Untitled
Nate Jack
4/7/2022
#1
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)
setwd("~/Data 101")
heroes_df <-read.csv("heroes_information.csv") str(heroes_df)## 'data.frame': 734 obs. of 11 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ name : chr "A-Bomb" "Abe Sapien" "Abin Sur" "Abomination" ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Eye.color : chr "yellow" "blue" "blue" "green" ...
## $ Race : chr "Human" "Icthyo Sapien" "Ungaran" "Human / Radiation" ...
## $ Hair.color: chr "No Hair" "No Hair" "No Hair" "No Hair" ...
## $ Height : num 203 191 185 203 -99 193 -99 185 173 178 ...
## $ Publisher : chr "Marvel Comics" "Dark Horse Comics" "DC Comics" "Marvel Comics" ...
## $ Skin.color: chr "-" "blue" "red" "-" ...
## $ Alignment : chr "good" "good" "good" "bad" ...
## $ Weight : num 441 65 90 441 -99 122 -99 88 61 81 ...dim(heroes_df)## [1] 734 11head(heroes_df)## X name Gender Eye.color Race Hair.color Height
## 1 0 A-Bomb Male yellow Human No Hair 203
## 2 1 Abe Sapien Male blue Icthyo Sapien No Hair 191
## 3 2 Abin Sur Male blue Ungaran No Hair 185
## 4 3 Abomination Male green Human / Radiation No Hair 203
## 5 4 Abraxas Male blue Cosmic Entity Black -99
## 6 5 Absorbing Man Male blue Human No Hair 193
## Publisher Skin.color Alignment Weight
## 1 Marvel Comics - good 441
## 2 Dark Horse Comics blue good 65
## 3 DC Comics red good 90
## 4 Marvel Comics - bad 441
## 5 Marvel Comics - bad -99
## 6 Marvel Comics - bad 122#Some problems include such things as the “-” in the skin color, race, and eye color columns
#2
human_df <-
heroes_df%>%
filter(Race=="Human")
# 208 are humanlength(human_df$Race)## [1] 208table(heroes_df$Race)%>%
sort()%>%
tail(3)##
## Mutant Human -
## 63 208 304#Mutants have the second highest number of superheroes
#3
table(heroes_df$Gender)##
## - Female Male
## 29 200 505#200 male and 505 female
#4
table <- table(heroes_df$Gender, heroes_df$Alignment)
View(table)Overall, there are more “good” & “bad”male superheroes than women. Overall more male superheroes.
#5
New_df <- subset(heroes_df, Height > 0 & Weight > 0)
View(New_df)#6
New_df%>%
summarise(MeanWeight = mean(Weight), MedianWeight = median(Weight), SDWeight = sd(Weight))## MeanWeight MedianWeight SDWeight
## 1 112.1796 81 104.4227New_df%>%
summarise(MeanHeight = mean(Height), MedianHeight = median(Height), SDHeight = sd(Height))## MeanHeight MedianHeight SDHeight
## 1 187.1239 183 58.99002#7
hist(New_df$Weight)
This histogram shows us that most of the superhero are between the weight of 0 and 200, then the frequency slowly trickles down as the weight of superheroes increase.
#8
ggplot(data = New_df, aes(x = Height, y = Weight)) +
geom_point()
There is strong correlation between shorter and less weighed superheroes where there are much more of them than taller and heavier heroes. The correlation being plotted is the shorter the hero, the less they weigh.
9
summary(New_df$Height)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 15.2 173.5 183.0 187.1 188.0 975.0summary(New_df$Weight)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.0 61.0 81.0 112.2 106.0 900.0boxplot(New_df$Weight ~ New_df$Alignment) 
The chart shows us the Weight vs the alignment, which show that most of the heroes are neutral in regard to alignment and are mostly between 50 and 200 pounds when it comes to weight.
#10
table(New_df$Publisher)##
## Dark Horse Comics DC Comics George Lucas
## 1 11 143 5
## Image Comics Marvel Comics Shueisha Sony Pictures
## 1 318 4 1
## Star Trek Team Epic TV
## 2 4Publisher_df <- New_df %>%
filter(Publisher == "Marvel Comics" | Publisher == "DC Comics") %>%
group_by(Publisher, Alignment)View(Publisher_df)print(filter(Publisher_df,Alignment=="bad"))## # A tibble: 132 x 11
## # Groups: Publisher, Alignment [2]
## X name Gender Eye.color Race Hair.color Height Publisher Skin.color
## <int> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 3 Abominat~ Male green Huma~ No Hair 203 Marvel C~ -
## 2 5 Absorbin~ Male blue Human No Hair 193 Marvel C~ -
## 3 11 Air-Walk~ Male blue - White 188 Marvel C~ -
## 4 12 Ajax Male brown Cybo~ Black 193 Marvel C~ -
## 5 19 Amazo Male red Andr~ - 257 DC Comics -
## 6 20 Ammo Male brown Human Black 188 Marvel C~ -
## 7 28 Annihilus Male green - No Hair 180 Marvel C~ -
## 8 34 Apocalyp~ Male red Muta~ Black 213 Marvel C~ grey
## 9 40 Arclight Female violet - Purple 173 Marvel C~ -
## 10 48 Atlas Male blue God ~ Brown 198 DC Comics -
## # ... with 122 more rows, and 2 more variables: Alignment <chr>, Weight <dbl>print(filter(Publisher_df,Alignment=="good"))## # A tibble: 309 x 11
## # Groups: Publisher, Alignment [2]
## X name Gender Eye.color Race Hair.color Height Publisher Skin.color
## <int> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 0 A-Bomb Male yellow Human No Hair 203 Marvel C~ -
## 2 2 Abin Sur Male blue Unga~ No Hair 185 DC Comics red
## 3 7 Adam Str~ Male blue Human Blond 185 DC Comics -
## 4 8 Agent 13 Female blue - Blond 173 Marvel C~ -
## 5 9 Agent Bob Male brown Human Brown 178 Marvel C~ -
## 6 10 Agent Ze~ Male - - - 191 Marvel C~ -
## 7 13 Alan Sco~ Male blue - Blond 180 DC Comics -
## 8 16 Alfred P~ Male blue Human Black 178 DC Comics -
## 9 22 Angel Male blue - Blond 183 Marvel C~ -
## 10 24 Angel Du~ Female yellow Muta~ Black 165 Marvel C~ -
## # ... with 299 more rows, and 2 more variables: Alignment <chr>, Weight <dbl>data_pub<-
paste(Publisher_df$Publisher,Publisher_df$Alignment)
table(data_pub)## data_pub
## DC Comics - DC Comics bad DC Comics good
## 1 40 94
## DC Comics neutral Marvel Comics - Marvel Comics bad
## 8 2 92
## Marvel Comics good Marvel Comics neutral
## 215 9