Can I use categorical and numerical data variable at the same time in randomForest in r? I have data in which few columns contain categorical data whereas remaining columns contain numerical data. I want to use random forest regressor from the randomForest library in r. So does this library work with such data out of the box (assuming I have preprocessed the data and is ready to directly feed in the regressor)?

DATA (something along this line) :

Target | product | status | revenue | MS
  0.8      abc       NC      1000    0.5
  0.5      abc       UR      200     0.2
  0.2      pqr       NC      800     0.04
In general you can always transform categorical data into numeric data. Many experienced STATA users need to do routinely. If you have gender variable coded as "male/female" you need one binary gendervvariable recorded as 0/1. If you have four levels variable such as main role in a football team "goalkeeper/defense/midfield/attack"  you could take midfield as reference and generate the goalkeeper, midfield and attack variables all coded as 0/1.
In caret, the dummyVars function allow to create easily dummy variables. 
Here an example with cylinders transformed as factors and then dummyfied:

 library(caret) 
 mtcars$cyl &lt;- as.factor(mtcars$cyl)
 dmy&lt;- dummyVars(" ~ .", data = mtcars, fullRank=T)
 dummy.mtcars &lt;- data.frame(predict(dmy, newdata = mtcars))
 head(dummy.mtcars)