PA1_template.Rmd

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

This is an R Markdown document, created for the Coursera course "Reproducible Research", in completion of "Peer Assessment 1". The assignment requires students to write an R markdown document evidencing literate programming, using markdown and R programming techniques. There are 5 primary questions to be answered, dealing with processing and analysing data. The data provided to be worked upon, is called "activity monitoring data".


## Loading and preprocessing the data
```{r}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, fig.width = 10, fig.height = 5,
                      fig.keep = 'all' ,fig.path = 'figures\ ', dev = 'png')
```


```{r}
# Loading packages
library(ggplot2)
library(ggthemes)

# Unzipping the file and reading it
path = getwd()
unzip("repdata_data_activity.zip", exdir = path)

activity <- read.csv("activity.csv")

# Setting date format to help get the weekdays of the dates
activity$date <- as.POSIXct(activity$date, "%Y%m%d")

# Getting the days of all the dates on the dataset
day <- weekdays(activity$date)

# Combining the dataset with the weekday of the dates
activity <- cbind(activity, day)

# Viewing the processed data
summary(activity)
```




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

```{r}
# Calculating total steps taken on a day
activityTotalSteps <- with(activity, aggregate(steps, by = list(date), sum, na.rm = TRUE))
# Changing col names
names(activityTotalSteps) <- c("Date", "Steps")

# Converting the data set into a data frame to be able to use ggplot2
totalStepsdf <- data.frame(activityTotalSteps)

# Plotting a histogram using ggplot2
g <- ggplot(totalStepsdf, aes(x = Steps)) + 
  geom_histogram(breaks = seq(0, 25000, by = 2500), fill = "#83CAFF", col = "black") + 
  ylim(0, 30) + 
  xlab("Total Steps Taken Per Day") + 
  ylab("Frequency") + 
  ggtitle("Total Number of Steps Taken on a Day") + 
  theme_calc(base_family = "serif")

print(g)
```

The mean of the total number of steps taken per day is:
```{r}
mean(activityTotalSteps$Steps)

```
The median of the total number of steps taken per day is:
```{r}
median(activityTotalSteps$Steps)

```

## What is the average daily activity pattern?

```{r}
# Calculating the average number of steps taken, averaged across all days by 5-min intervals.
averageDailyActivity <- aggregate(activity$steps, by = list(activity$interval), 
                                  FUN = mean, na.rm = TRUE)
# Changing col names
names(averageDailyActivity) <- c("Interval", "Mean")

# Converting the data set into a dataframe
averageActivitydf <- data.frame(averageDailyActivity)

# Plotting on ggplot2
da <- ggplot(averageActivitydf, mapping = aes(Interval, Mean)) + 
  geom_line(col = "blue") +
  xlab("Interval") + 
  ylab("Average Number of Steps") + 
  ggtitle("Average Number of Steps Per Interval") +
  theme_calc(base_family = "serif")
  
print(da)
```

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


```{r}
averageDailyActivity[which.max(averageDailyActivity$Mean), ]$Interval

```



## Imputing missing 

Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs.

```{r}
sum(is.na(activity$steps))

```
Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
```{r}
# Matching the mean of daily activity with the missing values
imputedSteps <- averageDailyActivity$Mean[match(activity$interval, averageDailyActivity$Interval)]
```

Create a new dataset that is equal to the original dataset but with the missing data filled in.

```{r}
# Transforming steps in activity if they were missing values with the filled values from above.
activityImputed <- transform(activity, 
                             steps = ifelse(is.na(activity$steps), yes = imputedSteps, no = activity$steps))

# Forming the new dataset with the imputed missing values.
totalActivityImputed <- aggregate(steps ~ date, activityImputed, sum)

# Changing col names
names(totalActivityImputed) <- c("date", "dailySteps")
```
Testing the new dataset to check if it still has any missing values -

```{r}
sum(is.na(totalActivityImputed$dailySteps))

```
```{r}
# Converting the data set into a data frame to be able to use ggplot2
totalImputedStepsdf <- data.frame(totalActivityImputed)

# Plotting a histogram using ggplot2
p <- ggplot(totalImputedStepsdf, aes(x = dailySteps)) + 
  geom_histogram(breaks = seq(0, 25000, by = 2500), fill = "#83CAFF", col = "black") + 
  ylim(0, 30) + 
  xlab("Total Steps Taken Per Day") + 
  ylab("Frequency") + 
  ggtitle("Total Number of Steps Taken on a Day") + 
  theme_calc(base_family = "serif")

print(p)
```
The mean of the total number of steps taken per day is:

```{r}
mean(totalActivityImputed$dailySteps)

```
The median of the total number of steps taken per day is:

```{r}
median(totalActivityImputed$dailySteps)

```




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

Create a new factor variable in the dataset with two levels – “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.

```{r}
# Updating format of the dates
activity$date <- as.Date(strptime(activity$date, format="%Y-%m-%d"))

# Creating a function that distinguises weekdays from weekends
activity$dayType <- sapply(activity$date, function(x) {
  if(weekdays(x) == "Saturday" | weekdays(x) == "Sunday")
  {y <- "Weekend"}
  else {y <- "Weekday"}
  y
})
```


```{r}
# Creating the data set that will be plotted
activityByDay <-  aggregate(steps ~ interval + dayType, activity, mean, na.rm = TRUE)

# Plotting using ggplot2
dayPlot <-  ggplot(activityByDay, aes(x = interval , y = steps, color = dayType)) + 
  geom_line() + ggtitle("Average Daily Steps by Day Type") + 
  xlab("Interval") + 
  ylab("Average Number of Steps") +
  facet_wrap(~dayType, ncol = 1, nrow=2) +
  scale_color_discrete(name = "Day Type") +
  theme_calc(base_family = "serif")

print(dayPlot) 
```