I have a data.table with a column that has NAs. I want to drop rows where that column takes a particular value (which happens to be ""). However, my first attempt lead me to lose rows with NAs as well:
> a = c(1,"",NA)
> x <- data.table(a);x
a
1: 1
2:
3: NA
> y <- x[a!=""];y
a
1: 1
After looking at ?`!=`, I found a one liner that works, but it's a pain:
> z <- x[!sapply(a,function(x)identical(x,""))]; z
a
1: 1
2: NA
I'm wondering if there's a better way to do this? Also, I see no good way of extending this to excluding multiple non-NA values. Here's a bad way:
> drop_these <- function(these,where){
+ argh <- !sapply(where,
+ function(x)unlist(lapply(as.list(these),function(this)identical(x,this)))
+ )
+ if (is.matrix(argh)){argh <- apply(argh,2,all)}
+ return(argh)
+ }
> x[drop_these("",a)]
a
1: 1
2: NA
> x[drop_these(c(1,""),a)]
a
1: NA
I looked at ?J and tried things out with a data.frame, which seems to work differently, keeping NAs when subsetting:
> w <- data.frame(a,stringsAsFactors=F); w
a
1 1
2
3 <NA>
> d <- w[a!="",,drop=F]; d
a
1 1
NA <NA>
To provide a solution to your question:
You should use %in%. It gives you back a logical vector.
a %in% ""
# [1] FALSE TRUE FALSE
x[!a %in% ""]
# a
# 1: 1
# 2: NA
To find out why this is happening in data.table:
(as opposted to data.frame)
If you look at the data.table source code on the file data.table.R under the function "[.data.table", there's a set of if-statements that check for i argument. One of them is:
if (!missing(i)) {
# Part (1)
isub = substitute(i)
# Part (2)
if (is.call(isub) && isub[[1L]] == as.name("!")) {
notjoin = TRUE
if (!missingnomatch) stop("not-join '!' prefix is present on i but nomatch is provided. Please remove nomatch.");
nomatch = 0L
isub = isub[[2L]]
}
.....
# "isub" is being evaluated using "eval" to result in a logical vector
# Part 3
if (is.logical(i)) {
# see DT[NA] thread re recycling of NA logical
if (identical(i,NA)) i = NA_integer_
# avoids DT[!is.na(ColA) & !is.na(ColB) & ColA==ColB], just DT[ColA==ColB]
else i[is.na(i)] = FALSE
}
....
}
To explain the discrepancy, I've pasted the important piece of code here. And I've also marked them into 3 parts.
First, why dt[a != ""] doesn't work as expected (by the OP)?
First, part 1 evaluates to an object of class call. The second part of the if statement in part 2 returns FALSE. Following that, the call is "evaluated" to give c(TRUE, FALSE, NA) . Then part 3 is executed. So, NA is replaced to FALSE (the last line of the logical loop).
why does x[!(a== "")] work as expected (by the OP)?
part 1 returns a call once again. But, part 2 evaluates to TRUE and therefore sets:
1) `notjoin = TRUE`
2) isub <- isub[[2L]] # which is equal to (a == "") without the ! (exclamation)
That is where the magic happened. The negation has been removed for now. And remember, this is still an object of class call. So this gets evaluated (using eval) to logical again. So, (a=="") evaluates to c(FALSE, TRUE, NA).
Now, this is checked for is.logical in part 3. So, here, NA gets replaced to FALSE. It therefore becomes, c(FALSE, TRUE, FALSE). At some point later, a which(c(F,T,F)) is executed, which results in 2 here. Because notjoin = TRUE (from part 2) seq_len(nrow(x))[-2] = c(1,3) is returned. so, x[!(a=="")] basically returns x[c(1,3)] which is the desired result. Here's the relevant code snippet:
if (notjoin) {
if (bywithoutby || !is.integer(irows) || is.na(nomatch)) stop("Internal error: notjoin but bywithoutby or !integer or nomatch==NA")
irows = irows[irows!=0L]
# WHERE MAGIC HAPPENS (returns c(1,3))
i = irows = if (length(irows)) seq_len(nrow(x))[-irows] else NULL # NULL meaning all rows i.e. seq_len(nrow(x))
# Doing this once here, helps speed later when repeatedly subsetting each column. R's [irows] would do this for each
# column when irows contains negatives.
}
Given that, I think there are some inconsistencies with the syntax.. And if I manage to get time to formulate the problem, then I'll write a post soon.
Background answer from Matthew :
The behaviour with != on NA as highlighted by this question wasn't intended, thinking about it. The original intention was indeed to be different than [.data.frame w.r.t. == and NA and I believe everyone is happy with that. For example, FAQ 2.17 has :
DT[ColA==ColB] is simpler than DF[!is.na(ColA) & !is.na(ColB) &
ColA==ColB,]
That convenience is achieved by dint of :
DT[c(TRUE,NA,FALSE)] treats the NA as FALSE, but DF[c(TRUE,NA,FALSE)]
returns NA rows for each NA
The motivation is not just convenience but speed, since each and every !, is.na, & and == are themselves vector scans with associated memory allocation of each of their results (explained in intro vignette). So although x[is.na(a) | a!=""] is a working solution, it's exactly the type of logic I was trying to avoid needing in data.table. x[!a %in% ""] is slightly better; i.e, 2 scans (%in% and !) rather than 3 (is.na, | and !=). But really x[a != ""] should do what Frank expected (include NA) in a single scan.
New feature request filed which links back to this question :
DT[col!=""] should include NA
Thanks to Frank, Eddi and Arun. If I haven't understood correctly feel free to correct, otherwise the change will get made eventually. It will need to be done in a way that considers compound expressions; e.g., DT[colA=="foo" & colB!="bar"] should exclude rows with NA in colA but include rows where colA is non-NA but colB is NA. Similarly, DT[colA!=colB] should include rows where either colA or colB is NA but not both. And perhaps DT[colA==colB] should include rows where both colA and colB are NA (which it doesn't currently, I believe).
As you have already figured out, this is the reason:
a != ""
#[1] TRUE NA FALSE
You can do what you figured out already, i.e. x[is.na(a) | a != ""] or you could setkey on a and do the following:
setkey(x, a)
x[!J("")]
I want to update one column of a dataframe, referencing it using its original name, is this possible? For example say I had the table 'data'
a b c
1 2 2
3 2 3
4 1 2
and I wanted to update the name of column b to 'd'. I know I could use
colnames(data)[2] <- 'd'
but can I make the change by specifically referencing b, i.e. something like
colnames(data)['b'] <- 'd'
so that if the column ordering of the dataframe changes the correct column name will still be updated.
Thanks in advance
There is a function setnames built into package data.table for exactly that.
setnames(DT, "b", "d")
It changes the names by reference with no copy at all. Any other method using names(data)<- or names(data)[i]<- or similar will copy the entire object, usually several times. Even though all you're doing is changing a column name.
DT must be type data.table for setnames to work, though. So you'd need to switch to data.table or convert using as.data.table, to use it.
Here is the extract from ?setnames. The intention is that you run example(setnames) at the prompt and then the comments relate to the copies you see being reported by tracemem.
DF = data.frame(a=1:2,b=3:4) # base data.frame to demo copies
tracemem(DF)
colnames(DF)[1] <- "A" # 4 copies of entire object
names(DF)[1] <- "A" # 3 copies of entire object
names(DF) <- c("A", "b") # 2 copies of entire object
`names<-`(DF,c("A","b")) # 1 copy of entire object
x=`names<-`(DF,c("A","b")) # still 1 copy (so not print method)
# What if DF is large, say 10GB in RAM. Copy 10GB just to change a column name?
DT = data.table(a=1:2,b=3:4,c=5:6)
tracemem(DT)
setnames(DT,"b","B") # by name; no match() needed. No copy.
setnames(DT,3,"C") # by position. No copy.
setnames(DT,2:3,c("D","E")) # multiple. No copy.
setnames(DT,c("a","E"),c("A","F")) # multiple by name. No copy.
setnames(DT,c("X","Y","Z")) # replace all. No copy.
As of October 2014 this can now be done easily in the dplyr package:
rename(data, d = b)
This seems like a hack, but the first thing that came to mind was to use grepl() with a sufficiently detailed enough search string to only get the column you want. I'm sure there are better options:
dat <- data.frame(a = 1:3, b = 1:3, c = 1:3)
colnames(dat)[grepl("b", colnames(dat))] <- "foo"
dat
#------
a foo c
1 1 1 1
2 2 2 2
3 3 3 3
As Joran points out below, I overcomplicated things...no need for a regex at all. This saves a few characters on the typing too.
colnames(dat)[colnames(dat) == "foo"] <- "bar"
#------
a bar c
1 1 1 1
2 2 2 2
3 3 3 3
Yes but it's more difficult (as far as I know) than numeric indexing. I'm going to provide a dirty function that will do this and if you want to see how to do it just tear the function apart line by line:
rename <- function(df, column, new){
x <- names(df) #Did this to avoid typing twice
if (is.numeric(column)) column <- x[column] #Take numeric input by indexing
names(df)[x %in% column] <- new #What you're interested in
return(df)
}
#try it out
rename(mtcars, 'mpg', 'NEW')
rename(mtcars, 1, 'NEW')
I disagree with #Chase - the grepl solution ain't the luckiest one. I'd say: go with simple ==. Here's why:
d <- data.frame(matrix(rnorm(100), 10))
colnames(d) <- replicate(10, paste(sample(letters[1:5], size = 5, replace=TRUE, prob=c(.1, .6, .1, .1, .1)), collapse = ""))
Now try doing grepl("b", colnames(d)). Either pass fixed = TRUE, or even better do simple colnames(d) == "b" like #joran suggested. Regex matching will always be slower than ==, so for simple tasks like this you may want to use simple ==.
I have three variables in a data frame and would like to swap the 4 columns around from
"dam" "piglet" "fdate" "ssire"
to
"piglet" "ssire" "dam" "tdate"
Is there any way I can do the swapping using R?
Any help would be very much appreciated.
Baz
dfrm <- dfrm[c("piglet", "ssire", "dam", "tdate")]
OR:
dfrm <- dfrm[ , c("piglet", "ssire", "dam", "tdate")]
d <- data.frame(a=1:3, b=11:13, c=21:23)
d
# a b c
#1 1 11 21
#2 2 12 22
#3 3 13 23
d2 <- d[,c("b", "c", "a")]
d2
# b c a
#1 11 21 1
#2 12 22 2
#3 13 23 3
or you can do same thing using index:
d3 <- d[,c(2, 3, 1)]
d3
# b c a
#1 11 21 1
#2 12 22 2
#3 13 23 3
To summarise the other posts, there are three ways of changing the column order, and two ways of specifying the indexing in each method.
Given a sample data frame
dfr <- data.frame(
dam = 1:5,
piglet = runif(5),
fdate = letters[1:5],
ssire = rnorm(5)
)
Kohske's answer: You can use standard matrix-like indexing using column numbers
dfr[, c(2, 4, 1, 3)]
or using column names
dfr[, c("piglet", "ssire", "dam", "fdate")]
DWin & Gavin's answer: Data frames allow you to omit the row argument when specifying the index.
dfr[c(2, 4, 1, 3)]
dfr[c("piglet", "ssire", "dam", "fdate")]
PaulHurleyuk's answer: You can also use subset.
subset(dfr, select = c(2, 4, 1, 3))
subset(dfr, select = c(c("piglet", "ssire", "dam", "fdate")))
You can use subset's 'select' argument;
#Assume df contains "dam" "piglet" "fdate" "ssire"
newdf<-subset(df, select=c("piglet", "ssire", "dam", "tdate"))
I noticed that this is almost an 8-year old question. But for people who are starting to learn R and might stumble upon this question, like I did, you can now use a much flexible select() function from dplyr package to accomplish the swapping operation as follows.
# Install and load the dplyr package
install.packages("dplyr")
library("dplyr")
# Override the existing data frame with the desired column order
df <- select(df, piglet, ssire, dam, tdate)
This approach has following advantages:
You will have to type less as the select() does not require variable names to be enclosed within quotes.
In case your data frame has more than 4 variables, you can utilize select helper functions such as starts_with(), ends_with(), etc. to select multiple columns without having to name each column and rearrange them with much ease.
Relevance Note: In response to some users (myself included) that would like to swap columns without having to specify every column, I wrote this answer up.
TL;DR: A one-liner for numerical indices is provided herein and a function for swapping exactly 2 nominal and numerical indices at the end, neither using imports, that will correctly swap any two columns in a data frame of any size is provided. A function that allows the reassignment of an arbitrary number of columns that may cause unavoidable superfluous swaps if not used carefully is also made available (read more & get functions in Summary section)
Preliminary Solution
Suppose you have some huge (or not) data frame, DF, and you only know the indices of the two columns you want to swap, say 1 < n < m < length(DF). (Also important is that your columns are not adjacent, i.e. |n-m| > 1 which is very likely to be the case in our "huge" data frame but not necessarily for smaller ones; work-arounds for all degenerate cases are provided at the end).
Because it is huge, there are a ton of columns and you don't want to have to specify every other column by hand, or it isn't huge and you're just lazy someone with fine taste in coding, either way, this one-liner will do the trick:
DF <- DF[ c( 1:(n-1), m, (n+1):(m-1), n, (m+1):length(DF) ) ]
Each piece works like this:
1:(n-1) # This keeps every column before column `n` in place
m # This places column `m` where column `n` was
(n+1):(m-1) # This keeps every column between the two in place
n # This places column `n` where column `m` was
(m+1):length(DF) # This keeps every column after column `m` in place
Generalizing for Degenerates
Because of how the : operator works, i.e. allowing "backwards-ranges" like this,
> 10:0
[1] 10 9 8 7 6 5 4 3 2 1 0
we have to be careful about our choices and placements of n and m, hence our previous restrictions. For instance, n < m doesn't lose us any generality (one of the columns has to be before the other one if they are different), however, it means we do need to be careful about which goes where in our line of code. We can make it so that we don't have to check this condition with the following modification:
DF <- DF[ c( 1:(min(n,m)-1), max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m), (max(n,m)+1):length(DF) ) ]
We have replaced every instance of n and m with min(n,m) and max(n,m) respectively, meaning that the correct ordering for our code will be preserved even in the case that m > n.
In the cases where min(n,m) == 1, max(n,m) == length(DF), both of those at the same time, and |n-m| == 1, we we will make some unreadable less aesthetic modifications involving if\else to forget about having to check if these are the case. Versions for where you know that one of these are the case, (i.e. you are always swapping some interior column with the first column, swapping some interior column with the last column, swapping the first and last columns, or swapping two adjacent columns), you can actually express these actions more succinctly because they usually just require omitting parts from our restricted case:
# Swapping not the last column with the first column
# We just got rid of 1:(min(n,m)-1) because it would be invalid and not what we meant
# since min(n,m) == 1
# Now we just stick the other column right at the front
DF <- DF[ c( max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m), (max(n,m)+1):length(DF) ) ]
# Also equivalent since we know min(n,m) == 1, for the leftover index i
DF <- DF[ c( i, 2:(i-1), 1, (i+1):length(DF) ) ]
# Swapping not the first column with the last column
# Similarly, we just got rid of (max(n,m)+1):length(DF) because it would also be invalid
# and not what we meant since max(n,m) == length(DF)
# Now we just stick the other column right at the end
DF <- DF[ c( 1:(min(n,m)-1), max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m) ) ]
# Also equivalent since we know max(n,m) == length(DF), for the leftover index, say i
DF <- DF[ c( 1:(i-1), length(DF), (i+1):(length(DF)-1), i ) ]
# Swapping the first column with the last column
DF <- DF[ c( max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m) ) ]
# Also equivalent (for if you don't actually know the length beforehand, as assumed
# elsewhere)
DF <- DF[ c( length(DF), 2:(length(DF)-1), 1 ) ]
# Swapping two interior adjacent columns
# Here we drop the explicit swap on either side of our middle column segment
# This is actually enough because then the middle segment becomes a backwards range
# because we know that `min(n,m) + 1 = max(n,m)`
# The range is just an ordering of the two adjacent indices from largest to smallest
DF <- DF[ c( 1:(min(n,m)-1), (min(n,m)+1):(max(n,m)-1), (max(n,m)+1):length(DF) )]
"But!", I hear you saying, "What if more than one of these cases occur simultaneously, like did in the third version in the block above!?". Right, coding up versions for each case is an enormous waste of time if one wants to be able to "swap columns" in the most general sense.
Swapping any Two Columns
It will be easiest to generalize our code to cover all of the cases at the same time, because they all employ essentially the same strategy. We will use if\else to keep our code a one-liner:
DF <- DF[ if (n==m) 1:length(DF) else c( (if (min(n,m)==1) c() else 1:(min(n,m)-1) ), (if (min(n,m)+1 == max(n,m)) (min(n,m)+1):(max(n,m)-1) else c( max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m))), (if (max(n,m)==length(DF)) c() else (max(n,m)+1):length(DF) ) ) ]
That's totally unreadable and probably pretty unfriendly to anyone who might try to understand or recreate your code (including yourself), so better to box it up in a function.
# A function that swaps the `n` column and `m` column in the data frame DF
swap <- function(DF, n, m)
{
return (DF[ if (n==m) 1:length(DF) else c( (if (min(n,m)==1) c() else 1:(min(n,m)-1) ), (if (min(n,m)+1 == max(n,m)) (min(n,m)+1):(max(n,m)-1) else c( max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m))), (if (max(n,m)==length(DF)) c() else (max(n,m)+1):length(DF) ) ) ])
}
A more robust version that can also swap on column names and has semi-explanatory comments:
# Returns data frame object with columns `n` and `m` swapped
# `n` and `m` can be column names, numerical indices, or a heterogeneous pair of both
swap <- function(DF, n, m)
{
# Of course, first, we want to make sure that n != m,
# because if they do, we don't need to do anything
if (n==m) return(DF)
# Next, if either n or m is a column name, we want to get its index
# We assume that if they aren't column names, they are indices (integers)
n <- if (class(n)=="character" & is.na(suppressWarnings(as.integer(n)))) which(colnames(DF)==n) else as.integer(n)
m <- if (class(m)=="character" & is.na(supressWarnings(as.integer(m)))) which(colnames(DF)==m) else as.integer(m)
# Make sure each index is actually valid
if (!(1<=n & n<=length(DF))) stop( "`n` represents invalid index!" )
if (!(1<=m & m<=length(DF))) stop( "`m` represents invalid index!" )
# Also, for readability, lets go ahead and set which column is earlier, and which is later
earlier <- min(n,m)
later <- max(n,m)
# This constructs the first third of the indices
# These are the columns that, if any, come before the earlier column you are swapping
firstThird <- if ( earlier==1 ) c() else 1:(earlier-1)
# This constructs the last third of the the indices
# These are the columns, if any, that come after the later column you are swapping
lastThird <- if ( later==length(DF) ) c() else (later+1):length(DF)
# This checks if the columns to be swapped are adjacent and then constructs the
# secondThird accordingly
if ( earlier+1 == later )
{
# Here; the second third is a list of the two columns ordered from later to earlier
secondThird <- (earlier+1):(later-1)
}
else
{
# Here; the second third is a list of
# the later column you want to swap
# the columns in between
# and then the earlier column you want to swap
secondThird <- c( later, (earlier+1):(later-1), earlier)
}
# Now we assemble our indices and return our permutation of DF
return (DF[ c( firstThird, secondThird, lastThird ) ])
}
And, for ease of repatriation with less of the spatial cost, a comment-less version that checks index validity and can handle column names, i.e. does everything in pretty close to the smallest space it can (yes, you could vectorize, using ifelse(...), the two checks that get performed, but then you'd have to unpack the vector back into n,m or change how the final line is written):
swap <- function(DF, n, m)
{
n <- if (class(n)=="character" & is.na(suppressWarnings(as.integer(n)))) which(colnames(DF)==n) else as.integer(n)
m <- if (class(m)=="character" & is.na(suppressWarnings(as.integer(m)))) which(colnames(DF)==m) else as.integer(m)
if (!(1<=n & n<=length(DF))) stop( "`n` represents invalid index!" )
if (!(1<=m & m<=length(DF))) stop( "`m` represents invalid index!" )
return (DF[ if (n==m) 1:length(DF) else c( (if (min(n,m)==1) c() else 1:(min(n,m)-1) ), (if (min(n,m)+1 == max(n,m)) (min(n,m)+1):(max(n,m)-1) else c( max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m))), (if (max(n,m)==length(DF)) c() else (max(n,m)+1):length(DF) ) ) ])
}
Permutations (or How to Do Specifically What the Question Asked and More!)
With our swap function in tow, we can try to actually do what the original question asked. The easiest way to do this, is to build a function that utilizes the really cool power that comes with a choice of heterogeneous arguments. Create a mapping:
mapping <- data.frame( "piglet" = 1, "ssire" = 2, "dam" = 3, "tdate" = 4)
In the case of the original question, these are all of the columns in our original data frame, but we will build a function where this doesn't have to be the case:
# A function that takes two data frames, one with actual data: DF, and the other with a
# rearrangement of the columns: R
# R must be structured so that colnames(R) is a subset of colnames(DF)
# Alternatively, R can be structured so that 1 <= as.integer(colnames(R)) <= length(DF)
# Further, 1 <= R$column <= length(DF), and length(R$column) == 1
# These structural requirements on R are not checked
# This is for brevity and because most likely R has been created specifically for use with
# this function
rearrange <- function(DF, R)
{
for (col in colnames(R))
{
DF <- swap(DF, col, R[col])
}
return (DF)
}
Wait, that's it? Yup. This will swap every column name to the appropriate placement. The power for such simplicity comes from swap taking heterogeneous arguments meaning we can specify the moving column name that we want to put somewhere, and so long as we only ever try to put one column in each position (which we should), once we put that column where it belongs, it won't move again. This means that even though it seems like later swaps could undo previous placements, the heterogeneous arguments make certain that won't happen, and so additionally, the order of the columns in our mapping also doesn't matter. This is a really nice quality because it means that we aren't kicking this whole "organizing the data" issue down the road too much. You only have to be able to determine which placement you want to send each column you want to move to.
Ok, ok, there is a catch. If you don't reassign the entire data frame when you do this, then you have superfluous swaps that occur, meaning that if you re-arrange over a subset of columns that isn't "closed", i.e. not every column name has an index that is represented in the rearrangement, then other columns that you didn't explicitly say to move may get moved to other places they don't exactly belong. This can be handled by creating your mapping very carefully, or simply using numerical indices mapping to other numerical indices. In the latter case, this doesn't solve the issue, but it makes more explicit what swaps are taking place and in what order so planning the rearrangement is more explicit and thus less likely to lead to problematic superfluous swaps.
Summary
You can use the swap function that we built to successfully swap exactly two columns or the rearrange function with a "rearrangement" data frame specifying where to send each column name you want to move. In the case of the rearrange function, if any of the placements chosen for each column name are not already occupied by one of the specified columns (i.e. not in colnames(R)), then superfluous swaps can and are very likely to occur (The only instance they won't is when every superfluous swap has a partner superfluous swap that undoes it before the end. This is, as stated, very unlikely to happen by accident, but the mapping can be structured to accomplish this outcome in practice).
swap <- function(DF, n, m)
{
n <- if (class(n)=="character" & is.na(suppressWarnings(as.integer(n)))) which(colnames(DF)==n) else as.integer(n)
m <- if (class(m)=="character" & is.na(suppressWarnings(as.integer(m)))) which(colnames(DF)==m) else as.integer(m)
if (!(1<=n & n<=length(DF))) stop( "`n` represents invalid index!" )
if (!(1<=m & m<=length(DF))) stop( "`m` represents invalid index!" )
return (DF[ if (n==m) 1:length(DF) else c( (if (min(n,m)==1) c() else 1:(min(n,m)-1) ), (if (min(n,m)+1 == max(n,m)) (min(n,m)+1):(max(n,m)-1) else c( max(n,m), (min(n,m)+1):(max(n,m)-1), min(n,m))), (if (max(n,m)==length(DF)) c() else (max(n,m)+1):length(DF) ) ) ])
}
rearrange <- function(DF, R)
{
for (col in colnames(R))
{
DF <- swap(DF, col, R[col])
}
return (DF)
}
I quickly wrote a function that takes a vector v and column indexes a and b which you want to swap.
swappy = function(v,a,b){ # where v is a dataframe, a and b are the columns indexes to swap
name = deparse(substitute(v))
helpy = v[,a]
v[,a] = v[,b]
v[,b] = helpy
name1 = colnames(v)[a]
name2 = colnames(v)[b]
colnames(v)[a] = name2
colnames(v)[b] = name1
assign(name,value = v , envir =.GlobalEnv)
}
I was using the function by Khôra Willis, which is helpful. But I encountered an error. I tried to make corrections. Here is R code that finally works. The arguments n and m could either be column names or column numbers in data frame DF.
require(tidyverse)
swap <- function(DF, n, m)
{
if (class(n)=="character") n <- which(colnames(DF)==n)
if (class(m)=="character") m <- which(colnames(DF)==m)
p <- NCOL(DF)
if (!(1<=n & n<=p)) stop("`n` represents invalid index!")
if (!(1<=m & m<=p)) stop("`m` represents invalid index!")
index <- 1:p
index[n] <- m; index[m] <- n
DF0 <- DF %>% select(all_of(index))
return(DF0)
}