Strangled by factors
I am exaggerating, but sometimes stringsAsFactors is almost this deadly. I work with genomic data, and a common quest in my job is to identify interesting features (in most cases, genes) from a pool of 25,000+.
To climb onto giants’ shoulders, genes that were repoted to be important in a certain process, say cell fate determination, are invaluable, and these genes often come in the supplementary tables of the original papers.
Reading the supplments into R and using those for analysis look dead simple, but this is when a wrong setting of stringsAsFactors can bite one hard.
I’ll demonstrate this with everyone’s favorite example set: mtcars.
Imagine it was reported that Honda Civic and Volvo 142E are particularly safe in a crash scene, and we want to fetch their information from mtcars for some detailed analysis. We notice he paper that reported Honda Civic and Volvo 142E provide a csv file containing the models that found like this:
Now, I could not wait to find what are the specs of these models! Let’s load the table and do some analysis!
candidate_models <- read.csv(file = "example.csv", header = TRUE) print(candidate_models) ## Model
## 1 Honda Civic
## 2 Volvo 142E
It looks great, so we can finally get the data we want.
print(mtcars[candidate_models$Model, ]) ## mpg cyl disp hp drat wt qsec vs am gear carb
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Oh no. I get two completely different rows though I even print the candidates after reading the csv file.
This weird phenomenon results from the way factor works: A factor is categorical and coded by integers under the table, but displayed as character strings ( levels). So, when I thought I was filtering rows with Honda Civic and Volvo 142E, I was actually doing mtcars[c(1,2), ].
head(mtcars) ## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
In this example, it might be so obvious that you start wondering why people even see this as a problem, but when you are facing several dozens of candidates among thousands of features, this kind of errors can go unnoticed for really long.
To prevent this from happening, it is good to make it a habit to set stringsAsFactors = FALSE. To inspect the data you load, str() often provides more information than print() or head() (On the other hand, if you work with a tibble, print() shows variable types by default.)
str(candidate_models) ## 'data.frame': 2 obs. of 1 variable:
## $ Model: chr "Honda Civic" "Volvo 142E"
Originally published at https://www.yccbio.net on March 28, 2019.
