Replace NAs in split lists

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 <esawiek at 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 <esawiek at 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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