README.md

In trendct/ctnamecleaner: Connecticut Town Name and Population Matcher

ctnamecleaner, ctpopulator, ctcorrelator

ctnamecleaner

An R package that finds that takes a list of Connecticut hamlets or neighborhoods and adds a column with the matching official town names.

• Matches hamlets or neighborhoods to equivalent town names
• Adds additional column with population
• Optionally exports a new CSV file
• Lists names that could not be matched to towns
• Based on an ever-growing list of town names that TrendCT.org comes across.

What function ctnamecleaner() does

Let's assume you have a dataframe in R called towncoffeeshops that looks like

Town | Coffeeshops --- | ---: Andover | 2 Centerbrook | 5 Yalesville | 1

Run this in R

ctnamecleaner(Town, towncoffeeshops, filename="towncoffeecleaned", case="Upper")

You'll get a new file called towncoffeecleaned.csv that looks like

Town | Coffeeshops | real.town.name --- | ---: | --- Andover | 2 | ANDOVER Centerbrook | 5 | ESSEX Yalesville | 1 | WALLINGFORD

Alternatively

ctnamecleaner(Town, towncoffeeshops)

The command above will create a dataframe without exporting.

Usage

ctnamecleaner(name, data, filename="nope", case="Title")

Arguments

• name - Column with town names
• data - Name of data frame.
• filename Name of CSV to save. If skipped, CSV will not export.
• case Output of town string. Options are Upper, Lower, and Title

ctpopulator

An R package that appends the most-recent population of Connecticut towns to a dataframe for efficient per-capita calculations.

• Matches 2013 town population to equivalent town names
• Adds additional column with population
• Optionally exports a new CSV file
• Works best if the dataframe has originally been checked with ctnamecleaner()

What function ctpopulator() does

Let's assume you've collapsed duplicate town names column real.town.name in the CTNAMECLEANED dataframe above and summed up or averaged the figures you were working with.

Run this in R

ctpopulator(real.town.name, CTNAMECLEANED, filename="towncoffeepop")

You'll get a new file called towncoffeepop.csv that looks like the table below. Note: if you exclude the CSV filename parameter only the dataframe will be exported and can be assigned to an object.

Town | Coffeeshops | real.town.name | pop2013 --- | ---: | --- | ---: Andover | 2 | ANDOVER | 3095 Centerbrook | 5 | ESSEX | 6668 Yalesville | 1 | WALLINGFORD | 45112

Usage

ctnamecleaner(name, data, filename="nope")

Arguments

• name - Column with town names
• data - Name of data frame.
• filename Name of CSV to save. If skipped, CSV will not export.
• case Output of town string. Options are Upper, Lower, and Title

ctcorrelator

An R package that takes a town dataframe and checks for correlations between the original data set and 500 different variables including demographics, median income, education attainment, and poverty from an ever-growing list. Why? Correlation does not mean causation. But having a quickly generated list could help point a researcher of journalist into unforseen directions with respect to the original data.

• Appends the original dataframe with ~500 other variables from ~20 spreadsheets
• Determines the Pearson product-moment correlation coefficient for each variable as compared to the original data
• Notifies the users which variables had the strongest correlations by exporting tables and charts to browse
• Exports a summary shreadsheet Exports a dataframe of the variables with strong correlations for analysis later Exports small-multiple scatterplots based on user-set coefficient threshold
• Works best if the dataframe has originally been checked with ctnamecleaner()

What function ctcorrelator() does

Let's assume you've collapsed duplicate town names column real.town.name in the CTNAMECLEANED dataframe above and summed up or averaged the figures you were working with.

This is a dataframe called ctcoffeeshops.

Town | Coffeeshops --- | ---: Andover | 2 Essex | 5 Wallingford | 1

Run this in R

ctcorrelator(ctcoffeeshops, p=.9)

You'll get a new file called array_summary.csv that looks similar to this:

row | correlation | n() --- | --- | ---: 1 | moderate.negative.correlation | 7 2 | moderate.positive.correlation | 70 3 | no.correlation | 12 4 | strong.negative.correlation | 3 5 | strong.positive.correlation | 103 6 | very.strong.positive.correlation | 8 7 | weak.negative.correlation | 6 8 | weak.positive.correlation | 49

You'll get a new file called strong.very.strong.csv that looks similar to this:

row | column.abbrev | corre | correlation | raw | column.name --- | --- | --- | --- | --- | --- 1 | below.poverty | 0.947982822 | very.strong.positive.correlation | 0.947982822 | Below poverty 2 | g11 | 0.934302408 | very.strong.positive.correlation | 0.934302408 | Educational Attainment for the Population 25 Years and Over, 11th grade (City) 3 | female.householder.male.partner | 0.931860863 | very.strong.positive.correlation | 0.931860863 | Unmarried-partner Households by Sex of Partner, Female householder and male partner (City)

And then you'll also get a new file called plot.png that looks similar to

Usage

ctcorrelator(dat_data, p=.9)

Arguments

• dat_data - dataframe with two columns: town with CT town names and one other one with raw numbers representing whatever you need.
• p - The minimum threshold for the correlation coefficient that will be rendered in the charts.

What you'll need to start

• R
• RStudio (not really, but sure)

What to run within R or RStudio

Assuming user is starting from scratch

install.packages("devtools")
library(devtools)

install_github("trendct/ctnamecleaner")
library(ctnamecleaner)

Future versions

Will account for zip codes and census tracts or possibly blocks in Connecticut.

Version

0.3.1

MIT

trendct/ctnamecleaner documentation built on May 31, 2019, 7:47 p.m.