Learning Objectives

Importing the Survey Data

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 survey_id , month, day, year, plot, species (a 2 letter code, see the species.csv file for correspondance), sex (“M” for males and “F” for females), wgt (the weight in grams).

The first few rows of the survey dataset look like this:


To load our survey data, we need to locate the surveys.csv file. We will use read.csv() to load into memory (as a data.frame) the content of the CSV file.

surveys <- read.csv('data/surveys.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

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():

##   record_id month day year plot species sex wgt
## 1         1     7  16 1977    2      NL   M  NA
## 2         2     7  16 1977    3      NL   M  NA
## 3         3     7  16 1977    2      DM   F  NA
## 4         4     7  16 1977    7      DM   M  NA
## 5         5     7  16 1977    3      DM   M  NA
## 6         6     7  16 1977    1      PF   M  NA

Let’s now check the structure of this data.frame in more details with the function str():



Read in another CSV file, species.csv, and store it in a variable.


Based on the output of str(surveys), can you answer the following questions?

  • What is the class of the object surveys?
  • What is the class of the column species?

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. We will talk about data frames in more detail in the next lesson.


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():


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:

food <- factor(c("low", "high", "medium", "high", "low", "medium", "high"))
food <- factor(food, levels=c("low", "medium", "high"))
min(food) ## doesn't work
## Error: min not meaningful for factors
food <- factor(food, levels=c("low", "medium", "high"), ordered=TRUE)
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", 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.


R has a built-in function called sort(), for sorting data.

We can make a vector of factors and then sort it like this:

sizes = factor(c("large","small","small","medium","large","large"));
## [1] large  large  large  medium small  small 
## Levels: large medium small

Unfortunately it looks like our vector has been sorted in the order large, medium, small. How could we use an ordered factor make sure it sorts in the order small, medium, large?

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