我在R中有数据框,其中两列是日期时间(POSIX类)。我需要按每一行计算平均日期时间。
以下是一些可重现的示例:
a <- c(
"2018-10-11 15:22:17",
"2018-10-10 16:30:37",
"2018-10-10 16:52:46",
"2018-10-10 16:58:33",
"2018-10-10 16:32:24")
b <- c(
"2018-10-11 15:25:12",
"2018-10-10 16:30:39",
"2018-10-10 16:55:14",
"2018-10-10 16:58:53",
"2018-10-10 16:32:27")
a <- strptime(a, format = "%Y-%m-%d %H:%M:%S")
b <- strptime(b, format = "%Y-%m-%d %H:%M:%S")
f <- data.frame(a, b)
结果应该是这样的:
a b time_mean
1 2018-10-11 15:22:17 2018-10-11 15:25:12 2018-10-11 15:23:44
2 2018-10-10 16:30:37 2018-10-10 16:30:39 2018-10-10 16:30:38
3 2018-10-10 16:52:46 2018-10-10 16:55:14 2018-10-10 16:54:00
4 2018-10-10 16:58:33 2018-10-10 16:58:53 2018-10-10 16:58:43
5 2018-10-10 16:32:24 2018-10-10 16:32:27 2018-10-10 16:32:25
我尝试了以下操作:
apply(f, 1, function(x) mean)
apply(f, 1, function(x) mean(c(x[1], x[2])))
答案 0 :(得分:1)
使用apply
matrix
(可以将其转换为class
然后去除Map
属性)。
f$time_mean <- do.call(c, Map(function(x, y) mean(c(x, y)), a, b))
f$time_mean
#[1] "2018-10-11 15:23:44 EDT" "2018-10-10 16:30:38 EDT" "2018-10-10 16:54:00 EDT" "2018-10-10 16:58:43 EDT"
#[5] "2018-10-10 16:32:25 EDT"
或者来自data.frame
f
do.call(c, Map(function(x, y) mean(c(x, y)), f$a, f$b))
此外,另一种选择是使用numeric
转换为?xtfrm
类(也具有POSIXlt
方法分派),执行rowMeans
并转换为DateTime类,如@ jay.sf的帖子
as.POSIXlt(rowMeans(sapply(f, xtfrm)), origin = "1970-01-01")
#[1] "2018-10-11 15:23:44 EDT" "2018-10-10 16:30:38 EDT" "2018-10-10 16:54:00 EDT" "2018-10-10 16:58:43 EDT"
#[5] "2018-10-10 16:32:25 EDT"
答案 1 :(得分:1)
您可以使用数字进行计算。
f$time_mean <- as.POSIXct(sapply(seq(nrow(f)), function(x)
mean(as.numeric(f[x, ]))), origin="1970-01-01")
f
# a b time_mean
# 1 2018-10-11 15:22:17 2018-10-11 15:25:12 2018-10-11 15:23:44
# 2 2018-10-10 16:30:37 2018-10-10 16:30:39 2018-10-10 16:30:38
# 3 2018-10-10 16:52:46 2018-10-10 16:55:14 2018-10-10 16:54:00
# 4 2018-10-10 16:58:33 2018-10-10 16:58:53 2018-10-10 16:58:43
# 5 2018-10-10 16:32:24 2018-10-10 16:32:27 2018-10-10 16:32:25