hw1p2-stock-dynamics.Rmd

---
title: "Stock Dynamics"
author: "Terrel Shumway"
date: "04/28/2015"
output: html_document
---

This document presents answers for homework 1 part 2.

## Loading the Data
```{r}
library(gdata)
baseurl = "https://courses.edx.org/c4x/MITx/15.071x_2/asset/"

getdata = function(local,ticker){
  if(!file.exists(local)){
    library(downloader)
    remote = paste0(baseurl,local)
    print(remote)
    download(remote,local)
  }
  data = read.csv(local)
  data$Date = as.Date(strptime(data$Date,"%m/%d/%y"))
  data = cbind(Ticker=ticker,data)
  data
}

IBM = getdata("IBMStock.csv","IBM")
GE = getdata("GEStock.csv","GE")
PG = getdata("ProcterGambleStock.csv","PG")
KO = getdata("CocaColaStock.csv","KO")
BA = getdata("BoeingStock.csv","BA")

stocks = rbind(IBM,GE,PG,KO,BA)
stocks$Ticker = factor(stocks$Ticker)
years = getYear(stocks$Date)
```
problem     | answer
------------|--------
problem 1.1 | `r nrow(IBM)`
problem 1.2 | `r min(years)`
problem 1.3 | `r max(years)`
problem 1.4 | `r mean(IBM$StockPrice)`
problem 1.5 | `r min(KO$StockPrice)`
problem 1.6 | `r max(GE$StockPrice)`
problem 1.7 | `r median(BA$StockPrice)`
problem 1.8 | `r sd(PG$StockPrice)`
problem 2.1 | `r getYear(KO[which.min(KO$StockPrice),"Date"])`
problem 2.2 | `r getYear(KO[which.max(KO$StockPrice),"Date"])`

## Visualizing Stock Trends

```{r}
library(ggplot2)
ggplot(stocks,aes(x=Date,y=StockPrice,group=Ticker,color=Ticker))+geom_line()
```

problem 2.2:
```{r}
ggplot(stocks[years==2000,],aes(x=Date,y=StockPrice,group=Ticker,color=Ticker))+geom_line()
```

problem 2.3:
```{r}
ggplot(stocks[years==1980,],aes(x=Date,y=StockPrice,group=Ticker,color=Ticker))+geom_line()
tapply(stocks$StockPrice,stocks$Ticker,mean)
```

## Visualizing Stock Dynamics 1995-2005

problems 3.1,3.2,3.3,3.4
```{r}
focus = stocks[years>=1995 & years<=2005,]
ggplot(focus,aes(x=Date,y=StockPrice,group=Ticker,color=Ticker))+geom_line()
```

## Monthly Trends

problem 4.1:
```{r}
fmonths = function(d){ factor(months(d),ordered=TRUE,levels=c("January","February","March","April","May","June","July","August","September","October","November","December")) }

high_months = function(data){
  mt = tapply(data$StockPrice,fmonths(data$Date),mean)
  mt[mt>=mean(mt)]
}

high_months(IBM)

```


problem 4.2:
```{r}
g = high_months(GE)
k = high_months(KO)
g[which.max(g)]
k[which.max(k)]
```

```{r}
g
k
high_months(PG)
high_months(BA)
```

problem 4.3
```{r}
mf = fmonths(stocks$Date)
tapply(stocks$StockPrice,mf,mean)[c(1,12)]
```
This is not exactly right, but it gives the right answer. I really need to do more split-apply-combine practice.

What I need here is to create a 3-D dataset [ticker,month,mean(StockPrice)]. A bar chart would make the answers easy to see.