HOMEWORK1
Nika Strmčnik
2023-01-09
mydata <- read.csv("./POKEMON.csv")str(mydata)## 'data.frame': 721 obs. of 23 variables:
## $ Number : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Name : chr "Bulbasaur" "Ivysaur" "Venusaur" "Charmander" ...
## $ Type_1 : chr "Grass" "Grass" "Grass" "Fire" ...
## $ Type_2 : chr "Poison" "Poison" "Poison" "" ...
## $ Total : int 318 405 525 309 405 534 314 405 530 195 ...
## $ HP : int 45 60 80 39 58 78 44 59 79 45 ...
## $ Attack : int 49 62 82 52 64 84 48 63 83 30 ...
## $ Defense : int 49 63 83 43 58 78 65 80 100 35 ...
## $ Sp_Atk : int 65 80 100 60 80 109 50 65 85 20 ...
## $ Sp_Def : int 65 80 100 50 65 85 64 80 105 20 ...
## $ Speed : int 45 60 80 65 80 100 43 58 78 45 ...
## $ Generation : int 1 1 1 1 1 1 1 1 1 1 ...
## $ isLegendary : chr "False" "False" "False" "False" ...
## $ Color : chr "Green" "Green" "Green" "Red" ...
## $ hasGender : chr "True" "True" "True" "True" ...
## $ Pr_Male : num 0.875 0.875 0.875 0.875 0.875 0.875 0.875 0.875 0.875 0.5 ...
## $ Egg_Group_1 : chr "Monster" "Monster" "Monster" "Monster" ...
## $ Egg_Group_2 : chr "Grass" "Grass" "Grass" "Dragon" ...
## $ hasMegaEvolution: chr "False" "False" "True" "False" ...
## $ Height_m : num 0.71 0.99 2.01 0.61 1.09 1.7 0.51 0.99 1.6 0.3 ...
## $ Weight_kg : num 6.9 13 100 8.5 19 90.5 9 22.5 85.5 2.9 ...
## $ Catch_Rate : int 45 45 45 45 45 45 45 45 45 255 ...
## $ Body_Style : chr "quadruped" "quadruped" "quadruped" "bipedal_tailed" ...head(mydata)## Number Name Type_1 Type_2 Total HP Attack Defense Sp_Atk Sp_Def Speed
## 1 1 Bulbasaur Grass Poison 318 45 49 49 65 65 45
## 2 2 Ivysaur Grass Poison 405 60 62 63 80 80 60
## 3 3 Venusaur Grass Poison 525 80 82 83 100 100 80
## 4 4 Charmander Fire 309 39 52 43 60 50 65
## 5 5 Charmeleon Fire 405 58 64 58 80 65 80
## 6 6 Charizard Fire Flying 534 78 84 78 109 85 100
## Generation isLegendary Color hasGender Pr_Male Egg_Group_1 Egg_Group_2
## 1 1 False Green True 0.875 Monster Grass
## 2 1 False Green True 0.875 Monster Grass
## 3 1 False Green True 0.875 Monster Grass
## 4 1 False Red True 0.875 Monster Dragon
## 5 1 False Red True 0.875 Monster Dragon
## 6 1 False Red True 0.875 Monster Dragon
## hasMegaEvolution Height_m Weight_kg Catch_Rate Body_Style
## 1 False 0.71 6.9 45 quadruped
## 2 False 0.99 13.0 45 quadruped
## 3 True 2.01 100.0 45 quadruped
## 4 False 0.61 8.5 45 bipedal_tailed
## 5 False 1.09 19.0 45 bipedal_tailed
## 6 True 1.70 90.5 45 bipedal_tailedThe dataset that I have imported has 721 units of observation, the categorical variables being name and type1 and the numerical variables being total value, base health points, base special attack, base special defence, speed and weight.
The dataset was found on the KAGGLE website, with the author being Alopez247. Retrieved January 5th 2023 from: https://www.kaggle.com/datasets/alopez247/pokemon
The main goal of the analysis of this data is to see which type of Pokemon has on average the highest stats.
I have replaced the existing value of 150 in the 5th row and 7th column with the value of 200.
mydata [5,7]=200
head(mydata)## Number Name Type_1 Type_2 Total HP Attack Defense Sp_Atk Sp_Def Speed
## 1 1 Bulbasaur Grass Poison 318 45 49 49 65 65 45
## 2 2 Ivysaur Grass Poison 405 60 62 63 80 80 60
## 3 3 Venusaur Grass Poison 525 80 82 83 100 100 80
## 4 4 Charmander Fire 309 39 52 43 60 50 65
## 5 5 Charmeleon Fire 405 58 200 58 80 65 80
## 6 6 Charizard Fire Flying 534 78 84 78 109 85 100
## Generation isLegendary Color hasGender Pr_Male Egg_Group_1 Egg_Group_2
## 1 1 False Green True 0.875 Monster Grass
## 2 1 False Green True 0.875 Monster Grass
## 3 1 False Green True 0.875 Monster Grass
## 4 1 False Red True 0.875 Monster Dragon
## 5 1 False Red True 0.875 Monster Dragon
## 6 1 False Red True 0.875 Monster Dragon
## hasMegaEvolution Height_m Weight_kg Catch_Rate Body_Style
## 1 False 0.71 6.9 45 quadruped
## 2 False 0.99 13.0 45 quadruped
## 3 True 2.01 100.0 45 quadruped
## 4 False 0.61 8.5 45 bipedal_tailed
## 5 False 1.09 19.0 45 bipedal_tailed
## 6 True 1.70 90.5 45 bipedal_tailedI have expressed only the 12th row.
mydata1 <- mydata[12, ]
head(mydata1)## Number Name Type_1 Type_2 Total HP Attack Defense Sp_Atk Sp_Def Speed
## 12 12 Butterfree Bug Flying 395 60 45 50 90 80 70
## Generation isLegendary Color hasGender Pr_Male Egg_Group_1 Egg_Group_2
## 12 1 False White True 0.5 Bug
## hasMegaEvolution Height_m Weight_kg Catch_Rate Body_Style
## 12 False 1.09 32 45 four_wingsI have renamed the 3rd and 4th column to TYPE1 and TYPE2.
colnames(mydata)[3]="TYPE1"
colnames(mydata)[4]="TYPE2"
head(mydata)## Number Name TYPE1 TYPE2 Total HP Attack Defense Sp_Atk Sp_Def Speed
## 1 1 Bulbasaur Grass Poison 318 45 49 49 65 65 45
## 2 2 Ivysaur Grass Poison 405 60 62 63 80 80 60
## 3 3 Venusaur Grass Poison 525 80 82 83 100 100 80
## 4 4 Charmander Fire 309 39 52 43 60 50 65
## 5 5 Charmeleon Fire 405 58 200 58 80 65 80
## 6 6 Charizard Fire Flying 534 78 84 78 109 85 100
## Generation isLegendary Color hasGender Pr_Male Egg_Group_1 Egg_Group_2
## 1 1 False Green True 0.875 Monster Grass
## 2 1 False Green True 0.875 Monster Grass
## 3 1 False Green True 0.875 Monster Grass
## 4 1 False Red True 0.875 Monster Dragon
## 5 1 False Red True 0.875 Monster Dragon
## 6 1 False Red True 0.875 Monster Dragon
## hasMegaEvolution Height_m Weight_kg Catch_Rate Body_Style
## 1 False 0.71 6.9 45 quadruped
## 2 False 0.99 13.0 45 quadruped
## 3 True 2.01 100.0 45 quadruped
## 4 False 0.61 8.5 45 bipedal_tailed
## 5 False 1.09 19.0 45 bipedal_tailed
## 6 True 1.70 90.5 45 bipedal_tailedI have put the total values of the pokemons in order from smallest to largest.
mydata2 <- mydata[order(mydata$Total) , ]
head(mydata2)## Number Name TYPE1 TYPE2 Total HP Attack Defense Sp_Atk Sp_Def Speed
## 191 191 Sunkern Grass 180 30 30 30 30 30 30
## 298 298 Azurill Normal Fairy 190 50 20 40 20 40 20
## 401 401 Kricketot Bug 194 37 25 41 25 41 25
## 10 10 Caterpie Bug 195 45 30 35 20 20 45
## 13 13 Weedle Bug Poison 195 40 35 30 20 20 50
## 265 265 Wurmple Bug 195 45 45 35 20 30 20
## Generation isLegendary Color hasGender Pr_Male Egg_Group_1 Egg_Group_2
## 191 2 False Yellow True 0.50 Grass
## 298 3 False Blue True 0.25 Undiscovered
## 401 4 False Red True 0.50 Bug
## 10 1 False Green True 0.50 Bug
## 13 1 False Brown True 0.50 Bug
## 265 3 False Red True 0.50 Bug
## hasMegaEvolution Height_m Weight_kg Catch_Rate Body_Style
## 191 False 0.3 1.8 235 head_only
## 298 False 0.2 2.0 150 head_legs
## 401 False 0.3 2.2 255 bipedal_tailless
## 10 False 0.3 2.9 255 insectoid
## 13 False 0.3 3.2 255 serpentine_body
## 265 False 0.3 3.6 255 serpentine_bodyI have put the base health points of the pokemons in order from smallest to largest.
mydata3 <- mydata[order(mydata$HP) , ]
head(mydata3)## Number Name TYPE1 TYPE2 Total HP Attack Defense Sp_Atk Sp_Def Speed
## 292 292 Shedinja Bug Ghost 236 1 90 45 30 30 40
## 50 50 Diglett Ground 265 10 55 25 35 45 95
## 129 129 Magikarp Water 200 20 10 55 15 20 80
## 172 172 Pichu Electric 205 20 40 15 35 35 60
## 213 213 Shuckle Bug Rock 505 20 10 230 10 230 5
## 349 349 Feebas Water 200 20 15 20 10 55 80
## Generation isLegendary Color hasGender Pr_Male Egg_Group_1 Egg_Group_2
## 292 3 False Brown False NA Mineral
## 50 1 False Brown True 0.5 Field
## 129 1 False Red True 0.5 Water_2 Dragon
## 172 2 False Yellow True 0.5 Undiscovered
## 213 2 False Yellow True 0.5 Bug
## 349 3 False Brown True 0.5 Water_1 Dragon
## hasMegaEvolution Height_m Weight_kg Catch_Rate Body_Style
## 292 False 0.79 1.2 45 head_base
## 50 False 0.20 0.8 255 head_base
## 129 False 0.89 10.0 255 with_fins
## 172 False 0.30 2.0 190 quadruped
## 213 False 0.61 20.5 190 insectoid
## 349 False 0.61 7.4 255 with_finsI have removed the following columns.
mydata <- mydata[c(-1,-4,-9,-10,-12,-13,-14,-15,-16,-17,-18,-19,-23)]
head(mydata)## Name TYPE1 Total HP Attack Defense Speed Height_m Weight_kg Catch_Rate
## 1 Bulbasaur Grass 318 45 49 49 45 0.71 6.9 45
## 2 Ivysaur Grass 405 60 62 63 60 0.99 13.0 45
## 3 Venusaur Grass 525 80 82 83 80 2.01 100.0 45
## 4 Charmander Fire 309 39 52 43 65 0.61 8.5 45
## 5 Charmeleon Fire 405 58 200 58 80 1.09 19.0 45
## 6 Charizard Fire 534 78 84 78 100 1.70 90.5 45VARIABLES
Name: Name of unit of observation TYPE1: Type of pokemon Total: Total value HP: Base health points Attack: Base special attack Defense: Base special defense Speed: Maximal speed Height_m: Height in meters Weight_kg: Weight in kilograms Catch_Rate Catch rate of pokemon
round(describe(mydata[c(-1,-2)]),1)## vars n mean sd median trimmed mad min max range skew
## Total 1 721 417.9 109.7 424 416.7 127.5 180.0 720.0 540.0 0.1
## HP 2 721 68.4 25.8 65 66.3 22.2 1.0 255.0 254.0 1.7
## Attack 3 721 75.2 29.4 75 74.1 29.7 5.0 200.0 195.0 0.4
## Defense 4 721 70.8 29.3 65 68.1 25.2 5.0 230.0 225.0 1.1
## Speed 5 721 65.7 27.3 65 64.9 29.7 5.0 160.0 155.0 0.3
## Height_m 6 721 1.1 1.0 1 1.0 0.6 0.1 14.5 14.4 5.5
## Weight_kg 7 721 56.8 89.1 28 36.9 31.9 0.1 950.0 949.9 4.0
## Catch_Rate 8 721 100.2 76.6 65 92.5 37.1 3.0 255.0 252.0 0.8
## kurtosis se
## Total -0.7 4.1
## HP 7.7 1.0
## Attack 0.0 1.1
## Defense 2.4 1.1
## Speed -0.5 1.0
## Height_m 49.6 0.0
## Weight_kg 23.6 3.3
## Catch_Rate -0.7 2.9MEAN
Total Mean value of total value is 417.9
HP Mean value of Base health points is 68.4
Attack Mean value of Base health points is 68.4
Defense Mean value of defense is 70.8
Speed Mean value of speed is 65.7
Height_m Mean value of height is 1.1 meter.
Weight_kg Mean value of attack is 56.8 kg.
Catch_Rate Mean value of attack is 56.8 kg.
RANGE
Total The difference between maximal and minimal value is 540.
HP The difference between maximal and minimal value is 254.
Attack The difference between maximal and minimal value is 195.
Defense The difference between maximal and minimal value is 225.
Speed The difference between maximal and minimal value is 155.
Height_m The difference between maximal and minimal value is 14.4.
Weight_kg The difference between maximal and minimal value is 949.9.
Catch_Rate The difference between maximal and minimal value is 252.
ggplot(mydata, aes(y=Total, fill=TYPE1)) +
geom_boxplot()
The five – number summary is the minimum, first quartile, median, third quartile, and the maximum. The horizontal line in each boxplot represents the median value. In the ggplot we can see the that the biggest range is for the normal type of pokemons, and the highest median being of the Flying type of pokemons.
ATTACK <- ggplot(mydata, aes(x=Attack)) +
geom_histogram(position=position_identity(), binwidth=10) +
ggtitle("Attack Rating") +
xlab("Rating")
DEFENSE <- ggplot(mydata, aes(x=Defense)) +
geom_histogram(position=position_identity(), binwidth=10) +
ggtitle("Defense Rating") +
xlab("Rating")
SPEED <- ggplot(mydata, aes(x=Speed)) +
geom_histogram(position=position_identity(), binwidth=10) +
ggtitle("Speed Rating") +
xlab("Rating")
TOTAL <- ggplot(mydata, aes(x=Total)) +
geom_histogram(position=position_identity(), binwidth=10) +
ggtitle("Total Rating") +
xlab("Rating")
ggarrange(ATTACK, DEFENSE, SPEED, TOTAL,
ncol = 2, nrow = 2)
The attack rating histogram has a pretty normal distribution, the defense rating on the other hand is slightly skewed to the right and so is the speed rating.
scatterplotMatrix(mydata[,c(6,7,9)],
smooth = FALSE)
Looking at the scatterplots we can see the correlation between defense and weight, whereas there is no correlation between speed and weight.