doc - 1 month ago 3

R Question

Example:

`a b p.a p.b`

1 2 x y

3 4 c d

2 1 y x

5 6 f e

1 1 x x

I would like to join

`p.a`

`p.b`

`a`

`b`

`a`

`b`

`b`

`a`

`a`

`b`

`paste`

The result should be as follows:

`a b p.a p.b joined`

1 2 x y xy

3 4 c d cd

1 2 x y xy

5 6 f e fe

1 1 x x xx

It is not important, whether the rows for

`a`

`b`

`1 2`

`2 1`

`1 2`

`2 1`

Answer

We can use `pmin/pmax`

to get the elements in the right order and then `paste`

them

```
library(dplyr)
df1 %>%
mutate(a1 = pmin(a,b), b1 = pmax(a,b), p.a1 = pmin(p.a, p.b),
p.b1 = pmax(p.a, p.b), joined = paste0(p.a1, p.b1)) %>%
select(-a, -b, -p.a, -p.b) %>%
rename(a=a1, b= b1, p.a= p.a1, p.b = p.b1)
# a b p.a p.b joined
#1 1 2 x y xy
#2 3 4 c d cd
#3 1 2 x y xy
```

Or we can use `base R`

```
lst <- lapply(seq(1, ncol(df1), by = 2), function(i) {
x1 <- df1[i:(i+1)]
list(do.call(pmin, x1), do.call(pmax, x1))})
df1[1:2] <- lst[[1]]
df1[3:4] <- lst[[2]]
df1$joined <- do.call(paste0, df1[3:4])
df1
# a b p.a p.b joined
#1 1 2 x y xy
#2 3 4 c d cd
#3 1 2 x y xy
```

Using the updated dataset, we can loop through the rows, get the `order`

based on the 1st two column elements, and `order`

the columns based on that.

```
df2 <- do.call(rbind, lapply(seq_len(nrow(df1)), function(i) {
x1 <- df1[i,]
i1 <- order(unlist(x1[1:2]))
x1[1:2] <- unlist(x1[1:2])[i1]
x1[3:4] <- unlist(x1[3:4])[i1]
x1}))
df2$joined <- do.call(paste0, df2[3:4])
df2
# a b p.a p.b joined
#1 1 2 x y xy
#2 3 4 c d cd
#3 1 2 x y xy
#4 5 6 f e fe
#5 1 1 x x xx
```

Source (Stackoverflow)

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