R: Changes to data when changing class - r

Question

Prior to running a randomForest model, I load my data and sort variables into categorical and numerical so the model can process it.
Data as first loaded from the .csv file looks like this:
> str(DataFrame)
'data.frame': 1060 obs. of 6 variables:
$ VarX : int 1 1 1 1 0 0 0 0 1 0 ...
$ Var1 : num 127 135 137 138 138 ...
$ Var2 : Factor w/ 200 levels "#N/A","1690",..: 190 190 190 191 191 191 189 185 183 181 ...
$ Var3 : Factor w/ 138 levels "#N/A","100","101",..: 44 43 43 43 43 43 43 43 43 42 ...
$ Var4 : int 15 15 15 15 15 16 16 16 16 16 ...
$ Var5 : Factor w/ 189 levels "#N/A","10029",..: 87 87 87 87 87 85 85 85 85 85 ...
> head(DataFrame, 3)
VarX Var1 Var2 Var3 Var4 Var5
1 1 126.58 3660 152 15 7159.5
2 1 135.17 3660 150 15 7159.5
3 1 137.25 3660 150 15 7159.5
I then attempt to sort the variables in the following way:
##Sort numerical and categorical values
options(digits = 5)
cols <- c("VarX")
for (i in cols) {
DataFrame[,i] = as.factor(DataFrame[,i])
}
cols2 <- c("Var1", "Var2", "Var3", "Var4", "Var5")
for (i in cols2) {
DataFrame[,i] = as.numeric(DataFrame[,i])
}
However, this does something strange and undesirable to the data:
> str(DataFrame)
'data.frame': 1060 obs. of 6 variables:
$ VarX : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 1 2 1 ...
$ Var1 : num 127 135 137 138 138 ...
$ Var2 : num 190 190 190 191 191 191 189 185 183 181 ...
$ Var3 : num 44 43 43 43 43 43 43 43 43 42 ...
$ Var4 : num 15 15 15 15 15 16 16 16 16 16 ...
$ Var5 : num 87 87 87 87 87 85 85 85 85 85 ...
> head(DataFrame,3)
VarX Var1 Var2 Var3 Var4 Var5
1 1 126.58 190 44 15 87
2 1 135.17 190 43 15 87
3 1 137.25 190 43 15 87
Also, while not shown in the above excerpt it turns all NA values into 1, which, depending on the data, can skew the results.
Q: What would be the correct way to process the data so that there is no corruption of the data, while ensuring that it can be used by the randomForest package?

Answer 1

You should have used as.numeric(as.character(variable_name)) to convert a factor column to numeric column, otherwise information will be lost.
If you see the documentation of ?factor it says in the WARNING section:
The interpretation of a factor depends on both the codes and the
"levels" attribute. Be careful only to compare factors with the same
set of levels (in the same order). In particular, as.numeric applied
to a factor is meaningless, and may happen by implicit coercion. To
transform a factor f to approximately its original numeric values,
as.numeric(levels(f))[f] is recommended and slightly more efficient
than as.numeric(as.character(f)).
Instead of for loops you can also use the power of sapply to convert these column into numeric like below:
dfnew <- sapply(df[,colms_to_be_converted],function(x)as.numeric(as.character(x)))