我遇到的问题与here类似，但我尝试过的解决方案都没有。
给出一个这样的表：
Date Exercise Category Weight Reps EstMax RepxWeight Note
4/2/16 Deadlift Legs 135 7 166.4685 7x135 easy
4/2/16 Deadlift Legs 135 7 166.4685 7x135 kinda easy
4/2/16 Deadlift Legs 135 7 166.4685 7x135 tired
4/2/16 Bench Press Chest 95 5 110.8175 5x95 hard
4/2/16 Bench Press Chest 135 2 143.991 2x135 not hard
4/9/16 Bench Press Chest 135 2 143.991 2x135 a little hard
4/9/16 Bench Press Chest 135 2 143.991 2x135 super tired
4/18/16 Deadlift Legs 155 8 196.292 8x155 …
4/18/16 Deadlift Legs 155 5 180.8075 5x155 bad day
5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
5/8/16 Deadlift Legs 185 3 203.4815 3x185 felt easy
5/8/16 Bench Press Chest 115 4 130.318 4x115 easy
5/8/16 Bench Press Chest 115 4 130.318 4x115 hard
我希望aggregate根据多个其他列（例如max和{{}获取某个列的EstMax值（例如Date） {1}}），但也保留行中的所有其他列。如果多个条目具有相同的最大值，请取第一个条目。
预期输出如下：
Exercise我试过的一些方法的例子;在每种情况下，'额外列'最终都被用作聚合的因素，这不是我想要的。
Date Exercise Category Weight Reps EstMax RepxWeight Note
4/2/16 Deadlift Legs 135 7 166.4685 7x135 easy
4/2/16 Bench Press Chest 135 2 143.991 2x135 not hard
4/9/16 Bench Press Chest 135 2 143.991 2x135 a little hard
4/18/16 Deadlift Legs 155 8 196.292 8x155 …
5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
5/8/16 Bench Press Chest 115 4 130.318 4x115 hard
特别喜欢碱性R溶液。还看到了data <- structure(list(Date = structure(c(2L, 2L, 2L, 2L, 2L, 3L, 3L,
1L, 1L, 4L, 4L, 4L, 4L), .Label = c("4/18/16", "4/2/16", "4/9/16",
"5/8/16"), class = "factor"), Exercise = structure(c(2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L), .Label = c("Bench Press",
"Deadlift"), class = "factor"), Category = structure(c(2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L), .Label = c("Chest",
"Legs"), class = "factor"), Weight = c(135L, 135L, 135L, 95L,
135L, 135L, 135L, 155L, 155L, 185L, 185L, 115L, 115L), Reps = c(7L,
7L, 7L, 5L, 2L, 2L, 2L, 8L, 5L, 3L, 3L, 4L, 4L), EstMax = c(166.4685,
166.4685, 166.4685, 110.8175, 143.991, 143.991, 143.991, 196.292,
180.8075, 203.4815, 203.4815, 130.318, 130.318), RepxWeight = structure(c(6L,
6L, 6L, 5L, 1L, 1L, 1L, 7L, 4L, 2L, 2L, 3L, 3L), .Label = c("2x135",
"3x185", "4x115", "5x155", "5x95", "7x135", "8x155"), class = "factor"),
Note = structure(c(4L, 8L, 11L, 7L, 9L, 2L, 10L, 1L, 3L,
6L, 5L, 4L, 7L), .Label = c("…", "a little hard", "bad day",
"easy", "felt easy", "good day", "hard", "kinda easy", "not hard",
"super tired", "tired"), class = "factor")), .Names = c("Date",
"Exercise", "Category", "Weight", "Reps", "EstMax", "RepxWeight",
"Note"), class = "data.frame", row.names = c(NA, -13L))
# base R
aggregate(EstMax ~ Date + Exercise, data = data, FUN = max)
# Date Exercise EstMax
# 1 4/2/16 Bench Press 143.9910
# 2 4/9/16 Bench Press 143.9910
# 3 5/8/16 Bench Press 130.3180
# 4 4/18/16 Deadlift 196.2920
# 5 4/2/16 Deadlift 166.4685
# 6 5/8/16 Deadlift 203.4815
aggregate(EstMax ~ Date + Exercise + RepxWeight + Note, data = data, FUN = max)
# Date Exercise RepxWeight Note EstMax
# 1 4/18/16 Deadlift 8x155 … 196.2920
# 2 4/9/16 Bench Press 2x135 a little hard 143.9910
# 3 4/18/16 Deadlift 5x155 bad day 180.8075
# 4 5/8/16 Bench Press 4x115 easy 130.3180
# 5 4/2/16 Deadlift 7x135 easy 166.4685
# 6 5/8/16 Deadlift 3x185 felt easy 203.4815
# 7 5/8/16 Deadlift 3x185 good day 203.4815
# 8 5/8/16 Bench Press 4x115 hard 130.3180
# 9 4/2/16 Bench Press 5x95 hard 110.8175
# 10 4/2/16 Deadlift 7x135 kinda easy 166.4685
# 11 4/2/16 Bench Press 2x135 not hard 143.9910
# 12 4/9/16 Bench Press 2x135 super tired 143.9910
# 13 4/2/16 Deadlift 7x135 tired 166.4685
# data table
library("data.table")
data_dt <- data.table(data)
data_dt[ , max(EstMax), by = c("Date", "Exercise")]
# Date Exercise V1
# 1: 4/2/16 Deadlift 166.4685
# 2: 4/2/16 Bench Press 143.9910
# 3: 4/9/16 Bench Press 143.9910
# 4: 4/18/16 Deadlift 196.2920
# 5: 5/8/16 Deadlift 203.4815
# 6: 5/8/16 Bench Press 130.3180
data_dt[, max(EstMax), .(Date, Exercise, Weight, Reps, RepxWeight, Note)]
# Date Exercise Weight Reps RepxWeight Note V1
# 1: 4/2/16 Deadlift 135 7 7x135 easy 166.4685
# 2: 4/2/16 Deadlift 135 7 7x135 kinda easy 166.4685
# 3: 4/2/16 Deadlift 135 7 7x135 tired 166.4685
# 4: 4/2/16 Bench Press 95 5 5x95 hard 110.8175
# 5: 4/2/16 Bench Press 135 2 2x135 not hard 143.9910
# 6: 4/9/16 Bench Press 135 2 2x135 a little hard 143.9910
# 7: 4/9/16 Bench Press 135 2 2x135 super tired 143.9910
# 8: 4/18/16 Deadlift 155 8 8x155 … 196.2920
# 9: 4/18/16 Deadlift 155 5 5x155 bad day 180.8075
# 10: 5/8/16 Deadlift 185 3 3x185 good day 203.4815
# 11: 5/8/16 Deadlift 185 3 3x185 felt easy 203.4815
# 12: 5/8/16 Bench Press 115 4 4x115 easy 130.3180
# 13: 5/8/16 Bench Press 115 4 4x115 hard 130.3180
函数，该函数可能会有所帮助，但无法弄清楚如何将其应用于此。
我看过的其他相关问题却没有解决这个问题：
Adding a non-aggregated column to an aggregated data set based on the aggregation of another column
Only keep min value for each factor level
How to select the row with the maximum value in each group
aggregating multiple columns in data.table
How to aggregate some columns while keeping other columns in R?
答案 0 :(得分：8)
我知道您寻求基本的R解决方案，但与此同时，这里有一个dplyr：
library(dplyr)
data %>%
group_by(Date, Exercise) %>%
slice(which.max(EstMax))
# # A tibble: 6 x 8
# # Groups: Date, Exercise [6]
# Date Exercise Category Weight Reps EstMax RepxWeight Note
# <fctr> <fctr> <fctr> <int> <int> <dbl> <fctr> <fctr>
# 1 4/18/16 Deadlift Legs 155 8 196.2920 8x155 …
# 2 4/2/16 Bench Press Chest 135 2 143.9910 2x135 not hard
# 3 4/2/16 Deadlift Legs 135 7 166.4685 7x135 easy
# 4 4/9/16 Bench Press Chest 135 2 143.9910 2x135 a little hard
# 5 5/8/16 Bench Press Chest 115 4 130.3180 4x115 easy
# 6 5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
修改
data.table不是我的 forte ，但为了完整起见，我的尝试是：
library(data.table)
setDT(data)[, .SD[which.max(EstMax)], by = .(Date, Exercise)]
# Date Exercise Category Weight Reps EstMax RepxWeight Note
# 1: 4/2/16 Deadlift Legs 135 7 166.4685 7x135 easy
# 2: 4/2/16 Bench Press Chest 135 2 143.9910 2x135 not hard
# 3: 4/9/16 Bench Press Chest 135 2 143.9910 2x135 a little hard
# 4: 4/18/16 Deadlift Legs 155 8 196.2920 8x155 …
# 5: 5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
# 6: 5/8/16 Bench Press Chest 115 4 130.3180 4x115 easy
答案 1 :(得分：2)
这是dplyr的另一种方法：
library(dplyr)
library(lubridate)
data %>%
mutate(Date = mdy(Date)) %>%
group_by(Date, Exercise) %>%
arrange(desc(EstMax)) %>%
slice(1)
<强>结果：
# A tibble: 6 x 8
# Groups: Date, Exercise [6]
Date Exercise Category Weight Reps EstMax RepxWeight Note
<date> <fctr> <fctr> <int> <int> <dbl> <fctr> <fctr>
1 2016-04-02 Bench Press Chest 135 2 143.9910 2x135 not hard
2 2016-04-02 Deadlift Legs 135 7 166.4685 7x135 easy
3 2016-04-09 Bench Press Chest 135 2 143.9910 2x135 a little hard
4 2016-04-18 Deadlift Legs 155 8 196.2920 8x155 …
5 2016-05-08 Bench Press Chest 115 4 130.3180 4x115 easy
6 2016-05-08 Deadlift Legs 185 3 203.4815 3x185 good day
或者您也可以使用sqldf：
library(sqldf)
library(lubridate)
data$Date = mdy(data$Date)
sqldf("select *, max(EstMax) as EstMax2 from data
group by Date, Exercise
order by Date, Exercise")
<强>结果：
Date Exercise Category Weight Reps EstMax RepxWeight Note EstMax2
1 2016-04-02 Bench Press Chest 135 2 143.9910 2x135 not hard 143.9910
2 2016-04-02 Deadlift Legs 135 7 166.4685 7x135 easy 166.4685
3 2016-04-09 Bench Press Chest 135 2 143.9910 2x135 a little hard 143.9910
4 2016-04-18 Deadlift Legs 155 8 196.2920 8x155 … 196.2920
5 2016-05-08 Bench Press Chest 115 4 130.3180 4x115 easy 130.3180
6 2016-05-08 Deadlift Legs 185 3 203.4815 3x185 good day 203.4815
答案 2 :(得分：2)
一个（不正确的）方法，为了显示一个问题而独立汇总所有数字列：
grpvar <- c("Date", "Exercise", "Category")
merge(
aggregate(data[,c("Weight", "Reps", "EstMax")], by = data[grpvar], FUN = max),
aggregate(data[,c("RepxWeight", "Note")], by = data[grpvar], FUN = function(a) a[1]),
by = grpvar
)
# Date Exercise Category Weight Reps EstMax RepxWeight Note
# 1 4/18/16 Deadlift Legs 155 8 196.2920 8x155 ...
# 2 4/2/16 Bench Press Chest 135 5 143.9910 5x95 hard
# 3 4/2/16 Deadlift Legs 135 7 166.4685 7x135 easy
# 4 4/9/16 Bench Press Chest 135 2 143.9910 2x135 a little hard
# 5 5/8/16 Bench Press Chest 115 4 130.3180 4x115 easy
# 6 5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
在4/2/16上，您的卧推显示最大重量为135，最大重复次数为5，但两者并未出现在数据的同一行。
这是一种稍微（更正确）的不同方法，使用您对which.max的想法：
do.call(rbind,
by(data, data[c("Date", "Exercise")],
function(x) x[which.max(x$Weight),])
)
# Date Exercise Category Weight Reps EstMax RepxWeight Note
# 5 4/2/16 Bench Press Chest 135 2 143.9910 2x135 not hard
# 6 4/9/16 Bench Press Chest 135 2 143.9910 2x135 a little hard
# 12 5/8/16 Bench Press Chest 115 4 130.3180 4x115 easy
# 8 4/18/16 Deadlift Legs 155 8 196.2920 8x155 ...
# 1 4/2/16 Deadlift Legs 135 7 166.4685 7x135 easy
# 10 5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
如果出于某种原因，可能在一个Exercise内有一个Category，您可能希望by的第二个参数改为data[c("Date","Exercise","Category")]。< / p>
（您可以使用类似x[order(as.Date(x$Date, format="%m/%d/%Y")),]的内容订购输出...实际上您可能认为$Date列是实际的Date - 类。）
答案 3 :(得分：1)
我知道你更喜欢基础R解决方案，但dplyr提供了一个功能＆＃39; top_n＆＃39;这正是你所要求的。
使用它一次来检索所有EstMax实例：
library(dplyr)
data %>%
group_by(Exercise) %>%
top_n(1, EstMax)
# A tibble: 5 x 8
# Groups: Exercise [2]
Date Exercise Category Weight Reps EstMax RepxWeight Note
<fctr> <fctr> <fctr> <int> <int> <dbl> <fctr> <fctr>
1 4/2/16 Bench Press Chest 135 2 143.9910 2x135 not hard
2 4/9/16 Bench Press Chest 135 2 143.9910 2x135 a little hard
3 4/9/16 Bench Press Chest 135 2 143.9910 2x135 super tired
4 5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
5 5/8/16 Deadlift Legs 185 3 203.4815 3x185 felt easy
使用它两次来检索最大结果的第一个结果：
data %>%
group_by(Exercise) %>%
top_n(1, EstMax) %>%
top_n(1, Date)
Selecting by Note
# A tibble: 2 x 8
# Groups: Exercise [2]
Date Exercise Category Weight Reps EstMax RepxWeight Note
<fctr> <fctr> <fctr> <int> <int> <dbl> <fctr> <fctr>
1 4/9/16 Bench Press Chest 135 2 143.9910 2x135 super tired
2 5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
请注意，这是第一个结果，不一定是最早的日期。因此，您必须在使用第二个＆＃39; top_n＆＃39;：
之前按日期排列data %>%
group_by(Exercise) %>%
top_n(1, EstMax) %>%
mutate(Date = as.Date(Date, format = '%d/%m/%y')) %>%
arrange(Date) %>%
top_n(1)
Selecting by Note
# A tibble: 2 x 8
# Groups: Exercise [2]
Date Exercise Category Weight Reps EstMax RepxWeight Note
<date> <fctr> <fctr> <int> <int> <dbl> <fctr> <fctr>
1 2016-09-04 Bench Press Chest 135 2 143.9910 2x135 super tired
2 2016-08-05 Deadlift Legs 185 3 203.4815 3x185 good day
[edit]稍微误读了这个问题，这是一个解决方案，它提供了你要求的输出：
data %>%
group_by(Date, Exercise) %>%
top_n(1, EstMax) %>%
top_n(1)
Selecting by Note
# A tibble: 6 x 8
# Groups: Date, Exercise [6]
Date Exercise Category Weight Reps EstMax RepxWeight Note
<fctr> <fctr> <fctr> <int> <int> <dbl> <fctr> <fctr>
1 4/2/16 Deadlift Legs 135 7 166.4685 7x135 tired
2 4/2/16 Bench Press Chest 135 2 143.9910 2x135 not hard
3 4/9/16 Bench Press Chest 135 2 143.9910 2x135 super tired
4 4/18/16 Deadlift Legs 155 8 196.2920 8x155 …
5 5/8/16 Deadlift Legs 185 3 203.4815 3x185 good day
6 5/8/16 Bench Press Chest 115 4 130.3180 4x115 hard