Chapter 5 Manipulating and analyzing data with dplyr

5.1 Data Manipulation using dplyr and tidyr

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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?

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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...
## 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

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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    GSM25…       1170     8 Fema… Influenz…     8 Cereb…    14   109900
##  2 Apod   GSM25…      36194     8 Fema… Influenz…     8 Cereb…    14    11815
##  3 Cyp2d… GSM25…       4060     8 Fema… Influenz…     8 Cereb…    14    56448
##  4 Klk6   GSM25…        287     8 Fema… Influenz…     8 Cereb…    14    19144
##  5 Fcrls  GSM25…         85     8 Fema… Influenz…     8 Cereb…    14    80891
##  6 Slc2a4 GSM25…        782     8 Fema… Influenz…     8 Cereb…    14    20528
##  7 Exd2   GSM25…       1619     8 Fema… Influenz…     8 Cereb…    14    97827
##  8 Gjc2   GSM25…        288     8 Fema… Influenz…     8 Cereb…    14   118454
##  9 Plp1   GSM25…      43217     8 Fema… Influenz…     8 Cereb…    14    18823
## 10 Gnb4   GSM25…       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   GSM25…        626 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  2 Apod  GSM25…      13021 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  3 Cyp2… GSM25…       2171 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  4 Klk6  GSM25…        448 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  5 Fcrls GSM25…        180 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  6 Slc2… GSM25…        313 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  7 Exd2  GSM25…       2366 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  8 Gjc2  GSM25…        310 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
##  9 Plp1  GSM25…      53126 Mus mus…     8 Male  Influenz… C57BL…     4 Cereb…
## 10 Gnb4  GSM25…       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   GSM25…        535 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  2 Apod  GSM25…      13668 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  3 Cyp2… GSM25…       2008 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  4 Klk6  GSM25…       1101 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  5 Fcrls GSM25…        375 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  6 Slc2… GSM25…        249 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  7 Exd2  GSM25…       3126 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  8 Gjc2  GSM25…        791 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
##  9 Plp1  GSM25…      98658 Mus mus…     8 Male  NonInfec… C57BL…     0 Cereb…
## 10 Gnb4  GSM25…       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

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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

rna |>
  filter(expression > 50000, sex == "Male", time == 0 ) |>
  select(gene, sample, time, expression, age)

## # A tibble: 8 × 5
##   gene   sample      time expression   age
##   <chr>  <chr>      <dbl>      <dbl> <dbl>
## 1 Plp1   GSM2545343     0      98658     8
## 2 Glul   GSM2545343     0      54808     8
## 3 Atp1b1 GSM2545343     0      60083     8
## 4 Plp1   GSM2545349     0      82722     8
## 5 Atp1b1 GSM2545349     0      59094     8
## 6 Plp1   GSM2545354     0      84540     8
## 7 Glul   GSM2545354     0      50584     8
## 8 Atp1b1 GSM2545354     0      57409     8

5.5 Mutate

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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

rna |>
  filter(chromosome_name != "X", chromosome_name != "Y") |>
  mutate(log_expression = log(expression)) |>
  select(gene, chromosome_name, phenotype_description, sample, log_expression) |>
  filter(!is.na(phenotype_description))

## # A tibble: 21,054 × 5
##    gene    chromosome_name phenotype_description        sample log_expression
##    <chr>   <chr>           <chr>                        <chr>           <dbl>
##  1 Asl     5               abnormal circulating amino … GSM25…           7.06
##  2 Apod    16              abnormal lipid homeostasis   GSM25…          10.5 
##  3 Cyp2d22 15              abnormal skin morphology     GSM25…           8.31
##  4 Klk6    7               abnormal cytokine level      GSM25…           5.66
##  5 Fcrls   3               decreased CD8-positive alph… GSM25…           4.44
##  6 Slc2a4  11              abnormal circulating glucos… GSM25…           6.66
##  7 Gjc2    11              Purkinje cell degeneration   GSM25…           5.66
##  8 Gnb4    3               decreased anxiety-related r… GSM25…           6.98
##  9 Tnc     4               abnormal CNS synaptic trans… GSM25…           5.39
## 10 Trf     9               abnormal circulating phosph… GSM25…           9.18
## # ℹ 21,044 more rows

5.6 Pulling a variable

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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

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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   GSM25…       1170 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  2 Apod  GSM25…      36194 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  3 Cyp2… GSM25…       4060 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  4 Klk6  GSM25…        287 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  5 Fcrls GSM25…         85 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  6 Slc2… GSM25…        782 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  7 Exd2  GSM25…       1619 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  8 Gjc2  GSM25…        288 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  9 Plp1  GSM25…      43217 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## 10 Gnb4  GSM25…       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   GSM25…       1170 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  2 Apod  GSM25…      36194 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  3 Cyp2… GSM25…       4060 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  4 Klk6  GSM25…        287 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  5 Fcrls GSM25…         85 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  6 Slc2… GSM25…        782 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  7 Exd2  GSM25…       1619 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  8 Gjc2  GSM25…        288 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  9 Plp1  GSM25…      43217 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## 10 Gnb4  GSM25…       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   GSM25…       1170 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  2 Apod  GSM25…      36194 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  3 Cyp2… GSM25…       4060 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  4 Klk6  GSM25…        287 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  5 Fcrls GSM25…         85 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  6 Slc2… GSM25…        782 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  7 Exd2  GSM25…       1619 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  8 Gjc2  GSM25…        288 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
##  9 Plp1  GSM25…      43217 Mus mus…     8 Fema… Influenz… C57BL…     8 Cereb…
## 10 Gnb4  GSM25…       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>

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

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

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

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

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

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

rna |> 
    count(infection)

## # A tibble: 2 × 2
##   infection       n
##   <chr>       <int>
## 1 InfluenzaA  22110
## 2 NonInfected 10318

rna |> 
    group_by(infection) |> 
    summarise(count = n())

## # A tibble: 2 × 2
##   infection   count
##   <chr>       <int>
## 1 InfluenzaA  22110
## 2 NonInfected 10318

rna |> 
    count(infection, sort = TRUE)

## # A tibble: 2 × 2
##   infection       n
##   <chr>       <int>
## 1 InfluenzaA  22110
## 2 NonInfected 10318

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

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

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

rna |>
  count(sample)

## # A tibble: 22 × 2
##    sample         n
##    <chr>      <int>
##  1 GSM2545336  1474
##  2 GSM2545337  1474
##  3 GSM2545338  1474
##  4 GSM2545339  1474
##  5 GSM2545340  1474
##  6 GSM2545341  1474
##  7 GSM2545342  1474
##  8 GSM2545343  1474
##  9 GSM2545344  1474
## 10 GSM2545345  1474
## # ℹ 12 more rows

rna |>
  group_by(sample) |>
  summarize(seq_depth = sum(expression)) |>
  arrange(desc(seq_depth))

## # A tibble: 22 × 2
##    sample     seq_depth
##    <chr>          <dbl>
##  1 GSM2545350   3255566
##  2 GSM2545352   3216163
##  3 GSM2545343   3105652
##  4 GSM2545336   3039671
##  5 GSM2545380   3036098
##  6 GSM2545353   2953249
##  7 GSM2545348   2913678
##  8 GSM2545362   2913517
##  9 GSM2545351   2782464
## 10 GSM2545349   2758006
## # ℹ 12 more rows

rna |>
  filter(gene == "Dok3") |>
  group_by(time) |>
  summarize(mean = mean(expression)) |>
  arrange(time)

## # A tibble: 3 × 2
##    time  mean
##   <dbl> <dbl>
## 1     0  169 
## 2     4  156.
## 3     8   61

rna |>
  filter(sample == "GSM2545336") |>
  group_by(gene_biotype) |>
  count(gene_biotype) |>
  arrange(desc(n))

## # A tibble: 13 × 2
## # Groups:   gene_biotype [13]
##    gene_biotype                           n
##    <chr>                              <int>
##  1 protein_coding                      1321
##  2 lncRNA                                69
##  3 processed_pseudogene                  59
##  4 miRNA                                  7
##  5 snoRNA                                 5
##  6 TEC                                    4
##  7 polymorphic_pseudogene                 2
##  8 unprocessed_pseudogene                 2
##  9 IG_C_gene                              1
## 10 scaRNA                                 1
## 11 transcribed_processed_pseudogene       1
## 12 transcribed_unitary_pseudogene         1
## 13 transcribed_unprocessed_pseudogene     1

rna |>
  filter(phenotype_description == "abnormal DNA methylation") |>
  group_by(gene, time) |>
  summarize(mean_expression = mean(log(expression))) |>
  arrange()

## `summarise()` has grouped output by 'gene'. You can override using the
## `.groups` argument.

## # A tibble: 6 × 3
## # Groups:   gene [2]
##   gene   time mean_expression
##   <chr> <dbl>           <dbl>
## 1 Xist      0            6.95
## 2 Xist      4            6.34
## 3 Xist      8            7.13
## 4 Zdbf2     0            6.27
## 5 Zdbf2     4            6.27
## 6 Zdbf2     8            6.19

5.8 Reshaping data

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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).

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

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>

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

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

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

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

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

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

rna |>
  filter(chromosome_name == "Y" | chromosome_name == "X") |>
  select(gene, sample, sex, expression, chromosome_name) |>
  arrange(gene)

## # A tibble: 1,298 × 5
##    gene   sample     sex    expression chromosome_name
##    <chr>  <chr>      <chr>       <dbl> <chr>          
##  1 Armcx5 GSM2545336 Female       1189 X              
##  2 Armcx5 GSM2545337 Female        513 X              
##  3 Armcx5 GSM2545338 Female        414 X              
##  4 Armcx5 GSM2545339 Female        543 X              
##  5 Armcx5 GSM2545340 Male          666 X              
##  6 Armcx5 GSM2545341 Male         1172 X              
##  7 Armcx5 GSM2545342 Female        857 X              
##  8 Armcx5 GSM2545343 Male          559 X              
##  9 Armcx5 GSM2545344 Female        570 X              
## 10 Armcx5 GSM2545345 Male          545 X              
## # ℹ 1,288 more rows

rna_1 <- rna |>
  filter(chromosome_name == "Y" | chromosome_name == "X") |>
  group_by(sex, chromosome_name) |>
  summarize(mean = mean(expression)) |>
  pivot_wider(names_from = sex,
              values_from = mean)

## `summarise()` has grouped output by 'sex'. You can override using the
## `.groups` argument.

rna_1

## # A tibble: 2 × 3
##   chromosome_name Female  Male
##   <chr>            <dbl> <dbl>
## 1 X                3504. 2497.
## 2 Y                   3  2117.

rna_1 |>
  pivot_longer(names_to = "gender",
               values_to = "mean",
               -chromosome_name)

## # A tibble: 4 × 3
##   chromosome_name gender  mean
##   <chr>           <chr>  <dbl>
## 1 X               Female 3504.
## 2 X               Male   2497.
## 3 Y               Female    3 
## 4 Y               Male   2117.

rna |>
  group_by(gene, time) |>
  summarize(mean_exp = mean(expression)) |>
  pivot_wider(names_from = time,
              values_from = mean_exp)

## `summarise()` has grouped output by 'gene'. You can override using the
## `.groups` argument.

## # A tibble: 1,474 × 4
## # Groups:   gene [1,474]
##    gene         `0`     `4`     `8`
##    <chr>      <dbl>   <dbl>   <dbl>
##  1 AI504432 1034.   1104.   1014   
##  2 AW046200  155.    152.     81   
##  3 AW551984  238     302.    342.  
##  4 Aamp     4603.   4870    4763.  
##  5 Abca12      5.29    4.25    4.14
##  6 Abcc8    2576.   2609.   2292.  
##  7 Abhd14a   591.    547.    432.  
##  8 Abi2     4881.   4903.   4945.  
##  9 Abi3bp   1175.   1061.    762.  
## 10 Abl2     2170.   2078.   2131.  
## # ℹ 1,464 more rows

rna |>
  group_by(gene, time) |>
  summarize(mean_exp = mean(expression)) |>
  pivot_wider(names_from = time,
              values_from = mean_exp) |>
  select(gene, 4)

## `summarise()` has grouped output by 'gene'. You can override using the
## `.groups` argument.

## # A tibble: 1,474 × 2
## # Groups:   gene [1,474]
##    gene         `8`
##    <chr>      <dbl>
##  1 AI504432 1014   
##  2 AW046200   81   
##  3 AW551984  342.  
##  4 Aamp     4763.  
##  5 Abca12      4.14
##  6 Abcc8    2292.  
##  7 Abhd14a   432.  
##  8 Abi2     4945.  
##  9 Abi3bp    762.  
## 10 Abl2     2131.  
## # ℹ 1,464 more rows

rna |>
  group_by(gene, time) |>
  summarize(mean_exp = mean(expression)) |>
  pivot_wider(names_from = time,
              values_from = mean_exp) |>
  select(gene, `4`)

## `summarise()` has grouped output by 'gene'. You can override using the
## `.groups` argument.

## # A tibble: 1,474 × 2
## # Groups:   gene [1,474]
##    gene         `4`
##    <chr>      <dbl>
##  1 AI504432 1104.  
##  2 AW046200  152.  
##  3 AW551984  302.  
##  4 Aamp     4870   
##  5 Abca12      4.25
##  6 Abcc8    2609.  
##  7 Abhd14a   547.  
##  8 Abi2     4903.  
##  9 Abi3bp   1061.  
## 10 Abl2     2078.  
## # ℹ 1,464 more rows

rna |>
  group_by(gene, time) |>
  summarize(mean_exp = mean(expression)) |>
  pivot_wider(names_from = time,
              values_from = mean_exp) |>
  rename("time0" = `0`, "time4" = `4`, "time8" = `8`) |>
  select(gene, time4)

## `summarise()` has grouped output by 'gene'. You can override using the
## `.groups` argument.

## # A tibble: 1,474 × 2
## # Groups:   gene [1,474]
##    gene       time4
##    <chr>      <dbl>
##  1 AI504432 1104.  
##  2 AW046200  152.  
##  3 AW551984  302.  
##  4 Aamp     4870   
##  5 Abca12      4.25
##  6 Abcc8    2609.  
##  7 Abhd14a   547.  
##  8 Abi2     4903.  
##  9 Abi3bp   1061.  
## 10 Abl2     2078.  
## # ℹ 1,464 more rows

rna_fc <- rna |>
  group_by(gene, time) |>
  summarize(mean_exp = mean(expression)) |>
  pivot_wider(names_from = time,
              values_from = mean_exp) |>
    mutate(time_8_vs_0 = `8` / `0`, time_8_vs_4 = `8` / `4`)

## `summarise()` has grouped output by 'gene'. You can override using the
## `.groups` argument.

rna_fc

## # A tibble: 1,474 × 6
## # Groups:   gene [1,474]
##    gene         `0`     `4`     `8` time_8_vs_0 time_8_vs_4
##    <chr>      <dbl>   <dbl>   <dbl>       <dbl>       <dbl>
##  1 AI504432 1034.   1104.   1014          0.981       0.918
##  2 AW046200  155.    152.     81          0.522       0.532
##  3 AW551984  238     302.    342.         1.44        1.13 
##  4 Aamp     4603.   4870    4763.         1.03        0.978
##  5 Abca12      5.29    4.25    4.14       0.784       0.975
##  6 Abcc8    2576.   2609.   2292.         0.889       0.878
##  7 Abhd14a   591.    547.    432.         0.731       0.791
##  8 Abi2     4881.   4903.   4945.         1.01        1.01 
##  9 Abi3bp   1175.   1061.    762.         0.649       0.719
## 10 Abl2     2170.   2078.   2131.         0.982       1.03 
## # ℹ 1,464 more rows

rna_fc |>
  pivot_longer(names_to = "comparisons",
               values_to = "Fold_changes",
               time_8_vs_0:time_8_vs_4)

## # A tibble: 2,948 × 6
## # Groups:   gene [1,474]
##    gene         `0`     `4`     `8` comparisons Fold_changes
##    <chr>      <dbl>   <dbl>   <dbl> <chr>              <dbl>
##  1 AI504432 1034.   1104.   1014    time_8_vs_0        0.981
##  2 AI504432 1034.   1104.   1014    time_8_vs_4        0.918
##  3 AW046200  155.    152.     81    time_8_vs_0        0.522
##  4 AW046200  155.    152.     81    time_8_vs_4        0.532
##  5 AW551984  238     302.    342.   time_8_vs_0        1.44 
##  6 AW551984  238     302.    342.   time_8_vs_4        1.13 
##  7 Aamp     4603.   4870    4763.   time_8_vs_0        1.03 
##  8 Aamp     4603.   4870    4763.   time_8_vs_4        0.978
##  9 Abca12      5.29    4.25    4.14 time_8_vs_0        0.784
## 10 Abca12      5.29    4.25    4.14 time_8_vs_4        0.975
## # ℹ 2,938 more rows

5.9 Exporting data

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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

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data(beers)

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.