Starting with data
Last updated on 2024-04-02 | Edit this page
Estimated time: 60 minutes
Overview
Questions
- First data analysis in R
Objectives
- Describe what a
data.frameis. - Load external data from a .csv file into a data frame.
- Summarize the contents of a data frame.
- Describe what a factor is.
- Convert between strings and factors.
- Reorder and rename factors.
- Format dates.
- Export and save data.
This episode is based on the Data Carpentries’s Data Analysis and Visualisation in R for Ecologists lesson.
Presentation of the gene expression data
We are going to use part of the data published by Blackmore et al. (2017), The effect of upper-respiratory infection on transcriptomic changes in the CNS. The goal of the study was to determine the effect of an upper-respiratory infection on changes in RNA transcription occurring in the cerebellum and spinal cord post infection. Gender matched eight week old C57BL/6 mice were inoculated with saline or with Influenza A by intranasal route and transcriptomic changes in the cerebellum and spinal cord tissues were evaluated by RNA-seq at days 0 (non-infected), 4 and 8.
The dataset is stored as a comma-separated values (CSV) file. Each row holds information for a single RNA expression measurement, and the first eleven columns represent:
| Column | Description |
|---|---|
| gene | The name of the gene that was measured |
| sample | The name of the sample the gene expression was measured in |
| expression | The value of the gene expression |
| organism | The organism/species - here all data stem from mice |
| age | The age of the mouse (all mice were 8 weeks here) |
| sex | The sex of the mouse |
| infection | The infection state of the mouse, i.e. infected with Influenza A or not infected. |
| strain | The Influenza A strain. |
| time | The duration of the infection (in days). |
| tissue | The tissue that was used for the gene expression experiment, i.e. cerebellum or spinal cord. |
| mouse | The mouse unique identifier. |
We are going to use the R function download.file() to
download the CSV file that contains the gene expression data, and we
will use read.csv() to load into memory the content of the
CSV file as an object of class data.frame. Inside the
download.file command, the first entry is a character
string with the source URL. This source URL downloads a CSV file from a
GitHub repository. The text after the comma
("data/rnaseq.csv") is the destination of the file on your
local machine. You’ll need to have a folder on your machine called
"data" where you’ll download the file. So this command
downloads the remote file, names it "rnaseq.csv" and adds
it to a preexisting folder named "data".
R
download.file(url = "https://github.com/carpentries-incubator/bioc-intro/raw/main/episodes/data/rnaseq.csv",
destfile = "data/rnaseq.csv")
You are now ready to load the data:
R
rna <- read.csv("data/rnaseq.csv")
This statement doesn’t produce any output because, as you might recall, assignments don’t display anything. If we want to check that our data has been loaded, we can see the contents of the data frame by typing its name:
R
rna
Wow… that was a lot of output. At least it means the data loaded
properly. Let’s check the top (the first 6 lines) of this data frame
using the function head():
R
head(rna)
OUTPUT
gene sample expression organism age sex infection strain time
1 Asl GSM2545336 1170 Mus musculus 8 Female InfluenzaA C57BL/6 8
2 Apod GSM2545336 36194 Mus musculus 8 Female InfluenzaA C57BL/6 8
3 Cyp2d22 GSM2545336 4060 Mus musculus 8 Female InfluenzaA C57BL/6 8
4 Klk6 GSM2545336 287 Mus musculus 8 Female InfluenzaA C57BL/6 8
5 Fcrls GSM2545336 85 Mus musculus 8 Female InfluenzaA C57BL/6 8
6 Slc2a4 GSM2545336 782 Mus musculus 8 Female InfluenzaA C57BL/6 8
tissue mouse ENTREZID
1 Cerebellum 14 109900
2 Cerebellum 14 11815
3 Cerebellum 14 56448
4 Cerebellum 14 19144
5 Cerebellum 14 80891
6 Cerebellum 14 20528
product
1 argininosuccinate lyase, transcript variant X1
2 apolipoprotein D, transcript variant 3
3 cytochrome P450, family 2, subfamily d, polypeptide 22, transcript variant 2
4 kallikrein related-peptidase 6, transcript variant 2
5 Fc receptor-like S, scavenger receptor, transcript variant X1
6 solute carrier family 2 (facilitated glucose transporter), member 4
ensembl_gene_id external_synonym chromosome_name gene_biotype
1 ENSMUSG00000025533 2510006M18Rik 5 protein_coding
2 ENSMUSG00000022548 <NA> 16 protein_coding
3 ENSMUSG00000061740 2D22 15 protein_coding
4 ENSMUSG00000050063 Bssp 7 protein_coding
5 ENSMUSG00000015852 2810439C17Rik 3 protein_coding
6 ENSMUSG00000018566 Glut-4 11 protein_coding
phenotype_description
1 abnormal circulating amino acid level
2 abnormal lipid homeostasis
3 abnormal skin morphology
4 abnormal cytokine level
5 decreased CD8-positive alpha-beta T cell number
6 abnormal circulating glucose level
hsapiens_homolog_associated_gene_name
1 ASL
2 APOD
3 CYP2D6
4 KLK6
5 FCRL2
6 SLC2A4R
## Try also
## View(rna)
Note
read.csv() assumes that fields are delineated by commas,
however, in several countries, the comma is used as a decimal separator
and the semicolon (;) is used as a field delineator. If you want to read
in this type of files in R, you can use the read.csv2()
function. It behaves exactly like read.csv() but uses
different parameters for the decimal and the field separators. If you
are working with another format, they can be both specified by the user.
Check out the help for read.csv() by typing
?read.csv to learn more. There is also the
read.delim() function for reading tab separated data files.
It is important to note that all of these functions are actually wrapper
functions for the main read.table() function with different
arguments. As such, the data above could have also been loaded by using
read.table() with the separation argument as
,. The code is as follows:
R
rna <- read.table(file = "data/rnaseq.csv",
sep = ",",
header = TRUE)
The header argument has to be set to TRUE to be able to read the
headers as by default read.table() has the header argument
set to FALSE.
What are data frames?
Data frames are the de facto data structure for most tabular data, and what we use for statistics and plotting.
A data frame can be created by hand, but most commonly they are
generated by the functions read.csv() or
read.table(); in other words, when importing spreadsheets
from your hard drive (or the web).
A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.
We can see this when inspecting the structure of a data frame
with the function str():
R
str(rna)
OUTPUT
'data.frame': 32428 obs. of 19 variables:
$ gene : chr "Asl" "Apod" "Cyp2d22" "Klk6" ...
$ sample : chr "GSM2545336" "GSM2545336" "GSM2545336" "GSM2545336" ...
$ expression : int 1170 36194 4060 287 85 782 1619 288 43217 1071 ...
$ organism : chr "Mus musculus" "Mus musculus" "Mus musculus" "Mus musculus" ...
$ age : int 8 8 8 8 8 8 8 8 8 8 ...
$ sex : chr "Female" "Female" "Female" "Female" ...
$ infection : chr "InfluenzaA" "InfluenzaA" "InfluenzaA" "InfluenzaA" ...
$ strain : chr "C57BL/6" "C57BL/6" "C57BL/6" "C57BL/6" ...
$ time : int 8 8 8 8 8 8 8 8 8 8 ...
$ tissue : chr "Cerebellum" "Cerebellum" "Cerebellum" "Cerebellum" ...
$ mouse : int 14 14 14 14 14 14 14 14 14 14 ...
$ ENTREZID : int 109900 11815 56448 19144 80891 20528 97827 118454 18823 14696 ...
$ product : chr "argininosuccinate lyase, transcript variant X1" "apolipoprotein D, transcript variant 3" "cytochrome P450, family 2, subfamily d, polypeptide 22, transcript variant 2" "kallikrein related-peptidase 6, transcript variant 2" ...
$ ensembl_gene_id : chr "ENSMUSG00000025533" "ENSMUSG00000022548" "ENSMUSG00000061740" "ENSMUSG00000050063" ...
$ external_synonym : chr "2510006M18Rik" NA "2D22" "Bssp" ...
$ chromosome_name : chr "5" "16" "15" "7" ...
$ gene_biotype : chr "protein_coding" "protein_coding" "protein_coding" "protein_coding" ...
$ phenotype_description : chr "abnormal circulating amino acid level" "abnormal lipid homeostasis" "abnormal skin morphology" "abnormal cytokine level" ...
$ hsapiens_homolog_associated_gene_name: chr "ASL" "APOD" "CYP2D6" "KLK6" ...Inspecting data.frame Objects
We already saw how the functions head() and
str() can be useful to check the content and the structure
of a data frame. Here is a non-exhaustive list of functions to get a
sense of the content/structure of the data. Let’s try them out!
Size:
-
dim(rna)- returns a vector with the number of rows as the first element, and the number of columns as the second element (the dimensions of the object). -
nrow(rna)- returns the number of rows. -
ncol(rna)- returns the number of columns.
Content:
-
head(rna)- shows the first 6 rows. -
tail(rna)- shows the last 6 rows.
Names:
-
names(rna)- returns the column names (synonym ofcolnames()fordata.frameobjects). -
rownames(rna)- returns the row names.
Summary:
-
str(rna)- structure of the object and information about the class, length and content of each column. -
summary(rna)- summary statistics for each column.
Note: most of these functions are “generic”, they can be used on
other types of objects besides data.frame.
- class: data frame
- how many rows: 66465, how many columns: 11
Indexing and subsetting data frames
Our rna data frame has rows and columns (it has 2
dimensions); if we want to extract some specific data from it, we need
to specify the “coordinates” we want. Row numbers come first, followed
by column numbers. However, note that different ways of specifying these
coordinates lead to results with different classes.
R
# first element in the first column of the data frame (as a vector)
rna[1, 1]
# first element in the 6th column (as a vector)
rna[1, 6]
# first column of the data frame (as a vector)
rna[, 1]
# first column of the data frame (as a data.frame)
rna[1]
# first three elements in the 7th column (as a vector)
rna[1:3, 7]
# the 3rd row of the data frame (as a data.frame)
rna[3, ]
# equivalent to head_rna <- head(rna)
head_rna <- rna[1:6, ]
head_rna
: is a special function that creates numeric vectors of
integers in increasing or decreasing order, test 1:10 and
10:1 for instance. See section @ref(sec:genvec) for details.
You can also exclude certain indices of a data frame using the
“-” sign:
R
rna[, -1] ## The whole data frame, except the first column
rna[-c(7:66465), ] ## Equivalent to head(rna)
Data frames can be subsetted by calling indices (as shown previously), but also by calling their column names directly:
R
rna["gene"] # Result is a data.frame
rna[, "gene"] # Result is a vector
rna[["gene"]] # Result is a vector
rna$gene # Result is a vector
In RStudio, you can use the autocompletion feature to get the full and correct names of the columns.
Challenge
Create a
data.frame(rna_200) containing only the data in row 200 of thernadataset.Notice how
nrow()gave you the number of rows in adata.frame?
Use that number to pull out just that last row in the initial
rnadata frame.Compare that with what you see as the last row using
tail()to make sure it’s meeting expectations.Pull out that last row using
nrow()instead of the row number.Create a new data frame (
rna_last) from that last row.
Use
nrow()to extract the row that is in the middle of thernadataframe. Store the content of this row in an object namedrna_middle.Combine
nrow()with the-notation above to reproduce the behavior ofhead(rna), keeping just the first through 6th rows of the rna dataset.
R
## 1.
rna_200 <- rna[200, ]
## 2.
## Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(rna)
rna_last <- rna[n_rows, ]
## 3.
rna_middle <- rna[n_rows / 2, ]
## 4.
rna_head <- rna[-(7:n_rows), ]
Factors
Factors represent categorical data. They are stored as integers associated with labels and they can be ordered or unordered. While factors look (and often behave) like character vectors, they are actually treated as integer vectors by R. So you need to be very careful when treating them as strings.
Once created, factors can only contain a pre-defined set of values, known as levels. By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:
R
sex <- factor(c("male", "female", "female", "male", "female"))
R will assign 1 to the level "female" and
2 to the level "male" (because f
comes before m, even though the first element in this
vector is "male"). You can see this by using the function
levels() and you can find the number of levels using
nlevels():
R
levels(sex)
OUTPUT
[1] "female" "male" R
nlevels(sex)
OUTPUT
[1] 2Sometimes, the order of the factors does not matter, other times you
might want to specify the order because it is meaningful (e.g., “low”,
“medium”, “high”), it improves your visualization, or it is required by
a particular type of analysis. Here, one way to reorder our levels in
the sex vector would be:
R
sex ## current order
OUTPUT
[1] male female female male female
Levels: female maleR
sex <- factor(sex, levels = c("male", "female"))
sex ## after re-ordering
OUTPUT
[1] male female female male female
Levels: male femaleIn R’s memory, these factors are represented by integers (1, 2, 3),
but are more informative than integers because factors are self
describing: "female", "male" is more
descriptive than 1, 2. Which one is “male”?
You wouldn’t be able to tell just from the integer data. Factors, on the
other hand, have this information built-in. It is particularly helpful
when there are many levels (like the gene biotype in our example
dataset).
When your data is stored as a factor, you can use the
plot() function to get a quick glance at the number of
observations represented by each factor level. Let’s look at the number
of males and females in our data.
R
plot(sex)
Converting to character
If you need to convert a factor to a character vector, you use
as.character(x).
R
as.character(sex)
OUTPUT
[1] "male" "female" "female" "male" "female"Renaming factors
If we want to rename these factor, it is sufficient to change its levels:
R
levels(sex)
OUTPUT
[1] "male" "female"R
levels(sex) <- c("M", "F")
sex
OUTPUT
[1] M F F M F
Levels: M FR
plot(sex)
R
levels(sex)
OUTPUT
[1] "M" "F"R
levels(sex) <- c("Male", "Female")
- missing quotations around the names of the animals
- missing one entry in the “feel” column (probably for one of the furry animals)
- missing one comma in the weight column
Challenge:
Can you predict the class for each of the columns in the following example?
Check your guesses using str(country_climate):
Are they what you expected? Why? Why not?
Try again by adding
stringsAsFactors = TRUEafter the last variable when creating the data frame. What is happening now?stringsAsFactorscan also be set when reading text-based spreadsheets into R usingread.csv().
R
country_climate <- data.frame(
country = c("Canada", "Panama", "South Africa", "Australia"),
climate = c("cold", "hot", "temperate", "hot/temperate"),
temperature = c(10, 30, 18, "15"),
northern_hemisphere = c(TRUE, TRUE, FALSE, "FALSE"),
has_kangaroo = c(FALSE, FALSE, FALSE, 1)
)
R
country_climate <- data.frame(
country = c("Canada", "Panama", "South Africa", "Australia"),
climate = c("cold", "hot", "temperate", "hot/temperate"),
temperature = c(10, 30, 18, "15"),
northern_hemisphere = c(TRUE, TRUE, FALSE, "FALSE"),
has_kangaroo = c(FALSE, FALSE, FALSE, 1)
)
str(country_climate)
OUTPUT
'data.frame': 4 obs. of 5 variables:
$ country : chr "Canada" "Panama" "South Africa" "Australia"
$ climate : chr "cold" "hot" "temperate" "hot/temperate"
$ temperature : chr "10" "30" "18" "15"
$ northern_hemisphere: chr "TRUE" "TRUE" "FALSE" "FALSE"
$ has_kangaroo : num 0 0 0 1The automatic conversion of data type is sometimes a blessing, sometimes an annoyance. Be aware that it exists, learn the rules, and double check that data you import in R are of the correct type within your data frame. If not, use it to your advantage to detect mistakes that might have been introduced during data entry (a letter in a column that should only contain numbers for instance).
Learn more in this RStudio tutorial
Matrices
Before proceeding, now that we have learnt about data frames, let’s
recap package installation and learn about a new data type, namely the
matrix. Like a data.frame, a matrix has two
dimensions, rows and columns. But the major difference is that all cells
in a matrix must be of the same type: numeric,
character, logical, … In that respect,
matrices are closer to a vector than a
data.frame.
The default constructor for a matrix is matrix. It takes
a vector of values to populate the matrix and the number of row and/or
columns1.
The values are sorted along the columns, as illustrated below.
R
m <- matrix(1:9, ncol = 3, nrow = 3)
m
OUTPUT
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9R
## create the matrix
ip <- installed.packages()
head(ip)
## try also View(ip)
## number of package
nrow(ip)
## names of all installed packages
rownames(ip)
## type of information we have about each package
colnames(ip)
It is often useful to create large random data matrices as test data.
The exercise below asks you to create such a matrix with random data
drawn from a normal distribution of mean 0 and standard deviation 1,
which can be done with the rnorm() function.
R
set.seed(123)
m <- matrix(rnorm(3000), ncol = 3)
dim(m)
OUTPUT
[1] 1000 3R
head(m)
OUTPUT
[,1] [,2] [,3]
[1,] -0.56047565 -0.99579872 -0.5116037
[2,] -0.23017749 -1.03995504 0.2369379
[3,] 1.55870831 -0.01798024 -0.5415892
[4,] 0.07050839 -0.13217513 1.2192276
[5,] 0.12928774 -2.54934277 0.1741359
[6,] 1.71506499 1.04057346 -0.6152683Formatting Dates
One of the most common issues that new (and experienced!) R users have is converting date and time information into a variable that is appropriate and usable during analyses.
Note on dates in spreadsheet programs
Dates in spreadsheets are generally stored in a single column. While this seems the most natural way to record dates, it actually is not best practice. A spreadsheet application will display the dates in a seemingly correct way (to a human observer) but how it actually handles and stores the dates may be problematic. It is often much safer to store dates with YEAR, MONTH and DAY in separate columns or as YEAR and DAY-OF-YEAR in separate columns.
Spreadsheet programs such as LibreOffice, Microsoft Excel, OpenOffice, Gnumeric, … have different (and often incompatible) ways of encoding dates (even for the same program between versions and operating systems). Additionally, Excel can turn things that aren’t dates into dates (@Zeeberg:2004), for example names or identifiers like MAR1, DEC1, OCT4. So if you’re avoiding the date format overall, it’s easier to identify these issues.
The Dates as data section of the Data Carpentry lesson provides additional insights about pitfalls of dates with spreadsheets.
We are going to use the ymd() function from the package
lubridate (which belongs to the
tidyverse; learn more here). .
lubridate gets installed as part of the
tidyverse installation. When you load the
tidyverse
(library(tidyverse)), the core packages (the packages used
in most data analyses) get loaded.
lubridate however does not belong to the
core tidyverse, so you have to load it explicitly with
library(lubridate).
Start by loading the required package:
R
library("lubridate")
ymd() takes a vector representing year, month, and day,
and converts it to a Date vector. Date is a
class of data recognized by R as being a date and can be manipulated as
such. The argument that the function requires is flexible, but, as a
best practice, is a character vector formatted as “YYYY-MM-DD”.
Let’s create a date object and inspect the structure:
R
my_date <- ymd("2015-01-01")
str(my_date)
OUTPUT
Date[1:1], format: "2015-01-01"Now let’s paste the year, month, and day separately - we get the same result:
R
# sep indicates the character to use to separate each component
my_date <- ymd(paste("2015", "1", "1", sep = "-"))
str(my_date)
OUTPUT
Date[1:1], format: "2015-01-01"Let’s now familiarise ourselves with a typical date manipulation
pipeline. The small data below has stored dates in different
year, month and day columns.
R
x <- data.frame(year = c(1996, 1992, 1987, 1986, 2000, 1990, 2002, 1994, 1997, 1985),
month = c(2, 3, 3, 10, 1, 8, 3, 4, 5, 5),
day = c(24, 8, 1, 5, 8, 17, 13, 10, 11, 24),
value = c(4, 5, 1, 9, 3, 8, 10, 2, 6, 7))
x
OUTPUT
year month day value
1 1996 2 24 4
2 1992 3 8 5
3 1987 3 1 1
4 1986 10 5 9
5 2000 1 8 3
6 1990 8 17 8
7 2002 3 13 10
8 1994 4 10 2
9 1997 5 11 6
10 1985 5 24 7Now we apply this function to the x dataset. We first
create a character vector from the year,
month, and day columns of x using
paste():
R
paste(x$year, x$month, x$day, sep = "-")
OUTPUT
[1] "1996-2-24" "1992-3-8" "1987-3-1" "1986-10-5" "2000-1-8" "1990-8-17"
[7] "2002-3-13" "1994-4-10" "1997-5-11" "1985-5-24"This character vector can be used as the argument for
ymd():
R
ymd(paste(x$year, x$month, x$day, sep = "-"))
OUTPUT
[1] "1996-02-24" "1992-03-08" "1987-03-01" "1986-10-05" "2000-01-08"
[6] "1990-08-17" "2002-03-13" "1994-04-10" "1997-05-11" "1985-05-24"The resulting Date vector can be added to x
as a new column called date:
R
x$date <- ymd(paste(x$year, x$month, x$day, sep = "-"))
str(x) # notice the new column, with 'date' as the class
OUTPUT
'data.frame': 10 obs. of 5 variables:
$ year : num 1996 1992 1987 1986 2000 ...
$ month: num 2 3 3 10 1 8 3 4 5 5
$ day : num 24 8 1 5 8 17 13 10 11 24
$ value: num 4 5 1 9 3 8 10 2 6 7
$ date : Date, format: "1996-02-24" "1992-03-08" ...Let’s make sure everything worked correctly. One way to inspect the
new column is to use summary():
R
summary(x$date)
OUTPUT
Min. 1st Qu. Median Mean 3rd Qu. Max.
"1985-05-24" "1988-01-11" "1993-03-24" "1993-03-18" "1997-01-20" "2002-03-13" Note that ymd() expects to have the year, month and day,
in that order. If you have for instance day, month and year, you would
need dmy().
R
dmy(paste(x$day, x$month, x$year, sep = "-"))
OUTPUT
[1] "1996-02-24" "1992-03-08" "1987-03-01" "1986-10-05" "2000-01-08"
[6] "1990-08-17" "2002-03-13" "1994-04-10" "1997-05-11" "1985-05-24"lubdridate has many functions to address all date
variations.
Summary of R objects
So far, we have seen several types of R object varying in the number of dimensions and whether they could store a single or multiple data types:
-
vector: one dimension (they have a length), single type of data. -
matrix: two dimensions, single type of data. -
data.frame: two dimensions, one type per column.
Lists
A data type that we haven’t seen yet, but that is useful to know, and follows from the summary that we have just seen are lists:
-
list: one dimension, every item can be of a different data type.
Below, let’s create a list containing a vector of numbers, characters, a matrix, a dataframe and another list:
R
l <- list(1:10, ## numeric
letters, ## character
installed.packages(), ## a matrix
cars, ## a data.frame
list(1, 2, 3)) ## a list
length(l)
OUTPUT
[1] 5R
str(l)
OUTPUT
List of 5
$ : int [1:10] 1 2 3 4 5 6 7 8 9 10
$ : chr [1:26] "a" "b" "c" "d" ...
$ : chr [1:169, 1:16] "abind" "askpass" "backports" "base64enc" ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:169] "abind" "askpass" "backports" "base64enc" ...
.. ..$ : chr [1:16] "Package" "LibPath" "Version" "Priority" ...
$ :'data.frame': 50 obs. of 2 variables:
..$ speed: num [1:50] 4 4 7 7 8 9 10 10 10 11 ...
..$ dist : num [1:50] 2 10 4 22 16 10 18 26 34 17 ...
$ :List of 3
..$ : num 1
..$ : num 2
..$ : num 3List subsetting is done using [] to subset a new
sub-list or [[]] to extract a single element of that list
(using indices or names, if the list is named).
R
l[[1]] ## first element
OUTPUT
[1] 1 2 3 4 5 6 7 8 9 10R
l[1:2] ## a list of length 2
OUTPUT
[[1]]
[1] 1 2 3 4 5 6 7 8 9 10
[[2]]
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"R
l[1] ## a list of length 1
OUTPUT
[[1]]
[1] 1 2 3 4 5 6 7 8 9 10Exporting and saving tabular data
We have seen how to read a text-based spreadsheet into R using the
read.table family of functions. To export a
data.frame to a text-based spreadsheet, we can use the
write.table set of functions (write.csv,
write.delim, …). They all take the variable to be exported
and the file to be exported to. For example, to export the
rna data to the my_rna.csv file in the
data_output directory, we would execute:
R
write.csv(rna, file = "data_output/my_rna.csv")
This new csv file can now be shared with other collaborators who
aren’t familiar with R. Note that even though there are commas in some
of the fields in the data.frame (see for example the
“product” column), R will by default surround each field with quotes,
and thus we will be able to read it back into R correctly, despite also
using commas as column separators.
Either the number of rows or columns are enough, as the other one can be deduced from the length of the values. Try out what happens if the values and number of rows/columns don’t add up.↩︎