---
title: "R Notebook"
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. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r}
plot(cars)
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```{r}
#Loading packages we need today
library(tidyverse)
library(ggplot2)
library(psych)
#If you don't already have these packages, please install first
if (FALSE) {
  install.packages("tidyverse")
  install.packages("ggplot2")
  install.packages("psych")
}
#Open dataset
d<-as.data.frame(state.x77)
#What does each variable measure?
?state.x77
```

```{r}
# REVIEW for mutate(): Make a new variable by combining two other variables, or transforming a single variable
#Population density = Population/Area
WithDen<-as.data.frame(d%>% mutate(Density = Population/Area))
#OR
d$Density<-d$Population/d$Area
```

```{r}
#Add Comments
## Covid Cases by State, 2020-04-03.  CDC website.
covid.x2020 <- read.csv("covidCases.csv", header=FALSE)

stat.covid <- data.frame(state.x77,Covid=covid.x2020[[2]], region=state.region)

```

```{r}
# Calculate summary statistics (e.g., Mean, Median, SD, Skewness & Kurtosis statistics) for a variable
mean(d$Income)
median(d$Income)
sd(d$Income)
skew(d$Income)
kurtosi(d$Income)
describe(d$Income)
```

```{r}
describe(stat.covid$Covid)
stat.covid %>% mutate(CovidDensity = Covid/Area) -> covid1
png("covidCases.png")
hist(covid1$CovidDensity, main="Covid Density by State 2020-04-03",xlab="Covid cases per square mile" )
dev.off()
```



```{r}
## Calculate summary statistics separately for groups of cases
Wealthy <- d %>% 
  filter(Income > 4435.8)
NotWealthy <- d %>% 
  filter(Income < 4435.8)
describe(NotWealthy)
```

```{r}
# Make a histogram, adjust the number of bins, and add a normal curve
ggplot(data = d) +
  geom_histogram(mapping = aes(x = Income), binwidth = 100) 

#OR

hist(d$Income,breaks = 30,main = "Income", freq=FALSE, col="gray", xlab="Income")#freq = false because we want to graph probablity densities here instead of representation of frequencies
# add a normal distribution line in histogram
curve(dnorm(x, mean=mean(d$Income), sd=sd(d$Income)), add=TRUE, col="red")
```

```{r}
# Panel histograms by rows according to a grouping variable - Income in geographically bigger vs smaller state
  #Creating a group variable based on area
mean(d$Area)
d$BigOrSmall<-ifelse(d$Area>=70735.08,"Big","Small")
  #Histogram of population based on geographically big vs small state
ggplot(data = d, mapping = aes(x = Population)) + 
  geom_freqpoly(mapping = aes(colour = BigOrSmall), binwidth = 100)
```

```{r}
#Make boxplots to compare groups of variables - population in geographically bigger or smaller states
ggplot(data = d, mapping = aes(x = BigOrSmall, y = Population)) +
  geom_boxplot()
```

```{r}
#Remove outliers or groups
NoOutliers<-d
NoOutliers$Population[NoOutliers$Population > 10000] <-NA
#Checking the boxplots again
ggplot(data = NoOutliers, mapping = aes(x = BigOrSmall, y = Population)) +
  geom_boxplot()
```

```{r}
# Save your output to a picture file.
#jpeg file
jpeg("boxplot.jpg", width = 350, height = 350) 
ggplot(data = NoOutliers, mapping = aes(x = BigOrSmall, y = Population)) +
  geom_boxplot()
dev.off()
#OR if you would like a PDF export
pdf("boxplot.pdf") 
ggplot(data = NoOutliers, mapping = aes(x = BigOrSmall, y = Population)) +
  geom_boxplot()
dev.off()
#Where did I save this image to?
getwd()
```

