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This question is a spin on my previous question . (I'm hoping it's okay to ask a new question, if it's based on a previously answered question.) The answer uses the dplyr function group_by. I was messing around with group_by on the same data frame from the previous question and got this result:

> gpri.l %>%  group_by(Point) %>%  count(Point)
Source: local data frame [188 x 2]

   Point n
1     99 1
2    100 1
3    101 5
4    102 3
5    103 1
6    104 8
7    105 6
8    106 7
9    107 7
10   108 7
..   ... .
> group_by(mtcars, vsam = vs + am)

I tried the example from dplyr intro

> group_by(mtcars, vsam = vs + am)
Source: local data frame [32 x 12]
Groups: vsam

    mpg cyl  disp  hp drat    wt  qsec vs am gear carb vsam
1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4    1
2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4    1
3  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1    2
4  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1    1
5  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2    0
6  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1    1
7  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4    0
8  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2    1
9  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2    1
10 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4    1
11 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4    1
12 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3    0
13 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3    0
14 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3    0
15 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4    0
16 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4    0
17 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4    0
18 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1    2
19 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2    2
20 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1    2
21 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1    1
22 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2    0
23 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2    0
24 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4    0
25 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2    0
26 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1    2
27 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2    1
28 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2    2
29 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4    1
30 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6    1
31 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8    1
32 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2    2

All the rows are returned in the example. I saw a previous question about a problem with group_by and long variable names, but nothing to explain why group_by doesn't return all 188 rows. I'm using R 3.1.2 in RStudio and I just installed dplyr as instructed in the answer to this question.

It occurred to me that the problem might not be with group_by. I had done a few different combinations of group_by, summarise, and count, but had lost track of exactly what had seemed to point to group_by as the problem. So, perhaps, it's not group_by, exactly, that's causing the shortened list.

shea
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  • you _create_ groups but do nothing with them (except that a new variable is introduced). So the `data.frame` is the same. – ckluss Jan 08 '15 at 18:12

1 Answers1

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Ckluss explained the issue to me. I'm quoting from the comment:

"you create groups but do nothing with them (except that a new variable is introduced). So the data.frame is the same. – ckluss"

EDIT some months later: another question gets at what I wanted. This question has an answer that says to use print.data.frame() or use as.data.frame() to view the output from a dplyr operation.

shea
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