How to subset all rows from a data frame for longitudinal study

[yalem]

I have a dataset consisting of repeated measures(4 waves), one data point per row. These data are from a longitudinal study and therefore at the moment, not every subject has all data points.

the data looks like

<subject_id waves Value>
1 wave1 23
1 Wave2 13
1 Wave3 12
2 Wave1 25
2 Wave2 31
2 Wave3 34
2 Wave4 25
3 Wave1 18
3 Wave2 34
4 Wave1 30
5 Wave1 19
5 Wave2 28
5 Wave3 25
5 Wave4 31
6 Wave1 50
6 Wave2 35
7 Wave1 33
7 Wave2 35
7 Wave3 41
7 Wave4 25

So,
subject 1 has three repeated measures
subject 2,5, & 7 have four repeated measures
subject 3 has two repeated measures
subject 4 has one measure.

I would like to be able to extract all data points involved in all repeated measures or those with two repeated measures.
The background of my question was to create a new data frame for those who have measured in all waves removing subject 1, 3 and 4.
Any thoughts, please?

[siddharthprabhu]

I find dplyr::add_count() very useful for this sort of thing.

library(dplyr, warn.conflicts = FALSE)

data <- tribble(~ subject_id, ~ waves, ~ Value,
                1, "wave1", 23,
                1, "Wave2", 13,
                1, "Wave3", 12,
                2, "Wave1", 25,
                2, "Wave2", 31,
                2, "Wave3", 34,
                2, "Wave4", 25,
                3, "Wave1", 18,
                3, "Wave2", 34,
                4, "Wave1", 30,
                5, "Wave1", 19,
                5, "Wave2", 28,
                5, "Wave3", 25,
                5, "Wave4", 31,
                6, "Wave1", 50,
                6, "Wave2", 35,
                7, "Wave1", 33,
                7, "Wave2", 35,
                7, "Wave3", 41,
                7, "Wave4", 25)


# Data points with all repeated measures.
data %>% 
  add_count(subject_id) %>% 
  filter(n == 4)
#> # A tibble: 12 x 4
#>    subject_id waves Value     n
#>         <dbl> <chr> <dbl> <int>
#>  1          2 Wave1    25     4
#>  2          2 Wave2    31     4
#>  3          2 Wave3    34     4
#>  4          2 Wave4    25     4
#>  5          5 Wave1    19     4
#>  6          5 Wave2    28     4
#>  7          5 Wave3    25     4
#>  8          5 Wave4    31     4
#>  9          7 Wave1    33     4
#> 10          7 Wave2    35     4
#> 11          7 Wave3    41     4
#> 12          7 Wave4    25     4

# Data points with only 2 repeated measures.
data %>% 
  add_count(subject_id) %>% 
  filter(n == 2)
#> # A tibble: 4 x 4
#>   subject_id waves Value     n
#>        <dbl> <chr> <dbl> <int>
#> 1          3 Wave1    18     2
#> 2          3 Wave2    34     2
#> 3          6 Wave1    50     2
#> 4          6 Wave2    35     2

^{Created on 2020-06-07 by the reprex package (v0.3.0)}

[yalem]

@siddharthprabhu

data %>% add_count(subject_id) %>% filter(n == 4

Thank you so much for your suggestions. Still, it doesn't work. It could be the value col has many NA's. The dim of the data is 448 x 123. I was trying to remove NA's using a code
Do you have suggestions to create a data frame removing the NA's?

[siddharthprabhu]

You could use dplyr::filter() to remove the NA values before count(). Maybe something like filter(!is.na(Value)).

[yalem]

Thank you so much. Now it works.