 male 77 a lot no 1241. 1651. 1217. 1266.
3 127 female 96 a lot no 1179. 1090 1546 4820
4 152 female 68 none no 1030. 1095 1137 1075.
5 171 female 92 a lot no 1025. 1026. 1379. 1310.
# ℹ 4 more rows
Filter data with across()
filter(blomkvist, across(starts_with("rt_"), ~ . > 1000 ))
# A tibble: 9 × 9
id sex age meds_cat smoker rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 70 female 85 a lot no 1102. 1352. 1458 1335.
2 96 male 77 a lot no 1241. 1651. 1217. 1266.
3 127 female 96 a lot no 1179. 1090 1546 4820
4 152 female 68 none no 1030. 1095 1137 1075.
5 171 female 92 a lot no 1025. 1026. 1379. 1310.
# ℹ 4 more rows
Summarise variables with across()
summarise(blomkvist, rt_hand_d_mean = mean(rt_hand_d),
rt_hand_nd_mean = mean(rt_hand_nd))
# A tibble: 1 × 2
rt_hand_d_mean rt_hand_nd_mean
<dbl> <dbl>
1 639. 637.
Summarise variables with across()
summarise(blomkvist, rt_hand_d_mean = mean(rt_hand_d),
rt_hand_nd_mean = mean(rt_hand_nd))
# A tibble: 1 × 2
rt_hand_d_mean rt_hand_nd_mean
<dbl> <dbl>
1 639. 637.
summarise(blomkvist, across(c(rt_hand_d, rt_hand_nd), mean))
# A tibble: 1 × 2
rt_hand_d rt_hand_nd
<dbl> <dbl>
1 639. 637.
Summarise variables with across()
summarise(blomkvist, across(starts_with("rt_"), mean))
# A tibble: 1 × 4
rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl>
1 639. 637. 847. 867.
Summarise variables with across()
summarise(blomkvist, across(starts_with("rt_"), mean))
# A tibble: 1 × 4
rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd
<dbl> <dbl> <dbl> <dbl>
1 639. 637. 847. 867.
summarise(blomkvist, across(starts_with("rt_"), list(mean = mean, sd = sd)))
# A tibble: 1 × 8
rt_hand_d_mean rt_hand_d_sd rt_hand_nd_mean rt_hand_nd_sd rt_foot_d_mean
<dbl> <dbl> <dbl> <dbl> <dbl>
1 639. 191. 637. 179. 847.
# ℹ 3 more variables: rt_foot_d_sd <dbl>, rt_foot_nd_mean <dbl>,
# rt_foot_nd_sd <dbl>
Mutate data with c_across()
Apply a function across different columns.
Example: get the sum for of all rts for each participant (row).
# Long way mutate(blomkvist, sum_rt = rt_hand_d + rt_hand_nd + rt_foot_d + rt_foot_nd)
# A tibble: 264 × 8
id sex age rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd sum_rt
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 male 84 702. 780. 1009 963. 3454.
2 2 female 37 471. 497 738. 692. 2398.
3 3 female 62 639. 638 878 786 2941.
4 4 female 85 708 639. 902. 1374. 3623.
5 5 male 73 607. 652 923 805 2987.
# ℹ 259 more rows
Mutate data with c_across()
# Long way mutate(blomkvist, sum_rt = rt_hand_d + rt_hand_nd + rt_foot_d + rt_foot_nd)
# A tibble: 264 × 8
id sex age rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd sum_rt
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 male 84 702. 780. 1009 963. 3454.
2 2 female 37 471. 497 738. 692. 2398.
3 3 female 62 639. 638 878 786 2941.
4 4 female 85 708 639. 902. 1374. 3623.
5 5 male 73 607. 652 923 805 2987.
# ℹ 259 more rows
Mutate data with c_across()
# Using sum function doesn't give the desired result mutate(blomkvist, sum_rt = sum(c(rt_hand_d, rt_hand_nd, rt_foot_d, rt_foot_nd), na.rm = TRUE))
# A tibble: 264 × 8
id sex age rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd sum_rt
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 male 84 702. 780. 1009 963. 789356.
2 2 female 37 471. 497 738. 692. 789356.
3 3 female 62 639. 638 878 786 789356.
4 4 female 85 708 639. 902. 1374. 789356.
5 5 male 73 607. 652 923 805 789356.
# ℹ 259 more rows
Mutate data with c_across()
# Long way mutate(blomkvist, sum_rt = rt_hand_d + rt_hand_nd + rt_foot_d + rt_foot_nd)
# Still a lot of typing :( blomkvist_rowwise <- rowwise(blomkvist) # each row / ppt is a group mutate(blomkvist_rowwise, sum_rt = sum(c_across(c(rt_hand_d, rt_hand_nd, rt_foot_d, rt_foot_nd))))
# A tibble: 264 × 8
# Rowwise:
id sex age rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd sum_rt
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 male 84 702. 780. 1009 963. 3454.
2 2 female 37 471. 497 738. 692. 2398.
3 3 female 62 639. 638 878 786 2941.
4 4 female 85 708 639. 902. 1374. 3623.
5 5 male 73 607. 652 923 805 2987.
# ℹ 259 more rows
Mutate data with c_across()
# Long way mutate(blomkvist, sum_rt = rt_hand_d + rt_hand_nd + rt_foot_d + rt_foot_nd)
# That's better :)
blomkvist_rowwise <- rowwise(blomkvist) # each row / ppt is a group
mutate(blomkvist_rowwise, sum_rt = sum(c_across(starts_with("rt_"))))
# A tibble: 264 × 8
# Rowwise:
id sex age rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd sum_rt
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 male 84 702. 780. 1009 963. 3454.
2 2 female 37 471. 497 738. 692. 2398.
3 3 female 62 639. 638 878 786 2941.
4 4 female 85 708 639. 902. 1374. 3623.
5 5 male 73 607. 652 923 805 2987.
# ℹ 259 more rows
Mutate data with c_across()
# Without rowwise grouping returns the overall sum:
mutate(blomkvist, sum_rt = sum(c_across(starts_with("rt_"))))
# A tibble: 264 × 8
id sex age rt_hand_d rt_hand_nd rt_foot_d rt_foot_nd sum_rt
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 male 84 702. 780. 1009 963. 789356.
2 2 female 37 471. 497 738. 692. 789356.
3 3 female 62 639. 638 878 786 789356.
4 4 female 85 708 639. 902. 1374. 789356.
5 5 male 73 607. 652 923 805 789356.
# ℹ 259 more rows
# which is not what we want.
Mutating data: case_when()
case_when(condition ~ do) # similar to ifelse()
mutate(blomkvist_rt, age_cat = case_when(age > 70 ~ "high",
age > 40 ~ "middle",
is.na(smoker) ~ "dunno",
TRUE ~ "on the young side"))
# A tibble: 267 × 5
id smoker age rt age_cat
<dbl> <chr> <dbl> <dbl> <chr>
1 1 former 84 702. high
2 2 no 37 471. on the young side
3 3 yes 62 639. middle
4 4 former 85 708 high
5 5 former 73 607. high
# ℹ 262 more rows
Using across() to mutate and summarise variables
Open exercise 9 script.
References
Andrews, Mark. 2021. Doing data science in R: An Introduction for Social Scientists. London, UK: SAGE Publications Ltd.
Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc.