Pokemon
Qorinul Huda
July 26, 2020
Dataset Pokemon
Pokemon merupakan dataset yang berisikan karakteristik pokemon seperti: nama pokemon, jenis pokemon, dan karakteristik kekuatan pokemon.
Kolom-kolom pada dataset tersebut, antara lain:
number : nomor seri pokemon name : nama pokemon type : jenis pokemon total : total nilai karakteristik serangan, kecepatan, health point, dan pertahanan pokemon hp : health point attack : Kekuatan serangan defense : kekuatan pertahanan special_attack : kekuatan serangan khusus special_defense : kekuatan pertahanan khusus speed : tingkat kecepatan
Persiapan
library(readr)
library(skimr)
library(DataExplorer)
library(tidyr)
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, unionlibrary(ggplot2)
library(purrr)Import Dataset
#Fungsi memanggil dataset pokemon dalam bentuk CSV
pokemon <- library(readr)
pokemon <- read_csv("D:/Development/proyek data/pokemon.csv")## Parsed with column specification:
## cols(
## number = col_character(),
## name = col_character(),
## type = col_character(),
## total = col_double(),
## hp = col_double(),
## attack = col_double(),
## defense = col_double(),
## special_attack = col_double(),
## special_defense = col_double(),
## speed = col_double()
## )View(pokemon)Ringkasan Data
glimpse(pokemon)## Rows: 1,168
## Columns: 10
## $ number <chr> " 001", " 001", " 002", " 002", " 003", " 003", " 0...
## $ name <chr> "Bulbasaur", "Bulbasaur", "Ivysaur", "Ivysaur", "Ve...
## $ type <chr> "GRASS", "POISON", "GRASS", "POISON", "GRASS", "POI...
## $ total <dbl> 318, 318, 405, 405, 525, 525, 625, 625, 309, 405, 5...
## $ hp <dbl> 45, 45, 60, 60, 80, 80, 80, 80, 39, 58, 78, 78, 78,...
## $ attack <dbl> 49, 49, 62, 62, 82, 82, 100, 100, 52, 64, 84, 84, 1...
## $ defense <dbl> 49, 49, 63, 63, 83, 83, 123, 123, 43, 58, 78, 78, 1...
## $ special_attack <dbl> 65, 65, 80, 80, 100, 100, 122, 122, 60, 80, 109, 10...
## $ special_defense <dbl> 65, 65, 80, 80, 100, 100, 120, 120, 50, 65, 85, 85,...
## $ speed <dbl> 45, 45, 60, 60, 80, 80, 80, 80, 65, 80, 100, 100, 1...summary(pokemon)## number name type total
## Length:1168 Length:1168 Length:1168 Min. :180.0
## Class :character Class :character Class :character 1st Qu.:334.0
## Mode :character Mode :character Mode :character Median :453.0
## Mean :435.6
## 3rd Qu.:515.0
## Max. :780.0
## hp attack defense special_attack
## Min. : 1.00 Min. : 5.00 Min. : 5.00 Min. : 10.00
## 1st Qu.: 50.00 1st Qu.: 55.00 1st Qu.: 50.00 1st Qu.: 50.00
## Median : 66.00 Median : 75.00 Median : 70.00 Median : 65.00
## Mean : 69.53 Mean : 78.82 Mean : 74.37 Mean : 72.62
## 3rd Qu.: 82.00 3rd Qu.:100.00 3rd Qu.: 90.00 3rd Qu.: 95.00
## Max. :255.00 Max. :190.00 Max. :230.00 Max. :194.00
## special_defense speed
## Min. : 20.00 Min. : 5.00
## 1st Qu.: 50.00 1st Qu.: 47.00
## Median : 70.00 Median : 65.50
## Mean : 71.72 Mean : 68.59
## 3rd Qu.: 90.00 3rd Qu.: 90.00
## Max. :230.00 Max. :180.00skim(pokemon)| Name | pokemon |
| Number of rows | 1168 |
| Number of columns | 10 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| number | 0 | 1 | 4 | 6 | 0 | 772 | 0 |
| name | 0 | 1 | 3 | 26 | 0 | 773 | 0 |
| type | 0 | 1 | 3 | 8 | 0 | 18 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total | 0 | 1 | 435.63 | 116.53 | 180 | 334 | 453.0 | 515 | 780 | ▃▆▇▂▁ |
| hp | 0 | 1 | 69.53 | 24.92 | 1 | 50 | 66.0 | 82 | 255 | ▃▇▁▁▁ |
| attack | 0 | 1 | 78.82 | 31.71 | 5 | 55 | 75.0 | 100 | 190 | ▂▇▆▂▁ |
| defense | 0 | 1 | 74.37 | 30.76 | 5 | 50 | 70.0 | 90 | 230 | ▃▇▂▁▁ |
| special_attack | 0 | 1 | 72.62 | 31.77 | 10 | 50 | 65.0 | 95 | 194 | ▅▇▅▂▁ |
| special_defense | 0 | 1 | 71.72 | 27.27 | 20 | 50 | 70.0 | 90 | 230 | ▇▇▂▁▁ |
| speed | 0 | 1 | 68.59 | 28.32 | 5 | 47 | 65.5 | 90 | 180 | ▃▇▆▁▁ |
plot_intro(pokemon)
plot_missing(pokemon)
Variasi
***Data Kontinu
plot_histogram(pokemon)
***Data Kategorikal
plot_bar(pokemon)## 2 columns ignored with more than 50 categories.
## number: 772 categories
## name: 773 categories
Kovarian
plot_correlation(pokemon)## 2 features with more than 20 categories ignored!
## number: 772 categories
## name: 773 categories
#Pada plot correlation dataset pokemon, setiap feature pada pokemon memeiliki korelasi yang baik.
#dimana nilai minimum korelasi yaitu -1 dan maksimum yaitu 1. plot_boxplot(pokemon, by="type")
plot_scatterplot(pokemon, by="type")
Jenis Pokemon Terkuat
pokemon %>%
ggplot() +
geom_boxplot(aes(x = type, y = total)) +
coord_flip()
Pokemon Terlemah
pokemon %>%
arrange(total)## # A tibble: 1,168 x 10
## number name type total hp attack defense special_attack special_defense
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 191 Sunk~ GRASS 180 30 30 30 30 30
## 2 298 Azur~ NORM~ 190 50 20 40 20 40
## 3 298 Azur~ FAIRY 190 50 20 40 20 40
## 4 401 Kric~ BUG 194 37 25 41 25 41
## 5 010 Cate~ BUG 195 45 30 35 20 20
## 6 013 Weed~ BUG 195 40 35 30 20 20
## 7 013 Weed~ POIS~ 195 40 35 30 20 20
## 8 265 Wurm~ BUG 195 45 45 35 20 30
## 9 280 Ralts PSYC~ 198 28 25 25 45 35
## 10 280 Ralts FAIRY 198 28 25 25 45 35
## # ... with 1,158 more rows, and 1 more variable: speed <dbl>Pokemon Tercepat
pokemon %>%
arrange(desc(speed))## # A tibble: 1,168 x 10
## number name type total hp attack defense special_attack special_defense
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 386.3 Deox~ PSYC~ 600 50 95 90 95 90
## 2 291 Ninj~ BUG 456 61 90 45 50 50
## 3 291 Ninj~ FLYI~ 456 61 90 45 50 50
## 4 065.1 Mega~ PSYC~ 590 55 50 65 175 95
## 5 142.1 Mega~ ROCK 615 80 135 85 70 95
## 6 142.1 Mega~ FLYI~ 615 80 135 85 70 95
## 7 386 Deox~ PSYC~ 600 50 150 50 150 50
## 8 386.1 Deox~ PSYC~ 600 50 180 20 180 20
## 9 617 Acce~ BUG 495 80 70 40 100 60
## 10 101 Elec~ ELEC~ 480 60 50 70 80 80
## # ... with 1,158 more rows, and 1 more variable: speed <dbl>Pokemon Terkuat dari jenisnya
max <- pokemon %>%
group_by(type) %>%
summarise(total = max(total)) ## `summarise()` ungrouping output (override with `.groups` argument)pokemon %>%
right_join(max, by = c("type", "total"))## # A tibble: 31 x 10
## number name type total hp attack defense special_attack special_defense
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 003.1 Mega~ GRASS 625 80 100 123 122 120
## 2 003.1 Mega~ POIS~ 625 80 100 123 122 120
## 3 127.1 Mega~ BUG 600 65 155 120 65 90
## 4 150.1 Mega~ PSYC~ 780 106 190 100 154 100
## 5 150.1 Mega~ FIGH~ 780 106 190 100 154 100
## 6 150.2 Mega~ PSYC~ 780 106 150 70 194 120
## 7 150.2 Mega~ PSYC~ 780 106 150 70 194 120
## 8 212.1 Mega~ BUG 600 70 150 140 65 100
## 9 214.1 Mega~ BUG 600 80 185 115 40 105
## 10 248.1 Mega~ ROCK 700 100 164 150 95 120
## # ... with 21 more rows, and 1 more variable: speed <dbl>Pokemon dengan Tingkat Serangan Spesial Tertinggi tiap Jenisnya
max <- pokemon %>%
group_by(type) %>%
summarise(special_attack = max(special_attack)) ## `summarise()` ungrouping output (override with `.groups` argument)pokemon %>%
right_join(max, by = c("type", "special_attack"))## # A tibble: 21 x 10
## number name type total hp attack defense special_attack special_defense
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 006.2 Mega~ FIRE 634 78 104 78 159 115
## 2 006.2 Mega~ FLYI~ 634 78 104 78 159 115
## 3 094.1 Mega~ GHOST 600 60 65 80 170 95
## 4 094.1 Mega~ POIS~ 600 60 65 80 170 95
## 5 139 Omas~ ROCK 495 70 60 125 115 70
## 6 150.1 Mega~ FIGH~ 780 106 190 100 154 100
## 7 150.2 Mega~ PSYC~ 780 106 150 70 194 120
## 8 150.2 Mega~ PSYC~ 780 106 150 70 194 120
## 9 181.1 Mega~ ELEC~ 610 90 95 105 165 110
## 10 229.1 Mega~ DARK 600 75 90 90 140 90
## # ... with 11 more rows, and 1 more variable: speed <dbl>Pokemon dengan Tingkat Pertahanan Spesial Tertinggi tiap Jenisnya
max <- pokemon %>%
group_by(type) %>%
summarise(special_defense = max(special_defense)) ## `summarise()` ungrouping output (override with `.groups` argument)pokemon %>%
right_join(max, by = c("type", "special_defense"))## # A tibble: 23 x 10
## number name type total hp attack defense special_attack special_defense
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 130.1 Mega~ DARK 640 95 155 109 70 130
## 2 181.1 Mega~ ELEC~ 610 90 95 105 165 110
## 3 197 Umbr~ DARK 525 95 65 110 60 130
## 4 213 Shuc~ BUG 505 20 10 230 10 230
## 5 213 Shuc~ ROCK 505 20 10 230 10 230
## 6 226 Mant~ WATER 465 65 40 70 80 140
## 7 242 Blis~ NORM~ 540 255 10 10 75 135
## 8 249 Lugia FLYI~ 680 106 90 130 90 154
## 9 250 Ho-oh FIRE 680 106 130 90 110 154
## 10 250 Ho-oh FLYI~ 680 106 130 90 110 154
## # ... with 13 more rows, and 1 more variable: speed <dbl>Apakah Pokemon dengan Tingkat Serangan Spesial Tinggi akan Memiliki Tingkat Pertahanan yang Tinggi juga?
ggplot(pokemon, aes(x = special_attack, y = special_defense)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'