Quiz on EDA for Numerical Data
Brooke Seguin
11/6/2017
Suppose that your boss gave you a task to report an exploratory data analysis including visualization and summary statistics of city_mpg (mileage per gallon) in the cars data set.
Load packages
library(ggplot2)
library(dplyr)Import Data
# Load data
cars <- read.csv("/resources/data/cars.csv")
str(cars)
## 'data.frame': 428 obs. of 19 variables:
## $ name : Factor w/ 425 levels "Acura 3.5 RL 4dr",..: 66 67 68 69 70 114 115 133 129 130 ...
## $ sports_car : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ suv : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ wagon : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ minivan : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ pickup : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ all_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ rear_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ msrp : int 11690 12585 14610 14810 16385 13670 15040 13270 13730 15460 ...
## $ dealer_cost: int 10965 11802 13697 13884 15357 12849 14086 12482 12906 14496 ...
## $ eng_size : num 1.6 1.6 2.2 2.2 2.2 2 2 2 2 2 ...
## $ ncyl : int 4 4 4 4 4 4 4 4 4 4 ...
## $ horsepwr : int 103 103 140 140 140 132 132 130 110 130 ...
## $ city_mpg : int 28 28 26 26 26 29 29 26 27 26 ...
## $ hwy_mpg : int 34 34 37 37 37 36 36 33 36 33 ...
## $ weight : int 2370 2348 2617 2676 2617 2581 2626 2612 2606 2606 ...
## $ wheel_base : int 98 98 104 104 104 105 105 103 103 103 ...
## $ length : int 167 153 183 183 183 174 174 168 168 168 ...
## $ width : int 66 66 69 68 69 67 67 67 67 67 ...
summary(cars)
## name sports_car suv
## Infiniti G35 4dr : 2 Mode :logical Mode :logical
## Mercedes-Benz C240 4dr : 2 FALSE:379 FALSE:368
## Mercedes-Benz C320 4dr : 2 TRUE :49 TRUE :60
## Acura 3.5 RL 4dr : 1 NA's :0 NA's :0
## Acura 3.5 RL w/Navigation 4dr: 1
## Acura MDX : 1
## (Other) :419
## wagon minivan pickup all_wheel
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:398 FALSE:408 FALSE:404 FALSE:336
## TRUE :30 TRUE :20 TRUE :24 TRUE :92
## NA's :0 NA's :0 NA's :0 NA's :0
##
##
##
## rear_wheel msrp dealer_cost eng_size
## Mode :logical Min. : 10280 Min. : 9875 Min. :1.300
## FALSE:318 1st Qu.: 20334 1st Qu.: 18866 1st Qu.:2.375
## TRUE :110 Median : 27635 Median : 25294 Median :3.000
## NA's :0 Mean : 32775 Mean : 30015 Mean :3.197
## 3rd Qu.: 39205 3rd Qu.: 35710 3rd Qu.:3.900
## Max. :192465 Max. :173560 Max. :8.300
##
## ncyl horsepwr city_mpg hwy_mpg
## Min. :-1.000 Min. : 73.0 Min. :10.00 Min. :12.00
## 1st Qu.: 4.000 1st Qu.:165.0 1st Qu.:17.00 1st Qu.:24.00
## Median : 6.000 Median :210.0 Median :19.00 Median :26.00
## Mean : 5.776 Mean :215.9 Mean :20.09 Mean :26.91
## 3rd Qu.: 6.000 3rd Qu.:255.0 3rd Qu.:21.00 3rd Qu.:29.00
## Max. :12.000 Max. :500.0 Max. :60.00 Max. :66.00
## NA's :14 NA's :14
## weight wheel_base length width
## Min. :1850 Min. : 89.0 Min. :143.0 Min. :64.00
## 1st Qu.:3102 1st Qu.:103.0 1st Qu.:177.0 1st Qu.:69.00
## Median :3474 Median :107.0 Median :186.0 Median :71.00
## Mean :3577 Mean :108.2 Mean :185.1 Mean :71.29
## 3rd Qu.:3974 3rd Qu.:112.0 3rd Qu.:193.0 3rd Qu.:73.00
## Max. :7190 Max. :144.0 Max. :227.0 Max. :81.00
## NA's :2 NA's :2 NA's :26 NA's :28Visualize
# Create faceted histogram
ggplot(cars, aes(x = wheel_base)) +
geom_histogram()
# Create box plots of city mpg by suv
ggplot(cars, aes(x = 1, y = wheel_base)) +
geom_boxplot()
# Create overlaid density plots for same data
ggplot(cars, aes(x = wheel_base)) +
geom_density(alpha = .3)
Summary statistics
# If data has extreme values
cars %>%
summarize(median(wheel_base, na.rm = TRUE),
IQR(wheel_base, na.rm = TRUE))
## median(wheel_base, na.rm = TRUE) IQR(wheel_base, na.rm = TRUE)
## 1 107 9
# If data doesn't have extreme values
cars %>%
summarize(mean(wheel_base, na.rm = TRUE),
sd(wheel_base, na.rm = TRUE))
## mean(wheel_base, na.rm = TRUE) sd(wheel_base, na.rm = TRUE)
## 1 108.1737 8.326449Interpretation
- Wheels of a typical car are 107
- I picked the median because it is usually more accurate and the mean wasn’t too far off at 108.
Add the second variable,
# Create box plots of city mpg by suv
ggplot(cars, aes(x = sports_car, y = wheel_base)) +
geom_boxplot()
# If data has extreme values
cars %>%
group_by(sports_car) %>%
summarize(median(wheel_base, na.rm = TRUE),
IQR(wheel_base, na.rm = TRUE))
## # A tibble: 2 x 3
## sports_car `median(wheel_base, na.rm = TRUE)`
## <lgl> <int>
## 1 FALSE 107
## 2 TRUE 101
## # ... with 1 more variables: `IQR(wheel_base, na.rm = TRUE)` <dbl>
# If data doesn't have extreme values
cars %>%
group_by(sports_car) %>%
summarize(mean(wheel_base, na.rm = TRUE),
sd(wheel_base, na.rm = TRUE))
## # A tibble: 2 x 3
## sports_car `mean(wheel_base, na.rm = TRUE)`
## <lgl> <dbl>
## 1 FALSE 109.23873
## 2 TRUE 99.97959
## # ... with 1 more variables: `sd(wheel_base, na.rm = TRUE)` <dbl>Interpretation
The wheels on sports cars are smaller in width than the ones on non sports cars, regardless of which measure of center were taken. On average non sports cars have values that are farther away from the center.