R: use of factor

Factors vs character vectors when doing stats: In terms of doing statistics, there's no difference in how R treats factors and character vectors. In fact, its often easier to leave factor variables as character vectors.

If you do a regression or ANOVA with lm() with a character vector as a categorical variable you'll get normal model output but with the message:

Warning message:
In model.matrix.default(mt, mf, contrasts) :
  variable 'character_x' converted to a factor

Factors vs character vectors when manipulating dataframes: When manipulating dataframes, however, character vectors and factors are treated very differently. Some information on the annoyances of R & factors can be found on the Quantum Forest blog, R pitfall #3: friggin’ factors.

Its useful to use stringsAsFactors = FALSE when reading data in from a .csv or .txt using read.table or read.csv. As noted in another reply you have to make sure that everything in your character vector is consistent, or else every typo will be designated as a different factor. You can use the function gsub() to fix typos.

Here is a worked example showing how lm() gives you the same results with a character vector and a factor.

A random independent variable:

continuous_x <- rnorm(10,10,3)

A random categorical variable as a character vector:

character_x  <- (rep(c("dog","cat"),5))

Convert the character vector to a factor variable. factor_x <- as.factor(character_x)

Give the two categories random values:

character_x_value <- ifelse(character_x == "dog", 5*rnorm(1,0,1), rnorm(1,0,2))

Create a random relationship between the indepdent variables and a depen

type will be converted from a character to a factor. The main difference is that factors have predefined levels. Thus their value can only be one of those levels or NA. Whereas characters can be anything.