在这种情况下，我们的大数据集将如下所示:
structure(list(Car = c("Mazda RX4", "Maserati Bora", "Leticia",
"Hornet 4 Drive", "Hornet Sportabout", "Alex", "Duster 360",
"Merc 240D", "Merc 230", "Merc 280", "Merc 280C", "Merc 450SE",
"Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood", "Lincoln Continental",
"Chrysler Imperial", "Fiat 128", "Honda Civic", "Toyota Corolla",
"Toyota Corona", "Datsun 710", "AMC Javelin", "Camaro Z28",
"Datsun 710", "Fiat X1-9", "Mazda RX4", "Lotus Europa",
"Ford Pantera L", "Ferrari Dino", "Mazda RX4 Wag", "Volvo 142E"
), Name = c("Mark", "Random", "Datsun 710", "Trevor", "Joanna",
"Valiant", "Random", "Random", "Random", "Random", "Random",
"Random", "Random", "Random", "Random", "Random", "Random", "Random",
"Random", "Trevor", "Random", "Random", "Random", "Random", "Random",
"Random", "Mazda RX4", "Random", "Alex", "Random", "John", "Random"
), disp = c(160, 160, 108, 258, 360, 225, 360, 146.7, 140.8,
167.6, 167.6, 275.8, 275.8, 275.8, 472, 460, 440, 78.7, 75.7,
71.1, 120.1, 318, 304, 350, 400, 79, 120.3, 95.1, 351, 145, 301,
121), hp = c(110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123,
180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245,
175, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9, 3.9, 3.85,
3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07,
2.93, 3, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76, 3.15, 3.73, 3.08,
4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11), wt = c(2.62, 2.875,
2.32, 3.215, 3.44, 3.46, 3.57, 3.19, 3.15, 3.44, 3.44, 4.07,
3.73, 3.78, 5.25, 5.424, 5.345, 2.2, 1.615, 1.835, 2.465, 3.52,
3.435, 3.84, 3.845, 1.935, 2.14, 1.513, 3.17, 2.77, 3.57, 2.78
), qsec = c(16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84,
20, 22.9, 18.3, 18.9, 17.4, 17.6, 18, 17.98, 17.82, 17.42, 19.47,
18.52, 19.9, 20.01, 16.87, 17.3, 15.41, 17.05, 18.9, 16.7, 16.9,
14.5, 15.5, 14.6, 18.6), vs = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
1), am = c(1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1), gear = c(4, 4,
4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3,
3, 3, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1, 2, 1, 4, 2,
2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4,
6, 8, 2)), .Names = c("Car", "Name", "disp", "hp", "drat", "wt",
"qsec", "vs", "am", "gear", "carb"), row.names = c(NA, -32L), class = "data.frame")
我想通过从中提取一些行来对这个数据集进行子集化。我想提取的行存储在另一个数据框中:
> dput(list_save)
structure(list(Car = c("Mazda RX4", "Mazda RX4 Wag", "Datsun 710",
"Hornet 4 Drive", "Hornet Sportabout", "Valiant"), Name = c("Mark",
"John", "Leticia", "Trevor", "Joanna", "Alex")), .Names = c("Car",
"Name"), class = "data.frame", row.names = c(NA, -6L))
查看
list_save 因为根据 df 可以在不同的列中找到一些字符串，但也必须提取它。所需的输出应该是这样的:
Car Name disp hp drat wt qsec vs am gear carb
1 Mazda RX4 Mark 160 110 3.90 2.620 16.46 0 1 4 4
2 Mazda RX4 Wag John 301 335 3.54 3.570 14.60 0 1 5 8
3 Leticia Datsun 710 108 93 3.85 2.320 18.61 1 1 4 1
4 Hornet 4 Drive Trevor 258 110 3.08 3.215 19.44 1 0 3 1
5 Hornet Sportabout Joanna 360 175 3.15 3.440 17.02 0 0 3 2
6 Alex Valiant 225 105 2.76 3.460 20.22 1 0 3 1
我想到了类似于下面显示的功能:
test <- df[df[,1:2] %in% list_save, ]
最佳答案
我只会使用 data.table 运行两个二进制连接，一次 Car 和 Name 对它们自己，一次对彼此，然后将它们结合起来。为此，我们将使用 CRAN 上的最新版本(v 1.9.6+)
library(data.table) # v 1.9.6+
res <- setDT(df)[list_save, on = c("Car", "Name")]
res2 <- df[list_save, on = c(Name = "Car", Car = "Name"), nomatch = 0L]
res[is.na(disp), (names(res)) := res2]
# Car Name disp hp drat wt qsec vs am gear carb
# 1: Mazda RX4 Mark 160 110 3.90 2.620 16.46 0 1 4 4
# 2: Mazda RX4 Wag John 301 335 3.54 3.570 14.60 0 1 5 8
# 3: Leticia Datsun 710 108 93 3.85 2.320 18.61 1 1 4 1
# 4: Hornet 4 Drive Trevor 258 110 3.08 3.215 19.44 1 0 3 1
# 5: Hornet Sportabout Joanna 360 175 3.15 3.440 17.02 0 0 3 2
# 6: Alex Valiant 225 105 2.76 3.460 20.22 1 0 3 1
或者，一种更安全的方法是
rbind 仅匹配结果，但这样您将丢失原始行顺序res <- setDT(df)[list_save, on = c("Car", "Name"), nomatch = 0L]
res2 <- df[list_save, on = c(Name = "Car", Car = "Name"), nomatch = 0L]
rbind(res, res2)
# Car Name disp hp drat wt qsec vs am gear carb
# 1: Mazda RX4 Mark 160 110 3.90 2.620 16.46 0 1 4 4
# 2: Mazda RX4 Wag John 301 335 3.54 3.570 14.60 0 1 5 8
# 3: Hornet 4 Drive Trevor 258 110 3.08 3.215 19.44 1 0 3 1
# 4: Hornet Sportabout Joanna 360 175 3.15 3.440 17.02 0 0 3 2
# 5: Leticia Datsun 710 108 93 3.85 2.320 18.61 1 1 4 1
# 6: Alex Valiant 225 105 2.76 3.460 20.22 1 0 3 1
关于r - 使用来自其他数据集的两列中的字符串对大数据集进行子集，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/33150519/