在R data.table中通过引用分配字符串变量中提供的i表达式

时间:2017-11-22 01:53:42

标签: r data.table

我正在使用plotly构建一个Shiny应用程序,并且需要根据许多参数过滤数据。目前我在data.table中使用一个标志,通过引用更新。实际数据有很多列,我非常喜欢可扩展的方法来添加列以进行可视化。我在一个方面做得很短:基于数值实际过滤数据。

我将要过滤的列的名称存储在一个字符数组中,但似乎我不能使用它来定义选择行的表达式(即i表达式)。这可能吗?或者我是以错误的方式接近这个?

library(data.table)

set.seed(12345)

dt = data.table(mtcars)
dt[,filtered := FALSE]


filterColumnNames = c('cyl','gear','carb')

filterValues = list(cyl = c(4,6),
                    gear = c(3),
                    carb = c(1))

for (columnName in filterColumnNames) {
  dt[columnName %in% filterValues[columnName][[1]], filtered := TRUE]
}

# Working, but not loopy enough.
# dt[cyl %in% filterValues['cyl'][[1]], filtered := TRUE]
# dt[gear %in% filterValues['gear'][[1]], filtered := TRUE]
# dt[carb %in% filterValues['carb'][[1]], filtered := TRUE]

print(dt)

3 个答案:

答案 0 :(得分:2)

原因是columnName未评估%in%以获取该列的值。我们可以使用get

for (columnName in filterColumnNames) {
  dt[get(columnName) %in% filterValues[columnName][[1]], filtered := TRUE][]
}

eval(as.name(

for (columnName in filterColumnNames) {
    dt[eval(as.name(columnName)) %in% filterValues[columnName][[1]], filtered := TRUE][]
}

答案 1 :(得分:2)

实现此目的的另一种方法是使用 join 来选择行:

library(data.table)
dt <- as.data.table(mtcars)
filterValues <- list(cyl = c(4,6),
                     gear = c(3),
                     carb = c(1))
dt[do.call(CJ, filterValues), on = names(filterValues), filtered := TRUE][]
     mpg cyl  disp  hp drat    wt  qsec vs am gear carb filtered
 1: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4       NA
 2: 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4       NA
 3: 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1       NA
 4: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1     TRUE
 5: 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2       NA
 6: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1     TRUE
 7: 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4       NA
 8: 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2       NA
 9: 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2       NA
10: 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4       NA
11: 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4       NA
12: 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3       NA
13: 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3       NA
14: 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3       NA
15: 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4       NA
16: 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4       NA
17: 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4       NA
18: 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1       NA
19: 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2       NA
20: 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1       NA
21: 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1     TRUE
22: 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2       NA
23: 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2       NA
24: 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4       NA
25: 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2       NA
26: 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1       NA
27: 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2       NA
28: 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2       NA
29: 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4       NA
30: 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6       NA
31: 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8       NA
32: 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2       NA
     mpg cyl  disp  hp drat    wt  qsec vs am gear carb filtered

dt <- as.data.table(mtcars)
dt[do.call(CJ, filterValues), on = names(filterValues), nomatch = 0L]
    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1: 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
2: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
3: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1

您只需指定filterValues的列表。 do.call(CJ, filterValues)交叉连接)创建一个data.table,其中包含所有组合以按以下方式选择行:

   cyl gear carb
1:   4    3    1
2:   6    3    1

修改

OP可以asked,如果这可以扩展到不平等。

这可以使用data.table非等联接来完成,但设置稍有不同。如,

filterIntervals <- list(disp = c(200, 300),
                        mpg = c(10, 20))
mDT <- dcast(melt(filterIntervals), . ~ L1 + rowid(L1))
filterCondition <- c("disp>=disp_1", "disp<disp_2", "mpg>mpg_1", "mpg<mpg_2")
dt[mDT, on = filterCondition, filtered := TRUE][]
     mpg cyl  disp  hp drat    wt  qsec vs am gear carb filtered
 1: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4       NA
 2: 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4       NA
 3: 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1       NA
 4: 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1       NA
 5: 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2       NA
 6: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1     TRUE
 7: 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4       NA
 8: 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2       NA
 9: 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2       NA
10: 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4       NA
11: 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4       NA
12: 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3     TRUE
13: 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3     TRUE
14: 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3     TRUE
15: 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4       NA
16: 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4       NA
17: 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4       NA
18: 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1       NA
19: 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2       NA
20: 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1       NA
21: 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1       NA
22: 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2       NA
23: 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2       NA
24: 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4       NA
25: 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2       NA
26: 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1       NA
27: 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2       NA
28: 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2       NA
29: 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4       NA
30: 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6       NA
31: 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8       NA
32: 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2       NA
     mpg cyl  disp  hp drat    wt  qsec vs am gear carb filtered

答案 2 :(得分:1)

您可以根据要应用的过滤条件创建角色向量。请参阅以下示例:

strerror()