Converting Column into Multiple Columns Using String Value in R - r

Question

I'm having trouble cleaning my dataset in R. I have a dataset with three variables(name, day, data). The third variable actually contains all of my data but it needs to be parsed. I need to split this column into multiple columns based on a value in the column. For example, in the following database:
x <- data.frame("name" = c("John","John","John","John","John","Sarah","Sarah","Sarah"), "Day" = c(1,1,1,1,1,2,2,2), "Data" = c("Map 28", 2,3,"Transfer","Time","Map 18",2,3))
which looks like:
name Day Data
1 John 1 Map 28
2 John 1 2
3 John 1 3
4 John 1 Transfer
5 John 1 Time
6 Sarah 2 Map 18
7 Sarah 2 2
8 Sarah 2 3
I need to look through the 'data' column and find any time the word 'map' is used, and then convert all of the data under that into another column. Like so:
name Day Data Val1 Val2 Val3 Val4
1 John 1 Map 28 2 3 Transfer Time
2 Sarah 2 Map 18 2 3 <NA> <NA>
Any help with this would be greatly appreciated!
[EDIT]
Sorry all, I think I oversimplified my example... the issue is that each person on each day will have multiple "Map" values that need to be located. So it looks more like the following.
x <- data.frame("name" = c("John","John","John","John","John","John","John","John","John","John","John","John","Sarah","Sarah","Sarah","Sarah","Sarah","Sarah"),
"Day" = c(1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2),
"Data" = c("Map 28", 2,3,"Transfer","Time","Map 15",2,3,"Text","Map3",2,4,"Map 18",2,3,"Map 22",2,3))
name Day Data
1 John 1 Map 28
2 John 1 2
3 John 1 3
4 John 1 Transfer
5 John 1 Time
6 John 1 Map 15
7 John 1 2
8 John 1 3
9 John 1 Text
10 John 1 Map3
11 John 1 2
12 John 1 4
13 Sarah 2 Map 18
14 Sarah 2 2
15 Sarah 2 3
16 Sarah 2 Map 22
17 Sarah 2 2
18 Sarah 2 3
and then the final output would be...
y <- data.frame("name" = c("John","John","John","Sarah", "Sarah"),
"Day" =c(1,1,1,2,2),
"Data"= c("Map 28","Map 15","Map 3","Map 18","Map 22"),
"Val1" =c(2,2,2,2,2),
"Val2"=c(3,3,4,3,3),
"Val3"=c("Transfer","Text",NA,NA,NA),
"Val4"=c("Time",NA,NA,NA,NA))
name Day Data Val1 Val2 Val3 Val4
1 John 1 Map 28 2 3 Transfer Time
2 John 1 Map 15 2 3 Text <NA>
3 John 1 Map 3 2 4 <NA> <NA>
4 Sarah 2 Map 18 2 3 <NA> <NA>
5 Sarah 2 Map 22 2 3 <NA> <NA>

Answer 1

you could use reshape from base R after adding a time variable using ave
reshape(transform(x,time=ave(Day,Day,FUN=seq_along)),v.names="Data",dir="wide",idvar = "name")
name Day Data.1 Data.2 Data.3 Data.4 Data.5
1 John 1 Map 28 2 3 Transfer Time
6 Sarah 2 Map 18 2 3 <NA> <NA>
with your new edit: Using base R you could do:
d = transform(x,ID=cumsum(grepl('Map',Data))->a,time=ave(a,a,FUN=seq_along))
reshape(d,v.names="Data",idvar = "ID",dir="wide")
name Day ID Data.1 Data.2 Data.3 Data.4 Data.5
1 John 1 1 Map 28 2 3 Transfer Time
6 John 1 2 Map 15 2 3 Text <NA>
10 John 1 3 Map3 2 4 <NA> <NA>
13 Sarah 2 4 Map 18 2 3 <NA> <NA>
16 Sarah 2 5 Map 22 2 3 <NA> <NA>
with tidyverse you could do:
library(tidyverse)
x%>%
group_by(ID = cumsum(str_detect(Data,"Map")))%>%
mutate(time=1:n())%>%
spread(time,Data)
# A tibble: 5 x 8
# Groups: ID [5]
name Day ID `1` `2` `3` `4` `5`
<fct> <dbl> <int> <fct> <fct> <fct> <fct> <fct>
1 John 1 1 Map 28 2 3 Transfer Time
2 John 1 2 Map 15 2 3 Text NA
3 John 1 3 Map3 2 4 NA NA
4 Sarah 2 4 Map 18 2 3 NA NA
5 Sarah 2 5 Map 22 2 3 NA NA