| layout | title | author | minutes |
|---|---|---|---|
topic |
Starting with data |
Data Carpentry contributors |
20 |
- load external data (CSV files) in memory using the survey table (
surveys.csv) as an example- explore the structure and the content of the data in R
- understand what are factors and how to manipulate them
We are studying the species and weight of animals caught in plots in our study
area. The dataset is stored as a csv file: each row holds information for a
single animal, and the columns represent:
| Column | Description |
|---|---|
| record_id | Unique id for the observation |
| month | month of observation |
| day | day of observation |
| year | year of observation |
| plot_id | ID of a particular plot |
| species_id | 2-letter code |
| sex | sex of animal ("M", "F") |
| hindfoot_length | length of the hindfoot in mm |
| weight | weight of the animal in grams |
| genus | genus of animal |
| species | species of animal |
| taxa | e.g. Rodent, Reptile, Bird, Rabbit |
| plot_type | type of plot |
We are going to use the R function download.file() to download the CSV file
that contains the survey data from figshare, and we will use read.csv() to
load into memory (as a data.frame) the content of the CSV file:
- First, make sure you are in the correct working directory by typing
getwd(). - Second, create a new directory within this working directory called
data. You can do this by clicking on the new folder icon in RStudio under the file tab, or by typingdir.create("data")at the terminal. - Third, download the data:
>getwd()
>dir.create("data")
>download.file("http://files.figshare.com/2236372/combined.csv", "data/my_survey_data.csv")
You should be able to see the my_survey_data.csv in the data folder are now ready to load the data:
>surveys <- read.csv('data/my_survey_data.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: surveys
Alternatively, wrapping an assignment in parentheses will perform the assignment and display it at the same time.
>(surveys <- read.csv('data/my_survey_data.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(surveys)
Let's now check the structure of this data.frame in more details with the
function str():
>str(surveys)
Some times data are in a format different from csv
Based on the output of str(surveys), can you answer the following questions?
- What is the class of the object
surveys? - How many rows and how many columns are in this object?
- How many species have been recorded during these surveys?
As you can see, the columns species and sex 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 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.
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, if you have a factor with 2 levels:
>sex <- factor(c("male", "female", "female", "male"))
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 check this by using the function levels(), and check the
number of levels using nlevels():
>levels(sex)
>nlevels(sex)
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 us to compare levels:
>food <- factor(c("low", "high", "medium", "high", "low", "medium", "high"))
>levels(food)
>
>food <- factor(food, levels=c("low", "medium", "high"))
>levels(food)
>
>min(food) ## doesn't work
>
>food <- factor(food, levels=c("low", "medium", "high"), ordered=TRUE)
>levels(food)
>min(food) ## 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", "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:
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.
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))
How can you recreate this plot but by having "control" being listed last instead of first?