reshaping a dataframe for prediction - r

Question

I just picked up on the package reshape today and I'm having some trouble to understand how it works.
I have the following dataframe:
name workoutnum time weight raceid final position
tommy 1 12 140 1 2
tommy 2 14 140 1 2
tommy 3 11 140 1 2
sarah 1 10 115 1 1
sarah 2 10 115 1 1
sarah 3 11 115 1 1
sarah 4 15 115 1 1
How would I put all this in one row? So the dataframe would look like:
name workoutnum1 workoutnum2 workoutnum3 workoutnum4 time1 time2 time3 time4 weight raceid final_position
tommy 1 1 1 0 12 14 11 NA 140 1 2
sarah 1 1 1 1 10 10 11 15 115 1 1
So all columns would be attached to the workout values.
Is this even the proper way to do it?

Answer 1

reshape seems like a natural part of what you want to do, but won't get you all the way there.
Here's a reshape2 approach that fully melts the data, then casts it back to data.frame, with some tweaks along the way to get the desired output.
Note that in the call to melt(), the variables in the id.vars arguments will remain wide. Then in dcast(), the variable that'll be cast wide is on the RHS of the ~.
library(reshape2)
library(dplyr)
# fully melt the data
d_melt <- melt(d, id.vars = c("name", "raceid", "position", "weight"))
# index the variables within name and variable
d_melt <- d_melt %>%
group_by(name, variable) %>%
mutate(i = row_number(),
wide_variable = paste0(variable, i))
# cast as wide
d_wide <- dcast(d_melt, name + raceid + position + weight ~ wide_variable, value.var = "value")
# replace the workoutnum indices with indicators for missingness
d_wide %>% mutate_each(funs(ifelse(!is.na(.), 1L, 0L)), matches("workoutnum\\d"))
# name raceid position weight time1 time2 time3 time4 workoutnum1 workoutnum2
# 1 sarah 1 1 115 10 10 11 15 1 1
# 2 tommy 1 2 140 12 14 11 NA 1 1
# workoutnum3 workoutnum4
# 1 1 1
# 2 1 0
Data:
structure(list(name = structure(c(2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("sarah", "tommy"), class = "factor"), workoutnum = c(1L, 2L, 3L, 1L, 2L, 3L, 4L), time = c(12L, 14L, 11L, 10L, 10L, 11L, 15L), weight = c(140L, 140L, 140L, 115L, 115L, 115L, 115L), raceid = c(1L, 1L, 1L, 1L, 1L, 1L, 1L), position = c(2L, 2L, 2L, 1L, 1L, 1L, 1L)), .Names = c("name", "workoutnum", "time", "weight", "raceid", "position"), class = "data.frame", row.names = c(NA, -7L))

Answer 2

Here's an approach using dcast from "data.table", which reshapes a little more like the reshape function in base R.
The only change I've made to the data is the inclusion of another "time" variable though, as pointed out by #rawr in the comments, it almost seems like your "workoutnum" is the time variable.
I've used getanID from my "splitstackshape" package to generate the "time" variable, but you can create this variable in many different ways.
library(splitstackshape)
dcast(getanID(mydf, c("name", "raceid", "final_position")),
name + raceid + final_position ~ .id,
value.var = c("workoutnum", "time", "weight"))
## name raceid final_position workoutnum_1 workoutnum_2 workoutnum_3
## 1: sarah 1 1 1 2 3
## 2: tommy 1 2 1 2 3
## workoutnum_4 time_1 time_2 time_3 time_4 weight_1 weight_2 weight_3 weight_4
## 1: 4 10 10 11 15 115 115 115 115
## 2: NA 12 14 11 NA 140 140 140 NA
If you're using getanID, you can also use reshape like this:
reshape(getanID(mydf, c("name", "raceid", "final_position")),
idvar = c("name", "raceid", "final_position"), timevar = ".id",
direction = "wide")
## name raceid final_position workoutnum.1 time.1 weight.1 workoutnum.2 time.2
## 1: tommy 1 2 1 12 140 2 14
## 2: sarah 1 1 1 10 115 2 10
## weight.2 workoutnum.3 time.3 weight.3 workoutnum.4 time.4 weight.4
## 1: 140 3 11 140 NA NA NA
## 2: 115 3 11 115 4 15 115
but dcast would be more efficient in general.