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plot(cars)

"C:/Users/Adria/Downloads"

[1] "C:/Users/Adria/Downloads"

setwd("C:/Users/Adria/Downloads")

usedcars <- read.csv("usedcars.csv", stringsAsFactors = FALSE)

# get structure of used car data
str(usedcars)

'data.frame':   150 obs. of  6 variables:
 $ year        : int  2011 2011 2011 2011 2012 2010 2011 2010 2011 2010 ...
 $ model       : chr  "SEL" "SEL" "SEL" "SEL" ...
 $ price       : int  21992 20995 19995 17809 17500 17495 17000 16995 16995 16995 ...
 $ mileage     : int  7413 10926 7351 11613 8367 25125 27393 21026 32655 36116 ...
 $ color       : chr  "Yellow" "Gray" "Silver" "Gray" ...
 $ transmission: chr  "AUTO" "AUTO" "AUTO" "AUTO" ...

# summarize numeric variables
summary(usedcars$year)

   Min. 1st Qu.  Median    Mean 3rd Qu. 
   2000    2008    2009    2009    2010 
   Max. 
   2012 

summary(usedcars[c("price", "mileage", "year", "transmission")])

     price          mileage      
 Min.   : 3800   Min.   :  4867  
 1st Qu.:10995   1st Qu.: 27200  
 Median :13592   Median : 36385  
 Mean   :12962   Mean   : 44261  
 3rd Qu.:14904   3rd Qu.: 55125  
 Max.   :21992   Max.   :151479  
      year      transmission      
 Min.   :2000   Length:150        
 1st Qu.:2008   Class :character  
 Median :2009   Mode  :character  
 Mean   :2009                     
 3rd Qu.:2010                     
 Max.   :2012                     

mean(c(36000, 44000, 56000))

[1] 45333.33

# the min/max of used car prices
range(usedcars$price)

[1]  3800 21992

# the difference of the range
diff(range(usedcars$price))

[1] 18192

# IQR for used car prices
IQR(usedcars$price)

[1] 3909.5

# use quantile to calculate five-number summary
quantile(usedcars$price)

     0%     25%     50%     75%    100% 
 3800.0 10995.0 13591.5 14904.5 21992.0 

# the 99th percentile
quantile(usedcars$price, probs = c(0.01, 0.99))

      1%      99% 
 5428.69 20505.00 

# quintiles
quantile(usedcars$price, seq(from = 0, to = 1, by = 0.20))

     0%     20%     40%     60%     80% 
 3800.0 10759.4 12993.8 13992.0 14999.0 
   100% 
21992.0 

# boxplot of used car prices and mileage
boxplot(usedcars$price, main="Boxplot of Used Car Prices",
      ylab="Price ($)")

boxplot(usedcars$mileage, main="Boxplot of Used Car Mileage",
      ylab="Odometer (mi.)")

# histograms of used car prices and mileage
hist(usedcars$price, main = "Histogram of Used Car Prices",
     xlab = "Price ($)")

# histograms of used car prices and mileage
hist(usedcars$price, main = "Histogram of Used Car Prices",
     xlab = "Price ($)")

# variance and standard deviation of the used car data
var(usedcars$price)

[1] 9749892

sd(usedcars$price)

[1] 3122.482

var(usedcars$mileage)

[1] 728033954

sd(usedcars$mileage)

[1] 26982.1

#EXPLORING NUMERIC VARIABLES
# one-way tables for the used car data
table(usedcars$year)

2000 2001 2002 2003 2004 2005 2006 2007 
   3    1    1    1    3    2    6   11 
2008 2009 2010 2011 2012 
  14   42   49   16    1 

table(usedcars$model)

 SE SEL SES 
 78  23  49 

# compute table proportions
model_table <- table(usedcars$model)
prop.table(model_table)

       SE       SEL       SES 
0.5200000 0.1533333 0.3266667 

# round the data
color_table <- table(usedcars$color)
color_pct <- prop.table(color_table) * 100
round(color_pct, digits = 1)

 Black   Blue   Gold   Gray  Green 
  23.3   11.3    0.7   10.7    3.3 
   Red Silver  White Yellow 
  16.7   21.3   10.7    2.0 

# scatterplot of price vs. mileage
plot(x = usedcars$mileage, y = usedcars$price,
     main = "Scatterplot of Price vs. Mileage",
     xlab = "Used Car Odometer (mi.)",
     ylab = "Used Car Price ($)")

# new variable indicating conservative colors
usedcars$conservative <-
  usedcars$color %in% c("Black", "Gray", "Silver", "White")

# checking our variable
table(usedcars$conservative)

FALSE  TRUE 
   51    99 

install.packages("gmodels")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/Adria/AppData/Local/R/win-library/4.4’
(as ‘lib’ is unspecified)
also installing the dependencies ‘gtools’, ‘gdata’

trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/gtools_3.9.5.zip'
Content type 'application/zip' length 368663 bytes (360 KB)
downloaded 360 KB

trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/gdata_3.0.1.zip'
Content type 'application/zip' length 505048 bytes (493 KB)
downloaded 493 KB

trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/gmodels_2.19.1.zip'
Content type 'application/zip' length 119350 bytes (116 KB)
downloaded 116 KB

package ‘gtools’ successfully unpacked and MD5 sums checked
package ‘gdata’ successfully unpacked and MD5 sums checked
package ‘gmodels’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\Adria\AppData\Local\Temp\RtmpMJxHc5\downloaded_packages

# Crosstab of conservative by model
library(gmodels)

CrossTable(x = usedcars$model, y = usedcars$conservative)

 
   Cell Contents
|-------------------------|
|                       N |
| Chi-square contribution |
|           N / Row Total |
|           N / Col Total |
|         N / Table Total |
|-------------------------|

 
Total Observations in Table:  150 

 
               | usedcars$conservative 
usedcars$model |     FALSE |      TRUE | Row Total | 
---------------|-----------|-----------|-----------|
            SE |        27 |        51 |        78 | 
               |     0.009 |     0.004 |           | 
               |     0.346 |     0.654 |     0.520 | 
               |     0.529 |     0.515 |           | 
               |     0.180 |     0.340 |           | 
---------------|-----------|-----------|-----------|
           SEL |         7 |        16 |        23 | 
               |     0.086 |     0.044 |           | 
               |     0.304 |     0.696 |     0.153 | 
               |     0.137 |     0.162 |           | 
               |     0.047 |     0.107 |           | 
---------------|-----------|-----------|-----------|
           SES |        17 |        32 |        49 | 
               |     0.007 |     0.004 |           | 
               |     0.347 |     0.653 |     0.327 | 
               |     0.333 |     0.323 |           | 
               |     0.113 |     0.213 |           | 
---------------|-----------|-----------|-----------|
  Column Total |        51 |        99 |       150 | 
               |     0.340 |     0.660 |           | 
---------------|-----------|-----------|-----------|