Expand a dataframe IN R into a larger dataframe - r

Question

I Have used the code below to generate a dataframe df1
df1<-data.frame("ID"=c("A", "A", "A", "A", "A", "B", "B", "B", 'B', "B"),
"X_Fr"=c(NA, NA, NA, NA,NA,1,2,3,4,5), "X_Ax"=c(NA, NA, NA, NA, NA,
.2,.3,.4,.2, .3),
"Y_Fr"=c(1,2,3,4,5,1,2,3,4,5),
"Y_Ax"=c(.1,.2,.3,.4,.1,.3,.4,.5,.2,.3),"Z_Fr"=c(1,2,NA, NA, 3,
1,3,4,5,10),
"Z_Ax"=c(.1,.2,NA,NA,.5, .1,.2,.4,.3,.5) )
ID X_Fr X_Ax Y_Fr Y_Ax Z_Fr Z_Ax
1 A NA NA 1 0.1 1 0.1
2 A NA NA 2 0.2 2 0.2
3 A NA NA 3 0.3 NA NA
4 A NA NA 4 0.4 NA NA
5 A NA NA 5 0.1 3 0.5
6 B 1 0.2 1 0.3 1 0.1
7 B 2 0.3 2 0.4 3 0.2
8 B 3 0.4 3 0.5 4 0.4
9 B 4 0.2 4 0.2 5 0.3
10 B 5 0.3 5 0.3 10 0.5
I would like to expand the dataframe to give the following data frame as output
ID X_Fr Y_Fr Z_Fr X_Ax Y_Ax Z_Ax
1 A 1 1 1 NA 0.1 0.1
2 A 2 2 2 NA 0.2 0.2
3 A 3 3 3 NA 0.3 0.5
4 A 4 4 4 NA 0.4 NA
5 A 5 5 5 NA 0.1 NA
6 B 1 1 1 0.2 0.3 0.1
7 B 2 2 2 0.3 0.4 NA
8 B 3 3 3 0.4 0.5 0.2
9 B 4 4 4 0.2 0.2 0.4
10 B 5 5 5 0.3 0.3 0.3
11 B 6 6 6 NA NA NA
12 B 7 7 7 NA NA NA
13 B 8 8 8 NA NA NA
14 B 9 9 9 NA NA NA
15 B 10 10 10 NA NA 0.5
I have tried the following code to obtain the above dataframe
library(tidyr)
library(dplyr)
df2<-df1 %>% complete(ID, nesting(X_Fr=full_seq(na.omit(c(X_Fr, Y_Fr,
Z_Fr)),1), Y_Fr=full_seq(na.omit(c(X_Fr, Y_Fr, Z_Fr)),1),
Z_Fr=full_seq(na.omit(c(X_Fr, Y_Fr, Z_Fr)),1)))
I am unable to obtain this result. I request someone to take a look.

Answer 1

I think it's two steps process so I create two new dataframes processed them and joined them later
library(dplyr)
library(tidyr)
df1x<-df1 %>% select(ID,matches('^X|^Y')) #select ID and any cloumn start with X or Y
df1y<-df1 %>% select(ID,matches('^Z'))
df1y %>% group_by(ID) %>% #group by ID column
arrange(Z_Fr, .by_group=TRUE) %>% #arrange Z_Fr column in ascending order so we can use row_number later
mutate(Z_Fr=coalesce(Z_Fr,as.numeric(row_number()))) %>% #Use row_number to fill NA's in Z_Fr.
#See ?dplyr::row_number() for more details
ungroup() %>% #Before using complete we need to ungroup
complete(ID, nesting(Z_Fr=full_seq(Z_Fr,1))) %>%
left_join(df1x, by=c('ID','Z_Fr'='Y_Fr')) #left join using "on" ID and Z_Fr from df1y and Y_Fr from df1x