Course: Reproducible Research

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Except where otherwise indicated, this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License . Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site.

The materials below are used by students in this course. To sign up to take the course online, please visit the Johns Hopkins Data Science Specialization.

Course Projects

The plotting assignments will be assessed via peer assessment. In these assignments you will be asked to construct or reproduce certain plots. You will be evaluated by your classmates on the plot that you produce and the code that you write to construct the plot. Assignments evaluted via peer assessment will make use of your GitHub account.

» Peer Assessment 1

Introduction

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a FitbitNike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.

This assignment makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day.

Data

The data for this assignment can be downloaded from the course web site:

The variables included in this dataset are:

  • steps: Number of steps taking in a 5-minute interval (missing values are coded as NA)

  • date: The date on which the measurement was taken in YYYY-MM-DD format

  • interval: Identifier for the 5-minute interval in which measurement was taken

The dataset is stored in a comma-separated-value (CSV) file and there are a total of 17,568 observations in this dataset.

Assignment

This assignment will be described in multiple parts. You will need to write a report that answers the questions detailed below. Ultimately, you will need to complete the entire assignment in a single R markdown document that can be processed by knitr and be transformed into an HTML file.

Throughout your report make sure you always include the code that you used to generate the output you present. When writing code chunks in the R markdown document, always use echo = TRUE so that someone else will be able to read the code. This assignment will be evaluated via peer assessment so it is essential that your peer evaluators be able to review the code for your analysis.

For the plotting aspects of this assignment, feel free to use any plotting system in R (i.e., base, lattice, ggplot2)

Fork/clone the GitHub repository created for this assignment. You will submit this assignment by pushing your completed files into your forked repository on GitHub. The assignment submission will consist of the URL to your GitHub repository and the SHA-1 commit ID for your repository state.

NOTE: The GitHub repository also contains the dataset for the assignment so you do not have to download the data separately.

Loading and preprocessing the data

Show any code that is needed to

  1. Load the data (i.e. read.csv())

  2. Process/transform the data (if necessary) into a format suitable for your analysis

What is mean total number of steps taken per day?

For this part of the assignment, you can ignore the missing values in the dataset.

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

  2. Calculate and report the mean and median total number of steps taken per day

What is the average daily activity pattern?

  1. 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)

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

Imputing missing values

Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.

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

  2. 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.

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

  4. Make a histogram of the total number of steps taken each day and Calculate and report the mean and mediantotal number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?

Are there differences in activity patterns between weekdays and weekends?

For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.

  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.

  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). See the README file in the GitHub repository to see an example of what this plot should look like using simulated data.

Submitting the Assignment

To submit the assignment:

  1. Commit the your completed PA1_template.Rmd file to the master branch of your git repository (you should already be on the master branch unless you created new ones)

  2. Commit your PA1_template.md and PA1_template.html files produced by processing your R markdown file with knit2html() function in R (from the knitr package) by running the function from the console.

  3. If your document has figures included (it should) then they should have been placed in the figure/ directory by default (unless you overrided the default). Add and commit the figure/ directory to yoru git repository so that the figures appear in the markdown file when it displays on github.

  4. Push your master branch to GitHub.

  5. Submit the URL to your GitHub repository for this assignment on the course web site.

In addition to submitting the URL for your GitHub repository, you will need to submit the 40 character SHA-1 hash (as string of numbers from 0-9 and letters from a-f) that identifies the repository commit that contains the version of the files you want to submit. You can do this in GitHub by doing the following

  1. Going to your GitHub repository web page for this assignment

  2. Click on the “?? commits” link where ?? is the number of commits you have in the repository. For example, if you made a total of 10 commits to this repository, the link should say “10 commits”.

  3. You will see a list of commits that you have made to this repository. The most recent commit is at the very top. If this represents the version of the files you want to submit, then just click the “copy to clipboard” button on the right hand side that should appear when you hover over the SHA-1 hash. Paste this SHA-1 hash into the course web site when you submit your assignment. If you don't want to use the most recent commit, then go down and find the commit you want and copy the SHA-1 hash.

» Peer Assessment 2

Introduction

Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

This project involves exploring the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.

Data

The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:

There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.

The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.

Assignment

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.

Questions

Your data analysis must address the following questions:

  1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

  2. Across the United States, which types of events have the greatest economic consequences?

Consider writing your report as if it were to be read by a government or municipal manager who might be responsible for preparing for severe weather events and will need to prioritize resources for different types of events. However, there is no need to make any specific recommendations in your report.

Requirements

For this assignment you will need some specific tools

  • RStudio: You will need RStudio to publish your completed analysis document to RPubs. You can also use RStudio to edit/write your analysis.

  • knitr: You will need the knitr package in order to compile your R Markdown document and convert it to HTML

Document Layout

  • Language: Your document should be written in English.

  • Title: Your document should have a title that briefly summarizes your data analysis

  • Synopsis: Immediately after the title, there should be a synopsis which describes and summarizes your analysis in at most 10 complete sentences.

  • There should be a section titled Data Processing which describes (in words and code) how the data were loaded into R and processed for analysis. In particular, your analysis must start from the raw CSV file containing the data. You cannot do any preprocessing outside the document. If preprocessing is time-consuming you may consider using the cache = TRUE option for certain code chunks.

  • There should be a section titled Results in which your results are presented.

  • You may have other sections in your analysis, but Data Processing and Results are required.

  • The analysis document must have at least one figure containing a plot.

  • Your analyis must have no more than three figures. Figures may have multiple plots in them (i.e. panel plots), but there cannot be more than three figures total.

  • You must show all your code for the work in your analysis document. This may make the document a bit verbose, but that is okay. In general, you should ensure that echo = TRUE for every code chunk (this is the default setting in knitr).

Publishing Your Analysis

For this assignment you will need to publish your analysis on RPubs.com. If you do not already have an account, then you will have to create a new account. After you have completed writing your analysis in RStudio, you can publish it to RPubs by doing the following:

  1. In RStudio, make sure your R Markdown document (.Rmd) document is loaded in the editor

  2. Click the Knit HTML button in the doc toolbar to preview your document.

  3. In the preview window, click the Publish button.

Once your document is published to RPubs, you should get a unique URL to that document. Make a note of this URL as you will need it to submit your assignment.

NOTE: If you are having trouble connecting with RPubs due to proxy-related or other issues, you can upload your final analysis document file as a PDF to Coursera instead.

Submitting Your Assignment

In order to submit this assignment, you must copy the RPubs URL for your completed data analysis document in to the peer assessment question.