R Remove Groups with Only Nas

Removing groups with all NA in Data.Table or DPLYR in R

Is this what you want?

library(dplyr)

dataHAVE %>%
    group_by(student) %>%
    filter(!all(is.na(score)))

  student  time score
    <dbl> <dbl> <dbl>
1       1     1     7
2       1     2     9
3       1     3     5
4       3     1    NA
5       3     2     3
6       3     3     9
7       5    NA     7
8       5     2    NA
9       5     3     5

Each student is only kept if not (!) all score values are NA

Remove NAs in each column by group

Here is one possible solution using data.table package:

library(data.table)

setDT(na_data)[, lapply(.SD, function(x) if(length(y<-na.omit(x))) y else first(x)), by=Year]

#     Year Peter  Paul  John
# 1:  2011     1     1    NA
# 2:  2011     2     2    NA
# 3:  2011     3     3    NA
# 4:  2012     1     3    NA
# 5:  2012     2     2    NA
# 6:  2012     3     1    NA
# 7:  2013     1     1     4
# 8:  2013     2     2     5
# 9:  2013     3     3     6

dplyr equivalent:

library(dplyr)

na_data |> 
  group_by(Year) |> 
  summarise(across(.fns = ~ if(length(y<-na.omit(.x))) y else first(.x)))

# # A tibble: 9 x 4
# # Groups:   Year [3]
#    Year Peter  Paul  John
#   <dbl> <dbl> <dbl> <int>
# 1  2011     1     1    NA
# 2  2011     2     2    NA
# 3  2011     3     3    NA
# 4  2012     1     3    NA
# 5  2012     2     2    NA
# 6  2012     3     1    NA
# 7  2013     1     1     4
# 8  2013     2     2     5
# 9  2013     3     3     6

Remove groups which do not have non-consecutive NA values in R

How about using difference between the index of NA-values per group?

library(dplyr)
df %>% group_by(group) %>% filter(any(diff(which(is.na(D))) > 1))

## A tibble: 8 x 2
## Groups:   group [2]
#  group     D
#  <dbl> <dbl>
#1    2.   NA 
#2    2.    2.
#3    2.   NA 
#4    2.   NA 
#5    4.   NA 
#6    4.    2.
#7    4.    3.
#8    4.   NA 

I'm not sure this would catch all potential edge cases but it seems to work for the given example.

remove group if any member containes NA in R

We can use filter after grouping by 'category'

library(dplyr)
tbl %>% 
    group_by(category) %>% 
    filter(!any(is.na(values))) %>%
    ungroup

-output

# A tibble: 2 x 2
  category values
  <chr>     <dbl>
1 A             2
2 A             3

Exclude groups with NAs in tidy dataset

Using all() will evaluate the entire group, so you can skip the mutate step.

MWA %>% 
  group_by(Dir) %>% 
  filter(all(!is.na(time_seg)))

# A tibble: 8 x 5
# Groups:   Dir [1]
     VP   Con   Dir   Seg time_seg
  <int> <int> <int> <int>    <int>
1    10     2     2     1      320
2    10     2     2     2     1110
3    10     2     2     3      450
4    10     2     2     4      600
5    10     2     2     5     1680
6    10     2     2     6      730
7    10     2     2     7      850
8    10     2     2     8      840

Remove rows with NA in a group, given the group contains at-least one non NA value

We could use data.table. Convert the 'data.frame' to 'data.table' (setDT(df)). Grouped by 'class', we check with an if/else condition about occurrence of 'NA' elements in the 'value' and subset with .SD

library(data.table)
setDT(df)[, if(any(!is.na(value))) .SD[!is.na(value)] else .SD , by = class]
#    class value
#1: orange    NA
#2:  apple     1
#3:  grape     1
#4:  berry    NA

Or we can change the condition from any to all by slightly modifying the condition

setDT(df)[, if(all(is.na(value))) .SD else .SD[!is.na(value)], by = class]
#    class value
#1: orange    NA
#2:  apple     1
#3:  grape     1
#4:  berry    NA

Or we get the row index (.I) and then subset the dataset.

indx <- setDT(df)[, if(any(!is.na(value))) .I[!is.na(value)] else .I, class]$V1
df[indx]

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