Chapter 5 Manipulating and analyzing data with dplyr

Learning Objectives

  • Describe the purpose of the dplyr and tidyr packages.

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

  • Select certain rows in a data frame 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 %>% or |>.

  • Add new columns to a data frame 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 data frame into groups of observations, apply 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.

  • Reshape a data frame from long to wide format and back with the pivot_wider() and pivot_longer() commands from the tidyr package.

5.1 Data Manipulation using dplyr and tidyr

Bracket subsetting is handy, but it can be cumbersome and difficult to read, especially for complicated operations. Enter dplyr. dplyr is a package for making tabular data manipulation easier. It pairs nicely with tidyr which enables you to swiftly convert between different data formats for plotting and analysis.

Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like str() or data.frame(), come built into R; packages give you access to more of them. Before you use a package for the first time you need to install it on your machine, and then you should import it in every subsequent R session when you need it. You should already have installed the tidyverse package. This is an “umbrella-package” that installs several packages useful for data analysis which work together well such as tidyr, dplyr, ggplot2, tibble, etc.

The tidyverse packages address 3 common issues that arise when doing data analysis with some of functions that come with R:

  1. The results from a base R function sometimes depend on the type of data.
  2. Using R expressions in a non standard way, which can be confusing for new learners.
  3. Hidden arguments, having default operations that new learners are not aware of.

Let’s start by loading several of the tidyverse packages with:

library("tidyverse")

The Data Transformation Cheat Sheet provides an overview of the dplyr grammar, offering more details and functions that we will see in this chapter. The Tidy Data Tutor is a wonderful tool to visually describe what the tidy data operations do. The following tutorial offers nice animations of mutate(), summarize(), group_by(), and ungroup().

5.2 What are dplyr and tidyr?

The package dplyr provides easy tools for the most common data manipulation tasks. It is built to work directly with data frames, with many common tasks optimized by being written in a compiled language (C++). An additional feature is the ability to work directly with data stored in an external database. The benefits of doing this are that the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of the query are returned.

This addresses a common problem with R in that all operations are conducted in-memory and thus the amount of data you can work with is limited by available memory. The database connections essentially remove that limitation in that you can connect to a database of many hundreds of GB, conduct queries on it directly, and pull back into R only what you need for analysis.

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 data frame where each measurement type has its own column, and rows are instead more aggregated groups - like plots or aquaria. Moving back and forth between these formats is nontrivial, and tidyr gives you tools for this and more sophisticated data manipulation.

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.

We’ll read in our data using the read_csv() function, from the tidyverse package readr, instead of read.csv().

rna <- read_csv("data/rnaseq.csv")
## Rows: 32428 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (14): gene, sample, organism, sex, infection, strain, tissue, product, e...
## dbl  (5): expression, age, time, mouse, ENTREZID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## inspect the data
str(rna)
## preview the data
# View(rna)

Notice that the class of the data is now tbl_df

This is referred to as a “tibble”. Tibbles tweak some of the behaviors of the data frame objects we introduced in the previous episode. The data structure is very similar to a data frame. For our purposes the only differences are that:

  1. In addition to displaying the data type of each column under its name, it only prints the first few rows of data and only as many columns as fit on one screen.
  2. Columns of class character are never converted into factors.

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

  • select(): subset columns
  • filter(): subset rows on conditions
  • mutate(): create new columns by using information from other columns
  • group_by() and summarize(): create summary statisitcs on grouped data
  • arrange(): sort results
  • count(): count discrete values

5.3 Selecting columns and filtering rows

To select columns of a data frame, use select(). The first argument to this function is the data frame (rna), and the subsequent arguments are the columns to keep.

select(rna, gene, sample, tissue, expression)
## # A tibble: 32,428 × 4
##    gene    sample     tissue     expression
##    <chr>   <chr>      <chr>           <dbl>
##  1 Asl     GSM2545336 Cerebellum       1170
##  2 Apod    GSM2545336 Cerebellum      36194
##  3 Cyp2d22 GSM2545336 Cerebellum       4060
##  4 Klk6    GSM2545336 Cerebellum        287
##  5 Fcrls   GSM2545336 Cerebellum         85
##  6 Slc2a4  GSM2545336 Cerebellum        782
##  7 Exd2    GSM2545336 Cerebellum       1619
##  8 Gjc2    GSM2545336 Cerebellum        288
##  9 Plp1    GSM2545336 Cerebellum      43217
## 10 Gnb4    GSM2545336 Cerebellum       1071
## # ℹ 32,418 more rows

To select all columns except certain ones, put a “-” in front of the variable to exclude it.

select(rna, -organism, -strain)
## # A tibble: 32,428 × 17
##    gene    sample   expression   age sex   infection  time tissue mouse ENTREZID
##    <chr>   <chr>         <dbl> <dbl> <chr> <chr>     <dbl> <chr>  <dbl>    <dbl>
##  1 Asl     GSM2545…       1170     8 Fema… Influenz…     8 Cereb…    14   109900
##  2 Apod    GSM2545…      36194     8 Fema… Influenz…     8 Cereb…    14    11815
##  3 Cyp2d22 GSM2545…       4060     8 Fema… Influenz…     8 Cereb…    14    56448
##  4 Klk6    GSM2545…        287     8 Fema… Influenz…     8 Cereb…    14    19144
##  5 Fcrls   GSM2545…         85     8 Fema… Influenz…     8 Cereb…    14    80891
##  6 Slc2a4  GSM2545…        782     8 Fema… Influenz…     8 Cereb…    14    20528
##  7 Exd2    GSM2545…       1619     8 Fema… Influenz…     8 Cereb…    14    97827
##  8 Gjc2    GSM2545…        288     8 Fema… Influenz…     8 Cereb…    14   118454
##  9 Plp1    GSM2545…      43217     8 Fema… Influenz…     8 Cereb…    14    18823
## 10 Gnb4    GSM2545…       1071     8 Fema… Influenz…     8 Cereb…    14    14696
## # ℹ 32,418 more rows
## # ℹ 7 more variables: product <chr>, ensembl_gene_id <chr>,
## #   external_synonym <chr>, chromosome_name <chr>, gene_biotype <chr>,
## #   phenotype_description <chr>, hsapiens_homolog_associated_gene_name <chr>

This will select all the variables in rna except organism and strain.

To choose rows based on a specific criteria, use filter():

filter(rna, sex == "Male")
## # A tibble: 14,740 × 19
##    gene    sample  expression organism   age sex   infection strain  time tissue
##    <chr>   <chr>        <dbl> <chr>    <dbl> <chr> <chr>     <chr>  <dbl> <chr> 
##  1 Asl     GSM254…        626 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  2 Apod    GSM254…      13021 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  3 Cyp2d22 GSM254…       2171 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  4 Klk6    GSM254…        448 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  5 Fcrls   GSM254…        180 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  6 Slc2a4  GSM254…        313 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  7 Exd2    GSM254…       2366 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  8 Gjc2    GSM254…        310 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  9 Plp1    GSM254…      53126 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
## 10 Gnb4    GSM254…       1355 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
## # ℹ 14,730 more rows
## # ℹ 9 more variables: mouse <dbl>, ENTREZID <dbl>, product <chr>,
## #   ensembl_gene_id <chr>, external_synonym <chr>, chromosome_name <chr>,
## #   gene_biotype <chr>, phenotype_description <chr>,
## #   hsapiens_homolog_associated_gene_name <chr>
filter(rna, sex == "Male" & infection == "NonInfected")
## # A tibble: 4,422 × 19
##    gene    sample  expression organism   age sex   infection strain  time tissue
##    <chr>   <chr>        <dbl> <chr>    <dbl> <chr> <chr>     <chr>  <dbl> <chr> 
##  1 Asl     GSM254…        535 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  2 Apod    GSM254…      13668 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  3 Cyp2d22 GSM254…       2008 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  4 Klk6    GSM254…       1101 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  5 Fcrls   GSM254…        375 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  6 Slc2a4  GSM254…        249 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  7 Exd2    GSM254…       3126 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  8 Gjc2    GSM254…        791 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  9 Plp1    GSM254…      98658 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
## 10 Gnb4    GSM254…       2437 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
## # ℹ 4,412 more rows
## # ℹ 9 more variables: mouse <dbl>, ENTREZID <dbl>, product <chr>,
## #   ensembl_gene_id <chr>, external_synonym <chr>, chromosome_name <chr>,
## #   gene_biotype <chr>, phenotype_description <chr>,
## #   hsapiens_homolog_associated_gene_name <chr>

5.4 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 data frame and use that as input to the next function, like this:

rna2 <- filter(rna, sex == "Male")
rna3 <- select(rna2, gene, sample, tissue, expression)
rna3
## # A tibble: 14,740 × 4
##    gene    sample     tissue     expression
##    <chr>   <chr>      <chr>           <dbl>
##  1 Asl     GSM2545340 Cerebellum        626
##  2 Apod    GSM2545340 Cerebellum      13021
##  3 Cyp2d22 GSM2545340 Cerebellum       2171
##  4 Klk6    GSM2545340 Cerebellum        448
##  5 Fcrls   GSM2545340 Cerebellum        180
##  6 Slc2a4  GSM2545340 Cerebellum        313
##  7 Exd2    GSM2545340 Cerebellum       2366
##  8 Gjc2    GSM2545340 Cerebellum        310
##  9 Plp1    GSM2545340 Cerebellum      53126
## 10 Gnb4    GSM2545340 Cerebellum       1355
## # ℹ 14,730 more rows

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:

rna3 <- select(filter(rna, sex == "Male"), gene, sample, tissue, expression)
rna3
## # A tibble: 14,740 × 4
##    gene    sample     tissue     expression
##    <chr>   <chr>      <chr>           <dbl>
##  1 Asl     GSM2545340 Cerebellum        626
##  2 Apod    GSM2545340 Cerebellum      13021
##  3 Cyp2d22 GSM2545340 Cerebellum       2171
##  4 Klk6    GSM2545340 Cerebellum        448
##  5 Fcrls   GSM2545340 Cerebellum        180
##  6 Slc2a4  GSM2545340 Cerebellum        313
##  7 Exd2    GSM2545340 Cerebellum       2366
##  8 Gjc2    GSM2545340 Cerebellum        310
##  9 Plp1    GSM2545340 Cerebellum      53126
## 10 Gnb4    GSM2545340 Cerebellum       1355
## # ℹ 14,730 more rows

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 %>% (as made available via the magrittr package, installed automatically with dplyr) or |> (as available in base R). If you use RStudio, you can type the pipe with Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac.

rna |>
  filter(sex == "Male") |>
  select(gene, sample, tissue, expression)
## # A tibble: 14,740 × 4
##    gene    sample     tissue     expression
##    <chr>   <chr>      <chr>           <dbl>
##  1 Asl     GSM2545340 Cerebellum        626
##  2 Apod    GSM2545340 Cerebellum      13021
##  3 Cyp2d22 GSM2545340 Cerebellum       2171
##  4 Klk6    GSM2545340 Cerebellum        448
##  5 Fcrls   GSM2545340 Cerebellum        180
##  6 Slc2a4  GSM2545340 Cerebellum        313
##  7 Exd2    GSM2545340 Cerebellum       2366
##  8 Gjc2    GSM2545340 Cerebellum        310
##  9 Plp1    GSM2545340 Cerebellum      53126
## 10 Gnb4    GSM2545340 Cerebellum       1355
## # ℹ 14,730 more rows

Or

rna  |>
  filter(sex == "Male") |>
  select(gene, sample, tissue, expression)
## # A tibble: 14,740 × 4
##    gene    sample     tissue     expression
##    <chr>   <chr>      <chr>           <dbl>
##  1 Asl     GSM2545340 Cerebellum        626
##  2 Apod    GSM2545340 Cerebellum      13021
##  3 Cyp2d22 GSM2545340 Cerebellum       2171
##  4 Klk6    GSM2545340 Cerebellum        448
##  5 Fcrls   GSM2545340 Cerebellum        180
##  6 Slc2a4  GSM2545340 Cerebellum        313
##  7 Exd2    GSM2545340 Cerebellum       2366
##  8 Gjc2    GSM2545340 Cerebellum        310
##  9 Plp1    GSM2545340 Cerebellum      53126
## 10 Gnb4    GSM2545340 Cerebellum       1355
## # ℹ 14,730 more rows

In the above code, we use the pipe to send the rna dataset first through filter() to keep rows where sex is Male, then through select() to keep only the gene, sample, tissue, and expressioncolumns. Since %>% (and |>) 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 data frame 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 took the data frame rna, then we filtered for rows with sex == "Male", then we selected columns gene, sample, tissue, and expression. The dplyr functions by themselves are somewhat simple, but by combining them into linear workflows with the pipe, we can accomplish more complex manipulations of data frames.

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

rna3 <- rna |>
  filter(sex == "Male") |>
  select(gene, sample, tissue, expression)

rna3
## # A tibble: 14,740 × 4
##    gene    sample     tissue     expression
##    <chr>   <chr>      <chr>           <dbl>
##  1 Asl     GSM2545340 Cerebellum        626
##  2 Apod    GSM2545340 Cerebellum      13021
##  3 Cyp2d22 GSM2545340 Cerebellum       2171
##  4 Klk6    GSM2545340 Cerebellum        448
##  5 Fcrls   GSM2545340 Cerebellum        180
##  6 Slc2a4  GSM2545340 Cerebellum        313
##  7 Exd2    GSM2545340 Cerebellum       2366
##  8 Gjc2    GSM2545340 Cerebellum        310
##  9 Plp1    GSM2545340 Cerebellum      53126
## 10 Gnb4    GSM2545340 Cerebellum       1355
## # ℹ 14,730 more rows

► Question

Using pipes, subset the rna data to genes with an expression higher than 50000 in male mice at time 0, and retain only the columns gene, sample, time, expression and age

► Solution

5.5 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().

To create a new column of time in hours:

rna |>
  mutate(time_hours = time * 24) |>
  select(time, time_hours)
## # A tibble: 32,428 × 2
##     time time_hours
##    <dbl>      <dbl>
##  1     8        192
##  2     8        192
##  3     8        192
##  4     8        192
##  5     8        192
##  6     8        192
##  7     8        192
##  8     8        192
##  9     8        192
## 10     8        192
## # ℹ 32,418 more rows

You can also create a second new column based on the first new column within the same call of mutate():

rna |>
  mutate(time_hours = time * 24, time_mn = time_hours * 60) |>
  select(time, time_hours, time_mn)
## # A tibble: 32,428 × 3
##     time time_hours time_mn
##    <dbl>      <dbl>   <dbl>
##  1     8        192   11520
##  2     8        192   11520
##  3     8        192   11520
##  4     8        192   11520
##  5     8        192   11520
##  6     8        192   11520
##  7     8        192   11520
##  8     8        192   11520
##  9     8        192   11520
## 10     8        192   11520
## # ℹ 32,418 more rows

If this runs off your screen and you just want to see the first few rows, you can use a pipe to view the head() of the data. (Pipes work with non-dplyr functions, too, as long as the dplyr or magrittr package is loaded).

rna |>
  mutate(time_hours = time * 24, time_mn = time_hours * 60) |>
  select(time, time_hours, time_mn) |>
  head()
## # A tibble: 6 × 3
##    time time_hours time_mn
##   <dbl>      <dbl>   <dbl>
## 1     8        192   11520
## 2     8        192   11520
## 3     8        192   11520
## 4     8        192   11520
## 5     8        192   11520
## 6     8        192   11520

Let’s imagine we are interested in the human homologs of the mouse genes analysed in this dataset. This information can be found in the last column of the rna tibble, named hsapiens_homolog_associated_gene_name.

rna |>
  select(gene, hsapiens_homolog_associated_gene_name)
## # A tibble: 32,428 × 2
##    gene    hsapiens_homolog_associated_gene_name
##    <chr>   <chr>                                
##  1 Asl     ASL                                  
##  2 Apod    APOD                                 
##  3 Cyp2d22 CYP2D6                               
##  4 Klk6    KLK6                                 
##  5 Fcrls   FCRL2                                
##  6 Slc2a4  SLC2A4                               
##  7 Exd2    EXD2                                 
##  8 Gjc2    GJC2                                 
##  9 Plp1    PLP1                                 
## 10 Gnb4    GNB4                                 
## # ℹ 32,418 more rows

Some mouse gene have no human homologs. These can be retrieved using a filter() in the chain, and the is.na() function that determines whether something is an NA.

rna |>
  select(gene, hsapiens_homolog_associated_gene_name) |>
  filter(is.na(hsapiens_homolog_associated_gene_name))
## # A tibble: 4,290 × 2
##    gene     hsapiens_homolog_associated_gene_name
##    <chr>    <chr>                                
##  1 Prodh    <NA>                                 
##  2 Tssk5    <NA>                                 
##  3 Vmn2r1   <NA>                                 
##  4 Gm10654  <NA>                                 
##  5 Hexa     <NA>                                 
##  6 Sult1a1  <NA>                                 
##  7 Gm6277   <NA>                                 
##  8 Tmem198b <NA>                                 
##  9 Adam1a   <NA>                                 
## 10 Ebp      <NA>                                 
## # ℹ 4,280 more rows

If we want to keep only mouse gene that have a human homolog, we can insert a ! symbol that negates the result, so we’re asking for every row where hsapiens_homolog_associated_gene_name is not an NA.

The first few rows of the output are full of NAs, so if we wanted to remove those we could insert a filter() in the chain:

rna |>
  select(gene, hsapiens_homolog_associated_gene_name) |>
  filter(!is.na(hsapiens_homolog_associated_gene_name))
## # A tibble: 28,138 × 2
##    gene    hsapiens_homolog_associated_gene_name
##    <chr>   <chr>                                
##  1 Asl     ASL                                  
##  2 Apod    APOD                                 
##  3 Cyp2d22 CYP2D6                               
##  4 Klk6    KLK6                                 
##  5 Fcrls   FCRL2                                
##  6 Slc2a4  SLC2A4                               
##  7 Exd2    EXD2                                 
##  8 Gjc2    GJC2                                 
##  9 Plp1    PLP1                                 
## 10 Gnb4    GNB4                                 
## # ℹ 28,128 more rows

► Question

Create a new data frame from the rna data that meets the following criteria: contains only the gene, chromosome_name, phenotype_description, sample, and expression columns and a new column giving the log expression the gene. This data frame must only contain gene located on autosomes and associated with a phenotype_description.

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

► Solution

5.6 Pulling a variable

The tidy functions that we have seen (and will see below) always take a tidy table (typically a tibble) as input, and return a new, transformed one, possibly composed of a single column/variables. This is an important feature that enables the piping of commands one into another. Sometimes however, one needs to extract the variable that composes the column of a table. This can be done with the pull() function.

Below, note the difference between select(), that here returns a one-column tibble …

mini_rna <- head(rna)

mini_rna |> select(gene)
## # A tibble: 6 × 1
##   gene   
##   <chr>  
## 1 Asl    
## 2 Apod   
## 3 Cyp2d22
## 4 Klk6   
## 5 Fcrls  
## 6 Slc2a4

… and pull(), that returns a vector:

mini_rna |> pull(gene)
## [1] "Asl"     "Apod"    "Cyp2d22" "Klk6"    "Fcrls"   "Slc2a4"

After using pull(), on can’t pipe the output into any of the functions that expect to get a tibble.

5.7 Split-apply-combine data analysis

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.

rna |>
  group_by(gene)
## # A tibble: 32,428 × 19
## # Groups:   gene [1,474]
##    gene    sample  expression organism   age sex   infection strain  time tissue
##    <chr>   <chr>        <dbl> <chr>    <dbl> <chr> <chr>     <chr>  <dbl> <chr> 
##  1 Asl     GSM254…       1170 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  2 Apod    GSM254…      36194 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  3 Cyp2d22 GSM254…       4060 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  4 Klk6    GSM254…        287 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  5 Fcrls   GSM254…         85 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  6 Slc2a4  GSM254…        782 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  7 Exd2    GSM254…       1619 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  8 Gjc2    GSM254…        288 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  9 Plp1    GSM254…      43217 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## 10 Gnb4    GSM254…       1071 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## # ℹ 32,418 more rows
## # ℹ 9 more variables: mouse <dbl>, ENTREZID <dbl>, product <chr>,
## #   ensembl_gene_id <chr>, external_synonym <chr>, chromosome_name <chr>,
## #   gene_biotype <chr>, phenotype_description <chr>,
## #   hsapiens_homolog_associated_gene_name <chr>

The group_by() function doesn’t perform any data processing, it groups the data into subsets: in the example above, our initial tibble of 32428 observations is split into 1474 groups based on the gene variable.

Once the data have been combined, subsequent operations will be applied on each group independently. To remove this grouping, simply use the ungroup() function.

grouped_rna <- rna |>
    group_by(gene)
grouped_rna
## # A tibble: 32,428 × 19
## # Groups:   gene [1,474]
##    gene    sample  expression organism   age sex   infection strain  time tissue
##    <chr>   <chr>        <dbl> <chr>    <dbl> <chr> <chr>     <chr>  <dbl> <chr> 
##  1 Asl     GSM254…       1170 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  2 Apod    GSM254…      36194 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  3 Cyp2d22 GSM254…       4060 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  4 Klk6    GSM254…        287 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  5 Fcrls   GSM254…         85 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  6 Slc2a4  GSM254…        782 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  7 Exd2    GSM254…       1619 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  8 Gjc2    GSM254…        288 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  9 Plp1    GSM254…      43217 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## 10 Gnb4    GSM254…       1071 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## # ℹ 32,418 more rows
## # ℹ 9 more variables: mouse <dbl>, ENTREZID <dbl>, product <chr>,
## #   ensembl_gene_id <chr>, external_synonym <chr>, chromosome_name <chr>,
## #   gene_biotype <chr>, phenotype_description <chr>,
## #   hsapiens_homolog_associated_gene_name <chr>
ungroup(rna)
## # A tibble: 32,428 × 19
##    gene    sample  expression organism   age sex   infection strain  time tissue
##    <chr>   <chr>        <dbl> <chr>    <dbl> <chr> <chr>     <chr>  <dbl> <chr> 
##  1 Asl     GSM254…       1170 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  2 Apod    GSM254…      36194 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  3 Cyp2d22 GSM254…       4060 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  4 Klk6    GSM254…        287 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  5 Fcrls   GSM254…         85 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  6 Slc2a4  GSM254…        782 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  7 Exd2    GSM254…       1619 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  8 Gjc2    GSM254…        288 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  9 Plp1    GSM254…      43217 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## 10 Gnb4    GSM254…       1071 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## # ℹ 32,418 more rows
## # ℹ 9 more variables: mouse <dbl>, ENTREZID <dbl>, product <chr>,
## #   ensembl_gene_id <chr>, external_synonym <chr>, chromosome_name <chr>,
## #   gene_biotype <chr>, phenotype_description <chr>,
## #   hsapiens_homolog_associated_gene_name <chr>

5.7.1 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 mean expression by gene:

rna |>
  group_by(gene) |>
  summarize(mean_expression = mean(expression))
## # A tibble: 1,474 × 2
##    gene     mean_expression
##    <chr>              <dbl>
##  1 AI504432         1053.  
##  2 AW046200          131.  
##  3 AW551984          295.  
##  4 Aamp             4751.  
##  5 Abca12              4.55
##  6 Abcc8            2498.  
##  7 Abhd14a           525.  
##  8 Abi2             4909.  
##  9 Abi3bp           1002.  
## 10 Abl2             2124.  
## # ℹ 1,464 more rows

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 data frame.

You can also group by multiple columns:

rna |>
  group_by(gene, infection, time) |>
  summarize(mean_expression = mean(expression))
## `summarise()` has grouped output by 'gene', 'infection'. You can override using
## the `.groups` argument.
## # A tibble: 4,422 × 4
## # Groups:   gene, infection [2,948]
##    gene     infection    time mean_expression
##    <chr>    <chr>       <dbl>           <dbl>
##  1 AI504432 InfluenzaA      4           1104.
##  2 AI504432 InfluenzaA      8           1014 
##  3 AI504432 NonInfected     0           1034.
##  4 AW046200 InfluenzaA      4            152.
##  5 AW046200 InfluenzaA      8             81 
##  6 AW046200 NonInfected     0            155.
##  7 AW551984 InfluenzaA      4            302.
##  8 AW551984 InfluenzaA      8            342.
##  9 AW551984 NonInfected     0            238 
## 10 Aamp     InfluenzaA      4           4870 
## # ℹ 4,412 more rows

Here, again, the output from these calls doesn’t run off the screen anymore. If you want to display more data, you can use the print() function at the end of your chain with the argument n specifying the number of rows to display:

rna |>
  group_by(gene, infection, time) |>
  summarize(mean_expression = mean(expression)) |>
  print(n = 15)
## `summarise()` has grouped output by 'gene', 'infection'. You can override using
## the `.groups` argument.
## # A tibble: 4,422 × 4
## # Groups:   gene, infection [2,948]
##    gene     infection    time mean_expression
##    <chr>    <chr>       <dbl>           <dbl>
##  1 AI504432 InfluenzaA      4         1104.  
##  2 AI504432 InfluenzaA      8         1014   
##  3 AI504432 NonInfected     0         1034.  
##  4 AW046200 InfluenzaA      4          152.  
##  5 AW046200 InfluenzaA      8           81   
##  6 AW046200 NonInfected     0          155.  
##  7 AW551984 InfluenzaA      4          302.  
##  8 AW551984 InfluenzaA      8          342.  
##  9 AW551984 NonInfected     0          238   
## 10 Aamp     InfluenzaA      4         4870   
## 11 Aamp     InfluenzaA      8         4763.  
## 12 Aamp     NonInfected     0         4603.  
## 13 Abca12   InfluenzaA      4            4.25
## 14 Abca12   InfluenzaA      8            4.14
## 15 Abca12   NonInfected     0            5.29
## # ℹ 4,407 more rows

Once the data is grouped, you can also summarize multiple variables at the same time (and not necessarily on the same variable). For instance, we could add columns indicating the median expression by gene and by condition:

rna |>
  group_by(gene, infection, time) |>
  summarize(mean_expression = mean(expression),
            median_expression = median(expression))
## `summarise()` has grouped output by 'gene', 'infection'. You can override using
## the `.groups` argument.
## # A tibble: 4,422 × 5
## # Groups:   gene, infection [2,948]
##    gene     infection    time mean_expression median_expression
##    <chr>    <chr>       <dbl>           <dbl>             <dbl>
##  1 AI504432 InfluenzaA      4           1104.             1094.
##  2 AI504432 InfluenzaA      8           1014               985 
##  3 AI504432 NonInfected     0           1034.             1016 
##  4 AW046200 InfluenzaA      4            152.              144.
##  5 AW046200 InfluenzaA      8             81                82 
##  6 AW046200 NonInfected     0            155.              163 
##  7 AW551984 InfluenzaA      4            302.              245 
##  8 AW551984 InfluenzaA      8            342.              287 
##  9 AW551984 NonInfected     0            238               265 
## 10 Aamp     InfluenzaA      4           4870              4708 
## # ℹ 4,412 more rows

It is sometimes useful to rearrange the result of a query to inspect the values. For instance, we can sort on mean_expression to put the genes lowly expressed first:

rna |>
  group_by(gene, infection, time) |>
  summarize(mean_expression = mean(expression),
            median_expression = median(expression)) |>
  arrange(mean_expression)
## `summarise()` has grouped output by 'gene', 'infection'. You can override using
## the `.groups` argument.
## # A tibble: 4,422 × 5
## # Groups:   gene, infection [2,948]
##    gene    infection    time mean_expression median_expression
##    <chr>   <chr>       <dbl>           <dbl>             <dbl>
##  1 Gm5415  InfluenzaA      4           0.375               0  
##  2 Selp    NonInfected     0           0.429               0  
##  3 Ascl5   NonInfected     0           0.571               1  
##  4 Gm28178 NonInfected     0           0.571               1  
##  5 Il18r1  NonInfected     0           0.571               0  
##  6 Pdcd1   InfluenzaA      8           0.571               0  
##  7 Rln3    NonInfected     0           0.571               0  
##  8 Gm19637 InfluenzaA      4           0.625               0.5
##  9 Gm6177  InfluenzaA      4           0.625               1  
## 10 Gm7241  InfluenzaA      4           0.625               0.5
## # ℹ 4,412 more rows

To sort in descending order, we need to add the desc() function:

rna |> 
  group_by(gene, infection, time) |> 
  summarize(mean_expression = mean(expression),
            median_expression = median(expression)) |> 
  arrange(desc(mean_expression))
## `summarise()` has grouped output by 'gene', 'infection'. You can override using
## the `.groups` argument.
## # A tibble: 4,422 × 5
## # Groups:   gene, infection [2,948]
##    gene   infection    time mean_expression median_expression
##    <chr>  <chr>       <dbl>           <dbl>             <dbl>
##  1 Plp1   NonInfected     0          91103.            96534 
##  2 Glul   InfluenzaA      8          73948.            71706 
##  3 Plp1   InfluenzaA      4          67198.            63840 
##  4 Atp1b1 InfluenzaA      4          60364.            56546.
##  5 Atp1b1 InfluenzaA      8          59229             61672 
##  6 Atp1b1 NonInfected     0          57351.            59094 
##  7 Sparc  InfluenzaA      8          56106.            57409 
##  8 Glul   InfluenzaA      4          55358.            52836.
##  9 Glul   NonInfected     0          48123.            49099 
## 10 Nrep   NonInfected     0          40060.            37493 
## # ℹ 4,412 more rows

5.7.2 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 infected and non infected, we would do:

rna |> 
    count(infection)
## # A tibble: 2 × 2
##   infection       n
##   <chr>       <int>
## 1 InfluenzaA  22110
## 2 NonInfected 10318

The count() function is shorthand for something we’ve already seen: grouping by a variable, and summarizing it by counting the number of observations in that group. In other words, rna |> count() is equivalent to:

rna |> 
    group_by(infection) |> 
    summarise(count = n())
## # A tibble: 2 × 2
##   infection   count
##   <chr>       <int>
## 1 InfluenzaA  22110
## 2 NonInfected 10318

For convenience, count() provides the sort argument:

rna |> 
    count(infection, sort = TRUE)
## # A tibble: 2 × 2
##   infection       n
##   <chr>       <int>
## 1 InfluenzaA  22110
## 2 NonInfected 10318

Previous example shows the use of count() to count the number of rows/observations for one factor (i.e., infection). If we wanted to count combination of factors, such as infection and time, we would specify the first and the second factor as the arguments of count():

rna |> 
    count(infection, time)
## # A tibble: 3 × 3
##   infection    time     n
##   <chr>       <dbl> <int>
## 1 InfluenzaA      4 11792
## 2 InfluenzaA      8 10318
## 3 NonInfected     0 10318

With the above code, we can proceed with arrange() to sort the table according to a number of criteria so that we have a better comparison. For instance, we might want to arrange the table above by time:

rna |> 
  count(infection, time) |> 
  arrange(time)
## # A tibble: 3 × 3
##   infection    time     n
##   <chr>       <dbl> <int>
## 1 NonInfected     0 10318
## 2 InfluenzaA      4 11792
## 3 InfluenzaA      8 10318

or by counts:

rna |>
  count(infection, time) |>
  arrange(n)
## # A tibble: 3 × 3
##   infection    time     n
##   <chr>       <dbl> <int>
## 1 InfluenzaA      8 10318
## 2 NonInfected     0 10318
## 3 InfluenzaA      4 11792

► Question

  1. How many genes were analysed in each sample?

  2. Use group_by() and summarize() to evaluate the sequencing depth (the sum of all expressions) in each sample. Which sample has the highest sequencing depth?

  3. Calculate the mean expression level of gene “Dok3” by timepoints.

  4. Pick one sample and evaluate the number of genes by biotype

  5. Identify genes associated with “abnormal DNA methylation” phenotype description, and calculate their mean expression (in log) at time 0, time 4 and time 8.

► Solution

It is important to be able to conceptually visualise how these different functions operate on data. Being able to do that allows to mentally run a set of commands and get a feeling whether this will work, rather that randomly try things out. The Tidy Data Tutor is a wonderful tool to do exactly that.

5.8 Reshaping data

In rna, the rows contain expression values that are associated with a combination of 2 other variables: gene and sample. All the other columns correspond to variables describing either the sample (age, sex, organism…) or the gene (gene_biotype, ENTREZ_ID, product…). The variables for the same gene and sample pair have the same value in all the rows. This structure is called a long format, as one column contains all the values, and other columns describe the context of the value. These data also tend to be quite long.

In certain cases, the long format is not really human-readable or the most appropriate for the task, and another format, called wide format is preferred. It is also generally as a more compact way of representing the data (although we don’t need to worry about the size of the data here). This is typically the case with gene expression values that scientists are used to look as matrices of quantitative data, were rows represent genes (or more generally features or variables) and columns represent samples.

To convert the gene expression values from rna into a wide-format, we need to create a new table where each row is composed of expression values associated with each gene. In practical terms this means the values of the sample column in rna would become the names of column variables, and the cells would contain the expression values measured on each gene.

The key point here is that we are still following a tidy data structure (a single value per cell), but we have reshaped the data according to the observations of interest: expression levels per gene instead of recording them per gene and per sample.

With this new table, it would become therefore straightforward to explore the relationship between the gene expression levels within, and between, the samples.

The opposite transformation would be to transform column names into values of a new variable.

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

5.8.1 Pivoting the data into a wider format

For simplicity, let’s first select the 3 first columns of rna and use pivot_wider() to transform data in a wide-format.

rna_exp <- rna |>
  select(gene, sample, expression)
rna_exp
## # A tibble: 32,428 × 3
##    gene    sample     expression
##    <chr>   <chr>           <dbl>
##  1 Asl     GSM2545336       1170
##  2 Apod    GSM2545336      36194
##  3 Cyp2d22 GSM2545336       4060
##  4 Klk6    GSM2545336        287
##  5 Fcrls   GSM2545336         85
##  6 Slc2a4  GSM2545336        782
##  7 Exd2    GSM2545336       1619
##  8 Gjc2    GSM2545336        288
##  9 Plp1    GSM2545336      43217
## 10 Gnb4    GSM2545336       1071
## # ℹ 32,418 more rows

pivot_wider takes the following three main arguments:

  1. the data to be transformed;
  2. the names_from column name whose values will become new column names;
  3. the values_from column name whose values will fill the new columns.

pivot_wider() generates a new table with 1474 gene for 22 samples - one row for each gene, one column for each sample. We can also directly pipe the data into the pivot_wider(), as illustrated below:

rna_wide <- rna_exp |>
  pivot_wider(names_from = sample,
              values_from = expression)
rna_wide
## # A tibble: 1,474 × 23
##    gene    GSM2545336 GSM2545337 GSM2545338 GSM2545339 GSM2545340 GSM2545341
##    <chr>        <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
##  1 Asl           1170        361        400        586        626        988
##  2 Apod         36194      10347       9173      10620      13021      29594
##  3 Cyp2d22       4060       1616       1603       1901       2171       3349
##  4 Klk6           287        629        641        578        448        195
##  5 Fcrls           85        233        244        237        180         38
##  6 Slc2a4         782        231        248        265        313        786
##  7 Exd2          1619       2288       2235       2513       2366       1359
##  8 Gjc2           288        595        568        551        310        146
##  9 Plp1         43217     101241      96534      58354      53126      27173
## 10 Gnb4          1071       1791       1867       1430       1355        798
## # ℹ 1,464 more rows
## # ℹ 16 more variables: GSM2545342 <dbl>, GSM2545343 <dbl>, GSM2545344 <dbl>,
## #   GSM2545345 <dbl>, GSM2545346 <dbl>, GSM2545347 <dbl>, GSM2545348 <dbl>,
## #   GSM2545349 <dbl>, GSM2545350 <dbl>, GSM2545351 <dbl>, GSM2545352 <dbl>,
## #   GSM2545353 <dbl>, GSM2545354 <dbl>, GSM2545362 <dbl>, GSM2545363 <dbl>,
## #   GSM2545380 <dbl>

We can now easily compare the gene expression levels in different samples.

Note that the pivot_wider() function comes with an optional values_fill argument that can be useful when dealing with missing values. Let’s imagine that for some reason, we had some missing expression values for some genes in certain samples. In the following example, the gene Cyp2d22 has only one expression value, in GSM2545338 sample.

rna_with_missing_values <- rna |>
  select(gene, sample, expression) |>
  filter(gene %in% c("Asl", "Apod", "Cyp2d22")) |>
  filter(sample %in% c("GSM2545336", "GSM2545337", "GSM2545338")) |>
  arrange(sample) |>
  filter(!(gene == "Cyp2d22" & sample != "GSM2545338"))
rna_with_missing_values
## # A tibble: 7 × 3
##   gene    sample     expression
##   <chr>   <chr>           <dbl>
## 1 Asl     GSM2545336       1170
## 2 Apod    GSM2545336      36194
## 3 Asl     GSM2545337        361
## 4 Apod    GSM2545337      10347
## 5 Asl     GSM2545338        400
## 6 Apod    GSM2545338       9173
## 7 Cyp2d22 GSM2545338       1603

By default, the pivot_wider() function will add NA for missing values.

rna_with_missing_values |>
  pivot_wider(names_from = sample,
              values_from = expression)
## # A tibble: 3 × 4
##   gene    GSM2545336 GSM2545337 GSM2545338
##   <chr>        <dbl>      <dbl>      <dbl>
## 1 Asl           1170        361        400
## 2 Apod         36194      10347       9173
## 3 Cyp2d22         NA         NA       1603

But in some cases, we may wish to fill in the missing values by setting values_fill to a specific value.

rna_with_missing_values |>
  pivot_wider(names_from = sample,
              values_from = expression,
              values_fill = 0)
## # A tibble: 3 × 4
##   gene    GSM2545336 GSM2545337 GSM2545338
##   <chr>        <dbl>      <dbl>      <dbl>
## 1 Asl           1170        361        400
## 2 Apod         36194      10347       9173
## 3 Cyp2d22          0          0       1603

5.8.2 Pivoting data into a longer format

The opposing situation could occur if we had been provided with data in the form of rna_wide, where the sample IDs are column names, but we wished to treat them as values of a sample variable instead.

In this situation we are using the column names and turn them into a pair of new variables and need to arrange the expression values accordingly in a new variable. This can be done with the pivot_longer() function. It takes the following four main arguments:

  1. the data to be transformed;
  2. the new names_to column we wish to create and populate with the current column names;
  3. the new values_to column we wish to create and populate with current values;
  4. the names of the columns to be used to populate the names_to and values_to variables (or altarnatively, those to drop using a -).

To recreate rna_long from rna_long we would create a key called sample and value called expression and use all columns except gene for the key variable. Here we drop gene column with a minus sign.

Notice how the new variable names are to be quoted here.

rna_long <- rna_wide |>
    pivot_longer(names_to = "sample",
                 values_to = "expression",
                 -gene)
rna_long
## # A tibble: 32,428 × 3
##    gene  sample     expression
##    <chr> <chr>           <dbl>
##  1 Asl   GSM2545336       1170
##  2 Asl   GSM2545337        361
##  3 Asl   GSM2545338        400
##  4 Asl   GSM2545339        586
##  5 Asl   GSM2545340        626
##  6 Asl   GSM2545341        988
##  7 Asl   GSM2545342        836
##  8 Asl   GSM2545343        535
##  9 Asl   GSM2545344        586
## 10 Asl   GSM2545345        597
## # ℹ 32,418 more rows

Note that if we had missing values in the wide-format, the NA would be included in the new wide format. Pivoting to wider and longer formats can be a useful way to balance out a dataset so every replicate has the same composition.

wide_with_NA <- rna_with_missing_values |>
  pivot_wider(names_from = sample,
              values_from = expression)
wide_with_NA
## # A tibble: 3 × 4
##   gene    GSM2545336 GSM2545337 GSM2545338
##   <chr>        <dbl>      <dbl>      <dbl>
## 1 Asl           1170        361        400
## 2 Apod         36194      10347       9173
## 3 Cyp2d22         NA         NA       1603
wide_with_NA |>
    pivot_longer(names_to = "sample",
                 values_to = "expression",
                 -gene)
## # A tibble: 9 × 3
##   gene    sample     expression
##   <chr>   <chr>           <dbl>
## 1 Asl     GSM2545336       1170
## 2 Asl     GSM2545337        361
## 3 Asl     GSM2545338        400
## 4 Apod    GSM2545336      36194
## 5 Apod    GSM2545337      10347
## 6 Apod    GSM2545338       9173
## 7 Cyp2d22 GSM2545336         NA
## 8 Cyp2d22 GSM2545337         NA
## 9 Cyp2d22 GSM2545338       1603

We could also have used a specification for what columns to include. This can be useful if you have a large number of identifying columns, and it’s easier to specify what to gather than what to leave alone. Here the starts_with() function can help to retrieve sample names without having to list them all! Another possibility would be to use the : operator!

rna_wide |>
    pivot_longer(names_to = "sample",
                 values_to = "expression",
                 cols = starts_with("GSM"))
## # A tibble: 32,428 × 3
##    gene  sample     expression
##    <chr> <chr>           <dbl>
##  1 Asl   GSM2545336       1170
##  2 Asl   GSM2545337        361
##  3 Asl   GSM2545338        400
##  4 Asl   GSM2545339        586
##  5 Asl   GSM2545340        626
##  6 Asl   GSM2545341        988
##  7 Asl   GSM2545342        836
##  8 Asl   GSM2545343        535
##  9 Asl   GSM2545344        586
## 10 Asl   GSM2545345        597
## # ℹ 32,418 more rows
rna_wide |>
    pivot_longer(names_to = "sample",
                 values_to = "expression",
                 GSM2545336:GSM2545380)
## # A tibble: 32,428 × 3
##    gene  sample     expression
##    <chr> <chr>           <dbl>
##  1 Asl   GSM2545336       1170
##  2 Asl   GSM2545337        361
##  3 Asl   GSM2545338        400
##  4 Asl   GSM2545339        586
##  5 Asl   GSM2545340        626
##  6 Asl   GSM2545341        988
##  7 Asl   GSM2545342        836
##  8 Asl   GSM2545343        535
##  9 Asl   GSM2545344        586
## 10 Asl   GSM2545345        597
## # ℹ 32,418 more rows

► Question

Subset genes located on X and Y chromosomes from the rna data.frame and create a new (wide) data.frame with sex as columns, chromosome_name as rows, containing the mean expression of genes located in each chromosome as the values, as shown below.

You will need to summarize before reshaping!

► Solution

► Question

Now take that data frame and transform it with pivot_longer() so each row is a unique chromosome_name by gender combination.

► Solution

► Question

Use the rna dataset to create an new table were each row represents the mean expression levels of genes and columns represent the different timepoints.

► Solution

► Question

Use the previous data frame containing mean expression levels per timepoint and create a new column containing fold-changes between timepoint 8 and timepoint 0, and fold-changes between timepoint 8 and timepoint 4. Convert this table in a long-format table gathering the foldchanges calculated.

► Solution

5.9 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 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 are going to prepare a table representing for each gene, the fold-changes (in log values) between timepoint 8 and timepoint 0, and the fold-changes between timepoint 8 and timepoint 0.

rna_fc <- rna |>
  mutate(expression_log = log(expression)) |>
  group_by(gene, time) |>
  summarize(mean_exp = mean(expression_log)) |>
  pivot_wider(names_from = time,
              values_from = mean_exp) |>
  mutate(time_8_vs_0 = `8` - `0`, time_4_vs_0 = `4` - `0`) |>
  select(gene, time_8_vs_0, time_4_vs_0)
## `summarise()` has grouped output by 'gene'. You can override using the
## `.groups` argument.
rna_fc
## # A tibble: 1,474 × 3
## # Groups:   gene [1,474]
##    gene     time_8_vs_0 time_4_vs_0
##    <chr>          <dbl>       <dbl>
##  1 AI504432     -0.0248    0.0523  
##  2 AW046200     -0.650    -0.0348  
##  3 AW551984      0.232    -0.428   
##  4 Aamp          0.0271    0.0522  
##  5 Abca12       -0.116    -0.114   
##  6 Abcc8        -0.114     0.0163  
##  7 Abhd14a      -0.309    -0.0800  
##  8 Abi2          0.0110   -0.000894
##  9 Abi3bp       -0.432    -0.107   
## 10 Abl2         -0.0188   -0.0550  
## # ℹ 1,464 more rows

We can save the table as a CSV file in our data_output folder.

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

5.10 Additional exercises

► Question

We are going to re-analyse beer consumption in 48 individuals using dplyr. The data are available in the rWSBIM1207 package. The data illustrated the fictive beer consumption in litres per year at different age according to gender and employment.

  • Load the rWSBIM1207 package. If the package isn’t installed of its version is older than 0.1.1, install it from the UCLouvain-CBIO/rWSBIM1207 GitHub repository using the BiocManager::install() function.
  • Directly load the data by typing
data(beers)

  • Remove observations with missing values.

  • Using the Year, Month and Day columns, create a new column Date using dplyr::mutate and lubridate::ymd. What is the class of Date ?

  • Create a new table, containing observations for women older than 35 years old, employed, and select all columns except Day, Month and Year, and order in descending value of consumption of beers.

  • Export the new table to a csv file.

Beer consumption analysis:

  • Does employment status have an impact on beer consumption?
  • Do men drink more than women?
  • Does employment status have an influence on beer consumption according to gender?
  • Do men drink more than women according to age and employment status?

As we can see from the last exercise, it become difficult to read and interpret multiple results. In the next chapter, we will see how to complement such analysis questions with visualisations such as the following one, that clearly highlight important patterns in our data.

Figure 5.1: Visualisation of beer consumption, highlighting different patterns of beer consumption in employed and unemployed males and females.

Visualisation of beer consumption, highlighting different patterns of beer consumption in employed and unemployed males and females.

► Question

The Cancer Genome Atlas (TCGA) is a large scale effort that has collected high throughput biology data from hundreds of patients samples. In this exercise, we are going to analyse the clinical variables recorded for a subset of the patients.

  • Load the rWSBIM1207 package. If the package isn’t installed of its version is older than 0.1.1, install it from the UCLouvain-CBIO/rWSBIM1207' GitHub repository using thedevtools::install_github` function.

  • Using the clinical1.csv() function from rWSBIM1207, find the path the clinical1.csv file and read it to produce a data.frame named clinical.

  • Familiarise yourself with the data.

  • Create a smaller data frame called clinical_mini containing only the columns corresponding to patientID, gender, age_at_diagnosis, smoking_history, number_pack_years_smoked, year_of_tobacco_smoking_onset, and stopped_smoking_year.

  • Calculate the number of males and females in the cohort.

  • Create a new variable years_at_diagnosis corresponding to the age at diagnosis converted from days into years.

  • Calculate the mean and median age at diagnosis (in years). Pay attention to missing values!

  • Calculate the mean and median age at diagnosis for males and females.

  • How many patient were diagnosed before 50 years?

  • Compare the mean age at diagnosis between current smoker and lifelong non-smoker.

  • Select patients who stopped smoking more than 15 years ago and calculate the number of smoking years for these cases. Display only cases for which you were able to calculate the data.

  • How many of them smoked less than 5 years?

  • Try to recreate the following table, reporting the number of smokers and lifelong-non smoker between males and females. Note: the layout can be different.

gender current smoker lifelong non-smoker
female 51 55
male 69 20

► Question

Using the interroA.csv() function from the rWSBIM1207 package to get the path to the spreadsheet file, read the data into R using the read_csv function. This data is in the wide format, with the results of each test stored as a separate column.

Using the appropriate pivot function, convert the data into a long table with a column interro informing which test that line refers to and a column result with the student’s mark.

► Question

Make sure you have rWSBIM1207 version >= 0.1.16 and load the 2022 Belgian road accidents statistics and the associated metadata, describing the variables. The path to the former as an rds file is available with road_accidents_be_2022.rds(). The road_accidents_be_meta.csv() returns the path to the metadata csv file.

The data provides the Number of killed, seriously injured, slightly injured and uninjured victims of road accidents, by age group, type of user, sex and various characteristics of the accident in Belgium in 20222.

  • Using the appropriate functions, load both files into R and familiarise yourself with the data.

  • Compare the numbers for man and women over the hours of the day for all age classes. Ignore any unknown information.

  • Compare the number of victims in the different provinces. Do this comparison for the different type of victims. Ignore any unknown information.

  • Come up with additional question/comparisons that you could ask these data.

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