Starting with data
Data Carpentry contributors
Learning Objectives
- load external data (CSV files) in memory using the survey table (
Ecoli_metadata.csv) as an example- explore the structure and the content of the data in R
- understand what are factors and how to manipulate them
Looking at Metadata
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 downlaoded by clicking on this link
- First, make sure you are in the correct working directory by typing
getwd(). - Second, create a new directory within this working directory called
data - Third, move the downloaded file into this directory
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('files/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('files/Ecoli_metadata.csv'))## sample generation clade strain cit run genome_size
## 1 REL606 0 <NA> REL606 unknown 4.62
## 2 REL1166A 2000 unknown REL606 unknown SRR098028 4.63
## 3 ZDB409 5000 unknown REL606 unknown SRR098281 4.60
## 4 ZDB429 10000 UC REL606 unknown SRR098282 4.59
## 5 ZDB446 15000 UC REL606 unknown SRR098283 4.66
## 6 ZDB458 20000 (C1,C2) REL606 unknown SRR098284 4.63
## 7 ZDB464* 20000 (C1,C2) REL606 unknown SRR098285 4.62
## 8 ZDB467 20000 (C1,C2) REL606 unknown SRR098286 4.61
## 9 ZDB477 25000 C1 REL606 unknown SRR098287 4.65
## 10 ZDB483 25000 C3 REL606 unknown SRR098288 4.59
## 11 ZDB16 30000 C1 REL606 unknown SRR098031 4.61
## 12 ZDB357 30000 C2 REL606 unknown SRR098280 4.62
## 13 ZDB199* 31500 C1 REL606 minus SRR098044 4.62
## 14 ZDB200 31500 C2 REL606 minus SRR098279 4.63
## 15 ZDB564 31500 Cit+ REL606 plus SRR098289 4.74
## 16 ZDB30* 32000 C3 REL606 minus SRR098032 4.61
## 17 ZDB172 32000 Cit+ REL606 plus SRR098042 4.77
## 18 ZDB158 32500 C2 REL606 minus SRR098041 4.63
## 19 ZDB143 32500 Cit+ REL606 plus SRR098040 4.79
## 20 CZB199 33000 C1 REL606 minus SRR098027 4.59
## 21 CZB152 33000 Cit+ REL606 plus SRR097977 4.80
## 22 CZB154 33000 Cit+ REL606 plus SRR098026 4.76
## 23 ZDB83 34000 Cit+ REL606 minus SRR098034 4.60
## 24 ZDB87 34000 C2 REL606 plus SRR098035 4.75
## 25 ZDB96 36000 Cit+ REL606 plus SRR098036 4.74
## 26 ZDB99 36000 C1 REL606 minus SRR098037 4.61
## 27 ZDB107 38000 Cit+ REL606 plus SRR098038 4.79
## 28 ZDB111 38000 C2 REL606 minus SRR098039 4.62
## 29 REL10979 40000 Cit+ REL606 plus SRR098029 4.78
## 30 REL10988 40000 C2 REL606 minus SRR098030 4.62Wow… 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)## sample generation clade strain cit run genome_size
## 1 REL606 0 <NA> REL606 unknown 4.62
## 2 REL1166A 2000 unknown REL606 unknown SRR098028 4.63
## 3 ZDB409 5000 unknown REL606 unknown SRR098281 4.60
## 4 ZDB429 10000 UC REL606 unknown SRR098282 4.59
## 5 ZDB446 15000 UC REL606 unknown SRR098283 4.66
## 6 ZDB458 20000 (C1,C2) REL606 unknown SRR098284 4.63We’ve just done two very useful things.
We’ve read our data in to R, so now we can work with it in R
We’ve created a data frame (with the read.csv command) the standard way R works with data.
What are data frames?
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': 30 obs. of 7 variables:
## $ sample : Factor w/ 30 levels "CZB152","CZB154",..: 7 6 18 19 20 21 22 23 24 25 ...
## $ generation : int 0 2000 5000 10000 15000 20000 20000 20000 25000 25000 ...
## $ clade : Factor w/ 7 levels "(C1,C2)","C1",..: NA 7 7 6 6 1 1 1 2 4 ...
## $ strain : Factor w/ 1 level "REL606": 1 1 1 1 1 1 1 1 1 1 ...
## $ cit : Factor w/ 3 levels "minus","plus",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ run : Factor w/ 30 levels "","SRR097977",..: 1 5 22 23 24 25 26 27 28 29 ...
## $ genome_size: num 4.62 4.63 4.6 4.59 4.66 4.63 4.62 4.61 4.65 4.59 ...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.
- Size:
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 columns
- Content:
head()- shows the first 6 rowstail()- shows the last 6 rows
- Names:
names()- returns the column names (synonym ofcolnames()fordata.frameobjects)
rownames()- returns the row names- Summary:
str()- structure of the object and information about the class, length and content of each columnsummary()- summary statistics for each column
Note: most of these functions are “generic”, they can be used on other types of objects besides data.frame.
Challenge
Based on the give table of functions to asses data structure, can you answer the following questions?
- What is the class of the object
metadata? - How many rows and how many columns are in this object?
- How many citrate+ mutants have been recorded in this population?
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)## 'data.frame': 30 obs. of 7 variables:
## $ sample : Factor w/ 30 levels "CZB152","CZB154",..: 7 6 18 19 20 21 22 23 24 25 ...
## $ generation : int 0 2000 5000 10000 15000 20000 20000 20000 25000 25000 ...
## $ clade : Factor w/ 7 levels "(C1,C2)","C1",..: NA 7 7 6 6 1 1 1 2 4 ...
## $ strain : Factor w/ 1 level "REL606": 1 1 1 1 1 1 1 1 1 1 ...
## $ cit : Factor w/ 3 levels "minus","plus",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ run : Factor w/ 30 levels "","SRR097977",..: 1 5 22 23 24 25 26 27 28 29 ...
## $ genome_size: num 4.62 4.63 4.6 4.59 4.66 4.63 4.62 4.61 4.65 4.59 ...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).
Converting factors
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:
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:
## Question: How can you recreate this plot but by having "control"
## being listed last instead of first?
exprmt <- factor(c("treat1", "treat2", "treat1", "treat3", "treat1", "control",
"treat1", "treat2", "treat3"))
table(exprmt)## exprmt
## control treat1 treat2 treat3
## 1 4 2 2barplot(table(exprmt))
exprmt <- factor(exprmt, levels=c("treat1", "treat2", "treat3", "control"))
barplot(table(exprmt))
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