Data Wrangling with dplyr and tidyr

Overview

Teaching: 50 min
Exercises: 30 min
Questions
  • How can I select specific rows and/or columns from a dataframe?

  • How can I combine multiple commands into a single command?

  • How can create new columns or remove existing columns from a dataframe?

  • How can I reformat a dataframe to meet my needs?

Objectives
  • Describe the purpose of an R package and the dplyr and tidyr packages.

  • Select certain columns in a dataframe with the dplyr function select.

  • Select certain rows in a dataframe according to filtering conditions with the dplyr function filter.

  • Link the output of one dplyr function to the input of another function with the ‘pipe’ operator %>%.

  • Add new columns to a dataframe that are functions of existing columns with mutate.

  • Use the split-apply-combine concept for data analysis.

  • Use summarize, group_by, and count to split a dataframe into groups of observations, apply a summary statistics for each group, and then combine the results.

  • 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 is a package for making tabular data wrangling easier by using a limited set of functions that can be combined to extract and summarize insights from your data. It pairs nicely with tidyr which enables you to swiftly convert between different data formats (long vs. wide) for plotting and analysis.

Similarly to readr, dplyr and tidyr are also part of the tidyverse. These packages were loaded in R’s memory when we called library(tidyverse) earlier.

Note

The packages in the tidyverse, namely dplyr, tidyr and ggplot2 accept both the British (e.g. summarise) and American (e.g. summarize) spelling variants of different function and option names. For this lesson, we utilize the American spellings of different functions; however, feel free to use the regional variant for where you are teaching.

What is an R package?

The package dplyr provides easy tools for the most common data wrangling tasks. It is built to work directly with dataframes, with many common tasks optimized by being written in a compiled language (C++) (not all R packages are written in R!).

The package tidyr addresses the common problem of wanting to reshape your data for plotting and use by different R functions. Sometimes we want data sets where we have one row per measurement. Sometimes we want a dataframe where each measurement type has its own column, and rows are instead more aggregated groups. Moving back and forth between these formats is nontrivial, and tidyr gives you tools for this and more sophisticated data wrangling.

But there are also packages available for a wide range of tasks including building plots (ggplot2, which we’ll see later), downloading data from the NCBI database, or performing statistical analysis on your data set. Many packages such as these are housed on, and downloadable from, the Comprehensive R Archive Network (CRAN) using install.packages. This function makes the package accessible by your R installation with the command library(), as you did with tidyverse earlier.

To easily access the documentation for a package within R or RStudio, use help(package = "package_name").

To learn more about dplyr and tidyr after the workshop, you may want to check out this handy data transformation with dplyr cheatsheet and this one about tidyr.

Learning dplyr and tidyr

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
library(tidyverse)

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

## inspect the data
interviews

## preview the data
# View(interviews)

We’re going to learn some of the most common dplyr functions:

Selecting columns and filtering rows

To select columns of a dataframe, use select(). The first argument to this function is the dataframe (interviews), and the subsequent arguments are the columns to keep, separated by commas. Alternatively, if you are selecting columns adjacent to each other, you can use a : to select a range of columns, read as “select columns from __ to __.”

# to select columns throughout the dataframe
select(interviews, village, no_membrs, months_lack_food)
# to select a series of connected columns
select(interviews, village:respondent_wall_type)

To choose rows based on a specific criteria, we can use the filter() function. The arguments after the dataframe are the condition(s) we want for our final dataframe to adhere to (e.g. village name is Chirodzo). We can chain a series of conditions together using commas between each condition.

# one condition
filter(interviews, village == "Chirodzo")
# A tibble: 39 x 14
   key_ID village interview_date      no_membrs years_liv respondent_wall… rooms
    <dbl> <chr>   <dttm>                  <dbl>     <dbl> <chr>            <dbl>
 1      8 Chirod… 2016-11-16 00:00:00        12        70 burntbricks          3
 2      9 Chirod… 2016-11-16 00:00:00         8         6 burntbricks          1
 3     10 Chirod… 2016-12-16 00:00:00        12        23 burntbricks          5
 4     34 Chirod… 2016-11-17 00:00:00         8        18 burntbricks          3
 5     35 Chirod… 2016-11-17 00:00:00         5        45 muddaub              1
 6     36 Chirod… 2016-11-17 00:00:00         6        23 sunbricks            1
 7     37 Chirod… 2016-11-17 00:00:00         3         8 burntbricks          1
 8     43 Chirod… 2016-11-17 00:00:00         7        29 muddaub              1
 9     44 Chirod… 2016-11-17 00:00:00         2         6 muddaub              1
10     45 Chirod… 2016-11-17 00:00:00         9         7 muddaub              1
# … with 29 more rows, and 7 more variables: memb_assoc <chr>,
#   affect_conflicts <chr>, liv_count <dbl>, items_owned <chr>, no_meals <dbl>,
#   months_lack_food <chr>, instanceID <chr>
# multiple conditions
filter(interviews, village == "Chirodzo", rooms > 1, no_meals > 2)
# A tibble: 10 x 14
   key_ID village interview_date      no_membrs years_liv respondent_wall… rooms
    <dbl> <chr>   <dttm>                  <dbl>     <dbl> <chr>            <dbl>
 1     10 Chirod… 2016-12-16 00:00:00        12        23 burntbricks          5
 2     49 Chirod… 2016-11-16 00:00:00         6        26 burntbricks          2
 3     52 Chirod… 2016-11-16 00:00:00        11        15 burntbricks          3
 4     56 Chirod… 2016-11-16 00:00:00        12        23 burntbricks          2
 5     65 Chirod… 2016-11-16 00:00:00         8        20 burntbricks          3
 6     66 Chirod… 2016-11-16 00:00:00        10        37 burntbricks          3
 7     67 Chirod… 2016-11-16 00:00:00         5        31 burntbricks          2
 8     68 Chirod… 2016-11-16 00:00:00         8        52 burntbricks          3
 9    199 Chirod… 2017-06-04 00:00:00         7        17 burntbricks          2
10    200 Chirod… 2017-06-04 00:00:00         8        20 burntbricks          2
# … with 7 more variables: memb_assoc <chr>, affect_conflicts <chr>,
#   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
#   instanceID <chr>

Pipes

What if you want to select and filter at the same time? There are three ways to do this: use intermediate steps, nested functions, or pipes.

With intermediate steps, you create a temporary dataframe and use that as input to the next function, like this:

interviews2 <- filter(interviews, village == "Chirodzo")
interviews_god <- select(interviews2, village:respondent_wall_type)

This is readable, but can clutter up your workspace with lots of objects that you have to name individually. With multiple steps, that can be hard to keep track of.

You can also nest functions (i.e. one function inside of another), like this:

interviews_god <- select(filter(interviews, village == "Chirodzo"), 
                         village:respondent_wall_type)

This is handy, but can be difficult to read if too many functions are nested, as R evaluates the expression from the inside out (in this case, filtering, then selecting).

The last option, pipes, are a recent addition to R. Pipes let you take the output of one function and send it directly to the next, which is useful when you need to do many things to the same dataset. Pipes in R look like %>% and are made available via the magrittr package, installed automatically with dplyr. If you use RStudio, you can type the pipe with Ctrl

interviews %>%
    filter(village == "Chirodzo") %>%
    select(village:respondent_wall_type)
# A tibble: 39 x 5
   village  interview_date      no_membrs years_liv respondent_wall_type
   <chr>    <dttm>                  <dbl>     <dbl> <chr>               
 1 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
 2 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
 3 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
 4 Chirodzo 2016-11-17 00:00:00         8        18 burntbricks         
 5 Chirodzo 2016-11-17 00:00:00         5        45 muddaub             
 6 Chirodzo 2016-11-17 00:00:00         6        23 sunbricks           
 7 Chirodzo 2016-11-17 00:00:00         3         8 burntbricks         
 8 Chirodzo 2016-11-17 00:00:00         7        29 muddaub             
 9 Chirodzo 2016-11-17 00:00:00         2         6 muddaub             
10 Chirodzo 2016-11-17 00:00:00         9         7 muddaub             
# … with 29 more rows

In the above code, we use the pipe to send the interviews dataset first through filter() to keep rows where village is “Chirodzo”, then through select() to keep only the no_membrs and years_liv columns. Since %>% takes the object on its left and passes it as the first argument to the function on its right, we don’t need to explicitly include the dataframe as an argument to the filter() and select() functions any more.

Some may find it helpful to read the pipe like the word “then”. For instance, in the above example, we take the dataframe interviews, then we filter for rows with village == "Chirodzo", then we select columns no_membrs and years_liv. The dplyr functions by themselves are somewhat simple, but by combining them into linear workflows with the pipe, we can accomplish more complex data wrangling operations.

If we want to create a new object with this smaller version of the data, we can assign it a new name:

interviews_god <- interviews %>%
    filter(village == "Chirodzo") %>%
    select(village:respondent_wall_type)

interviews_god
# A tibble: 39 x 5
   village  interview_date      no_membrs years_liv respondent_wall_type
   <chr>    <dttm>                  <dbl>     <dbl> <chr>               
 1 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
 2 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
 3 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
 4 Chirodzo 2016-11-17 00:00:00         8        18 burntbricks         
 5 Chirodzo 2016-11-17 00:00:00         5        45 muddaub             
 6 Chirodzo 2016-11-17 00:00:00         6        23 sunbricks           
 7 Chirodzo 2016-11-17 00:00:00         3         8 burntbricks         
 8 Chirodzo 2016-11-17 00:00:00         7        29 muddaub             
 9 Chirodzo 2016-11-17 00:00:00         2         6 muddaub             
10 Chirodzo 2016-11-17 00:00:00         9         7 muddaub             
# … with 29 more rows

Note that the final dataframe (interviews_god) is the leftmost part of this expression.

Exercise

Using pipes, subset the interviews data to include interviews where respondents were members of an irrigation association (memb_assoc) and retain only the columns affect_conflicts, liv_count, and no_meals.

Solution

interviews %>%
    filter(memb_assoc == "yes") %>%
    select(affect_conflicts, liv_count, no_meals)
# A tibble: 33 x 3
   affect_conflicts liv_count no_meals
   <chr>                <dbl>    <dbl>
 1 once                     3        2
 2 never                    2        2
 3 never                    2        3
 4 once                     3        2
 5 frequently               1        3
 6 more_once                5        2
 7 more_once                3        2
 8 more_once                2        3
 9 once                     3        3
10 never                    3        3
# … with 23 more rows

Mutate

Frequently you’ll want to create new columns based on the values in existing columns, for example to do unit conversions, or to find the ratio of values in two columns. For this we’ll use mutate().

We might be interested in the ratio of number of household members to rooms used for sleeping (i.e. avg number of people per room):

interviews %>%
    mutate(people_per_room = no_membrs / rooms)
# A tibble: 131 x 15
   key_ID village interview_date      no_membrs years_liv respondent_wall… rooms
    <dbl> <chr>   <dttm>                  <dbl>     <dbl> <chr>            <dbl>
 1      1 God     2016-11-17 00:00:00         3         4 muddaub              1
 2      1 God     2016-11-17 00:00:00         7         9 muddaub              1
 3      3 God     2016-11-17 00:00:00        10        15 burntbricks          1
 4      4 God     2016-11-17 00:00:00         7         6 burntbricks          1
 5      5 God     2016-11-17 00:00:00         7        40 burntbricks          1
 6      6 God     2016-11-17 00:00:00         3         3 muddaub              1
 7      7 God     2016-11-17 00:00:00         6        38 muddaub              1
 8      8 Chirod… 2016-11-16 00:00:00        12        70 burntbricks          3
 9      9 Chirod… 2016-11-16 00:00:00         8         6 burntbricks          1
10     10 Chirod… 2016-12-16 00:00:00        12        23 burntbricks          5
# … with 121 more rows, and 8 more variables: memb_assoc <chr>,
#   affect_conflicts <chr>, liv_count <dbl>, items_owned <chr>, no_meals <dbl>,
#   months_lack_food <chr>, instanceID <chr>, people_per_room <dbl>

We may be interested in investigating whether being a member of an irrigation association had any effect on the ratio of household members to rooms. To look at this relationship, we will first remove data from our dataset where the respondent didn’t answer the question of whether they were a member of an irrigation association. These cases are recorded as “NULL” in the dataset.

To remove these cases, we could insert a filter() in the chain:

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    mutate(people_per_room = no_membrs / rooms)
# A tibble: 92 x 15
   key_ID village interview_date      no_membrs years_liv respondent_wall… rooms
    <dbl> <chr>   <dttm>                  <dbl>     <dbl> <chr>            <dbl>
 1      1 God     2016-11-17 00:00:00         7         9 muddaub              1
 2      7 God     2016-11-17 00:00:00         6        38 muddaub              1
 3      8 Chirod… 2016-11-16 00:00:00        12        70 burntbricks          3
 4      9 Chirod… 2016-11-16 00:00:00         8         6 burntbricks          1
 5     10 Chirod… 2016-12-16 00:00:00        12        23 burntbricks          5
 6     12 God     2016-11-21 00:00:00         7        20 burntbricks          3
 7     13 God     2016-11-21 00:00:00         6         8 burntbricks          1
 8     15 God     2016-11-21 00:00:00         5        30 sunbricks            2
 9     21 God     2016-11-21 00:00:00         8        20 burntbricks          1
10     24 Ruaca   2016-11-21 00:00:00         6         4 burntbricks          2
# … with 82 more rows, and 8 more variables: memb_assoc <chr>,
#   affect_conflicts <chr>, liv_count <dbl>, items_owned <chr>, no_meals <dbl>,
#   months_lack_food <chr>, instanceID <chr>, people_per_room <dbl>

The ! symbol negates the result of the is.na() function. Thus, if is.na() returns a value of TRUE (because the memb_assoc is missing), the ! symbol negates this and says we only want values of FALSE, where memb_assoc is not missing.

Exercise

Create a new dataframe from the interviews data that meets the following criteria: contains only the village column and a new column called total_meals containing a value that is equal to the total number of meals served in the household per day on average (no_membrs times no_meals). Only the rows where total_meals is greater than 20 should be shown in the final dataframe.

Hint: think about how the commands should be ordered to produce this data frame!

Solution

interviews_total_meals <- interviews %>%
    mutate(total_meals = no_membrs * no_meals) %>%
    filter(total_meals > 20) %>%
    select(village, total_meals)

Split-apply-combine data analysis and the summarize() function

Many data analysis tasks can be approached using the split-apply-combine paradigm: split the data into groups, apply some analysis to each group, and then combine the results. dplyr makes this very easy through the use of the group_by() function.

The summarize() function

group_by() is often used together with summarize(), which collapses each group into a single-row summary of that group. group_by() takes as arguments the column names that contain the categorical variables for which you want to calculate the summary statistics. So to compute the average household size by village:

interviews %>%
    group_by(village) %>%
    summarize(mean_no_membrs = mean(no_membrs))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  village  mean_no_membrs
  <chr>             <dbl>
1 Chirodzo           7.08
2 God                6.86
3 Ruaca              7.57

You may also have noticed that the output from these calls doesn’t run off the screen anymore. It’s one of the advantages of tbl_df over dataframe.

You can also group by multiple columns:

interviews %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs))
`summarise()` regrouping output by 'village' (override with `.groups` argument)
# A tibble: 9 x 3
# Groups:   village [3]
  village  memb_assoc mean_no_membrs
  <chr>    <chr>               <dbl>
1 Chirodzo no                   8.06
2 Chirodzo yes                  7.82
3 Chirodzo <NA>                 5.08
4 God      no                   7.13
5 God      yes                  8   
6 God      <NA>                 6   
7 Ruaca    no                   7.18
8 Ruaca    yes                  9.5 
9 Ruaca    <NA>                 6.22

Note that the output is a grouped tibble. To obtain an ungrouped tibble, use the ungroup function:

interviews %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs)) %>%
    ungroup()
`summarise()` regrouping output by 'village' (override with `.groups` argument)
# A tibble: 9 x 3
  village  memb_assoc mean_no_membrs
  <chr>    <chr>               <dbl>
1 Chirodzo no                   8.06
2 Chirodzo yes                  7.82
3 Chirodzo <NA>                 5.08
4 God      no                   7.13
5 God      yes                  8   
6 God      <NA>                 6   
7 Ruaca    no                   7.18
8 Ruaca    yes                  9.5 
9 Ruaca    <NA>                 6.22

When grouping both by village and membr_assoc, we see rows in our table for respondents who did not specify whether they were a member of an irrigation association. We can exclude those data from our table using a filter step.

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs))
`summarise()` regrouping output by 'village' (override with `.groups` argument)
# A tibble: 6 x 3
# Groups:   village [3]
  village  memb_assoc mean_no_membrs
  <chr>    <chr>               <dbl>
1 Chirodzo no                   8.06
2 Chirodzo yes                  7.82
3 God      no                   7.13
4 God      yes                  8   
5 Ruaca    no                   7.18
6 Ruaca    yes                  9.5 

Once the data are grouped, you can also summarize multiple variables at the same time (and not necessarily on the same variable). For instance, we could add a column indicating the minimum household size for each village for each group (members of an irrigation association vs not):

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs),
              min_membrs = min(no_membrs))
`summarise()` regrouping output by 'village' (override with `.groups` argument)
# A tibble: 6 x 4
# Groups:   village [3]
  village  memb_assoc mean_no_membrs min_membrs
  <chr>    <chr>               <dbl>      <dbl>
1 Chirodzo no                   8.06          4
2 Chirodzo yes                  7.82          2
3 God      no                   7.13          3
4 God      yes                  8             5
5 Ruaca    no                   7.18          2
6 Ruaca    yes                  9.5           5

It is sometimes useful to rearrange the result of a query to inspect the values. For instance, we can sort on min_membrs to put the group with the smallest household first:

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs),
              min_membrs = min(no_membrs)) %>%
    arrange(min_membrs)
`summarise()` regrouping output by 'village' (override with `.groups` argument)
# A tibble: 6 x 4
# Groups:   village [3]
  village  memb_assoc mean_no_membrs min_membrs
  <chr>    <chr>               <dbl>      <dbl>
1 Chirodzo yes                  7.82          2
2 Ruaca    no                   7.18          2
3 God      no                   7.13          3
4 Chirodzo no                   8.06          4
5 God      yes                  8             5
6 Ruaca    yes                  9.5           5

To sort in descending order, we need to add the desc() function. If we want to sort the results by decreasing order of minimum household size:

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs),
              min_membrs = min(no_membrs)) %>%
    arrange(desc(min_membrs))
`summarise()` regrouping output by 'village' (override with `.groups` argument)
# A tibble: 6 x 4
# Groups:   village [3]
  village  memb_assoc mean_no_membrs min_membrs
  <chr>    <chr>               <dbl>      <dbl>
1 God      yes                  8             5
2 Ruaca    yes                  9.5           5
3 Chirodzo no                   8.06          4
4 God      no                   7.13          3
5 Chirodzo yes                  7.82          2
6 Ruaca    no                   7.18          2

Counting

When working with data, we often want to know the number of observations found for each factor or combination of factors. For this task, dplyr provides count(). For example, if we wanted to count the number of rows of data for each village, we would do:

interviews %>%
    count(village)
# A tibble: 3 x 2
  village      n
  <chr>    <int>
1 Chirodzo    39
2 God         43
3 Ruaca       49

For convenience, count() provides the sort argument to get results in decreasing order:

interviews %>%
    count(village, sort = TRUE)
# A tibble: 3 x 2
  village      n
  <chr>    <int>
1 Ruaca       49
2 God         43
3 Chirodzo    39

Exercise

How many households in the survey have an average of two meals per day? Three meals per day? Are there any other numbers of meals represented?

Solution

interviews %>%
   count(no_meals)
# A tibble: 2 x 2
  no_meals     n
     <dbl> <int>
1        2    52
2        3    79

Use group_by() and summarize() to find the mean, min, and max number of household members for each village. Also add the number of observations (hint: see ?n).

Solution

interviews %>%
  group_by(village) %>%
  summarize(
      mean_no_membrs = mean(no_membrs),
      min_no_membrs = min(no_membrs),
      max_no_membrs = max(no_membrs),
      n = n()
  )
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 5
  village  mean_no_membrs min_no_membrs max_no_membrs     n
  <chr>             <dbl>         <dbl>         <dbl> <int>
1 Chirodzo           7.08             2            12    39
2 God                6.86             3            15    43
3 Ruaca              7.57             2            19    49

What was the largest household interviewed in each month?

Solution

# if not already included, add month, year, and day columns
library(lubridate) # load lubridate if not already loaded

Attaching package: 'lubridate'
The following objects are masked from 'package:base':

    date, intersect, setdiff, union
interviews %>%
    mutate(month = month(interview_date),
           day = day(interview_date),
           year = year(interview_date)) %>%
    group_by(year, month) %>%
    summarize(max_no_membrs = max(no_membrs))
`summarise()` regrouping output by 'year' (override with `.groups` argument)
# A tibble: 5 x 3
# Groups:   year [2]
   year month max_no_membrs
  <dbl> <dbl>         <dbl>
1  2016    11            19
2  2016    12            12
3  2017     4            17
4  2017     5            15
5  2017     6            15

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

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 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 x 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      1 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
# … with 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 x 4
   key_ID village  interview_date      instanceID                               
    <dbl> <chr>    <dttm>              <chr>                                    
 1     68 Chirodzo 2016-11-16 00:00:00 uuid:ef04b3eb-b47d-412e-9b09-4f5e08fc66f9
 2     64 Chirodzo 2016-11-16 00:00:00 uuid:28cfd718-bf62-4d90-8100-55fafbe45d06
 3     54 Chirodzo 2016-11-16 00:00:00 uuid:273ab27f-9be3-4f3b-83c9-d3e1592de919
 4     34 Chirodzo 2016-11-17 00:00:00 uuid:14c78c45-a7cc-4b2a-b765-17c82b43feb4
 5      8 Chirodzo 2016-11-16 00:00:00 uuid:d6cee930-7be1-4fd9-88c0-82a08f90fb5a
 6     57 Chirodzo 2016-11-16 00:00:00 uuid:a7184e55-0615-492d-9835-8f44f3b03a71
 7     66 Chirodzo 2016-11-16 00:00:00 uuid:a457eab8-971b-4417-a971-2e55b8702816
 8     67 Chirodzo 2016-11-16 00:00:00 uuid:6c15d667-2860-47e3-a5e7-7f679271e419
 9    127 Chirodzo 2016-11-16 00:00:00 uuid:f6d04b41-b539-4e00-868a-0f62b427587d
10     59 Chirodzo 2016-11-16 00:00:00 uuid:1936db62-5732-45dc-98ff-9b3ac7a22518

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

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 housing construction materials?

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 housing material (e.g. the respondent_wall_type). In practical terms this means the values of the wall construction materials in respondent_wall_type (e.g. muddaub, burntbricks, cement, sunbricks) would become the names of column variables and the cells would contain values of TRUE or FALSE, for whether that house had a wall made of that material.

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 these of 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 type of wall construction material. We will make use of the pipe operator as have done before. Because both the names_from and values_from parameters must come from column values, we will create a dummy column (we’ll name it wall_type_logical) to hold the value TRUE, which we will then place into the appropriate column that corresponds to the wall construction material for that respondent. When using mutate() if you give a single value, it will be used for all observations in the dataset.

For each row in our newly pivoted table, only one of the newly created wall type columns will have a value of TRUE, since each house can only be made of one wall type. The default value that pivot_wider uses to fill the other wall types is NA.

If instead of the default value being NA, we wanted these values to be FALSE, we can insert a default value into the values_fill argument. By including values_fill = list(wall_type_logical = FALSE) inside pivot_wider(), we can fill the remainder of the wall type columns for that row with the value FALSE.

interviews_wide <- interviews %>%
    mutate(wall_type_logical = TRUE) %>%
    pivot_wider(names_from = respondent_wall_type, 
                values_from = wall_type_logical, 
                values_fill = list(wall_type_logical = FALSE))

View the interviews_wide dataframe and notice that there is no longer a column titled respondent_wall_type. 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 muddaub, burntbricks, sunbricks, and cement. You can use dim(interviews) and dim(interviews_wide) to see how the number of columns has changed between the two datasets.

Pivoting longer

The opposing situation could occur if we had been provided with data in the form of interviews_wide, where the building materials are column names, but we wish to treat them as values of a respondent_wall_type 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.

To recreate our original dataframe, we will use the following:

  1. the data - interviews_wide
  2. a list of cols (columns) that are to be reshaped; these can be specified using a : if the columns to be reshaped are in one area of the dataframe, or with a vector (c()) command if the columns are spread throughout the dataframe.
  3. the names_to column will be a character string of the name the column these columns will be collapsed into (“respondent_wall_type”).
  4. the values_to column will be a character string of the name of the column the values of the collapsed columns will be inserted into (“wall_type_logical”). This column will be populated with values of TRUE or FALSE.
interviews_long <- interviews_wide %>%
    pivot_longer(cols = burntbricks:sunbricks,
                 names_to = "respondent_wall_type", 
                 values_to = "wall_type_logical")

This creates a dataframe with 262 rows (4 rows per interview respondent). The four rows for each respondent differ only in the value of the “respondent_wall_type” and “wall_type_logical” columns. View the data to see what this looks like.

Only one row for each interview respondent is informative–we know that if the house walls are made of “sunbrick” they aren’t made of any other the other materials. Therefore, it would make sense to filter our dataset to only keep values where wall_type_logical is TRUE. Because wall_type_logical is already either TRUE or FALSE, when passing the column name to filter(), it will automatically already only keep rows where this column has the value TRUE. We can then remove the wall_type_logical column.

We do all of these steps together in the next chunk of code:

interviews_long <- interviews_wide %>%
    pivot_longer(cols = c(burntbricks, cement, muddaub, sunbricks),
                 names_to = "respondent_wall_type", 
                 values_to = "wall_type_logical") %>%
    filter(wall_type_logical) %>%
    select(-wall_type_logical)

View both interviews_long and interviews_wide and compare their structure.

Applying pivot_wider() to clean our data

Now that we’ve learned about pivot_longer() and pivot_wider() we’re going to put these functions to use to fix a problem with the way that 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 several columns which contain multiple pieces of information. For example, the items_owned column contains information about whether our respondents owned a fridge, a television, etc. To make this data easier to analyze, we will split this column and create a new column for each item. Each cell in that column will either be TRUE or FALSE and will indicate whether that interview respondent owned that item (similar to what we did previously with wall_type).

interviews_items_owned <- interviews %>% 
  separate_rows(items_owned, sep = ";") %>%
  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))

nrow(interviews_items_owned)
[1] 131

There are a couple of new concepts in this code chunk, so let’s walk through it line by line. First we create a new object (interviews_items_owned) based on the interviews dataframe.

interviews_items_owned <- interviews %>%

Then we use the new function separate_rows() 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_rows(items_owned, sep = ";") %>%

You may notice that one of the columns is called ´NA´. 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 since, since each item in every row was owned by that household. We are constructing this variable so that when 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) %>%

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))

View the interviews_items_owned dataframe. It should have 131 rows (the same number of rows you had originally), but extra columns for each item. How many columns were added?

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) %>%
  count(bicycle)
# A tibble: 3 x 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. We then group the data by villages and caluculate the mean number of items, so each average is grouped by village.

interviews_items_owned %>%
    mutate(number_items = rowSums(select(., bicycle:car))) %>%
    group_by(village) %>%
    summarize(mean_items = mean(number_items))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  village  mean_items
  <chr>         <dbl>
1 Chirodzo       4.62
2 God            4.07
3 Ruaca          5.63

Exercise

  1. Create a new dataframe (named interviews_months_lack_food) that has one column for each month and records TRUE or FALSE for whether each interview respondent was lacking food in that month.

Solution

interviews_months_lack_food <- interviews %>%
  separate_rows(months_lack_food, sep = ";") %>%
  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))
  1. How many months (on average) were respondents without food if they did belong to an irrigation association? What about if they didn’t?

Solution

interviews_months_lack_food %>%
  mutate(number_months = rowSums(select(., Jan:May))) %>%
  group_by(memb_assoc) %>%
  summarize(mean_months = mean(number_months))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  memb_assoc mean_months
  <chr>            <dbl>
1 no                2   
2 yes               2.30
3 <NA>              2.82

Exporting data

Now that you have learned how to use dplyr to extract information from or summarize your raw data, you may want to export these new data sets to share them with your collaborators or for archival.

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

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 are going to create a version of the dataset where each of the columns includes only one data value. To do this, we will use pivot_wider to expand the months_lack_food and items_owned columns. We will also create a couple of summary columns.

interviews_plotting <- interviews %>%
  ## pivot wider by items_owned
  separate_rows(items_owned, sep = ";") %>%
  ## 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_rows(months_lack_food, sep = ";") %>%
  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)))

Now we can save this dataframe to our data_output directory.

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

Key Points

  • Use the dplyr package to manipulate dataframes.

  • Use select() to choose variables from a dataframe.

  • Use filter() to choose data based on values.

  • Use group_by() and summarize() to work with subsets of data.

  • Use mutate() to create new variables.

  • Use the tidyr package to change the layout of dataframes.

  • Use pivot_wider() to go from long to wide format.

  • Use pivot_longer() to go from wide to long format.