我有二项式事件的表格（例如输赢数量），我想重塑为长格式，以便每个事件都在一个单独的行上。< / p>
数据看起来像这样：
#Fake data
team <- c("a","b","c","d")
wins.home <- c(0,1,0,2); wins.away <- c(1,1,1,1)
loss.home <- c(0,1,2,1); loss.away <- c(2,1,2,2)
df <- data.frame(team,wins.home,wins.away,loss.home,loss.away)
df$games.tot <- apply(df[,-1],1,sum)
#Dataframe in WIDE format
team wins.home wins.away loss.home loss.away games.tot
a 0 1 0 2 3
b 1 1 1 1 4
c 0 1 2 2 5
d 2 1 1 2 6
我想将这些广泛的数据重塑为LONG格式，如下所示：
team game where win
a 1 away 1
a 2 away 0
a 3 away 0
b 1 home 1
b 2 away 1
b 3 home 0
b 4 away 0
编辑：游戏索引是任意的，只是一个索引。
转换此内容以便reshape2::melt可以轻松计算每行中每个类别的计数，但我不知道如何将计数进一步细分为二进制事件。
答案 0 :(得分：3)
可能有一个较短的解决方案，但作为一个快速解决方案：
library(stringr)
library(dplyr)
library(reshape2)
#initial melt
df2 <- melt(df[-6], id.vars='team')
#split the variable column to get the 'away and home' column
df2 <- cbind(df2, str_split_fixed(df2$variable, "\\.", 2))
#create the wins column
df2$win <- ifelse(df2$`1` == 'wins',1,0)
#replicate the number of rows according to value
df2 <- df2[rep(1:nrow(df2), df2$value),]
#remove unnecessary columns
df2$variable <- df2$value <- df2$`1` <- NULL
#final group by to add the game column
df2 %>% group_by(team) %>% mutate(game = 1:n()) %>% arrange(team)
输出：
Source: local data frame [18 x 4]
Groups: team [4]
team 2 win game
(fctr) (fctr) (dbl) (int)
1 a away 1 1
2 a away 0 2
3 a away 0 3
4 b home 1 1
5 b away 1 2
6 b home 0 3
7 b away 0 4
#...and so on
答案 1 :(得分：2)
这是另一种考虑因素（尽管melt，因为第一步更有意义）：
使用apply创建列名列表，并stack将它们分成两列data.frame，其中包含＆＃34; ind＆＃34; （团队）和＆＃34;价值观＆＃34; （列名）。
temp <- stack(setNames(apply(df[-c(1, length(df))], 1, function(x)
rep(names(df[-c(1, length(df))]), x)), df[[1]]))
然后，加载我的＆＃34; splitstackshape＆＃34;打包并执行以下操作：
library(splitstackshape)
setnames( ## To create nice names
getanID( ## "game" numbers
cSplit(temp, "values", ".")[ ## Splits values into columns
, values_1 := as.numeric(values_1 == "wins")], ## "wins" = 1, others = 0
"ind"), ## game numbers based on team
c("team", "win", "where", "game"))[] ## final names we want
# team win where game
# 1: a 1 away 1
# 2: a 0 away 2
# 3: a 0 away 3
# 4: b 1 home 1
# 5: b 1 away 2
# 6: b 0 home 3
# 7: b 0 away 4
# 8: c 1 away 1
# 9: c 0 home 2
# 10: c 0 home 3
# 11: c 0 away 4
# 12: c 0 away 5
# 13: d 1 home 1
# 14: d 1 home 2
# 15: d 1 away 3
# 16: d 0 home 4
# 17: d 0 away 5
# 18: d 0 away 6
这是一种主要使用＆＃34; tidyr＆＃34;和＆＃34; dplyr＆＃34;，在＆＃34; splitstackshape＆＃34;的一点点帮助下：
library(dplyr)
library(tidyr)
library(splitstackshape)
gather(df[-6], var, val, -team) %>% ## A new take on melt
expandRows("val") %>% ## Replicates rows by values
separate(var, into = c("win", "where")) %>% ## Splits into two columns
group_by(team) %>% ## Grouping is required
mutate(win = as.numeric(win == "wins"), ## Numeric version of win
game = sequence(n())) %>% ## Sequence of games
arrange(team) ## Final sorting
而且，为了完整性，还有一个＆＃34; data.table＆＃34;替代方案可能是这样的：
library(data.table)
DT <- as.data.table(df)
sdcols <- names(DT)[-c(1, length(DT))]
DT[, list(val = rep(sdcols, .SD)), by = team, .SDcols = sdcols][
, c("win", "where") := tstrsplit(val, ".", TRUE)][
, `:=`(val = NULL, win = as.numeric(win == "wins"))][
, game := sequence(.N), by = team][]