CODEWITHSUNDEEP
rtypes
yCalleecharan
yCalleecharan

Reputation: 4704

R: use of factor

I have some data:

transaction <- c(1,2,3);
date <- c("2010-01-31","2010-02-28","2010-03-31");
type <- c("debit", "debit", "credit");
amount <- c(-500, -1000.97, 12500.81);
oldbalance <- c(5000, 4500, 17000.81)
evolution <- data.frame(transaction, date, type, amount, oldbalance, row.names=transaction,  stringsAsFactors=FALSE);
evolution$date <- as.Date(evolution$date, "%Y-%m-%d");
evolution <- transform(evolution, newbalance = oldbalance + amount);
evolution

If I enter the command:

type <- factor(type)

where type is nominal (categorical) variable,then what difference does it make to my data?

Thanks

Upvotes: 16

Views: 34457

Answers (3)

N Brouwer
N Brouwer

Reputation: 5078

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 dependent variable

continuous_y <- continuous_x*10*rnorm(1,0) + character_x_value

Compare the output of a linear model with the factor variable and the character vector. Note the warning that is given with the character vector.

summary(lm(continuous_y ~ continuous_x + factor_x))
summary(lm(continuous_y ~ continuous_x + character_x))

Upvotes: 17

Sean
Sean

Reputation: 3955

It all depends on what question you are asking of the data!

type.c <- c("debit", "debit", "credit")
type.f <- factor(type.c)

Here type.c is just a list of character strings, whereas type.f is a list of factors (is this correct? or is it an array?)

storage.mode(type.c)
# [1] "character"
storage.mode(type.f)
# [1] "integer"

when a factor variable is created it looks through all of the values that have been given and creates the "levels"... have a peek at:

 levels(type.f)
 # [1] "credit" "debit"

Then instead of storing the character strings "debit" "credit" "mis-spelt debbit" etc... it just stores the integer along with the levels... have a look at:

str(type.f)
# Factor w/ 2 levels "credit","debit": 2 2 1

i.e. in type.c it says c("debit", "debit",",credit") and levels(type.f) says "credit" "debit", you see that str(type.f) starts listing the first few values as they are stored, i.e. 2 2 1...

If you mis-type "debbit" and add it to the list, and then later do a levels(type.f) you'll see it as a new level... otherwise you could do table(type.c).

When there are only three elements in the list, it doesn't make much difference to the storage volume, but as your list gets longer, "credit" (6 characters) and "debit" (5 characters) will start take up much more storage than the 4 bytes it takes to hold an integer (plus the couple of bytes). A little experiment shows that for a randomly selected set of type.c, the threshold on object.size(type.c)>object.size(type.f) is about 96 elements.

dc <- c("debit", "credit")
N <- 300

# lets store the calculations as a matrix
# col1 = n
# col2 = sizeof(character)
# col3 = sizeof(factors)
res <- matrix(ncol=3, nrow=N)

for (i in c(1:N)) {
  type.c <- sample(dc, i, replace=T)
  type.f <- factor(type.c)
  res[i, 1] <- i
  res[i, 2] <- object.size(type.c)
  res[i, 3] <- object.size(type.f)
  cat('N=', i, '  object.size(type.c)=',object.size(type.c), '  object.size(type.f)=',object.size(type.f), '\n')
}
plot(res[,1], res[,2], col='blue', type='l', xlab='Number of items in type.x', ylab='bytes of storage')
lines(res[,1], res[,3], col='red')
mtext('blue for character; red for factor')

cat('Threshold at:', min(which(res[,2]>res[,3])), '\n')

Apologies for lack of R'ness as I thought it would help with clarity.

Upvotes: 12

Thierry
Thierry

Reputation: 18487

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.

Upvotes: 7

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