Expand a dataframe based on columns in the dataframe in R - r

Question

I have the following dataframe in R
df<-data.frame(
"Val1"=seq(from=1, to=40, by=5), 'Val2'=c(2,4,2,5,11,3,5,3),
"Val3"=seq(from=5, to=40, by=5), "Val4"=c(3,5,7,3,7,5,7,8))
The resulting dataframe looks as follows. Val 1, Val3 are the causal variables and Val2, Val4 are the dependent variables
Val1 Val2 Val3 Val4
1 1 2 5 3
2 6 4 10 5
3 11 2 15 7
4 16 5 20 3
5 21 11 25 7
6 26 3 30 5
7 31 5 35 7
8 36 3 40 8
I wish to obtain the following dataframe as an output
Val1 Val2 Val3 Val4
1 1 2 1 NA
2 2 NA 2 NA
3 3 NA 3 3
4 4 NA 4 NA
5 5 NA 5 NA
6 6 4 6 NA
7 7 NA 7 NA
8 8 NA 8 NA
9 9 NA 9 NA
10 10 NA 10 5
11 11 2 11 NA
12 12 NA 12 NA
13 13 NA 13 NA
14 14 NA 14 NA
15 15 NA 15 7
16 16 5 16 NA
17 17 NA 17 NA
18 18 NA 18 NA
19 19 NA 19 NA
20 20 NA 20 3
21 21 11 21 NA
22 22 NA 22 NA
23 23 NA 23 NA
24 24 NA 24 NA
25 25 NA 25 7
26 26 3 26 NA
27 27 NA 27 NA
28 28 NA 28 NA
29 29 NA 29 NA
30 30 NA 30 5
31 31 5 31 NA
32 32 NA 32 NA
33 33 NA 33 NA
34 34 NA 34 NA
35 35 NA 35 7
36 36 3 36 NA
37 37 NA 37 NA
38 38 NA 38 NA
39 39 NA 39 NA
40 40 NA 40 8
How do I accomplish this. I have created the following code but it involves creating a second dataframe and then copying data from the first to the second. Is there a way to overwrite the existing dataframe. I would like to avoid loops
df2<-data.frame('Val1'=
seq(from=min(na.omit(c(df$Val1, df$Val3))), to= max(na.omit(c(df$Val1,
df$Val3))), by=1), "Val3"=seq(from=min(na.omit(c(df$Val1, df$Val3))), to=
max(na.omit(c(df$Val1, df$Val3))), by=1))
###### Create two loops
for(i in df$Val1){
for(j in df2$Val1){
if(i==j){
df2$Val2[df2$Val1==j]=df$Val2[df$Val1==i]
} else{df2$Val2[df2$Val1==j]=NA}}}
for(i in df$Val3){ for(j in df2$Val3){
if(i==j){df2$Val4[df2$Val3==j]=df$Val4[df$Val3==i]
} else{df2$Val4[df2$Val3==j]=NA}}}
Is there a faster vectorised way to accomplish the same. requesting some one to help

Answer 1

Assuming there's a slight error in your output example (row 3 should show NA for Val4 and the 3 in row 3 should be in row 5), this works:
library(tidyverse)
df_new <- bind_cols(
df %>%
select(Val1, Val2) %>%
complete(., expand(., Val1 = 1:40)),
df %>%
select(Val3, Val4) %>%
complete(., expand(., Val3 = 1:40))
)
> df_new
# A tibble: 40 x 4
Val1 Val2 Val3 Val4
<dbl> <dbl> <dbl> <dbl>
1 1 2 1 NA
2 2 NA 2 NA
3 3 NA 3 NA
4 4 NA 4 NA
5 5 NA 5 3
6 6 4 6 NA
7 7 NA 7 NA
8 8 NA 8 NA
9 9 NA 9 NA
10 10 NA 10 5
# ... with 30 more rows
We use bind_cols() to put together two parts of the dataframe:
First we select the first two columns, expand() the causal variable and complete() the data, then we do it again for the third and fourth column.