Data Wrangling with tidyr

Last updated on 2023-12-05 | Edit this page

Estimated time 40 minutes



  • How can I reformat a data frame to meet my needs?


  • Describe the concept of a wide and a long table format and for which purpose those formats are useful.
  • Describe the roles of variable names and their associated values when a table is reshaped.
  • Reshape a dataframe from long to wide format and back with the pivot_wider and pivot_longer commands from the tidyr package.
  • Export a dataframe to a csv file.

dplyr pairs nicely with tidyr which enables you to swiftly convert between different data formats (long vs. wide) for plotting and analysis. To learn more about tidyr after the workshop, you may want to check out this handy data tidying with tidyr cheatsheet.

To make sure everyone will use the same dataset for this lesson, we’ll read again the SAFI dataset that we downloaded earlier.


## load the tidyverse

interviews <- read_csv(here("data", "SAFI_clean.csv"), na = "NULL")

## inspect the data

## preview the data
# view(interviews)

Reshaping with pivot_wider() and pivot_longer()

There are essentially three rules that define a “tidy” dataset:

  1. Each variable has its own column
  2. Each observation has its own row
  3. Each value must have its own cell

This graphic visually represents the three rules that define a “tidy” dataset:

R for Data Science, Wickham H and Grolemund G ( © Wickham, Grolemund 2017 This image is licenced under Attribution-NonCommercial-NoDerivs 3.0 United States (CC-BY-NC-ND 3.0 US)

In this section we will explore how these rules are linked to the different data formats researchers are often interested in: “wide” and “long”. This tutorial will help you efficiently transform your data shape regardless of original format. First we will explore qualities of the interviews data and how they relate to these different types of data formats.

Long and wide data formats

In the interviews data, each row contains the values of variables associated with each record collected (each interview in the villages), where it is stated that the key_ID was “added to provide a unique Id for each observation” and the instance_ID “does this as well but it is not as convenient to use.”

However, with some inspection, we notice that there are more than one row in the dataset with the same key_ID (as seen below). However, the instanceIDs associated with these duplicate key_IDs are not the same. Thus, we should think of instanceID as the unique identifier for observations!


interviews %>%
  select(key_ID, village, interview_date, instanceID)


# A tibble: 131 × 4
   key_ID village  interview_date      instanceID                               
    <dbl> <chr>    <dttm>              <chr>                                    
 1      1 God      2016-11-17 00:00:00 uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef
 2      2 God      2016-11-17 00:00:00 uuid:099de9c9-3e5e-427b-8452-26250e840d6e
 3      3 God      2016-11-17 00:00:00 uuid:193d7daf-9582-409b-bf09-027dd36f9007
 4      4 God      2016-11-17 00:00:00 uuid:148d1105-778a-4755-aa71-281eadd4a973
 5      5 God      2016-11-17 00:00:00 uuid:2c867811-9696-4966-9866-f35c3e97d02d
 6      6 God      2016-11-17 00:00:00 uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70
 7      7 God      2016-11-17 00:00:00 uuid:ae20a58d-56f4-43d7-bafa-e7963d850844
 8      8 Chirodzo 2016-11-16 00:00:00 uuid:d6cee930-7be1-4fd9-88c0-82a08f90fb5a
 9      9 Chirodzo 2016-11-16 00:00:00 uuid:846103d2-b1db-4055-b502-9cd510bb7b37
10     10 Chirodzo 2016-12-16 00:00:00 uuid:8f4e49bc-da81-4356-ae34-e0d794a23721
# ℹ 121 more rows

As seen in the code below, for each interview date in each village no instanceIDs are the same. Thus, this format is what is called a “long” data format, where each observation occupies only one row in the dataframe.


interviews %>%
  filter(village == "Chirodzo") %>%
  select(key_ID, village, interview_date, instanceID) %>%
  sample_n(size = 10)


# A tibble: 10 × 4
   key_ID village  interview_date      instanceID                               
    <dbl> <chr>    <dttm>              <chr>                                    
 1     36 Chirodzo 2016-11-17 00:00:00 uuid:c90eade0-1148-4a12-8c0e-6387a36f45b1
 2     68 Chirodzo 2016-11-16 00:00:00 uuid:ef04b3eb-b47d-412e-9b09-4f5e08fc66f9
 3     35 Chirodzo 2016-11-17 00:00:00 uuid:ff7496e7-984a-47d3-a8a1-13618b5683ce
 4     60 Chirodzo 2016-11-16 00:00:00 uuid:85465caf-23e4-4283-bb72-a0ef30e30176
 5     47 Chirodzo 2016-11-17 00:00:00 uuid:2d0b1936-4f82-4ec3-a3b5-7c3c8cd6cc2b
 6     54 Chirodzo 2016-11-16 00:00:00 uuid:273ab27f-9be3-4f3b-83c9-d3e1592de919
 7     58 Chirodzo 2016-11-16 00:00:00 uuid:a7a3451f-cd0d-4027-82d9-8dcd1234fcca
 8     63 Chirodzo 2016-11-16 00:00:00 uuid:86ed4328-7688-462f-aac7-d6518414526a
 9     57 Chirodzo 2016-11-16 00:00:00 uuid:a7184e55-0615-492d-9835-8f44f3b03a71
10     66 Chirodzo 2016-11-16 00:00:00 uuid:a457eab8-971b-4417-a971-2e55b8702816

We notice that the layout or format of the interviews data is in a format that adheres to rules 1-3, where

  • each column is a variable
  • each row is an observation
  • each value has its own cell

This is called a “long” data format. But, we notice that each column represents a different variable. In the “longest” data format there would only be three columns, one for the id variable, one for the observed variable, and one for the observed value (of that variable). This data format is quite unsightly and difficult to work with, so you will rarely see it in use.

Alternatively, in a “wide” data format we see modifications to rule 1, where each column no longer represents a single variable. Instead, columns can represent different levels/values of a variable. For instance, in some data you encounter the researchers may have chosen for every survey date to be a different column.

These may sound like dramatically different data layouts, but there are some tools that make transitions between these layouts much simpler than you might think! The gif below shows how these two formats relate to each other, and gives you an idea of how we can use R to shift from one format to the other.

Long and wide dataframe layouts mainly affect readability. You may find that visually you may prefer the “wide” format, since you can see more of the data on the screen. However, all of the R functions we have used thus far expect for your data to be in a “long” data format. This is because the long format is more machine readable and is closer to the formatting of databases.

Questions which warrant different data formats

In interviews, each row contains the values of variables associated with each record (the unit), values such as the village of the respondent, the number of household members, or the type of wall their house had. This format allows for us to make comparisons across individual surveys, but what if we wanted to look at differences in households grouped by different types of items owned?

To facilitate this comparison we would need to create a new table where each row (the unit) was comprised of values of variables associated with items owned (i.e., items_owned). In practical terms this means the values of the items in items_owned (e.g. bicycle, radio, table, etc.) would become the names of column variables and the cells would contain values of TRUE or FALSE, for whether that household had that item.

Once we we’ve created this new table, we can explore the relationship within and between villages. The key point here is that we are still following a tidy data structure, but we have reshaped the data according to the observations of interest.

Alternatively, if the interview dates were spread across multiple columns, and we were interested in visualizing, within each village, how irrigation conflicts have changed over time. This would require for the interview date to be included in a single column rather than spread across multiple columns. Thus, we would need to transform the column names into values of a variable.

We can do both of these transformations with two tidyr functions, pivot_wider() and pivot_longer().

Pivoting wider

pivot_wider() takes three principal arguments:

  1. the data
  2. the names_from column variable whose values will become new column names.
  3. the values_from column variable whose values will fill the new column variables.

Further arguments include values_fill which, if set, fills in missing values with the value provided.

Let’s use pivot_wider() to transform interviews to create new columns for each item owned by a household. There are a couple of new concepts in this transformation, so let’s walk through it line by line. First we create a new object (interviews_items_owned) based on the interviews data frame.


interviews_items_owned <- interviews %>%

Then we will actually need to make our data frame longer, because we have multiple items in a single cell. We will use a new function, separate_longer_delim(), from the tidyr package to separate the values of items_owned based on the presence of semi-colons (;). The values of this variable were multiple items separated by semi-colons, so this action creates a row for each item listed in a household’s possession. Thus, we end up with a long format version of the dataset, with multiple rows for each respondent. For example, if a respondent has a television and a solar panel, that respondent will now have two rows, one with “television” and the other with “solar panel” in the items_owned column.


separate_longer_delim(items_owned, delim = ";") %>%

After this transformation, you may notice that the items_owned column contains NA values. This is because some of the respondents did not own any of the items that was in the interviewer’s list. We can use the replace_na() function to change these NA values to something more meaningful. The replace_na() function expects for you to give it a list() of columns that you would like to replace the NA values in, and the value that you would like to replace the NAs. This ends up looking like this:


replace_na(list(items_owned = "no_listed_items")) %>%

Next, we create a new variable named items_owned_logical, which has one value (TRUE) for every row. This makes sense, since each item in every row was owned by that household. We are constructing this variable so that when we spread the items_owned across multiple columns, we can fill the values of those columns with logical values describing whether the household did (TRUE) or didn’t (FALSE) own that particular item.


mutate(items_owned_logical = TRUE) %>%
Two tables shown side-by-side. The first row of the left table is highlighted in blue, and the first four rows of the right table are also highlighted in blue to show how each of the values of 'items owned' are given their own row with the separate longer delim function. The 'items owned logical' column is highlighted in yellow on the right table to show how the mutate function adds a new column.

Lastly, we use pivot_wider() to switch from long format to wide format. This creates a new column for each of the unique values in the items_owned column, and fills those columns with the values of items_owned_logical. We also declare that for items that are missing, we want to fill those cells with the value of FALSE instead of NA.


pivot_wider(names_from = items_owned,
            values_from = items_owned_logical,
            values_fill = list(items_owned_logical = FALSE))
Two tables shown side-by-side. The 'items owned' column is highlighted in blue on the left table, and the column names are highlighted in blue on the right table to show how the values of the 'items owned' become the column names in the output of the pivot wider function. The 'items owned logical' column is highlighted in yellow on the left table, and the values of the bicycle, television, and solar panel columns are highlighted in yellow on the right table to show how the values of the 'items owned logical' column became the values of all three of the aforementioned columns.

Combining the above steps, the chunk looks like this:


interviews_items_owned <- interviews %>%
  separate_longer_delim(items_owned, delim = ";") %>%
  replace_na(list(items_owned = "no_listed_items")) %>%
  mutate(items_owned_logical = TRUE) %>%
  pivot_wider(names_from = items_owned,
              values_from = items_owned_logical,
              values_fill = list(items_owned_logical = FALSE))

View the interviews_items_owned data frame. It should have 131 rows (the same number of rows you had originally), but extra columns for each item. How many columns were added? Notice that there is no longer a column titled items_owned. This is because there is a default parameter in pivot_wider() that drops the original column. The values that were in that column have now become columns named television, solar_panel, table, etc. You can use dim(interviews) and dim(interviews_wide) to see how the number of columns has changed between the two datasets.

This format of the data allows us to do interesting things, like make a table showing the number of respondents in each village who owned a particular item:


interviews_items_owned %>%
  filter(bicycle) %>%
  group_by(village) %>%


# A tibble: 3 × 3
# Groups:   village [3]
  village  bicycle     n
  <chr>    <lgl>   <int>
1 Chirodzo TRUE       17
2 God      TRUE       23
3 Ruaca    TRUE       20

Or below we calculate the average number of items from the list owned by respondents in each village. This code uses the rowSums() function to count the number of TRUE values in the bicycle to car columns for each row, hence its name. Note that we replaced NA values with the value no_listed_items, so we must exclude this value in the aggregation. We then group the data by villages and calculate the mean number of items, so each average is grouped by village.


interviews_items_owned %>%
    select(-no_listed_items) %>% 
    mutate(number_items = rowSums(select(., bicycle:car))) %>%
    group_by(village) %>%
    summarize(mean_items = mean(number_items))


# A tibble: 3 × 2
  village  mean_items
  <chr>         <dbl>
1 Chirodzo       4.54
2 God            3.98
3 Ruaca          5.57

Pivoting longer

The opposing situation could occur if we had been provided with data in the form of interviews_wide, where the items owned are column names, but we wish to treat them as values of an items_owned variable instead.

In this situation we are gathering these columns turning them into a pair of new variables. One variable includes the column names as values, and the other variable contains the values in each cell previously associated with the column names. We will do this in two steps to make this process a bit clearer.

pivot_longer() takes four principal arguments:

  1. the data
  2. cols are the names of the columns we use to fill the a new values variable (or to drop).
  3. the names_to column variable we wish to create from the cols provided.
  4. the values_to column variable we wish to create and fill with values associated with the cols provided.


interviews_long <- interviews_items_owned %>%
  pivot_longer(cols = bicycle:car,
               names_to = "items_owned",
               values_to = "items_owned_logical")

View both interviews_long and interviews_items_owned and compare their structure.


We created some summary tables on interviews_items_owned using count and summarise. We can create the same tables on interviews_long, but this will require a different process.

  1. Make a table showing showing the number of respondents in each village who owned a particular item, and include all items. The difference between this format and the wide format is that you can now count all the items using the items_owned variable.


interviews_long %>%
  filter(items_owned_logical) %>% 
  group_by(village) %>% 


# A tibble: 47 × 3
# Groups:   village [3]
   village  items_owned         n
   <chr>    <chr>           <int>
 1 Chirodzo bicycle            17
 2 Chirodzo computer            2
 3 Chirodzo cow_cart            6
 4 Chirodzo cow_plough         20
 5 Chirodzo electricity         1
 6 Chirodzo fridge              1
 7 Chirodzo lorry               1
 8 Chirodzo mobile_phone       25
 9 Chirodzo motorcyle          13
10 Chirodzo no_listed_items     3
# ℹ 37 more rows

Exercise (continued)

  1. Calculate the average number of items from the list owned by respondents in each village. If you remove rows where items_owned_logical is FALSE you will have a data frame where the number of rows per household is equal to the number of items owned. You can use that to calculate the mean number of items per village.

Remember, you need to make sure we don’t count no_listed_items, since this is not an actual item, but rather the absence thereof.


interviews_long %>% 
         items_owned != "no_listed_items") %>% 
  # to keep information per household, we count key_ID
  count(key_ID, village) %>% # we want to also keep the village variable
  group_by(village) %>% 
  summarise(mean_items = mean(n))


# A tibble: 3 × 2
  village  mean_items
  <chr>         <dbl>
1 Chirodzo       4.92
2 God            4.38
3 Ruaca          5.93

Applying what we learned to clean our data

Now we have simultaneously learned about pivot_longer() and pivot_wider(), and fixed a problem in the way our data is structured. In the spreadsheets lesson, we learned that it’s best practice to have only a single piece of information in each cell of your spreadsheet. In this dataset, we have another column that stores multiple values in a single cell. Some of the cells in the months_lack_food column contain multiple months which, as before, are separated by semi-colons (;).

To create a data frame where each of the columns contain only one value per cell, we can repeat the steps we applied to items_owned and apply them to months_lack_food. Since we will be using this data frame for the next episode, we will call it interviews_plotting.


interviews_plotting <- interviews %>%
  ## pivot wider by items_owned
  separate_longer_delim(items_owned, delim = ";") %>%
  ## if there were no items listed, changing NA to no_listed_items
  replace_na(list(items_owned = "no_listed_items")) %>%
  mutate(items_owned_logical = TRUE) %>%
  pivot_wider(names_from = items_owned,
              values_from = items_owned_logical,
              values_fill = list(items_owned_logical = FALSE)) %>%
  ## pivot wider by months_lack_food
  separate_longer_delim(months_lack_food, delim = ";") %>%
  mutate(months_lack_food_logical = TRUE) %>%
  pivot_wider(names_from = months_lack_food,
              values_from = months_lack_food_logical,
              values_fill = list(months_lack_food_logical = FALSE)) %>%
  ## add some summary columns
  mutate(number_months_lack_food = rowSums(select(., Jan:May))) %>%
  mutate(number_items = rowSums(select(., bicycle:car)))

Exporting data

Now that you have learned how to use dplyr and tidyr to wrangle your raw data, you may want to export these new datasets to share them with your collaborators or for archival purposes.

Similar to the read_csv() function used for reading CSV files into R, there is a write_csv() function that generates CSV files from data frames.

Before using write_csv(), we are going to create a new folder, data_output, in our working directory that will store this generated dataset. We don’t want to write generated datasets in the same directory as our raw data. It’s good practice to keep them separate. The data folder should only contain the raw, unaltered data, and should be left alone to make sure we don’t delete or modify it. In contrast, our script will generate the contents of the data_output directory, so even if the files it contains are deleted, we can always re-generate them.

In preparation for our next lesson on plotting, we created a version of the dataset where each of the columns includes only one data value. Now we can save this data frame to our data_output directory.


write_csv (interviews_plotting, file = "data_output/interviews_plotting.csv")


  • Use the tidyr package to change the layout of data frames.
  • Use pivot_wider() to go from long to wide format.
  • Use pivot_longer() to go from wide to long format.