如何识别具有归一化向量的数据帧的每个实例为分位数（如0,0.25,0.5,0.75,1）？

Question

我有一个数据框有20个变量和400k实例。所有的变量用均值0和标准差1归一化。我想写一个函数，可以将每个变量的每个实例分类为分位数。

Lets say we have a normalized vector 
a <- c(0.2132821 -1.5136988 0.6450274 1.5085178 0.2132821 1.5085178 0.6450274) 

And the quantiles for this vector are 
quant.a <- c(-1.5136988 -1.0819535 0.2132821 1.0767726 1.5085178) 

where -1.5136988 is 0% 
     -1.0819535 is 25% 
     0.2132821 is 50% 
     1.0767726 is 75% 
     1.5085178 is 100% (all are elements in vector 'quant.a') 

Now, I want to classify each element of vector 'a' as follows 
new.a <- c(0.5, 0, 0.75, 1, 0.5, 1, 0.75) 

You can use the following code to workout through the example as it is not possible for me to share the actual data 

# Generate random data 
set.seed(99) 

# All variables are on a scale of 1-9 
a <- floor(runif(500, min = 1, max = 9)) 
b <- floor(runif(500, min = 1, max = 9)) 
c <- floor(runif(500, min = 1, max = 9)) 

# store variables as dataframe 
x <- data.frame(cbind(a,b,c)) 

#Scale variables 
scaled.dat <- data.frame(scale(x)) 

# check that we get mean of 0 and sd of 1 
colMeans(scaled.dat) 
apply(scaled.dat, 2, sd) 

# generate quantiles for each variables 
quantiles <- data.frame(apply(scaled.dat,2,quantile)) 

预先感谢

Answer 1

library(dplyr) 
yourdataframe %>% 
    mutate_all(funs(ntile(., 4)/4) 

Answer 2

a <- c(0.2132821, -1.5136988, 0.6450274 , 1.5085178 , 0.2132821 , 1.5085178 , 0.6450274) 

quant.a = quantile(a) 

aux_matrix = findInterval(a, quant.a) 

new.a = ifelse(aux_matrix == 1|aux_matrix == 0, 0, 
       ifelse(aux_matrix == 2, 0.5, 
         ifelse(aux_matrix==3,0.75, 
          1))) 

print(new.a) 

0.50 0.00 0.75 1.00 0.50 1.00 0.75