Upon closer examination I see that you are not using the split version of
df1 as I usually would, so here is a reproducible example:
#----
df1 <- read.table( text=
"ID ID_2 Firist Value
1 a aa TRUE 2
2 a ab FALSE NA
3 a ac FALSE NA
4 b aa TRUE 5
5 b ab FALSE NA
", header=TRUE, as.is=TRUE )
sdf <- split( df1, df1$ID )
# note the extra [ 1 ] in case you have more than one non-NA value
# per ID
sdf2 <- lapply( sdf
, function( z ) {
z$Value <- ifelse( is.na( z$Value )
, z$Value[ !is.na( z$Value ) ][ 1 ]
, z$Value
)
z
}
)
df2 <- do.call( rbind, sdf2 )
df2
#> ID ID_2 Firist Value
#> a.1 a aa TRUE 2
#> a.2 a ab FALSE 2
#> a.3 a ac FALSE 2
#> b.4 b aa TRUE 5
#> b.5 b ab FALSE 5
# or using tidyverse methods
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
df3 <- ( df1
%>% group_by( ID )
%>% do({
mutate( .
, Value = ifelse( is.na( Value )
, Value[ !is.na( Value ) ][ 1 ]
, Value
)
)
})
%>% ungroup
)
df3
#> # A tibble: 5 x 4
#> ID ID_2 Firist Value
#> <chr> <chr> <lgl> <int>
#> 1 a aa T 2
#> 2 a ab F 2
#> 3 a ac F 2
#> 4 b aa T 5
#> 5 b ab F 5
#----
On Sun, 7 Jan 2018, Jeff Newmiller wrote:
Why do you want to modify df1?
Why not just reassemble the parts as a new data frame and use that going
forward in your calculations? That is generally the preferred approach
in R so you can re-do your calculations easily if you find a mistake
later.
--
Sent from my phone. Please excuse my brevity.
On January 7, 2018 7:35:59 PM PST, Ek Esawi <esaw...@gmail.com> wrote:
I just came up with a solution right after i posted the question, but
i figured there must be a better and shorter one.than my solution
sdf1[[1]][1,4]<-lapplyresults[[1]]
sdf1[[2]][1,4]<-lapplyresults[[2]]
EK
On Sun, Jan 7, 2018 at 10:13 PM, Ek Esawi <esaw...@gmail.com> wrote:
Hi all--
I stumbled on this problem online. I did not like the solution given
there which was a long UDF. I thought why cannot split and l/s apply
work here. My aim is to split the data frame, use l/sapply, make
changes on the split lists and combine the split lists to new data
frame with the desired changes/output.
The data frame shown below has a column named ID which has 2
variables
a and b; i want to replace the NAs on the Value column by 2, which is
the only numeric entry, for ID=a and by 5 for ID=b.
I worked out the solution but could not replace the results in the
split lists.
Original dataframe , df1
ID ID_2 Firist Value
1 a aa TRUE 2
2 a ab FALSE NA
3 a ac FALSE NA
4 b aa TRUE 5
5 b ab FALSE NA
Sdf1
$a
ID ID_2 Firist Value
1 a aa TRUE 2
2 a ab FALSE NA
3 a ac FALSE NA
$b
ID ID_2 Firist Value
4 b aa TRUE 5
5 b ab FALSE NA
Desired results
ID ID_2 Firist Value
1 a aa TRUE 2
2 a ab FALSE 2
3 a ac FALSE 2
$b
ID ID_2 Firist Value
4 b aa TRUE 5
5 b ab FALSE 5
My code
sdf <- split(df1,df$ID)
lapply(sdf, function(z)
ifelse(is.na(z$Value),z$Value[!is.na(z$Value)],z$Value))
result:
$ a: num [1:3] 2 2 2
$ b: num [1:2] 5 5
How could I put these two lists back in the split data frame, sdf1?
Then I could use do.call to reassemble a data frame from the split
lists,
Thanks,
EK
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