Homework Week 1

Due: Friday 8 September 2017 by 8PM Mountain Time

1. Readings

Open Science Readings

Read the open science related articles below.

1. The value of open science
2. Five selfish reasons to work reproducibly - Florian Markowetz
3. Computing Workflows for Biologists: A Roadmap

View Slideshow: Share, Publish & Archive Code & Data

Flood Readings

Read the following articles and listen to the 7 minute interview with Suzanne Anderson (faculty here at CU Boulder).

1. Gochis, D. et al. (2014): The great Colorado flood of September 2013. Bull. Amer. Meteor. Soc. 96, 1461-1487, doi:10.1175/BAMS-D-13-00241.1.

2. Coe, J.A. et al (2014): New insights into debris-flow hazards from an extraordinary event in the Colorado Front Range. GSA Today 24 (10), 4-10, doi: 10.1130/GSATG214A.1.

3. Anderson, S.W., Anderson, S.P., & Anderson, R.S. (2015). Exhumation by debris flows in the 2013 Colorado Front Range storm. Geology 43 (5), 391-394, doi:10.1130/G36507.1. 

4. Read the short article and listen to the 7 minute interview with Suzanne Anderson: To listen - click on the “ Listen” icon on the page Study: 2013 Front Range floods caused a thousand year’s worth of erosion

2. Watch Flood Video

Watch this video to learn more about the 2013 Colorado floods and some of the data that can be used to better understand the drivers and impacts of those floods.

Before and After Google Earth Fly Through

3. Review Lessons

After you’ve read the readings and watched the videos, complete the homework assignment below. Review the Set Up R lessons. (see links on the left hand side of this page). Following the lessons, setup R, RStudio and a working directory that will contain the data you will use for the entire course. IMPORTANT: if you working directory is not setup right - you won’t be able to follow along in class. Also you won’t be able to test your code when you submit assignments to give you partial credit!

Once everything is setup, review the second set of lessons (R Markdown intro) which walk you through creating R Markdown documents and knitting them to .html format. If you already know rmarkdown, be sure to review the lessons anyway - particularly the ones about file organization and why you use this workflow in science.

Important: Review ALL of the lessons and have your computer setup BEFORE class begins next week. You will be behind if these things are not setup / complete before week 2.

4. Complete Assignment Below

After you have reviewed the lessons above, complete the assignment below. Submit your .html document and R Markdown document to the D2L course drop box by Friday 8 September 2017 by 8PM Mountain Time.

# load the ggplot2 library for plotting
library(ggplot2)

# download data from figshare
# note that you are downloading the data into your data directory
download.file(url = "https://ndownloader.figshare.com/files/7010681",
              destfile = "data/boulder-precip.csv",
              method = "libcurl")

# if the code above doesn't allow you to download the data, remove the method =
# "libcurl" argument! Also note that you may get a warning that you are not able to
# download the data twice. That is ok!

# import data
boulder_precip <- read.csv(file="data/boulder-precip.csv")

# view first few rows of the data
head(boulder_precip)
##     X       DATE PRECIP
## 1 756 2013-08-21    0.1
## 2 757 2013-08-26    0.1
## 3 758 2013-08-27    0.1
## 4 759 2013-09-01    0.0
## 5 760 2013-09-09    0.1
## 6 761 2013-09-10    1.0

# when you download the data you create a `data.frame`
# view each column of the data frame using it's name (or header)
boulder_precip$DATE
##  [1] "2013-08-21" "2013-08-26" "2013-08-27" "2013-09-01" "2013-09-09"
##  [6] "2013-09-10" "2013-09-11" "2013-09-12" "2013-09-13" "2013-09-15"
## [11] "2013-09-16" "2013-09-22" "2013-09-23" "2013-09-27" "2013-09-28"
## [16] "2013-10-01" "2013-10-04" "2013-10-11"

# view the precip column
boulder_precip$PRECIP
##  [1] 0.1 0.1 0.1 0.0 0.1 1.0 2.3 9.8 1.9 1.4 0.4 0.1 0.3 0.3 0.1 0.0 0.9
## [18] 0.1

# qplot stands for quick plot. It is a function in the ggplot2 library.
# Let's use it to plot our data
qplot(x = boulder_precip$DATE,
      y = boulder_precip$PRECIP)

If your code ran properly, the plot output should look like the image above.

Troubleshooting: Missing Plot

If the code above did not produce a plot, please check the following:

Check Your Working Directory

If the path to your file is not correct, then the data won’t load into R. If the data don’t load into R, then you can’t work with it or plot it.

To figure out your current working directory use the command: getwd() Next, go to your finder or file explorer on your computer. Navigate to the path that R gives you when you type getwd() in the console. It will look something like the path example: /Users/your-username/documents/earth-analytics

# check your working directory
getwd()

## [1] "/Users/lewa8222/documents/earth-analytics"

In the example above, note that my USER directory is called lewa8222. Yours is called something different. Is there a data directory within the earth-analytics directory?

If not, review the working directory lesson to ensure your working directory is SETUP properly on your computer and in RStudio.

Class Participation - Flood Diagram Activity

While attendance is not explicitly tracked, participation in this course counts towards your grade. This week, please be sure that your name is associated with one of the diagrams posted in the piazza forum. In class you worked in groups so it is ok if multiple people are associated with one diagram. Just be sure your name is there so you get credit and if it isn’t - please edit your post and add your names above the image!