8 Exercise
jerylann burke
February 15, 2019
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#8.1
library(tidyverse)## -- Attaching packages ------------------------------------------------ tidyverse 1.2.1 --## v ggplot2 3.0.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.6
## v tidyr 0.8.1 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0## -- Conflicts --------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2mtcars.tb <- as_tibble(mtcars)
mtcars.tb## # 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 rowsmtcars.tb %>% slice(1:5, )## # A tibble: 5 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 2mtcars.tb %>% slice(28:32)## # A tibble: 5 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 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
## 2 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
## 3 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
## 4 15 8 301 335 3.54 3.57 14.6 0 1 5 8
## 5 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2mtcars.tb %>% count()## # A tibble: 1 x 1
## n
## <int>
## 1 32mtcars.tb %>% select(mpg)## # A tibble: 32 x 1
## mpg
## * <dbl>
## 1 21
## 2 21
## 3 22.8
## 4 21.4
## 5 18.7
## 6 18.1
## 7 14.3
## 8 24.4
## 9 22.8
## 10 19.2
## # ... with 22 more rowsmtcars.tb %>%
filter(cyl == 6) %>%
select(mpg)## # A tibble: 7 x 1
## mpg
## <dbl>
## 1 21
## 2 21
## 3 21.4
## 4 18.1
## 5 19.2
## 6 17.8
## 7 19.7mtcars.tb %>%
filter(cyl == 6)## # A tibble: 7 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 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 4 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 5 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## 6 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
## 7 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6mtcars.tb %>%
filter(mpg > 25) %>%
select(mpg, cyl)## # A tibble: 6 x 2
## mpg cyl
## <dbl> <dbl>
## 1 32.4 4
## 2 30.4 4
## 3 33.9 4
## 4 27.3 4
## 5 26 4
## 6 30.4 4#8.2
diamonds.tb <- as_tibble(diamonds)
diamonds.tb## # A tibble: 53,940 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
## # ... with 53,930 more rowsdiamonds.tb %>%
slice(1:5,)## # A tibble: 5 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75diamonds.tb %>%
count()## # A tibble: 1 x 1
## n
## <int>
## 1 53940diamonds.tb %>%
filter(cut == "Very Good") %>%
count()## # A tibble: 1 x 1
## n
## <int>
## 1 12082diamonds.tb %>%
filter(carat > 3.0) %>%
count()## # A tibble: 1 x 1
## n
## <int>
## 1 32diamonds.tb %>%
filter(color == "D") %>%
select(color, cut)## # A tibble: 6,775 x 2
## color cut
## <ord> <ord>
## 1 D Very Good
## 2 D Very Good
## 3 D Very Good
## 4 D Good
## 5 D Good
## 6 D Premium
## 7 D Premium
## 8 D Ideal
## 9 D Ideal
## 10 D Very Good
## # ... with 6,765 more rowsdiamonds.tb %>%
summarise(mean(price))## # A tibble: 1 x 1
## `mean(price)`
## <dbl>
## 1 3933.#8.3
mtcars.tb %>%
group_by(cyl) %>%
count()## # A tibble: 3 x 2
## # Groups: cyl [3]
## cyl n
## <dbl> <int>
## 1 4 11
## 2 6 7
## 3 8 14mtcars.tb %>%
group_by(cyl) %>%
summarise(mpg.mean = mean(mpg), disp.mean = mean(disp))## # A tibble: 3 x 3
## cyl mpg.mean disp.mean
## <dbl> <dbl> <dbl>
## 1 4 26.7 105.
## 2 6 19.7 183.
## 3 8 15.1 353.diamonds.tb %>%
group_by(cut) %>%
summarise(max.price = max(price), min.price = min(price))## # A tibble: 5 x 3
## cut max.price min.price
## <ord> <dbl> <dbl>
## 1 Fair 18574 337
## 2 Good 18788 327
## 3 Very Good 18818 336
## 4 Premium 18823 326
## 5 Ideal 18806 326diamonds.tb %>%
group_by(color) %>%
summarise(max.price = max(price), mean.price = mean(price), min.price = min(price))## # A tibble: 7 x 4
## color max.price mean.price min.price
## <ord> <dbl> <dbl> <dbl>
## 1 D 18693 3170. 357
## 2 E 18731 3077. 326
## 3 F 18791 3725. 342
## 4 G 18818 3999. 354
## 5 H 18803 4487. 337
## 6 I 18823 5092. 334
## 7 J 18710 5324. 335