Getting the data…
Superheroes <- read.csv("~/Data Science/Datasets/superheroes_information.csv")
Look at a bit of the data
head(Superheroes, n=10)tail(Superheroes, n=5)
Make summary
summary(Superheroes) X name Gender Eye.color Race Hair.color
Min. : 0.0 Goliath : 3 - : 29 blue :225 - :304 - :172
1st Qu.:183.2 Spider-Man: 3 Female:200 - :172 Human :208 Black :158
Median :366.5 Angel : 2 Male :505 brown :126 Mutant : 63 Blond : 99
Mean :366.5 Atlas : 2 green : 73 God / Eternal : 14 Brown : 86
3rd Qu.:549.8 Atom : 2 red : 46 Cyborg : 11 No Hair: 75
Max. :733.0 Batgirl : 2 black : 23 Human / Radiation: 11 Red : 51
(Other) :720 (Other): 69 (Other) :123 (Other): 93
Height Publisher Skin.color Alignment Weight
Min. :-99.0 Marvel Comics :388 - :662 - : 7 Min. :-99.00
1st Qu.:-99.0 DC Comics :215 green : 21 bad :207 1st Qu.:-99.00
Median :175.0 NBC - Heroes : 19 blue : 9 good :496 Median : 62.00
Mean :102.3 Dark Horse Comics: 18 red : 9 neutral: 24 Mean : 43.86
3rd Qu.:185.0 : 15 white : 7 3rd Qu.: 90.00
Max. :975.0 George Lucas : 14 grey : 5 Max. :900.00
(Other) : 65 (Other): 21 NA's :2
More in depth information
str(Superheroes)'data.frame': 734 obs. of 11 variables:
$ X : int 0 1 2 3 4 5 6 7 8 9 ...
$ name : Factor w/ 715 levels "Abe Sapien","Abin Sur",..: 3 1 2 4 5 6 7 8 9 10 ...
$ Gender : Factor w/ 3 levels "-","Female","Male": 3 3 3 3 3 3 3 3 2 3 ...
$ Eye.color : Factor w/ 23 levels "-","amber","black",..: 20 4 4 9 4 4 4 4 4 7 ...
$ Race : Factor w/ 62 levels "-","Alien","Alpha",..: 24 33 56 29 12 24 1 24 1 24 ...
$ Hair.color: Factor w/ 30 levels "-","Auburn","black",..: 18 18 18 18 4 18 7 7 7 9 ...
$ Height : num 203 191 185 203 -99 193 -99 185 173 178 ...
$ Publisher : Factor w/ 25 levels "","ABC Studios",..: 13 3 4 13 13 13 15 4 13 13 ...
$ Skin.color: Factor w/ 17 levels "-","black","blue",..: 1 3 13 1 1 1 1 1 1 1 ...
$ Alignment : Factor w/ 4 levels "-","bad","good",..: 3 3 3 2 2 2 3 3 3 3 ...
$ Weight : num 441 65 90 441 -99 122 -99 88 61 81 ...
Quantiles of quantitative variables
quantile(Superheroes$Height, na.rm = T) 0% 25% 50% 75% 100%
-99 -99 175 185 975 quantile(Superheroes$Weight, probs = c(0, 0.33, 0.67, 1), na.rm = T) 0% 33% 67% 100%
-99.00 17.23 83.00 900.00
Make table
table(Superheroes$Race, useNA = "ifany")
- Alien Alpha Amazon Android
304 7 5 2 9
Animal Asgardian Atlantean Bizarro Bolovaxian
4 5 5 1 1
Clone Cosmic Entity Cyborg Czarnian Dathomirian Zabrak
1 4 11 1 1
Demi-God Demon Eternal Flora Colossus Frost Giant
2 6 2 1 2
God / Eternal Gorilla Gungan Human Human / Altered
14 1 1 208 3
Human / Clone Human / Cosmic Human-Kree Human / Radiation Human-Spartoi
1 2 2 11 1
Human-Vulcan Human-Vuldarian Icthyo Sapien Inhuman Kaiju
1 1 1 4 1
Kakarantharaian Korugaran Kryptonian Luphomoid Maiar
1 1 7 1 1
Martian Metahuman Mutant Mutant / Clone New God
1 2 63 1 3
Neyaphem Parademon Planet Rodian Saiyan
1 1 1 1 2
Spartoi Strontian Symbiote Talokite Tamaranean
1 1 9 1 1
Ungaran Vampire Xenomorph XX121 Yautja Yoda's species
1 2 1 1 1
Zen-Whoberian Zombie
1 1 table(Superheroes$Race, Superheroes$Gender)
- Female Male
- 22 98 184
Alien 0 2 5
Alpha 0 2 3
Amazon 0 2 0
Android 0 0 9
Animal 0 0 4
Asgardian 0 3 2
Atlantean 0 2 3
Bizarro 0 0 1
Bolovaxian 0 0 1
Clone 0 0 1
Cosmic Entity 2 0 2
Cyborg 0 3 8
Czarnian 0 0 1
Dathomirian Zabrak 0 0 1
Demi-God 0 1 1
Demon 0 0 6
Eternal 0 0 2
Flora Colossus 0 0 1
Frost Giant 0 0 2
God / Eternal 2 0 12
Gorilla 0 0 1
Gungan 0 0 1
Human 1 50 157
Human / Altered 0 0 3
Human / Clone 0 0 1
Human / Cosmic 0 1 1
Human-Kree 0 2 0
Human / Radiation 0 2 9
Human-Spartoi 0 0 1
Human-Vulcan 0 0 1
Human-Vuldarian 0 0 1
Icthyo Sapien 0 0 1
Inhuman 0 2 2
Kaiju 1 0 0
Kakarantharaian 0 0 1
Korugaran 0 0 1
Kryptonian 0 3 4
Luphomoid 0 1 0
Maiar 0 0 1
Martian 0 0 1
Metahuman 0 1 1
Mutant 0 20 43
Mutant / Clone 0 1 0
New God 0 1 2
Neyaphem 0 0 1
Parademon 1 0 0
Planet 0 0 1
Rodian 0 0 1
Saiyan 0 0 2
Spartoi 0 0 1
Strontian 0 0 1
Symbiote 0 0 9
Talokite 0 1 0
Tamaranean 0 1 0
Ungaran 0 0 1
Vampire 0 0 2
Xenomorph XX121 0 0 1
Yautja 0 0 1
Yoda's species 0 0 1
Zen-Whoberian 0 1 0
Zombie 0 0 1
Check for missing values
sum(is.na(Superheroes))[1] 2any(is.na(Superheroes[,c("Height", "Weight")]))[1] TRUEall(Superheroes$Weight > 0)[1] FALSE
Check for NAs with row and column sums
colSums(is.na(Superheroes)) X name Gender Eye.color Race Hair.color Height Publisher Skin.color
0 0 0 0 0 0 0 0 0
Alignment Weight
0 2 all(is.na(Superheroes)==0)[1] FALSE
Values with specific characteristics
table(Superheroes$Hair.color %in% "No Hair")
FALSE TRUE
659 75 table(Superheroes$Publisher %in% c("DC Comics", "Marvel Comics"))
FALSE TRUE
131 603
Subset by values with specific characteristics
Superheroes[Superheroes$Publisher %in% c("DC Comics", "Marvel Comics"), c("name", "Gender", "Publisher")]
Filtering data and add variable
Superheroes$BMI <- round((Superheroes$Weight/(Superheroes$Height*0.01)/(Superheroes$Height*0.01)), 3)
SH <- Superheroes[Superheroes$Height > 0 & Superheroes$Height <= 250 & Superheroes$Weight > 0 & Superheroes$Weight <= 300,]
SH[order(SH$BMI, SH$Publisher),]
Cross tabs 1
SHBMI <- xtabs(BMI ~ Gender + Alignment, data = SH)
SHBMI Alignment
Gender - bad good neutral
- 0.000 24.990 291.051 0.000
Female 0.000 522.165 2441.207 38.372
Male 49.085 5448.231 5687.908 458.538
Open a new dataset
data("UCBAdmissions")
DF = as.data.frame(UCBAdmissions)
summary(DF) Admit Gender Dept Freq
Admitted:12 Male :12 A:4 Min. : 8.0
Rejected:12 Female:12 B:4 1st Qu.: 80.0
C:4 Median :170.0
D:4 Mean :188.6
E:4 3rd Qu.:302.5
F:4 Max. :512.0
Cross tabs 2
xt <- xtabs(Freq ~ Gender + Admit, data=DF)
xt Admit
Gender Admitted Rejected
Male 1198 1493
Female 557 1278
Another new dataset for example
data("warpbreaks")
warpbreaks$replicate <- rep(1:9, len=54)
warpbreaks
Cross tabs 3
xt = xtabs(breaks ~., data=warpbreaks)
xt, , replicate = 1
tension
wool L M H
A 26 18 36
B 27 42 20
, , replicate = 2
tension
wool L M H
A 30 21 21
B 14 26 21
, , replicate = 3
tension
wool L M H
A 54 29 24
B 29 19 24
, , replicate = 4
tension
wool L M H
A 25 17 18
B 19 16 17
, , replicate = 5
tension
wool L M H
A 70 12 10
B 29 39 13
, , replicate = 6
tension
wool L M H
A 52 18 43
B 31 28 15
, , replicate = 7
tension
wool L M H
A 51 35 28
B 41 21 15
, , replicate = 8
tension
wool L M H
A 26 30 15
B 20 39 16
, , replicate = 9
tension
wool L M H
A 67 36 26
B 44 29 28
Flat tables
ftable(xt) replicate 1 2 3 4 5 6 7 8 9
wool tension
A L 26 30 54 25 70 52 51 26 67
M 18 21 29 17 12 18 35 30 36
H 36 21 24 18 10 43 28 15 26
B L 27 14 29 19 29 31 41 20 44
M 42 26 19 16 39 28 21 39 29
H 20 21 24 17 13 15 15 16 28
Size of the dataset
fakeData <- rnorm(10^6)object.size(Superheroes)102872 bytesobject.size(fakeData)8000040 bytes
Print file size
print(object.size(fakeData), units = "Mb")7.6 Mb