Factors

Overview

Teaching: 25 min
Exercises: 20 min

Questions

• How do I work with factors in R?

Objectives

• Describe what a factor is.

• Convert between strings and factors.

• Reorder and rename factors.

Factors in R

When we did str(tumor) we saw that most of the columns are numeric. The column Grp, however, isof the class character. This column contains categorical data, that is, it can only take on a limited number of values. To see this, use the unique() function.

unique(tumor$Grp)

[1] "1.CTR" "2.D"   "3.R"   "4.D+R"

As a reminder:

Cells from a human glioma cell line were implanted in the flank of n=37 nude mice and a subcutaneous tumor (xenograft) was allowed to grow. When a tumor grew to around 40-60 $mm^3$, the animal was assigned to one of 4 experimental groups (day 0):
1) Control (CTR, n=8);
2) Drug only (D, n=10);
3) Radiation only (R, n=10); and
4) Drug + Radiation (D+R, n=9).
The main outcome in xenograft experiments is the size (volume) of the tumor over time. The study’s two main scientific aims were to assess whether:
a. The drug has an effect on tumor growth.
b. The administration of the drug before radiation enhances the effect of the latter on tumor growth.

R has a special class for working with categorical data, called factor. Factors are very useful and actually contribute to making R particularly well suited to working with data. So we are going to spend a little time introducing them.

Once created, factors can only contain a pre-defined set of values, known as levels. Factors are stored as integers associated with labels and they can be ordered or unordered. While factors look (and often behave) like character vectors, they are actually treated as integer vectors by R. So you need to be very careful when treating them as strings.

When importing a data frame with read_csv(), the columns that contain text are not automatically coerced (=converted) into the factor data type, but once we have loaded the data we can do the conversion using the factor() function:

tumor$Grp <- factor(tumor$Grp)

We can see that the conversion has worked by using the summary() function again. This produces a table with the counts for each factor level:

summary(tumor$Grp)

1.CTR   2.D   3.R 4.D+R 
   97   171   161   145 

By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:

direction <- factor(c("right", "left", "left", "right"))

R will assign 1 to the level "left" and 2 to the level "right" (because l comes before r, even though the first element in this vector is "right"). You can see this by using the function levels() and you can find the number of levels using nlevels():

levels(direction)

[1] "left"  "right"

nlevels(direction)

[1] 2

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”), it improves your visualization, or it is required by a particular type of analysis. Here, one way to reorder our levels in the direction vector would be:

direction # current order

[1] right left  left  right
Levels: left right

direction <- factor(direction, levels = c("right", "left"))
direction # after re-ordering

[1] right left  left  right
Levels: right left

In R’s memory, these factors are represented by integers (1, 2), but are more informative than integers because factors are self describing: "left", "right" is more descriptive than 1, 2. Which one is “right”? 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 Grp names in our example dataset).

Exercise

1. Change the column Grp in the tumor data frame into a factor.
2. Using the functions you learned before, can you find out:
   • How many were in group D?
   • How many levels there are?

Solution

tumor$Grp <- factor(tumor$Grp)
summary(tumor$Grp)

1.CTR   2.D   3.R 4.D+R 
   97   171   161   145 

nlevels(tumor$Grp)

[1] 4

Converting factors

If you need to convert a factor to a character vector, you use as.character(x).

as.character(direction)

[1] "right" "left"  "left"  "right"

In some cases, you may have to convert factors where the levels appear as numbers (such as concentration levels or years) to a numeric vector. For instance, in one part of your analysis the years might need to be encoded as factors (e.g., comparing average weights across years) but in another part of your analysis they may need to be stored as numeric values (e.g., doing math operations on the years). This conversion from factor to numeric is a little trickier. The as.numeric() function returns the index values of the factor, not its levels, so it will result in an entirely new (and unwanted in this case) set of numbers. One method to avoid this is to convert factors to characters, and then to numbers.

Another method is to use the levels() function. Compare:

year_fct <- factor(c(1990, 1983, 1977, 1998, 1990))
as.numeric(year_fct)               # Wrong! And there is no warning...

[1] 3 2 1 4 3

as.numeric(as.character(year_fct)) # Works...

[1] 1990 1983 1977 1998 1990

as.numeric(levels(year_fct))[year_fct]    # The recommended way.

[1] 1990 1983 1977 1998 1990

Notice that in the levels() approach, three important steps occur:

• We obtain all the factor levels using levels(year_fct)
• We convert these levels to numeric values using as.numeric(levels(year_fct))
• We then access these numeric values using the underlying integers of the vector year_fct inside the square brackets

Renaming factors

When your data is stored as a factor, you can use the plot() function to get a quick glance at the number of observations represented by each factor level. Let’s look at the number in each group.

## bar plot of the numbers in each group:
plot(tumor$Grp)

Exercise

1. We have seen how data frames are created when using read_csv(), but they can also be created by hand with the data.frame() function. There are a few mistakes in this hand-crafted data.frame. Can you spot and fix them? Don’t hesitate to experiment!

    animal_data <- data.frame(
              animal = c(dog, cat, sea cucumber, sea urchin),
              feel = c("furry", "squishy", "spiny"),
              weight = c(45, 8 1.1, 0.8)
              )
   

2. Can you predict the class for each of the columns in the following example? Check your guesses using str(country_climate):

   • Are they what you expected? Why? Why not?
   • What would you need to change to ensure that each column had the accurate data type?

    country_climate <- data.frame(
           country = c("Canada", "Panama", "South Africa", "Australia"),
           climate = c("cold", "hot", "temperate", "hot/temperate"),
           temperature = c(10, 30, 18, "15"),
           northern_hemisphere = c(TRUE, TRUE, FALSE, "FALSE"),
           has_kangaroo = c(FALSE, FALSE, FALSE, 1)
           )
   

Solution

1. • missing quotations around the names of the animals
     * missing one entry in the feel column (probably for one of the furry animals)
     * missing one comma in the weight column
2. country, climate, temperature, and northern_hemisphere are characters; has_kangaroo is numeric
   • using factor() one could replace character columns with factors columns
   • removing the quotes in temperature and northern_hemisphere and replacing 1 by TRUE in the has_kangaroo column would give what was probably intended

The automatic conversion of data type is sometimes a blessing, sometimes an annoyance. Be aware that it exists, learn the rules, and double check that data you import in R are of the correct type within your data frame. If not, use it to your advantage to detect mistakes that might have been introduced during data entry (for instance, a letter in a column that should only contain numbers).

Learn more in this RStudio tutorial

Key Points