ZDaly

Homework 5: Fun Times with GGPlot2
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For this assignment I decided to go out and find some new data, as I was getting a bit tired of working with GapMinder. I decided to check out [NYC Open Data](https://data.cityofnewyork.us/) as I grew up in NY and thought it would be cool to check out. It turns out that there are over 1100 data sets, but luckily there was a good search and sort feature on the site. I ended up settling on a dataset on response times to emergencies by the NYC Fire Department located [here](https://data.cityofnewyork.us/Public-Safety/FDNY-Community-Board-Incident-Count/rtc6-e7ff).

The data has 5 different variables: Two are categorical (the borough or location, type of incident) and three are numerical (a count variable for each type of incident, an average time of response, and a date). After playing around a bit I manage to get the CSV loaded with the code below. Before doing so I should note that I had to go into the actual file and c

ZDaly

Homework Number Four (Stat545a)
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In this assignment we will be using the **Gapminder** dataset. (Located [here](http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt "Gapminder dataset on the course website") for those who are curious).

The goal of the present assignment is to make use of code which was written by another student. I will use the code from [Mina Park](http://rpubs.com/parkm87/stat545a-2013-hw03_park-min "Many thanks for the well written code!").

But first, we need to load the dataset and do out little sanity check with `str()`, followed by loading up the datasets we need: `lattice` and `plyr`.
```{r warning = FALSE}
gDat <- read.delim("gapminderDataFiveYear.txt")

ZDaly

Data Aggregation Fun Times!
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This assignment will once again make use of the **GapMinder** dataset, but this time we will be having fun with the `Plyr` package. 

Before beginning, I load the dataset, the needed packages, and then do a quick sanity check that my data is what I expect it to be. As a matter of personal preference I make use of the `stringsAsFactors = FALSE` argument when I load the data, to keep R from turning the variables for Country and Continent into factors.

```{r  warning = FALSE}
gDat <- read.delim("gapminderDataFiveYear.txt", stringsAsFactors = FALSE)
library(plyr)

ZDaly

Homework Number Two (Stat545a)
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In this assignment we will be taking a preliminary look at the **Gapminder** dataset. (Located [here](http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt "Gapminder dataset on the course website") for those who are curious).

This will include:
* Loading the dataset
* Get a sense of the sort of information in the dataset
* Summarizing the variables in the dataset
* Creating a quick plot of some of the information in the dataset

ZDaly

n <- 60
a <- 2
b <- 3
sigSq <- 0.5
x <- runif(n)
y <- a + b * x + rnorm(n, sd = sqrt(sigSq))

(avgX <- mean(x))
plot(x, y)
abline(a, b, col = "red")