Law firm analysis
Joyce Escatel-Flores
2025-10-13
Introduction
Hello! My name is Joyce Escatel-Flores and we are working as data scientists for a law firm analyzing NYC violation data. The NYC violation data can be found here The firm who hired us for this task wants to uncover hidden patterns in the data to inform their marketing strategy.
So far we have examined patterns by day of the week, time of day, and violation type. The firm wants us to do further investigation by exploring three questions:
Do certain agencies issue higher payments?
Do drivers from different states (NY, NJ, CT) pay more?
Do certain counties tend to have higher payment amounts?
Data
In this section, I ran some code to load the data for the analysis.
endpoint <-"https://data.cityofnewyork.us/resource/nc67-uf89.json"
resp<-GET(endpoint, query=list(
"$limit"=99999,
"$order"="issue_date DESC"
))
camera<- fromJSON(content(resp, as="text"), flatten=TRUE)
view(camera)Data cleaning
In this step, we will now clean the county column to replace any abbreviations with the full county names
camera<-camera %>%
mutate(county=case_when(
county=="Q" ~"Queens County",
county=="Qns" ~"Queens County",
county=="QN" ~"Queens County",
county=="K"~"Kings County",
county=="BK"~"Kings County",
county=="Kings"~"Kings County",
county=="NY"~"New York County",
county=="BX"~"Bronx County",
county=="Bronx"~"Bronx County",
county=="R"~"Richmond County",
county=="RICH"~"Richmond County",
county=="ST"~"Staten Island County",
county=="MN"~"Monroe County",
TRUE~county
))camera<- camera%>%
mutate(across(
c("payment_amount"),
~as.numeric(.)
))Question 1: Do certain agencies issue higher payments?
###Descriptive Statistics
Agency_Statistics<- favstats(payment_amount ~ issuing_agency, data = camera) %>% arrange(desc(mean))
Agency_Statistics## issuing_agency min Q1 median Q3 max
## 1 HEALTH DEPARTMENT POLICE 243.81 243.810 243.81 243.8100 243.81
## 2 SEA GATE ASSOCIATION POLICE 190.00 190.000 190.00 190.0000 190.00
## 3 FIRE DEPARTMENT 180.00 180.000 180.00 180.0000 180.00
## 4 NYS OFFICE OF MENTAL HEALTH POLICE 0.00 180.000 180.00 190.0000 210.00
## 5 ROOSEVELT ISLAND SECURITY 0.00 135.000 180.00 190.0000 246.68
## 6 PORT AUTHORITY 0.00 180.000 180.00 190.0000 242.76
## 7 NYS PARKS POLICE 0.00 45.000 180.00 190.0000 242.58
## 8 PARKS DEPARTMENT 0.00 90.000 180.00 190.0000 245.28
## 9 TAXI AND LIMOUSINE COMMISSION 125.00 125.000 125.00 125.0000 125.00
## 10 HEALTH AND HOSPITAL CORP. POLICE 0.00 0.000 180.00 190.0000 245.64
## 11 POLICE DEPARTMENT 0.00 0.000 180.00 190.0000 260.00
## 12 CON RAIL 0.00 0.000 95.00 228.8875 243.87
## 13 DEPARTMENT OF TRANSPORTATION 0.00 50.000 75.00 125.0000 690.04
## 14 TRAFFIC 0.00 65.000 115.00 115.0000 245.79
## 15 OTHER/UNKNOWN AGENCIES 0.00 40.115 80.23 120.3450 160.46
## 16 TRANSIT AUTHORITY 0.00 0.000 75.00 125.0000 190.00
## 17 SUNY MARITIME COLLEGE 65.00 65.000 65.00 65.0000 65.00
## 18 NYC OFFICE OF THE SHERIFF 0.00 28.750 57.50 86.2500 115.00
## 19 DEPARTMENT OF SANITATION 0.00 0.000 65.00 105.0000 115.00
## 20 LONG ISLAND RAILROAD 0.00 0.000 0.00 0.0000 0.00
## mean sd n missing
## 1 243.81000 NA 1 0
## 2 190.00000 0.00000 2 0
## 3 180.00000 NA 1 0
## 4 161.33333 65.99423 15 0
## 5 149.16083 90.57967 24 0
## 6 147.35792 82.58394 48 0
## 7 143.86176 89.24158 34 0
## 8 128.47736 78.92728 144 0
## 9 125.00000 NA 1 0
## 10 124.71373 98.60130 51 0
## 11 123.93855 88.00388 214 0
## 12 112.62000 124.87146 6 0
## 13 99.52822 82.88394 87273 0
## 14 94.59362 44.47453 12091 0
## 15 80.23000 113.46235 2 0
## 16 78.00000 82.05181 5 0
## 17 65.00000 NA 1 0
## 18 57.50000 81.31728 2 0
## 19 56.78571 48.26239 14 0
## 20 0.00000 NA 1 0###Inferential Statistics
anova_model_IA<-aov(payment_amount ~ issuing_agency, data = camera)
summary(anova_model_IA)## Df Sum Sq Mean Sq F value Pr(>F)
## issuing_agency 19 937675 49351 7.858 <2e-16 ***
## Residuals 99910 627464684 6280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingnesssupernova(anova_model_IA)## Refitting to remove 69 cases with missing value(s)
## ℹ aov(formula = payment_amount ~ issuing_agency, data = listwise_delete(camera,
## c("payment_amount", "issuing_agency")))## Analysis of Variance Table (Type III SS)
## Model: payment_amount ~ issuing_agency
##
## SS df MS F PRE p
## ----- --------------- | ------------- ----- --------- ----- ----- -----
## Model (error reduced) | 937675.432 19 49351.339 7.858 .0015 .0000
## Error (from model) | 627464683.951 99910 6280.299
## ----- --------------- | ------------- ----- --------- ----- ----- -----
## Total (empty model) | 628402359.383 99929 6288.488###Interpretation
In the ANOVA we just conducted, the variance explained by the sum of squares using the formula SSerror/SStotal gives us 0.99. This sum of squares does account for a large amount of variance. The proportion of variance using the formula SSmodel/SStotal is 0.002, which does not explain the variation portion of the data. The F value is 8.004 with a p value of .0000 which gives us a significant results. There are differences between agencies issuing higher payments.
###Visualization
ggplot(camera, aes(x=issuing_agency, y=payment_amount)) +
geom_boxplot(fill = "blue", color = "black") +
coord_flip() +
labs(
title ="Agencies issuing payments",
x="Agency",
y="payment") +
theme(plot.title = element_text(size=20, family="serif", face="bold"),
axis.title = element_text(size=15, family ="serif"),
axis.text = element_text(size = 10, family = "serif"))## Warning: Removed 65 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
Interpretation
The ANOVA performed showed that agency is statistically significant indicator for payment amount. Differences between the agencies are shown. The law firm might consider using the agency in their marketing strategy if they choose to look at agencies who have high payment amount.
Question 2: Do drivers from different states (NY, NJ, CT) pay more?
Descriptive Statistics
camera<- camera %>%
filter(state %in% c("NY","NJ","CT"))
Drivers_Statistics<- favstats(payment_amount ~ state, data = camera) %>% arrange(desc(mean))
Drivers_Statistics## state min Q1 median Q3 max mean sd n missing
## 1 NJ 0 50 75 115 682.35 101.5746 89.97170 8654 3
## 2 NY 0 50 75 125 690.04 101.0902 80.93015 79541 10
## 3 CT 0 50 75 100 276.57 80.6627 46.07849 1457 2Inferential Statistics
anova_model_Drivers<-aov(payment_amount ~ state, data = camera)
summary(anova_model_Drivers)## Df Sum Sq Mean Sq F value Pr(>F)
## state 2 602716 301358 45.48 <2e-16 ***
## Residuals 89649 594098897 6627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingnesssupernova(anova_model_Drivers)## Refitting to remove 15 cases with missing value(s)
## ℹ aov(formula = payment_amount ~ state, data = listwise_delete(camera,
## c("payment_amount", "state")))## Analysis of Variance Table (Type III SS)
## Model: payment_amount ~ state
##
## SS df MS F PRE p
## ----- --------------- | ------------- ----- ---------- ------ ----- -----
## Model (error reduced) | 602716.142 2 301358.071 45.475 .0010 .0000
## Error (from model) | 594098896.889 89649 6626.944
## ----- --------------- | ------------- ----- ---------- ------ ----- -----
## Total (empty model) | 594701613.031 89651 6633.519Interpretation
In the ANOVA we just conducted, the variance explained by the sum of squares using the formula SSerror/SStotal gives us 0.999. This sum of squares does account for a large amount of variance. The proportion of variance using the formula SSmodel/SStotal is 0.0007, which does not explain the variation portion of the data. The F value is 18.552 with a p value of .0000 which gives us a significant results. Drivers that come from different states do pay more.
Visualization
ggplot(camera, aes(x=state, y=payment_amount)) +
geom_boxplot(fill = "orange", color = "black") +
coord_flip() +
labs(
title ="Drivers payment by state",
x="State",
y="payment") +
theme(plot.title = element_text(size=20, family="serif", face="bold"),
axis.title = element_text(size=15, family ="serif"),
axis.text = element_text(size = 10, family = "serif"))## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
Interpretation
The ANOVA performed showed that Drivers from different states do pay higher, specifically those in New Jersey. State is statistically significant indicator for payment amount. Differences between the states payment amount are shown. The law firm should not really use this variable as it limits them to only certain states.
Question 3: Do certain counties tend to have higher payment amounts?
Descriptive Statistics
camera<- camera %>%
filter(!is.na(county))
County_Statistics<- favstats(payment_amount ~ county, data = camera) %>% arrange(desc(mean))
County_Statistics## county min Q1 median Q3 max mean sd n
## 1 Richmond County 0 65 180 180.0 245.79 138.80005 80.46141 811
## 2 Kings County 0 50 75 115.0 690.04 115.38500 132.61340 14184
## 3 Monroe County 0 50 75 150.0 280.38 102.46441 74.50960 13476
## 4 Bronx County 0 65 85 167.5 245.64 101.71333 66.51450 222
## 5 New York County 0 65 115 115.0 260.00 91.60696 38.32289 8144
## 6 Queens County 0 50 50 100.0 283.03 84.12366 60.74257 15897
## 7 Staten Island County 0 50 50 75.0 250.00 67.43513 41.86493 425
## missing
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0Inferential Statistics
anova_model_County<-aov(payment_amount ~ county, data = camera)
summary(anova_model_County)## Df Sum Sq Mean Sq F value Pr(>F)
## county 6 9642588 1607098 212.6 <2e-16 ***
## Residuals 53152 401810433 7560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1supernova(anova_model_County)## Analysis of Variance Table (Type III SS)
## Model: payment_amount ~ county
##
## SS df MS F PRE p
## ----- --------------- | ------------- ----- ----------- ------- ----- -----
## Model (error reduced) | 9642588.258 6 1607098.043 212.589 .0234 .0000
## Error (from model) | 401810432.596 53152 7559.648
## ----- --------------- | ------------- ----- ----------- ------- ----- -----
## Total (empty model) | 411453020.854 53158 7740.190Interpretation
In the ANOVA we just conducted, the variance explained by the sum of squares using the formula SSerror/SStotal gives us 0.977. This sum of squares does account for a large amount of variance. The proportion of variance using the formula SSmodel/SStotal is 0.02, which does not explain for a large variation portion of the data. The F value is 212.589 with a p value of .0000 which gives us a significant results. Some counties do tend to have higher payment amounts.
Visualization
ggplot(camera, aes(x=county, y=payment_amount)) +
geom_boxplot(fill = "green", color = "black") +
coord_flip() +
labs(
title ="County Payment Amounts",
x="County",
y="payment") +
theme(plot.title = element_text(size=20, family="serif", face="bold"),
axis.title = element_text(size=15, family ="serif"),
axis.text = element_text(size = 10, family = "serif"))
Interpretation
The ANOVA performed showed that different counties tend to have higher payment amounts, specifically Richmond County. The county variable is a statistically significant indicator for payment amount. Differences between the county payment amount are shown. The law firm should use this variable to investigate further which counties have the highest payment and use in their marketing strategies.
Final Recommendation
Out of the three variables we have explored today, I would recommend that the law firm further explores with county. Although all three variables were significant, county had the higher F value and largest portion of variance explained.