Load Packages

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(tinytex)
theme_set(theme_bw(16))

List the Files

getwd()
## [1] "C:/Users/MCuser/Desktop/DATA110_CourseMaterials"
setwd("C:/Users/MCuser/Desktop/DATA110_CourseMaterials")
list.files()
##  [1] "Data 110 syllabus sum22(2).pdf"                                                                           
##  [2] "eCig.csv"                                                                                                 
##  [3] "eCigHW---Submitted.docx"                                                                                  
##  [4] "eCigHW---Submitted.html"                                                                                  
##  [5] "eCigHW---Submitted.Rmd"                                                                                   
##  [6] "eCigHW - Submitted.Rmd"                                                                                   
##  [7] "eCigHW.html"                                                                                              
##  [8] "eCigHW.Rmd"                                                                                               
##  [9] "HW 1 - Reputable Sources"                                                                                 
## [10] "HW 2 - Elevator Pitch"                                                                                    
## [11] "HW 3 - Five Plots"                                                                                        
## [12] "HW 4 - Hate Crimes Data"                                                                                  
## [13] "HW 5 - Flights"                                                                                           
## [14] "HW 6 - eCigarette Project"                                                                                
## [15] "rmarkdown-reference.pdf"                                                                                  
## [16] "rsconnect"                                                                                                
## [17] "Week 1 Notes - Intro and Markdown and Rpubs sum.pdf"                                                      
## [18] "Week 2 Notes - Data 110 unit 2 Using GitHub and handling data sum21.docx"                                 
## [19] "Week 2 Notes - Data 110 unit 2 Using GitHub and handling data sum21.pdf"                                  
## [20] "Week 2 Screenshots.docx"                                                                                  
## [21] "Week 3 Notes - Data 110 unit 3 ethics p-hacking reproducibility heatmap treemap streamgraph sum21(1).docx"
## [22] "Week 3 Notes - Data 110 unit 3 ethics p-hacking reproducibility heatmap treemap streamgraph sum21.pdf"    
## [23] "Week 3 Screenshots.docx"                                                                                  
## [24] "Week 4 Screenshots.docx"

Data Dimensions

eCig <- read.csv("eCig.csv")
dim(eCig)
## [1] 2080   11

Structure of the Data

str(eCig)
## 'data.frame':    2080 obs. of  11 variables:
##  $ Filing.Period  : chr  "3/18/2022" "3/18/2022" "3/18/2022" "3/18/2022" ...
##  $ Taxable.Amount : num  13659 238 166 46264 0 ...
##  $ Total.AmountDue: num  4097.6 71.2 49.9 13879.3 0 ...
##  $ Date.Paid      : chr  "4/16/2018" "4/18/2018" "4/4/2018" "4/20/2018" ...
##  $ Name           : chr  "MJ Vape LLC" "C&S Wholesale Grocers, Inc. (AWI)" "Delhaize America Distribution, LLC" "AVAIL Vapor, LLC" ...
##  $ Street.Address : chr  "19328 Montgomery Village Ave" "10 Optical Avenue" "1703 East D Street" "820 Southlake Blvd" ...
##  $ Street.Address2: chr  "" "" "" "" ...
##  $ City           : chr  "Gaithersburg" "Keene" "Butner" "Richmond" ...
##  $ State          : chr  "Maryland" "New Hampshire" "NC" "Virginia" ...
##  $ Zip            : chr  "20886" "3431" "27509" "23236" ...
##  $ Location1      : chr  "19328 Montgomery Village Ave\nGaithersburg, Maryland 20886\n(39.173257, -77.203734)" "10 Optical Avenue\nKeene, New Hampshire 03431\n(42.922316, -72.263149)" "1703 East D Street\nButner, NC 27509\n(36.157157, -78.730843)" "820 Southlake Blvd\nRichmond, Virginia 23236\n(37.503787, -77.59775)" ...

Convert Date Characters into Date Format

strDates <- c("01/05/1965", "08/16/1975")
dates <- as.Date(strDates, "%m/%d/%Y")

Calculating Summary Statistics

mean(eCig$Total.AmountDue)
## [1] 3089.262
median(eCig$Total.AmountDue)
## [1] 371.21
sd(eCig$Total.AmountDue)
## [1] 9503.848

Select only certain eCig data

eCig2 <- eCig %>%
  select(Taxable.Amount, Total.AmountDue, City, State) %>%
  group_by(City, State)
head(eCig2)
## # A tibble: 6 × 4
## # Groups:   City, State [6]
##   Taxable.Amount Total.AmountDue City            State        
##            <dbl>           <dbl> <chr>           <chr>        
## 1         13659.          4098.  Gaithersburg    Maryland     
## 2           238.            71.2 Keene           New Hampshire
## 3           166.            49.9 Butner          NC           
## 4         46264.         13879.  Richmond        Virginia     
## 5             0              0   Madison Heights VA           
## 6             0              0   Rockville       MD

Check for Missing Values

dim(eCig2)
## [1] 2080    4

View Selected Data

view(eCig2)

Summary Data

summary(eCig2)
##  Taxable.Amount   Total.AmountDue       City              State          
##  Min.   :     0   Min.   :    0.0   Length:2080        Length:2080       
##  1st Qu.:     0   1st Qu.:    0.0   Class :character   Class :character  
##  Median :  1237   Median :  371.2   Mode  :character   Mode  :character  
##  Mean   : 10298   Mean   : 3089.3                                        
##  3rd Qu.:  5677   3rd Qu.: 1703.2                                        
##  Max.   :287233   Max.   :86169.8

Summarize for Top 10 Instances of Total Amount Due by City

eCig2 %>%
  group_by(City) %>%
  summarize(Total.AmountDue = n()) %>%
  arrange(desc(Total.AmountDue)) %>%
  head(10)
## # A tibble: 10 × 2
##    City            Total.AmountDue
##    <chr>                     <int>
##  1 Gaithersburg                338
##  2 Rockville                   245
##  3 Baltimore                   162
##  4 Keene                       158
##  5 silver spring                87
##  6 Fredericksburg               81
##  7 Halethorpe                   81
##  8 Mountville                   81
##  9 Westlake                     81
## 10 Madison Heights              80

Summarize for Top 10 Instances Taxable Amount by City

eCig2 %>%
  group_by(City) %>%
  summarize(Taxable.AmountDue = n()) %>%
  arrange(desc(Taxable.AmountDue)) %>%
  head(10)
## # A tibble: 10 × 2
##    City            Taxable.AmountDue
##    <chr>                       <int>
##  1 Gaithersburg                  338
##  2 Rockville                     245
##  3 Baltimore                     162
##  4 Keene                         158
##  5 silver spring                  87
##  6 Fredericksburg                 81
##  7 Halethorpe                     81
##  8 Mountville                     81
##  9 Westlake                       81
## 10 Madison Heights                80

Plot Total Amount Due by City

ggplot(data = eCig2) + 
  stat_summary(
    mapping = aes(x = City, y = Total.AmountDue/1000),
    fun.min = min,
    fun.max = max,
    fun = median) +
  theme(axis.text.x = element_text(angle = 90)) +
  ggtitle("Total Excise Tax Due by City")

Plot Taxable Amount by City

ggplot(data = eCig2) + 
  stat_summary(
    mapping = aes(x = City, y = Taxable.Amount/1000),
    fun.min = min,
    fun.max = max,
    fun = median) +
  theme(axis.text.x = element_text(angle = 90)) +
  ggtitle("Total Taxable Amount by City")

Sum of Taxable Amount Due by City

data <- tibble::as_tibble(eCig2)
data %>%
  group_by(City) %>%
  summarize(sum_Taxable.Amount = sum(Taxable.Amount))
## # A tibble: 32 × 2
##    City        sum_Taxable.Amount
##    <chr>                    <dbl>
##  1 Addison                     0 
##  2 Asheville               17512.
##  3 Baltimore              249492.
##  4 Beloit                    320.
##  5 Bensenville             86332.
##  6 Bentonville             31283.
##  7 Bethesda                83670.
##  8 Butner                   9440.
##  9 Costa Mesa              20755.
## 10 Damascus               223094.
## # … with 22 more rows

Plot Sum of Total Amount Due and Taxable Amount By City

ggplot(data = eCig2) +
  geom_col(aes(x = factor(City), y = Taxable.Amount/1000), 
           fill = "blue", width = 0.3) +
  geom_col(aes(x = City, y = Total.AmountDue/1000), 
           alpha = 0.3, fill = "red", width = 0.6) +
  labs(title = "Total Excise Tax Due and Taxable Amount By City", x = "City", y = "Dollars per 1,000") +
  theme(axis.text.x = element_text(angle = 90))

## ECig Excise Tax Essay

The e-cigarette Excise Tax dataset includes information about the excise tax paid by wholesale retailers that distribute e-cigarettes. The tax is imposed on any product containing or delivering nicotine or used to simulate smoking including e-cigarettes, e-pipe, e-cigar, e-hookah, or vape pens. Until 2015, the e-cigarette was not subjected to state tobacco taxes. Montgomery County, Maryland became the first county in the country to adopt an excise tax on e-cigarettes when the County Council approved a 30% excise tax on the wholesale price. In 2021, the excise tax was raised to 60% of the wholesale price.

According to the IRS, legislators have three needs to address when considering tax laws. The need to raise revenue, be fair to taxpayers, and influence taxpayer behavior. The taxes on e-cigarettes are commonly referred to as a “sin tax” and are meant to discourage risky behavior and consumption. Before increasing the excise tax, Montgomery County Public Schools and the local police department were looking for ways to crack down on school-age children vaping. The county tried suing the manufacturer, information campaigns about the dangers of vaping, and issuing citations to children found with vape pens.

The dataset was found on the Open Data Portal on the Montgomery County Data website. The dataset is updated daily and covers the date paid beginning 9/4/2015 – 6/16/2022. There are 2,080 entries and 11 categories that include the wholesale distributor, date paid, taxable amount, the total amount due, and the filing period. I was interested in where the wholesale distributors are located to observe if the excise tax funds were coming from within Montgomery County, Maryland, or outside of the jurisdiction.

As part of the data clean-up, I created eCig2 with 2,080 entries and four categories: taxable amount, the total amount due, city, and state. Limiting the visualization to these fields allowed me to group the taxes by city. The top 10 instances of the taxable amount by the city are shown on line 108. These are the wholesalers that had the highest number of transactions that generated excise tax. Of the 2,080 transactions, 338 originated in Gaithersburg, and 245 originated in Rockville, which represents 28% of the transactions recorded in the dataset. Within the top 10, 43% of the transactions took place with wholesalers in Maryland.

The first graph shows the total excise tax due by the city. The second graph shows the taxable amount by city. The third graph combines the graph using a bar graph overlay. I did not have success with producing the legends. The total excise tax due is shown in red and the taxable amount by the city is shown in blue.

An unexpected outcome appears in the plot on line 123. The graph shows the total excise tax due among cities. Fredericksburg, Virginia is an outlier with the highest amount of excise tax due. The dataset includes the full addresses of each wholesaler and there is only one business listed in Fredericksburg, so I did a search to see the location. The location is a truck distribution stop for McLane/Mid-Atlantic, Inc. which is a grocery wholesaler to convenience stores, mass merchants, and cigarette outlets.

If I had better mastery of coding, I would look at excise tax due over time since 2015. That graphic would show how much has been collected since the implementation of the tax which might generate questions about how the money is spent in the community.