如何将数据帧中的NaNs转换为NAs

I'm hoping and fearing this just has a one line answer, and am embarrassed about having to ask it:

我希望并担心这个问题只有一句话回答，不得不问这个问题让我很尴尬:

As a result of casting a dataframe using reshape2, I have a large dataframe whose first lines are as follows:

由于使用reshape2对dataframe进行了强制转换，我有一个大的dataframe，其第一行如下:

  ukgridcode      x       y year     pm10 pm2.5      no2      nox  co so2 ozone benzene
1      56361 460500 1218500 2001 8.583796   NaN 2.375508 3.031918 NaN NaN   NaN     NaN
2      57051 460500 1217500 2001 8.584764   NaN 2.368387 3.022830 NaN NaN   NaN     NaN
3      57052 461500 1217500 2001 8.587392   NaN 2.383438 3.042039 NaN NaN   NaN     NaN
4      57054 463500 1217500 2001 8.590163   NaN 2.455386 3.133869 NaN NaN   NaN     NaN
5      57741 460500 1216500 2001 8.588822   NaN 2.375839 3.032340 NaN NaN   NaN     NaN
6      57742 461500 1216500 2001 8.592360   NaN 2.394949 3.056732 NaN NaN   NaN     NaN

I would like to turn each of the NaNs into NAs. So far I've tried:

我想把每个NaNs转换成NAs。到目前为止我已经试过:

d2 <- d[is.nan(d)] <- NA
d2 <- d[!is.finite(d)] <- NA
d2 <- apply(d, 2, function (x) x[is.nan(x)] <- NA) # also tried row-wise (1)

But so far no success.

但到目前为止还没有成功。

Solutions gratefully received.

欣然接受的解决方案。

2 个解决方案

#1

Try

试一试

d[sapply(d, is.nan)] <- NA


#   ukgridcode      x       y year     pm10 pm2.5      no2      nox co so2 ozone benzene
# 1      56361 460500 1218500 2001 8.583796    NA 2.375508 3.031918 NA  NA    NA      NA
# 2      57051 460500 1217500 2001 8.584764    NA 2.368387 3.022830 NA  NA    NA      NA
# 3      57052 461500 1217500 2001 8.587392    NA 2.383438 3.042039 NA  NA    NA      NA
# 4      57054 463500 1217500 2001 8.590163    NA 2.455386 3.133869 NA  NA    NA      NA
# 5      57741 460500 1216500 2001 8.588822    NA 2.375839 3.032340 NA  NA    NA      NA
# 6      57742 461500 1216500 2001 8.592360    NA 2.394949 3.056732 NA  NA    NA      NA

Or using data.table

或者使用data.table

library(data.table)
setDT(d)[, lapply(.SD, function(x) ifelse(is.nan(x), NA, x))]

Or just look at @Rolands comment

或者看看@Rolands的评论

#2

You can try:

你可以尝试:

 is.nan(d$pm2.5)
 #[1] TRUE TRUE TRUE TRUE TRUE TRUE

 d[d=="NaN"] <- NA
 d
 #    ukgridcode      x       y year     pm10 pm2.5      no2      nox co so2 ozone
 #1      56361 460500 1218500 2001 8.583796    NA 2.375508 3.031918 NA  NA    NA
 #2      57051 460500 1217500 2001 8.584764    NA 2.368387 3.022830 NA  NA    NA
 #3      57052 461500 1217500 2001 8.587392    NA 2.383438 3.042039 NA  NA    NA
 #4      57054 463500 1217500 2001 8.590163    NA 2.455386 3.133869 NA  NA    NA
 #5      57741 460500 1216500 2001 8.588822    NA 2.375839 3.032340 NA  NA    NA
 #6      57742 461500 1216500 2001 8.592360    NA 2.394949 3.056732 NA  NA    NA
 #  benzene
 #1      NA
 #2      NA
 #3      NA
 #4      NA
 #5      NA
 #6      NA