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data.table offers advanced techniques for efficient data manipulation in R. This guide explores lesser-known features like .GRP for group indexing and precise subsetting using row and column indices. These methods enhance data processing speed and code readability, surpassing dplyr in performance.
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data.table Advanced Technics And Examples | Data Science With R
data.table是R語言的熱門套件，能夠快速處理大量資料，效率高於dplyr，且得利於語法結構的精巧設計，data.table更容易寫出版排整齊的程式。這篇文章會討論data.table的進階技巧，在Cheat Sheat上也不一定找得到。
.GRP – Add index for each group
dt <- data.table(C1 = c("A", "B", "C", "B", "A"),
C2 = c(1, 2, 3, 2, 1))
dt2 <- dt[, INDEX := .GRP, by = .(C1)] C1 C2 INDEX
1: A 1 1
2: B 2 2
3: C 3 3
4: B 2 2
5: A 1 1Subset with row index and column index
dt <- data.table(C1 = 1:3,
C2 = 101:103,
C3 = 901:903)
dt_row <- dt[1:2, ]
dt_col <- dt[, 2:3]
dt_row_col <- dt[1:2, 2:3]> dt_row
C1 C2 C3
1: 1 101 901
2: 2 102 902> dt_col
C2 C3
1: 101 901
2: 102 902
3: 103 903> dt_row_col
C2 C3
1: 101 901
2: 102 902
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