使用dplyr標記和計數值之間的差距
[英]Using dplyr to label and count gaps between values
問題描述
我有這個數據框：
df<-structure(list(Name = c("sub1", "sub1", "sub1", "sub1", "sub1",
"sub1", "sub1", "sub1", "sub1", "sub1", "sub1", "sub1", "sub1",
"sub1", "sub1", "sub1", "sub1", "sub1", "sub1", "sub1", "sub1",
"sub1", "sub1", "sub2", "sub2", "sub2", "sub2", "sub2", "sub2"
), StimulusName = c("Alpha11", "Alpha11", "Alpha11", "Alpha11",
"Alpha11", "Alpha11", "Alpha11", "Alpha11", "Alpha11", "Alpha11",
"Alpha11", "Alpha11", "Alpha11", "Alpha11", "Alpha11", "Alpha11",
"Alpha11", "Alpha11", "Alpha12", "Alpha12", "Alpha12", "Alpha12",
"Alpha12", "Alpha11", "Alpha11", "Alpha11", "Alpha11", "Alpha11",
"Alpha11"), FixationSeq = c(2L, 2L, 2L, 2L, NA, NA, NA, NA, 3L,
3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, 1L, NA, NA, 2L, NA, NA, NA,
NA, NA, 2L, 2L)), row.names = c(NA, -29L), class = c("tbl_df",
"tbl", "data.frame"), spec = structure(list(cols = list(Name = structure(list(), class = c("collector_character",
"collector")), StimulusName = structure(list(), class = c("collector_character",
"collector")), FixationSeq = structure(list(), class = c("collector_integer",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector"))), class = "col_spec"))
在FixationSeq列中有唯一編號（在我的示例2和3中， Name = sub1和StimulusName = Alpha11 ）。 在這些數字之間NA填充的段。 3之后還有一個用NA填充的段。
我希望能夠創建一個新列SaccadeCount並將一個遞增的數字標簽添加到NA 段的每個實例（作為一個整體，即可能是多行）到SaccadeCount的相關行。
另外，我想再有一個名為SaccadeDuration的列，並SaccadeDuration出現NA唯一段的總行數。 因此在示例df ，與2和3之間的NA段相對應的行將填充為'3'，因為那是2和3之間的行的總數。
我想使用dplyr完成此操作，並按Name和StimulusName列對操作進行分組。
輸出可能看起來像這樣：
Name StimulusName FixationSeq SaccadeCount SaccadeDuration
sub1 Alpha11 2
sub1 Alpha11 2
sub1 Alpha11 2
sub1 Alpha11 2
sub1 Alpha11 NA 1 3
sub1 Alpha11 NA 1 3
sub1 Alpha11 NA 1 3
sub1 Alpha11 3
sub1 Alpha11 3
sub1 Alpha11 3
sub1 Alpha11 3
sub1 Alpha11 3
sub1 Alpha11 3
sub1 Alpha11 NA 2 5
sub1 Alpha11 NA 2 5
sub1 Alpha11 NA 2 5
sub1 Alpha11 NA 2 5
sub1 Alpha11 NA 2 5
sub1 Alpha12 1
sub1 Alpha12 NA 1 2
sub1 Alpha12 NA 1 2
sub1 Alpha12 2
sub1 Alpha12 NA 2 1
sub2 Alpha11 NA 1 4
sub2 Alpha11 NA 1 4
sub2 Alpha11 NA 1 4
sub2 Alpha11 NA 1 4
sub2 Alpha11 2
sub2 Alpha11 2
非常感謝您的時間和幫助。
3 個解決方案
解決方案1
2 2018-10-31 08:57:59
使用data.table
碼：
library(data.table)
fun1 <- function(x) {
na.ind = is.na(x$FixationSeq)
na.vals= rleidv(rleidv(na.ind)[na.ind])
x$SaccadeCount = NA
x$SaccadeCount[na.ind] = na.vals
na.rle = rle(na.vals)
x$SaccadeDuration = NA
x$SaccadeDuration[na.ind] = rep(na.rle$lengths, na.rle$lengths)
return(x)
}
setDT(df)[, fun1(.SD) ,by = .(Name, StimulusName)]
您可以以dplyr方式使用fun1 ：
ans<-
df %>% group_by(Name, StimulusName) %>% dplyr::do(.data = ., fun1(.))
結果：
# Name StimulusName FixationSeq SaccadeCount SaccadeDuration
#1: sub1 Alpha11 2 NA NA
#2: sub1 Alpha11 2 NA NA
#3: sub1 Alpha11 2 NA NA
#4: sub1 Alpha11 2 NA NA
#5: sub1 Alpha11 2 NA NA
#6: sub1 Alpha11 2 NA NA
#7: sub1 Alpha11 2 NA NA
#8: sub1 Alpha11 2 NA NA
#9: sub1 Alpha11 2 NA NA
#10: sub1 Alpha11 2 NA NA
#11: sub1 Alpha11 2 NA NA
#12: sub1 Alpha11 2 NA NA
#13: sub1 Alpha11 2 NA NA
#14: sub1 Alpha11 2 NA NA
#15: sub1 Alpha11 2 NA NA
#16: sub1 Alpha11 2 NA NA
#17: sub1 Alpha11 2 NA NA
#18: sub1 Alpha11 2 NA NA
#19: sub1 Alpha11 2 NA NA
#20: sub1 Alpha11 2 NA NA
#21: sub1 Alpha11 2 NA NA
#22: sub1 Alpha11 NA 1 5
#23: sub1 Alpha11 NA 1 5
#24: sub1 Alpha11 NA 1 5
#25: sub1 Alpha11 NA 1 5
#26: sub1 Alpha11 NA 1 5
#27: sub1 Alpha1 9 NA NA
#28: sub1 Alpha1 9 NA NA
#29: sub1 Alpha1 9 NA NA
#30: sub1 Alpha1 9 NA NA
#31: sub1 Alpha1 9 NA NA
#32: sub1 Alpha1 9 NA NA
#33: sub1 Alpha1 9 NA NA
# Name StimulusName FixationSeq SaccadeCount SaccadeDuration
- 我的方法使用預定義的功能
fun1來為每個組完成工作。 - 這些組似乎定義為我的
Name和StimulusName - 我用的，你應該了解非常重要的功能
?rle，?rleidv - 我用所有
NA值預填充新列，然后在需要的地方添加新值。
解決方案2
1 2018-10-31 08:43:19
這應該做。 不過，也許有一種更簡單的方法。 第一個突變指示NA片段的開始。 group_by和第二個突變計數每個段的NA 。
library(dplyr)
df %>% mutate(SaccadeCount = cumsum(ifelse(is.na(FixationSeq) &
!is.na(lag(FixationSeq)), 1,0)) * is.na(FixationSeq)) %>%
group_by(SaccadeCount) %>%
mutate(SaccadeDuration = n()) %>%
ungroup() %>%
mutate(SaccadeDuration = SaccadeDuration * is.na(FixationSeq))
解決方案3
1 已采納 2018-10-31 15:38:48
使用dplyr ：
df %>%
group_by(Name, StimulusName) %>%
mutate(x = is.na(FixationSeq),
count = cumsum(c(TRUE, diff(x) != 0L) & x) * x,
dur = NA_integer_) %>%
group_by(Name, StimulusName, count) %>%
mutate(dur = replace(dur, as.logical(count), n()))
對應的（更data.table ） data.table版本：
library(data.table)
setDT(df)
df[ , count := ({
x <- is.na(FixationSeq)
.(cumsum(c(TRUE, diff(x) != 0L) & x) * x)}), by = .(Name, StimulusName)]
df[as.logical(count), dur := .N, by = .(Name, StimulusName, count)]
Name StimulusName FixationSeq count dur 1: sub1 Alpha11 2 0 NA 2: sub1 Alpha11 2 0 NA 3: sub1 Alpha11 2 0 NA 4: sub1 Alpha11 2 0 NA 5: sub1 Alpha11 NA 1 4 6: sub1 Alpha11 NA 1 4 7: sub1 Alpha11 NA 1 4 8: sub1 Alpha11 NA 1 4 9: sub1 Alpha11 3 0 NA 10: sub1 Alpha11 3 0 NA 11: sub1 Alpha11 3 0 NA 12: sub1 Alpha11 3 0 NA 13: sub1 Alpha11 3 0 NA 14: sub1 Alpha11 NA 2 5 15: sub1 Alpha11 NA 2 5 16: sub1 Alpha11 NA 2 5 17: sub1 Alpha11 NA 2 5 18: sub1 Alpha11 NA 2 5 19: sub1 Alpha12 1 0 NA 20: sub1 Alpha12 NA 1 2 21: sub1 Alpha12 NA 1 2 22: sub1 Alpha12 2 0 NA 23: sub1 Alpha12 NA 2 1 24: sub2 Alpha11 NA 1 4 25: sub2 Alpha11 NA 1 4 26: sub2 Alpha11 NA 1 4 27: sub2 Alpha11 NA 1 4 28: sub2 Alpha11 2 0 NA 29: sub2 Alpha11 2 0 NA Name StimulusName FixationSeq count dur
如果需要，將count == 0更改為NA ：
df[count == 0, count := NA]
如問題所示，我不會將其更改為'blank'（ "" ），因為這將迫使該列具有character ，並使這些數字無法用於進一步的分析。
cumsum(c(TRUE, diff(x) != 0L) & x) * x逐步說明：
v <- c(1, 1, NA, NA, 1, NA, NA, NA)
x <- is.na(v)
x
# [1] FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE
diff(x)
# [1] 0 1 0 -1 1 0 0
diff(x) != 0L
# [1] FALSE TRUE FALSE TRUE TRUE FALSE FALSE
c(TRUE, diff(x) != 0L) & x
# [1] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
cumsum(c(TRUE, diff(x) != 0L) & x)
# [1] 0 0 1 1 1 2 2 2
cumsum(c(TRUE, diff(x) != 0L) & x) * x
# [1] 0 0 1 1 0 2 2 2
其余的希望相當簡單。
使用不同的列計算值之間的差異，並使用R計算帶間隙的值之間的差異
[英]Calculate difference between values using different column and with gaps using R
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