r - 在数据框中查找最大值按另一列中的值分组的行

Question

我想在不使用 SQL 的情况下在 R 中解决这个问题。

如何在 SQL 中通过另一列选择具有 MAX（列值）、DISTINCT 的行？

当然，我可以使用 sqldf 来做到这一点，但是 R 中也必须有一个很酷的 apply 方法来做到这一点？

Answer 1

设置数据首先读入数据：

Lines <- "id  home  datetime  player   resource
1   10   04/03/2009  john    399 
2   11   04/03/2009  juliet  244
5   12   04/03/2009  borat   555
3   10   03/03/2009  john    300
4   11   03/03/2009  juliet  200
6   12   03/03/2009  borat   500
7   13   24/12/2008  borat   600
8   13   01/01/2009  borat   700
"
DF <- read.table(text = Lines, header = TRUE)
DF$datetime <- as.Date(DF$datetime, format = "%d/%m/%Y")

1) base - by有很多方法可以使用各种包来处理它，但在这里我们将首先展示一个基本解决方案：

> do.call("rbind", by(DF, DF$home, function(x) x[which.max(x$datetime), ]))
   id home   datetime player resource
10  1   10 2009-03-04   john      399
11  2   11 2009-03-04 juliet      244
12  5   12 2009-03-04  borat      555
13  8   13 2009-01-01  borat      700

1a) 基础 - ave和一个变体（也只使用 R 的基础）：

FUN <- function(x) which.max(x) == seq_along(x)
is.max <- ave(xtfrm(DF$datetime), DF$home, FUN = FUN) == 1
DF[is.max, ]

2) sqldf在这里它使用 sqldf 以防万一：

> library(sqldf)
> sqldf("select id, home, max(datetime) datetime, player, resource 
+        from DF 
+        group by home")
  id home   datetime player resource
1  1   10 2009-03-04   john      399
2  2   11 2009-03-04 juliet      244
3  5   12 2009-03-04  borat      555
4  8   13 2009-01-01  borat      700

Answer 2

I do not use SQL as well, so I would do it in this way.

1)

df <- read.table("your file", "your options") # I leave this to you

2)

row_with_max_value <- max(which(df$values & df$group_column=="desired_group"))

"row_with_max_value" contents the row number of your data frame (df), in which you find the maximum value of the column "values" (df$values) grouped by "group_column". If "group_column" is not of type character, remove the quotes and use the corresponding text format.

If you need the value, than

df$values[row_with_max_value]

Probably it is not the most elegant way, but you do not need SQL and it works (at least for me ;)