1

I have one dataframe DATA_SET, which is composed of three columnsc(TEST1, TEST2, TEST3) and a second dataframe DATA_SET1 which is composed of two columns c(key, value). 

DATA_SET<-data.frame(
                     TEST1=c(200,220,200,260,300,290,320,320,360,400),
                     TEST2=c(200,220,200,260,400,290,220,370,260,200),
                     TEST3=c(200,220,200,260,500,290,120,240,160,400)
                     )            

DATA_SET1<-data.frame(         
                      key=c(rep("TEST1",10),rep("TEST2",10),rep("TEST3",10)),
                      value=c(700,700,700,700,700,700,700,700,700,700)
                      )

So my intention is to change DATA_SET1 with melt() function or something similar and after that combine that with first table (i.e. DATA_SET) to get a table like the one below.

enter image description here

M--
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silent_hunter
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  • If I were you I would have had `DATA_SET1` with different values for `TEST1`, `TEST2`, etc. so it could be checked that whether the right columns has been combined with `DATA_SET` or not. In general, your example should be simple but exclusive to take as much advantage of a [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example). Cheers. – M-- May 15 '19 at 20:24

2 Answers2

1

You can do the following in base r.

unstack takes care of "change DATA_SET1 with melt() function or s̲o̲m̲e̲t̲h̲i̲n̲g̲ ̲si̲m̲i̲l̲a̲r̲" and rbind is for "combine with first table (DATA_SET)":

rbind(DATA_SET, unstack(DATA_SET1, form = value ~ key))

#>    TEST1 TEST2 TEST3
#> 1    200   200   200
#> 2    220   220   220
#> 3    200   200   200
#> 4    260   260   260
#> 5    300   400   500
#> 6    290   290   290
#> 7    320   220   120
#> 8    320   370   240
#> 9    360   260   160
#> 10   400   200   400
#> 11   700   700   700
#> 12   700   700   700
#> 13   700   700   700
#> 14   700   700   700
#> 15   700   700   700
#> 16   700   700   700
#> 17   700   700   700
#> 18   700   700   700
#> 19   700   700   700
#> 20   700   700   700

Created on 2019-05-15 by the reprex package (v0.2.1)

M--
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0

Using the tidyverse, tidyr and dplyr:

library(tidyr)
library(dplyr)

DATA_SET1 %>%
mutate(id = rep(1:10, 3)) %>%
spread(key, value) %>%
select(-id) %>%
bind_rows(DATA_SET)
Jet
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