LeapFromExcelAnalysis.Rmd

---
title: "LeapFromExcelAnalysis"
author: "MaryJoWebster"
date: "January 16, 2019"
output: html_document
#this is the YAML header and what it looks like by default


#let's improve it for our web page
#title: "Making charts with ggplot2"
#author: "By MaryJo Webster"
#date:  "Last updated: `r Sys.Date()`"
#output:
#  html_document: 
#    toc: true
#    to_depth: 1
#    toc_float: true
#    theme: cerulean
---

```{r setup, include=FALSE}
#set echo to FALSE so that your code doesn't show up in the HTML page
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
#load packages and data

library(readr) #importing csv files 
library(dplyr) #general analysis 
library(ggplot2) #making charts 
library(ggthemes) #this will help make our charts look better
library(janitor) #for data cleanup

#we'll need these new packages for creating this markdown page to an HTMl page
library(rmarkdown)
library(knitr)


#Import our data
deaths <- read_csv('./data/opiate_deaths.csv',col_types=cols(.default="c",BIRTHDATE=col_date("%m/%d/%Y"),DEATHDATE=col_date("%m/%d/%Y"),INJURY_DATE=col_date("%m/%d/%Y"),AGEYEARS="i")) %>%
  clean_names()


#If you use an Rscript for loading, cleaning data, you can connect it to this page so you don't have to repeat loading, etc.

```

# You can put text here as a title
It will show up as HTMl on the page we create (this will be body type)

```{r}
#Try displaying the top of the data frame
deaths

```

#Select and filter
```{r}
#this is showing all the black females 

deaths %>% 
   filter(gender=='F' , race=='African American') %>% 
   select(firstname, lastname, gender, race)
```

#Count records
```{r}
deaths %>% summarise(numrecords=n())
```

#Pivot Table
How many people of each gender died?
```{r}
deaths %>%
  group_by(gender) %>% 
  summarise(numdeaths = n())
```

What was the average age by gender?
```{r}
deaths %>% 
  group_by(gender) %>% 
  summarise(avgage = mean(ageyears))
```

#Group by two variables
This is something Excel Pivot Tables can't do
```{r}
deaths %>% 
  group_by(gender, race) %>% 
  summarise(numdeaths = n())
```

#Sort our results
```{r}
deaths %>% 
  group_by(gender, race) %>% 
  summarise(numdeaths = n()) %>% 
  arrange(desc(numdeaths))
```

#filter summarized results
Let's just look at the women
```{r}
deaths_bygender_race <- deaths %>% 
  group_by(gender, race) %>% 
  summarise(numdeaths = n()) %>% 
  arrange(desc(numdeaths)) %>% 
  filter(gender=='F')
```


#add a new calculated column
Percentage of people who died by gender
```{r}

deaths %>% 
  group_by(gender) %>% 
  summarise(numdeaths = n()) %>% 
  mutate(pct = numdeaths/sum(numdeaths)) %>% 
  arrange(desc(pct))

```

#Put it all together
Round numbers using a tool from janitor package called round_half_up
```{r}
deaths %>% 
  group_by(gender) %>% 
  summarise(numdeaths = n(), avgage = round(mean(ageyears),1)) %>% 
  mutate(pct = round_half_up(numdeaths/sum(numdeaths)*100)) %>% 
  arrange(desc(pct))
```
#data cleanup
How many people died each year?
We need a field with the year of death
```{r}
#We are going to create a new data frame (overwriting the old one)
#then we are going to tell it to use all the data from the old data frame
#plus tack on a new variable called "deathyr" 
#we'll use the mutate function from dplyr to do this
#plus we're going to use a date function from the lubridate package

library(lubridate)

deaths <-  deaths %>% mutate(deathyr=year(deathdate))

#now let's see what this gave us

deaths %>% 
  group_by(deathyr) %>% 
  summarise(numdeaths = n())

```


#More recoding

```{r}
# let's look at this field to see where we've got a problem
deaths %>% group_by(hispanicethnicity) %>% summarise(count=n())
```

#Recode values into a new column
```{r}
#we'll use a lot of the same things we did above
#plus a base R function called "case_when" (it's like an IF statement)

deaths <-  deaths %>%
  mutate(hispanic_new = 
           case_when(hispanicethnicity=='Non-Hispanic' | 
hispanicethnicity=='NOT HISPANIC' ~'NOT HISPANIC',
TRUE~toupper(hispanicethnicity)))

# the base R function called "toupper()" converts values to uppercase


#let's see our new column

deaths %>% 
  group_by(hispanic_new) %>% 
  summarise(numdeaths = n())

```

#Create a chart
How many people died each year?

```{r}
#because we need to summarise our data first, you need to create
# a new data frame to base the chart on

deaths_by_yr <-  deaths %>% 
  group_by(deathyr) %>% 
  summarise(numdeaths = n())


#Now we'll use ggplot() to build a bar chart
deaths_by_year_chart <- 
  ggplot(deaths_by_yr, aes(x = deathyr, y = numdeaths)) + 
  geom_bar(stat = "identity")

#this is necessary to actually plot it in the space below
plot(deaths_by_year_chart)


```

#Make the chart look better

```{r}
#Build the chart again, this time with labels and titles
deaths_by_year_chart <- 
  ggplot(deaths_by_yr, aes(x = deathyr, y = numdeaths)) + 
  geom_bar(stat = "identity") +
    labs(title = "Number of opiate deaths by year in MN", 
       subtitle = "2006-2015",
       caption = "Graphic by MaryJo Webster",
       x="Year",
       y="Number of deaths")

plot(deaths_by_year_chart)
```

#Add color and a theme
```{r}
deaths_by_year_chart <- ggplot(deaths_by_yr, aes(x = deathyr, y = numdeaths)) + 
  geom_bar(stat = "identity", color="black", fill="#59A897") +
  theme_hc()+
  labs(title = "Number of opiate deaths by year in MN", 
       subtitle = "2006-2015",
       caption = "Graphic by MaryJo Webster",
       x="Year",
       y="Number of deaths")

plot(deaths_by_year_chart)
```

#Fix the x axis values
```{r}
deaths_by_year_chart <- ggplot(deaths_by_yr, aes(x = deathyr, y = numdeaths)) + 
  geom_bar(stat = "identity", color="black", fill="#59A897") +
  scale_x_continuous(name="Year", breaks=seq(2006, 2015, 1))+
  theme_fivethirtyeight()+
  labs(title = "Opiate deaths in Minnesota", 
       subtitle = "2006-2015",
       caption = "Graphic by MaryJo Webster",
       x="Year",
       y="Number of deaths")

plot(deaths_by_year_chart)


```

#Export our chart
```{r}
#https://ggplot2.tidyverse.org/reference/ggsave.html

ggsave("deaths_by_year_chart.jpg", width=8, height=5, units="in", dpi="print")
```


#Export data
```{r}
deaths_export <-  deaths

#this will send a csv to your working directory
write.csv(deaths_export, 'deaths_export.csv', row.names=FALSE)


#can also export JSON

```



Empty environment and run again

Knit the page

change echo to false and knit again

show page built with kable()