Apply the following dplyr verbs to your data
Filter rows
filter(data, type_1 == "grass", type_2 == "poison")
## # A tibble: 15 × 23
## Column1 id pokemon species_id height weight base_experience type_1 type_2
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 1 1 bulbasa… 1 0.7 6.9 64 grass poison
## 2 2 2 ivysaur 2 1 13 142 grass poison
## 3 3 3 venusaur 3 2 100 236 grass poison
## 4 43 43 oddish 43 0.5 5.4 64 grass poison
## 5 44 44 gloom 44 0.8 8.6 138 grass poison
## 6 45 45 vileplu… 45 1.2 18.6 221 grass poison
## 7 69 69 bellspr… 69 0.7 4 60 grass poison
## 8 70 70 weepinb… 70 1 6.4 137 grass poison
## 9 71 71 victree… 71 1.7 15.5 221 grass poison
## 10 315 315 roselia 315 0.3 2 140 grass poison
## 11 406 406 budew 406 0.2 1.2 56 grass poison
## 12 407 407 roserade 407 0.9 14.5 232 grass poison
## 13 590 590 foongus 590 0.2 1 59 grass poison
## 14 591 591 amoongu… 591 0.6 10.5 162 grass poison
## 15 835 10033 venusau… 3 2.4 156. 281 grass poison
## # ℹ 14 more variables: hp <dbl>, attack <dbl>, defense <dbl>,
## # special_attack <dbl>, special_defense <dbl>, speed <dbl>, color_1 <chr>,
## # color_2 <chr>, color_f <chr>, egg_group_1 <chr>, egg_group_2 <chr>,
## # url_icon <chr>, generation_id <dbl>, url_image <chr>
Arrange rows
arrange(data, desc(height))
## # A tibble: 949 × 23
## Column1 id pokemon species_id height weight base_experience type_1 type_2
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 321 321 wailord 321 14.5 398 175 water NA
## 2 916 10114 exeggut… 103 10.9 416. 186 grass dragon
## 3 881 10079 rayquaz… 384 10.8 392 351 dragon flying
## 4 874 10072 steelix… 208 10.5 740 214 steel ground
## 5 879 10077 kyogre-… 382 9.8 430 347 water NA
## 6 208 208 steelix 208 9.2 400 179 steel ground
## 7 797 797 celeste… 797 9.2 1000. 114 steel flying
## 8 95 95 onix 95 8.8 210 77 rock ground
## 9 929 10127 wishiwa… 746 8.2 78.6 217 water NA
## 10 384 384 rayquaza 384 7 206. 306 dragon flying
## # ℹ 939 more rows
## # ℹ 14 more variables: hp <dbl>, attack <dbl>, defense <dbl>,
## # special_attack <dbl>, special_defense <dbl>, speed <dbl>, color_1 <chr>,
## # color_2 <chr>, color_f <chr>, egg_group_1 <chr>, egg_group_2 <chr>,
## # url_icon <chr>, generation_id <dbl>, url_image <chr>
Select columns
select(data, pokemon:weight)
## # A tibble: 949 × 4
## pokemon species_id height weight
## <chr> <dbl> <dbl> <dbl>
## 1 bulbasaur 1 0.7 6.9
## 2 ivysaur 2 1 13
## 3 venusaur 3 2 100
## 4 charmander 4 0.6 8.5
## 5 charmeleon 5 1.1 19
## 6 charizard 6 1.7 90.5
## 7 squirtle 7 0.5 9
## 8 wartortle 8 1 22.5
## 9 blastoise 9 1.6 85.5
## 10 caterpie 10 0.3 2.9
## # ℹ 939 more rows
Add columns
mutate(data,
estimated_body_mass = height * weight) %>%
select(height, weight, estimated_body_mass)
## # A tibble: 949 × 3
## height weight estimated_body_mass
## <dbl> <dbl> <dbl>
## 1 0.7 6.9 4.83
## 2 1 13 13
## 3 2 100 200
## 4 0.6 8.5 5.1
## 5 1.1 19 20.9
## 6 1.7 90.5 154.
## 7 0.5 9 4.5
## 8 1 22.5 22.5
## 9 1.6 85.5 137.
## 10 0.3 2.9 0.87
## # ℹ 939 more rows
Summarize by groups
data %>%
group_by(type_1) %>%
summarise(
avg_height = mean(height, na.rm =TRUE),
avg_weight = mean(weight, na.rm = TRUE)
)
## # A tibble: 18 × 3
## type_1 avg_height avg_weight
## <chr> <dbl> <dbl>
## 1 bug 0.937 35.8
## 2 dark 1.22 65.8
## 3 dragon 2.37 140.
## 4 electric 0.851 28.5
## 5 fairy 0.763 22.4
## 6 fighting 1.19 56.6
## 7 fire 1.19 68.6
## 8 flying 1.23 54.8
## 9 ghost 1.22 66.1
## 10 grass 1.12 42.3
## 11 ground 1.30 147.
## 12 ice 1.17 98.3
## 13 normal 1.04 45.0
## 14 poison 1.16 35.7
## 15 psychic 1.2 62.3
## 16 rock 1.09 82.1
## 17 steel 2.13 226.
## 18 water 1.46 57.4