R - How to add columns to a dataset incrementally using a loop? - r

Question

I'm trying to get the error rates for a Naive Bayes classifier, by adding in each variable incrementally. For example I have 25 variables in my dataset. I want to get the error rates of the model as I add in one variable at a time. So you know it would output the error rate of the model with the first 2 columns, the error rate with the first 3 columns, then with the first 4 columns, and so on up to the last column.
Here is the pseudocode of what I'm trying to achieve
START
IMPORT DATASET WITH ALL VARIABLES
num_variables = num_dataset_cols
i= 1
WHILE (i <= num_variables)
{
CREATE NEW DATASET WITH x COLUMNs
BUILD THE MODEL
GET THE ERROR RATE
ADD IN NEXT COLUMN
i = i + 1
}
Here is a reproducible question. Obviously you can't build a NB classifier with this data, but that's not my problem. My problem is adding in the columns one by one. So far, the only way I can do it is by overwriting each column. For a NB classifier, the first column is the class node, so there must be at least 2 columns starting off in order for it to run.
#REPRODUCIBLE EXAMPLE
col1 <- c("A", "B", "C", "D", "E")
col2 <- c(1,2,3,4,5)
col3 <- c(TRUE, FALSE, FALSE, TRUE, FALSE)
col4 <- c("n","y","y","n","y")
col5 <- c("10", "15", "50", "100", "20")
dataset <- data.frame(col1, col2, col3, col4,col5)
num_variables <- ncol(dataset)
i <- 1
while i <= num_variables
{
data <- dataset[c(1, i+1)]
str(data)
#BUILD MODEL AND GET VALIDATION ERROR
#INCREMENT i TO GET NEXT COLUMN
i <- i + 1
}
You should be able to see from the str(data) that each time the column is overwritten. Does anyone know how I could go about adding each column without overwriting the previous one? Someone suggested an array to me, but I'm not too familiar with arrays in R. Would this work?

Answer 1

I think this is what you want.
col1 <- c("A", "B", "C", "D", "E")
col2 <- c(1,2,3,4,5)
col3 <- c(TRUE, FALSE, FALSE, TRUE, FALSE)
col4 <- c("n","y","y","n","y")
col5 <- c("10", "15", "50", "100", "20")
dataset <- data.frame(col1, col2, col3, col4,col5)
dataset
num_variables <- ncol(dataset)
num_variables
i <- 1
while (i <= num_variables) {
data <- dataset[, 1:i]
print(str(data))
#BUILD MODEL AND GET VALIDATION ERROR
#INCREMENT i TO GET NEXT COLUMN
i <- i + 1
}
Factor w/ 5 levels "A","B","C","D",..: 1 2 3 4 5
NULL
'data.frame': 5 obs. of 2 variables:
$ col1: Factor w/ 5 levels "A","B","C","D",..: 1 2 3 4 5
$ col2: num 1 2 3 4 5
NULL
'data.frame': 5 obs. of 3 variables:
$ col1: Factor w/ 5 levels "A","B","C","D",..: 1 2 3 4 5
$ col2: num 1 2 3 4 5
$ col3: logi TRUE FALSE FALSE TRUE FALSE
NULL
'data.frame': 5 obs. of 4 variables:
$ col1: Factor w/ 5 levels "A","B","C","D",..: 1 2 3 4 5
$ col2: num 1 2 3 4 5
$ col3: logi TRUE FALSE FALSE TRUE FALSE
$ col4: Factor w/ 2 levels "n","y": 1 2 2 1 2
NULL
'data.frame': 5 obs. of 5 variables:
$ col1: Factor w/ 5 levels "A","B","C","D",..: 1 2 3 4 5
$ col2: num 1 2 3 4 5
$ col3: logi TRUE FALSE FALSE TRUE FALSE
$ col4: Factor w/ 2 levels "n","y": 1 2 2 1 2
$ col5: Factor w/ 5 levels "10","100","15",..: 1 3 5 2 4
NULL

Answer 2

You can use append function after defining output variable
data <- dataset[c(1, i+1)]
append(output, data)
str(data)

Answer 3

Using the "assign" function within a while loop can be helpful for issues like this. You don't show the model syntax, but something like this should work:
dataset$errorrate <- [whatever makes this calculation, assuming it is vectorized]
name <- paste0(errorrate, i)
assign(name, dataset$errorrate)
...
This should leave you with i variables containing error estimate for each model run. If you are looking for one parameter estimate per model you can assign the single estimate a unique name within the global environment using the process above and then rbind them together after the loop has finished