Function to automatically create vector in a large

Question

I have a single Dataframe with the following structure:

A.Data is a vector with numeric data

A.Quartile is a vector with the calculation of quartiles for each A.data and which quartile belongs to this data. (Q1,Q2,Q3,Q4).

I used a very similar code to create the quantile and the Q which belongs to.

quantile(x <- rnorm(1001))
list2env(setNames(as.list(quantile(x <- rnorm(1001))),paste0("Q",1:5)),.GlobalEnv)

Now, ( and here is my problem) I have a .csv that I imported into R, with more than 400 elements with XYZ.Data vectors

So when I imported the .csv file into my environment, I would like to create a function to create in one time all the XYZ.Quartile vectors and I don't know how.

The point would be to read all elements in my list loaded into environment from a .csv file with a function and have the function to create the B.Quartile,C.Quartile,D.Quartile, vectors... one for each element in the list.

Anyone can help please?

Thank you very much for any comment.

PD: New Code Example

quantile(x <- Orange$circumference)
Orange<- within(Orange, Quartile <- as.integer(cut(Orange$circumference, quantile(Orange$circumference, probs=0:4/4), include.lowest=TRUE)))

Answer 1

Your example data is confusing. It's not clear what the structure of your data is so I'm just pretending your lists are columns of a matrix/data.frame.

# proper example data
set.seed(1)
dat <- replicate(6, rnorm(20))
colnames(dat) <- LETTERS[1:6]
head(dat)
#              A           B          C           D          E           F
#[1,] -0.6264538  0.91897737 -0.1645236  2.40161776 -0.5686687 -0.62036668
#[2,]  0.1836433  0.78213630 -0.2533617 -0.03924000 -0.1351786  0.04211587
#[3,] -0.8356286  0.07456498  0.6969634  0.68973936  1.1780870 -0.91092165
#[4,]  1.5952808 -1.98935170  0.5566632  0.02800216 -1.5235668  0.15802877
#[5,]  0.3295078  0.61982575 -0.6887557 -0.74327321  0.5939462 -0.65458464
#[6,] -0.8204684 -0.05612874 -0.7074952  0.18879230  0.3329504  1.76728727

# for each column i
qdat <- apply(dat, 2, function(i){
  q <- quantile(i)
  # for each element j in column i
  sapply(i, function(j){
    paste0("Q",1:5)[sum(j > q)+1]
  })
})
head(qdat)
#     A    B    C    D    E    F   
#[1,] "Q2" "Q5" "Q3" "Q5" "Q2" "Q2"
#[2,] "Q3" "Q5" "Q3" "Q3" "Q3" "Q4"
#[3,] "Q2" "Q4" "Q5" "Q5" "Q5" "Q1"
#[4,] "Q5" "Q1" "Q4" "Q3" "Q1" "Q4"
#[5,] "Q3" "Q4" "Q2" "Q2" "Q4" "Q2"
#[6,] "Q2" "Q3" "Q2" "Q4" "Q4" "Q5"

EDIT 1 See the following code:

# example data
set.seed(1)
dat <- replicate(3, rnorm(20))
colnames(dat) <- paste0(LETTERS[1:3],".Data")

replacewithQ <- function(x) {
  as.integer(cut(x, 
                 quantile(x, 
                          probs=0:4/4), 
                 include.lowest=TRUE)
  )
}

qdat <- apply(dat, 2, replacewithQ)
colnames(qdat) <- gsub("Data","Quartile",colnames(dat))
newdat <- cbind(dat, qdat)
head(newdat)
#         A.Data      B.Data     C.Data A.Quartile B.Quartile C.Quartile
#[1,] -0.6264538  0.91897737 -0.1645236          1          4          2
#[2,]  0.1836433  0.78213630 -0.2533617          2          4          2
#[3,] -0.8356286  0.07456498  0.6969634          1          3          4
#[4,]  1.5952808 -1.98935170  0.5566632          4          1          3
#[5,]  0.3295078  0.61982575 -0.6887557          2          3          1
#[6,] -0.8204684 -0.05612874 -0.7074952          1          2          1