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#Install and load required packages
if (!require("dplyr")) install.packages("dplyr")

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

if (!require("tidyverse")) install.packages("tidyverse")

## Loading required package: tidyverse

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.4
## ✔ ggplot2   3.4.3     ✔ stringr   1.5.0
## ✔ lubridate 1.9.2     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
library(ggplot2)

#Read the data
mydata <- read.csv("TNBBAccessData.csv")

#Look at histograms of the PctBB and MedIncome distributions
ggplot(mydata, aes(x = PctBB))+geom_histogram(color="black",fill="dodgerblue")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mydata, aes(x = MedIncome))+geom_histogram(color="black",fill="dodgerblue")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#Compute descriptive statistics for PctBB and MedIncome
mydata %>%
  select(PctBB, MedIncome) %>%
  summarise_all(list(Median = median,
                     Mean = mean,
                     SD = sd,
                     Min = min,
                     Max = max))

##   PctBB_Median MedIncome_Median PctBB_Mean MedIncome_Mean PctBB_SD MedIncome_SD
## 1         71.9            44122   72.22947          47167 7.045458     10837.72
##   PctBB_Min MedIncome_Min PctBB_Max MedIncome_Max
## 1      51.2         30136      93.4        112962

#Look at a scatterplot of PctBB and MedIncome
ggplot(mydata,aes(x = MedIncome,
                  y = PctBB))+
  geom_point(size = 2)+
  geom_smooth(method = "lm",
              se = FALSE)

## `geom_smooth()` using formula = 'y ~ x'

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