Learning Objectives
- Load external tabular data from a .csv file into R.
- Describe what an R data frame is.
- Summarize the contents of a data frame in R.
- Manipulate categorical data in R using factors.
We are studying a population of Escherichia coli (designated Ara-3), which were propagated for more than 40,000 generations in a glucose-limited minimal medium. This medium was supplemented with citrate which E. coli cannot metabolize in the aerobic conditions of the experiment. Sequencing of the populations at regular time points reveals that spontaneous citrate-using mutants (Cit+) appeared at around 31,000 generations. This metadata describes information on the Ara-3 clones and the columns represent:
Column | Description |
---|---|
sample | clone name |
generation | generation when sample frozen |
clade | based on parsimony-based tree |
strain | ancestral strain |
cit | citrate-using mutant status |
run | Sequence read archive sample ID |
genome_size | size in Mbp (made up data for this lesson) |
The metadata file required for this lesson can be downloaded directly here.
getwd()
- for Azure the data uploaded into the notebook will be in the current directory but get familar with this command for R-shell setwd(path/of/directories)
is how to change the data directory when using R else where.Ecoli_metadata.csv
file into the notebook.You are now ready to load the data. We are going to use the R function read.csv()
to load the data file into memory (as a data.frame
):
metadata <- read.csv('Ecoli_metadata.csv')
This statement doesn’t produce any output because assignment doesn’t display
anything. If we want to check that our data has been loaded, we can print the
variable’s value: metadata
Alternatively, wrapping an assignment in parentheses will perform the assignment and display it at the same time.
(metadata <- read.csv('Ecoli_metadata.csv'))
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()
:
head(metadata)
We’ve just done two very useful things.
data.frame
is the de facto data structure for most tabular data and what we
use for statistics and plotting.
A data.frame
is a collection of vectors of identical lengths. Each vector
represents a column, and each vector can be of a different data type (e.g.,
characters, integers, factors). The str()
function is useful to inspect the
data types of the columns.
A data.frame
can be created by the functions read.csv()
or read.table()
, in
other words, when importing spreadsheets from your hard drive (or the web).
By default, data.frame
converts (= coerces) columns that contain characters
(i.e., text) into the factor
data type. Depending on what you want to do with
the data, you may want to keep these columns as character
. To do so,
read.csv()
and read.table()
have an argument called stringsAsFactors
which
can be set to FALSE
:
Let’s now check the __str__ucture of this data.frame
in more details with the
function str()
:
str(metadata)
data.frame
objectsWe 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.
dim()
- returns a vector with the number of rows in the first element, and
the number of columns as the second element (the __dim__ensions of the object)nrow()
- returns the number of rowsncol()
- returns the number of columnshead()
- shows the first 6 rowstail()
- shows the last 6 rowsnames()
- returns the column names (synonym of colnames()
for data.frame
objects)rownames()
- returns the row namesstr()
- structure of the object and information about the class, length and
content of each columnsummary()
- summary statistics for each columnNote: most of these functions are “generic”, they can be used on other types of
objects besides data.frame
.
Based on the give table of functions to asses data structure, can you answer the following questions?
metadata
?As you can see, many of the columns in our data frame are of a special class called
factor
. Before we learn more about the data.frame
class, we are going to
talk about factors. They are very useful but not necessarily intuitive, and
therefore require some attention.
### Factors
Factors are used to represent categorical data. Factors can be ordered or unordered and are an important class for statistical analysis and for plotting.
Factors are stored as integers, and have labels associated with these unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood, and you need to be careful when treating them like strings.
In the data frame we just imported, let’s do
str(metadata)
We can see the names of the multiple columns. And, we see that
some say things like Factor w/ 30 levels
When we read in a file, any column that contains text is automatically assumed to be a factor. Once created, factors can only contain a pre-defined set values, known as levels. By default, R always sorts levels in alphabetical order.
For instance, we see that cit
is a Factor w/ 3 levels, minus
, plus
and unknown
.
<!–
You can check this by using the function levels()
, and check the
number of levels using nlevels()
:
levels(citrate)
nlevels(citrate)
Sometimes, 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”) or it is required by particular type of analysis. Additionally, specifying the order of the levels allows to compare levels:
expression <- factor(c("low", "high", "medium", "high", "low", "medium", "high"))
levels(expression)
expression <- factor(expression, levels=c("low", "medium", "high"))
levels(expression)
min(expression) ## doesn't work
expression <- factor(expression, levels=c("low", "medium", "high"), ordered=TRUE)
levels(expression)
min(expression) ## works!
In R’s memory, these factors are represented by numbers (1, 2, 3). They are
better than using simple integer labels because factors are self describing:
"low"
, "medium"
, and "high"
” is more descriptive than 1
, 2
, 3
. Which
is low? You wouldn’t be able to tell with just integer data. Factors have this
information built in. It is particularly helpful when there are many levels
(like the species in our example data set).
If you need to convert a factor to a character vector, simply use
as.character(x)
.
Converting a factor to a numeric vector is however a little trickier, and you have to go via a character vector. Compare:
```{r, purl=TRUE, eval=FALSE} f <- factor(c(1, 5, 10, 2)) as.numeric(f) ## wrong! and there is no warning… as.numeric(as.character(f)) ## works… as.numeric(levels(f))[f] ## The recommended way.
### Challenge
The function `table()` tabulates observations and can be used to create
bar plots quickly. For instance:
exprmt <- factor(c(“treat1”, “treat2”, “treat1”, “treat3”, “treat1”, “control”, “treat1”, “treat2”, “treat3”)) table(exprmt) barplot(table(exprmt))
exprmt <- factor(exprmt, levels=c(“treat1”, “treat2”, “treat3”, “control”)) barplot(table(exprmt)) ``` —>