CP1-RepResearch.Rmd

---
title: "CourseProject1-ReproducibleResearch"
author: "pblack476"
date: "10/14/2020"
output: html_document
---
```{r}
require(knitr)
require(ggplot2)
require(data.table)
require(lubridate)
require(mice)
opts_chunk$set(echo = TRUE, results = 'hold')
```

Reading the data:
```{r}
act_data <- fread("activity.csv", header=TRUE, sep=",")
str(act_data)
```

Pre-Processing:
```{r}
act_data[,date := ymd(act_data[,date])]
act_data[,interval := as.factor(act_data[,interval])]
str(act_data)
```

## What is mean total number of steps taken per day?
### 1. Calculate the total number of steps taken per day and plot a histogram

```{r}
spd <- aggregate(steps ~ date,act_data,sum)
colnames(spd) <- c("date", "steps")
head(spd)
ggplot(spd, aes(x = steps)) + 
  geom_histogram(fill = "red", binwidth = 1000) + 
  labs(title = "Histogram - Steps Taken Per Day", x = "Steps Per Day", y = "Frequency")
```

### 2. Report the mean and median of total number of steps per day

```{r}
mean_steps_per_day <- mean(spd$steps)
mean_steps_per_day
median_steps_per_day <- median(spd$steps)
median_steps_per_day
```

## What is the average daily activity pattern?
### 1. Make a time series plot of the 5-minute interval and the average number of steps taken, averaged across all days

```{r}
steps_per_interval <- aggregate(steps ~ interval, data = act_data, FUN = mean, na.rm = TRUE)
steps_per_interval$interval <- as.integer(levels(steps_per_interval$interval)[steps_per_interval$interval])
colnames(steps_per_interval) <- c("interval", "steps")

ggplot(steps_per_interval, aes(x = interval, y = steps)) + 
  geom_line(col = "red", size = 1) + 
  labs(title = "Average Daily Activity Pattern", x = "Interval", y = "Steps")
```

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

```{r}
max_interval <- steps_per_interval[which.max(steps_per_interval$steps),]
max_interval
```

### Imputation of NA's

```{r}
new_act_data <- act_data
new_act_data<-complete(mice(new_act_data,m=1,method='mean'))
```

### 4. Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day.

```{r}
new_steps_per_day <- aggregate(steps ~ date, data = new_act_data, FUN=sum)
colnames(new_steps_per_day) <- c("date", "steps")
ggplot(new_steps_per_day, aes(x = steps)) + 
  geom_histogram(fill = "red", binwidth = 1000) + 
  labs(title = "Histogram - Steps Taken Per Day", x = "Steps Per Day", y = "Frequency")
```

### Impact of Imputation:
```{r}
new_mean_steps_per_day <- mean(new_steps_per_day$steps)
new_mean_steps_per_day
new_median_steps_per_day <- median(new_steps_per_day$steps)
new_median_steps_per_day
```

## Are there differences in activity patterns between weekdays and weekends?
### 1. 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}
dt <- data.table(new_act_data)
dt[, weekday := ifelse(weekdays(date) %in% c("Saturday", "Sunday"), "Weekend", "Weekday")]
dt$weekday <- as.factor(dt$weekday)
dt$interval <- as.integer(levels(dt$interval)[dt$interval])
```

### 2. Make a panel plot containing 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 weekday days or weekend days (y-axis)

```{r}
steps_per_weekday <- aggregate(steps ~ interval+weekday, data = dt, FUN = mean)
ggplot(steps_per_weekday, aes(x = interval, y = steps)) + 
  geom_line(col = "red", size = 1) + 
  facet_wrap(~ weekday, nrow=2, ncol=1) + 
  labs(x = "Interval", y = "Number of Steps")
```