Chapter 4 Tidy Data

A giant demon emerging from fire labelled 'messy data'; in front is a tiny human labelled 'R User'

4.1 Learning Objectives

4.1.1 Basic

Understand the concept of tidy data (video)
Be able to convert between long and wide formats using pivot functions (video)
- pivot_longer()
- pivot_wider()
Be able to use the 4 basic tidyr verbs (video)
Be able to chain functions using pipes (video)

4.1.2 Advanced

Be able to use regular expressions to separate complex columns

4.2 Resources

Tidy Data
Chapter 12: Tidy Data in R for Data Science
Chapter 18: Pipes in R for Data Science
Data wrangling cheat sheet

4.3 Setup

# libraries needed
library(tidyverse)
library(dataskills)

set.seed(8675309) # makes sure random numbers are reproducible

4.4 Tidy Data

4.4.1 Three Rules

Each variable must have its own column
Each observation must have its own row
Each value must have its own cell

This table has three observations per row and the total_meanRT column contains two values.

Table 4.1: Untidy table
id	score_1	score_2	score_3	rt_1	rt_2	rt_3	total_meanRT
1	4	3	7	857	890	859	14 (869)
2	3	1	1	902	900	959	5 (920)
3	2	5	4	757	823	901	11 (827)
4	6	2	6	844	788	624	14 (752)
5	1	7	2	659	764	690	10 (704)

This is the tidy version.

Table 4.1: Tidy table
id	trial	rt	score	total	mean_rt
1	1	857	4	14	869
1	2	890	3	14	869
1	3	859	7	14	869
2	1	902	3	5	920
2	2	900	1	5	920
2	3	959	1	5	920
3	1	757	2	11	827
3	2	823	5	11	827
3	3	901	4	11	827
4	1	844	6	14	752
4	2	788	2	14	752
4	3	624	6	14	752
5	1	659	1	10	704
5	2	764	7	10	704
5	3	690	2	10	704

4.4.2 Wide versus long

Data tables can be in wide format or long format (and sometimes a mix of the two). Wide data are where all of the observations about one subject are in the same row, while long data are where each observation is on a separate row. You often need to convert between these formats to do different types of analyses or data processing.

Imagine a study where each subject completes a questionnaire with three items. Each answer is an observation of that subject. You are probably most familiar with data like this in a wide format, where the subject id is in one column, and each of the three item responses is in its own column.

Table 4.2: Wide data
id	Q1	Q2	Q3
A	1	2	3
B	4	5	6

The same data can be represented in a long format by creating a new column that specifies what item the observation is from and a new column that specifies the value of that observation.

Table 4.3: Long data
id	item	value
A	Q1	1
B	Q1	4
A	Q2	2
B	Q2	5
A	Q3	3
B	Q3	6

Create a long version of the following table.

id	fav_colour	fav_animal
Lisa	red	echidna
Robbie	orange	babirusa
Steven	green	frog

Your answer doesn’t need to have the same column headers or be in the same order.

id	fav	answer
Lisa	colour	red
Lisa	animal	echidna
Robbie	colour	orange
Robbie	animal	babirusa
Steven	colour	green
Steven	animal	frog

4.5 Pivot Functions

The pivot functions allow you to transform a data table from wide to long or long to wide in one step.

4.5.1 Load Data

We will used the dataset personality from the dataskills package (or download the data from personality.csv). These data are from a 5-factor (personality) personality questionnaire. Each question is labelled with the domain (Op = openness, Co = conscientiousness, Ex = extroversion, Ag = agreeableness, and Ne = neuroticism) and the question number.

data("personality", package = "dataskills")

user_id	date	Op1	Ne1	Ne2	Op2	Ex1	Ex2	Co1	Co2	Ne3	Ag1	Ag2	Ne4	Ex3	Co3	Op3	Ex4	Op4	Ex5	Ag3	Co4	Co5	Ne5	Op5	Ag4	Op6	Co6	Ex6	Ne6	Co7	Ag5	Co8	Ex7	Ne7	Co9	Op7	Ne8	Ag6	Ag7	Co10	Ex8	Ex9
0	2006-03-23	3	4	0	6	3	3	3	3	0	2	1	3	3	2	2	1	3	3	1	3	0	3	6	1	0	6	3	1	3	3	3	3	NA	3	0	2	NA	3	1	2	4
1	2006-02-08	6	0	6	0	0	0	0	0	0	0	6	6	6	0	6	0	0	0	0	6	6	0	6	0	6	0	6	6	6	6	0	6	0	6	6	0	6	0	6	0	6
2	2005-10-24	6	0	6	0	0	0	0	0	0	0	6	6	5	1	5	1	1	1	1	5	5	1	5	1	5	1	5	5	5	5	1	5	1	5	5	1	5	1	5	1	5
5	2005-12-07	6	4	4	4	2	3	3	3	1	4	0	2	5	3	5	3	6	6	1	5	5	4	2	4	1	4	3	1	1	0	1	4	2	4	5	1	2	1	5	4	5
8	2006-07-27	6	1	2	6	2	3	5	4	0	6	5	3	3	4	5	3	6	3	0	5	5	1	5	6	6	6	0	0	3	2	3	1	0	3	5	1	3	1	3	3	5
108	2006-02-28	3	2	1	4	4	4	4	3	1	5	4	2	3	4	4	3	3	3	4	3	3	1	4	5	4	5	4	1	4	5	4	2	2	4	4	1	4	3	5	4	2

4.5.2 pivot_longer()

pivot_longer() converts a wide data table to long format by converting the headers from specified columns into the values of new columns, and combining the values of those columns into a new condensed column.

cols refers to the columns you want to make long You can refer to them by their names, like col1, col2, col3, col4 or col1:col4 or by their numbers, like 8, 9, 10 or 8:10.
names_to is what you want to call the new columns that the gathered column headers will go into; it’s “domain” and “qnumber” in this example.
names_sep is an optional argument if you have more than one value for names_to. It specifies the characters or position to split the values of the cols headers.
values_to is what you want to call the values in the columns ...; they’re “score” in this example.

personality_long <- pivot_longer(
  data = personality, 
  cols = Op1:Ex9,                    # columns to make long 
  names_to = c("domain", "qnumber"), # new column names for headers
  names_sep = 2,                     # how to split the headers
  values_to = "score"                # new column name for values
) %>%
  glimpse()

## Rows: 615,000
## Columns: 5
## $ user_id <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ date    <date> 2006-03-23, 2006-03-23, 2006-03-23, 2006-03-23, 2006-03-23, …
## $ domain  <chr> "Op", "Ne", "Ne", "Op", "Ex", "Ex", "Co", "Co", "Ne", "Ag", "…
## $ qnumber <chr> "1", "1", "2", "2", "1", "2", "1", "2", "3", "1", "2", "4", "…
## $ score   <dbl> 3, 4, 0, 6, 3, 3, 3, 3, 0, 2, 1, 3, 3, 2, 2, 1, 3, 3, 1, 3, 0…

You can pipe a data table to glimpse() at the end to have a quick look at it. It will still save to the object.

What would you set names_sep to in order to split the cols headers listed below into the results?

`cols`	`names_to`	`names_sep`
`A_1`, `A_2`, `B_1`, `B_2`	`c("condition", "version")`
`A1`, `A2`, `B1`, `B2`	`c("condition", "version")`
`cat-day&pre`, `cat-day&post`, `cat-night&pre`, `cat-night&post`, `dog-day&pre`, `dog-day&post`, `dog-night&pre`, `dog-night&post`	`c("pet", "time", "condition")`

4.5.3 pivot_wider()

We can also go from long to wide format using the pivot_wider() function.

names_from is the columns that contain your new column headers.
values_from is the column that contains the values for the new columns.
names_sep is the character string used to join names if names_from is more than one column.

personality_wide <- pivot_wider(
  data = personality_long,
  names_from = c(domain, qnumber),
  values_from = score,
  names_sep = ""
) %>%
  glimpse()

## Rows: 15,000
## Columns: 43
## $ user_id <dbl> 0, 1, 2, 5, 8, 108, 233, 298, 426, 436, 685, 807, 871, 881, 9…
## $ date    <date> 2006-03-23, 2006-02-08, 2005-10-24, 2005-12-07, 2006-07-27, …
## $ Op1     <dbl> 3, 6, 6, 6, 6, 3, 3, 6, 6, 3, 4, 5, 5, 5, 6, 4, 1, 2, 5, 6, 4…
## $ Ne1     <dbl> 4, 0, 0, 4, 1, 2, 3, 4, 0, 3, 3, 3, 2, 1, 1, 3, 4, 5, 2, 4, 5…
## $ Ne2     <dbl> 0, 6, 6, 4, 2, 1, 2, 3, 1, 2, 5, 5, 3, 1, 1, 1, 1, 6, 1, 2, 5…
## $ Op2     <dbl> 6, 0, 0, 4, 6, 4, 4, 0, 0, 3, 4, 3, 3, 4, 5, 3, 3, 4, 1, 6, 6…
## $ Ex1     <dbl> 3, 0, 0, 2, 2, 4, 4, 3, 5, 4, 1, 1, 3, 3, 1, 3, 5, 1, 0, 4, 1…
## $ Ex2     <dbl> 3, 0, 0, 3, 3, 4, 5, 2, 5, 3, 4, 1, 3, 2, 1, 6, 5, 3, 4, 4, 1…
## $ Co1     <dbl> 3, 0, 0, 3, 5, 4, 3, 4, 5, 3, 3, 3, 1, 5, 5, 4, 4, 5, 6, 4, 2…
## $ Co2     <dbl> 3, 0, 0, 3, 4, 3, 3, 4, 5, 3, 5, 3, 3, 4, 5, 1, 5, 4, 5, 2, 5…
## $ Ne3     <dbl> 0, 0, 0, 1, 0, 1, 4, 4, 0, 4, 2, 5, 1, 2, 5, 5, 2, 2, 1, 2, 5…
## $ Ag1     <dbl> 2, 0, 0, 4, 6, 5, 5, 4, 2, 5, 4, 3, 2, 4, 5, 3, 5, 5, 5, 4, 4…
## $ Ag2     <dbl> 1, 6, 6, 0, 5, 4, 5, 3, 4, 3, 5, 1, 5, 4, 2, 6, 5, 5, 5, 5, 2…
## $ Ne4     <dbl> 3, 6, 6, 2, 3, 2, 3, 3, 0, 4, 4, 5, 5, 4, 5, 3, 2, 5, 2, 4, 5…
## $ Ex3     <dbl> 3, 6, 5, 5, 3, 3, 3, 0, 6, 1, 4, 2, 3, 2, 1, 2, 5, 1, 0, 5, 5…
## $ Co3     <dbl> 2, 0, 1, 3, 4, 4, 5, 4, 5, 3, 4, 3, 4, 4, 5, 4, 2, 4, 5, 2, 2…
## $ Op3     <dbl> 2, 6, 5, 5, 5, 4, 3, 2, 4, 3, 3, 6, 5, 5, 6, 5, 4, 4, 3, 6, 5…
## $ Ex4     <dbl> 1, 0, 1, 3, 3, 3, 4, 3, 5, 3, 2, 0, 3, 3, 1, 2, NA, 4, 4, 4, …
## $ Op4     <dbl> 3, 0, 1, 6, 6, 3, 3, 0, 6, 3, 4, 5, 4, 5, 6, 6, 2, 2, 4, 5, 5…
## $ Ex5     <dbl> 3, 0, 1, 6, 3, 3, 4, 2, 5, 2, 2, 4, 2, 3, 0, 4, 5, 2, 3, 1, 1…
## $ Ag3     <dbl> 1, 0, 1, 1, 0, 4, 4, 4, 3, 3, 4, 4, 3, 4, 4, 5, 5, 4, 5, 3, 4…
## $ Co4     <dbl> 3, 6, 5, 5, 5, 3, 2, 4, 3, 1, 4, 3, 1, 2, 4, 2, NA, 5, 6, 1, …
## $ Co5     <dbl> 0, 6, 5, 5, 5, 3, 3, 1, 5, 1, 2, 4, 4, 4, 2, 1, 6, 4, 3, 1, 3…
## $ Ne5     <dbl> 3, 0, 1, 4, 1, 1, 4, 5, 0, 3, 4, 6, 2, 0, 1, 1, 0, 4, 3, 1, 5…
## $ Op5     <dbl> 6, 6, 5, 2, 5, 4, 3, 2, 6, 6, 2, 4, 3, 4, 6, 6, 6, 5, 3, 3, 5…
## $ Ag4     <dbl> 1, 0, 1, 4, 6, 5, 5, 6, 6, 6, 4, 2, 4, 5, 4, 5, 6, 4, 5, 6, 5…
## $ Op6     <dbl> 0, 6, 5, 1, 6, 4, 6, 0, 0, 3, 5, 3, 5, 5, 5, 2, 5, 1, 1, 6, 2…
## $ Co6     <dbl> 6, 0, 1, 4, 6, 5, 6, 5, 4, 3, 5, 5, 4, 6, 6, 1, 3, 4, 5, 4, 6…
## $ Ex6     <dbl> 3, 6, 5, 3, 0, 4, 3, 1, 6, 3, 2, 1, 4, 2, 1, 5, 6, 2, 1, 2, 1…
## $ Ne6     <dbl> 1, 6, 5, 1, 0, 1, 3, 4, 0, 4, 4, 5, 2, 1, 5, 6, 1, 2, 2, 3, 5…
## $ Co7     <dbl> 3, 6, 5, 1, 3, 4, NA, 2, 3, 3, 2, 2, 4, 2, 5, 2, 5, 5, 3, 1, …
## $ Ag5     <dbl> 3, 6, 5, 0, 2, 5, 6, 2, 2, 3, 4, 1, 3, 5, 2, 6, 5, 6, 5, 3, 3…
## $ Co8     <dbl> 3, 0, 1, 1, 3, 4, 3, 0, 1, 3, 2, 2, 1, 2, 4, 3, 2, 4, 5, 2, 6…
## $ Ex7     <dbl> 3, 6, 5, 4, 1, 2, 5, 3, 6, 3, 4, 3, 5, 1, 1, 6, 6, 3, 1, 1, 3…
## $ Ne7     <dbl> NA, 0, 1, 2, 0, 2, 4, 4, 0, 3, 2, 5, 1, 2, 5, 2, 2, 4, 1, 3, …
## $ Co9     <dbl> 3, 6, 5, 4, 3, 4, 5, 3, 5, 3, 4, 3, 4, 4, 2, 4, 6, 5, 5, 2, 2…
## $ Op7     <dbl> 0, 6, 5, 5, 5, 4, 6, 2, 1, 3, 2, 4, 5, 5, 6, 3, 6, 5, 2, 6, 5…
## $ Ne8     <dbl> 2, 0, 1, 1, 1, 1, 5, 4, 0, 4, 4, 5, 1, 2, 5, 2, 1, 5, 1, 2, 5…
## $ Ag6     <dbl> NA, 6, 5, 2, 3, 4, 5, 6, 1, 3, 4, 2, 3, 5, 1, 6, 2, 6, 6, 5, …
## $ Ag7     <dbl> 3, 0, 1, 1, 1, 3, 3, 5, 0, 3, 2, 1, 2, 3, 5, 6, 4, 4, 6, 6, 2…
## $ Co10    <dbl> 1, 6, 5, 5, 3, 5, 1, 2, 5, 2, 4, 3, 4, 4, 3, 2, 5, 5, 5, 2, 2…
## $ Ex8     <dbl> 2, 0, 1, 4, 3, 4, 2, 4, 6, 2, 4, 0, 4, 4, 1, 3, 5, 4, 3, 1, 1…
## $ Ex9     <dbl> 4, 6, 5, 5, 5, 2, 3, 3, 6, 3, 3, 4, 4, 3, 2, 5, 5, 4, 4, 0, 4…

4.6 Tidy Verbs

The pivot functions above are relatively new functions that combine the four basic tidy verbs. You can also convert data between long and wide formats using these functions. Many researchers still use these functions and older code will not use the pivot functions, so it is useful to know how to interpret these.

4.6.1 gather()

Much like pivot_longer(), gather() makes a wide data table long by creating a column for the headers and a column for the values. The main difference is that you cannot turn the headers into more than one column.

key is what you want to call the new column that the gathered column headers will go into; it’s “question” in this example. It is like names_to in pivot_longer(), but can only take one value (multiple values need to be separated after separate()).
value is what you want to call the values in the gathered columns; they’re “score” in this example. It is like values_to in pivot_longer().
... refers to the columns you want to gather. It is like cols in pivot_longer().

The gather() function converts personality from a wide data table to long format, with a row for each user/question observation. The resulting data table should have the columns: user_id, date, question, and score.

personality_gathered <- gather(
  data = personality, 
  key = "question", # new column name for gathered headers
  value = "score",  # new column name for gathered values
  Op1:Ex9           # columns to gather
) %>%
  glimpse()

## Rows: 615,000
## Columns: 4
## $ user_id  <dbl> 0, 1, 2, 5, 8, 108, 233, 298, 426, 436, 685, 807, 871, 881, …
## $ date     <date> 2006-03-23, 2006-02-08, 2005-10-24, 2005-12-07, 2006-07-27,…
## $ question <chr> "Op1", "Op1", "Op1", "Op1", "Op1", "Op1", "Op1", "Op1", "Op1…
## $ score    <dbl> 3, 6, 6, 6, 6, 3, 3, 6, 6, 3, 4, 5, 5, 5, 6, 4, 1, 2, 5, 6, …

4.6.2 separate()

col is the column you want to separate
into is a vector of new column names
sep is the character(s) that separate your new columns. This defaults to anything that isn’t alphanumeric, like .,_-/: and is like the names_sep argument in pivot_longer().

Split the question column into two columns: domain and qnumber.

There is no character to split on, here, but you can separate a column after a specific number of characters by setting sep to an integer. For example, to split “abcde” after the third character, use sep = 3, which results in c("abc", "de"). You can also use negative number to split before the nth character from the right. For example, to split a column that has words of various lengths and 2-digit suffixes (like “lisa03”“,”amanda38"), you can use sep = -2.

personality_sep <- separate(
  data = personality_gathered, 
  col = question,                # column to separate
  into = c("domain", "qnumber"), # new column names
  sep = 2                        # where to separate
) %>%
  glimpse()

## Rows: 615,000
## Columns: 5
## $ user_id <dbl> 0, 1, 2, 5, 8, 108, 233, 298, 426, 436, 685, 807, 871, 881, 9…
## $ date    <date> 2006-03-23, 2006-02-08, 2005-10-24, 2005-12-07, 2006-07-27, …
## $ domain  <chr> "Op", "Op", "Op", "Op", "Op", "Op", "Op", "Op", "Op", "Op", "…
## $ qnumber <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "…
## $ score   <dbl> 3, 6, 6, 6, 6, 3, 3, 6, 6, 3, 4, 5, 5, 5, 6, 4, 1, 2, 5, 6, 4…

If you want to separate just at full stops, you need to use sep = "\\.", not sep = ".". The two slashes escape the full stop, making it interpreted as a literal full stop and not the regular expression for any character.

4.6.3 unite()

col is your new united column
... refers to the columns you want to unite
sep is the character(s) that will separate your united columns

Put the domain and qnumber columns back together into a new column named domain_n. Make it in a format like “Op_Q1”.

personality_unite <- unite(
  data = personality_sep, 
  col = "domain_n", # new column name
  domain, qnumber,  # columns to unite
  sep = "_Q"        # separation characters
) %>%
  glimpse()

## Rows: 615,000
## Columns: 4
## $ user_id  <dbl> 0, 1, 2, 5, 8, 108, 233, 298, 426, 436, 685, 807, 871, 881, …
## $ date     <date> 2006-03-23, 2006-02-08, 2005-10-24, 2005-12-07, 2006-07-27,…
## $ domain_n <chr> "Op_Q1", "Op_Q1", "Op_Q1", "Op_Q1", "Op_Q1", "Op_Q1", "Op_Q1…
## $ score    <dbl> 3, 6, 6, 6, 6, 3, 3, 6, 6, 3, 4, 5, 5, 5, 6, 4, 1, 2, 5, 6, …

4.6.4 spread()

You can reverse the processes above, as well. For example, you can convert data from long format into wide format.

key is the column that contains your new column headers. It is like names_from in pivot_wider(), but can only take one value (multiple values need to be merged first using unite()).
value is the column that contains the values in the new spread columns. It is like values_from in pivot_wider().

personality_spread <- spread(
  data = personality_unite,
  key = domain_n, # column that contains new headers
  value = score   # column that contains new values
) %>%
  glimpse()

## Rows: 15,000
## Columns: 43
## $ user_id <dbl> 0, 1, 2, 5, 8, 108, 233, 298, 426, 436, 685, 807, 871, 881, 9…
## $ date    <date> 2006-03-23, 2006-02-08, 2005-10-24, 2005-12-07, 2006-07-27, …
## $ Ag_Q1   <dbl> 2, 0, 0, 4, 6, 5, 5, 4, 2, 5, 4, 3, 2, 4, 5, 3, 5, 5, 5, 4, 4…
## $ Ag_Q2   <dbl> 1, 6, 6, 0, 5, 4, 5, 3, 4, 3, 5, 1, 5, 4, 2, 6, 5, 5, 5, 5, 2…
## $ Ag_Q3   <dbl> 1, 0, 1, 1, 0, 4, 4, 4, 3, 3, 4, 4, 3, 4, 4, 5, 5, 4, 5, 3, 4…
## $ Ag_Q4   <dbl> 1, 0, 1, 4, 6, 5, 5, 6, 6, 6, 4, 2, 4, 5, 4, 5, 6, 4, 5, 6, 5…
## $ Ag_Q5   <dbl> 3, 6, 5, 0, 2, 5, 6, 2, 2, 3, 4, 1, 3, 5, 2, 6, 5, 6, 5, 3, 3…
## $ Ag_Q6   <dbl> NA, 6, 5, 2, 3, 4, 5, 6, 1, 3, 4, 2, 3, 5, 1, 6, 2, 6, 6, 5, …
## $ Ag_Q7   <dbl> 3, 0, 1, 1, 1, 3, 3, 5, 0, 3, 2, 1, 2, 3, 5, 6, 4, 4, 6, 6, 2…
## $ Co_Q1   <dbl> 3, 0, 0, 3, 5, 4, 3, 4, 5, 3, 3, 3, 1, 5, 5, 4, 4, 5, 6, 4, 2…
## $ Co_Q10  <dbl> 1, 6, 5, 5, 3, 5, 1, 2, 5, 2, 4, 3, 4, 4, 3, 2, 5, 5, 5, 2, 2…
## $ Co_Q2   <dbl> 3, 0, 0, 3, 4, 3, 3, 4, 5, 3, 5, 3, 3, 4, 5, 1, 5, 4, 5, 2, 5…
## $ Co_Q3   <dbl> 2, 0, 1, 3, 4, 4, 5, 4, 5, 3, 4, 3, 4, 4, 5, 4, 2, 4, 5, 2, 2…
## $ Co_Q4   <dbl> 3, 6, 5, 5, 5, 3, 2, 4, 3, 1, 4, 3, 1, 2, 4, 2, NA, 5, 6, 1, …
## $ Co_Q5   <dbl> 0, 6, 5, 5, 5, 3, 3, 1, 5, 1, 2, 4, 4, 4, 2, 1, 6, 4, 3, 1, 3…
## $ Co_Q6   <dbl> 6, 0, 1, 4, 6, 5, 6, 5, 4, 3, 5, 5, 4, 6, 6, 1, 3, 4, 5, 4, 6…
## $ Co_Q7   <dbl> 3, 6, 5, 1, 3, 4, NA, 2, 3, 3, 2, 2, 4, 2, 5, 2, 5, 5, 3, 1, …
## $ Co_Q8   <dbl> 3, 0, 1, 1, 3, 4, 3, 0, 1, 3, 2, 2, 1, 2, 4, 3, 2, 4, 5, 2, 6…
## $ Co_Q9   <dbl> 3, 6, 5, 4, 3, 4, 5, 3, 5, 3, 4, 3, 4, 4, 2, 4, 6, 5, 5, 2, 2…
## $ Ex_Q1   <dbl> 3, 0, 0, 2, 2, 4, 4, 3, 5, 4, 1, 1, 3, 3, 1, 3, 5, 1, 0, 4, 1…
## $ Ex_Q2   <dbl> 3, 0, 0, 3, 3, 4, 5, 2, 5, 3, 4, 1, 3, 2, 1, 6, 5, 3, 4, 4, 1…
## $ Ex_Q3   <dbl> 3, 6, 5, 5, 3, 3, 3, 0, 6, 1, 4, 2, 3, 2, 1, 2, 5, 1, 0, 5, 5…
## $ Ex_Q4   <dbl> 1, 0, 1, 3, 3, 3, 4, 3, 5, 3, 2, 0, 3, 3, 1, 2, NA, 4, 4, 4, …
## $ Ex_Q5   <dbl> 3, 0, 1, 6, 3, 3, 4, 2, 5, 2, 2, 4, 2, 3, 0, 4, 5, 2, 3, 1, 1…
## $ Ex_Q6   <dbl> 3, 6, 5, 3, 0, 4, 3, 1, 6, 3, 2, 1, 4, 2, 1, 5, 6, 2, 1, 2, 1…
## $ Ex_Q7   <dbl> 3, 6, 5, 4, 1, 2, 5, 3, 6, 3, 4, 3, 5, 1, 1, 6, 6, 3, 1, 1, 3…
## $ Ex_Q8   <dbl> 2, 0, 1, 4, 3, 4, 2, 4, 6, 2, 4, 0, 4, 4, 1, 3, 5, 4, 3, 1, 1…
## $ Ex_Q9   <dbl> 4, 6, 5, 5, 5, 2, 3, 3, 6, 3, 3, 4, 4, 3, 2, 5, 5, 4, 4, 0, 4…
## $ Ne_Q1   <dbl> 4, 0, 0, 4, 1, 2, 3, 4, 0, 3, 3, 3, 2, 1, 1, 3, 4, 5, 2, 4, 5…
## $ Ne_Q2   <dbl> 0, 6, 6, 4, 2, 1, 2, 3, 1, 2, 5, 5, 3, 1, 1, 1, 1, 6, 1, 2, 5…
## $ Ne_Q3   <dbl> 0, 0, 0, 1, 0, 1, 4, 4, 0, 4, 2, 5, 1, 2, 5, 5, 2, 2, 1, 2, 5…
## $ Ne_Q4   <dbl> 3, 6, 6, 2, 3, 2, 3, 3, 0, 4, 4, 5, 5, 4, 5, 3, 2, 5, 2, 4, 5…
## $ Ne_Q5   <dbl> 3, 0, 1, 4, 1, 1, 4, 5, 0, 3, 4, 6, 2, 0, 1, 1, 0, 4, 3, 1, 5…
## $ Ne_Q6   <dbl> 1, 6, 5, 1, 0, 1, 3, 4, 0, 4, 4, 5, 2, 1, 5, 6, 1, 2, 2, 3, 5…
## $ Ne_Q7   <dbl> NA, 0, 1, 2, 0, 2, 4, 4, 0, 3, 2, 5, 1, 2, 5, 2, 2, 4, 1, 3, …
## $ Ne_Q8   <dbl> 2, 0, 1, 1, 1, 1, 5, 4, 0, 4, 4, 5, 1, 2, 5, 2, 1, 5, 1, 2, 5…
## $ Op_Q1   <dbl> 3, 6, 6, 6, 6, 3, 3, 6, 6, 3, 4, 5, 5, 5, 6, 4, 1, 2, 5, 6, 4…
## $ Op_Q2   <dbl> 6, 0, 0, 4, 6, 4, 4, 0, 0, 3, 4, 3, 3, 4, 5, 3, 3, 4, 1, 6, 6…
## $ Op_Q3   <dbl> 2, 6, 5, 5, 5, 4, 3, 2, 4, 3, 3, 6, 5, 5, 6, 5, 4, 4, 3, 6, 5…
## $ Op_Q4   <dbl> 3, 0, 1, 6, 6, 3, 3, 0, 6, 3, 4, 5, 4, 5, 6, 6, 2, 2, 4, 5, 5…
## $ Op_Q5   <dbl> 6, 6, 5, 2, 5, 4, 3, 2, 6, 6, 2, 4, 3, 4, 6, 6, 6, 5, 3, 3, 5…
## $ Op_Q6   <dbl> 0, 6, 5, 1, 6, 4, 6, 0, 0, 3, 5, 3, 5, 5, 5, 2, 5, 1, 1, 6, 2…
## $ Op_Q7   <dbl> 0, 6, 5, 5, 5, 4, 6, 2, 1, 3, 2, 4, 5, 5, 6, 3, 6, 5, 2, 6, 5…

4.7 Pipes

Pipes are a way to order your code in a more readable format.

Let’s say you have a small data table with 10 participant IDs, two columns with variable type A, and 2 columns with variable type B. You want to calculate the mean of the A variables and the mean of the B variables and return a table with 10 rows (1 for each participant) and 3 columns (id, A_mean and B_mean).

One way you could do this is by creating a new object at every step and using that object in the next step. This is pretty clear, but you’ve created 6 unnecessary data objects in your environment. This can get confusing in very long scripts.

# make a data table with 10 subjects
data_original <- tibble(
  id = 1:10,
  A1 = rnorm(10, 0),
  A2 = rnorm(10, 1),
  B1 = rnorm(10, 2),
  B2 = rnorm(10, 3)
)

# gather columns A1 to B2 into "variable" and "value" columns
data_gathered <- gather(data_original, variable, value, A1:B2)

# separate the variable column at the _ into "var" and "var_n" columns
data_separated <- separate(data_gathered, variable, c("var", "var_n"), sep = 1)

# group the data by id and var
data_grouped <- group_by(data_separated, id, var)

# calculate the mean value for each id/var 
data_summarised <- summarise(data_grouped, mean = mean(value), .groups = "drop")

# spread the mean column into A and B columns
data_spread <- spread(data_summarised, var, mean)

# rename A and B to A_mean and B_mean
data <- rename(data_spread, A_mean = A, B_mean = B)

data

## # A tibble: 10 x 3
##       id  A_mean B_mean
##    <int>   <dbl>  <dbl>
##  1     1 -0.594   1.02 
##  2     2  0.744   2.72 
##  3     3  0.931   3.93 
##  4     4  0.720   1.97 
##  5     5 -0.0281  1.95 
##  6     6 -0.0983  3.21 
##  7     7  0.126   0.926
##  8     8  1.45    2.38 
##  9     9  0.298   1.66 
## 10    10  0.559   2.10

You can name each object data and keep replacing the old data object with the new one at each step. This will keep your environment clean, but I don’t recommend it because it makes it too easy to accidentally run your code out of order when you are running line-by-line for development or debugging.

One way to avoid extra objects is to nest your functions, literally replacing each data object with the code that generated it in the previous step. This can be fine for very short chains.

mean_petal_width <- round(mean(iris$Petal.Width), 2)

But it gets extremely confusing for long chains:

# do not ever do this!!
data <- rename(
  spread(
    summarise(
      group_by(
        separate(
          gather(
            tibble(
              id = 1:10,
              A1 = rnorm(10, 0),
              A2 = rnorm(10, 1),
              B1 = rnorm(10, 2),
              B2 = rnorm(10,3)), 
            variable, value, A1:B2), 
          variable, c("var", "var_n"), sep = 1), 
        id, var), 
      mean = mean(value), .groups = "drop"), 
    var, mean), 
  A_mean = A, B_mean = B)

The pipe lets you “pipe” the result of each function into the next function, allowing you to put your code in a logical order without creating too many extra objects.

# calculate mean of A and B variables for each participant
data <- tibble(
  id = 1:10,
  A1 = rnorm(10, 0),
  A2 = rnorm(10, 1),
  B1 = rnorm(10, 2),
  B2 = rnorm(10,3)
) %>%
  gather(variable, value, A1:B2) %>%
  separate(variable, c("var", "var_n"), sep=1) %>%
  group_by(id, var) %>%
  summarise(mean = mean(value), .groups = "drop") %>%
  spread(var, mean) %>%
  rename(A_mean = A, B_mean = B)

You can read this code from top to bottom as follows:

Make a tibble called data with
- id of 1 to 10,
- A1 of 10 random numbers from a normal distribution,
- A2 of 10 random numbers from a normal distribution,
- B1 of 10 random numbers from a normal distribution,
- B2 of 10 random numbers from a normal distribution; and then
Gather to create variable and value column from columns A_1 to B_2; and then
Separate the column variable into 2 new columns called varand var_n, separate at character 1; and then
Group by columns id and var; and then
Summarise and new column called mean as the mean of the value column for each group and drop the grouping; and then
Spread to make new columns with the key names in var and values in mean; and then
Rename to make columns called A_mean (old A) and B_mean (old B)

You can make intermediate objects whenever you need to break up your code because it’s getting too complicated or you need to debug something.

You can debug a pipe by highlighting from the beginning to just before the pipe you want to stop at. Try this by highlighting from data <- to the end of the separate function and typing cmd-return. What does data look like now?

Chain all the steps above using pipes.

personality_reshaped <- personality %>%
  gather("question", "score", Op1:Ex9) %>%
  separate(question, c("domain", "qnumber"), sep = 2) %>%
  unite("domain_n", domain, qnumber, sep = "_Q") %>%
  spread(domain_n, score)

4.8 More Complex Example

4.8.1 Load Data

Get data on infant and maternal mortality rates from the dataskills package. If you don’t have the package, you can download them here:

data("infmort", package = "dataskills")
head(infmort)

## # A tibble: 6 x 3
##   Country     Year `Infant mortality rate (probability of dying between birth a…
##   <chr>      <dbl> <chr>                                                        
## 1 Afghanist…  2015 66.3 [52.7-83.9]                                             
## 2 Afghanist…  2014 68.1 [55.7-83.6]                                             
## 3 Afghanist…  2013 69.9 [58.7-83.5]                                             
## 4 Afghanist…  2012 71.7 [61.6-83.7]                                             
## 5 Afghanist…  2011 73.4 [64.4-84.2]                                             
## 6 Afghanist…  2010 75.1 [66.9-85.1]

data("matmort", package = "dataskills")
head(matmort)

## # A tibble: 6 x 4
##   Country     `1990`               `2000`               `2015`           
##   <chr>       <chr>                <chr>                <chr>            
## 1 Afghanistan 1 340 [ 878 - 1 950] 1 100 [ 745 - 1 570] 396 [ 253 -  620]
## 2 Albania     71 [ 58 -  88]       43 [ 33 -  56]       29 [ 16 -  46]   
## 3 Algeria     216 [ 141 -  327]    170 [ 118 -  241]    140 [ 82 -  244] 
## 4 Angola      1 160 [ 627 - 2 020] 924 [ 472 - 1 730]   477 [ 221 -  988]
## 5 Argentina   72 [ 64 -  80]       60 [ 54 -  65]       52 [ 44 -  63]   
## 6 Armenia     58 [ 51 -  65]       40 [ 35 -  46]       25 [ 21 -  31]

4.8.2 Wide to Long

matmort is in wide format, with a separate column for each year. Change it to long format, with a row for each Country/Year observation.

This example is complicated because the column names to gather are numbers. If the column names are non-standard (e.g., have spaces, start with numbers, or have special characters), you can enclose them in backticks (`) like the example below.

matmort_long <- matmort %>%
  pivot_longer(cols = `1990`:`2015`,
               names_to = "Year",
               values_to = "stats") %>%
  glimpse()

## Rows: 543
## Columns: 3
## $ Country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Albania", "Alba…
## $ Year    <chr> "1990", "2000", "2015", "1990", "2000", "2015", "1990", "2000…
## $ stats   <chr> "1 340 [ 878 - 1 950]", "1 100 [ 745 - 1 570]", "396 [ 253 - …

You can put matmort at the first argument to pivot_longer(); you don’t have to pipe it in. But when I’m working on data processing I often find myself needing to insert or rearrange steps and I constantly introduce errors by forgetting to take the first argument out of a pipe chain, so now I start with the original data table and pipe from there.

Alternatively, you can use the gather() function.

matmort_long <- matmort %>%
  gather("Year", "stats", `1990`:`2015`) %>%
  glimpse()

## Rows: 543
## Columns: 3
## $ Country <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "…
## $ Year    <chr> "1990", "1990", "1990", "1990", "1990", "1990", "1990", "1990…
## $ stats   <chr> "1 340 [ 878 - 1 950]", "71 [ 58 -  88]", "216 [ 141 -  327]"…

4.8.3 One Piece of Data per Column

The data in the stats column is in an unusual format with some sort of confidence interval in brackets and lots of extra spaces. We don’t need any of the spaces, so first we’ll remove them with mutate(), which we’ll learn more about in the next lesson.

The separate function will separate your data on anything that is not a number or letter, so try it first without specifying the sep argument. The into argument is a list of the new column names.

matmort_split <- matmort_long %>%
  mutate(stats = gsub(" ", "", stats)) %>%
  separate(stats, c("rate", "ci_low", "ci_hi")) %>%
  glimpse()

## Warning: Expected 3 pieces. Additional pieces discarded in 543 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

## Rows: 543
## Columns: 5
## $ Country <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "…
## $ Year    <chr> "1990", "1990", "1990", "1990", "1990", "1990", "1990", "1990…
## $ rate    <chr> "1340", "71", "216", "1160", "72", "58", "8", "8", "64", "46"…
## $ ci_low  <chr> "878", "58", "141", "627", "64", "51", "7", "7", "56", "34", …
## $ ci_hi   <chr> "1950", "88", "327", "2020", "80", "65", "9", "10", "74", "61…

The gsub(pattern, replacement, x) function is a flexible way to do search and replace. The example above replaces all occurances of the pattern " " (a space), with the replacement "" (nothing), in the string x (the stats column). Use sub() instead if you only want to replace the first occurance of a pattern. We only used a simple pattern here, but you can use more complicated regex patterns to replace, for example, all even numbers (e.g., gsub(“[:02468:]”, "“,”id = 123456“)) or all occurances of the word colour in US or UK spelling (e.g., gsub(”colo(u)?r“,”**“,”replace color, colour, or colours, but not collors")).

4.8.3.1 Handle spare columns with `extra`

The previous example should have given you an error warning about “Additional pieces discarded in 543 rows”. This is because separate splits the column at the brackets and dashes, so the text 100[90-110] would split into four values c(“100”, “90”, “110”, "“), but we only specified 3 new columns. The fourth value is always empty (just the part after the last bracket), so we are happy to drop it, but separate generates a warning so you don’t do that accidentally. You can turn off the warning by adding the extra argument and setting it to “drop”. Look at the help for ??tidyr::separate to see what the other options do.

matmort_split <- matmort_long %>%
  mutate(stats = gsub(" ", "", stats)) %>%
  separate(stats, c("rate", "ci_low", "ci_hi"), extra = "drop") %>%
  glimpse()

## Rows: 543
## Columns: 5
## $ Country <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "…
## $ Year    <chr> "1990", "1990", "1990", "1990", "1990", "1990", "1990", "1990…
## $ rate    <chr> "1340", "71", "216", "1160", "72", "58", "8", "8", "64", "46"…
## $ ci_low  <chr> "878", "58", "141", "627", "64", "51", "7", "7", "56", "34", …
## $ ci_hi   <chr> "1950", "88", "327", "2020", "80", "65", "9", "10", "74", "61…

4.8.3.2 Set delimiters with `sep`

Now do the same with infmort. It’s already in long format, so you don’t need to use gather, but the third column has a ridiculously long name, so we can just refer to it by its column number (3).

infmort_split <- infmort %>%
  separate(3, c("rate", "ci_low", "ci_hi"), extra = "drop") %>%
  glimpse()

## Rows: 5,044
## Columns: 5
## $ Country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "…
## $ Year    <dbl> 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2…
## $ rate    <chr> "66", "68", "69", "71", "73", "75", "76", "78", "80", "82", "…
## $ ci_low  <chr> "3", "1", "9", "7", "4", "1", "8", "6", "4", "3", "4", "7", "…
## $ ci_hi   <chr> "52", "55", "58", "61", "64", "66", "69", "71", "73", "75", "…

Wait, that didn’t work at all! It split the column on spaces, brackets, and full stops. We just want to split on the spaces, brackets and dashes. So we need to manually set sep to what the delimiters are. Also, once there are more than a few arguments specified for a function, it’s easier to read them if you put one argument on each line.

{#regex}

You can use regular expressions to separate complex columns. Here, we want to separate on dashes and brackets. You can separate on a list of delimiters by putting them in parentheses, separated by “|”. It’s a little more complicated because brackets have a special meaning in regex, so you need to “escape” the left one with two backslashes “\”.

infmort_split <- infmort %>%
  separate(
    col = 3, 
    into = c("rate", "ci_low", "ci_hi"), 
    extra = "drop", 
    sep = "(\\[|-|])"
  ) %>%
  glimpse()

## Rows: 5,044
## Columns: 5
## $ Country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "…
## $ Year    <dbl> 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2…
## $ rate    <chr> "66.3 ", "68.1 ", "69.9 ", "71.7 ", "73.4 ", "75.1 ", "76.8 "…
## $ ci_low  <chr> "52.7", "55.7", "58.7", "61.6", "64.4", "66.9", "69.0", "71.2…
## $ ci_hi   <chr> "83.9", "83.6", "83.5", "83.7", "84.2", "85.1", "86.1", "87.3…

4.8.3.3 Fix data types with `convert`

That’s better. Notice the next to Year, rate, ci_low and ci_hi. That means these columns hold characters (like words), not numbers or integers. This can cause problems when you try to do thigs like average the numbers (you can’t average words), so we can fix it by adding the argument convert and setting it to TRUE.

infmort_split <- infmort %>%
  separate(col = 3, 
           into = c("rate", "ci_low", "ci_hi"), 
           extra = "drop", 
           sep = "(\\[|-|])", 
           convert = TRUE) %>%
  glimpse()

## Rows: 5,044
## Columns: 5
## $ Country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "…
## $ Year    <dbl> 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2…
## $ rate    <dbl> 66.3, 68.1, 69.9, 71.7, 73.4, 75.1, 76.8, 78.6, 80.4, 82.3, 8…
## $ ci_low  <dbl> 52.7, 55.7, 58.7, 61.6, 64.4, 66.9, 69.0, 71.2, 73.4, 75.5, 7…
## $ ci_hi   <dbl> 83.9, 83.6, 83.5, 83.7, 84.2, 85.1, 86.1, 87.3, 88.9, 90.7, 9…

Do the same for matmort.

matmort_split <- matmort_long %>%
  mutate(stats = gsub(" ", "", stats)) %>%
  separate(col = stats, 
           into = c("rate", "ci_low", "ci_hi"), 
           extra = "drop", 
           convert = TRUE) %>%
  glimpse()

## Rows: 543
## Columns: 5
## $ Country <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "…
## $ Year    <chr> "1990", "1990", "1990", "1990", "1990", "1990", "1990", "1990…
## $ rate    <int> 1340, 71, 216, 1160, 72, 58, 8, 8, 64, 46, 26, 569, 58, 33, 9…
## $ ci_low  <int> 878, 58, 141, 627, 64, 51, 7, 7, 56, 34, 20, 446, 47, 28, 7, …
## $ ci_hi   <int> 1950, 88, 327, 2020, 80, 65, 9, 10, 74, 61, 33, 715, 72, 38, …

4.8.4 All in one step

We can chain all the steps for matmort above together, since we don’t need those intermediate data tables.

matmort2<- dataskills::matmort %>%
  gather("Year", "stats", `1990`:`2015`) %>%
  mutate(stats = gsub(" ", "", stats)) %>%
  separate(
    col = stats, 
    into = c("rate", "ci_low", "ci_hi"), 
    extra = "drop", 
    convert = TRUE
  ) %>%
  glimpse()

## Rows: 543
## Columns: 5
## $ Country <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "…
## $ Year    <chr> "1990", "1990", "1990", "1990", "1990", "1990", "1990", "1990…
## $ rate    <int> 1340, 71, 216, 1160, 72, 58, 8, 8, 64, 46, 26, 569, 58, 33, 9…
## $ ci_low  <int> 878, 58, 141, 627, 64, 51, 7, 7, 56, 34, 20, 446, 47, 28, 7, …
## $ ci_hi   <int> 1950, 88, 327, 2020, 80, 65, 9, 10, 74, 61, 33, 715, 72, 38, …

4.8.5 Columns by Year

Spread out the maternal mortality rate by year.

matmort_wide <- matmort2 %>%
  spread(key = Year, value = rate) %>%
  print()

## # A tibble: 542 x 6
##    Country     ci_low ci_hi `1990` `2000` `2015`
##    <chr>        <int> <int>  <int>  <int>  <int>
##  1 Afghanistan    253   620     NA     NA    396
##  2 Afghanistan    745  1570     NA   1100     NA
##  3 Afghanistan    878  1950   1340     NA     NA
##  4 Albania         16    46     NA     NA     29
##  5 Albania         33    56     NA     43     NA
##  6 Albania         58    88     71     NA     NA
##  7 Algeria         82   244     NA     NA    140
##  8 Algeria        118   241     NA    170     NA
##  9 Algeria        141   327    216     NA     NA
## 10 Angola         221   988     NA     NA    477
## # … with 532 more rows

Nope, that didn’t work at all, but it’s a really common mistake when spreading data. This is because spread matches on all the remaining columns, so Afghanistan with ci_low of 253 is treated as a different observation than Afghanistan with ci_low of 745.

This is where pivot_wider() can be very useful. You can set values_from to multiple column names and their names will be added to the names_from values.

matmort_wide <- matmort2 %>%
  pivot_wider(
    names_from = Year,
    values_from = c(rate, ci_low, ci_hi)
  )
              
glimpse(matmort_wide)

## Rows: 181
## Columns: 10
## $ Country     <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Argentina…
## $ rate_1990   <int> 1340, 71, 216, 1160, 72, 58, 8, 8, 64, 46, 26, 569, 58, 3…
## $ rate_2000   <int> 1100, 43, 170, 924, 60, 40, 9, 5, 48, 61, 21, 399, 48, 26…
## $ rate_2015   <int> 396, 29, 140, 477, 52, 25, 6, 4, 25, 80, 15, 176, 27, 4, …
## $ ci_low_1990 <int> 878, 58, 141, 627, 64, 51, 7, 7, 56, 34, 20, 446, 47, 28,…
## $ ci_low_2000 <int> 745, 33, 118, 472, 54, 35, 8, 4, 42, 50, 18, 322, 38, 22,…
## $ ci_low_2015 <int> 253, 16, 82, 221, 44, 21, 5, 3, 17, 53, 12, 125, 19, 3, 5…
## $ ci_hi_1990  <int> 1950, 88, 327, 2020, 80, 65, 9, 10, 74, 61, 33, 715, 72, …
## $ ci_hi_2000  <int> 1570, 56, 241, 1730, 65, 46, 10, 6, 55, 74, 26, 496, 58, …
## $ ci_hi_2015  <int> 620, 46, 244, 988, 63, 31, 7, 5, 35, 124, 19, 280, 37, 6,…

4.8.6 Experimentum Data

Students in the Institute of Neuroscience and Psychology at the University of Glasgow can use the online experiment builder platform, Experimentum. The platform is also open source on github for anyone who can install it on a web server. It allows you to group questionnaires and experiments into projects with randomisation and counterbalancing. Data for questionnaires and experiments are downloadable in long format, but researchers often need to put them in wide format for analysis.

Look at the help menu for built-in dataset dataskills::experimentum_quests to learn what each column is. Subjects are asked questions about dogs to test the different questionnaire response types.

current: Do you own a dog? (yes/no)
past: Have you ever owned a dog? (yes/no)
name: What is the best name for a dog? (free short text)
good: How good are dogs? (1=pretty good:7=very good)
country: What country do borzois come from?
good_borzoi: How good are borzois? (0=pretty good:100=very good)
text: Write some text about dogs. (free long text)
time: What time is it? (time)

To get the dataset into wide format, where each question is in a separate column, use the following code:

q <- dataskills::experimentum_quests %>%
  pivot_wider(id_cols = session_id:user_age,
              names_from = q_name,
              values_from = dv) %>%
  type.convert(as.is = TRUE) %>%
  print()

## # A tibble: 24 x 15
##    session_id project_id quest_id user_id user_sex user_status user_age current
##         <int>      <int>    <int>   <int> <chr>    <chr>          <dbl>   <int>
##  1      34034          1        1   31105 female   guest           28.2       1
##  2      34104          1        1   31164 male     registered      19.4       1
##  3      34326          1        1   31392 female   guest           17         0
##  4      34343          1        1   31397 male     guest           22         1
##  5      34765          1        1   31770 female   guest           44         1
##  6      34796          1        1   31796 female   guest           35.9       0
##  7      34806          1        1   31798 female   guest           35         0
##  8      34822          1        1   31802 female   guest           58         1
##  9      34864          1        1   31820 male     guest           20         0
## 10      35014          1        1   31921 female   student         39.2       1
## # … with 14 more rows, and 7 more variables: past <int>, name <chr>,
## #   good <int>, country <chr>, text <chr>, good_borzoi <int>, time <chr>

The responses in the dv column have multiple types (e.g., r glossary(“integer”), r glossary(“double”), and r glossary(“character”)), but they are all represented as character strings when they’re in the same column. After you spread the data to wide format, each column should be given the ocrrect data type. The function type.convert() makes a best guess at what type each new column should be and converts it. The argument as.is = TRUE converts columns where none of the numbers have decimal places to integers.

4.9 Glossary

term	definition
long	Data where each observation is on a separate row
observation	All of the data about a single trial or question.
value	A single number or piece of data.
variable	A word that identifies and stores the value of some data for later use.
wide	Data where all of the observations about one subject are in the same row

4.10 Exercises

Download the exercises. See the answers only after you’ve attempted all the questions.

# run this to access the exercise
dataskills::exercise(4)

# run this to access the answers
dataskills::exercise(4, answers = TRUE)

id	trial	rt	score	total	mean_rt
1	1	857	4	14	869
1	2	890	3	14	869
1	3	859	7	14	869
2	1	902	3	5	920
2	2	900	1	5	920
2	3	959	1	5	920
3	1	757	2	11	827
3	2	823	5	11	827
3	3	901	4	11	827
4	1	844	6	14	752
4	2	788	2	14	752
4	3	624	6	14	752
5	1	659	1	10	704
5	2	764	7	10	704
5	3	690	2	10	704

id	trial	rt	score	total	mean_rt
1	1	857	4	14	869
1	2	890	3	14	869
1	3	859	7	14	869
2	1	902	3	5	920
2	2	900	1	5	920
2	3	959	1	5	920
3	1	757	2	11	827
3	2	823	5	11	827
3	3	901	4	11	827
4	1	844	6	14	752
4	2	788	2	14	752
4	3	624	6	14	752
5	1	659	1	10	704
5	2	764	7	10	704
5	3	690	2	10	704