How to select a subset of a dataframe using a variable dynamically

Question

I have an R dataframe with 300 columns. I have done Principal Component Analysis and grabbed the top 110 columns that explain the variability of dataset. How do we pass the 110 column names list to an R function to select the subset of original dataframe containing only these columns?

Please see the code below.

t.df = read.xlsx('t_dataset.xlsx', 1,startRow=1 )
X=t.df[ , 3:307]
t.pca=PCA(X, graph=FALSE)
write.infile(t.pca$var$contrib, "pca.csv", sep=',')

t.df.pca = read.xlsx('pca1.xlsx', 1,startRow=1 )
t.df.pca.r=subset(t.df.pca, Dim.1>mean(Dim.1) | Dim.2>mean(Dim.2) | Dim.3>mean(Dim.3) | Dim.4>mean(Dim.4) | Dim.5>mean(Dim.5))

c1=c(t.df.pca.r$Column)

#c1 contains the list of 110 column names.

c2=cat(paste(shQuote(c1), collapse=", "))
print(c2)

output of print(c2): "funct", "pronoun", "ppron", "i", "we", "you", "shehe", "they", "ipron", "article", "verb", "auxverb", "past", "present", "future", "adverb", "conj", upto 110 variables

t.df.2=t.df[c(c2)]
nrow(t.df.2)
ncol(t.df.2)

t.df.4=t.df[c2]
nrow(t.df.4)
ncol(t.df.4)

t.df.5=t.df[ ,c2]
nrow(t.df.5)
ncol(t.df.5)

Above code returns the result as follows: [1] 45498 [1] 0

[1] 45498 [1] 0

[1] 45498 [1] 0

What I need is: Pass these column names to an R function and get a subset of the original dataframe t.df. This subset will contain only the 110 columns present in c1

How to do this?

Answer 1

Here's one way to do it, with an example data frame:

library(tidyverse)

df <-
  tibble(
    col1 = c(1, 2, 3),
    col2 = c(2, 3, 4),
    col3 = c(3, 4, 5)
  )

cols_to_keep <- c("col1", "col3")

df %>% 
  select(cols_to_keep)

I don't know what format your data is in, but as long as you get a vector of column names you should be able to use the select command.