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2 changes: 2 additions & 0 deletions Cities/.gitignore
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MyEnv

119 changes: 60 additions & 59 deletions cities.csv → Cities/cities.csv
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Places to consider,
City,State
Madison,WI
Appleton,WI
Green.Bay,WI
Milwaukee,WI
Bremerton,WA
Bainbridge,WA
Spokane,WA
Burlington,VT
Provo,UT
Salt.Lake.City,UT
Ogden,UT
St..George,UT
Bend,OR
Portland,OR
Hanover,NH
Concord,NH
Bedford,NH
Manchester,NH
Portsmouth,NH
Lincoln,NE
Omaha,NE
Bozeman,MT
Billings,MT
Great.Falls,MT
Columbia.Falls,MT
St..Louis,MO
St..Cloud,MN
St..Paul,MN
Minneapolis,MN
Traverse.City,MI
Grand.Rapids,MI
Ann.Arbor,MI
Detroit,MI
Kalamazoo,MI
Lansing,MI
Portland.1,ME
Wichita,KS
Kansas.City,KS
Des.Moines,IO
Cedar.Rapids,IO
Urbandale,IO
Iowa.City,IO
Indianapolis,IN
Peoria,IL
Springfield,IL
Urbana.Champaign,IL
Boise,ID
Moscow,ID
Pocatello,ID
Idaho.Falls,ID
Coeur.d.Alene,ID
Wilmington,DE
Boulder,CO
Denver,CO
Colorado.Springs,CO
Greenley,CO
Fort.Collins,CO
Places to consider,
City,State
Madison,WI
Appleton,WI
Green.Bay,WI
Milwaukee,WI
Bremerton,WA
Bainbridge,WA
Spokane,WA
Burlington,VT
Provo,UT
Salt.Lake.City,UT
Ogden,UT
St..George,UT
Bend,OR
Portland,OR
Hanover,NH
Concord,NH
Bedford,NH
Manchester,NH
Portsmouth,NH
Lincoln,NE
Omaha,NE
Bozeman,MT
Billings,MT
Great.Falls,MT
Columbia.Falls,MT
St..Louis,MO
St..Cloud,MN
St..Paul,MN
Minneapolis,MN
Traverse.City,MI
Grand.Rapids,MI
Ann.Arbor,MI
Detroit,MI
Kalamazoo,MI
Lansing,MI
Portland.1,ME
Wichita,KS
Kansas.City,KS
Des.Moines,IO
Cedar.Rapids,IO
Urbandale,IO
Iowa.City,IO
Indianapolis,IN
Peoria,IL
Springfield,IL
Urbana.Champaign,IL
Boise,ID
Moscow,ID
Pocatello,ID
Idaho.Falls,ID
Coeur.d.Alene,ID
Wilmington,DE
Boulder,CO
Denver,CO
Colorado.Springs,CO
Greenley,CO
Fort.Collins,CO
Fake.City,WY
File renamed without changes.
14 changes: 14 additions & 0 deletions Cities/cities_reviewstates.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

cities = pd.read_csv("cities.csv")
print(cities.head(10))

df = pd.Categorical(cities)
print(df)

head(df)
3 changes: 3 additions & 0 deletions Notes.txt
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For taking notes? No, for testing Git.

It turns out, as collaborator we do not have to create PRs unless desired. However, doing so requires being more intentional.
21 changes: 9 additions & 12 deletions Outline.Rmd
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---
title: "Outline"
title: "Savings Report Outline"
author: "Team DAREZ"
date: "10/5/2020"
output: html_document
Expand All @@ -9,22 +9,19 @@ output: html_document
knitr::opts_chunk$set(echo = TRUE)
```

## R Markdown
## Summary

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
## Introduction

When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
This is going to be the outline of our ability to save over the next 1 - 3 years under various conditions. From this we can develop a flexible dashboard and use our personal financial data in a private repository (so as to avoid any potentially prying eyes). My goal is to create an interactive, practical app for informing us of our current savings at any given time and provide options on where we can reduce spending to increase the amount placed in savings.

```{r cars}
summary(cars)
```
The app results will be broken into three parts as I currently see it: a summary (which is listed at the top to give a shortened version of the most common/necessary info), methodology (to explain our thinking and how we arrive at our values), and Report (to include a breakdown by expenses categories and perhaps suggestions for improvement)

## Including Plots
## Methods

You can also embed plots, for example:
## Full Report

```{r pressure, echo=FALSE}
plot(pressure)
```{r introduction}
# import the data here (an old source)
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
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