Submission for Reproducable Research Project 1

PA1_template.Rmd

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+## 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)
+
+```
 
-## Imputing missing values
 
 
 
 ## 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) 
+```
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