重新组合列值低于 x 的数据框的行
Regroup lines of a data frame for which a column value is inferior to x
我有这个数据框：
> df
Z freq proba
1 17 1 0.0033289263
2 18 4 0.0055569026
3 19 2 0.0087878028
4 20 3 0.0132023556
5 21 16 0.0188900561
6 22 12 0.0257995234
7 23 30 0.0337042731
8 24 41 0.0421963455
9 25 56 0.0507149437
10 26 65 0.0586089198
11 27 65 0.0652230449
12 28 93 0.0699913154
13 29 82 0.0725182432
14 30 94 0.0726318551
15 31 72 0.0703990113
16 32 74 0.0661024717
17 33 58 0.0601873020
18 34 66 0.0531896431
19 35 38 0.0456625487
20 36 45 0.0381117389
21 37 27 0.0309498221
22 38 17 0.0244723502
23 39 15 0.0188543771
24 40 13 0.0141629367
25 41 4 0.0103793600
26 42 1 0.0074254435
27 43 2 0.0051886582
28 45 1 0.0023658767
29 46 1 0.0015453804
30 49 2 0.0003792308
# Here are my datas :
> dput(df)
structure(list(Z = c(17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
43, 45, 46, 49), freq = c(1, 4, 2, 3, 16, 12, 30, 41, 56, 65,
65, 93, 82, 94, 72, 74, 58, 66, 38, 45, 27, 17, 15, 13, 4, 1,
2, 1, 1, 2), proba = c(0.0033289262662263, 0.00555690264007235,
0.00878780282243439, 0.0132023555702843, 0.0188900560866825,
0.0257995234198431, 0.0337042730520012, 0.0421963455163949, 0.0507149437492447,
0.0586089198012906, 0.0652230449359029, 0.0699913153996099, 0.0725182432348992,
0.0726318551493006, 0.0703990113442269, 0.0661024716831246, 0.0601873020200862,
0.0531896430528685, 0.045662548708844, 0.0381117389181843, 0.030949822142559,
0.0244723501557229, 0.01885437705459, 0.0141629366839816, 0.0103793599644779,
0.00742544354411115, 0.00518865818999788, 0.00236587669133322,
0.00154538036835848, 0.000379230768851682)), .Names = c("Z",
"freq", "proba"), row.names = c(NA, -30L), class = "data.frame")
我想将值 "freq" 小于 5 的行与下一行重新组合，而下一行小于 5。
Idk 如果我足够清楚，那么这是我期望的输出：
> df2
labels effectifs pi
1 17;20 10 0.03087599
2 21 16 0.01889006
3 22 12 0.02579952
4 23 30 0.03370427
5 24 41 0.04219635
6 25 56 0.05071494
7 26 65 0.05860892
8 27 65 0.06522304
9 28 93 0.06999132
10 29 82 0.07251824
11 30 94 0.07263186
12 31 72 0.07039901
13 32 74 0.06610247
14 33 58 0.06018730
15 34 66 0.05318964
16 35 38 0.04566255
17 36 45 0.03811174
18 37 27 0.03094982
19 38 17 0.02447235
20 39 15 0.01885438
21 40 13 0.01416294
22 41;49 11 0.02728395
我用嵌套 while 做到了，但我发现这个解决方案非常痛苦，而且没有经过优化。
i <- 1
freqs <- c()
labels <- c()
pi <- c()
while(i < nrow(df)) {
if (df$freq[i] >= 5) {
freqs <- c(freqs, df$freq[i])
labels <- c(labels, df$Z[i])
pi <- c(pi, df$proba[i])
i <- i + 1
}
else {
count <- df$freq[i]
countPi <- df$proba[i]
k <- i
j <- i
while(df$freq[i] < 5 & i < nrow(df)) {
if (df$freq[i+1] < 5) {
count <- count + df$freq[i+1]
countPi <- countPi + df$proba[i+1]
j <- i + 1
}
i <- i + 1
}
labels <- c(labels, paste0(df$Z[k], ";", df$Z[j]))
freqs <- c(freqs, count)
pi <- c(pi, countPi)
}
}
df2 <- data.frame(labels, freqs, pi)
我敢肯定还有更好的，也许是 dplyr。如果您有更好的解决方案..谢谢！
我们可以使用 "data.table" 的 "devel" 版本，因为引入了新功能 (rleid)。在这里，我们将 "data.frame" 转换为 "data.table" (setDT(df))，根据逻辑索引 (freq <5) 使用 [= 创建一个分组变量 ("gr") 11=]。 'Z' 列是 'numeric/integer' class。从 "Z" 创建一个字符列 ("Z1")。按 'gr' 分组，如果该组所有元素的 "freq" 都小于 5，则通过对列 (.SD[1L]) 的第一次观察将行汇总为一行，删除不需要的列（因为 .SD 包含 "Z1"，这将导致列重复），将其附加 "Z1"，我们通过粘贴 min 和 max 值获得 "Z" 该组。否则，保持不变 (else .SD)。通过将其分配给 "NULL".
来删除我们不需要的列
library(data.table) #data.table_1.9.5
res <- setDT(df)[, gr:=rleid(freq<5)][, Z1:= as.character(Z)][,
if(all(freq<5)) c(.SD[1L][,-4, with=FALSE],
list(Z1=toString(c(min(Z), max(Z)))))
else .SD, gr][,1:2 :=NULL][]
head(res,3)
# freq proba Z1
#1: 1 0.003328926 17, 20
#2: 16 0.018890056 21
#3: 12 0.025799523 22
由于这是一个 dplyr 问题，这里有一个 dplyr 解决方案。首先，我使用分组函数来定义组（类似于 data.table 中的 rleid 函数）。然后总结，相当简单。
# grouping function
grouping <- function(condition){
# calculate runs for grouping
run <- rle((!condition) * 1:length(condition))
# revalue
run$values <- seq_along(run$values)
# invert to get grouping
inverse.rle(run)
}
# load dplyr
require(dplyr)
df %>%
mutate(group = grouping(freq<5)) %>% # add groups
group_by(group) %>% # group data
summarize(freq = sum(freq), # sum freq
proba = sum(proba), # sum proba
Z = toString(unique(range(Z)))) %>% # rename Z
mutate(group=NULL) # remove groups
## Source: local data table [22 x 3]
##
## freq proba Z
## 1 10 0.03087599 17, 20
## 2 16 0.01889006 21
## 3 12 0.02579952 22
## 4 30 0.03370427 23
## 5 41 0.04219635 24
## 6 56 0.05071494 25
## 7 65 0.05860892 26
## 8 65 0.06522304 27
## 9 93 0.06999132 28
## 10 82 0.07251824 29
## .. ... ... ...
我有这个数据框：
> df
Z freq proba
1 17 1 0.0033289263
2 18 4 0.0055569026
3 19 2 0.0087878028
4 20 3 0.0132023556
5 21 16 0.0188900561
6 22 12 0.0257995234
7 23 30 0.0337042731
8 24 41 0.0421963455
9 25 56 0.0507149437
10 26 65 0.0586089198
11 27 65 0.0652230449
12 28 93 0.0699913154
13 29 82 0.0725182432
14 30 94 0.0726318551
15 31 72 0.0703990113
16 32 74 0.0661024717
17 33 58 0.0601873020
18 34 66 0.0531896431
19 35 38 0.0456625487
20 36 45 0.0381117389
21 37 27 0.0309498221
22 38 17 0.0244723502
23 39 15 0.0188543771
24 40 13 0.0141629367
25 41 4 0.0103793600
26 42 1 0.0074254435
27 43 2 0.0051886582
28 45 1 0.0023658767
29 46 1 0.0015453804
30 49 2 0.0003792308
# Here are my datas :
> dput(df)
structure(list(Z = c(17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
43, 45, 46, 49), freq = c(1, 4, 2, 3, 16, 12, 30, 41, 56, 65,
65, 93, 82, 94, 72, 74, 58, 66, 38, 45, 27, 17, 15, 13, 4, 1,
2, 1, 1, 2), proba = c(0.0033289262662263, 0.00555690264007235,
0.00878780282243439, 0.0132023555702843, 0.0188900560866825,
0.0257995234198431, 0.0337042730520012, 0.0421963455163949, 0.0507149437492447,
0.0586089198012906, 0.0652230449359029, 0.0699913153996099, 0.0725182432348992,
0.0726318551493006, 0.0703990113442269, 0.0661024716831246, 0.0601873020200862,
0.0531896430528685, 0.045662548708844, 0.0381117389181843, 0.030949822142559,
0.0244723501557229, 0.01885437705459, 0.0141629366839816, 0.0103793599644779,
0.00742544354411115, 0.00518865818999788, 0.00236587669133322,
0.00154538036835848, 0.000379230768851682)), .Names = c("Z",
"freq", "proba"), row.names = c(NA, -30L), class = "data.frame")
我想将值 "freq" 小于 5 的行与下一行重新组合，而下一行小于 5。 Idk 如果我足够清楚，那么这是我期望的输出：
> df2
labels effectifs pi
1 17;20 10 0.03087599
2 21 16 0.01889006
3 22 12 0.02579952
4 23 30 0.03370427
5 24 41 0.04219635
6 25 56 0.05071494
7 26 65 0.05860892
8 27 65 0.06522304
9 28 93 0.06999132
10 29 82 0.07251824
11 30 94 0.07263186
12 31 72 0.07039901
13 32 74 0.06610247
14 33 58 0.06018730
15 34 66 0.05318964
16 35 38 0.04566255
17 36 45 0.03811174
18 37 27 0.03094982
19 38 17 0.02447235
20 39 15 0.01885438
21 40 13 0.01416294
22 41;49 11 0.02728395
我用嵌套 while 做到了，但我发现这个解决方案非常痛苦，而且没有经过优化。
i <- 1
freqs <- c()
labels <- c()
pi <- c()
while(i < nrow(df)) {
if (df$freq[i] >= 5) {
freqs <- c(freqs, df$freq[i])
labels <- c(labels, df$Z[i])
pi <- c(pi, df$proba[i])
i <- i + 1
}
else {
count <- df$freq[i]
countPi <- df$proba[i]
k <- i
j <- i
while(df$freq[i] < 5 & i < nrow(df)) {
if (df$freq[i+1] < 5) {
count <- count + df$freq[i+1]
countPi <- countPi + df$proba[i+1]
j <- i + 1
}
i <- i + 1
}
labels <- c(labels, paste0(df$Z[k], ";", df$Z[j]))
freqs <- c(freqs, count)
pi <- c(pi, countPi)
}
}
df2 <- data.frame(labels, freqs, pi)
我敢肯定还有更好的，也许是 dplyr。如果您有更好的解决方案..谢谢！
我们可以使用 "data.table" 的 "devel" 版本，因为引入了新功能 (rleid)。在这里，我们将 "data.frame" 转换为 "data.table" (setDT(df))，根据逻辑索引 (freq <5) 使用 [= 创建一个分组变量 ("gr") 11=]。 'Z' 列是 'numeric/integer' class。从 "Z" 创建一个字符列 ("Z1")。按 'gr' 分组，如果该组所有元素的 "freq" 都小于 5，则通过对列 (.SD[1L]) 的第一次观察将行汇总为一行，删除不需要的列（因为 .SD 包含 "Z1"，这将导致列重复），将其附加 "Z1"，我们通过粘贴 min 和 max 值获得 "Z" 该组。否则，保持不变 (else .SD)。通过将其分配给 "NULL".
library(data.table) #data.table_1.9.5
res <- setDT(df)[, gr:=rleid(freq<5)][, Z1:= as.character(Z)][,
if(all(freq<5)) c(.SD[1L][,-4, with=FALSE],
list(Z1=toString(c(min(Z), max(Z)))))
else .SD, gr][,1:2 :=NULL][]
head(res,3)
# freq proba Z1
#1: 1 0.003328926 17, 20
#2: 16 0.018890056 21
#3: 12 0.025799523 22
由于这是一个 dplyr 问题，这里有一个 dplyr 解决方案。首先，我使用分组函数来定义组（类似于 data.table 中的 rleid 函数）。然后总结，相当简单。
# grouping function
grouping <- function(condition){
# calculate runs for grouping
run <- rle((!condition) * 1:length(condition))
# revalue
run$values <- seq_along(run$values)
# invert to get grouping
inverse.rle(run)
}
# load dplyr
require(dplyr)
df %>%
mutate(group = grouping(freq<5)) %>% # add groups
group_by(group) %>% # group data
summarize(freq = sum(freq), # sum freq
proba = sum(proba), # sum proba
Z = toString(unique(range(Z)))) %>% # rename Z
mutate(group=NULL) # remove groups
## Source: local data table [22 x 3]
##
## freq proba Z
## 1 10 0.03087599 17, 20
## 2 16 0.01889006 21
## 3 12 0.02579952 22
## 4 30 0.03370427 23
## 5 41 0.04219635 24
## 6 56 0.05071494 25
## 7 65 0.05860892 26
## 8 65 0.06522304 27
## 9 93 0.06999132 28
## 10 82 0.07251824 29
## .. ... ... ...