Renamed Cherry Hill to Federal Hill

data_cleaning.rmd

@@ -44,6 +44,7 @@ cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2"
 
 Let's begin by loading the three data files in. Note that this data is from fall 2017. 
 We have data on: calls, arrests, and victims. Calls is a very large file, and arrests has pretty messy data, so we'll do a separate rmd of EDA on those.
+
 ```{r importing data}
 victims <- read.csv("Data/BPD_Part_1_Victim_Based_Crime_Data.csv")
 ```
@@ -354,7 +355,7 @@ victims$Location.1 <- NULL
 rm(latLon)
 ```
 
-####Weapons
+#### Weapons
 
 ```{r}
 #Grab em

cherryHillExploration.rmd → federalHillExploration.rmd

@@ -1,5 +1,5 @@
 ---
-title: "An Analysis of Crime in Cherry Hill"
+title: "An Analysis of Crime in Federal Hill"
 author: "Jason Hammett"
 date: "February 15, 2018"
 output: html_document
@@ -39,54 +39,55 @@ cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2"
 #scale_colour_manual(values=cbPalette)
 ```
 
-## Cherry Hill: A Neighborhood Level Analysis
+## Federal Hill: A Neighborhood Level Analysis
 
 
 To begin, I'll load the data cleaned in data_cleaning.rmd
+
 ```{r data import}
 load("cleanedData.RData")
 ```
 
 
- I'll grab the cherryHill neighborhood from the data set
-```{r cherryHill}
-cherryHill <- victims[which(victims$Neighborhood == "Cherry Hill"),]
+ I'll grab the Federal Hill neighborhood from the data set
+```{r federalHill}
+federalHill <- victims[which(victims$Neighborhood == "Federal Hill"),]
 ```
 
 I'm going to utilize ggmap to visualize these crimes.
-```{r mapping cherry hill}
-#Grab a map centered on the cherry hill neighborhood
-cherryHillMap <- get_map("cherry hill, baltimore, maryland", zoom = 15)
+```{r mapping federal hill}
+#Grab a map centered on the federal hill neighborhood
+federalHillMap <- get_map("federal hill, baltimore, maryland", zoom = 16)
 #Plot all crimes
-mapPoints <- ggmap(cherryHillMap) + geom_point(aes(x = Longitude, y = Latitude), data = cherryHill, alpha = .25)
+mapPoints <- ggmap(federalHillMap) + geom_point(aes(x = Longitude, y = Latitude), data = federalHill, alpha = .25)
 #Titles
-mapPoints <- mapPoints + ggtitle("Crime in Cherry Hill") + ylab("Latitude") + xlab("Longitude")
+mapPoints <- mapPoints + ggtitle("Crime in Federal Hill") + ylab("Latitude") + xlab("Longitude")
 
 #Display it
 mapPoints
 
-mapPoints2 <- ggmap(cherryHillMap) + geom_point(aes(x = Longitude, y = Latitude, colour = Weapon), data = cherryHill, alpha = .75) + scale_colour_manual(values=cbbPalette)
-mapPoints2 <- mapPoints2 + ggtitle("Crime in Cherry Hill by Weapon Used") + ylab("Latitude") + xlab("Longitude")
+mapPoints2 <- ggmap(federalHillMap) + geom_point(aes(x = Longitude, y = Latitude, colour = Weapon), data = federalHill, alpha = .75) + scale_colour_manual(values=cbbPalette)
+mapPoints2 <- mapPoints2 + ggtitle("Crime in Federal Hill by Weapon Used") + ylab("Latitude") + xlab("Longitude")
 
 #Display
 mapPoints2
 ```
 
-Basic rpart decision tree on the cherryhill neighborhood.
+Basic rpart decision tree on the Federal Hill neighborhood.
 
-```{r training tree on cherry hill}
+```{r training tree on federal hill}
 #Form a 90-10 train test split
-split <- sample(nrow(cherryHill), nrow(cherryHill) * 0.9)
+split <- sample(nrow(federalHill), nrow(federalHill) * 0.9)
 #Divide the set into train and test
-cherryHillTrain <- cherryHill[split,]
-cherryHillTest <- cherryHill[-split,]
+federalHillTrain <- federalHill[split,]
+federalHillTest <- federalHill[-split,]
 #Build the model
-cherryHillModel <- rpart(cherryHillTrain, formula = CrimeCode ~ Description)
+federalHillModel <- rpart(federalHillTrain, formula = CrimeCode ~ Description)
 #Plot it
-prp(cherryHillModel)
+prp(federalHillModel)
 #Make two more models
-cherryHillModel2 <- rpart(cherryHillTrain, formula = Weapon ~ Description)
-prp(cherryHillModel2)
+federalHillModel2 <- rpart(federalHillTrain, formula = Weapon ~ Description)
+prp(federalHillModel2)
 ```
 
 Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.