I'm trying to set up a random forest classifier in R. It needs to use my csv growth.csv and return a confusion matrix based on the factor "cm".
The R library `randomForest` has methods to create a confusion matrix. This code should work:

library(randomForest)
library(caret)

# Load the dataset
data  <- read.csv("growth.csv", sep=",", header= TRUE)

# Display the head and dimensions
head(data)
dim(head)

# Set the seed
set.seed(100)

# Set testing and training data
samples <- sample(nrow(data), 0.8 * nrow(data))
train <- data[samples,]
test <- data[samples,]

# Create the random forest
model <- randomForest(cm~., data=train)

# Predict the values
datapredict <- predict(model, test)

# Create the confusion matrix
confusionMatrix(datapredict, train$ cm)

It imports the `randomForest` and `caret` libraries, then loads the data from "growth.csv" with `read.csv` to the `data` variable. It displays the head and dimensions of the data, then sets a random seed to ensure the results will be the same if called with the seed again. This will allow you to produce the same results in the future if necessary by using the same random seed. Here, it is set to 100, but you can choose any seed. Once the seed is set, it breaks the data into 80% training data and 20% testing data with `sample()` and defines the `train` training data and `test` testing data with the samples. It uses the training data to train the random forest model with `randomForest()`, and it uses `predict()` to make predictions based on the testing data. Finally, it builds the confusion matrix you requested with `confusionMatrix()` comparing the predictions to the training data.