September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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答案：撰寫程式
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45
> extractCounts <- function(ws, gender, year) {
+ col <- switch(year, "11"="Col4", "12"="Col6", "13"="Col8")
+ countRows <- grep(gender, ws[, "Col1"])
+ if (length(countRows) > 0) {
+ counts <- ws[countRows, col]
+ as.numeric(counts[!is.na(counts)])
+ } else {
+ numeric(0)
+ }
+ }
以下的程式碼會在 Kappa College 的電子試算表上，取得不同組的資料。
> extractCounts(kappa, "Male", "11")
[1] 30 0 0
> extractCounts(kappa, "Male", "12")
[1] 42 42 0
> extractCounts(kappa, "Male", "13")
[1] 34 37 33
> extractCounts(kappa, "Female", "13")
[1] 26 21 20
現在，從 11 年級、12 年級以及 13 年級的男學生與 13 年級的女學生欄位中抽出的值，
我們能夠更詳細的確認整體狀況。我們將只會對，在 11 年級與 12 年級有零個男學生，
以及
參加每個層級 NCEA 的 13 年級男學生的數量小於 3，
以及
有一些 13
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.