The Graphs shows us the trends with number of complaints that has been filled through 1999 and 2016. The downward patern of the Data through graphs.

The below graphs explains the number of complaints recieved by years.

 ccrb<-read.csv("D:/MyRWork/Data506/CCRBP.csv")
ggplot(ccrb, aes(ccrb$Received.Year)) + geom_bar(stat = "count")+
  labs(title = "Number of complaints received by year", x = "Received Year", y= "Number of incidents")

The below graphs tell’s us about the incidents and caomplaints closed by year.

ggplot(ccrb, aes(ccrb$Close.Year)) + geom_bar(stat = "count")+
  labs(title = "Number of complaints closed by year", x = "Close Year", y= "Number of incidents")

The below graphs explains the distribution of complaints from diferent areas of New York.

ggplot(ccrb, aes(ccrb$Borough.of.Occurrence)) + geom_bar(stat = "count")+
  labs(title = "Number of complaints occured by geographical area", x = "Area Name", y= "Number of incidents")

Number of complaints occured by different boroughs of New York. And number of Inceidents. This explains us what was the pattern’s and how did it Increase and decrease through 1999to 2016.

ggplot(ccrb, aes(ccrb$Borough.of.Occurrence)) + geom_bar(stat = "count")+
  facet_wrap(~ccrb$Received.Year)+
  labs(title = "Number of complaints occured by geographical area by year", x = "Area Name", y= "Number of incidents")+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

Below graph tells us exactly which agency is and what incidents are being recorded. So that we can understand the types of cases, and can compare it to how many cases were closed.

ggplot(ccrb, aes(ccrb$Complaint.Filed.Place)) + geom_bar(stat = "count")+
  labs(title = "Number of complaints filed by place", x = "Name of Place", y= "Number of incidents")+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

Below graphs explains the modes of communication while reporting an incident, the below graphs tells us the uneasyness of people visiting in person.

ggplot(ccrb, aes(ccrb$Complaint.Filed.Mode)) + geom_bar(stat = "count")+
  labs(title = "Number of complaints filed by mode", x = "Mode", y= "Number of incidents")

Below graph we have clubed 1999 to 2016 and the modes of communication. There has been an increase in Website but phone still being an important mode of communication.

ggplot(ccrb, aes(ccrb$Complaint.Filed.Mode)) + geom_bar(stat = "count")+
  facet_wrap(~ccrb$Received.Year)+
  labs(title = "Number of complaints filed by mode by year", x = "Mode", y= "Number of incidents")+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

This below graph is a classic example of where police should concentrate to reduce the number of incidents.

ggplot(ccrb, aes(ccrb$Incident.Location)) + geom_bar(stat = "count")+
  labs(title = "Location of Incident", x = "Location", y= "Number of incidents")+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

This graph is very disturbing for a layman like me. When we talk about difference in rich and poor this is bound to happen. Abuse of Authority is general one of the allegation where the numbers are high through out the world.

ggplot(ccrb, aes(ccrb$Allegation.FADO.Type)) + geom_bar(stat = "count")+
  labs(title = "Allegation Type", x = "Allegation Type", y= "Number of incidents")

Below graphs tells us the how many cases which were filled and how many turned out to be serious or just hoax to start with!

ggplot(ccrb, aes(ccrb$Encounter.Outcome)) + geom_bar(stat = "count")+
  labs(title = "Actions taken on complaint", x = "Action taken", y= "Number of incidents")

When we compare actions taken against Video Evidence (9th Graph) Vs Investigations (10th Graph) We can see that authority comes into action after investigation as Video is taken as evidence but not a complelling factor wich decides if its true or otherwise.

ggplot(ccrb, aes(ccrb$Encounter.Outcome)) + geom_bar(stat = "count")+
  facet_wrap(~ccrb$Complaint.Has.Video.Evidence)+
  labs(title = "Actions taken on complaint upon video evidence", x = "Action taken", y= "Number of incidents")+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

ggplot(ccrb, aes(ccrb$Encounter.Outcome)) + geom_bar(stat = "count")+
  facet_wrap(~ccrb$Is.Full.Investigation)+
  labs(title = "Actions taken on complaint upon investigation", x = "Action taken", y= "Number of incidents")+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

Summary:

This summary is based on the data on the explanatory variables: . Incidents/complaints filed: abuse, discourtesy, force, and offensive language . Various government organizations incidents/complaints were filed at . Location of the incidents/complaints in the five boroughs of New York City (Bronx, Brooklyn, Manhattan, Staten Island, and Queens) . Duration of the incidents/complaints: 1999 to 2016 Based on the explanatory variables stated, the conclusions are: . The data of the type of allegations filed over the years and the number of complaints closed by years aka allegations dealt with, roughly convert to right skewed graph over the years. . Noting that most of these allegations occurred in Brooklyn, yearly and overall . The Civilian Complaint Review Board (CCRB) had nearly as twice many complaints filed compared to the second highest place where complaints were filed . With the turn of the phone/cellphone revolution, the mode of the which the complaint was filed in followed suit, putting complaints filed by phone at #1, both yearly and overall . The specific location of the allegations occurrence was the highest in street/highway at over 120,000 (nearly 60% of the initial data) . There is a critical difference in the actions taken based on whether video evidence was available or not. However the actions taken based on investigation being similar shows that investigation into incidents/complaints is necessary. The goal of this analysis was to find ways to assist the government of New York City to decrease the occurrence of future incidents/complaints. The trend of conclusions, clearly shown using the explanatory variables, can be used by the government of New York City to take the necessary steps to accomplish the respective goal.