RLab Test Claudia
CS
2025-03-04
- Below is a histogram of miles per gallon for the mtcars data set.This histogram is right skewed and shows that the majority of miles per gallon are between 15 and 20. This is the most frequent at 12. There is a fair range of miles per gallon from 10 to 35. There are multiple cars with a high mpg.
hist(mtcars$mpg)
Below is a summery of mpg. The mean is 20.9 and the median is 19.20. The mean being above the median reveals that the data is right skewed or positively skewed which confirms what out histogram showed. The median shows the middle value and the mean shows the average. Comparing these values show that most cars have mpg below the mean. There could be some outliers with high mpg.
summary(mtcars$mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90- Below is a scatterplot between mpg and hp. This shows that as horsepower increases, the miles per gallon decrease, revealing that this is a less effieient way to drive.
plot(mtcars$mpg,mtcars$hp)
- Below is a box plot to display the relationship between mpg and cyl. This shows that a 4 cylinder has the highest median mpg and this decreases with cylinders 4 and 8. The 4 cylinder also has the greatest range of mpg and the 6 has the smallest. This shows that cars with less cylinders are more fuel efficient.
boxplot(mpg~cyl,data=mtcars)
- The average mpg is 20.09062. The codes for this question are shown.
avgmpg=mean(mtcars$mpg)
mean(mtcars$mpg)## [1] 20.09062dfHigh<-mtcars[(mtcars$mpg>avgmpg),]
dfLow<-mtcars[(mtcars$mpg<=avgmpg),]
rbind(dfHigh,dfLow)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8PART 2 1. The colnames function shows what type of data each of the columns contain. Septal length/width, Petal length/width are all numeric. Species is a factor which includes setosa, vericolor and verginica. The pther columns all include numbers that are lengths and widths.
colnames(iris)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosastr(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...- The below is a scatter plot which graphs iris septal length and width. This shows that the data is all within a small range with a few outlines. There is no signifigant relationship.
plot(iris$Sepal.Length, iris$Sepal.Width,)
- This is a scatter plot between the iris species and septal length. This shpws that a shorter length (between 3 and 6) is likely to be setosa or iris species 1. Lengths between 5 and 7 are likely to be iris species 2. Lengths above this are the third iris species.
plot(iris$Sepal.Length, iris$Species,)
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