This is the second homework assignment for Intro to R. You must submit both the RMD and “knitted” HTML files are due before class on Wednesday (Day 3)

## you can add more, or change...these are suggestions
library(tidyverse)
library(readr)
library(dplyr)
library(ggplot2)
library(tidyr)

This is a dataset from the “Kaggle” website, which hosts competitions for prediction and machine learning. More details on this dataset are here: http://www.kaggle.com/c/DontGetKicked/details/Background

Questions:

  1. Get the dataset: http://johnmuschelli.com/intro_to_r/data/kaggleCarAuction.csv read the data set in using read_csv(), name the dataset cars.
cars = read_csv(
  "http://johnmuschelli.com/intro_to_r/data/kaggleCarAuction.csv",
  col_types = cols(VehBCost = col_double())
  )
  1. Read the “dictionary”: http://johnmuschelli.com/intro_to_r/data/Carvana_Data_Dictionary_formatted.txt use the read_tsv() function and name it key.
key = read_tsv("http://johnmuschelli.com/intro_to_r/data/Carvana_Data_Dictionary_formatted.txt")
  1. What would you do if the data was formatted like this: http://johnmuschelli.com/intro_to_r/data/Carvana_Data_Dictionary.txt with spaces and tabs and such?

  2. Save the key and data in an “.rda” file so you can access the data offline using the save() function.

save(cars, key, file = "kaggle.rda")
  1. How many cars are in the dataset? How many variables are recorded for each car?
dim(cars)
## [1] 72983    34
nrow(cars)
## [1] 72983

There are 72983 observations in the dataset.

  1. What is the range of the manufacturer’s years of the vehicles? Use VehYear
range(cars$VehYear)
## [1] 2001 2010
table(cars$VehYear)
## 
##  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010 
##  1481  3405  6227 10207 15489 17043 11423  6885   822     1
  1. How many cars were from before 2004, and what percent/proportion do these represent? Hint: use summarize/filter or sum
sum(cars$VehYear < 2004)
## [1] 11113
mean(cars$VehYear < 2004)
## [1] 0.1522683
table(cars$VehYear < 2004)
## 
## FALSE  TRUE 
## 61870 11113
  1. Drop any vehicles that cost less than or equal to $1500 (VehBCost), or missing - (filter() removes missing values!). How many vehicles were removed? Note the rest of the questions expect answers based on this reduced dataset.
sum(cars$VehBCost <= 1500 | is.na(cars$VehBCost))
## [1] 3
cars = filter(cars, VehBCost > 1500)
nrow(cars)
## [1] 72980

There are 72980 after filtering by price.

  1. How many different vehicle a) manufacturers/makes (Make), b) models (Model) and c) sizes (Size) are there? Hint: use table() or group_by() with tally()/summarize()
length(unique(cars$Make)) # a
## [1] 33
length(table(cars$Make))
## [1] 33
length(unique(cars$Model)) # b
## [1] 1063
length(unique(cars$Size)) # c
## [1] 13
  1. Which vehicle a) make, b) model and c) color had the highest average acquisition cost paid for the vehicle at time of purchase, and what was this cost? Hint: use group_by() with summarize()
cars %>% group_by(Make) %>% 
  summarize(mean = mean(VehBCost)) %>% 
  arrange(desc(mean))
## # A tibble: 33 x 2
##    Make       mean
##    <chr>     <dbl>
##  1 LEXUS    13023.
##  2 HUMMER   11920 
##  3 CADILLAC 10958.
##  4 INFINITI  9844.
##  5 VOLVO     9756.
##  6 ACURA     9039.
##  7 SUBARU    8958.
##  8 MINI      8560.
##  9 HONDA     8350.
## 10 GMC       8342.
## # ... with 23 more rows
cars %>% group_by(Model) %>% 
  summarize(mean = mean(VehBCost)) %>% 
  arrange(desc(mean))
## # A tibble: 1,063 x 2
##    Model               mean
##    <chr>              <dbl>
##  1 ESCALADE 2WD      45469 
##  2 GX470 4WD         38785 
##  3 RX400H AWD        33140 
##  4 GS450H            28560 
##  5 TOUAREG V8        28180 
##  6 FJ CRUISER 4WD V6 20380 
##  7 HIGHLANDER 4WD V6 16892.
##  8 M35               15419.
##  9 4 RUNNER 4WD V6   15300.
## 10 4 RUNNER 2WD V6   15148.
## # ... with 1,053 more rows
cars %>% group_by(Color) %>% 
  summarize(mean = mean(VehBCost)) %>% 
  arrange(desc(mean))
## # A tibble: 17 x 2
##    Color      mean
##    <chr>     <dbl>
##  1 BLACK     7146.
##  2 GREY      7093.
##  3 BROWN     6886.
##  4 OTHER     6845.
##  5 BEIGE     6760.
##  6 BLUE      6719.
##  7 RED       6718.
##  8 ORANGE    6695.
##  9 WHITE     6650.
## 10 MAROON    6636.
## 11 SILVER    6610.
## 12 NOT AVAIL 6514.
## 13 GREEN     6487.
## 14 GOLD      6375.
## 15 PURPLE    6304.
## 16 YELLOW    6299.
## 17 NULL      4891.
  1. Which vehicle a) make, b) model and c) color had the highest variability in acquisition cost paid for the vehicle at time of purchase?
cars %>% group_by(Make) %>% 
  summarize(var = var(VehBCost)) %>% 
  arrange(desc(var))
## # A tibble: 33 x 2
##    Make             var
##    <chr>          <dbl>
##  1 LEXUS      68614545.
##  2 CADILLAC   39008786.
##  3 INFINITI    8240801.
##  4 LINCOLN     4523891.
##  5 VOLKSWAGEN  4358504.
##  6 FORD        3824550.
##  7 DODGE       3650695.
##  8 TOYOTA      3646264.
##  9 MERCURY     3198687.
## 10 NISSAN      2803932.
## # ... with 23 more rows
cars %>% group_by(Model) %>% 
  summarize(var = var(VehBCost)) %>% 
  arrange(desc(var))
## # A tibble: 1,063 x 2
##    Model                       var
##    <chr>                     <dbl>
##  1 HIGHLANDER 4WD V6    164749479.
##  2 M35                   43817437.
##  3 RX400H AWD            22378050 
##  4 2500 SILVERADO PICKU  19220000 
##  5 4 RUNNER 2WD V6       19189012.
##  6 F150 PICKUP 4WD V8    16749639.
##  7 2500 RAM PICKUP 4WD    8224008.
##  8 G35                    7127987.
##  9 GS300 3.0L I6 EFI      6275149.
## 10 COMMANDER 4WD V8       6030312.
## # ... with 1,053 more rows
cars %>% group_by(Color) %>% 
  summarize(var = var(VehBCost)) %>% 
  arrange(desc(var))
## # A tibble: 17 x 2
##    Color          var
##    <chr>        <dbl>
##  1 MAROON    3388012.
##  2 BLACK     3302015.
##  3 OTHER     3257129.
##  4 GREY      3245465.
##  5 RED       3228092.
##  6 WHITE     3114782.
##  7 BROWN     3064809.
##  8 BEIGE     3032798.
##  9 SILVER    3025037.
## 10 GREEN     2984072.
## 11 YELLOW    2963724.
## 12 GOLD      2936305.
## 13 BLUE      2802224.
## 14 PURPLE    2645876.
## 15 NOT AVAIL 2637445.
## 16 ORANGE    2182411.
## 17 NULL      1027184.
  1. How many vehicles (using filter() or sum()):
  1. Were red and have fewer than 30,000 miles?
  2. Are made by GMC and were purchased in Texas?
  3. Are blue or red?
  4. Are made by Chrysler or Nissan and are white or silver?
  5. Are automatic, blue, Pontiac cars with under 40,000 miles?
sum(cars$Color == "RED" & cars$VehOdo < 30000)
## [1] 28
# b.    Are made by GMC and were purchased in Texas? 
sum(cars$Make == "GMC" & cars$VNST == "TX")
## [1] 137
# c.    Are blue or red?
sum(cars$Color == "BLUE" | cars$Color == "RED" )
## [1] 16603
sum(cars$Color %in% c("BLUE","RED"))
## [1] 16603
# d.    Are made by Chrysler or Nissan and are white or silver? 
sum((cars$Make == "CHRYSLER" | cars$Make=="NISSAN") & (cars$Color == "WHITE" | cars$Color == "WHITE" ))
## [1] 1748
sum(cars$Make %in% c("CHRYSLER","NISSAN") & 
    cars$Color %in% c("WHITE","SILVER" ))
## [1] 4335
# e.    Are automatic, blue, Pontiac cars with under 40,000 miles? 
sum(cars$Transmission == "AUTO" & cars$Color=="BLUE" & 
    cars$Make == "PONTIAC" & cars$VehOdo < 40000)
## [1] 1