For this quiz, you are going to use orange juice data. This data set is originally used in a machine learning (ML) class, with the goal to predict which of the two brands of orange juices the customers bought. Of course, you are not building a ML algorithm in this quiz. I just wanted to provide you with the context of the data.

The response variable (that ML algorithm is built to predict) is Purchase, which takes either CH (Citrus Hill) or MM (Minute Maid). The predictor variables (that ML algorithm uses to make predictions) are characteristics of the customer and the product itself. Together, the data set has 18 variables.WeekofPurchase is the week of purchase. LoyalCH is customer brand loyalty for CH (how loyal the customer is for CH on a scale of 0-1), and is the only variable that characterizes customers. All other variables are characteristics of the product or stores the sale occurred at. For more information on the data set, click the link below and scroll down to page 11. https://cran.r-project.org/web/packages/ISLR/ISLR.pdf

# Load the package
library(tidyverse)
## -- Attaching packages -------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.2
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## -- Conflicts ----------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
# Import data
Orange <- read.csv('https://raw.githubusercontent.com/selva86/datasets/master/orange_juice_withmissing.csv', stringsAsFactors = TRUE) %>%
  mutate(STORE = as.factor(STORE),
         StoreID = as.factor(StoreID))

# Print the first 6 rows
head(Orange)
##   Purchase WeekofPurchase StoreID PriceCH PriceMM DiscCH DiscMM SpecialCH
## 1       CH            237       1    1.75    1.99   0.00    0.0         0
## 2       CH            239       1    1.75    1.99   0.00    0.3         0
## 3       CH            245       1    1.86    2.09   0.17    0.0         0
## 4       MM            227       1    1.69    1.69   0.00    0.0         0
## 5       CH            228       7    1.69    1.69   0.00    0.0         0
## 6       CH            230       7    1.69    1.99   0.00    0.0         0
##   SpecialMM  LoyalCH SalePriceMM SalePriceCH PriceDiff Store7 PctDiscMM
## 1         0 0.500000        1.99        1.75      0.24     No  0.000000
## 2         1 0.600000        1.69        1.75     -0.06     No  0.150754
## 3         0 0.680000        2.09        1.69      0.40     No  0.000000
## 4         0 0.400000        1.69        1.69      0.00     No  0.000000
## 5         0 0.956535        1.69        1.69      0.00    Yes  0.000000
## 6         1 0.965228        1.99        1.69      0.30    Yes  0.000000
##   PctDiscCH ListPriceDiff STORE
## 1  0.000000          0.24     1
## 2  0.000000          0.24     1
## 3  0.091398          0.23     1
## 4  0.000000          0.00     1
## 5  0.000000          0.00     0
## 6  0.000000          0.30     0
# Get a sense of the dataset
glimpse(Orange)
## Rows: 1,070
## Columns: 18
## $ Purchase       <fct> CH, CH, CH, MM, CH, CH, CH, CH, CH, CH, CH, CH, CH, ...
## $ WeekofPurchase <int> 237, 239, 245, 227, 228, 230, 232, 234, 235, 238, 24...
## $ StoreID        <fct> 1, 1, 1, 1, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 2...
## $ PriceCH        <dbl> 1.75, 1.75, 1.86, 1.69, 1.69, 1.69, 1.69, 1.75, 1.75...
## $ PriceMM        <dbl> 1.99, 1.99, 2.09, 1.69, 1.69, 1.99, 1.99, 1.99, 1.99...
## $ DiscCH         <dbl> 0.00, 0.00, 0.17, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00...
## $ DiscMM         <dbl> 0.00, 0.30, 0.00, 0.00, 0.00, 0.00, 0.40, 0.40, 0.40...
## $ SpecialCH      <int> 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
## $ SpecialMM      <int> 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1...
## $ LoyalCH        <dbl> 0.500000, 0.600000, 0.680000, 0.400000, 0.956535, 0....
## $ SalePriceMM    <dbl> 1.99, 1.69, 2.09, 1.69, 1.69, 1.99, 1.59, 1.59, 1.59...
## $ SalePriceCH    <dbl> 1.75, 1.75, 1.69, 1.69, 1.69, 1.69, 1.69, 1.75, 1.75...
## $ PriceDiff      <dbl> 0.24, -0.06, 0.40, 0.00, 0.00, 0.30, -0.10, -0.16, -...
## $ Store7         <fct> No, No, No, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Y...
## $ PctDiscMM      <dbl> 0.000000, 0.150754, 0.000000, 0.000000, 0.000000, 0....
## $ PctDiscCH      <dbl> 0.000000, 0.000000, 0.091398, 0.000000, 0.000000, 0....
## $ ListPriceDiff  <dbl> 0.24, 0.24, 0.23, 0.00, 0.00, 0.30, 0.30, 0.24, 0.24...
## $ STORE          <fct> 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2...
summary(Orange)
##  Purchase WeekofPurchase  StoreID       PriceCH         PriceMM     
##  CH:653   Min.   :227.0   1   :157   Min.   :1.690   Min.   :1.690  
##  MM:417   1st Qu.:240.0   2   :222   1st Qu.:1.790   1st Qu.:1.990  
##           Median :257.0   3   :196   Median :1.860   Median :2.090  
##           Mean   :254.4   4   :139   Mean   :1.867   Mean   :2.085  
##           3rd Qu.:268.0   7   :355   3rd Qu.:1.990   3rd Qu.:2.180  
##           Max.   :278.0   NA's:  1   Max.   :2.090   Max.   :2.290  
##                                      NA's   :1       NA's   :4      
##      DiscCH            DiscMM         SpecialCH       SpecialMM     
##  Min.   :0.00000   Min.   :0.0000   Min.   :0.000   Min.   :0.0000  
##  1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0.0000  
##  Median :0.00000   Median :0.0000   Median :0.000   Median :0.0000  
##  Mean   :0.05196   Mean   :0.1234   Mean   :0.147   Mean   :0.1624  
##  3rd Qu.:0.00000   3rd Qu.:0.2300   3rd Qu.:0.000   3rd Qu.:0.0000  
##  Max.   :0.50000   Max.   :0.8000   Max.   :1.000   Max.   :1.0000  
##  NA's   :2         NA's   :4        NA's   :2       NA's   :5       
##     LoyalCH          SalePriceMM     SalePriceCH      PriceDiff       Store7   
##  Min.   :0.000011   Min.   :1.190   Min.   :1.390   Min.   :-0.6700   No :714  
##  1st Qu.:0.320000   1st Qu.:1.690   1st Qu.:1.750   1st Qu.: 0.0000   Yes:356  
##  Median :0.600000   Median :2.090   Median :1.860   Median : 0.2300            
##  Mean   :0.565203   Mean   :1.962   Mean   :1.816   Mean   : 0.1463            
##  3rd Qu.:0.850578   3rd Qu.:2.130   3rd Qu.:1.890   3rd Qu.: 0.3200            
##  Max.   :0.999947   Max.   :2.290   Max.   :2.090   Max.   : 0.6400            
##  NA's   :5          NA's   :5       NA's   :1       NA's   :1                  
##    PctDiscMM         PctDiscCH       ListPriceDiff    STORE    
##  Min.   :0.00000   Min.   :0.00000   Min.   :0.000   0   :356  
##  1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.140   1   :157  
##  Median :0.00000   Median :0.00000   Median :0.240   2   :222  
##  Mean   :0.05939   Mean   :0.02732   Mean   :0.218   3   :194  
##  3rd Qu.:0.11268   3rd Qu.:0.00000   3rd Qu.:0.300   4   :139  
##  Max.   :0.40201   Max.   :0.25269   Max.   :0.440   NA's:  2  
##  NA's   :5         NA's   :2

Q1 How many rows are there in the Orange dataset?

. 1,070 ## Q2 Interpret Row1 of the Orange dataset.

Week of purchase its when it was purchased , Store ID is 1, loyalCH is 0.5, Salesprice is how much is was bought for and there is a .24 cent difference in price

Q3 SalePriceMM What is the median price of Minute Maid orange joice?

The median price is 2.090

Q4 StoreID How many stores are there?

There are 5 stores

Q5 SalePriceMM Graph the distribution of Minute Maid orange joice prices.

Hint: Insert a code chunk below and the code to create a histogram.

p <- ggplot(data=orange, aes(x = SalePriceMM)) + geom_histogram(binwidth=900, bins = 30)

Q6 Are the prices normally distributed? Why or why not?

This is not normally distributed because its not symetrical ## Q7 StoreID In what store, the typical Minute Maid orange juice price appears to be lowest? Create a boxplot. Hint: Insert a code chunk below and the code to create a boxplot. See Data Visualization with R: Ch4.3.3 Box plots. Map SalePriceMM to the y-axis and StoreID to the x-axis. The typical value is represented by the median, the thick horizontal line inside the box.

Q8 Hide the messages, but display the code and its results on the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.