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 Describe a situation when the mean is an appropriate measure of centrality.

The mean is usually the best measure of centrality to use when your data is distribution is continuous and symmetrical. When data is normally distributed.

Q2 SalePriceCH Calculate the mean price of Citrus Hill orange joice.

Hint: Code it so that the outcome is a scalar, not a data frame. It’s the same code you learned in Quiz3-b. Save the result under mean_pr.

mean_pr <- mean((Orange$SalePriceCH) , na.rm = TRUE)
mean_pr
## [1] 1.815519

Q3 SalePriceCH Calculate the median price of Citrus Hill orange joice.

Hint: Replace mean with median in the code in Q2. Save the result under median_pr.

median_pr <- median((Orange$SalePriceCH) , na.rm = TRUE)
median_pr
## [1] 1.86

Q4 SalePriceCH Plot Citrus Hill orange juice prices in a histogram.

Hint: Refer to the code in Data Visualization with R: Ch3.2.1 Histogram.

  ggplot(Orange, aes(x = SalePriceCH)) +
  geom_histogram(fill = "black", 
                 color = "white") + 
  labs(title="CitrusHill OJ Prices",
       x = "Price")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (stat_bin).

Q5 Add the vertical lines of mean_pr and median_pr in the histogram.

Hint: Copy the code from Q4 and add two lines of the geom_vline() function in the code for vertical lines of the mean and the median home prices. Google geom_vline() for its documentation.

ggplot(Orange, aes(x = SalePriceCH)) +
  geom_histogram(fill = "black", 
                 color = "white") + 
  labs(title="Citrus Hill OJ Prices",
       x = "Sale Price") +
  geom_vline(xintercept = mean_pr, color = 'red') +
  geom_vline(xintercept = median_pr, color = 'blue')
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (stat_bin).

Q6 Which of the two measures would be more apprrpriate to represent the typical price? Why?

The median would be more appropriate because there are a lot of sales on the blue line and there are none on the red.

Q7 Law of Large Numbers We learned that the sample mean is not likley to be representative of the population mean when a sample is too small. Explain why?

If we have a town population of 50,000, and approximately 55% of their ages are between 25-44. The sample size is 5,000. If your 10% sample size does not include any of the majority adult ages, it is misrepresenting the population by not accounting for the majority of the town and the data would be falsely skewed.

Q8 Hide the messages and warnings, 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.