如果两个条件都满足，则平均两行
Average two rows if both conditions are met
我有一个名为 df 的数据框，它看起来像这样；
id face value
1 r 15
1 r 11
1 t 16
1 t 17
2 r 13
2 r 25
2 t 12
2 t 18
3 r 30
3 r 20
3 t 19
3 t 10
因此，如果满足两个条件，我需要对每一行进行平均。条件是；如果 id 和 face 相同，则平均 value.
例如，如果 id=1 和 face=r 则取平均值 15+11 并将计算出的值 13 放入新列中。我必须对整个数据框执行此操作（2000 行，500 行不同 id）。
PS; 对于每个 face 我必须有不同的列。我的意思是，例如，如果 id=1 和 face=r 将平均值 value 放在名为 newr 的新列中，如果 id=2 和 face=r 将平均值 value 在一个名为 newr 的新列中。然后，如果 id=1 和 face=t 将平均值 value 放入名为 newt 的新列中。输出将是这样的；
id face newr newt
1 r 13
1 t 16.5
2 r 19
2 t 15
这是我的 str(df1)
Classes ‘data.table’ and 'data.frame': 340 obs. of 26 variables:
$ id : int 5 5 5 5 5 5 5 5 7 7 ...
$ nirid : chr "bx5xtx1" "ax5xrx2" "bx5xrx2" "bx5xtx2" ...
$ group : Factor w/ 3 levels "a","b","r": 2 1 2 2 2 1 1 1 1 1 ...
$ section : Factor w/ 3 levels "","r","t": 3 2 2 3 2 3 2 3 2 3 ...
$ face : Factor w/ 3 levels "","1","2": 2 3 3 3 2 2 2 3 2 3 ...
$ sample : chr "B3C-3D" "B3C-3D" "B3C-3D" "B3C-3D" ...
$ treatment : chr "control" "control" "control" "control" ...
$ width : num 1 1 1 1 1 ...
$ thick : num 1.02 1.02 1.02 1.02 1.02 ...
$ length : num 16 16 16 16 16 ...
$ testweight : num 126 126 126 126 126 ...
$ maxload : num 418 418 418 418 418 418 418 418 436 436 ...
$ loadppl : num 251 251 251 251 251 251 251 251 258 258 ...
$ ppldistance: num 0.139 0.139 0.139 0.139 0.139 ...
$ scmor : num 0.399 0.399 0.399 0.399 0.399 ...
$ scmoe : num 5.53e-05 5.53e-05 5.53e-05 5.53e-05 5.53e-05 ...
$ failure : int 2 2 2 2 2 2 2 2 2 2 ...
$ mcweight : num 107 107 107 107 107 ...
$ odweight : num 94.1 94.1 94.1 94.1 94.1 94.1 94.1 94.1 90.3 90.3 ...
$ mc : num 13.3 13.3 13.3 13.3 13.3 ...
$ sgsc : num 0.415 0.415 0.415 0.415 0.415 ...
$ scmorpsi : num 58 58 58 58 58 ...
$ scmoepsi : num 8.03 8.03 8.03 8.03 8.03 ...
$ rows : chr "9" "10" "11" "12" ...
$ value :Class 'AsIs' num [1:238000] 0.0147 -0.0169 -0.0152 0.0135 -0.0107 ...
$ sg42 :Class 'AsIs' num [1:235280] 1.86e-04 9.39e-05 8.94e-05 1.83e-04 8.86e-05 ...
- attr(*, ".internal.selfref")=<externalptr>
已更新
这是使用 dput(droplevels(head(data, 20)))
的实际数据集的一小部分
structure(list(id = c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L), nirid = c("bx5xtx1",
"ax5xrx2", "bx5xrx2", "bx5xtx2", "bx5xrx1", "ax5xtx1", "ax5xrx1",
"ax5xtx2", "ax7xrx1", "ax7xtx2", "ax7xrx2", "ax7xtx1", "ax8xrx2",
"ax8xtx1", "ax8xrx1", "ax8xtx2", "ax9xtx2", "bx9xtx2", "ax9xrx2",
"ax9xtx1"), group = c("b", "a", "b", "b", "b", "a", "a", "a",
"a", "a", "a", "a", "a", "a", "a", "a", "a", "b", "a", "a"),
section = c("t", "r", "r", "t", "r", "t", "r", "t", "r",
"t", "r", "t", "r", "t", "r", "t", "t", "t", "r", "t"), face = c(1L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L), sample = c("B3C-3D", "B3C-3D", "B3C-3D",
"B3C-3D", "B3C-3D", "B3C-3D", "B3C-3D", "B3C-3D", "B3C-1E",
"B3C-1E", "B3C-1E", "B3C-1E", "B1C-2D", "B1C-2D", "B1C-2D",
"B1C-2D", "A3C-2C", "A3C-2C", "A3C-2C", "A3C-2C"), treatment = c("control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control"), width = c("1.003", "1.003", "1.003", "1.003",
"1.003", "1.003", "1.003", "1.003", "1.021", "1.021", "1.021",
"1.021", "1.02", "1.02", "1.02", "1.02", "1.033", "1.033",
"1.033", "1.033"), thick = c("1.02", "1.02", "1.02", "1.02",
"1.02", "1.02", "1.02", "1.02", "1.043", "1.043", "1.043",
"1.043", "1.025", "1.025", "1.025", "1.025", "1.029", "1.029",
"1.029", "1.029"), length = c("16", "16", "16", "16", "16",
"16", "16", "16", "15.98", "15.98", "15.98", "15.98", "16.016",
"16.016", "16.016", "16.016", "16.005", "16.005", "16.005",
"16.005"), testweight = c("126", "126", "126", "126", "126",
"126", "126", "126", "121.4", "121.4", "121.4", "121.4",
"144.1", "144.1", "144.1", "144.1", "119.6", "119.6", "119.6",
"119.6"), maxload = c(418L, 418L, 418L, 418L, 418L, 418L,
418L, 418L, 436L, 436L, 436L, 436L, 631L, 631L, 631L, 631L,
486L, 486L, 486L, 486L), loadppl = c("251", "251", "251",
"251", "251", "251", "251", "251", "258", "258", "258", "258",
"296", "296", "296", "296", "255", "255", "255", "255"),
ppldistance = c("0.1388", "0.1388", "0.1388", "0.1388", "0.1388",
"0.1388", "0.1388", "0.1388", "0.155", "0.155", "0.155",
"0.155", "0.1412", "0.1412", "0.1412", "0.1412", "0.1488",
"0.1488", "0.1488", "0.1488"), scmor = c("0.399330740757585",
"0.399330740757585", "0.399330740757585", "0.399330740757585",
"0.399330740757585", "0.399330740757585", "0.399330740757585",
"0.399330740757585", "0.391336060622532", "0.391336060622532",
"0.391336060622532", "0.391336060622532", "0.587001478757759",
"0.587001478757759", "0.587001478757759", "0.587001478757759",
"0.442958394865818", "0.442958394865818", "0.442958394865818",
"0.442958394865818"), scmoe = c("5.5328050375923e-05", "5.5328050375923e-05",
"5.5328050375923e-05", "5.5328050375923e-05", "5.5328050375923e-05",
"5.5328050375923e-05", "5.5328050375923e-05", "5.5328050375923e-05",
"4.6792031310635e-05", "4.6792031310635e-05", "4.6792031310635e-05",
"4.6792031310635e-05", "6.2150955161815e-05", "6.2150955161815e-05",
"6.2150955161815e-05", "6.2150955161815e-05", "4.9585347590597e-05",
"4.9585347590597e-05", "4.9585347590597e-05", "4.9585347590597e-05"
), failure = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), mcweight = c("106.6",
"106.6", "106.6", "106.6", "106.6", "106.6", "106.6", "106.6",
"102.1", "102.1", "102.1", "102.1", "121.9", "121.9", "121.9",
"121.9", "100.7", "100.7", "100.7", "100.7"), odweight = c("94.1",
"94.1", "94.1", "94.1", "94.1", "94.1", "94.1", "94.1", "90.3",
"90.3", "90.3", "90.3", "107.1", "107.1", "107.1", "107.1",
"88.3", "88.3", "88.3", "88.3"), mc = c("13.2837407013815",
"13.2837407013815", "13.2837407013815", "13.2837407013815",
"13.2837407013815", "13.2837407013815", "13.2837407013815",
"13.2837407013815", "13.0675526024363", "13.0675526024363",
"13.0675526024363", "13.0675526024363", "13.8188608776844",
"13.8188608776844", "13.8188608776844", "13.8188608776844",
"14.0430351075878", "14.0430351075878", "14.0430351075878",
"14.0430351075878"), sgsc = c("0.414649099500969", "0.414649099500969",
"0.414649099500969", "0.414649099500969", "0.414649099500969",
"0.414649099500969", "0.414649099500969", "0.414649099500969",
"0.385028360121945", "0.385028360121945", "0.385028360121945",
"0.385028360121945", "0.461392466167132", "0.461392466167132",
"0.461392466167132", "0.461392466167132", "0.376174963976185",
"0.376174963976185", "0.376174963976185", "0.376174963976185"
), scmorpsi = c("57.9580175265", "57.9580175265", "57.9580175265",
"57.9580175265", "57.9580175265", "57.9580175265", "57.9580175265",
"57.9580175265", "56.79768659253", "56.79768659253", "56.79768659253",
"56.79768659253", "85.1961507631", "85.1961507631", "85.1961507631",
"85.1961507631", "64.2900427962", "64.2900427962", "64.2900427962",
"64.2900427962"), scmoepsi = c("8.0301959907", "8.0301959907",
"8.0301959907", "8.0301959907", "8.0301959907", "8.0301959907",
"8.0301959907", "8.0301959907", "6.7912962715", "6.7912962715",
"6.7912962715", "6.7912962715", "9.0204579335", "9.0204579335",
"9.0204579335", "9.0204579335", "7.1967122773", "7.1967122773",
"7.1967122773", "7.1967122773"), rows = 9:28, value = c("0.014680833",
"-0.0169", "-0.015241563", "0.013507307", "-0.010687351",
"0.000479", "-0.0311", "-7.18e-05", "-0.037", "-0.00349",
"-0.0395", "-0.000859", "-0.018", "0.000127", "-0.0234",
"0.00215", "-0.0165", "-0.0162", "-0.0286", "-0.0214"), sg42 = c("0.000185853584415584",
"9.39393939393943e-05", "8.93772943722944e-05", "0.000183087277056277",
"8.86156017316018e-05", "0.000180270562770563", "9.02597402597403e-05",
"0.0001831779004329", "8.26839826839824e-05", "0.000167605411255411",
"8.44155844155841e-05", "0.000175891774891775", "9.1774891774892e-05",
"0.000180465367965368", "9.02597402597405e-05", "0.000178874458874459",
"0.000160822510822511", "0.000154978354978355", "8.26839826839826e-05",
"0.000159090909090909")), .Names = c("id", "nirid", "group",
"section", "face", "sample", "treatment", "width", "thick", "length",
"testweight", "maxload", "loadppl", "ppldistance", "scmor", "scmoe",
"failure", "mcweight", "odweight", "mc", "sgsc", "scmorpsi",
"scmoepsi", "rows", "value", "sg42"), row.names = c(NA, 20L), class = "data.frame")
预期结果列为 newr、newt、newrsg42 和 newtsg42
非常感谢:)
如果我们需要 'wide' 格式的输出，请使用 data.table 中的 dcast 并将 fun.aggregate 指定为 mean
library(data.table)
dcast(setDT(df1), id + face ~ paste0("new", face), value.var="value", mean)
# id face newr newt
#1: 1 r 13 NaN
#2: 1 t NaN 16.5
#3: 2 r 19 NaN
#4: 2 t NaN 15.0
#5: 3 r 25 NaN
#6: 3 t NaN 14.5
或者另一种选择是 dplyr/tidyr
library(dplyr)
library(tidyr)
df1 %>%
group_by(id, face) %>%
summarise(MeanValue = mean(value)) %>%
mutate(newface = paste0("new", face)) %>%
spread(newface, MeanValue)
# id face newr newt
# <int> <chr> <dbl> <dbl>
#1 1 r 13 NA
#2 1 t NA 16.5
#3 2 r 19 NA
#4 2 t NA 15.0
#5 3 r 25 NA
#6 3 t NA 14.5
基准
set.seed(24)
df1 <- data.frame(id = sample(1:50, 1e7, replace=TRUE),
face = sample(letters, 1e7, replace=TRUE),
value = rnorm(1e7), stringsAsFactors=FALSE)
df2 <- copy(df1)
system.time({
dcast(setDT(df1), id + face ~ paste0("new", face), value.var="value", mean)
})
# user system elapsed
# 1.95 0.01 1.96
system.time({
reshape(transform(aggregate(value~face+id,df1,mean),time=face),dir='w',
idvar=c('id','face'));
})
# user system elapsed
# 16.36 1.00 17.38
数据
df1 <- structure(list(id = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L), face = c("r", "r", "t", "t", "r", "r", "t", "t", "r",
"r", "t", "t"), value = c(15L, 11L, 16L, 17L, 13L, 25L, 12L,
18L, 30L, 20L, 19L, 10L)), .Names = c("id", "face", "value"),
class = "data.frame", row.names = c(NA, -12L))
这是使用 aggregate() 和 reshape() 的解决方案：
df <- data.frame(id=c(1L,1L,1L,1L,2L,2L,2L,2L,3L,3L,3L,3L),face=c('r','r','t','t','r','r','t','t','r','r','t','t'),value=c(15L,11L,16L,17L,13L,25L,12L,18L,30L,20L,19L,10L),stringsAsFactors=F);
reshape(transform(aggregate(value~face+id,df,mean),time=face),dir='w',idvar=c('id','face'));
## face id value.r value.t
## 1 r 1 13 NA
## 2 t 1 NA 16.5
## 3 r 2 19 NA
## 4 t 2 NA 15.0
## 5 r 3 25 NA
## 6 t 3 NA 14.5
for( i in unique(df1$id)){
for(j in unique(df1$face=="r"[df1$id==i])){
for(l in unique(df1$face == "t"[df1$id==i])){
df1$newr[df1$id==i & df1$face=="r"] <- mean(df1$value[df1$id==i & df1$face=="r"])
df1$newt[df1$id==i & df1$face=="t"] <- mean(df1$value[df1$id==i & df1$face=="t"])
}
}
}
df1 <- df1[!duplicated(df1[,c("id","face")]),]
> df1
id face newr newt
1 1 r 13 NA
3 1 t NA 16.5
5 2 r 19 NA
7 2 t NA 15.0
9 3 r 25 NA
11 3 t NA 14.5
我有一个名为 df 的数据框，它看起来像这样；
id face value
1 r 15
1 r 11
1 t 16
1 t 17
2 r 13
2 r 25
2 t 12
2 t 18
3 r 30
3 r 20
3 t 19
3 t 10
因此，如果满足两个条件，我需要对每一行进行平均。条件是；如果 id 和 face 相同，则平均 value.
例如，如果 id=1 和 face=r 则取平均值 15+11 并将计算出的值 13 放入新列中。我必须对整个数据框执行此操作（2000 行，500 行不同 id）。
PS; 对于每个 face 我必须有不同的列。我的意思是，例如，如果 id=1 和 face=r 将平均值 value 放在名为 newr 的新列中，如果 id=2 和 face=r 将平均值 value 在一个名为 newr 的新列中。然后，如果 id=1 和 face=t 将平均值 value 放入名为 newt 的新列中。输出将是这样的；
id face newr newt
1 r 13
1 t 16.5
2 r 19
2 t 15
这是我的 str(df1)
Classes ‘data.table’ and 'data.frame': 340 obs. of 26 variables:
$ id : int 5 5 5 5 5 5 5 5 7 7 ...
$ nirid : chr "bx5xtx1" "ax5xrx2" "bx5xrx2" "bx5xtx2" ...
$ group : Factor w/ 3 levels "a","b","r": 2 1 2 2 2 1 1 1 1 1 ...
$ section : Factor w/ 3 levels "","r","t": 3 2 2 3 2 3 2 3 2 3 ...
$ face : Factor w/ 3 levels "","1","2": 2 3 3 3 2 2 2 3 2 3 ...
$ sample : chr "B3C-3D" "B3C-3D" "B3C-3D" "B3C-3D" ...
$ treatment : chr "control" "control" "control" "control" ...
$ width : num 1 1 1 1 1 ...
$ thick : num 1.02 1.02 1.02 1.02 1.02 ...
$ length : num 16 16 16 16 16 ...
$ testweight : num 126 126 126 126 126 ...
$ maxload : num 418 418 418 418 418 418 418 418 436 436 ...
$ loadppl : num 251 251 251 251 251 251 251 251 258 258 ...
$ ppldistance: num 0.139 0.139 0.139 0.139 0.139 ...
$ scmor : num 0.399 0.399 0.399 0.399 0.399 ...
$ scmoe : num 5.53e-05 5.53e-05 5.53e-05 5.53e-05 5.53e-05 ...
$ failure : int 2 2 2 2 2 2 2 2 2 2 ...
$ mcweight : num 107 107 107 107 107 ...
$ odweight : num 94.1 94.1 94.1 94.1 94.1 94.1 94.1 94.1 90.3 90.3 ...
$ mc : num 13.3 13.3 13.3 13.3 13.3 ...
$ sgsc : num 0.415 0.415 0.415 0.415 0.415 ...
$ scmorpsi : num 58 58 58 58 58 ...
$ scmoepsi : num 8.03 8.03 8.03 8.03 8.03 ...
$ rows : chr "9" "10" "11" "12" ...
$ value :Class 'AsIs' num [1:238000] 0.0147 -0.0169 -0.0152 0.0135 -0.0107 ...
$ sg42 :Class 'AsIs' num [1:235280] 1.86e-04 9.39e-05 8.94e-05 1.83e-04 8.86e-05 ...
- attr(*, ".internal.selfref")=<externalptr>
已更新
这是使用 dput(droplevels(head(data, 20)))
structure(list(id = c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L), nirid = c("bx5xtx1",
"ax5xrx2", "bx5xrx2", "bx5xtx2", "bx5xrx1", "ax5xtx1", "ax5xrx1",
"ax5xtx2", "ax7xrx1", "ax7xtx2", "ax7xrx2", "ax7xtx1", "ax8xrx2",
"ax8xtx1", "ax8xrx1", "ax8xtx2", "ax9xtx2", "bx9xtx2", "ax9xrx2",
"ax9xtx1"), group = c("b", "a", "b", "b", "b", "a", "a", "a",
"a", "a", "a", "a", "a", "a", "a", "a", "a", "b", "a", "a"),
section = c("t", "r", "r", "t", "r", "t", "r", "t", "r",
"t", "r", "t", "r", "t", "r", "t", "t", "t", "r", "t"), face = c(1L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L), sample = c("B3C-3D", "B3C-3D", "B3C-3D",
"B3C-3D", "B3C-3D", "B3C-3D", "B3C-3D", "B3C-3D", "B3C-1E",
"B3C-1E", "B3C-1E", "B3C-1E", "B1C-2D", "B1C-2D", "B1C-2D",
"B1C-2D", "A3C-2C", "A3C-2C", "A3C-2C", "A3C-2C"), treatment = c("control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control"), width = c("1.003", "1.003", "1.003", "1.003",
"1.003", "1.003", "1.003", "1.003", "1.021", "1.021", "1.021",
"1.021", "1.02", "1.02", "1.02", "1.02", "1.033", "1.033",
"1.033", "1.033"), thick = c("1.02", "1.02", "1.02", "1.02",
"1.02", "1.02", "1.02", "1.02", "1.043", "1.043", "1.043",
"1.043", "1.025", "1.025", "1.025", "1.025", "1.029", "1.029",
"1.029", "1.029"), length = c("16", "16", "16", "16", "16",
"16", "16", "16", "15.98", "15.98", "15.98", "15.98", "16.016",
"16.016", "16.016", "16.016", "16.005", "16.005", "16.005",
"16.005"), testweight = c("126", "126", "126", "126", "126",
"126", "126", "126", "121.4", "121.4", "121.4", "121.4",
"144.1", "144.1", "144.1", "144.1", "119.6", "119.6", "119.6",
"119.6"), maxload = c(418L, 418L, 418L, 418L, 418L, 418L,
418L, 418L, 436L, 436L, 436L, 436L, 631L, 631L, 631L, 631L,
486L, 486L, 486L, 486L), loadppl = c("251", "251", "251",
"251", "251", "251", "251", "251", "258", "258", "258", "258",
"296", "296", "296", "296", "255", "255", "255", "255"),
ppldistance = c("0.1388", "0.1388", "0.1388", "0.1388", "0.1388",
"0.1388", "0.1388", "0.1388", "0.155", "0.155", "0.155",
"0.155", "0.1412", "0.1412", "0.1412", "0.1412", "0.1488",
"0.1488", "0.1488", "0.1488"), scmor = c("0.399330740757585",
"0.399330740757585", "0.399330740757585", "0.399330740757585",
"0.399330740757585", "0.399330740757585", "0.399330740757585",
"0.399330740757585", "0.391336060622532", "0.391336060622532",
"0.391336060622532", "0.391336060622532", "0.587001478757759",
"0.587001478757759", "0.587001478757759", "0.587001478757759",
"0.442958394865818", "0.442958394865818", "0.442958394865818",
"0.442958394865818"), scmoe = c("5.5328050375923e-05", "5.5328050375923e-05",
"5.5328050375923e-05", "5.5328050375923e-05", "5.5328050375923e-05",
"5.5328050375923e-05", "5.5328050375923e-05", "5.5328050375923e-05",
"4.6792031310635e-05", "4.6792031310635e-05", "4.6792031310635e-05",
"4.6792031310635e-05", "6.2150955161815e-05", "6.2150955161815e-05",
"6.2150955161815e-05", "6.2150955161815e-05", "4.9585347590597e-05",
"4.9585347590597e-05", "4.9585347590597e-05", "4.9585347590597e-05"
), failure = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), mcweight = c("106.6",
"106.6", "106.6", "106.6", "106.6", "106.6", "106.6", "106.6",
"102.1", "102.1", "102.1", "102.1", "121.9", "121.9", "121.9",
"121.9", "100.7", "100.7", "100.7", "100.7"), odweight = c("94.1",
"94.1", "94.1", "94.1", "94.1", "94.1", "94.1", "94.1", "90.3",
"90.3", "90.3", "90.3", "107.1", "107.1", "107.1", "107.1",
"88.3", "88.3", "88.3", "88.3"), mc = c("13.2837407013815",
"13.2837407013815", "13.2837407013815", "13.2837407013815",
"13.2837407013815", "13.2837407013815", "13.2837407013815",
"13.2837407013815", "13.0675526024363", "13.0675526024363",
"13.0675526024363", "13.0675526024363", "13.8188608776844",
"13.8188608776844", "13.8188608776844", "13.8188608776844",
"14.0430351075878", "14.0430351075878", "14.0430351075878",
"14.0430351075878"), sgsc = c("0.414649099500969", "0.414649099500969",
"0.414649099500969", "0.414649099500969", "0.414649099500969",
"0.414649099500969", "0.414649099500969", "0.414649099500969",
"0.385028360121945", "0.385028360121945", "0.385028360121945",
"0.385028360121945", "0.461392466167132", "0.461392466167132",
"0.461392466167132", "0.461392466167132", "0.376174963976185",
"0.376174963976185", "0.376174963976185", "0.376174963976185"
), scmorpsi = c("57.9580175265", "57.9580175265", "57.9580175265",
"57.9580175265", "57.9580175265", "57.9580175265", "57.9580175265",
"57.9580175265", "56.79768659253", "56.79768659253", "56.79768659253",
"56.79768659253", "85.1961507631", "85.1961507631", "85.1961507631",
"85.1961507631", "64.2900427962", "64.2900427962", "64.2900427962",
"64.2900427962"), scmoepsi = c("8.0301959907", "8.0301959907",
"8.0301959907", "8.0301959907", "8.0301959907", "8.0301959907",
"8.0301959907", "8.0301959907", "6.7912962715", "6.7912962715",
"6.7912962715", "6.7912962715", "9.0204579335", "9.0204579335",
"9.0204579335", "9.0204579335", "7.1967122773", "7.1967122773",
"7.1967122773", "7.1967122773"), rows = 9:28, value = c("0.014680833",
"-0.0169", "-0.015241563", "0.013507307", "-0.010687351",
"0.000479", "-0.0311", "-7.18e-05", "-0.037", "-0.00349",
"-0.0395", "-0.000859", "-0.018", "0.000127", "-0.0234",
"0.00215", "-0.0165", "-0.0162", "-0.0286", "-0.0214"), sg42 = c("0.000185853584415584",
"9.39393939393943e-05", "8.93772943722944e-05", "0.000183087277056277",
"8.86156017316018e-05", "0.000180270562770563", "9.02597402597403e-05",
"0.0001831779004329", "8.26839826839824e-05", "0.000167605411255411",
"8.44155844155841e-05", "0.000175891774891775", "9.1774891774892e-05",
"0.000180465367965368", "9.02597402597405e-05", "0.000178874458874459",
"0.000160822510822511", "0.000154978354978355", "8.26839826839826e-05",
"0.000159090909090909")), .Names = c("id", "nirid", "group",
"section", "face", "sample", "treatment", "width", "thick", "length",
"testweight", "maxload", "loadppl", "ppldistance", "scmor", "scmoe",
"failure", "mcweight", "odweight", "mc", "sgsc", "scmorpsi",
"scmoepsi", "rows", "value", "sg42"), row.names = c(NA, 20L), class = "data.frame")
预期结果列为 newr、newt、newrsg42 和 newtsg42
非常感谢:)
如果我们需要 'wide' 格式的输出，请使用 data.table 中的 dcast 并将 fun.aggregate 指定为 mean
library(data.table)
dcast(setDT(df1), id + face ~ paste0("new", face), value.var="value", mean)
# id face newr newt
#1: 1 r 13 NaN
#2: 1 t NaN 16.5
#3: 2 r 19 NaN
#4: 2 t NaN 15.0
#5: 3 r 25 NaN
#6: 3 t NaN 14.5
或者另一种选择是 dplyr/tidyr
library(dplyr)
library(tidyr)
df1 %>%
group_by(id, face) %>%
summarise(MeanValue = mean(value)) %>%
mutate(newface = paste0("new", face)) %>%
spread(newface, MeanValue)
# id face newr newt
# <int> <chr> <dbl> <dbl>
#1 1 r 13 NA
#2 1 t NA 16.5
#3 2 r 19 NA
#4 2 t NA 15.0
#5 3 r 25 NA
#6 3 t NA 14.5
基准
set.seed(24)
df1 <- data.frame(id = sample(1:50, 1e7, replace=TRUE),
face = sample(letters, 1e7, replace=TRUE),
value = rnorm(1e7), stringsAsFactors=FALSE)
df2 <- copy(df1)
system.time({
dcast(setDT(df1), id + face ~ paste0("new", face), value.var="value", mean)
})
# user system elapsed
# 1.95 0.01 1.96
system.time({
reshape(transform(aggregate(value~face+id,df1,mean),time=face),dir='w',
idvar=c('id','face'));
})
# user system elapsed
# 16.36 1.00 17.38
数据
df1 <- structure(list(id = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L), face = c("r", "r", "t", "t", "r", "r", "t", "t", "r",
"r", "t", "t"), value = c(15L, 11L, 16L, 17L, 13L, 25L, 12L,
18L, 30L, 20L, 19L, 10L)), .Names = c("id", "face", "value"),
class = "data.frame", row.names = c(NA, -12L))
这是使用 aggregate() 和 reshape() 的解决方案：
df <- data.frame(id=c(1L,1L,1L,1L,2L,2L,2L,2L,3L,3L,3L,3L),face=c('r','r','t','t','r','r','t','t','r','r','t','t'),value=c(15L,11L,16L,17L,13L,25L,12L,18L,30L,20L,19L,10L),stringsAsFactors=F);
reshape(transform(aggregate(value~face+id,df,mean),time=face),dir='w',idvar=c('id','face'));
## face id value.r value.t
## 1 r 1 13 NA
## 2 t 1 NA 16.5
## 3 r 2 19 NA
## 4 t 2 NA 15.0
## 5 r 3 25 NA
## 6 t 3 NA 14.5
for( i in unique(df1$id)){
for(j in unique(df1$face=="r"[df1$id==i])){
for(l in unique(df1$face == "t"[df1$id==i])){
df1$newr[df1$id==i & df1$face=="r"] <- mean(df1$value[df1$id==i & df1$face=="r"])
df1$newt[df1$id==i & df1$face=="t"] <- mean(df1$value[df1$id==i & df1$face=="t"])
}
}
}
df1 <- df1[!duplicated(df1[,c("id","face")]),]
> df1
id face newr newt
1 1 r 13 NA
3 1 t NA 16.5
5 2 r 19 NA
7 2 t NA 15.0
9 3 r 25 NA
11 3 t NA 14.5