r - R 中带有子集的 For 循环

Question

我在 csv 文件中有以下数据:

Date        Model      Color    Value   Samples
6/19/2017   Gold       Blue     0.5     500
6/19/2017   Gold       Red      0.0     449
6/19/2017   Silver     Blue     0.75    1320
6/19/2017   Silver     Blue     1.5     103
6/19/2017   Gold       Red      0.7     891
6/19/2017   Gold       Blue     0.41    18103
6/19/2017   Copper     Blue     0.83    564
6/19/2017   Silver     Pink     1.17    173
6/19/2017   Platinum   Brown    0.43    793
6/19/2017   Platinum   Red      0.71    1763
6/19/2017   Gold       Orange   1.92    503

我使用 fread 函数来创建一个 data.table:

library(dplyr)
library(data.table)

df <- fread("test_data.csv", 
                 header = TRUE,
                 fill = TRUE,
                 sep = ",")

然后我通过 Model 对数据进行子集化，如下所示:

df_subset <- subset(df, df$Model=='Gold' & df$Value > 0)

然后，我根据 Color 变量创建了一些百分位数，如下所示:

df_subset[, .(Samples = sum(Samples),
    '50th'    = quantile(AvgValue, probs = c(0.50)),
    '99th'    = quantile(AvgValue, probs = c(0.99)),
    '99.9th'  = quantile(AvgValue, probs = c(0.999)), 
    '99.99th' = quantile(AvgValue, probs = c(0.9999))),
by = Color]

这给出了以下输出:

    Color Samples  50th   99th  99.9th  99.99th
1:   Blue   18603 0.455 0.4991 0.49991 0.499991
2:    Red    1340 0.975 1.2445 1.24945 1.249945
3: Orange     503 1.920 1.9200 1.92000 1.920000

我正在尝试遍历 Model 值列表并输出每个 Model 值的相关百分位值。

我已经尝试了以下(失败):

models <- unique(df$Model)

for (model in models){

  df$model[, .(Samples = sum(Samples),
                '50th'    = quantile(Value, probs = c(0.50)),
                '99th'    = quantile(Value, probs = c(0.99)),
                '99.9th'  = quantile(Value, probs = c(0.999)), 
                '99.99th' = quantile(Value, probs = c(0.9999))),
            by = Color]
}

错误信息是:

Error in .(Samples = sum(Samples), `50th` = quantile(Value, probs = c(0.5)),  :  could not find function "."

Answer 1

fread 创建一个 data.table 对象而不是一个数据框，所以我建议坚持使用 data.table 语法而不是将它与 dplyr 混淆。也不需要 for 循环，我们可以使用 by 参数中的两个变量列表在一行代码中循环模型和颜色:

qs = df[Value > 0, .(Samples = sum(Samples),
              '50th'    = quantile(Value, probs = c(0.50)),
              '99th'    = quantile(Value, probs = c(0.99)),
              '99.9th'  = quantile(Value, probs = c(0.999)), 
              '99.99th' = quantile(Value, probs = c(0.9999))),
          by = .(Model, Color)]
setkey(qs, 'Model')

#       Model  Color Samples  50th   99th  99.9th  99.99th
# 1:   Copper   Blue     564 0.830 0.8300 0.83000 0.830000
# 2:     Gold   Blue   18603 0.455 0.4991 0.49991 0.499991
# 3:     Gold    Red     891 0.700 0.7000 0.70000 0.700000
# 4:     Gold Orange     503 1.920 1.9200 1.92000 1.920000
# 5: Platinum  Brown     793 0.430 0.4300 0.43000 0.430000
# 6: Platinum    Red    1763 0.710 0.7100 0.71000 0.710000
# 7:   Silver   Blue    1423 1.125 1.4925 1.49925 1.499925
# 8:   Silver   Pink     173 1.170 1.1700 1.17000 1.170000