我想计算SACCADIC_RT的每个COMMISSION_ERROR =1的平均值，每{5}连续HITS=1，每个ID每个条件。
ID | TRIAL | TRIAL_TYPE| CONDITION | COMMISSION_ERROR | HITS| SACCADIC_RT
1 183 nogo square_1 1 -1 175
1 54 go square_1 -1 1 259
1 26 nogo square_1 1 -1 365
1 3 nogo square_1 1 -1 346
1 100 nogo square_1 1 -1 287
1 11 go square_1 -1 1 164
1 52 go square_1 -1 1 244
1 8 go square_1 -1 1 223
1 10 go square_1 -1 1 183
1 21 go square_1 -1 1 234
1 32 go square_1 1 -1 221
1 2 go square_1 -1 1 183
1 13 nogo square_1 0 -1 -1
1 87 nogo square_2 1 -1 228
1 95 nogo square_2 1 -1 274
1 111 go square_2 -1 1 305
1 28 nogo square_2 0 -1 309
1 65 go square_2 -1 0 -1
1 40 nogo square_1 0 -1 199
1 19 nogo square_1 0 -1 207
1 28 go square_1 -1 1 257
2 45 nogo square_1 1 -1 169
2 197 nogo square_1 1 -1 350
2 115 nogo square_1 1 -1 321
2 65 go square_2 -1 1 298
2 24 go square_2 -1 0 -1
2 1 nogo square_2 1 -1 183
2 77 go square_2 -1 1 225
2 90 go square_2 -1 1 305
2 89 go square_2 -1 1 210
2 104 go square_2 -1 1 199
2 116 go square_2 -1 1 175
2 29 nogo square_2 1 -1 99
2 41 go square_2 -1 1 104
可以在r中重新创建样本表：
structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), X..TRIAL.. = c(183L, 54L, 26L,
3L, 100L, 11L, 52L, 8L, 10L, 21L, 32L, 2L, 13L, 87L, 95L, 111L,
28L, 65L, 40L, 19L, 28L, 45L, 197L, 115L, 65L, 24L, 1L, 77L,
90L, 89L, 104L, 116L, 29L, 41L), TRIAL_TYPE. = structure(c(2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L), .Label = c("go", "nogo"), class = "factor"), CONDITION = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("square_1", "square_2"), class = "factor"), X..COMMISSION_ERROR = c(1L,
-1L, 1L, 1L, 1L, -1L, -1L, -1L, -1L, -1L, 1L, -1L, 0L, 1L, 1L,
-1L, 0L, -1L, 0L, 0L, -1L, 1L, 1L, 1L, -1L, -1L, 1L, -1L, -1L,
-1L, -1L, -1L, 1L, -1L), X..HITS. = c(-1L, 1L, -1L, -1L, -1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, -1L, -1L, 1L, -1L, 0L, -1L,
-1L, 1L, -1L, -1L, -1L, 1L, 0L, -1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L), SACCADIC_RT = c(175L, 259L, 365L, 346L, 287L, 164L, 244L,
223L, 183L, 234L, 221L, 183L, -1L, 228L, 274L, 305L, 309L, -1L,
199L, 207L, 257L, 169L, 350L, 321L, 298L, -1L, 183L, 225L, 305L,
210L, 199L, 175L, 99L, 104L)), .Names = c("ID", "X..TRIAL..",
"TRIAL_TYPE.", "CONDITION", "X..COMMISSION_ERROR", "X..HITS.",
"SACCADIC_RT"), class = "data.frame", row.names = c(NA, -34L))
因此，此示例的结果将如下：
ID | CONDITION | x
1 square_1 221
2 square_2 99
答案 0 :(得分：0)
您可以使用数据包data.table来执行此任务。
步骤如下：
1）对于每个ID和条件计算命中的滚动总和
2）只占用满足2个条件的行：前一行的commision_error = 1，滚动总和列中的数字为5
3）计算在步骤2中创建的表中每个ID和条件的平均值
.emacs # load your data
data <- read.csv("./yourData.csv")
# load data table library
library(data.table)
# convert your data to data.table object
data <- data.table(data)
# group data by ID and Condition, calculate rolling sum over 5 rows
data[, roll := Reduce('+', shift(HITS, 0:4)), by = list(ID, CONDITION)]
# take only rows where there were 5 hits in a row and commission error is 1
newData <- data[shift(roll, 1) == 5 & COMMISSION_ERROR == 1]
# calculate mean of SACCADIC_RT for each ID and Condition in the new dataset
newData[, meanSacc := mean(SACCADIC_RT), by = list(ID, CONDITION)]
shift函数允许您根据前一行中的值计算当前行中的值。这里＆＃39; Reduce（＆＃39; +＆＃39;，shift（HITS，0：4））＆＃39;获取给定行上的点击值，并将其添加到3之前的点击值行。然后将该值写入名为roll的新列。
roll := Reduce('+', shift(HITS, 0:4))
上述代码仅保留原始数据集中的行，其中列newData <- data[shift(roll, 1) == 5 & COMMISSION_ERROR == 1]的上一行中的值为5，当前行的roll值等于1。 / p>
COMISSION_ERROR
以上片段为每个newData[, meanSacc := mean(SACCADIC_RT), by = list(ID, CONDITION)]和SACCADIC计算ID _ RT的平均值，并且仅根据上面创建的新数据集中的行计算均值。然后将均值写入名为CONDITION