Using R’s dplyr package, write R code that shows the average mpg and average weight based on # of cylinders and whether there is automatic transmission; show only for cars that get more than 20 mpg.

require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Assign mtcars data to a table data frame

mtcars_df <- tbl_df(mtcars)

Group by # of cylendars,am

(cylendars.am <- group_by(mtcars_df, cyl, am))
## Source: local data frame [32 x 11]
## Groups: cyl, am
## 
##     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

Now summarise - avergae mpg, average wt

(summary.results <- summarise(cylendars.am,
                             avgmpg = mean(mpg, na.rm = TRUE),
                             avgwt = mean(wt, na.rm = TRUE)))
## Source: local data frame [6 x 4]
## Groups: cyl
## 
##   cyl am avgmpg avgwt
## 1   4  0  22.90 2.935
## 2   4  1  28.07 2.042
## 3   6  0  19.12 3.389
## 4   6  1  20.57 2.755
## 5   8  0  15.05 4.104
## 6   8  1  15.40 3.370

show only for cars that get more than 20 mpg.

filter(summary.results, avgmpg > 20)
## Source: local data frame [3 x 4]
## Groups: cyl
## 
##   cyl am avgmpg avgwt
## 1   4  0  22.90 2.935
## 2   4  1  28.07 2.042
## 3   6  1  20.57 2.755

The above steps can be combined/chained into a single top-down step using operator “%>%”

mtcars %>%
  group_by(cyl, am) %>%
      summarise(avgmpg = mean(mpg), avgwt = mean(wt)) %>%
            filter(avgmpg > 20)
## Source: local data frame [3 x 4]
## Groups: cyl
## 
##   cyl am avgmpg avgwt
## 1   4  0  22.90 2.935
## 2   4  1  28.07 2.042
## 3   6  1  20.57 2.755