How to order variables in blocks-CodePudding

I have a large data set with variables like this:

df <- data.frame(ID=c(1,2),
                 Height_1=c(180,192),
                 Weight_1=c(70,90),
                 Hip_1=c(25,29),
                 Height_2=c(167,168),
                 Weight_2=c(50,50),
                 Hip_2=c(23,27),
                 Height_3=c(175,176),
                 Weight_3=c(50,70),
                 Hip_3=c(28,28))

I would like to order the variables as follows:

ID, Height_1, Height_2, Height_3, Weight_1, Weight_2, Weight_3, Hip_1, Hip_2, Hip_3

I have tried with:

df <- df[sort(names(df))]

But I do not want all the variables alphabetically.

Thank you so much in advance.

CodePudding user response：

An option in base R would be to convert the column names to a matrix and then to a vector:

n <- length(unique(sub("_\\d ", "", names(df)[-1])))
df[c('ID', c(matrix(names(df)[-1], ncol = n, byrow = TRUE)))]

Output:

ID Height_1 Height_2 Height_3 Weight_1 Weight_2 Weight_3 Hip_1 Hip_2 Hip_3
1  1      180      167      175       70       50       50    25    23    28
2  2      192      168      176       90       50       70    29    27    28

Or you may use

library(data.table)
library(dplyr)
df %>%
   select(ID, order(rowid(readr::parse_number(names(.)[-1]))) 1)

Output:

 ID Height_1 Height_2 Height_3 Weight_1 Weight_2 Weight_3 Hip_1 Hip_2 Hip_3
1  1      180      167      175       70       50       50    25    23    28
2  2      192      168      176       90       50       70    29    27    28

CodePudding user response：

This is the long version of @akrun's solution, the core idea is to make pivot longer, transform to factor, and the arrange and pivot back:

library(tidyverse)

df %>% 
  pivot_longer(-ID) %>% 
  mutate(helper = str_replace_all(name, "[[:punct:]][0-9] ", ""),
         helper = factor(helper, levels = c("Height", "Weight", "Hip"))) %>% 
  group_by(ID) %>% 
  arrange(helper, .by_group = TRUE) %>% 
  select(-helper) %>% 
  pivot_wider(names_from = name, values_from = value)

     ID Height_1 Height_2 Height_3 Weight_1 Weight_2 Weight_3 Hip_1 Hip_2 Hip_3
  <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> <dbl> <dbl> <dbl>
1     1      180      167      175       70       50       50    25    23    28
2     2      192      168      176       90       50       70    29    27    28