Order columns of a dataframe with Thousands of Rows and Columns-CodePudding

I need some help in sorting data that has more than 100 objects and thousands of variables and I want to sort them according to numeric order. (E.g. 1,2,3,4)

This is a sample Dataframe


print(df)
   P1 P5 P3 P2 P15
1   1 11  6 21  35
2   2 12  7 22  34
3   3 13  8 23  33
4   4 14  9 24  32
5   5 15 10 25  31
6   6 16 11 26  30
7   7 17 12 27  29
8   8 18 13 28  28
9   9 19 14 29  27
10 10 20 15 30  26

But I want the columns to be ordered by their names like below

   P1 P2 P3 P5 P15
1   1 21  6 11  35
2   2 22  7 12  34
3   3 23  8 13  33
4   4 24  9 14  32
5   5 25 10 15  31
6   6 26 11 16  30
7   7 27 12 17  29
8   8 28 13 18  28
9   9 29 14 19  27
10 10 30 15 20  26

And also I am not using any libraries which basically is base-R.

CodePudding user response：

You may use mixedorder/mixedsort from gtools.

df <- data.frame(P10 = rnorm(5), P1 = rnorm(5), P15 = rnorm(5))
df[gtools::mixedsort(names(df))]

#          P1        P10        P15
#1  0.2306336 -0.3343381  0.9183412
#2 -1.6918624 -0.1055599 -0.4527006
#3  0.6597919 -0.7305097 -1.7483723
#4 -1.0236236  1.9050436  1.7699041
#5 -0.8915216  0.3326217 -2.3774069

CodePudding user response：

Since you have a letter at the beginning of the column name, you can't just sort by names, but have use a string sort.

library(stringr)    
df <- data.frame(matrix(runif(16), ncol = 8, nrow = 2))
names(df) <- c("P100", "R3", "P1", "P2", "P10", "P11", "P100C", "PP9")

df

#        P100        R3        P1         P2       P10       P11     P100C       PP9
# 1 0.1774975 0.5982241 0.6745210 0.31186217 0.5893895 0.2070617 0.4891480 0.5275640
# 2 0.8936577 0.7666618 0.1734269 0.09079242 0.9984647 0.7152058 0.9342534 0.7952669

df[, str_sort(names(df), numeric = TRUE)]

 #         P1         P2       P10       P11      P100     P100C       PP9        R3
# 1 0.6745210 0.31186217 0.5893895 0.2070617 0.1774975 0.4891480 0.5275640 0.5982241
# 2 0.1734269 0.09079242 0.9984647 0.7152058 0.8936577 0.9342534 0.7952669 0.7666618