6.4 Transforming tables with tidyr

Tidy datasets are all alike
but every messy dataset is messy in its own way.

Hadley Wickham (2014b, p. 2)

This quote by Hadley Wickham is a variation of the initial sentence of Tolstoy’s novel Anna Karenina. In both version, it is implied that all entities (i.e., happy families or tidy datasets) share some characteristic feature, whereas there is a variety of ways in which things can go wrong (i.e., become unhappy or messy). Both sentences convey great insights, of course, but explaining why they are true is a bit like explaining a joke — and not quite as catchy.

The term “tidy data” comes from the related concept of “data cleaning.” Cleaning up data is an effortful and expensive phase of data analysis. The “tidy” concept was defined in contrast to “messy data” by Wickham (2014b) and is the eponym for the tidyverse and the tidyr package.

The following sections merely provide a summary of essential tidyr functions. More extensive resources for this section include:

6.4.1 What is tidy data?

What is tidy data? The notion of tidy data is a key inspiration for the tidyverse. The concept of “tidy” is neat and intuitive, but also a bit vague. Rather than attempting a formal definition, we will characterize the term by describing the intentions behind its uses:

  • Informally, tidy data serves as the antagonist of messy data. As messy data is data that is difficult to work with, data is considered “tidy” when it is easy to work with.

  • The key idea of tidy data is that each variable should be in its own column.

Both these descriptions sound rather trivial, of course. The second point raises the question: When is a variable not in its own column?

The trick here is that the term “variable” is used in a functional sense: A variable is some measure or description that we want to use as a variable in an analysis. For instance, depending on the particular task at hand, a “variable” could be a particular date, or the month, year, or century that corresponds to a date. Thus, what is considered to be “tidy” partly lies in the eyes of the beholder and depends on what someone wants to do with data, rather than on some inherent property of the data itself.

More generally, the notion of tidy data is illuminated within our framework of ecological rationality (see Section 1.2.6): The difference between messy and tidy data depends on (a) our goals or the intended use of the data (e.g., which task do we want to address?) and (b) the tools with which we typically carry out our tasks (e.g., which functions are we familiar with?). Given the tools provided by R and prominent R packages (e.g., dplyr or ggplot2), it makes sense to first identify the variables of our analysis and then reshape the data so that each variable is in its own column. Although this format is informationally equivalent to many alternative formats, it provides practical benefits for further transforming the data. For instance, we can use the variables to filter, select, group, or pivot the data to reshape or reduce it to answer our questions.

6.4.2 Essential tidyr functions

The tidyr package (Wickham & Henry, 2020) provides commands to create and transform messy into tidy data (or vice versa). Although the package provides many functions, we consider two pairs of two complementary tidyr commands as essential:

  1. separate() splits one variable into two variables;
  2. unite() combines two variables into one variable;
  3. pivot_longer() or gather() make (“wide”) data longer (by gathering many variables into one variable);
  4. pivot_wider() or spread() make (“long”) data wider (by spreading one variable into many variables).

The first two commands allow splitting or combining variables: separate() is the complement to or inverse/opposite of unite(). The second two pairs of commands allow changing the data layout by making a given table longer or wider: spread() and pivot_wider() are the complements to or inverses/opposites of gather() and pivot_longer().

In the following sections, we briefly explain how to use these four essential commands.

The table5 tibble of the tidyr package presents two obstacles to tidy data:

#> # A tibble: 6 × 4
#>   country     century year  rate             
#> * <chr>       <chr>   <chr> <chr>            
#> 1 Afghanistan 19      99    745/19987071     
#> 2 Afghanistan 20      00    2666/20595360    
#> 3 Brazil      19      99    37737/172006362  
#> 4 Brazil      20      00    80488/174504898  
#> 5 China       19      99    212258/1272915272
#> 6 China       20      00    213766/1280428583
  1. Instead of solving typical tasks in terms of century and a 2-digit code of year, we normally would encode the year as a 4-digit number. Doing so would require combining two variables into one — which is what the unite() function is for.

  2. The variable rate suffers from the opposite problem: It contains both the number of TB cases and the population count, separated by a forward slash “/.” Splitting one variable into two is the job of the separate() function.

6.4.3 Unite variables

To combine the variables century and year of table5 into a single variable, we can use the following unite() expression:

table5 %>%
  unite(col = "yr", c("century", "year"), sep = "")
#> # A tibble: 6 × 3
#>   country     yr    rate             
#>   <chr>       <chr> <chr>            
#> 1 Afghanistan 1999  745/19987071     
#> 2 Afghanistan 2000  2666/20595360    
#> 3 Brazil      1999  37737/172006362  
#> 4 Brazil      2000  80488/174504898  
#> 5 China       1999  212258/1272915272
#> 6 China       2000  213766/1280428583

Note that this unite() function is preceded by the pipe operator, so that table5 is used as its first argument (run ?unite to discover its arguments as unite(data, col, ..., sep, remove, na.rm)). Evaluating the pipe creates a 6 x 3 tibble with a new variable yr and removes the two original variables (century and year) from the table, as the remove argument of unite() is set to TRUE by default. Incidentally, omitting the quotation marks around the variable names (here: yr, century, and year) would work as well. Additionally, the new variable yr is of type “character.” To create a corresponding number or factor, we could use mutate() to create additional variables:

table5 %>%
  unite(col = "yr", c("century", "year"), sep = "") %>%
  mutate(yr_num = as.numeric(yr),
         yr_fac = as.factor(yr))
#> # A tibble: 6 × 5
#>   country     yr    rate              yr_num yr_fac
#>   <chr>       <chr> <chr>              <dbl> <fct> 
#> 1 Afghanistan 1999  745/19987071        1999 1999  
#> 2 Afghanistan 2000  2666/20595360       2000 2000  
#> 3 Brazil      1999  37737/172006362     1999 1999  
#> 4 Brazil      2000  80488/174504898     2000 2000  
#> 5 China       1999  212258/1272915272   1999 1999  
#> 6 China       2000  213766/1280428583   2000 2000

or use transmute() to drop yr in favor of an alternative variable.

In case of wondering why we specified sep = "" and whether the order of variables in the expression matters, we can try the following variants:

table5 %>% 
  unite(col = "yr", c("century", "year"))

table5 %>% 
  unite(col = yr, c(year, century), sep = "")

Thus, sep = "_" by default, and the order of variables matters, but variable names can be quoted or unquoted.

6.4.4 Separate variables

The second issue of table5 involved the rate variable. The separate() function of tidyr allows creating two variables:

table5 %>%
  separate(rate, into = c("cases", "popu"))
#> # A tibble: 6 × 5
#>   country     century year  cases  popu      
#>   <chr>       <chr>   <chr> <chr>  <chr>     
#> 1 Afghanistan 19      99    745    19987071  
#> 2 Afghanistan 20      00    2666   20595360  
#> 3 Brazil      19      99    37737  172006362 
#> 4 Brazil      20      00    80488  174504898 
#> 5 China       19      99    212258 1272915272
#> 6 China       20      00    213766 1280428583

Note that we did not need to specify sep = "/", as separate() was smart enough to identify this as the only plausible splitting point. If we wanted to separate a variable without a dedicated symbol that signals the separation, we can also specify the numeric position at which we want to split a variable:

table5 %>%
  separate(country, into = c("first_3", "rest"), sep = 3)
#> # A tibble: 6 × 5
#>   first_3 rest     century year  rate             
#>   <chr>   <chr>    <chr>   <chr> <chr>            
#> 1 Afg     hanistan 19      99    745/19987071     
#> 2 Afg     hanistan 20      00    2666/20595360    
#> 3 Bra     zil      19      99    37737/172006362  
#> 4 Bra     zil      20      00    80488/174504898  
#> 5 Chi     na       19      99    212258/1272915272
#> 6 Chi     na       20      00    213766/1280428583

As the rate and country variables that were separated are no longer part of the resulting tibble, we now have remove = FALSE by default. Finally, the new variables are still of type “character” (i.e., would require a round of “as.numeric()” to be turned into numbers).


  1. Combine the above unite() step (to create the yr variable) with a separate() step that reverses its effect in a single dplyr pipe.


table5 %>%
  unite(col = "yr", c("century", "year"), sep = "") %>%
  separate(yr, into = c("century", "year"), sep = 2)
  1. Combine the above separate() step (to split the rate variable) with a unite() step that reverses its effect in a single dplyr pipe.


table5 %>%
  separate(rate, into = c("cases", "popu")) %>%
  unite(col = "rate", c("cases", "popu"), sep = "/")

See Section 7.2 Essential tidyr commands for additional examples and practice tasks.

6.4.5 Making tables longer

When describing the shape of datasets, notions like “wide” and “long” make more sense when describing changes than in absolute terms. For instance, data tables in “wide format” can contain thousands of cases (which most people would consider quite “long”) and tables in “long format” can contain a large number of variables (i.e., be considered quite “wide”). However, the relative terms “longer” and “wider” make sense when describing a change in format of the same data table.

The first change considered here makes a (“wide”) data table (e.g.,, a table in which a single variable is distributed over multiple columns) longer. A simple case for such a task is provided by the data of table4a of the tidyr package:

#> # A tibble: 3 × 3
#>   country     `1999` `2000`
#> * <chr>        <int>  <int>
#> 1 Afghanistan    745   2666
#> 2 Brazil       37737  80488
#> 3 China       212258 213766

Using gather() for making wide tables longer

Making wide tables longer can be achieved with the gather() command of tidyr. Gathering multiple columns requires that each cell value to be moved is described by a so-called key (e.g., the name of a variable that describes or identifies the value) and its value (e.g., the name of the variable that is being measured). Additionally, we need to specify which variables of a table are to be gathered (by specifying either a vector or range of variables). In the case of table4a, the corresponding gather() command could be:

table4a %>% 
  gather(key = "year", value = "cases", '1999':'2000')
#> # A tibble: 6 × 3
#>   country     year   cases
#>   <chr>       <chr>  <int>
#> 1 Afghanistan 1999     745
#> 2 Brazil      1999   37737
#> 3 China       1999  212258
#> 4 Afghanistan 2000    2666
#> 5 Brazil      2000   80488
#> 6 China       2000  213766

Note that the range of to-be-gathered variables (i.e., 1999:2000) lacks an argument name, uses the :-notation to specify a range of variables (but could also be expressed as a two-variable vector c('1999', '2000')), and encloses the variable name in single quotes because the variables '1999' and '2000' start with a number, rather than a letter (as variables in R normally should).

A potential limitation of gather() is that it is primarily designed for cases in which the values of one variable are spread over multiple columns. However, a common case in many datasets is that there are several variables whose values are scattered over multiple columns. For instance, consider the following table8 from the ds4psy package:

#> # A tibble: 3 × 5
#>   country     cases_1999 cases_2000  popu_1999  popu_2000
#>   <chr>            <dbl>      <dbl>      <dbl>      <dbl>
#> 1 Afghanistan        745       2666   19987071   20595360
#> 2 Brazil           37737      80488  172006362  174504898
#> 3 China           212258     213766 1272915272 1280428583

In table8, the contents of table4a are followed by two more columns from table4b, and the meaning of each column (and the variables it contains) is signaled by the column names. One way of reshaping table8 into a longer format would be to split it into two parts (i.e., table4a and table4b), use gather() on each part, and then join the two resulting tables (see the Practice exercises below). An alternative consists in using gather() on all variables with values and later separate() the key variable (containing the previous column names) into two variables:

table8 %>%
  gather(key = "key", value = "nr", cases_1999:popu_2000) %>%
  separate(col = key, into = c("type", "year"))
#> # A tibble: 12 × 4
#>   country     type  year      nr
#>   <chr>       <chr> <chr>  <dbl>
#> 1 Afghanistan cases 1999     745
#> 2 Brazil      cases 1999   37737
#> 3 China       cases 1999  212258
#> 4 Afghanistan cases 2000    2666
#> 5 Brazil      cases 2000   80488
#> 6 China       cases 2000  213766
#> # … with 6 more rows

However, this only works for table8 because the columns had sensible and systematic column names. A more flexibel range of solutions is supported by the pivot_longer() function.

Using pivot_longer() rather than gather()

In recent versions of tidyr (e.g., version 1.1.0+), the documentation of gather() carries a warning label: “Development on gather() is complete, and for new code we recommend switching to pivot_longer(), which is easier to use, more featureful, and still under active development.” Hence, we can still use gather(), but using the more recent pivot_longer() command is a safer choice. Making table4a wider with this function can be achieved as follows:

table4a %>% 
  pivot_longer(cols = `1999`:`2000`, 
               names_to = "year", values_to = "cases")
#> # A tibble: 6 × 3
#>   country     year   cases
#>   <chr>       <chr>  <int>
#> 1 Afghanistan 1999     745
#> 2 Afghanistan 2000    2666
#> 3 Brazil      1999   37737
#> 4 Brazil      2000   80488
#> 5 China       1999  212258
#> 6 China       2000  213766

For simple cases, pivot_longer() works just like gather(): The range of variables (columns) to be changed to a longer format are specified by the cols argument, and the cryptic key and value arguments of gather() are replaced by more intuitive names_to and values_to arguments.

The main benefit of pivot_longer() is that it provides functionalities beyond those of gather(). For instance, the function can gather the two distributed variables of table8 in one expression:

ds4psy::table8 %>%
  pivot_longer(cols = cases_1999:popu_2000, 
               names_to = c("type", "year"), 
               names_sep = "_", 
               # names_pattern = "(.*)_(.*)", 
               values_to = "nr")
#> # A tibble: 12 × 4
#>   country     type  year        nr
#>   <chr>       <chr> <chr>    <dbl>
#> 1 Afghanistan cases 1999       745
#> 2 Afghanistan cases 2000      2666
#> 3 Afghanistan popu  1999  19987071
#> 4 Afghanistan popu  2000  20595360
#> 5 Brazil      cases 1999     37737
#> 6 Brazil      cases 2000     80488
#> # … with 6 more rows

As names_to argument specified two variables, we need to say how the column names are to be mapped to the two new variables. This can either be done by specifying a separation point sep (which can be a character or a numeric value) or by providing a names_pattern (as a regular expression, see Appendix E: Using regular expressions for an introduction).

Overall, pivot_longer() can do more than the gather() function, but the price of using the more advanced features is additional complexity within the function.

More advanced features of pivot_longer()

Here are some helpful examples from the documentation of pivot_longer() (see the vignette("pivot") for explanations):

  1. Using the relig_income data:

  2. Using the billboard data:

The tidyr::billboard data provides top-100 song rankings for the year 2000. The data provides variables for the artist name, the track (or song) name, and the date on which a song first appeared in the top-100 ratings (date.enter). After this, a range of variables from wk1 to wk76 notes a track’s rank in each week after it entered. As most songs disappear from the rankings after some weeks, later variables (i.e., columns on the right side of the billbard table) contain an increasing number of missing (or NA) values:

# Data documentation: 

# Inspect data:

# Note: 
sum(!is.na(billboard))  # existing values
sum(is.na(billboard))   # missing values

The following pivot_longer() command identifies the variables (or cols) to be gathered (or moved from a wide to a longer format) by their common prefix "wk". Specifying names_prefix = "wk" further ensures that the values in the new variable week drop the "wk" prefix (and could thus be transformed into numbers). Finally, the values_drop_na = TRUE argument ensures that the NA values are removed from the resulting tibble:

billboard %>%
   cols = starts_with("wk"),
   names_to = "week",
   names_prefix = "wk",
   values_to = "rank",
   values_drop_na = TRUE
#> # A tibble: 5,307 × 5
#>   artist track                   date.entered week   rank
#>   <chr>  <chr>                   <date>       <chr> <dbl>
#> 1 2 Pac  Baby Don't Cry (Keep... 2000-02-26   1        87
#> 2 2 Pac  Baby Don't Cry (Keep... 2000-02-26   2        82
#> 3 2 Pac  Baby Don't Cry (Keep... 2000-02-26   3        72
#> 4 2 Pac  Baby Don't Cry (Keep... 2000-02-26   4        77
#> 5 2 Pac  Baby Don't Cry (Keep... 2000-02-26   5        87
#> 6 2 Pac  Baby Don't Cry (Keep... 2000-02-26   6        94
#> # … with 5,301 more rows

Note that the new week variable is of type “character,” but we could easily turn it into a numeric variable by adding a mutate() step of mutate(nr_wk = as.numeric(week)).

  1. Using the who data:

The tidyr::who data encodes a lot of information (regarding the diagnosis, gender, and age, of the people captured in the corresponding frequency counts) in the name of its 56 rightmost variables. To extract this information from the variable names, the names_to argument of pivot_longer() can use multiple names of new variables and the names_pattern argument accepts a regular expression that parses the names according to some pattern:

# Data:
# who

# Multiple variables stored in column names: 
who %>% pivot_longer(
  cols = new_sp_m014:newrel_f65,
  names_to = c("diagnosis", "gender", "age_group"),
  names_pattern = "new_?(.*)_(.)(.*)",
  values_to = "count"

This is nicely illustrates the superior powers of pivot_longer(), but requires some expertise in using regular expressions in R (see Appendix E: Using regular expressions).

  1. Using the anscombe data:

An example only intelligible to expert users is the following (using the anscombe data from the datasets package):


# Multiple observations per row
anscombe %>%
   names_to = c(".value", "set"),
   names_pattern = "(.)(.)"

An explanation of this example, and many others, is available in vignette("pivot"). The examples show that people spend a lot of time and effort on reshaping data files. Our more modest goal is to understand the basics…

Practice: Making tables longer

The shape of table4b is identical to table4a, but the values represent population counts, rather than counts of cases.

  1. Use both gather() and pivot_longer() to make table4b (containing the countries’ population values) longer:



table4b %>% 
  gather(key = "year", value = "population", `1999`:`2000`)

table4b %>% 
  pivot_longer(c(`1999`, `2000`), 
               names_to = "year", values_to = "population")
  1. What would we need to create table1 from the longer versions of the same data in table4a and table4b?
#> # A tibble: 6 × 4
#>   country      year  cases population
#>   <chr>       <int>  <int>      <int>
#> 1 Afghanistan  1999    745   19987071
#> 2 Afghanistan  2000   2666   20595360
#> 3 Brazil       1999  37737  172006362
#> 4 Brazil       2000  80488  174504898
#> 5 China        1999 212258 1272915272
#> 6 China        2000 213766 1280428583


We could create table1 from table4a and table4b in two steps:

  • First, we would transform both table4a and table4b from their wide format into a longer format, so that cases and population would be a variable with a single column (as we have just done).
  • In a second step, we would need to combine both resulting tables: Provided that both outputs in longer formats are arranged the same way, we could add one of the variables (either cases or population) to the other table.

A safer way of combining both tables would join them based on their common variables (e.g., by using the merge() function of base R or one of the join functions of dplyr, see Chapter 8: Joining data of the ds4psy book).

6.4.6 Making tables wider

The opposite of making (“wide”) tables longer is making (“long”) tables wider. And the inverse of gather() in the tidyr package is spread().

Using spread() for making long tables wider

The simplest case for the spread() function is that we have a single key variable (whose values are to be turned into new variable names) and a single value variable (whose values are to be distributed over multiple variables). For instance, if we removed the population variable from table1, we could spread the values of the cases variable over two new variables whose names are generated from the year variable (to obtain table4a):

table1 %>%
  select(-population) %>% 
  spread(key = year, value = cases)
#> # A tibble: 3 × 3
#>   country     `1999` `2000`
#>   <chr>        <int>  <int>
#> 1 Afghanistan    745   2666
#> 2 Brazil       37737  80488
#> 3 China       212258 213766

# same as: table4a

Removing the cases variable would allow spreading the remaining values of table1 so that the values of the population variable are spread over two new variables whose names are generated from the year variable (to obtain table4b):

table1 %>%
  select(-cases) %>% 
  spread(key = year, value = population)
#> # A tibble: 3 × 3
#>   country         `1999`     `2000`
#>   <chr>            <int>      <int>
#> 1 Afghanistan   19987071   20595360
#> 2 Brazil       172006362  174504898
#> 3 China       1272915272 1280428583

# same as: table4b

Unfortunately, just like we saw for gather() above, the spread() function is designed for a single dependent variable. However, we often deal with multiple variables (e.g., table1 contains the variables cases and populations, both of which contain frequency counts that we may want to spread into a wider table format. How could we spread multiple variables at once?

Using pivot_wider() rather than spread()

Perhaps not surprisingly, the more potent replacement of spread() in the tidyr package is called pivot_wider(). To illustrate its potential, we can distinguish between two cases:

Case 1: Our table1 data

#> # A tibble: 6 × 4
#>   country      year  cases population
#>   <chr>       <int>  <int>      <int>
#> 1 Afghanistan  1999    745   19987071
#> 2 Afghanistan  2000   2666   20595360
#> 3 Brazil       1999  37737  172006362
#> 4 Brazil       2000  80488  174504898
#> 5 China        1999 212258 1272915272
#> 6 China        2000 213766 1280428583

can be viewed as containing one independent variable (year) and two dependent variables (cases and population). When reshaping data by spreading it from a (“long”) table into a wider table, the independent variable should provide the names of a new variable and the two dependent variables should become the values of the new variable. The following pivot_wider() expression does this in one step:

# 1. One IV, two DVs: 
table1 %>%
  pivot_wider(names_from = year, 
              values_from = c(cases, population))
#> # A tibble: 3 × 5
#>   country     cases_1999 cases_2000 population_1999 population_2000
#>   <chr>            <int>      <int>           <int>           <int>
#> 1 Afghanistan        745       2666        19987071        20595360
#> 2 Brazil           37737      80488       172006362       174504898
#> 3 China           212258     213766      1272915272      1280428583

Note that values_from argument contains both dependent variables — and that they are combined with the values of the independent variable (year) to form the names of the new variables (using names_sep = "_" by default).

Case 2: Alternatively, our table2 data

#> # A tibble: 12 × 4
#>   country      year type           count
#>   <chr>       <int> <chr>          <int>
#> 1 Afghanistan  1999 cases            745
#> 2 Afghanistan  1999 population  19987071
#> 3 Afghanistan  2000 cases           2666
#> 4 Afghanistan  2000 population  20595360
#> 5 Brazil       1999 cases          37737
#> 6 Brazil       1999 population 172006362
#> # … with 6 more rows

can be viewed as containing two independent variables (year and type) and and one dependent variable (count). When spreading this table, the two independent variables should form the names of new variables and the values of the dependent variable should be distributed over these variables. This task can be addressed by the following pivot_wider() command:

table2 %>%
  pivot_wider(names_from = c(year, type), 
              values_from = c(count))
#> # A tibble: 3 × 5
#>   country     `1999_cases` `1999_population` `2000_cases` `2000_population`
#>   <chr>              <int>             <int>        <int>             <int>
#> 1 Afghanistan          745          19987071         2666          20595360
#> 2 Brazil             37737         172006362        80488         174504898
#> 3 China             212258        1272915272       213766        1280428583

Note that the names_from argument now contains both independent variables. Their values are combined into the names of four new variables (using names_sep = "_" by default). As the names of the new variables start with a number (rather than a letter), they are enclosed in quotes. To obtain exactly the same wider table as above, we could add two dplyr steps for shuffling the order of columns (by using select()) and renaming some variables (using rename()):

table2 %>%
  pivot_wider(names_from = c(year, type), 
              values_from = c(count)) %>%
  select(country, `1999_cases`, `2000_cases`, `1999_population`, everything()) %>%
  rename(cases_1999 = `1999_cases`,
         cases_2000 = `2000_cases`,
         pop_1999 = `1999_population`,
         pop_2000 = `2000_population`)
#> # A tibble: 3 × 5
#>   country     cases_1999 cases_2000   pop_1999   pop_2000
#>   <chr>            <int>      <int>      <int>      <int>
#> 1 Afghanistan        745       2666   19987071   20595360
#> 2 Brazil           37737      80488  172006362  174504898
#> 3 China           212258     213766 1272915272 1280428583

Thus, both the names_from() and the values_from() arguments of pivot_wider() can contain multiple variables.

More advanced features of pivot_wider()

Here are some more advanced examples from the documentation of pivot_wider() (see the vignette("pivot") for explanations):

  1. Using the fish_encounters data:
# Data:

# Simplest case: 
fish_encounters %>%
  pivot_wider(names_from = station, values_from = seen)

# Fill in missing values (as 0): 
fish_encounters %>%
  pivot_wider(names_from = station, values_from = seen, 
              values_fill = 0)
  1. Using the us_rent_income data:
# Data:

# Generate column names from multiple variables:
us_rent_income %>%
  pivot_wider(names_from = variable, values_from = c(estimate, moe))

# When there are multiple `names_from` or `values_from`, we can use 
# `names_sep` or `names_glue` to control the output variable names: 
us_rent_income %>%
    names_from = variable,
    names_sep = ".",
    values_from = c(estimate, moe)

us_rent_income %>%
    names_from = variable,
    names_glue = "{variable}_{.value}",
    values_from = c(estimate, moe)
  1. Using the warpbreaks data:
# Data: 
warpbreaks <- as_tibble(warpbreaks[c("wool", "tension", "breaks")])

# Can perform aggregation with values_fn:
warpbreaks %>%
    names_from = wool,
    values_from = breaks,
    values_fn = mean

Overall, these examples show that pivot_wider() is a more flexible replacement for spread().

Practice: Making tables wider

  1. Demonstrate that spreading is the opposite of gathering by starting with table4a and using first gather() and then spread() to re-create the same table.


table4a %>% 
  gather(key = year, value = cases, `1999`:`2000`) %>%
  spread(key = year, value = cases)
  1. Make the table8 from ds4psy package tidy.


ds4psy::table8 %>%
  tidyr::pivot_longer(cols = cases_1999:popu_2000,
                      names_to = c(".value", "year"),
                      names_sep = "_")

This concludes our summary of essential tidyr functions (Wickham & Henry, 2020). To sum up:

  1. separate() splits one variable into two;
  2. unite() combines two variables into one;
  3. pivot_longer() or gather() make (“wide”) data longer;
  4. pivot_wider() or spread() make (“long”) data wider.

The combination of dplyr and tidyr provides a powerful combination for transforming, summarizing, and reshaping data.


Neth, H. (2022a). Data science for psychologists. Social Psychology; Decision Sciences, University of Konstanz. https://bookdown.org/hneth/ds4psy/
Wickham, H. (2014b). Tidy data. Journal of Statistical Software, 59(10), 1–23. https://doi.org/10.18637/jss.v059.i10
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. http://r4ds.had.co.nz
Wickham, H., & Henry, L. (2020). tidyr: Tidy messy data. https://CRAN.R-project.org/package=tidyr