PA1_template.Rmd

---
title: "Reproducible Research: Peer Assessment 1"
output: 
  html_document:
    keep_md: true
---


## Loading and preprocessing the data

```{r loadlib, echo=T, results='hide', message=F, warning=F}
library (dplyr)
library (lubridate)
```

```{r}
if (!file.exists ("activity.csv"))
  unzip ("activity.zip")
act <- read.csv ("activity.csv")
```

## What is mean total number of steps taken per day?

```{r}
dailyStepTotal <- aggregate (. ~ date, data = act, sum)
hist (dailyStepTotal$steps, xlab = "Total Steps per Day")
abline (v = mean (dailyStepTotal$steps), col = "red")
legend ("topright", lty = 1, bty = "n", col = "red", legend = "Mean Steps per Day")

# There are up to five significant digits in each number; by reporting integers, we keep that 
#   relatively taken care of
meanStepsPerDay <- sprintf ("%.0f", mean (dailyStepTotal$steps))
medianStepsPerDay <- sprintf ("%.0f",median (dailyStepTotal$steps))
```

**Mean steps per day: `r meanStepsPerDay`**    
**Median steps per day: `r medianStepsPerDay`**  


## What is the average daily activity pattern?

```{r}
stepsEachInterval <- aggregate (steps ~ interval, data = act, mean)
with (stepsEachInterval, plot (interval, steps, type = "l", 
      xlab = "5 Minute Intervals Throughout Each Day", ylab = "Steps"))

rowNum <- which (stepsEachInterval$steps == max (stepsEachInterval$steps))
intervalWithMaxSteps <- stepsEachInterval$interval[rowNum]

abline (v = intervalWithMaxSteps, col = "blue")
legend ("topright", lty = 1, bty = "n", col = "blue", legend = "Interval With Max Steps")

```

**The interval number with the maximum average number of steps is: `r intervalWithMaxSteps`.**  

## Imputing missing values

```{r}
numMissingValues <- sum (is.na(act$steps))
```

**Total number of missing values: `r numMissingValues`.**    

```{r}
# Strategy for imputing missing values: use average for that interval over all days in dataset
#   Create a reverse lookup vector so that we can get the average steps for a given interval, given the interval
#   intervals start with 0, vectors start with 1, so I'm looking up (interval + 1) in the vector
avgLookupVector <- rep (0L, max (stepsEachInterval$interval) + 1)
for (i in 1:length (stepsEachInterval$steps)) {
  interval <- stepsEachInterval$interval[i]
  avgLookupVector[interval+1] <- stepsEachInterval$steps[i]
}
imputedAct <- act
for (i in 1:length (imputedAct$steps)) {
  if (is.na (imputedAct$steps[i])) {
      imputedAct$steps[i] <- avgLookupVector[imputedAct$interval[i] + 1]
  }
}

dailyStepTotal <- aggregate (. ~ date, data = imputedAct, sum)
hist (dailyStepTotal$steps, main = "After Imputing Missing Data", xlab = "Total Steps per Day")
abline (v = mean (dailyStepTotal$steps), col = "red")
legend ("topright", lty = 1, bty = "n", col = "red", legend = "Mean Steps per Day")

meanStepsPerDay <- sprintf ("%.0f", mean (dailyStepTotal$steps))
medianStepsPerDay <- sprintf ("%.0f",median (dailyStepTotal$steps))


```

**Mean steps per day, using imputed missing values: `r meanStepsPerDay`**  
**Median steps per day, using imputed missing values: `r medianStepsPerDay`**  

**These values do not differ significantly from those in the first part of the assignment.**   
**Imputing missing data on the estimates of the total daily number of steps made no significant difference.**

## Are there differences in activity patterns between weekdays and weekends?

```{r}
imputedDaysOfWeek <- weekdays (ymd (imputedAct$date))
imputedWeekends <- imputedDaysOfWeek == "Saturday" | imputedDaysOfWeek == "Sunday"
imputedWeekParts <- rep ("weekday", length (imputedWeekends))
imputedWeekParts[imputedWeekends] = "weekend"
imputedAct[["partOfWeek"]] <- factor (imputedWeekParts)

weekendSteps <- subset (imputedAct, partOfWeek == "weekend")
aggWeekendSteps <- aggregate (steps ~ interval, data = weekendSteps, mean)
weekdaySteps <- subset (imputedAct, partOfWeek == "weekday")
aggWeekdaySteps <- aggregate (steps ~ interval, data = weekdaySteps, mean)

par (mfrow = c(2, 1), mar = c(2,4,2,1), oma = c(0, 2, 0, 0))
with (aggWeekendSteps, plot (interval, steps, type = "l", ylim = c(0, 250), main = "weekend", xlab = "", ylab = ""))
par (mar = c(5,4,2,1))
with (aggWeekdaySteps, plot (interval, steps, type = "l", ylim = c(0, 250), main = "weekday", xlab = "Interval", ylab = ""))
mtext ("Number of steps", side = 2, line = 0, outer = TRUE)

```

**Weekdays have more steps per day on average then do weekend days.**