Sorry if this is too simple a question but for the life of me cant sort this out.
I have a data frame with a continuous variable and need to create multiple logical variables based on the value of the continuous variable.
Hopefully the example below illustrates this:
x <- as.integer(rnorm(n=1000, mean=10, sd=5))
y <- 1:1000
df <- data.frame(x,y)
for i in 1:10 {
df$[i] <- ifelse (df$x<[i],1,0)
}
I suspect I am going wrong at the df$[i] expression...any solutions?
Thanks in advance
You can add new columns to a data.frame easily by assigning to them. On the LHS specify the column dereference and on the RHS specify an expression that computes to a vector, which should have the same length as the other columns that already exist in the data.frame. For example, if you want to add a logical column that captures if the x value in the same row is less than the mean of the normal distribution you used to calculate all x values, you can do this:
df$z <- df$x<10;
You don't need a for loop to do this. Many operations in R are vectorized, meaning they automatically loop through all elements of vector operands. The df$x<10 snippet in that line of code tests every one of the 1000 values in df$x to see if it's less than 10, and the whole operation returns a vector of 1000 logical values (each TRUE, FALSE, or NA) with the results of the vectorized operation. So you can assign that result directly to a new column in the data.frame.
May be you want (using your code)
for(i in 1:10){
df[paste0("Col",i)] <- ifelse(df$x <i, 1, 0)
}
head(df,3)
# x y Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10
#1 7 1 0 0 0 0 0 0 0 1 1 1
#2 12 2 0 0 0 0 0 0 0 0 0 0
#3 12 3 0 0 0 0 0 0 0 0 0 0
Or
df[paste0('Col',1:10)] <- Vectorize(function(x) df$x < x)(1:10)+0
Or
df[paste0('Col', 1:10)] <- (do.call(cbind,Map(`<`, list(df$x), 1:10)))+0
Or
df[paste0('Col', 1:10)] <- matrix((df$x < rep(1:10, each=nrow(df)))+0, ncol=10)
Or
df[paste0('Col', 1:10)] <- `dim<-` ((df$x < rep(1:10, each=nrow(df)))+0,
c(nrow(df),10))
set.seed(24)
x <- as.integer(rnorm(n=1000, mean=10, sd=5))
y <- 1:1000
df <- data.frame(x,y)
