CODEWITHSUNDEEP
r
Abhishek Bhatia
Abhishek Bhatia

Reputation: 9806

Subsetting dataframes removing false values

I have a data of following sort:

head(df)

enter image description here

Sometimes I have value "Nan" in the answer column. enter image description here

What I wish to do create a separate dataframe of all rows with this value and one without.

I used the following code:

dfwith <-df[ !grepl("Nan", df$answer) , ]
dfwithout <-df[ grepl("Nan", df$answer) , ]

But I get incorrect results, I don't understand why, where am I going wrong. Thanks in advance. Please help.

Edit:

> dput(droplevels(head(df)))
structure(list(X.run.number. = c(16L, 9L, 3L, 18L, 1L, 19L), 
    density = c(0.52, 0.52, 0.52, 0.52, 0.52, 0.52), k = c(100L, 
    100L, 100L, 100L, 100L, 100L), knt = c(2900L, 1700L, 500L, 
    2900L, 500L, 2900L), threshold = c(0.2, 0.2, 0.3, 0.4, 0.1, 
    0.5), X.step. = c(0L, 0L, 0L, 0L, 0L, 0L), answer = structure(c(4L, 
    5L, 1L, 6L, 2L, 3L), .Label = c("100.2767857", "106.9588889", 
    "107.1467647", "53.13833333", "64.54785714", "95.61115385"
    ), class = "factor"), percent = c(16.04938272, 18.51851852, 
    38.27160494, 34.56790123, 11.11111111, 45.67901235)), .Names = c("X.run.number.", 
"density", "k", "knt", "threshold", "X.step.", "answer", "percent"
), row.names = c(NA, 6L), class = "data.frame")

Edit 2:

> dput(droplevels(tail(df)))
structure(list(X.run.number. = c(4488L, 4509L, 4502L, 4537L, 
4530L, 4544L), density = c(0.52, 0.52, 0.52, 0.52, 0.52, 0.52
), k = c(600L, 600L, 600L, 600L, 600L, 600L), knt = c(19700L, 
23300L, 22100L, 28100L, 26900L, 29300L), threshold = c(0.1, 0.1, 
0.1, 0.1, 0.1, 0.1), X.step. = c(0L, 0L, 0L, 0L, 0L, 0L), answer = structure(c(1L, 
1L, 1L, 1L, 1L, 1L), .Label = "\"Nan\"", class = "factor"), percent = c(11.11111111, 
12.34567901, 6.172839506, 8.641975309, 11.11111111, 11.11111111
)), .Names = c("X.run.number.", "density", "k", "knt", "threshold", 
"X.step.", "answer", "percent"), row.names = 4545:4550, class = "data.frame")

Upvotes: 0

Views: 73

Answers (2)

Danny M.
Danny M.

Reputation: 281

I think it has something to do with you having NaN instead of NA. Try this. 

df[complete.cases(df), ]

Upvotes: 1

Danny M.
Danny M.

Reputation: 281

For only getting rows without NA, you can use:

dfwithout <- na.omit(df)

And if you need to change Nan to NA or NaN, you can use:

df <- gsub("Nan", "NA", df)

You can see which rows and columns have NA with:

which(is.na(df), arr.ind=TRUE)

Upvotes: 1

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