Project 2 - Pokemon API

Yi Ren and Paula Bailey 2022-10-14

• This document will walk the user through the process of connecting to & collecting data from the pokemon API within R.

Reference to Render Code

rmarkdown::render("Project2.Rmd", "github_document","README.md")

Packages for Vignette

• The following packages are required for connecting and retrieving data from the API.

1. httr - Retrieves Data from an API
2. jsonlite - Parses Results from an API Query
3. dplyr - A part of the tidyverse used for manipulating data
4. tidyr - A part of the tidyverse used for data cleaning
5. ggplot2 - A part of the tidyverse used for creating graphics

# Read in Required Packages 
library(httr)
library(jsonlite)
library(tidyverse)
library(ggplot2)

Create functions

• First, we creates functions for users to easily look up pokemon by its id or name. Then we proceed to obtain the related basic, training, base stats and moves information. Lastly, we wrapped all functions as a combined function to effectively track the desired data information.

1. Function for look up by id

lookid<-function(id){
  base<-"https://pokeapi.co/api/v2/pokemon-species/"
  url<-(paste0(base,id,"/"))
  pokemon_api<-GET(url) %>% content("text") %>% fromJSON(flatten=TRUE)
  pokemon<-pokemon_api[["name"]]
  return(pokemon)
}

2. Function for look up by name

lookname<-function(pokemon){
  base<-"https://pokeapi.co/api/v2/pokemon/"
  url<-tolower((paste0(base,pokemon,"/")))
  pokemon_api<<-GET(url) %>% content("text") %>% fromJSON(flatten=TRUE)
  return(pokemon_api)
}

3. Function for basic information

basic<-function(pokemon_api){
  species_api<-GET(pokemon_api[["species"]][["url"]]) %>% content("text") %>% fromJSON(flatten=TRUE)
  if (length(pokemon_api[["types"]][["type.name"]]) >1) {
    type=paste(pokemon_api[["types"]][["type.name"]], collapse = '/')
  } else{
    type=pokemon_api[["types"]][["type.name"]]
  }
  basic<-data.frame(id=pokemon_api[["id"]], name=pokemon_api[["name"]], type=type, height=pokemon_api[["height"]], weight=pokemon_api[["weight"]], habitat=species_api[["habitat"]][["name"]], shape=species_api[["shape"]][["name"]], color=species_api[["color"]][["name"]])
  return(basic)
}

4. Function for training information

traning<-function(pokemon_api){
  species_api<-GET(pokemon_api[["species"]][["url"]]) %>% content("text") %>% fromJSON(flatten=TRUE)
  traning<-data.frame(base_happiness=species_api[["base_happiness"]], capture_rate=species_api[["capture_rate"]],growth_rate=species_api[["growth_rate"]][["name"]],gender_rate=species_api[["gender_rate"]])
  return(traning)
}

5. Function for base stats

stats<-function(pokemon_api){
  stats<-pokemon_api[["stats"]] %>% select(stat.name,base_stat) %>% pivot_wider(names_from = "stat.name", values_from = "base_stat") 
  return(stats)
}

6. Function for moves information

get_moves <- function(type){
  base<-"https://pokeapi.co/api/v2/move/"
  url<-(paste0(base,type,"/"))
  move_api<-GET(url) %>% content("text") %>% fromJSON(flatten=TRUE)
  moves <- tibble(idMove =move_api[["id"]],
  nameMove=move_api[["name"]],
  accuracy = move_api[["accuracy"]],
  power = move_api[["power"]],
  PowerPoint = move_api[["pp"]])
  return(moves)
}

7. Function to get your pokemon

get_pokemon <- function(pokemon = 132, tran = 1, stat = 1, move = 1, berry = 0){
    poke_One<-NULL
    
  if (is.numeric(pokemon) == "TRUE"){
    pokemon_mini<-lookname(lookid(pokemon))
  } else {
    pokemon_mini<-lookname(pokemon)
  }

  poke_One<-NULL  
  if(move == 0 & tran == 1 & stat == 0){
    basic <-basic(pokemon_mini)                  
    traning<-traning(pokemon_mini)
    poke_One<-merge(basic,traning)
  }else if(move == 0 & tran == 0 & stat == 1){
    basic <-basic(pokemon_mini)         
    stat<-stats(pokemon_mini)                   
    poke_One<-merge(basic,stat)
  }else if(move == 1 & tran == 1 & stat == 1){
    poke_One[1]<-lapply(pokemon, FUN=info)      
  }else{
    poke_One[1]<-lapply(pokemon, FUN=info)   
  }
   
  if(berry == 1){
    berry <- get_berry()
  }

return(list(poke_One, berry))
}

Wrapper function

info<-function(pokemon){
  if (is.numeric(pokemon) == "TRUE"){
    pokemon_api<-lookname(lookid(pokemon))
  } else {
    pokemon_api<-lookname(pokemon)
  }
  basic<-basic(pokemon_api)
  traning<-traning(pokemon_api)
  stats<-stats(pokemon_api)
  move <- get_moves(pokemon_api[["id"]])
  info<-merge(basic,traning) %>% merge(stats) %>% merge(move)
  return(info)
}

Function for berries

• This function does not require any input arguments. The function loops through the pokemon api and pulls the information for the 64 berries. There is not a direct link to pokemon data itself. It will be used for future analytical purposes.

get_berry <-function(){
  api_json<-GET("https://pokeapi.co/api/v2/berry/1/") %>% content("text") %>% fromJSON(flatten=TRUE)
  berries_df <- as.data.frame(api_json)
  for(i in 2:64){
    base <- "https://pokeapi.co/api/v2/berry/"
    call_next <- (paste0(base,i,"/"))
    api_json2 <- GET(call_next) %>% content("text") %>% fromJSON(flatten=TRUE)
    next_df <- as.data.frame(api_json2)
    berries_df <- rbind(berries_df,next_df)
  }
    berries_df <<-berries_df %>% 
                select(- firmness.url,-flavors.flavor.url,-item.url, -natural_gift_type.url )
}

Subset dataframes for anaysis

• We generated first 200 pokemon and all 64 berries from pokemon api through our functions. We noticed that some pokemon have dual-type. For convenience, we only include main type as our interest. Then we saved them as data frames to efficiently handle the actual exploratory data analysis.

Function for subset

sub<-function(n){
  poke<-NULL
  for (i in 1:n){
    poke[i]<-lapply(i, FUN=info)
  }
  poke_com<-poke[[1]]
  for (i in 2:n){
    poke_com<-bind_rows(poke_com,poke[[i]])
  }
  return(poke_com)
}

Get Pokemon

To request information from the api, the user has 3 options:

1. Get Basic information and Training
2. Get Basic information and Stats
3. Get All - Basic information, Training, and Stats
4. Get Berry information.

The default arguments are (pokemon = 132, tran = 1, stat = 1, move = 1, berry=0), where 1 is return this information and 0 do not return this information.

get_pokemon("DITTO")

## [[1]]
## [[1]][[1]]
##    id  name   type height weight habitat shape  color base_happiness capture_rate growth_rate gender_rate hp
## 1 132 ditto normal      3     40   urban  ball purple             50           35      medium          -1 48
##   attack defense special-attack special-defense speed idMove  nameMove accuracy power PowerPoint
## 1     48      48             48              48    48    132 constrict      100    10         35
## 
## 
## [[2]]
## [1] 0

get_pokemon(18, tran = 1, stat = 0, move = 0, berry=0)

## [[1]]
##   id    name          type height weight habitat shape color base_happiness capture_rate growth_rate
## 1 18 pidgeot normal/flying     15    395  forest wings brown             70           45 medium-slow
##   gender_rate
## 1           4
## 
## [[2]]
## [1] 0

get_pokemon(132, tran = 0, stat = 1, move = 0, berry = 0)

## [[1]]
##    id  name   type height weight habitat shape  color hp attack defense special-attack special-defense speed
## 1 132 ditto normal      3     40   urban  ball purple 48     48      48             48              48    48
## 
## [[2]]
## [1] 0

Pokemon subset

poke<-sub(200)
sep<-separate(poke, col=type, into=c('main.type', 'co.type'), sep='/')

Berries subset

get_berry()

Basic Exploratory Data Analysis (EDA)

Bar plot

ggplot(poke,aes(y = habitat, fill = color)) + geom_bar(position="dodge") + 
  labs(y="Habitat", title ="Bar Plot of Pokemon Habitat by Color") +
  scale_fill_discrete(name="Color") + 
  theme(legend.text = element_text(size = 6), plot.title = element_text(hjust = 0.5))

• This bar plot shows the relationship between habitat and the pokemon’s color. According to Bulbadpedia, color is “usually the color most apparent or covering each pokemon’s body”. We believe color is based on where the pokemon lives.
• We observed above blue pokemons are mainly creatures that live by the water’s edge. We can confirm this by viewing images. These pokemon are aquatic or living by the water’s edge.
• The most diverse region is grassland with seven colors (types) of pokemon living in that habitat. The missing habitat is white. Most of these pokemon have an arctic appearance.

ggplot(sep, aes(x = habitat, fill = main.type)) +
  geom_bar(position="dodge") +
  facet_grid(. ~growth_rate, labeller=label_both) + scale_fill_discrete(name="Main Type") + 
  labs(x="Habitat", title ="Side-by-side Bar Plot of Pokemon Growth Rate by Habitat") + theme(axis.text.x = element_text(angle=45, size=5), legend.text = element_text(size = 6), plot.title = element_text(hjust = 0.5) )

• This side-by-side bar plot of pokemon growth rate indicates that generally the grass-type pokemon commonly lives in grassland has lower-middle growth rate. Most of fairy-type and bug-type pokemon grow relatively faster than others. In the contrast, dragon-type pokemon grows relatively slower.
• We can also observed that it seems like there is strong relationship between pokemon’s habitat and their type. We will explore that information later in our contingency analysis.

Contingency Table

  table(sep$habitat,sep$main.type)

##                
##                 bug dark dragon electric fairy fighting fire ghost grass ground ice normal poison psychic
##   cave            0    0      0        0     0        0    0     4     0      2   0      0      3       0
##   forest         16    1      0        3     2        0    0     0     5      0   0      6      0       2
##   grassland       1    0      0        4     0        0    9     0    16      0   0     13      8       2
##   mountain        0    0      0        0     3        5    4     0     0      2   0      1      0       0
##   rare            0    0      0        1     0        0    1     0     0      0   1      0      0       2
##   rough-terrain   0    0      0        2     0        0    0     0     0      4   0      2      0       0
##   sea             0    0      0        0     0        0    0     0     0      0   0      0      0       0
##   urban           0    1      0        3     0        2    1     0     0      0   1      6      4       5
##   waters-edge     0    0      3        0     0        0    0     0     0      0   0      0      0       0
##                
##                 rock water
##   cave             1     0
##   forest           1     0
##   grassland        0     0
##   mountain         4     0
##   rare             0     0
##   rough-terrain    0     0
##   sea              4    13
##   urban            0     1
##   waters-edge      0    25

• As expected, bug-type pokemon lives in forest, grass-type pokemon lives in grassland, and water-type pokemon lives in sea or waters-edge.
• The other type of pokemon’s habitat also make lot of sense, where rock-type pokemon lives near mountain and sea, but they also can find near cave and forest. In that case, those pokemon might have dual-type based on their habitat.

Scatter plot

ggplot(berries_df,aes(x = growth_time, y = soil_dryness)) + 
  geom_smooth(formula = y~x, method = "loess") + geom_point() + 
  labs(x = "Growth Time", y= "Soil Dryness",title = "Impact of Soil Dryness on Growth") +
  theme(legend.title = element_text(size = 6), legend.text = element_text(size = 6), plot.title = element_text(hjust = 0.5))

• This scatter plot shows the relationship between berry growth and soil dryness. It visually verifies the negative relationship between the growth of berries and soil dryness. As the berry grows, the soil becomes drier.

  • This negative relationship is also confirmed in the correlation score of -.68.

cor(berries_df$growth_time,berries_df$soil_dryness)

## [1] -0.6768502

ggplot(sep, aes(x = round((weight/height),2), y = speed)) +
    geom_point(aes(color = main.type)) +
    labs(x = "BMI", y= "Speed",title = "Body Mass Index (BMI) and Speed") +
    theme(legend.title = element_text(size = 5), legend.text = element_text(size = 5))

• This scatter plot shows the relationship between BMI (weight/height) and speed. We used type of pokemon to highlight the color. This chart shows no clear relationship between a higher BMI and moving at slower speeds. We can see a few pokemon to the right with BMIs around 50; However, they are not the slowest pokemon.

cor(sep$weight,sep$height)

## [1] 0.5966108

  ggplot(sep, aes(x=weight, y=height)) + geom_point(aes(color = main.type, shape = habitat)) + geom_smooth(method=lm, formula = y~x) + 
  labs(x = "Weight", y= "Height",title = "Weight vs. Height") +
  theme(legend.title = element_text(size = 6), legend.text = element_text(size = 6), plot.title = element_text(hjust = 0.5))

• Correlation between weight and height is 0.5966, which considered as moderate correlations. From the scatter plot, we can conclude the same, which weight and height have relationship but not that strong.

Histogram

ggplot(poke, aes(x=shape)) + 
  geom_histogram(binwidth=5,stat="count",colour="black", fill="steelblue") + 
  labs(x = "Shape", y= "Density", title = 'Histogram for Pokemon Shape') + 
  geom_text(stat='count', aes(label=..count..), vjust=-0.5) + theme(axis.text.x = element_text(angle=45)) + theme(plot.title = element_text(hjust = 0.5))

• This histogram shows upright, quadruped, humanoid and wings are the most common shapes for first 200 pokemon. Arms, heads and tentacles are the most rare shapes.

Box plot

poke %>% ggplot(aes(x=growth_rate, y=capture_rate)) + geom_boxplot() + geom_jitter(aes(colour=growth_rate)) + labs(title = 'Boxplot for Capture Rate') + theme(legend.title = element_text(size = 5), legend.text = element_text(size = 5))

• This box plot shows capture rate segmented by growth rates - medium-slow, medium and fast. The medium growth rate has the largest median value of the three at about 125. It also has more outliers. It is interesting, the medium-slow box plot does not have a first quantile. All of its values lie between the median and 3rd quantile.

Numeric summaries

1. Numeric summaries for pokemon base stats

pokestats <- poke %>% select(hp, attack, defense, 'special-attack','special-defense',speed) %>% apply(2, function(x){summary(x[!is.na(x)])}) 
knitr::kable(pokestats, caption = 'Summary of Pokemon Stats', digits = 2)

	hp	attack	defense	special-attack	special-defense	speed
Min.	10.00	5.00	5.00	15.00	20.00	15.00
1st Qu.	45.00	50.00	48.75	45.00	50.00	45.00
Median	60.00	65.00	65.00	60.50	65.00	65.00
Mean	64.58	69.38	66.30	66.26	66.75	66.72
3rd Qu.	80.00	86.25	80.00	85.00	83.50	87.75
Max.	250.00	134.00	180.00	154.00	130.00	150.00

Summary of Pokemon Stats

The summary statistics for hp, attack, defense, special-attack, special-defense, and speed.

• HP : The difference between the mean (60.00) and median (60.04) is quite small. So we can infer, the shape is symmetrically distributed. The range is between 10 and 140.

• For Attack, Defense, Special Attack, and Speed, their respective mean is greater than the median. So, we can expect the shape to be slightly skewed to the right. The outliers are also in the right tail of the distribution.

• Special-Defense : The difference between the mean (60.88) and median (64.50). We can infer the shape is symmetrically distributed; however, skewed to the left. The median is higher than mean.

1. Numeric summaries for berries

berrySummary <- berries_df %>% select(growth_time, max_harvest, size, smoothness, soil_dryness) %>% apply(2, function(x){summary(x[!is.na(x)])}) 
knitr::kable(berrySummary, caption = 'Summary of Berry Stats', digits = 2)

	growth_time	max_harvest	size	smoothness	soil_dryness
Min.	2.00	5.00	20.00	20.00	4.0
1st Qu.	5.00	5.00	45.75	25.00	6.0
Median	15.00	5.00	98.50	30.00	8.0
Mean	12.86	6.48	120.53	31.56	10.2
3rd Qu.	18.00	5.00	155.25	35.00	10.0
Max.	24.00	15.00	300.00	60.00	35.0

Summary of Berry Stats

The summary statistics for growth time, maximum harvest, size, smoothness, and soil dryness.

• Growth Time : The difference between the mean (12.86) and median (15.00) is small, so we can infer HP is symmetrically distributed and slightly skewed to the left. Median is higher than mean.

• For Maximum Harvest and Smoothness, the difference between their respective mean and median is small, so we can infer the shape is symmetrically distributed; however, slightly skewed to the right. The mean is higher than median.

• For Size and Soil Dryness, the difference between each respective mean and median is relatively large. Since the mean is greater than the median, we can infer the shape is right skewed.