demographics.Rmd

---
title: "Demographics"

output: html_document
---


```{r exp_yaa_lib}
library(tidyverse)
```



```{r exp_yaa_data}
felony_crime = readRDS(file = "./datasets/sex_drug_weapons.rds")

overall_felony = readRDS(file = "./datasets/nyc_felony_crimes.rds")

complete_data = inner_join(overall_felony, felony_crime, by = "cmplnt_num") %>%
  janitor::clean_names(dat = .) 
```



**Number of crimes in 3 crime groups**

```{r}
complete_data %>%
  group_by(crime_group) %>%
  summarise(number = n()) %>%
  arrange(desc(number)) %>%
  knitr::kable()
```


** For sex related and weapon related crimes what are the dempgraphics of the victims?**


**Weapon-Related Crimes**

Race
```{r}
complete_data %>%
  filter(crime_group == "Weapon-Related") %>%
  filter(!is.na(vic_race_y), vic_race_y != "unknown") %>% 
  group_by(vic_race_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable() 
```




Age
```{r}
complete_data %>%
  filter(crime_group == "Weapon-Related") %>%
  filter(!is.na(vic_age_group_y), vic_age_group_y != "unknown") %>% 
  group_by(vic_age_group_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable() 
```




Sex
```{r}
complete_data %>%
  filter(crime_group == "Weapon-Related") %>%
  filter(!is.na(vic_sex_y), vic_sex_y != "unknown") %>% 
  group_by(vic_sex_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable() 
```



**Drug-Related**

Race
```{r}
complete_data %>%
  filter(crime_group == "Drug-Related") %>%
  filter(!is.na(vic_race_y), vic_race_y != "unknown") %>% 
  group_by(vic_race_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable()
```



Age
```{r}
complete_data %>%
  filter(crime_group == "Drug-Related") %>%
  filter(!is.na(vic_age_group_y), vic_age_group_y != "unknown") %>% 
  group_by(vic_age_group_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable() 
```



Sex
```{r}
complete_data %>%
  filter(crime_group == "Drug-Related") %>%
  filter(!is.na(vic_sex_y), vic_sex_y != "unknown") %>% 
  group_by(vic_sex_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable() 
```




**Sex-related**
Race
```{r}
complete_data %>%
  filter(crime_group == "Sex-Related") %>%
  filter(!is.na(vic_race_y), vic_race_y != "unknown") %>% 
  group_by(vic_race_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable() 
```


Age
```{r}
complete_data %>%
  filter(crime_group == "Sex-Related") %>%
  filter(!is.na(vic_age_group_y), vic_age_group_y != "unknown") %>% 
  group_by(vic_age_group_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable()
```



Sex
```{r}
complete_data %>%
  filter(crime_group == "Sex-Related") %>%
  filter(!is.na(vic_sex_y), vic_sex_y != "unknown") %>% 
  group_by(vic_sex_y) %>%
  summarise(number = n()) %>% arrange(desc(number)) %>% 
  knitr::kable() 
```