MassDataByZipCodeWithSeparateDataFile.Rmd

---
title: "Massachusetts Data by Zip Code"
output: html_document
---

```{r setup, include=FALSE}

### This R Markdown document needs a separate .rdata file in the same directory

knitr::opts_chunk$set(echo = FALSE)

if(!require("pacman")){
  install.packages("pacman")
}
pacman::p_load(DT, dplyr, ggplot2, scales)
load("mamarkdowndata.rdata") # or whatever you called your file storing data

```


# Find highest and lowest median incomes. Ignores ties for code simplicity.

```{r}
zip_highest_income_row <- filter(markdowndata, MedianHouseholdIncome == max(MedianHouseholdIncome, na.rm = TRUE))
zip_lowest_income_row <- filter(markdowndata, MedianHouseholdIncome == min(MedianHouseholdIncome, na.rm = TRUE))
```




```


Zip code `r zip_highest_income_row$ZipCode[1]` in `r zip_highest_income_row$City[1]` has the highest median income in Massachusetts, `r scales::dollar(zip_highest_income_row$MedianHouseholdIncome[1])`.

Zip code `r zip_lowest_income_row$ZipCode[1]` in `r zip_lowest_income_row$City[1]` has the lowest median income in Massachusetts, `r scales::dollar(zip_lowest_income_row$MedianHouseholdIncome[1])`.


#### Distribution of median household incomes

```{r histo, fig.width = 4, fig.height = 2, warning=FALSE, message=FALSE}
ggplot(markdowndata, aes(x = MedianHouseholdIncome)) +
  geom_histogram(binwidth = 10000, color = "black", fill = "darkgreen") +
  theme_classic() +
   xlab("") + ylab("")  +
   scale_x_continuous(labels = dollar)
```

#### Income, Housing Costs, and Population


```{r table}
DT::datatable(markdowndata, filter = 'top') %>%
  formatCurrency(4:5, digits = 0) %>%
  formatCurrency(6, currency = "", digits = 0)
```