library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
# Before dplyr v0.5. this worked,
# so cyl was considered in arranging
mtcars %>%
  group_by(cyl) %>%
  arrange(mpg) %>%
  print(.,n = 30)
## Source: local data frame [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   10.4     8 472.0   205  2.93 5.250 17.98     0     0     3     4
## 2   10.4     8 460.0   215  3.00 5.424 17.82     0     0     3     4
## 3   13.3     8 350.0   245  3.73 3.840 15.41     0     0     3     4
## 4   14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
## 5   14.7     8 440.0   230  3.23 5.345 17.42     0     0     3     4
## 6   15.0     8 301.0   335  3.54 3.570 14.60     0     1     5     8
## 7   15.2     8 275.8   180  3.07 3.780 18.00     0     0     3     3
## 8   15.2     8 304.0   150  3.15 3.435 17.30     0     0     3     2
## 9   15.5     8 318.0   150  2.76 3.520 16.87     0     0     3     2
## 10  15.8     8 351.0   264  4.22 3.170 14.50     0     1     5     4
## 11  16.4     8 275.8   180  3.07 4.070 17.40     0     0     3     3
## 12  17.3     8 275.8   180  3.07 3.730 17.60     0     0     3     3
## 13  17.8     6 167.6   123  3.92 3.440 18.90     1     0     4     4
## 14  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
## 15  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2
## 16  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
## 17  19.2     8 400.0   175  3.08 3.845 17.05     0     0     3     2
## 18  19.7     6 145.0   175  3.62 2.770 15.50     0     1     5     6
## 19  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
## 20  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
## 21  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
## 22  21.4     4 121.0   109  4.11 2.780 18.60     1     1     4     2
## 23  21.5     4 120.1    97  3.70 2.465 20.01     1     0     3     1
## 24  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
## 25  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
## 26  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
## 27  26.0     4 120.3    91  4.43 2.140 16.70     0     1     5     2
## 28  27.3     4  79.0    66  4.08 1.935 18.90     1     1     4     1
## 29  30.4     4  75.7    52  4.93 1.615 18.52     1     1     4     2
## 30  30.4     4  95.1   113  3.77 1.513 16.90     1     1     5     2
## ..   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...
# Now with v0.5 cyl needs to be prefixed
# in arrange to restore old behaviour
mtcars %>%
  group_by(cyl) %>%
  arrange(cyl, mpg) %>%
  print(.,n = 30)
## Source: local data frame [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   21.4     4 121.0   109  4.11 2.780 18.60     1     1     4     2
## 2   21.5     4 120.1    97  3.70 2.465 20.01     1     0     3     1
## 3   22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
## 4   22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
## 5   24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
## 6   26.0     4 120.3    91  4.43 2.140 16.70     0     1     5     2
## 7   27.3     4  79.0    66  4.08 1.935 18.90     1     1     4     1
## 8   30.4     4  75.7    52  4.93 1.615 18.52     1     1     4     2
## 9   30.4     4  95.1   113  3.77 1.513 16.90     1     1     5     2
## 10  32.4     4  78.7    66  4.08 2.200 19.47     1     1     4     1
## 11  33.9     4  71.1    65  4.22 1.835 19.90     1     1     4     1
## 12  17.8     6 167.6   123  3.92 3.440 18.90     1     0     4     4
## 13  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
## 14  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
## 15  19.7     6 145.0   175  3.62 2.770 15.50     0     1     5     6
## 16  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
## 17  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
## 18  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
## 19  10.4     8 472.0   205  2.93 5.250 17.98     0     0     3     4
## 20  10.4     8 460.0   215  3.00 5.424 17.82     0     0     3     4
## 21  13.3     8 350.0   245  3.73 3.840 15.41     0     0     3     4
## 22  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
## 23  14.7     8 440.0   230  3.23 5.345 17.42     0     0     3     4
## 24  15.0     8 301.0   335  3.54 3.570 14.60     0     1     5     8
## 25  15.2     8 275.8   180  3.07 3.780 18.00     0     0     3     3
## 26  15.2     8 304.0   150  3.15 3.435 17.30     0     0     3     2
## 27  15.5     8 318.0   150  2.76 3.520 16.87     0     0     3     2
## 28  15.8     8 351.0   264  4.22 3.170 14.50     0     1     5     4
## 29  16.4     8 275.8   180  3.07 4.070 17.40     0     0     3     3
## 30  17.3     8 275.8   180  3.07 3.730 17.60     0     0     3     3
## ..   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...