amandammor

STAT 545A Homework #5
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Prepared by: Amanda Yuen

Up to this point, we have learned how to generate plots using the lattice package. For homework #5, we will practice generating plots using the ggplot2 package. I'm going to attempt to reproduce some plots from Homework #4 using ggplot2 to compare the results from the lattice and ggplot2 packages. The goal is to make at least two figures, with one being a stripplot-type of figure (one quantitative variable and one categorical variable) and one being a scatterplot (two quantitative variables).

As always, we start by importing the [Gapminder](http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt) data into R and then doing a quick check that the data has been imported properly.

```{r}
gDat <- read.delim("gapminderDataFiveYear.txt")

amandammor

STAT 545A Homework #4
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Prepared by: Amanda Yuen

For homework #4, we will evaluate code prepared by someone else for last week's data aggregation tasks and produce graphics to accompany these tasks.

As always, we begin by importing the [Gapminder](http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt) data into R and do a quick check that the data has been imported properly.


```{r}

amandammor

STAT 545A Homework #3
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Prepared by: Amanda Yuen

This is an R Markdown document. For homework #3, we will perform some data aggregation tasks on the [Gapminder](http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt) data.

First, let's import the data into R.

```{r}
gDat <- read.delim("gapminderDataFiveYear.txt")

amandammor

STAT 545A Homework #2
========================================================
Prepared by: Amanda Yuen


This is an R Markdown document. For homework #2, we will carry out a brief analysis of the Gapminder data located [here](http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt).

Let's import the data into R:

```{r}

amandammor

a <- 2
b <- -3
n <- 40
sigSq <- 0.5
x <- runif(n)
y <- a + b * x + rnorm(n, sd = sqrt(sigSq))
(avgX <- mean(x))
write(avgX, "avgX.txt")
plot(x,y)
abline(a, b, col = "purple")