descritivo.Rmd

---
title: "descritivo.Rmd"
author: "Tatiana Saturno da Silva"
date: "26 de outubro de 2016"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
if(!require(plotly)){
  install.packages("plotly", dependencies = T)
}
if(!require(Cairo)){
  install.packages('Cairo')
}
if(!require(ggplot2)){
  install.packages("ggplot2", dependencies = T)
}
if(!require(dplyr)){
  install.packages("dplyr", dependencies = T)
}
if(!require(grid)){
  install.packages("grid", dependencies = T)
}
if(!require(tidyr)){
  install.packages("tidyr", dependencies = T)
}
library(Cairo)
library(ggplot2)
library(scales)
library(dplyr)
library(grid)
library(plotly)
library(tidyr)
theme_set(theme_bw())
```

## R Markdown

Exploratory Data Analysis for the Crime Forecasting Challenge

## Import Data
```{r import}
d <- read.csv(file = "data/mp_data.csv", header = T, sep = ";", stringsAsFactors = F)

data <- d

# convert character to date
data$occ_date <- as.POSIXct(data$occ_date, format="%d/%m/%Y")

# removing na's
data <- na.omit(data)
```

## Brief Data Description
```{r description, echo=FALSE}
# see general aspects of the data
str(data)

# view top few records of data
head(data)

# Five Numbersummary & interqurtile range
# Returns Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum) for the input data.
fivenum(data$census_trac)

# data's summary
summary(data)

# uniques
unique(data$CATEGORY)
unique(data$CALL.GROUPS)
unique(data$final_case_type)
unique(data$CASE.DESC)
unique(data$census_tract)
```

## Including Plots

Category's percentage:

```{r category_percentage, echo=FALSE}
p <-ggplot(data, aes(CATEGORY)) +
  geom_bar(aes(y = (..count..)/sum(..count..), fill = CATEGORY), stat="count") +
  geom_text(aes( label = scales::percent((..count..)/sum(..count..)),
                 y= (..count..)/sum(..count..) ), stat= "count", vjust = -.5) +
  labs(x = "Category" ,y = "Percent", fill="Category") +
  scale_y_continuous(labels=percent)+
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly()
```

Call description's percentage:

```{r call_percentage, echo=FALSE}
p <-ggplot(data, aes(CALL.GROUPS)) +
  geom_bar(aes(y = (..count..)/sum(..count..), fill = CALL.GROUPS), stat="count") +
  geom_text(aes( label = scales::percent((..count..)/sum(..count..)),
                 y= (..count..)/sum(..count..) ), stat= "count", vjust = -.5) +
  labs(x = "Call Groups" ,y = "Percent", fill="Call Groups") +
  scale_y_continuous(labels=percent) +
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly()
```
Final case type's percentage:

```{r final_percentage, echo=FALSE}
p <-ggplot(data, aes(final_case_type)) +
  geom_bar(aes(y = (..count..)/sum(..count..), fill = final_case_type), stat="count") +
  geom_text(aes( label = scales::percent((..count..)/sum(..count..)),
                 y= (..count..)/sum(..count..) ), stat= "count", vjust = -.5) +
  labs(x = "Final Case" ,y = "Percent", fill="Final Case") +
  scale_y_continuous(labels=percent)+
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly()
```
Census tract's percentage:

```{r census_percentage, echo=FALSE}
p <-ggplot(data, aes(as.character(census_tract))) +
  geom_bar(aes(y = (..count..)/sum(..count..), fill = as.character(census_tract)), stat="count") +
  geom_text(aes( label = scales::percent((..count..)/sum(..count..)),
                 y= (..count..)/sum(..count..) ), stat= "count", vjust = -.5) +
  labs(x = "Census Tract" ,y = "Percent", fill="Census Tract") +
  scale_y_continuous(labels=percent) +
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly()
```
District's percentage:

```{r district_percentage, echo=FALSE}
p <-ggplot(data, aes(as.character(DISTRICT))) +
  geom_bar(aes(y = (..count..)/sum(..count..), fill = as.character(DISTRICT)), stat="count") +
  geom_text(aes( label = scales::percent((..count..)/sum(..count..)),
                 y= (..count..)/sum(..count..) ), stat= "count", vjust = -.5) +
  labs(x = "District" ,y = "Percent", fill="District") +
  scale_y_continuous(labels=percent) +
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly()
```
Date's percentage:

```{r date_percentage, echo=FALSE}
p <-ggplot(data, aes(strftime(occ_date, format = "%Y"))) +
  geom_bar(aes(y = (..count..)/sum(..count..), fill = strftime(occ_date, format = "%Y")), stat="count") +
  geom_text(aes( label = scales::percent((..count..)/sum(..count..)),
                 y= (..count..)/sum(..count..) ), stat= "count", vjust = -.5) +
  labs(x = "Date" ,y = "Percent", fill="Date") +
  scale_y_continuous(labels=percent) +
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly()
```

## Multivariate Analysis
District Density Analysis:

```{r bivariable, echo=FALSE}
districts <- distinct(data, DISTRICT, .keep_all = TRUE)

# calculate percentage
get_perc <- data %>%
  group_by(DISTRICT) %>%
  summarise (n = n()) %>%
  mutate(freq = n / sum(n)) %>%
  mutate(perc = freq*100)

# join district with percentage
districts <- inner_join(districts, get_perc, by = "DISTRICT")
districts <-   select(districts, DISTRICT, x_coordinate, y_coordinate, perc)

# normalize coordinates
districts$x_coordinate <- districts$x_coordinate/100000
districts$y_coordinate <- districts$y_coordinate/100000

districts2 <- with(districts, districts[rep(1:nrow(districts), perc),])

# density's colors
colfunc <- colorRampPalette(c("darkblue", "lightblue", "green", "yellow", "red"))

p<-ggplot(districts2, aes(x_coordinate, y_coordinate)) +
  stat_density2d(geom="tile", aes(fill = ..density..), contour = FALSE) +
  scale_fill_gradientn(colours=colfunc(400)) + 
  xlim(c(min(districts$x_coordinate), max(districts$x_coordinate))) + ylim(c(min(districts$y_coordinate), max(districts$y_coordinate))) +
  geom_density2d(colour="black", bins=10) +
  geom_point() + 
  geom_text(aes(label=DISTRICT), size=3, hjust=-.25, vjust=.75) +
  labs(x = "x", y = "y", fill = "Density")
guides(fill = guide_colorbar(barwidth = 0.5, barheight = 10)) +
  theme(legend.title=element_blank())+
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly()
```

## District histrogram's density

District vs Category:
```{r districtXcategory_hist_den, echo=FALSE}
g <- ggplot(d, aes(DISTRICT)) 
  
g1 <- g +  geom_histogram(aes(y=..density.., fill = CATEGORY), binwidth = 10) +
  labs(x = "District", y = "Density", fill = "CATEGORY")+
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly(g1)
```

District vs Final Case Type:
```{r districtXfinal_hist_den, echo=FALSE}
g2 <- g +  geom_histogram(aes(y=..density.., fill = final_case_type), binwidth = 10) +
  labs(x = "District", y = "Density", fill = "Final Case Type")+
  theme(axis.text.x=element_text(angle=45, hjust=1))
ggplotly(g2)
```

## Date histrogram's density

Load data:
```{r load_data, echo=FALSE}
#date to numeric
data$occ_date <- as.numeric(data$occ_date)

# removing na's
data <- na.omit(data)
```

Date vs Category:
```{r density_dateXcategory, echo=FALSE}
p <- ggplot(data, aes(occ_date)) +
  scale_y_continuous(labels=percent) +
  theme(legend.position="none")

p1 <- p + geom_bar(aes(y = ..density.., fill = CATEGORY), stat="density") +
  labs(x = "Date" ,y = "Density", fill="Category")+
  theme(axis.text.x=element_text(angle=45, hjust=1))

ggplotly(p1)
```

Date vs District:
```{r density_dateXdistrict, echo=FALSE}
p2 <- p + geom_bar(aes(y = ..density.., fill = as.character(DISTRICT)), stat="density") +
  labs(x = "Date" ,y = "Density", fill="District")+
  theme(axis.text.x=element_text(angle=45, hjust=1))

ggplotly(p2)
```