First, a note to the reader. This is a two part series. This part we will look at some data reported to the FTC and in the second part, I have a surprise of sorts that will be ruined, if I give too much information. Reddington is back just because he’s ridiculously cute. He has nothing to do with scamming, but if you look into his adorable little eyes, it makes you thankful he is spared from this harm humans inflict on other humans.

The FTC files complaints from individuals who claim to have been scammed by someone posing as a government representative. What do we mean by scammed? Scamming can come in many different forms, but let’s talk about the general concept first and then provide some examples. According to the Merriam Webster online dictionary, a scam is “to obtain (something, such as money) by a scam,” and I believe defining the word with the word is never a good practice, so I am going to combine both definitions provided by Merriam Webster and here is the finalized definition, “to obtain (something, such as money) by” an act of deception. This definition is very broad so let’s look at first the list provided by the FTC of different cases they have received.

1. Most recently, the FTC stated that these government imposters were posing as companies providing Coronavirus treatment.
2. People claimed being contacted by someone claiming to be a member of “the government or a well-known business, a romantic interest, or a family member with an emergency” were received by consumers in 2019 and reported to the FTC.  These consumers reported a total of “$667 million” in losses.
3. Within the scams that occurred that were labeled government imposters for 2019, the top imposter scam reported were Social Security imposters. There were “166,190 reports about Social Security scam, and the median individual loss was $1,500” (consumer.FTC.gov).

So how do these individuals scam people out of their money? “Gift cards” are reported to be the payment of choice (consumer.FTC.gov). Now that you have some context, let’s look at a small sample of this massive collection of data, and let’s focus on the Ohio fraud and theft reports from 2018. This analysis was completed in R and is available on my github repository (github.com/sterlingn/scammerdata).

A note to the reader on formatting: If you read this on a phone or tablet the scatter plots and bar charts should show with little issue, however, if you are viewing on a desktop, the images will appear ginormous. To get a better view of the scatter plots and the bar charts, simply click on the image and you can change the scale yourself.

library(tidyverse)
library(ggplot2)
library(sqldf)

Ohiofraud <- read.csv("Desktop:/Ohio_normalized.csv")

knitr::kable(Ohiofraud, caption = "Ohio Fraud")

knitr::kable((Ohiofraud[1:3,] %>%
  arrange(desc(reports))), caption ="Cities with the Highest Number of Incidents")

Ohiofraud %>%filter(str_detect(metro_area,"Cincinn")

mean(Ohiofraud$reports)# 559.9375

summary(Ohiofraud$reports)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   454.0   487.8   573.5   559.9   600.5   714.0

d <- Ohiofraud$reports

View(d)

qplot(Ohiofraud$reports,Ohiofraud$metro_area)

Now let’s also look at add a second column which will contain the data on id theft. I will need to revert to SQL for this query.

Ohioidtheft <- read.csv("Desktop:/Ohio_idtheft_BCNF.csv")

View(Ohioidtheft)


Ohioidtheft %
  rename(metro_area_t=Metropolitan.Area)%>%
  rename(reportsoftheft= X) %>%
  rename(state=Reports.per.100K.Population)

sqldf("SELECT *
      FROM Ohioidtheft")

##                  metro_area_t                                   state
## 1            Cleveland-Elyria        OH Metropolitan Statistical Area
## 2                    Columbus        OH Metropolitan Statistical Area
## 3                      Dayton        OH Metropolitan Statistical Area
## 4                       Akron        OH Metropolitan Statistical Area
## 5                      Toledo        OH Metropolitan Statistical Area
## 6  Youngstown-Warren-Boardman     OH-PA Metropolitan Statistical Area
## 7                  Cincinnati  OH-KY-IN Metropolitan Statistical Area
## 8                     Wooster        OH Micropolitan Statistical Area
## 9            Canton-Massillon        OH Metropolitan Statistical Area
## 10                  Mansfield        OH Metropolitan Statistical Area
## 11                       Lima        OH Metropolitan Statistical Area
## 12                      Salem        OH Micropolitan Statistical Area
## 13                Springfield        OH Metropolitan Statistical Area
## 14       Weirton-Steubenville     WV-OH Metropolitan Statistical Area
## 15         Huntington-Ashland  WV-KY-OH Metropolitan Statistical Area
## 16                   Wheeling     WV-OH Metropolitan Statistical Area
##    reportsoftheft
## 1             226
## 2             121
## 3             116
## 4             115
## 5             104
## 6              96
## 7              95
## 8              88
## 9              77
## 10             73
## 11             72
## 12             68
## 13             64
## 14             59
## 15             51
## 16             43

Do we have a primary key? Is it unique, irreducible, and not null… There should be 16 rows in this next query.

sqldf("SELECT DISTINCT metro_area_f
      FROM Ohiofraud")

So the easiest way I know of as of now, is to sort based on my primary key and add the data that way. If I had more data, I would need to first create a database, but for our purposes, this is fine.

sqldf("SELECT *
      FROM Ohiofraud f
      LEFT JOIN Ohioidtheft t
      ON metro_area.f = metro_area.t")#Error: No such column: metro_area.f?

Since the SQL join statment isn’t working. I will have to resort to the join function within…tidyverse. I am not going to show all of the queries I ran, but I used the sample queiries for the tidyverse package to figure out how to apply the same method to my dataset.

Using left join?

View(Ohiofraud)
View(Ohioidtheft)

Ohio_reports %
left_join( Ohioidtheft, by=c("metro_area_f"="metro_area_t"))

View(Ohio_reports)

I need to remove a column and rename a column. For Simplicities sake we will call our collection of data ‘r’

r %
  rename(metro_area= Ohio_reports.metro_area_f) %>%
  rename(state=Ohio_reports.state.x) %>%
  rename(fraud_reports=Ohio_reports.reports) %>%
  rename(theft_reports=Ohio_reports.reportsoftheft)
)

r %
  rename(metro_area= Ohio_reports.metro_area_f) %>%
  rename(state=Ohio_reports.state.x) %>%
  rename(fraud_reports=Ohio_reports.reports) %>%
  rename(theft_reports=Ohio_reports.reportsoftheft)

View(r)

Now we finally have our dataset to work with. Truly 80% of the work.

rp <- ggplot(r, aes(r$metro_area, r$fraud_reports))

rp+ geom_point(color = "blue")+coord_flip()

rp2 <- ggplot(r, aes(r$metro_area, r$theft_reports))
 
rp2 + geom_point(color ="red")+coord_flip()

What is the correlation between the two variables?

cor(r$theft_reports, r$fraud_reports)

## [1] 0.7238463

# 0.7238 

rp <- ggplot(r, aes(r$theft_reports, r$fraud_reports, colour = r$metro_area))

rp+geom_point(mapping = aes(color=r$metro_area))+
  labs(title = "The Imperfect High Fraud High Theft Relationship",
         subtitle = "The relationship between fraud reports and theft reports.")

As you can see there is somewhat of an upward trend. If the fraud reports increase, so do the theft reports. Although a definite outlier does seem to be Cleveland-Elyria.

theft<- ggplot(r, aes(r$metro_area,r$theft_reports, colour = r$metro_area))

theft+coord_flip()+geom_col()

fraud<- ggplot(r, aes(r$metro_area,r$fraud_reports, colour = r$metro_area))

fraud+coord_flip()+geom_col()

Clevland-ELyria also has the highest number of reports here too with 714 fraud repots.

Take a look at more data at FTC.gov/data. Check out their incredible Tableau vizzes here: https://www.ftc.gov/enforcement/data-visualizations/explore-data.

https://rpubs.com/natester

Thank you for reading and stay tuned for part 2!!!

References:

1. Merriam Webster Online Dictionary https://www.merriam-webster.com/dictionary/scam
2. https://www.consumer.ftc.gov/blog/2020/03/ftc-fda-warnings-sent-sellers-scam-coronavirus-treatments
3. https://www.consumer.ftc.gov/blog/2020/01/top-frauds-2019
4. ​https://www.consumer.ftc.gov/features/feature-0037-imposter-scams
5. Tableau Interactive Viz: https://public.tableau.com/profile/federal.trade.commission#!/vizhome/GovernmentImposter/Infographic
6. https://www.ftc.gov/enforcement/data-visualizations/explore-data