creating dataframe of vectors and arrange it o three row levels in R-CodePudding

HI everyone i have 3 vectors:

v1 = c(32,60,20)
v2 = c(60,10,40)
v3 = c(90,45,15)

i want to put them into DataFrame as shown in the screenshot below where: low for value less than 30 average for values more than 30 and less 50 High for value more than 50.

the DataFrame should be like this:

          v1 : v2 : v3
low:
average:
High:

CodePudding user response：

This isn't what you asked for but in my opinion it is a more sensible way of coding your data (long format).

#### Library ####
library(tidyverse)

#### Create Vars ####
v1 = c(30,60,20)
v2 = c(40,10,30)
v3 = c(90,20,10)

#### Make Data Frame ####
df <- data.frame(v1,v2,v3)
df_fix <- df %>% 
  pivot_longer(cols = everything(),
               names_to = "Variable",
               values_to = "Score") %>% 
  mutate(Variable_Type = ifelse(Score < 30,
                       "Low",
                       ifelse(Score > 50,
                              "High",
                              "Average"))) %>% 
  select(-Variable) 

df_fix

Which gives you this cleaner column by variable format:

# A tibble: 9 × 2
  Score Variable_Type
  <dbl> <chr>        
1    30 Average      
2    40 Average      
3    90 High         
4    60 High         
5    10 Low          
6    20 Low          
7    20 Low          
8    30 Average      
9    10 Low

And if you want to leave the variable names in place by row:

df_row <- df %>% 
  pivot_longer(cols = everything(),
               names_to = "Variable",
               values_to = "Score") %>% 
  mutate(Variable_Type = ifelse(Score < 30,
                                "Low",
                                ifelse(Score > 50,
                                       "High",
                                       "Average")))
    df_row

Which looks like this:

# A tibble: 9 × 3
  Variable Score Variable_Type
  <chr>    <dbl> <chr>        
1 v1          30 Average      
2 v2          40 Average      
3 v3          90 High         
4 v1          60 High         
5 v2          10 Low          
6 v3          20 Low          
7 v1          20 Low          
8 v2          30 Average      
9 v3          10 Low

CodePudding user response：

Maybe you can use cut to split the vectors in the classes Low, Average and High. With table you can get the counts per class.

v1 <- c(32,60,20)
v2 <- c(60,10,40)
v3 <- c(90,45,15)

. <- lapply(mget(c("v1", "v2", "v3")), cut, c(-Inf,30,50,Inf), c("Low", "Average", "High"))
sapply(., table)
#        v1 v2 v3
#Low      1  1  1
#Average  1  1  1
#High     1  1  1