How to delete missing observations for a subset of columns: the R equivalent of dropna(subset) from python pandas - r

Question

Consider a dataframe in R where I want to drop row 6 because it has missing observations for the variables var1:var3. But the dataframe has valid observations for id and year. See code below.
In python, this can be done in two ways:
use df.dropna(subset = ['var1', 'var2', 'var3'], inplace=True)
use df.set_index(['id', 'year']).dropna()
How to do this in R with tidyverse?
library(tidyverse)
df <- tibble(id = c(seq(1,10)), year=c(seq(2001,2010)),
var1 = c(sample(1:100, 10, replace=TRUE)),
var2 = c(sample(1:100, 10, replace=TRUE)),
var3 = c(sample(1:100, 10, replace=TRUE)))
df[3,4] = NA
df[6,3:5] = NA
df[8,3:4] = NA
df[10,4:5] = NA

Answer 1

We may use complete.cases
library(dplyr)
df %>%
filter(if_any(var1:var3, complete.cases))
-output
# A tibble: 9 x 5
id year var1 var2 var3
<int> <int> <int> <int> <int>
1 1 2001 48 55 82
2 2 2002 22 83 67
3 3 2003 89 NA 19
4 4 2004 56 1 38
5 5 2005 17 58 35
6 7 2007 4 30 94
7 8 2008 NA NA 36
8 9 2009 97 100 80
9 10 2010 37 NA NA

Answer 2

We can use pmap for this case also:
library(dplyr)
library(purrr)
df %>%
filter(!pmap_lgl(., ~ {x <- c(...)[-c(1, 2)];
all(is.na(x))}))
# A tibble: 9 x 5
id year var1 var2 var3
<int> <int> <int> <int> <int>
1 1 2001 90 55 77
2 2 2002 77 5 18
3 3 2003 17 NA 70
4 4 2004 72 33 33
5 5 2005 10 55 77
6 7 2007 22 81 17
7 8 2008 NA NA 46
8 9 2009 93 28 100
9 10 2010 50 NA NA
Or we could also use complete.cases function in pmap as suggested by dear #akrun:
df %>%
filter(pmap_lgl(select(., 3:5), ~ any(complete.cases(c(...)))))

Answer 3

You can use if_any in filter -
library(dplyr)
df %>% filter(if_any(var1:var3, Negate(is.na)))
# id year var1 var2 var3
# <int> <int> <int> <int> <int>
#1 1 2001 14 99 43
#2 2 2002 25 72 76
#3 3 2003 90 NA 15
#4 4 2004 91 7 32
#5 5 2005 69 42 7
#6 7 2007 57 83 41
#7 8 2008 NA NA 74
#8 9 2009 9 78 23
#9 10 2010 93 NA NA
In base R, we can use rowSums to select rows which has atleast 1 non-NA value.
cols <- grep('var', names(df))
df[rowSums(!is.na(df[cols])) > 0, ]

Answer 4

If looking for complete cases, use the following (kernel of this is based on other answers):
library(tidyverse)
df <- tibble(id = c(seq(1,10)), year=c(seq(2001,2010)),
var1 = c(sample(1:100, 10, replace=TRUE)),
var2 = c(sample(1:100, 10, replace=TRUE)),
var3 = c(sample(1:100, 10, replace=TRUE)))
df[3,4] = NA
df[6,3:5] = NA
df[8,3:4] = NA
df[10,4:5] = NA
df %>% filter(!if_any(var1:var3, is.na))
#> # A tibble: 6 x 5
#> id year var1 var2 var3
#> <int> <int> <int> <int> <int>
#> 1 1 2001 13 28 26
#> 2 2 2002 61 77 58
#> 3 4 2004 95 38 58
#> 4 5 2005 38 34 91
#> 5 7 2007 85 46 14
#> 6 9 2009 45 60 40
Created on 2021-06-24 by the reprex package (v2.0.0)