I’m trying to do some that preprocessing, and want to convert the classe factors values {A,B,C,D,E} to {1,2,3,4,5}.
The classe column is of type factor, I have provided all steps, see below:
#get the data
training <- read.table("http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv",header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
training_df <- data.frame(training,stringsAsFactors=FALSE)
#split to training & test sets
inTrain <- createDataPartition(y=training$classe, p=0.75, list=FALSE)
training_data <- training[inTrain,]
testing_data <- training[-inTrain,]
#subset based on columns of interest, based on previous studies
training_data_subset <- subset(training_data, select=c("avg_roll_belt","var_roll_belt","var_total_accel_belt","amplitude_roll_belt","max_roll_belt","var_roll_belt",
"var_accel_arm","magnet_arm_x","magnet_arm_y","magnet_arm_z","accel_dumbbell_y","accel_dumbbell_z","magnet_dumbbell_x","gyros_dumbbell_x",
"gyros_dumbbell_y","gyros_dumbbell_z","pitch_forearm","gyros_forearm_x","gyros_forearm_y","classe"))
#see which columns are factors, the training_data_subset#classe feature is a factor
sapply(training_data_subset, class)
#sapply output
avg_roll_belt var_roll_belt var_total_accel_belt amplitude_roll_belt max_roll_belt
"numeric" "numeric" "numeric" "numeric" "numeric"
var_roll_belt.1 var_accel_arm magnet_arm_x magnet_arm_y magnet_arm_z
"numeric" "numeric" "integer" "integer" "integer"
accel_dumbbell_y accel_dumbbell_z magnet_dumbbell_x gyros_dumbbell_x gyros_dumbbell_y
"integer" "integer" "integer" "numeric" "numeric"
gyros_dumbbell_z pitch_forearm gyros_forearm_x gyros_forearm_y classe
"numeric" "numeric" "numeric" "numeric" "factor"
I created a function that attempts to replace A=1,B=2,C=3,D=4,E=5, see below:
factorsToNumeric <- function(data)
{
data_numeric <- data
data_numeric$classe <-as.numeric(factor(toupper(as.character(data_numeric$classe))))
#loop through the data frame based on replace values
for(i in 1:nrow(data_numeric))
{
if ((data_numeric[i,]$classe == "A") || (data_numeric[i,]$classe == "a"))
{data_numeric[i,]$classe <- "1"}
else if ((data_numeric[i,]$classe == "B") || (data_numeric[i,]$classe == "b"))
{data_numeric[i,]$classe <- "2"}
else if ((data_numeric[i,]$classe == "C") || (data_numeric[i,]$classe == "c"))
{data_numeric[i,]$classe <- "3"}
else if ((data_numeric[i,]$classe == "D") || (data_numeric[i,]$classe == "d"))
{data_numeric[i,]$classe <- "4"}
else if ((data_numeric[i,]$classe == "E") || (data_numeric[i,]$classe == "e"))
{data_numeric[i,]$classe <- "5"}
else
{
#do nothing
}
}
return (data_numeric)
}
However, I get this error:
training_data_subset_numeric <- factorsToNumeric(training_data_subset)
Error:
Warning messages:
1: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
2: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
3: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
4: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
5: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
6: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
7: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
8: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
9: In `[<-.factor`(`*tmp*`, iseq, value = "1") :
invalid factor level, NA generated
Further inspection shows the column "classe"'s class is converted to "numeric":
sapply(training_data_subset_numeric, class)
avg_roll_belt var_roll_belt var_total_accel_belt amplitude_roll_belt max_roll_belt
"numeric" "numeric" "numeric" "numeric" "numeric"
var_roll_belt.1 var_accel_arm magnet_arm_x magnet_arm_y magnet_arm_z
"numeric" "numeric" "integer" "integer" "integer"
accel_dumbbell_y accel_dumbbell_z magnet_dumbbell_x gyros_dumbbell_x gyros_dumbbell_y
"integer" "integer" "integer" "numeric" "numeric"
gyros_dumbbell_z pitch_forearm gyros_forearm_x gyros_forearm_y classe
"numeric" "numeric" "numeric" "numeric" "numeric"
However, the head function confirms the error above & all the values A,B,C,D,E have been replaced with NA incorrectly.
training_data_subset$classe <- as.numeric(factor(toupper(as.character(training_data_subset$classe))))