About the data

The data is collected from the R package: fueleconomy. The fueleconomy package’s data was sourced from the EPA (Environmental Protection Agency). In this package, the data is stored in vehicles dataset.

Fuel economy data contains data for all cars sold in the US from 1984 to 2015. The package fueleconomy has 33,442 rows and 12 variables

id Unique EPA identifier
make Manufacturer
model Model name
year Model year
class EPA vehicle size class, http://www.fueleconomy.gov/feg/ws/wsData.shtml#VClass
trans Transmission
drive Drive train
cyl Number of cylinders
displ Engine displacement, in litres
fuel Fuel type
hwy Highway fuel economy, in mpg
cty City fuel economy, in mpg

Data Source

Further data information in this study can be found in below links:

Load the data

The dataset is loaded from fueleconomy dataset of R in vehicles. kable library is used to display data tables.

# load data
library(fueleconomy)

kable(head(vehicles))

The dimensions of data shows 33384 rows and 13 columns in the dataset.

# dimensions
dim(vehicles)

## [1] 33442    12

Tidying the data

Approach: My goal is to clean the data and create new columns like mpg for further analysis.

Rename columns where column names are abbreviated. Here I renamed the columns oscar_yrhwy and cty to highway and city.
Next is to mutate new column called mpg as Per EPA (Environmental protection agency) guidelines which says combined fuel economy is a weighted average of City and Highway MPG values that is calculated by weighting the City value by 55% and the Highway value by 45%.
Then I grouped the dataset by cyl, displacement, year and summarise them.

# rename columns
vehicles <- vehicles %>% 
  rename("highway"=hwy) %>% 
  rename("city"=cty)

# show intial rows through head
kable(head(vehicles), align = "l")

id	make	model	year	class	trans	drive	cyl	displ	fuel	highway	city
27550	AM General	DJ Po Vehicle 2WD	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	4	2.5	Regular	17	18
28426	AM General	DJ Po Vehicle 2WD	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	4	2.5	Regular	17	18
27549	AM General	FJ8c Post Office	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	6	4.2	Regular	13	13
28425	AM General	FJ8c Post Office	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	6	4.2	Regular	13	13
1032	AM General	Post Office DJ5 2WD	1985	Special Purpose Vehicle 2WD	Automatic 3-spd	Rear-Wheel Drive	4	2.5	Regular	17	16
1033	AM General	Post Office DJ8 2WD	1985	Special Purpose Vehicle 2WD	Automatic 3-spd	Rear-Wheel Drive	6	4.2	Regular	13	13

summary(vehicles)

##        id            make              model                year     
##  Min.   :    1   Length:33442       Length:33442       Min.   :1984  
##  1st Qu.: 8361   Class :character   Class :character   1st Qu.:1991  
##  Median :16724   Mode  :character   Mode  :character   Median :1999  
##  Mean   :17038                                         Mean   :1999  
##  3rd Qu.:25265                                         3rd Qu.:2008  
##  Max.   :34932                                         Max.   :2015  
##                                                                      
##     class              trans              drive                cyl        
##  Length:33442       Length:33442       Length:33442       Min.   : 2.000  
##  Class :character   Class :character   Class :character   1st Qu.: 4.000  
##  Mode  :character   Mode  :character   Mode  :character   Median : 6.000  
##                                                           Mean   : 5.772  
##                                                           3rd Qu.: 6.000  
##                                                           Max.   :16.000  
##                                                           NA's   :58      
##      displ           fuel              highway            city       
##  Min.   :0.000   Length:33442       Min.   :  9.00   Min.   :  6.00  
##  1st Qu.:2.300   Class :character   1st Qu.: 19.00   1st Qu.: 15.00  
##  Median :3.000   Mode  :character   Median : 23.00   Median : 17.00  
##  Mean   :3.353                      Mean   : 23.55   Mean   : 17.49  
##  3rd Qu.:4.300                      3rd Qu.: 27.00   3rd Qu.: 20.00  
##  Max.   :8.400                      Max.   :109.00   Max.   :138.00  
##  NA's   :57

any(is.na(vehicles))

## [1] TRUE

vehicles_subdf <- vehicles[rowSums(is.na(vehicles)) > 0,]
kable(head(vehicles_subdf))

id	make	model	year	class	trans	drive	cyl	displ	fuel	highway	city
31893	Azure Dynamics	Transit Connect Electric Van	2012	Special Purpose Vehicle 2WD	Automatic (A1)	Front-Wheel Drive	NA	NA	Electricity	62	62
31894	Azure Dynamics	Transit Connect Electric Wagon	2012	Special Purpose Vehicle 2WD	Automatic (A1)	Front-Wheel Drive	NA	NA	Electricity	62	62
32276	BMW	Active E	2011	Subcompact Cars	Automatic (A1)	Rear-Wheel Drive	NA	NA	Electricity	96	107
33383	BYD	e6	2012	Sport Utility Vehicle - 2WD	Automatic (A1)	Front-Wheel Drive	NA	NA	Electricity	64	60
34860	BYD	e6	2013	Small Sport Utility Vehicle 2WD	Automatic (A1)	Front-Wheel Drive	NA	NA	Electricity	65	61
34859	BYD	e6	2014	Small Sport Utility Vehicle 2WD	Automatic (A1)	Front-Wheel Drive	NA	NA	Electricity	65	61

Per EPA (Environmental protection agency) guidelines, combined fuel economy is a weighted average of City and Highway MPG values that is calculated by weighting the City value by 55% and the Highway value by 45%.

# remove null values
vehicles <- na.omit(vehicles)

vehicles <- vehicles %>% mutate(mpg = 0.55 * vehicles$city + 0.45 * vehicles$highway)
kable(head(vehicles))

id	make	model	year	class	trans	drive	cyl	displ	fuel	highway	city	mpg
27550	AM General	DJ Po Vehicle 2WD	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	4	2.5	Regular	17	18	17.55
28426	AM General	DJ Po Vehicle 2WD	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	4	2.5	Regular	17	18	17.55
27549	AM General	FJ8c Post Office	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	6	4.2	Regular	13	13	13.00
28425	AM General	FJ8c Post Office	1984	Special Purpose Vehicle 2WD	Automatic 3-spd	2-Wheel Drive	6	4.2	Regular	13	13	13.00
1032	AM General	Post Office DJ5 2WD	1985	Special Purpose Vehicle 2WD	Automatic 3-spd	Rear-Wheel Drive	4	2.5	Regular	17	16	16.45
1033	AM General	Post Office DJ8 2WD	1985	Special Purpose Vehicle 2WD	Automatic 3-spd	Rear-Wheel Drive	6	4.2	Regular	13	13	13.00

any(is.na(vehicles$cyl))

## [1] FALSE

vehicles_cyl <- vehicles %>% filter(!is.na(cyl))

vehicles_cyl %>%
  group_by(cyl) %>%
  summarise(n = n(), mean = mean(mpg), median = median(mpg), sd = sd(mpg))

## # A tibble: 9 x 5
##     cyl     n  mean median    sd
##   <int> <int> <dbl>  <dbl> <dbl>
## 1     2    45  18.4   18.6 0.529
## 2     3   182  37.1   35.9 6.04 
## 3     4 12381  24.1   23.5 4.29 
## 4     5   718  20.9   20.6 2.73 
## 5     6 11885  18.9   19.0 2.63 
## 6     8  7550  15.5   15.2 2.62 
## 7    10   138  14.5   14.6 1.80 
## 8    12   478  13.4   13.7 1.76 
## 9    16     7  11.0   11.2 0.241

vehicles_displ <- vehicles %>% filter(!is.na(displ))

vehicles_displ %>%
  group_by(displ) %>%
  summarise(n = n(), mean = mean(mpg), median = median(mpg), sd = sd(mpg))

## # A tibble: 64 x 5
##    displ     n  mean median    sd
##    <dbl> <int> <dbl>  <dbl> <dbl>
##  1   0       3  17.8   17.2  1.30
##  2   1     157  37.9   36.6  6.33
##  3   1.1     8  21.7   21.4  4.56
##  4   1.2    35  30.6   30.2  3.28
##  5   1.3   177  29.1   28.2  7.90
##  6   1.4    80  31.5   31.4  3.67
##  7   1.5   610  29.7   28.7  4.68
##  8   1.6  1264  26.8   26.7  3.19
##  9   1.7    50  29.9   29.7  4.35
## 10   1.8  1353  25.0   24.2  3.82
## # … with 54 more rows

vehicles %>%
  group_by(year) %>%
  summarise(n = n(), mean = mean(mpg), median = median(mpg), sd = sd(mpg))

## # A tibble: 32 x 5
##     year     n  mean median    sd
##    <int> <int> <dbl>  <dbl> <dbl>
##  1  1984   784  17.2   17.2  4.18
##  2  1985  1699  20.2   19.6  5.32
##  3  1986  1209  19.9   19.6  5.26
##  4  1987  1247  19.6   19.4  5.14
##  5  1988  1130  19.7   19.2  5.04
##  6  1989  1153  19.5   19.2  5.18
##  7  1990  1078  19.4   19.0  4.96
##  8  1991  1132  19.3   18.7  4.92
##  9  1992  1121  19.3   19.0  4.89
## 10  1993  1093  19.6   19.0  4.87
## # … with 22 more rows

Data Analysis

Approach: To analyze the data, I have used below described graphs to get the insights from vehicles dataset.

First I tried to find make and model having max mpg
First plot is to show the mpg distribution in vehicles dataset.
Second plot is to show the box plot for cyl and mpg.
Third one is to show the details of mpg by factoring with cyl.
The last graph is to draw qq plot for mpg variable. It helps to assess the distribution.

# to find make and model having max mpg
vehicles[which.max(vehicles$mpg),]

## # A tibble: 1 x 13
##      id make  model  year class trans drive   cyl displ fuel  highway  city
##   <int> <chr> <chr> <int> <chr> <chr> <chr> <int> <dbl> <chr>   <int> <int>
## 1 15606 Honda Insi…  2000 Two … Manu… Fron…     3     1 Regu…      61    49
## # … with 1 more variable: mpg <dbl>

ggplot(data=vehicles, aes(vehicles$mpg)) + 
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vehicles_cyl, aes(cyl, mpg)) + geom_boxplot(aes(fill = factor(cyl)))

ggplot(vehicles, aes(mpg)) + geom_histogram(bins = 50, aes(fill = factor(cyl)))

qqnorm(vehicles$mpg)
qqline(vehicles$mpg)

Summary/Conclusion

After doing the above analysis of fueleconomy package from R, the data shows that the average MPG has increased from ~17MPG in 1984 to ~23MPG in 2015. There is signicant evidence that combined MPG differs for engines having different number of cylinders. Also its clear that vehicle with more cylinders give less combined MPG. The data also shows that fuel economy is different for engines having different number of displacments. More displacment performs less on overall MPG. The data provides a number of different variables that could be further analyzed. We could perform multiple regression to predict the gas mileage of a vehicle based upon these characteristics.

Data607 - Project2B

Amit Kapoor