Learning Objectives

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 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:

download.file("http://datacarpentry.github.io/dc_zurich/data/portal_data_joined.csv",
              "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 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/portal_data_joined.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)
#>   record_id month day year plot_id species_id sex hindfoot_length weight
#> 1         1     7  16 1977       2         NL   M              32     NA
#> 2         2     7  16 1977       3         NL   M              33     NA
#> 3         3     7  16 1977       2         DM   F              37     NA
#> 4         4     7  16 1977       7         DM   M              36     NA
#> 5         5     7  16 1977       3         DM   M              35     NA
#> 6         6     7  16 1977       1         PF   M              14     NA
#>         genus  species   taxa                plot_type
#> 1     Neotoma albigula Rodent                  Control
#> 2     Neotoma albigula Rodent Long-term Krat Exclosure
#> 3   Dipodomys merriami Rodent                  Control
#> 4   Dipodomys merriami Rodent         Rodent Exclosure
#> 5   Dipodomys merriami Rodent Long-term Krat Exclosure
#> 6 Perognathus   flavus Rodent        Spectab exclosure

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

str(surveys)

Challenge

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

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 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
#> 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 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:

## Challenge

## 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       2
barplot(table(exprmt))