How to select range of columns in a dataframe base

Question

In a pandas dataframe created like this:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(10, size=(6, 6)),
                  columns=['c' + str(i) for i in range(6)],
                  index=["r" + str(i) for i in range(6)])

which could look as follows:

    c0  c1  c2  c3  c4  c5
r0   2   7   3   3   2   8
r1   6   9   6   7   9   1
r2   4   0   9   8   4   2
r3   9   0   4   3   5   4
r4   7   6   8   8   0   8
r5   0   6   1   8   2   2

I can easily select certain rows and/or a range of columns using .loc:

print df.loc[['r1', 'r5'], 'c1':'c4']

That would return:

    c1  c2  c3  c4
r1   9   6   7   9
r5   6   1   8   2

So, particular rows/columns I can select in a list, a range of rows/columns using a colon.

How would one do this in R? Here and here one always has to specify the desired range of columns by their index but one cannot - or at least I did not find it - access those by name. To give an example:

df <- data.frame(c1=1:6, c2=2:7, c3=3:8, c4=4:9, c5=5:10, c6=6:11)
rownames(df) <- c('r1', 'r2', 'r3', 'r4', 'r5', 'r6')

The command

df[c('r1', 'r5'),'c1':'c4']

does not work and throws an error. The only thing that worked for me is

df[c('r1', 'r5'), 1:4]

which returns

   c1 c2 c3 c4
r1  1  2  3  4
r5  5  6  7  8

But how would I select the columns by their name and not by their index (which might be important when I drop certain columns throughout the analysis)? In this particular case I could of course use grep but how about columns that have arbitrary names?

So I don't want to use

df[c('r1', 'r5'),c('c1','c2', 'c3', 'c4')]

but an actual slice.

EDIT:

A follow-up question can be found here.

Answer 1

A solution using dplyr package but you need to specify the row you want to select before hand

rowName2Match <- c("r1", "r5")

df1 <- df %>% 
  select(matches("2"):matches("4")) %>% 
  add_rownames() %>% 
  mutate(idRow = match(rowname, rowName2Match)) %>% 
  slice(which(!is.na(idRow))) %>% 
  select(-idRow)
df1

> df1
Source: local data frame [2 x 4]

  rowname    c2    c3    c4
   <chr> <int> <int> <int>
1      r1     2     3     4
2      r5     6     7     8

Answer 2

An alternative approach to subset if you don't mind to work with data.table would be:

data.table::setDT(df)
df[1:3, c2:c4, with=F]
   c2 c3 c4
1:  2  3  4
2:  3  4  5
3:  4  5  6

This still does not solve the problem of subsetting row range though.

Answer 3

This seems way too easy so perhaps I'm doing something wrong.

df <- data.frame(c1=1:6, c2=2:7, c3=3:8, c4=4:9, c5=5:10, c6=6:11,
                 row.names=c('r1', 'r2', 'r3', 'r4', 'r5', 'r6'))


df[c('r1','r2'),c('c1','c2')]

   c1 c2
r1  1  2
r2  2  3

Answer 4

It looks like you can accomplish this with a subset:

> df <- data.frame(c1=1:6, c2=2:7, c3=3:8, c4=4:9, c5=5:10, c6=6:11)
> rownames(df) <- c('r1', 'r2', 'r3', 'r4', 'r5', 'r6')
> subset(df, select=c1:c4)
   c1 c2 c3 c4
r1  1  2  3  4
r2  2  3  4  5
r3  3  4  5  6
r4  4  5  6  7
r5  5  6  7  8
r6  6  7  8  9
> subset(df, select=c1:c2)
   c1 c2
r1  1  2
r2  2  3
r3  3  4
r4  4  5
r5  5  6
r6  6  7

If you want to subset by row name range, this hack would do:

> gRI <- function(df, rName) {which(match(rNames, rName) == 1)}
> df[gRI(df,"r2"):gRI(df,"r4"),]
   c1 c2 c3 c4 c5 c6
r2  2  3  4  5  6  7
r3  3  4  5  6  7  8
r4  4  5  6  7  8  9

Answer 5

Adding onto @evan058's answer:

subset(df[rownames(df) %in% c("r3", "r4", "r5"),], select=c1:c4)

c1 c2 c3 c4
r3  3  4  5  6
r4  4  5  6  7
r5  5  6  7  8

But note, the : operator will probably not work here; you will have to write out the name of each row you want to include explicitly. It might be easier to group by a particular value of one of your other columns or to create an index column as @evan058 mentioned in comments.