PA1_template.Rmd

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



## Loading and preprocessing the data
```{R}
##Load the data
data<-read.csv("activity.csv")
##Date transformation
data$date <- as.Date(data$date)
```

## What is mean total number of steps taken per day?
Calculate the total number of steps taken per day
```{r}
dailysteps<-aggregate(steps~date,data,sum)
dailysteps

#Show a histogram of the total number of steps taken each day
hist(dailysteps$steps, breaks=20, main="Total steps taken per day", xlab="Number of steps")

meansteps<-mean(dailysteps$steps)
meansteps<-format(meansteps, scientific=FALSE) ##Display this nicely rather than as an exponent
medsteps<-median(dailysteps$steps)

```

"Calculate and report the mean and median of the total number of steps taken per day"  
Answer:  
Mean number of steps taken per day (excluding NA values): `r meansteps`  
Median number of steps taken per day (excluding NA values): `r medsteps`

## What is the average daily activity pattern?

Make a time series plot (i.e. 𝚝𝚢𝚙𝚎 = "𝚕") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)

```{R}
stepsPerInterval<-aggregate(steps~interval, data, mean)
maxsteps<-max(stepsPerInterval$steps)
interval <- stepsPerInterval$interval[stepsPerInterval$steps == maxsteps] 

plot(stepsPerInterval$interval, stepsPerInterval$steps, xlab="Interval", ylab="Average steps", type="l")

```

"Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?"  

Answer: `r interval`

## Imputing missing values

Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with 𝙽𝙰s)
```{R}
missingvals<-sum(is.na(data$steps))
```
Number of NA values: `r missingvals`


For the missing values, use the mean calculated earlier for that particular 5-minute interval.
Creating a new dataset that is equal to the original dataset but with the missing data filled in...
```{R}
completeData <- data ## Copy data

##Run through data and replace any NAs by average for that interval - use modulus operator to determine interval needed
intervalCount<-dim(stepsPerInterval) ## Number of intervals per day
for(i in seq_along(completeData$steps))
  {
    if (is.na(completeData$steps[i]))
      {
        index<-(i %% intervalCount[1])
        if (index==0)
          index<-intervalCount[1] ##No zero indexing in R
          
        completeData$steps[i]<-stepsPerInterval$steps[index]
      }
  }

```

Make a histogram of the total number of steps taken each day

```{r}
completedailysteps<-aggregate(steps~date,completeData,sum)
completedailysteps

hist(completedailysteps$steps, breaks=20, main="Total steps taken per day", xlab="Number of steps")

```

Calculate and report the mean and median total number of steps taken per day. 
```{r}

completemean<-mean(completedailysteps$steps)

completemed<-median(completedailysteps$steps)

```

Answer:  
Mean number of steps taken per day (imputing NA values): `r completemean`  
Median number of steps taken per day (imputing NA values): `r completemed`

Do these values differ from the estimates from the first part of the assignment? 

Answer: Not significantly


What is the impact of imputing missing data on the estimates of the total daily number of steps?
Answer: Step count is averaged for those with all data missing, otherwise not changed significantly

## Are there differences in activity patterns between weekdays and weekends?
I've run out of time to complete this - sorry. 
Thanks for reading!