Calculate stats in concatenated strings in R - r

Question

Suppose I have a dataframe like this:
X. Name Type Total HP Attack Defense Sp..Atk Sp..Def Speed
795 718 Zygarde50% Forme Dragon/Ground 600 108 100 121 81 95 95
796 719 Diancie Rock/Fairy 600 50 100 150 100 150 50
797 719 DiancieMega Diancie Rock/Fairy 700 50 160 110 160 110 110
798 720 HoopaHoopa Confined Psychic/Ghost 600 80 110 60 150 130 70
799 720 HoopaHoopa Unbound Psychic/Dark 680 80 160 60 170 130 80
800 721 Volcanion Fire/Water 600 80 110 120 130 90 70
If I want to calculate the average stats (Total, HP, Attack, Defense, etc...), per type Dragon, type Ground, type Rock, type Fairy, etc... (instead of type Dragon/Ground, Rock/Fairy), how would I proceed? The stats of pokemons that belong to any two types would be used in calculating the average stats for both.
I have written the code using functions in the dplyr package:
summaryStats_byType<- summarise(byType,
count = n(),
averageTotal = mean(Total, na.rm = T),
averageHP = mean(HP, na.rm = T),
averageDefense = mean(Defense, na.rm = T),
averageSpAtk = mean(Sp..Atk, na.rm = T),
averageSpDef = mean(Sp..Def, na.rm = T),
averageSpeed = mean(Speed, na.rm = T))
but obviously it counts "Dragon/Ground" as a type instead of two.

Answer 1

One way is to split the Type column in long format (I chose cSplit from splitstackshape to do this) and group_by as usual, i.e.
library(splitstackshape)
library(dplyr)
df1 <- cSplit(df, 'Type', sep = '/', 'long')
df1 %>%
group_by(Type) %>%
summarise_each(funs(mean), -c(X., Name))
# A tibble: 9 × 8
# Type Total HP Attack Defense Sp..Atk Sp..Def Speed
# <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 Dark 680 80 160 60 170 130 80
#2 Dragon 600 108 100 121 81 95 95
#3 Fairy 650 50 130 130 130 130 80
#4 Fire 600 80 110 120 130 90 70
#5 Ghost 600 80 110 60 150 130 70
#6 Ground 600 108 100 121 81 95 95
#7 Psychic 640 80 135 60 160 130 75
#8 Rock 650 50 130 130 130 130 80
#9 Water 600 80 110 120 130 90 70
Alternatively (as noted by #DavidArenburg) we can also use separate_rows from tidyr as part of the pipe, i.e.
library(tidyr)
library(dplyr)
df %>%
separate_rows(Type) %>%
group_by(Type) %>%
summarise_each(funs(mean), -c(X., Name))
which of course yields the same results
DATA
dput(df)
structure(list(X. = c(718L, 719L, 719L, 720L, 720L, 721L), Name = structure(c(6L,
1L, 2L, 3L, 4L, 5L), .Label = c("Diancie", "DiancieMega_Diancie",
"HoopaHoopa_Confined", "HoopaHoopa_Unbound", "Volcanion", "Zygarde50%_Forme"
), class = "factor"), Type = structure(c(1L, 5L, 5L, 4L, 3L,
2L), .Label = c("Dragon/Ground", "Fire/Water", "Psychic/Dark",
"Psychic/Ghost", "Rock/Fairy"), class = "factor"), Total = c(600L,
600L, 700L, 600L, 680L, 600L), HP = c(108L, 50L, 50L, 80L, 80L,
80L), Attack = c(100L, 100L, 160L, 110L, 160L, 110L), Defense = c(121L,
150L, 110L, 60L, 60L, 120L), Sp..Atk = c(81L, 100L, 160L, 150L,
170L, 130L), Sp..Def = c(95L, 150L, 110L, 130L, 130L, 90L), Speed = c(95L,
50L, 110L, 70L, 80L, 70L)), .Names = c("X.", "Name", "Type",
"Total", "HP", "Attack", "Defense", "Sp..Atk", "Sp..Def", "Speed"
), class = "data.frame", row.names = c("795", "796", "797", "798",
"799", "800"))