Does the dplyr function is_grouped_df() actually require the input to be a date frame (vs a data table, tibble, etc.)? If it does not require a data frame why isn't it named is_grouped instead of is_grouped_df?
mtcars %>% group_by(cyl) %>% is_grouped_df()
#> [1] TRUE
mtcars %>% group_by(cyl) %>% class()
#> [1] "grouped_df" "tbl_df" "tbl" "data.frame"
I can group a multiple class mtcars data set, and confirm with the is_grouped_df() function that the data set is grouped.
mtcars %>% group_by(cyl) %>% as_tibble() %>% is_grouped_df()
#> [1] FALSE
mtcars %>% group_by(cyl) %>% as_tibble() %>% class()
#> [1] "tbl_df" "tbl" "data.frame"
I can try to force mtcars to be a tibble and notice that when I check if it is a is_grouped_df I get FALSE as the answer. Even though that doesn't seem to be the case. I never called the ungroup() function in my pipe after grouping. Why FALSE?
mtcars %>% group_by(cyl) %>% as_tibble() %>% as.data.frame() %>% is_grouped_df()
#> [1] FALSE
mtcars %>% group_by(cyl) %>% as_tibble() %>% as.data.frame() %>% class()
#> [1] "data.frame"
I can try to force mtcars to be a data frame and notice that when I check if it is a is_grouped_df I get FALSE as the answer. Even though that doesn't seem to be the case. I never called the ungroup() function in my pipe after grouping. Why FALSE?
And now I circle back to the original question, "Does the dplyr function is_grouped_df() actually require the input to be a date frame (vs a data table, tibble, etc.)?". And why all the inconsistencies in my three examples above?
As soon as you add the as_tibble or as.data.frame functions, the groups that were created by the group_by function are deleted.
You can't create groups on a data frame. The data frame is converted to a tibble as soon as you use group_by
class(mtcars)
[1] "data.frame"
mtcars %>%
group_by(cyl) %>%
class()
[1] "grouped_df" "tbl_df" "tbl" "data.frame"
You can see how the data frame gets converted into a tibble by using group_by
mtcars %>%
group_by(cyl)
# A tibble: 32 x 11
# Groups: cyl [3]
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# ... with 22 more rows
But as soon as you call as_tibble again, the groups dissapear.
mtcars %>%
group_by(cyl) %>%
as_tibble()
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# ... with 22 more rows
Same thing happens if you call as.data.frame after using group_by
mtcars %>%
group_by(cyl) %>%
as.data.frame()
mpg cyl disp hp drat wt qsec vs am gear carb
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
So basically, is_grouped_df works as intended, only detecting tibbles that have groups. In this case, it's important to note that as_tibble will effectively reset the groups that have already been created, so it ends up acting as ungroup if called on a tibble.