我找不到令人满意的教程,可以解释我如何使用应用函数的所有可能性.我仍然是一个新手,但这通常会派上用场,并显着简化我的代码.所以这是我的例子......我有一个看起来像这样的数据框:
> head(p01)
time key dwell
1 8.13 z 0.00
3 8.13 x 1.25
5 9.38 l 0.87
7 10.25 x 0.15
9 10.40 l 1.13
11 11.53 x 0.45
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进入R:
p01 <- structure(list(time = c(8.13, 8.13, 9.38, 10.25, 10.4, 11.53),
key = c("z", "x", "l", "x", "l", "x"), dwell = c(0, 1.25,
0.869, 0.15, 1.13, 0.45)), .Names = c("time", "key", "dwell"), row.names = c(1L, 3L, 5L, 7L, 9L, 11L), class = "data.frame")
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现在我想计算每个字母的出现次数p01$key并打印出来p01$occurences,这样结果看起来像这样:
time key dwell occurences
1 8.13 z 0.00 1
3 8.13 x 1.25 3
5 9.38 l 0.87 2
7 10.25 x 0.15 3
9 10.40 l 1.13 2
11 11.53 x 0.45 3
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我现在这样做的方式是:
p01[p01$key == "l", "occurences"] <- table(p01$key)["l"]
p01[p01$key == "x", "occurences"] <- table(p01$key)["x"]
p01[p01$key == "z", "occurences"] <- table(p01$key)["z"]
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......当然这不是最好的解决方案.特别是因为真实数据包含更多可能性p01$key(16个不同字母之一).
最重要的是,我想计算dwell每个字母的总数,所以我现在正在做的是:
p01[p01$key == "l", "total_dwell"] <- tapply(p01$dwell, p01$key, sum)["l"]
p01[p01$key == "x", "total_dwell"] <- tapply(p01$dwell, p01$key, sum)["x"]
p01[p01$key == "z", "total_dwell"] <- tapply(p01$dwell, p01$key, sum)["z"]
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为了得到:
time key dwell total_dwell
1 8.13 z 0.00 0.00
3 8.13 x 1.25 1.85
5 9.38 l 0.87 2.00
7 10.25 x 0.15 1.85
9 10.40 l 1.13 2.00
11 11.53 x 0.45 1.85
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在过去的6个小时里,我一直在谷歌上搜索并阅读几本书.真的很感激优雅的解决方案和/或一些综合教程的链接.我的解决方案显然有效,但这不是我第一次解决这个问题,我的脚本文件开始看起来很荒谬!
Rol*_*and 10
如果您的数据集很大,请尝试data.table.
library(data.table)
DT <- data.table(p01)
DT[,occurences:=.N,by=key]
DT[,total_dwell:=sum(dwell),by=key]
time key dwell occurences total_dwell
1: 8.13 z 0.000 1 0.000
2: 8.13 x 1.250 3 1.850
3: 9.38 l 0.869 2 1.999
4: 10.25 x 0.150 3 1.850
5: 10.40 l 1.130 2 1.999
6: 11.53 x 0.450 3 1.850
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通过引用分配的两行可以组合如下:
DT[, `:=`(occurences = .N, total_dwell = sum(dwell)), by=key]
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我用的是plyr:
res = ddply(p01, .(key), transform,
occurrences = length(key),
total_dwell = sum(dwell))
res
time key dwell occurrences total_dwell
1 9.38 l 0.869 2 1.999
2 10.40 l 1.130 2 1.999
3 8.13 x 1.250 3 1.850
4 10.25 x 0.150 3 1.850
5 11.53 x 0.450 3 1.850
6 8.13 z 0.000 1 0.000
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请注意,在此之后,表按字母顺序排序key.你可以order用来求助time:
res[order(res$time),]
time key dwell occurrences total_dwell
3 8.13 x 1.250 3 1.850
6 8.13 z 0.000 1 0.000
1 9.38 l 0.869 2 1.999
4 10.25 x 0.150 3 1.850
2 10.40 l 1.130 2 1.999
5 11.53 x 0.450 3 1.850
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