基于其他列R中的多个标准平均列值
my.df1是data.frame，具有许多独特的观察结果，但具有相似的特征（在此示例中为Colour，Type＆amp; Size）。对于my.df2中的每个特征组合，我想计算mean中符合条件的所有观察值的SD和my.df1。因此，例如在my.df2的第一行中，我想计算具有以下特征的来自mean的所有观察值的PriceOne和PriceTwo的SD和my.df1：颜色为蓝色，类型1和大小S.注意：对于第5行，我想计算PriceOne的mean和SD以及来自my.df1的所有具有蓝色的观察值的PriceTwo，所以不管它们的类型和大小。我的原始数据集有更多的观察，标准变量和价格列，因此高度赞赏可扩展的解决方案。
my.df1 <- data.frame(Colour = c('Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Red','Red','Red','Red','Red','Red','Red','Red','Red','Red','Red','Red'),
Type = c(1,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1,2),
Size = c('S','S','S','S','S','S','M','M','M','M','M','M','S','S','S','S','S','S','M','M','M','M','M','M'),
PriceOne = c(10,15,20,18,19,11,12,16,20,21,10,11,10,15,10,18,20,14,21,15,28,19,10,11),
PriceTwo = c(10,15,10,18,20,14,21,15,28,19,10,11,10,15,20,18,19,11,12,16,20,21,10,11))
my.df1(head)
Colour Type Size PriceOne PriceTwo
1 Blue 1 S 10 10
2 Blue 1 S 15 15
3 Blue 2 S 20 10
4 Blue 2 S 18 18
5 Blue 1 S 19 20
my.df2 <- data.frame(Colour = c('Blue','Blue','Blue','Blue','Blue','Blue','Red','Red','Red','Red','Red','Red'),
Type = c(1,1,2,2,2,'-',1,1,2,2,2,'-'),
Size = c('S','M','S','M','-','-','S','M','S','M','-','-'),
PriceOneMean = NA,
PriceOneStDev = NA,
PriceTwoMean = NA,
PriceTwoStDev = NA)
my.df2
Colour Type Size PriceOneMean PriceOneStDev PriceTwoMean PriceTwoStDev
1 Blue 1 S NA NA NA NA
2 Blue 1 M NA NA NA NA
3 Blue 2 S NA NA NA NA
4 Blue 2 M NA NA NA NA
5 Blue 2 - NA NA NA NA
6 Blue - - NA NA NA NA
7 Red 1 S NA NA NA NA
8 Red 1 M NA NA NA NA
9 Red 2 S NA NA NA NA
10 Red 2 M NA NA NA NA
11 Red 2 - NA NA NA NA
12 Red - - NA NA NA NA
编辑：我已将第5行和第11行添加到my.df2，以便更好地匹配原始数据集。我如何才能使我的问题在这些行上面工作呢？
3 个答案:
答案 0 :(得分：3)
你可以尝试
library(tidyverse)
as.tbl(my.df1) %>%
mutate(Type=NA, Size=NA) %>%
bind_rows(my.df1) %>%
group_by(Colour, Type, Size) %>%
summarise_all(c("mean", "sd"))
# A tibble: 10 x 7
# Groups: Colour, Type [?]
Colour Type Size PriceOne_mean PriceTwo_mean PriceOne_sd PriceTwo_sd
<fctr> <dbl> <fctr> <dbl> <dbl> <dbl> <dbl>
1 Blue 1 M 12.66667 15.33333 3.055050 5.507571
2 Blue 1 S 14.66667 15.00000 4.509250 5.000000
3 Blue 2 M 17.33333 19.33333 5.507571 8.504901
4 Blue 2 S 16.33333 14.00000 4.725816 4.000000
5 Blue NA <NA> 15.25000 15.91667 4.287932 5.534328
6 Red 1 M 15.33333 12.66667 5.507571 3.055050
7 Red 1 S 15.00000 14.66667 5.000000 4.509250
8 Red 2 M 19.33333 17.33333 8.504901 5.507571
9 Red 2 S 14.00000 16.33333 4.000000 4.725816
10 Red NA <NA> 15.91667 15.25000 5.534328 4.287932
参考您的修改，我会这样做：
as.tbl(my.df1) %>%
bind_rows(mutate(my.df1, Type=NA, Size=NA)) %>%
bind_rows(mutate(my.df1, Size=NA)) %>%
group_by(Colour, Type, Size) %>%
summarise_all(c("mean", "sd"))
答案 1 :(得分：1)
dplyr库允许您分组，汇总和绑定。编辑添加额外的分组。为了简洁起见，我更喜欢@ Jimbou的回答 - 这可能是他/她的一行编辑。
my.df1 <- data.frame(Colour = c('Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Blue','Red','Red','Red','Red','Red','Red','Red','Red','Red','Red','Red','Red'),
Type = c(1,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1,2),
Size = c('S','S','S','S','S','S','M','M','M','M','M','M','S','S','S','S','S','S','M','M','M','M','M','M'),
PriceOne = c(10,15,20,18,19,11,12,16,20,21,10,11,10,15,10,18,20,14,21,15,28,19,10,11),
PriceTwo = c(10,15,10,18,20,14,21,15,28,19,10,11,10,15,20,18,19,11,12,16,20,21,10,11))
library(dplyr)
# make detailed summaries
my.df1.ColourTypeSize = my.df1 %>%
group_by(Colour, Type, Size) %>%
summarise(
PriceOneMean = mean(PriceOne),
PriceOneStDev = sd(PriceOne),
PriceTwoMean = mean(PriceTwo),
PriceTwoStDev = sd(PriceTwo))
my.df1.ColourType = my.df1 %>%
group_by(Colour, Type) %>%
summarise(
PriceOneMean = mean(PriceOne),
PriceOneStDev = sd(PriceOne),
PriceTwoMean = mean(PriceTwo),
PriceTwoStDev = sd(PriceTwo)) %>%
mutate(Size = NA)
# Make summary for colour alone and add NA for Size and Type
my.df1.Colour = my.df1 %>%
group_by(Colour) %>%
summarise(
PriceOneMean = mean(PriceOne),
PriceOneStDev = sd(PriceOne),
PriceTwoMean = mean(PriceTwo),
PriceTwoStDev = sd(PriceTwo)) %>%
mutate(Type = NA, Size = NA)
# Bind the summaries together and sort and arrange to make it look nice
my.df2 =
my.df1.Colour %>%
bind_rows(my.df1.ColourTypeSize) %>%
bind_rows(my.df1.ColourType) %>%
arrange(Colour, Type, Size) %>%
select(Colour, Type, Size, everything())
答案 2 :(得分：0)
创建要在子集函数中调用的特征的所有可用组合：
call_combo <- function(frame) {
combo_list <- list()
for(i in 1:nrow(frame)) {
combo <- frame[i,c(1,2,3)]
combo_left <- combo[combo != '-']
combo_left_cols <- names(combo[1:length(combo_left)])
call_string <- paste(combo_left_cols, '==', combo_left, '&', sep=' ', collapse=' ')
ind <- unlist(gregexpr('&',call_string))
res <- substring(call_string, 1, ind[length(ind)]-1)
combo_list[i] <- list(res)
}
return(combo_list)
}
特征组合：
combo_list <- call_combo(my.df2)
combo_list
评估子集内的所有组合并附加到第二个数据框：
# define attributes as objects
Blue <- 'Blue'
Red <- 'Red'
S <- 'S'
M <- 'M'
L <- 'L'
# evaluate combo_list entries inside subset function
for(p in 1:length(combo_list)) {
sub_frame <- subset(my.df1, eval(parse(text=combo_list[[p]])))
# calculate sd and mean for each combination and attach to 2nd frame
my.df2[p,]$PriceOneStDev <- sd(sub_frame$PriceOne)
my.df2[p,]$PriceTwoStDev <- sd(sub_frame$PriceTwo)
my.df2[p,]$PriceOneMean <- mean(sub_frame$PriceOne)
my.df2[p,]$PriceTwoMean <- mean(sub_frame$PriceTwo)
}
<强>结果：
my.df2
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