### Problem 4 title:"R Notebook" a) b) c) d) output: html_notebook --- This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.

### Problem 4 After the hurricane Irma, September 2017, many citizens in Miami Dade County called 311 to report something. The dataset 311_MDC_9_10-18 includes a detailed information about the incidents reported.

a) Upload the dataset ("https://raw.githubusercontent.com/reisanar/datasets/master/311_MDC_9_10-18.csv") and asign it to the variable mdc_calls. Since the data set is external, use the appropriate command and use the operator head to verify it was uploaded properly and get familiar with all of the information included. http://www.lock5stat.com/datasets1e/

b) Draw a barplot for the Zip Postal Code column in order to see from which part of the County calls were generated the most. Find a zip code map for Miami Dade County in Google, and tell where approximately these codes are located in the city. <<<<<< not complete.. cannot figure out ```{r} # Creating table structure for zip code count_zip <- table(mdc_calls $ `Zip / Postal Code`) # Making bar plot for zip code barplot(count_zip, main = Highest Calls, xlab = "zipcode") # installing ggmap package for getting google map install.packages("ggmap") # Loading the package library(ggmap) # Getting the map of miami map = get_map(location = "miami", zoom = 10, color="bw") # Displaying zip code area on the map ggmap(map, extent='device') + geom_point(aes(x=Longitude, y=Latitude, colour= Zip / Postal.code), + data=mdc_calls) + ggtitle('Zipcode Location') + theme(plot.title = element_text(hjust = 0.5))

c) Draw a barplot for the Issue Type column in order to see which issues generated most of the calls.

```{r}

mdc_calls <- read.csv("https://raw.githubusercontent.com/reisanar/datasets/master/311_MDC_9_10-18.csv", header = TRUE, sep = ",")

head(mdc_calls)

# Displaying summary of the dataset

summary(mdc_calls)

```

d) St. Thomas University is located in the City of Miami Gardens. Filter the dataset by the City of Miami Gardens and then, draw a bar plot of the issues that generated most of the calls within this city.

```{r} # Type R code below this line

```