Starting with data

Presentation of 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 comma separated value (CSV) file. Each row holds information for a single animal, and the columns represent:

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 the content of the CSV file as a data.frame.

To download the data into the data/ subdirectory, do:

download.file("https://ndownloader.figshare.com/files/2292169",
              "data/portal_data_joined.csv")

You are now ready to load the data:

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

This statement doesn’t produce any output because, as you might recall, 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():

head(surveys)

#>   record_id month day year plot_id species_id sex hindfoot_length weight
#> 1         1     7  16 1977       2         NL   M              32     NA
#> 2        72     8  19 1977       2         NL   M              31     NA
#> 3       224     9  13 1977       2         NL                  NA     NA
#> 4       266    10  16 1977       2         NL                  NA     NA
#> 5       349    11  12 1977       2         NL                  NA     NA
#> 6       363    11  12 1977       2         NL                  NA     NA
#>     genus  species   taxa plot_type
#> 1 Neotoma albigula Rodent   Control
#> 2 Neotoma albigula Rodent   Control
#> 3 Neotoma albigula Rodent   Control
#> 4 Neotoma albigula Rodent   Control
#> 5 Neotoma albigula Rodent   Control
#> 6 Neotoma albigula Rodent   Control

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 the column are vectors, they all contain the same type of data. We can see this when inspecting the __str__ucture of a data.frame with the function str():

str(surveys)

Factors

Factors are used to represent categorical data. Factors can be ordered or unordered, and understanding them is necessary 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 of 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 a particular type of analysis. Additionally, specifying the order of the levels allows for level comparison:

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

#> Error in Summary.factor(structure(c(1L, 3L, 2L, 3L, 1L, 2L, 3L), .Label = c("low", : 'min' not meaningful for factors

food <- factor(food, levels=c("low", "medium", "high"), ordered=TRUE)
levels(food)
min(food) ## works!

In R’s memory, these factors are represented by integers (1, 2, 3), but are more informative than integers 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 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 species in our example data set).

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.