R中全字符列类型的data.table如何删除NaN与Inf值

我有如下data.table：

data = structure(list(date = c("2021-11-24", "2021-11-24", "2021-11-26", 
"2021-11-24", "2021-11-26", "2021-11-24", "2021-11-24", "2021-11-26", 
"2021-11-26", "2021-11-26", "2021-11-26"), open = c("NaN", "NaN", 
"0.43", "0.17", "0.19", "0.15", "NaN", "NaN", "NaN", "NaN", "NaN"
), high = c("NaN", "NaN", "0.43", "0.17", "0.19", "0.15", "NaN", 
"NaN", "NaN", "NaN", "NaN"), low = c("NaN", "NaN", "0.43", "0.17", 
"0.19", "0.15", "NaN", "NaN", "NaN", "NaN", "NaN"), close = c("NaN", 
"NaN", "0.43", "0.17", "0.19", "0.15", "NaN", "NaN", "NaN", "NaN", 
"NaN"), volume = c(0L, 0L, 2L, 10L, 75L, 1L, 0L, 0L, 0L, 0L, 
0L)), row.names = c(NA, -11L), class = c("data.table", "data.frame"
))

我需要删除该data.table中所有NaN和Inf值，原始数据预览如下：

date      open high  low close volume
 1: 2021-11-24  NaN  NaN  NaN   NaN      0
 2: 2021-11-24  NaN  NaN  NaN   NaN      0
 3: 2021-11-26 0.43 0.43 0.43  0.43      2
 4: 2021-11-24 0.17 0.17 0.17  0.17     10
 5: 2021-11-26 0.19 0.19 0.19  0.19     75
 6: 2021-11-24 0.15 0.15 0.15  0.15      1
 7: 2021-11-24  NaN  NaN  NaN   NaN      0
 8: 2021-11-26  NaN  NaN  NaN   NaN      0
 9: 2021-11-26  NaN  NaN  NaN   NaN      0
10: 2021-11-26  NaN  NaN  NaN   NaN      0
11: 2021-11-26  NaN  NaN  NaN   NaN      0

受NaN值影响，open、high、low、close列均为字符类型，请问有没有可直接在data.table中快速删除NaN的方法？

各方案性能测试结果 #

以下是不同实现方案的性能对比测试代码：

p_load(dtplyr, dplyr)
microbenchmark::microbenchmark(
  
  user438383 = data[!unique(which(data == "NaN" | data == "Inf", arr.ind=T)[,1])],
  
  langtang = na.omit(cbind(data[, .(date,volume)], data[, lapply(.SD, as.numeric), .SDcols = 2:5])),
  
  akrun  = {data <- type.convert(data, as.is = TRUE);
  data[data[, Reduce(`&`, lapply(.SD, function(x)
    !is.nan(x) & is.finite(x))), .SDcols = -1]]},

  paul = {data <- type.convert(data, as.is = TRUE);
  data[data[,is.finite(rowSums(.SD)), .SDcols=-1]]},
  
  Macosso = {data$Row <- row.names(data);
  rm_rw <- data[apply(data, 1, 
                      function(X) any(X== "NaN"|X== "Inf")),] %>% dplyr::pull(Row);
  data[!row.names(data) %in% rm_rw ,] %>% dplyr::select(-Row)} 
)

测试输出结果：

Unit: microseconds
       expr      min       lq      mean   median       uq       max neval  cld
 user438383  893.843  931.243  976.4554  974.011 1005.673  1093.929   100 a   
   langtang 2694.987 2779.411 2904.5124 2877.927 3003.832  3420.539   100   c 
      akrun 1664.476 1694.780 2253.8962 1731.392 1838.755 26035.268   100  b  
       paul 1663.552 1718.956 1792.2313 1770.511 1843.051  2151.975   100  b  
    Macosso 5899.961 6140.244 6429.9634 6368.072 6604.615  8180.782   100    d

从测试结果可以看出，user438383提供的方案性能最优，无需提前转换列数据类型，直接匹配字符串形式的NaN和Inf定位待删除行，执行速度是其他方案的2-6倍。

内容的提问来源于stack exchange，提问作者Saurabh