Honey Berk - IS607 - Week 6 Assignment
Load dataset and identify variable types
- Choose and load any R dataset (except for diamonds!) that has at least two numeric variables and at least two categorical variables. Identify which variables in your data set are numeric, and which are categorical (factors).
#Identify which variables in your data set are numeric, and which are categorical (factors).
require(ggplot2)## Loading required package: ggplot2str(mpg)## 'data.frame': 234 obs. of 11 variables:
## $ manufacturer: Factor w/ 15 levels "audi","chevrolet",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ model : Factor w/ 38 levels "4runner 4wd",..: 2 2 2 2 2 2 2 3 3 3 ...
## $ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : Factor w/ 10 levels "auto(av)","auto(l3)",..: 4 9 10 1 4 9 1 9 4 10 ...
## $ drv : Factor w/ 3 levels "4","f","r": 2 2 2 2 2 2 2 1 1 1 ...
## $ cty : int 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : Factor w/ 5 levels "c","d","e","p",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ class : Factor w/ 7 levels "2seater","compact",..: 2 2 2 2 2 2 2 2 2 2 ...Generate descriptive statistics
- Generate summary level descriptive statistics: Show the mean, median, 25th and 75th quartiles, min, and max for each of the applicable variables in your data set.
summary(mpg)## manufacturer model displ year
## dodge :37 caravan 2wd : 11 Min. :1.600 Min. :1999
## toyota :34 ram 1500 pickup 4wd: 10 1st Qu.:2.400 1st Qu.:1999
## volkswagen:27 civic : 9 Median :3.300 Median :2004
## ford :25 dakota pickup 4wd : 9 Mean :3.472 Mean :2004
## chevrolet :19 jetta : 9 3rd Qu.:4.600 3rd Qu.:2008
## audi :18 mustang : 9 Max. :7.000 Max. :2008
## (Other) :74 (Other) :177
## cyl trans drv cty hwy
## Min. :4.000 auto(l4) :83 4:103 Min. : 9.00 Min. :12.00
## 1st Qu.:4.000 manual(m5):58 f:106 1st Qu.:14.00 1st Qu.:18.00
## Median :6.000 auto(l5) :39 r: 25 Median :17.00 Median :24.00
## Mean :5.889 manual(m6):19 Mean :16.86 Mean :23.44
## 3rd Qu.:8.000 auto(s6) :16 3rd Qu.:19.00 3rd Qu.:27.00
## Max. :8.000 auto(l6) : 6 Max. :35.00 Max. :44.00
## (Other) :13
## fl class
## c: 1 2seater : 5
## d: 5 compact :47
## e: 8 midsize :41
## p: 52 minivan :11
## r:168 pickup :33
## subcompact:35
## suv :62Determine frequencies
- Determine the frequency for one of the categorical variables.
table(mpg$model)##
## 4runner 4wd a4 a4 quattro
## 6 7 8
## a6 quattro altima c1500 suburban 2wd
## 3 6 5
## camry camry solara caravan 2wd
## 7 7 11
## civic corolla corvette
## 9 5 5
## dakota pickup 4wd durango 4wd expedition 2wd
## 9 7 3
## explorer 4wd f150 pickup 4wd forester awd
## 6 7 6
## grand cherokee 4wd grand prix gti
## 8 5 5
## impreza awd jetta k1500 tahoe 4wd
## 8 9 4
## land cruiser wagon 4wd malibu maxima
## 2 5 3
## mountaineer 4wd mustang navigator 2wd
## 4 9 3
## new beetle passat pathfinder 4wd
## 6 7 4
## ram 1500 pickup 4wd range rover sonata
## 10 4 7
## tiburon toyota tacoma 4wd
## 7 7- Determine the frequency for one of the categorical variables, by a different categorical variable.
table(mpg$model, mpg$class)##
## 2seater compact midsize minivan pickup subcompact
## 4runner 4wd 0 0 0 0 0 0
## a4 0 7 0 0 0 0
## a4 quattro 0 8 0 0 0 0
## a6 quattro 0 0 3 0 0 0
## altima 0 2 4 0 0 0
## c1500 suburban 2wd 0 0 0 0 0 0
## camry 0 0 7 0 0 0
## camry solara 0 7 0 0 0 0
## caravan 2wd 0 0 0 11 0 0
## civic 0 0 0 0 0 9
## corolla 0 5 0 0 0 0
## corvette 5 0 0 0 0 0
## dakota pickup 4wd 0 0 0 0 9 0
## durango 4wd 0 0 0 0 0 0
## expedition 2wd 0 0 0 0 0 0
## explorer 4wd 0 0 0 0 0 0
## f150 pickup 4wd 0 0 0 0 7 0
## forester awd 0 0 0 0 0 0
## grand cherokee 4wd 0 0 0 0 0 0
## grand prix 0 0 5 0 0 0
## gti 0 5 0 0 0 0
## impreza awd 0 4 0 0 0 4
## jetta 0 9 0 0 0 0
## k1500 tahoe 4wd 0 0 0 0 0 0
## land cruiser wagon 4wd 0 0 0 0 0 0
## malibu 0 0 5 0 0 0
## maxima 0 0 3 0 0 0
## mountaineer 4wd 0 0 0 0 0 0
## mustang 0 0 0 0 0 9
## navigator 2wd 0 0 0 0 0 0
## new beetle 0 0 0 0 0 6
## passat 0 0 7 0 0 0
## pathfinder 4wd 0 0 0 0 0 0
## ram 1500 pickup 4wd 0 0 0 0 10 0
## range rover 0 0 0 0 0 0
## sonata 0 0 7 0 0 0
## tiburon 0 0 0 0 0 7
## toyota tacoma 4wd 0 0 0 0 7 0
##
## suv
## 4runner 4wd 6
## a4 0
## a4 quattro 0
## a6 quattro 0
## altima 0
## c1500 suburban 2wd 5
## camry 0
## camry solara 0
## caravan 2wd 0
## civic 0
## corolla 0
## corvette 0
## dakota pickup 4wd 0
## durango 4wd 7
## expedition 2wd 3
## explorer 4wd 6
## f150 pickup 4wd 0
## forester awd 6
## grand cherokee 4wd 8
## grand prix 0
## gti 0
## impreza awd 0
## jetta 0
## k1500 tahoe 4wd 4
## land cruiser wagon 4wd 2
## malibu 0
## maxima 0
## mountaineer 4wd 4
## mustang 0
## navigator 2wd 3
## new beetle 0
## passat 0
## pathfinder 4wd 4
## ram 1500 pickup 4wd 0
## range rover 4
## sonata 0
## tiburon 0
## toyota tacoma 4wd 0R Graphics
- Create a graph for a single numeric variable.
Plot mpg city for each manufacturer.
hist(mpg$cty)
Look at the same single numeric variable, in ggplot2.
qplot(cty, data = mpg)## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
- Create a scatterplot of two numeric variables.
First, in base R:
plot(mpg$cyl, mpg$cty, xlab="# Cylinders",ylab="Miles Per Gallon", pch=19)
Look at the same scatterplot in ggplot2
ggplot(mpg, aes(cyl, cty)) + geom_point()
Extra plots
# Manufacturer vs. mpg city
ggplot(mpg, aes(x = manufacturer, y = cty)) + geom_boxplot() + coord_flip()
# City mpg by manufacturer
ggplot(mpg, aes(x = cty, fill = manufacturer)) + geom_bar()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
#City mpg by year, with manufacturer
ggplot(mpg, aes(x = year, y = cty, fill = manufacturer)) + geom_bar(stat="identity", position=position_dodge())