要创建简化示例，这是dput（df）的输出：
df <- structure(list(SubjectID = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("1", "2", "3"), class = "factor"), EventNumber = structure(c(1L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"),
EventType = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L
), .Label = c("A", "B"), class = "factor"), Param1 = c(0.3,
0.21, 0.87, 0.78, 0.9, 1.2, 1.4, 1.3, 0.6, 0.45, 0.45, 0.04,
0, 0.1, 0.03, 0.01, 0.09, 0.06, 0.08, 0.09, 0.03, 0.04, 0.04,
0.02), Param2 = c(45, 38, 76, 32, 67, 23, 27, 784, 623, 54,
54, 1056, 487, 341, 671, 859, 7769, 2219, 4277, 4060, 411,
440, 224, 57), Param3 = c(1.5, 1.7, 1.65, 1.32, 0.6, 0.3,
2.5, 0.4, 1.4, 0.67, 0.67, 0.32, 0.1, 0.15, 0.22, 0.29, 0.3,
0.2, 0.8, 1, 0.9, 0.8, 0.3, 0.1), Param4 = c(0.14, 0, 1,
0.86, 0, 0.6, 1, 1, 0.18, 0, 0, 0.39, 0, 1, 0.29, 0.07, 0.33,
0.53, 0.29, 0.23, 0.84, 0.61, 0.57, 0.59), Param5 = c(0.18,
0, 1, 0, 1, 0, 0.09, 1, 0.78, 0, 0, 1, 0.2, 0, 0.46, 0.72,
0.16, 0.22, 0.77, 0.52, 0.2, 0.68, 0.58, 0.17), Param6 = c(0,
1, 0.75, 0, 0.14, 0, 1, 0, 1, 0.27, 0, 1, 0, 0.23, 0.55,
0.86, 1, 0.33, 1, 1, 0.88, 0.75, 0, 0), AbsoluteTime = structure(c(1522533600,
1522533602, 1522533604, 1522533604, 1525125600, 1525125602,
1525125604, 1519254000, 1519254002, 1519254004, 1519254006,
1521759600, 1521759602, 1521759604, 1521759606, 1521759608,
1517353224, 1517353226, 1517353228, 1517353230, 1517439600,
1517439602, 1517439604, 1517439606), class = c("POSIXct",
"POSIXt"), tzone = "")), row.names = c(NA, -24L), class = "data.frame")
df
真实数据有20个主题，EventNumbers范围从1到100，参数从Param1到Param40（取决于实验）。 行数约为60 000观察。
对于df，创建n * 40个新列。 ＃（40或将在以后选择的任意数量的参数。）
将n视为“迈向未来的步骤”。
将40 * n新创建的列命名为：
Param1_2，Param2_2，Param3_2，...，Param39_2，Param40_2，...，
Param1_3，Param2_3，Param3_3，...，Param39_3，Param40_3，...，
...
Param1_n，Param2_n，Param3_n，...，Param39_n，Param40_n
导致列
Param1_1，Param2_1，Param1_2，Param2_2，Param1_3，Param2_3，Param1_4，Param2_4，... Param1_n，Param2_n
因此，子集df[X, c(4:9)]的每次观察都会获得一组额外的变量，其值从df[X+1, c(4:9)]到df[X+n, c(4:9)]。
对于n = 1，这就是新的df.extended：
df.extended <- structure(list(SubjectID = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2,
2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3), EventNumber = c(1, 1,
1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2,
2), EventType = c("A", "A", "A", "A", "B", "B", "B", "A", "A",
"A", "A", "B", "B", "B", "B", "B", "A", "A", "A", "A", "B", "B",
"B", "B"), Param1 = c(0.3, 0.21, 0.87, 0.78, 0.9, 1.2, 1.4, 1.3,
0.6, 0.45, 0.45, 0.04, 0, 0.1, 0.03, 0.01, 0.05, 0.07, 0.06,
0.01, 0.01, 0.01, 0.07, 0.04), Param2 = c(45, 38, 76, 32, 67,
23, 27, 784, 623, 54, 54, 1056, 487, 341, 671, 859, 1858, 640,
8181, 220, 99, 86, 170, 495), Param3 = c(1.5, 1.7, 1.65, 1.32,
0.6, 0.3, 2.5, 0.4, 1.4, 0.67, 0.67, 0.32, 0.1, 0.15, 0.22, 0.29,
1.5, 0.9, 0.8, 0.9, 0.1, 0, 0.8, 0.1), Param4 = c(0.14, 0, 1,
0.86, 0, 0.6, 1, 1, 0.18, 0, 0, 0.39, 0, 1, 0.29, 0.07, 0.64,
0.11, 0.12, 0.32, 0.55, 0.67, 0.83, 0.82), Param5 = c(0.18, 0,
1, 0, 1, 0, 0.09, 1, 0.78, 0, 0, 1, 0.2, 0, 0.46, 0.72, 0.27,
0.14, 0.7, 0.67, 0.23, 0.44, 0.61, 0.76), Param6 = c(0, 1, 0.75,
0, 0.14, 0, 1, 0, 1, 0.27, 0, 1, 0, 0.23, 0.55, 0.86, 1, 0.56,
0.45, 0.5, 0, 0, 0.89, 0.11), AbsoluteTime = c("2018-04-01 00:00:00",
"2018-04-01 00:00:02", "2018-04-01 00:00:04", "2018-04-01 00:00:04",
"2018-05-01 00:00:00", "2018-05-01 00:00:02", "2018-05-01 00:00:04",
"2018-02-22 00:00:00", "2018-02-22 00:00:02", "2018-02-22 00:00:04",
"2018-02-22 00:00:06", "2018-03-23 00:00:00", "2018-03-23 00:00:02",
"2018-03-23 00:00:04", "2018-03-23 00:00:06", "2018-03-23 00:00:08",
"2018-01-31 00:00:24", "2018-01-31 00:00:26", "2018-01-31 00:00:28",
"2018-01-31 00:00:30", "2018-02-01 00:00:00", "2018-02-01 00:00:02",
"2018-02-01 00:00:04", "2018-02-01 00:00:06"), Param1_2 = c(0.21,
0.87, 0.78, NA, 1.2, 1.4, NA, 0.6, 0.45, 0.45, NA, 0, 0.1, 0.03,
0.01, NA, 0.07, 0.07, 0.08, NA, 0.09, 0.06, 0.01, NA), Param2_2 = c(38,
76, 32, NA, 23, 27, NA, 623, 54, 54, NA, 487, 341, 671, 859,
NA, 6941, 4467, 808, NA, 143, 301, 219, NA), Param3_2 = c(1.7,
1.65, 1.32, NA, 0.3, 2.5, NA, 1.4, 0.67, 0.67, NA, 0.1, 0.15,
0.22, 0.29, NA, 1, 1, 0.1, NA, 0.5, 1, 0.3, NA), Param4_2 = c(0,
1, 0.86, NA, 0.6, 1, NA, 0.18, 0, 0, NA, 0, 1, 0.29, 0.07, NA,
0.31, 0.16, 0.68, NA, 0.86, 0.47, 0.47, NA), Param5_2 = c(0,
1, 0, NA, 0, 0.09, NA, 0.78, 0, 0, NA, 0.2, 0, 0.46, 0.72, NA,
0.29, 0.26, 0.1, NA, 0.88, 0.86, 0.95, NA), Param6_2 = c(1, 0,
0, NA, 0, 1, NA, 1, 0.27, 0, NA, 0, 0.23, 0.55, 0.86, NA, 0.68,
0.66, 0, NA, 0.44, 1, 0.22, NA)), row.names = c(NA, 24L), class = "data.frame")
df.extended
doBy？我的想法和迄今为止我为解决这个问题所做的工作：
试用1：
我遇到的问题：第5步：
df.temp <- df[1,]
df.temp <- merge(df.temp, df[2, !(colnames(df) == "AbsoluteTime")], by = c("SubjectID", "EventNumber", "EventType"))
df.temp <- merge(df.temp, df[3, !(colnames(df) == "AbsoluteTime")], by = c("SubjectID", "EventNumber", "EventType"))
df.temp <- merge(df.temp, df[4, !(colnames(df) == "AbsoluteTime")], by = c("SubjectID", "EventNumber", "EventType"))
Warning:
In merge.data.frame(df.temp, df[4, ], by = c("SubjectID", "EventNumber", :
column names ‘Param1.x’, ‘Param2.x’, ‘Param3.x’, ‘Param4.x’, ‘Param5.x’, ‘Param6.x’, ‘AbsoluteTime.x’, ‘Param1.y’, ‘Param2.y’,
'Param3.y'，'Param4.y'，'Param5.y'，'Param6.y'，'AbsoluteTime.y'是 结果重复。
必须有一个比这更好的方法：
n <- 3
names_vector <- c()
for (n in seq(from = c(1), to = n)) {
for (i in names(df[4:9])) {
names_vector <- c(names_vector, paste0(i, "_", c(n+1)))
}
}
names(df.temp)[c(4:9)] <- parameters
names(df.temp)[c(11:ncol(df.temp))] <- names_vector
names(df.temp)
试用2：
这是一个SubjectID和一个EventNumber的代码：
df.temp <- df[which(df$SubjectID == "1" & df$EventNumber == "1"), ]
df.temp2 <- df.temp[2:nrow(df.temp)-1, parameters]
df.temp2 <- rbind(df.temp2, NA)
df.temp <- cbind(df.temp, df.temp2)
df.temp2 <- df.temp[3:nrow(df.temp)-1, parameters]
df.temp2 <- rbind(df.temp2, NA, NA)
df.temp <- cbind(df.temp, df.temp2)
df.temp2 <- df.temp[4:nrow(df.temp)-1, parameters]
df.temp2 <- rbind(df.temp2, NA, NA, NA)
df.temp <- cbind(df.temp, df.temp2)
n <- 3
names_vector <- c()
for (n in seq(from = c(1), to = n)) {
for (i in names(df[4:9])) {
print(i)
print(n)
names_vector <- c(names_vector, paste0(i, "_", c(n+1)))
}
}
names(df.temp)[c(4:9)] <- parameters
names(df.temp)[c(11:ncol(df.temp))] <- names_vector
df.temp
答案 0 :(得分：1)
这样的事情：
您可以使用包dplyr的{{3}}根据数据中感兴趣的各种子集添加和重命名变量。 dplyr还提供了函数lead()和lag()，可用于查找向量（或此处行）中的“下一个”或“上一个”值。您可以将lead()与函数mutate_at()结合使用，从后续的“第n个”-row中提取值，并使用它们创建新的变量集。
在这里，我使用您在示例中提供的数据：
# load dplyr package
require(dplyr)
# creacte new data frame "df.extended"
df.extended <- df
# number of observations per group (e.g., SubjectID)
# or desired number of successions
obs = 3
# loop until number of successions achieved
for (i in 1:obs) {
# overwrite df.extended with new information
df.extended <- df.extended %>%
# group by subjects and events
group_by(SubjectID, EventNumber) %>%
# create new variable for each parameter
mutate_at( vars(Param1:Param6),
# using the lead function
.funs = funs(step = lead),
# for the nth followning row
n = i) %>%
# rename the new variables to show the succession number
rename_at(vars(contains("_step")), funs(sub("step", as.character(i), .)))
}
这应该粗略地重新创建您发布的数据作为期望的结果。
# Look at first part of "df.extended"
> head(df.extended)
# A tibble: 6 x 28
# Groups: SubjectID, EventNumber [2]
SubjectID EventNumber EventType Param1 Param2 Param3 Param4 Param5 Param6 AbsoluteTime Param1_1 Param2_1 Param3_1 Param4_1 Param5_1 Param6_1
<fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 A 0.300 45. 1.50 0.140 0.180 0. 2018-04-01 00:00:00 0.210 38. 1.70 0. 0. 1.00
2 1 1 A 0.210 38. 1.70 0. 0. 1.00 2018-04-01 00:00:02 0.870 76. 1.65 1.00 1.00 0.750
3 1 1 A 0.870 76. 1.65 1.00 1.00 0.750 2018-04-01 00:00:04 0.780 32. 1.32 0.860 0. 0.
4 1 1 A 0.780 32. 1.32 0.860 0. 0. 2018-04-01 00:00:04 NA NA NA NA NA NA
5 1 2 B 0.900 67. 0.600 0. 1.00 0.140 2018-05-01 00:00:00 1.20 23. 0.300 0.600 0. 0.
6 1 2 B 1.20 23. 0.300 0.600 0. 0. 2018-05-01 00:00:02 1.40 27. 2.50 1.00 0.0900 1.00
# ... with 12 more variables: Param1_2 <dbl>, Param2_2 <dbl>, Param3_2 <dbl>, Param4_2 <dbl>, Param5_2 <dbl>, Param6_2 <dbl>, Param1_3 <dbl>,
# Param2_3 <dbl>, Param3_3 <dbl>, Param4_3 <dbl>, Param5_3 <dbl>, Param6_3 <dbl>
答案 1 :(得分：1)
对于基础R，请考虑by按 SubjectID ， EventNumber 和 EventType 进行切片，然后运行{{1使用帮助器 group_num 。要运行一系列参数，请在merge中包装by进程，以获取在外部链接合并的数据帧列表，以便与原始数据帧进行最终合并：
lapply<强>输出
df_list <- lapply(2:3, function(i) {
# BUILD LIST OF DATAFRAMES
by_list <- by(df, df[c("SubjectID", "EventNumber", "EventType")], FUN=function(sub){
sub$grp_num <- 1:nrow(sub)
row_less_sub <- transform(sub, AbsoluteTime=NULL, grp_num=grp_num-(i-1))
merge(sub, row_less_sub, by=c("SubjectID", "EventNumber", "EventType", "grp_num"),
all.x=TRUE, suffixes = c("", paste0("_", i)))
})
# APPEND ALL DATAFRAMES IN LIST
grp_df <- do.call(rbind, by_list)
grp_df <- with(grp_df, grp_df[order(SubjectID, EventNumber),])
# KEEP NEEDED COLUMNS
grp_df <- grp_df[c("SubjectID", "EventNumber", "EventType", "grp_num",
names(grp_df)[grep("Param[0-9]_", names(grp_df))])]
row.names(grp_df) <- NULL
return(grp_df)
})
# ALL PARAMS_* CHAIN MERGE
params_df <- Reduce(function(x,y) merge(x, y, by=c("SubjectID", "EventNumber", "EventType", "grp_num")), df_list)
# ORIGINAL DF AND PARAMS MERGE
df$grp_num <- ave(df$Param1, df$SubjectID, df$EventNumber, df$EventType,
FUN=function(x) cumsum(rep(1, length(x))))
final_df <- transform(merge(df, params_df, by=c("SubjectID", "EventNumber", "EventType", "grp_num")), grp_num=NULL)