A handful of miscellaneous R scripts

README.md

A few R scripts, for my own reference.

Calls to 911.R

## Load the file into a dataframe
calls.to.911 <- read.csv('/tmp/Calls_for_Service_2013.csv')

calls.to.911 <- read.csv('Calls_for_Service_2013.csv')

## Try `table` to summarize the data
table(calls.to.911$DispositionText)

## Make a few new frames out of those tables
disposition.summary <- as.data.frame(table(calls.to.911$DispositionText))
crime.summary <- as.data.frame(table(calls.to.911$TypeText))
crime.by.disposition <- as.data.frame(table(calls.to.911$TypeText,calls.to.911$DispositionText))


## And write the frames to a CSV
write.csv(crime.summary, file='count_by_crime.csv')
write.csv(disposition.summary, file='count_by_disposition.csv')
write.csv(crime.by.disposition, file='summary.csv')

geocoding.R

library(ggmap)
library(ggplot2)

## If you don't have ggmap, install it with `install.packages("ggmap")`

setwd("~/mydir")

## Read the CSV into a data frame
alums <- read.csv('where_weve_gone.csv')
## geocode() will return 2 vectors, latitude and longitude. Attach those with cbind:
alums <- cbind(alums, geocode(as.character(alums$City)))

## write that all back to a CSV:
write.csv(alums, file="alums_geo.csv")

## To work with the data later, you can just read from the geocoded CSV
alums <- read.csv('alums_geo.csv')


## Draw the world map  
library(maps)
library(mapdata)

map("world", col="#f2f2f2", fill=TRUE, bg="#FFFFFF", lwd=0.05)
points(alums$lon, alums$lat)

# Should help:
# http://stackoverflow.com/questions/19262805/r-geom-point-and-ggmap
# http://www.mpi.nl/departments/independent-research-groups/evolutionary-process

nyc_prek.R

library(ggmap)

## Read your csv into a data frame
setwd('Data/PreK')
prek <- read.csv('prek.csv')

## Create a new column that has a complete address in the form
## 219 W 40th Street, New York, NY 10018
## out of the relevant columns in my CSV (Address, Boro, ZIP)

prek$fulladdy <- paste(prek$Address, prek$Boro, ', NY', prek$ZIP)

## Geocode that "fulladdy" column and attach it to the data frame. This adds two columns, lon and lat.
prek <- cbind(prek, geocode(prek$fulladdy))

## look into the N/As
subset(prek, is.na(prek$lat))

## Write it out to a new text file for mapping
write.csv(prek, file="prek.txt")