extract rows from data.frame with only numeric values

Question

To my surprise, I couldn't find this being asked before.

My input data.frame

a <- c(1,5.3,3,1,-8,6,-1)
b <- c(4,-2,1.0,"1 2","-","1.2.3","x")
df <- data.frame(a,b)
df
   a     b
1  1     4
2  5.3  -2
3  3     1
4  1   1 2
5 -8     -
6  6 1.2.3
7 -1     x

Desired output

What I came up with

df[apply(df, 1, function(r) !any(is.na(as.numeric(r)))) ,]

It works but it throws some ugly warnings

    a  b
1 1.0  4
2 5.3 -2
3 3.0  1
Warning messages:
1: In FUN(newX[, i], ...) : NAs introduced by coercion
2: In FUN(newX[, i], ...) : NAs introduced by coercion
3: In FUN(newX[, i], ...) : NAs introduced by coercion
4: In FUN(newX[, i], ...) : NAs introduced by coercion

Any better idea using R base if possible?

Is there any reason why your source data frame contains both text and non text data in the same column? — Tim Biegeleisen
– Tim Biegeleisen, Commented Nov 23, 2021 at 1:30
It's just to avoid wrong input from the user. Note that all values in b are considered as text, but some of them can (and should) be coerced to numeric. — asg
– asg, Commented Nov 23, 2021 at 8:27
This may look a bit silly now, but I just realized that suppressWarnings( df[apply(df, 1, function(r) !any(is.na(as.numeric(r)))) ,] ) is probably the best way to do this. — asg
– asg, Commented Dec 17, 2021 at 11:29

neilfws · Accepted Answer · 2021-11-24 05:39:35Z

2

note: edited after you altered your question

I don't think the warnings are a big problem. They just tell you what you know already; that 4 character values return NA when coerced to numeric.

You could filter the data frame for only positive or negative digit values, then convert to numeric, using dplyr:

library(dplyr)

df %>% 
  filter(if_all(everything(), ~ grepl("^-?\\d+(\\.\\d+)?$", .x))) %>% 
  mutate(across(everything(), ~ as.numeric(.x)))

Result:

edited Nov 24, 2021 at 5:39

answered Nov 23, 2021 at 1:46

neilfws

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Andre Wildberg · Accepted Answer · 2021-11-23 15:38:59Z

1

A couple base R solutions using strtoi (without warnings)

rowSums (all integer)

df[ !is.na( rowSums( sapply( df, strtoi ) ) ), ]

  a  b
1 1  4
2 5 -2
3 3  1

complete.cases (all integer)

df[ complete.cases( sapply( df, strtoi ) ), ]

  a  b
1 1  4
2 5 -2
3 3  1

EDIT after changes (some float)

The next uses double sapply to touch every single value, no vectors. It's important in cases where you have conflicting modes, i.e. ifelse can't decide.

df
    a     b
1   1     4
2 5.3    -2
3   3     1
4   1   1 2
5  -8     -
6   6   1.3
7  -1     x
8 2.5 1.2.3

data.frame( na.omit( sapply( df, function(x) 
  sapply( x, function(y) 
    ifelse(grepl("^-?\\d+\\.\\d+$", y), as.numeric(y), strtoi(y)) ) )))

      a    b
1   1.0  4.0
5.3 5.3 -2.0
3   3.0  1.0
6   6.0  1.3

edited Nov 23, 2021 at 15:38

answered Nov 23, 2021 at 1:54

Andre Wildberg

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1 Comment

asg Over a year ago

Thanks for answering, but this solution also removes float numerics. I have edited the input data.frame of my question

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