diff --git a/2024-10-coreR/index.html b/2024-10-coreR/index.html index 57870691..37748ef8 100644 --- a/2024-10-coreR/index.html +++ b/2024-10-coreR/index.html @@ -175,7 +175,7 @@
  • - 10  Practice Session I + 10  Practice Session: Joins
  • @@ -199,7 +199,7 @@
  • - 14  Practice Session II + 14  Practice Session: Collaborative Report
  • @@ -223,7 +223,7 @@
  • - 18  Practice Session III + 18  Practice Session: Functions
  • diff --git a/2024-10-coreR/search.json b/2024-10-coreR/search.json index 8ccc56a7..865204ba 100644 --- a/2024-10-coreR/search.json +++ b/2024-10-coreR/search.json @@ -882,141 +882,141 @@ { "objectID": "session_10.html", "href": "session_10.html", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "", "text": "Learning Objectives", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#learning-objectives", "href": "session_10.html#learning-objectives", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "", "text": "Practice joining tables together\nPractice identifying primary and foreign keys\nPractice using common cleaning and wrangling functions\n\n\n\n\n\n\n\nAcknowledgements\n\n\n\nThese exercises are adapted from Allison Horst’s EDS 221: Scientific Programming Essentials Course for the Bren School’s Master of Environmental Data Science program.", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#about-the-data", "href": "session_10.html#about-the-data", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "About the data", "text": "About the data\nThese exercises will be using bird survey data collected from the central Arizona-Phoenix metropolitan area by Arizona State University researchers (Warren et al. 2021).", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#exercise-1-practice-joins", "href": "session_10.html#exercise-1-practice-joins", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "Exercise 1: Practice Joins", "text": "Exercise 1: Practice Joins\n\n\n\n\n\n\nSetup\n\n\n\n\nMake sure you’re in the right project (training_{USERNAME}) and use the Git workflow by Pulling to check for any changes in the remote repository (aka repository on GitHub).\nCreate a new Quarto Document.\n\nTitle it “R Practice: Tidy Data and Joins”.\nSave the file and name it “r-practice-tidy-data-joins”.\n\n\nNote: Double check that you’re in the right project. Where in RStudio can you check where you are?\n\nLoad the following libraries at the top of your Quarto Document.\n\n\nlibrary(readr)\nlibrary(dplyr)\nlibrary(lubridate) # for bonus question\n\n# Quick question: Do you get a message after loading the libraries? What is it telling you? Talk to your neighbor about it or write a note in your qmd.\n\n\nObtain data from the EDI Data Portal Ecological and social interactions in urban parks: bird surveys in local parks in the central Arizona-Phoenix metropolitan area. Download the following files:\n\n\n52_pp52_birds_1.csv\n52_pp52_surveys_1.csv\n52_pp52_sites_1.csv\n52_pp52_taxalist_1.csv\n\nNote: It’s up to you on how you want to download and load the data! You can either use the download links (obtain by right-clicking the “Download” button and select “Copy Link Address” for each data entity) or manually download the data and then upload the files to RStudio server.\n\nOrganize your Quarto Document in a meaningful way. Organization is personal - so this is up to you! Consider the different ways we’ve organized previous files using: headers, bold text, naming code chunks, comments in code chunks. What is most important is organizing and documenting the file so that your future self (or if you share this file with others!) understands it as well as your current self does right now.\nUse the Git workflow. After you’ve set up your project and uploaded your data go through the workflow: Stage (add) -> Commit -> Pull -> Push\n\nNote: You also want to Pull when you first open a project.", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#read-in-the-data", "href": "session_10.html#read-in-the-data", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.1 Read in the data", "text": "10.1 Read in the data\n\n\n\n\n\n\nQuestion 1\n\n\n\nRead in the data and store the data frames as bird_observations, sites, surveys, and taxalist (it should be clear from the raw file names which is which).\n\n\n\n\nAnswer\n# read in data using download links\nbird_observations <- read_csv(\"https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-cap.256.10&entityid=53edaa7a0e083013d9bf20322db1780e\")\nsurveys <- read_csv(\"https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-cap.256.10&entityid=b2466fa5cb5ed7ee1ea91398fc291c59\")\nsites <- read_csv(\"https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-cap.256.10&entityid=81bf72420e69077097fb0790dcdc63a6\")\ntaxalist <- read_csv(\"https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-cap.256.10&entityid=58f863b7e3066e68536a9cacdc7bd58e\")\n\n# read in data from the data directory after manually downloading data \nbird_observations <- read_csv(\"data/52_pp52_birds_1.csv\")\nsurveys <- read_csv(\"data/52_pp52_surveys_1.csv\")\nsites <- read_csv(\"data/52_pp52_sites_1.csv\")\ntaxalist <- read_csv(\"data/52_pp52_taxalist_1.csv\")", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#get-familiar-with-the-data", "href": "session_10.html#get-familiar-with-the-data", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.2 Get familiar with the data", "text": "10.2 Get familiar with the data\n\n\n\n\n\n\nQuestion 2a\n\n\n\nWhat functions can you use to explore the data you just read in? Think about which functions we’ve been using to explore the structure of the data frame, information about columns, unique observations, etc. Tip: run `View(name_of_your_data_frame) in the console to see data in a spreadsheet-style viewer.\n\n\n\n\nAnswer\n# returns dimensions of the dataframe by number of rows and number of cols\ndim(bird_observations)\n\n# returns the top six rows of the dataframe\nhead(bird_observations)\n\n# returns all the columns and some info about the cols\nglimpse(bird_observations)\n\n# similar to glimpse but returns some summary statistics about the cols\nsummary(bird_observations)\n\n# returns column names \nnames(bird_observations)\n\n# returns unique values in a column. In this case we can see all the different bird species IDs\nunique(bird_observations$species_id)\n\n\n\n\n\n\n\n\nQuestion 2b\n\n\n\nWhat are the primary and foreign keys for the tables bird_observations and taxalist? Recall that a primary key is a unique identifier for each observed entity, one per row. And a foreign key references to a primary key in another table (linkage).\nHint: First identify the primary keys for all the tables, then identify the foreign keys.\n\n\n\n\nAnswer\n\n\nbird_observations: Primary key is a compound key made up of survey_id, site_id, and species_id. The foreign key is species_id.\ntaxalist: Primary key is species_id and does not have a foreign key that match the primary key in bird_observations.\n\nHowever, we could join bird_observations and taxalist by species_id, but depending on the type of join some values would be droped or NAs would be introduce in the resulting data frame.", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#create-a-subset-of-bird_observations", "href": "session_10.html#create-a-subset-of-bird_observations", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.3 Create a subset of bird_observations", "text": "10.3 Create a subset of bird_observations\n\n\n\n\n\n\nQuestion 3\n\n\n\nWrite code to create a subset of bird_observations called birds_subset that only contains observations for birds with species id BHCO and RWBL, and from sites with site ID LI-W and NU-C.\nHint: What function do you use to subset data by rows?\n\n\n\n\nAnswer\nbirds_subset <- bird_observations %>% \n filter(species_id %in% c(\"BHCO\", \"RWBL\")) %>% \n filter(site_id %in% c(\"LI-W\", \"NU-C\"))", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#use-left_join-to-merge-birds_subset-with-the-tables-sites", "href": "session_10.html#use-left_join-to-merge-birds_subset-with-the-tables-sites", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.4 Use left_join() to merge birds_subset with the tables sites", "text": "10.4 Use left_join() to merge birds_subset with the tables sites\n\n\n\n\n\n\nQuestion 4a\n\n\n\nFirst, answer: what do you expect the outcome data frame when doing left_join() between birds_subset and sites to look like? What observations do you expect in the outcome data frame.\nWrite this in your Quarto Document or tell a neighbor.\n\n\n\n\nAnswer\n\nI expect to see all columns and all observations from birds_subset and from sites, I expect to see the columns park_code, park_district, park-name, point_code, point_location and park_acreage and only observations for NU-C and LI-W because those are the only site_id values in birds_subset and in a left join only the observations matching the left table (in this case, birds_subset is the left table) will be kept.\n\n\n\n\n\n\n\nQustion 4b\n\n\n\nUse a left join to update birds_subset so that it also includes sites information. For each join, include an explicit argument saying which key you are joining by (even if it will just assume the correct one for you). Store the updated data frame as birds_left. Make sure to look at the output - is what it contains consistent with what you expected it to contain?\n\n\n\n\nAnswer\n# syntax using pipe\nbirds_left <- birds_subset %>% \n left_join(y = sites, by = \"site_id\")\n# don't see x = birds_subset here because piping in birds_subset means it automatically assumes birds_subset as x.\n\n# syntax without pipe\nbirds_left <- left_join(x = birds_subset, y = sites, by = \"site_id\")", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#use-full_join-to-merge-birds_subset-and-sites-tables", "href": "session_10.html#use-full_join-to-merge-birds_subset-and-sites-tables", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.5 Use full_join() to merge birds_subset and sites tables", "text": "10.5 Use full_join() to merge birds_subset and sites tables\n\n\n\n\n\n\nQuestion 5a\n\n\n\nFirst, answer: what do you expect a full_join() between birds_subset and sites to contain? Write this in your Quarto Document or tell a neighbor.\n\n\n\n\nAnswer\n\nI expect to see all columns and all observations from birds_subset and all columns and all observations from sites to be merged into one data frame because in a full join everything is kept.\n\n\n\n\n\n\n\nQuestions 5b\n\n\n\nWrite code to full_join() the birds_subset and sites data into a new object called birds_full. Explicitly include the variable you’re joining by. Look at the output. Is it what you expected?\n\n\n\n\nAnswer\n# syntax using pipe\nbirds_full <- birds_subset %>% \n full_join(y = sites, by = \"site_id\")\n\n# syntax without pipe\nbirds_full <- full_join(x = birds_subset, y = sites, by = \"site_id\")", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#use-inner_join-to-merge-birds_subset-and-taxalist-data", "href": "session_10.html#use-inner_join-to-merge-birds_subset-and-taxalist-data", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.6 Use inner_join() to merge birds_subset and taxalist data", "text": "10.6 Use inner_join() to merge birds_subset and taxalist data\n\n\n\n\n\n\nQuestion 6a\n\n\n\nFirst, answer: what do you expect an inner_join() between birds_subset and taxalist to contain? Write this in your Quarto Document or tell a neighbor.\n\n\n\n\nAnswer\n\nI expect to only have data merge together based on species_id and since there is only BHCO and RWBL in birds_subset then I will only retain data related to those two species. I will also expect to see the columns from taxalist: common_name and asu_itis to be merged into the joined table.\n\n\n\n\n\n\n\nQuestion 6b\n\n\n\nWrite code to inner_join() the birds_subset and taxalist, called birds_inner. Include an argument for what variable you’ll be joining by. Make sure you check the output.\n\n\n\n\nAnswer\n# syntax using pipe\nbirds_inner <- birds_subset %>% \n inner_join(y = taxalist, by = \"species_id\")\n\n# syntax without pipe\nbirds_inner <- inner_join(x = birds_subset, y = taxalist, by = \"species_id\" )\n\n\n\n\n\n\n\n\nQuestion 6c\n\n\n\nWhat would you get if instead of inner_join() you’d used left_join() for this example? Write code for the left join and check.\n\n\n\n\nAnswer\n# syntax using pipe\nbirds_inner_left <- birds_subset %>% \n left_join(y = taxalist, by = \"species_id\")\n\n# syntax without pipe\nbirds_inner_left <- left_join(x = birds_subset, y = taxalist, by = \"species_id\")\n\n\n\n\n\n\n\n\nQuestion 6d\n\n\n\nWhy does that make sense for this scenario? In what case would you expect the outcome to differ from an inner_join()? Write this in your Quarto Document or tell a neighbor.\n\n\n\n\nAnswer\n\nYou have the same resulting data set regardless of using inner_join() or left_join() to merge bird_subset and taxalist. The reasons for this are:\n\ninner_join() keeps only the rows (observations) that have a matching key across both data sets - here, species_id is our key, and the only rows that match across both data sets are those where species_id equals BHCO or RWBL\nleft_join() keeps all rows from the left table (in our case, the left table is birds_subset) and merges on data with matching keys (species_id) on the right (here, the right table is taxalist). Because our left data set (birds_subset) only contains species_ids equal to BHCO or RWBL, only rows with those species will be kept from the right data set (taxalist)\n\nYou’d expect the outcome to differ from an inner_join() if birds_subset contained an observation with a species_id that was not found in taxalist. If there was an observation of a species_id in birds_subset that was not in taxalist, then that observation would be kept, and NAs would be assigned to the common_name and asu_itis columns for that observations", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#exercise-2-practice-wrangling-joining-data", "href": "session_10.html#exercise-2-practice-wrangling-joining-data", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "Exercise 2: Practice Wrangling & Joining Data", "text": "Exercise 2: Practice Wrangling & Joining Data", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#wrangle-bird_observations-data-and-merge-the-data-with-all-the-other-tables-sites-surveys-and-taxalist", "href": "session_10.html#wrangle-bird_observations-data-and-merge-the-data-with-all-the-other-tables-sites-surveys-and-taxalist", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.7 Wrangle bird_observations data and merge the data with all the other tables (sites, surveys, and taxalist)", "text": "10.7 Wrangle bird_observations data and merge the data with all the other tables (sites, surveys, and taxalist)\n\n\n\n\n\n\nQuestion 7a\n\n\n\nStarting with your object bird_observations, rename the notes column to bird_obs_notes (so this doesn’t conflict with notes in the surveys table).\n\n\n\n\nAnswer\nbird_observations <- bird_observations %>% \n rename(bird_obs_notes = notes)\n\n\n\n\n\n\n\n\nQuestion 7b\n\n\n\n\nCreate a subset that contains all observations in the birds_observations data frame,\nthen join the taxalist, sites and surveys tables to it,\nand finally limit to only columns survey_date, common_name, park_name, bird_count, and observer.\n\nHint: What function do you use to subset data by columns?\n\n\n\n\nAnswer\nbird_obs_subset <- bird_observations %>% \n full_join(y = taxalist, by = \"species_id\") %>% \n full_join(y = sites, by = \"site_id\") %>% \n full_join(y = surveys, by = c(\"site_id\", \"survey_id\")) %>% \n select(survey_date, common_name, park_name, bird_count, observer)", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#explore-observer-data-and-fix-the-values-within-this-column-so-that-all-values-are-in-the-same-format", "href": "session_10.html#explore-observer-data-and-fix-the-values-within-this-column-so-that-all-values-are-in-the-same-format", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.8 Explore observer data and fix the values within this column so that all values are in the same format", "text": "10.8 Explore observer data and fix the values within this column so that all values are in the same format\n\n\n\n\n\n\nQuestion 8a\n\n\n\nContinuing with bird_obs_subset, first use unique() to see the different unique values in the column observer. How many observers are there? Which value is unlike the others?\n\n\n\n\nAnswer\nunique(bird_obs_subset$observer)\n\n\n\n\n\n\n\n\nQuestion 8b\n\n\n\nReplace “Josh Burns” with a format that matches the other observer names. Then use unique() again to check your work.\nHint: What function do you use when you are making a change to an entire column?\n\n\n\n\nAnswer\nbird_obs_subset <- bird_obs_subset %>% \n mutate(observer = if_else(condition = observer == \"Josh Burns\", \n true = \"J. Burns\", \n false = observer))\n\nunique(bird_obs_subset$observer)\n\n\n\n\n\n\n\n\nSave your work and use Git\n\n\n\nDon’t forget the Git workflow! After you’ve completed the exercises or reached a significant stopping point, use the workflow: Stage (add) -> Commit -> Pull -> Push", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { "objectID": "session_10.html#bonus-use-a-new-package-lubridate-to-wrangle-the-date-data-and-find-the-total-number-of-birds-by-park-and-month", "href": "session_10.html#bonus-use-a-new-package-lubridate-to-wrangle-the-date-data-and-find-the-total-number-of-birds-by-park-and-month", - "title": "10  Practice Session I", + "title": "10  Practice Session: Joins", "section": "10.9 Bonus: Use a new package lubridate to wrangle the date data and find the total number of birds by park and month", "text": "10.9 Bonus: Use a new package lubridate to wrangle the date data and find the total number of birds by park and month\nHint: How do you learn about a new function or package?\n\n\n\n\n\n\nBonus Question(s)\n\n\n\n\nUse lubridate::month() to add a new column to bird_obs_subset called survey_month, containing only the month number. Then, convert the month number to a factor (again within mutate()).\nUse dplyr::relocate() to move the new survey_month column to immediately after the survey_date column. You can do this in a separate code chunk, or pipe straight into it from your existing code.\nFilter to only include parks Lindo, Orme, Palomino, and Sonrisa.\nFind the total number of birds observed by park and month (Hint: You can use group_by() and summarize()).\n\n\n\n\n\nAnswer\nbird_obs_subset <- bird_obs_subset %>% \n mutate(survey_month = lubridate::month(survey_date)) %>% \n mutate(survey_month = as.factor(survey_month)) %>% \n dplyr::relocate(survey_month, .after = survey_date) %>% \n filter(park_name %in% c(\"Lindo\", \"Orme\", \"Palomino\", \"Sonrisa\")) %>% \n group_by(park_name, survey_month) %>% \n summarize(tot_bird_count_month = n())\n\n\nTake a look at your final data frame. Does it give you the outcome you expected? Is it informative? How would you improve this wrangling process?\n\n\n\n\nWarren, Paige S., Ann Kinzig, Chris A Martin, and Louis Machabee. 2021. “Ecological and Social Interactions in Urban Parks: Bird Surveys in Local Parks in the Central Arizona-Phoenix Metropolitan Area.” Environmental Data Initiative. https://doi.org/10.6073/PASTA/F6F004BC7112CE266FDE2B80FAD19FF4.", "crumbs": [ - "10  Practice Session I" + "10  Practice Session: Joins" ] }, { @@ -1232,91 +1232,91 @@ { "objectID": "session_14.html", "href": "session_14.html", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "", "text": "Learning Objectives", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#learning-objectives", "href": "session_14.html#learning-objectives", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "", "text": "Practice using common cleaning and wrangling functions\nPractice creating plots using common visualization functions in ggplot\nPractice saving and sharing data visualizations\nPractice Git and GitHub workflow and collaborating with a collegue\n\n\n\n\n\n\n\nAcknowledgements\n\n\n\nThese exercises are adapted from Allison Horst’s EDS 221: Scientific Programming Essentials Course for the Bren School’s Master of Environmental Data Science program.", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#about-the-data", "href": "session_14.html#about-the-data", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "About the data", "text": "About the data\nThese exercises will be using data on abundance, size, and trap counts (fishing pressure) of California spiny lobster (Panulirus interruptus) and were collected along the mainland coast of the Santa Barbara Channel by Santa Barbara Coastal LTER researchers (LTER, Reed, and Miller 2022).", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#exercise-collaborate-on-an-analysis-and-create-a-report-to-publish-using-github-pages", "href": "session_14.html#exercise-collaborate-on-an-analysis-and-create-a-report-to-publish-using-github-pages", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "14.1 Exercise: Collaborate on an analysis and create a report to publish using GitHub Pages", "text": "14.1 Exercise: Collaborate on an analysis and create a report to publish using GitHub Pages\n\n\n\n\n\n\nSetup\n\n\n\n\nCreate a new repository with a partner\n\nDetermine who is the Owner and who is the Collaborator\nThe Owner creates a repository on GitHub titled with both your names (i.e. If Angel and Camila were partners, and Angel is the Owner, she would create a repo called angel-camila)\n\nWhen creating the repository, add a brief description (i.e. R Practice Session: Collaborating on, Wrangling & Visualizing Data), keep the repo Public, and Initialize the repo with a README file and an R .gitignore template.\n\nThe Owner adds the Collaborator to the repo\nBoth the Collaborator and the Owner clone the repo into their RStudio\nBoth the Collaborator and the Owner run git config pull.rebase false in the Terminal to set the Git default strategy for Pulling\n\n\nStep 2 and Step 3 are meant to be completed at the same time. Step 2 is for the Collaborator to complete, and Step 3 is for the Owner to complete.\n\nCollaborator creates new files for exercise\n\nThe Collaborator creates the following directory:\n\nanalysis\n\nAfter creating the directories, create the following Quarto Documents and store them in the listed folders:\n\nTitle it: “Owner Analysis”, save it as: owner-analysis.qmd, and store in analysis folder\nTitle it: “Collaborator Analysis”, save it as: collaborator-analysis.qmd, and store in analysis folder\nTitle it: “Lobster Report” and save it as: lobster-report.qmd (do not need to put in a folder)\n\nAfter creating the files, the Collaborator will stage (add), commit, write a commit message, pull, and push the files to the remote repository (on GitHub)\nThe Owner pulls the changes and Quarto Documents into their local repository (their workspace)\n\nOwner downloads data from the EDI Data Portal SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012.\n\nCreate two new directories one called data and one called figs\n\nNote: Git does not track empty directories, so you won’t see figs when you push to GitHub\n\nDownload the following data and upload them to the data folder:\n\nTime-series of lobster abundance and size\nTime-series of lobster trap buoy counts\n\nAfter creating the data folder and adding the data, the Owner will stage (add), commit, write a commit message,pull, and push the files to the remote repository (on GitHub)\nThe Collaborator pulls the changes and data into their local repository (their workspace)\n\n\n\n\n\n14.1.1 Explore, clean and wrangle data\nFor this portion of the exercise, the Owner will be working with the lobster abundance and size data, and the Collaborator will be working with the lobster trap buoy counts data\nQuestions 1-3 you will be working independently since you’re working with different data frames, but you’re welcome to check in with each other.\n\nOwnerCollaborator\n\n\n\n\n\n\n\n\nSetup\n\n\n\n\nOpen the Quarto Document owner-analysis.qmd\n\nCheck the YAML and add your name to the author field\nCreate a new section with a level 2 header and title it “Exercise: Explore, Clean, and Wrangle Data”\n\nLoad the following libraries at the top of your Quarto Document\n\n\nlibrary(readr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(here)\n\n\nRead in the data and store the data frame as lobster_abundance\n\n\nlobster_abundance <- read_csv(here(\"data/Lobster_Abundance_All_Years_20220829.csv\"))\n\n\nLook at your data. Take a minute to explore what your data structure looks like, what data types are in the data frame, or use a function to get a high-level summary of the data you’re working with.\nUse the Git workflow: Stage (add) -> Commit -> Pull -> Push\n\nNote: You also want to Pull when you first open a project\n\n\n\n\n\n14.2 Convert missing values using mutate() and na_if()\n\n\n\n\n\n\nQuestion 1\n\n\n\nThe variable SIZE_MM uses -99999 as the code for missing values (see metadata or use unique()). This has the potential to cause conflicts with our analyses, so let’s convert -99999 to an NA value. Do this using mutate() and na_if(). Look up the help page to see how to use na_if(). Check your output data using unique().\n\n\n\nlobster_abundance <- lobster_abundance %>% \n mutate(SIZE_MM = na_if(SIZE_MM, -99999))\n\n\n\n14.3 filter() practice\n\n\n\n\n\n\nQuestion 2\n\n\n\nCreate and store a subset that does NOT include observations from Naples Reef (NAPL). Check your output data frame to ensure that NAPL is NOT in the data frame.\n\n\n\nnot_napl <- lobster_abundance %>% \n filter(SITE != \"NAPL\")\n\n\n\n\n\n\n\nQuestion 3\n\n\n\nCreate and store a subset with lobsters at Arroyo Quemado (AQUE) AND with a carapace length greater than 70 mm. Check your output.\n\n\n\naque_70mm <- lobster_abundance %>% \n filter(SITE == \"AQUE\" & SIZE_MM >= 70)\n\n\n\n\n\n\n\nSave your work and use Git\n\n\n\nDon’t forget the Git workflow! After you’ve completed the exercises or reached a significant stopping point, use the workflow: Stage (add) -> Commit -> Pull -> Push\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSetup\n\n\n\n\nOpen the Quarto Document collaborator-analysis.qmd\n\nCheck the YAML and add your name to the author field\nCreate a new section with a level 2 header and title it “Exercise: Explore, Clean, and Wrangle Data”\n\nLoad the following libraries at the top of your Quarto Document.\n\n\nlibrary(readr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(here)\n\n\nRead in the data and store the data frame as lobster_traps\n\n\nlobster_traps <- read_csv(here(\"data/Lobster_Trap_Counts_All_Years_20210519.csv\"))\n\n\nLook at your data. Take a minute to explore what your data structure looks like, what data types are in the data frame, or use a function to get a high-level summary of the data you’re working with.\nUse the Git workflow: Stage (add) -> Commit -> Pull -> Push\n\nNote: You also want to Pull when you first open a project\n\n\n\n\n\n14.4 Convert missing values using mutate() and na_if()\n\n\n\n\n\n\nQuestion 1\n\n\n\nThe variable TRAPS uses -99999 as the code for missing values (see metadata or use unique()). This has the potential to cause conflicts with our analyses, so let’s convert -99999 to an NA value. Do this using mutate() and na_if(). Look up the help page to see how to use na_if(). Check your output data using unique().\n\n\n\nlobster_traps <- lobster_traps %>% \n mutate(TRAPS = na_if(TRAPS, -99999))\n\n\n\n14.5 filter() practice\n\n\n\n\n\n\nQuestion 2\n\n\n\nCreate and store a subset that does NOT include observations from Naples Reef (NAPL). Check your output data frame to ensure that NAPL is NOT in the data frame.\n\n\n\nnot_napl <- lobster_traps %>% \n filter(SITE != \"NAPL\")\n\n\n\n\n\n\n\nQuestion 3\n\n\n\nCreate and store a subset with lobsters at Carpinteria Reef (CARP) AND number of commercial trap floats is greater than 20. Check your output.\n\n\n\ncarp_20_traps <- lobster_traps %>% \n filter(SITE == \"CARP\" & TRAPS > 20)\n\n\n\n\n\n\n\nSave your work and use Git\n\n\n\nDon’t forget the Git workflow! After you’ve completed the exercises or reached a significant stopping point, use the workflow: Stage (add) -> Commit -> Pull -> Push\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n14.5.1 Create visually appealing and informative data visualization\n\nOwnerCollaborator\n\n\n\n\n\n\n\n\nSetup\n\n\n\n\nStay in the Quarto Document owner-analysis.qmd and create a new section with a level 2 header and title it “Exercise: Data Visualization”\n\nStructure of the data visualization exercises:\n\nQuestions 7-9 will have you create the necessary subsets to create the data visualizations, as well as the basic code to create a visualization.\nQuestion 10, return to the data visualization code you’ve written and add styling code to it. For this exercise, only add styling code to the visualization you want to include in the lobster-report.qmd (start with just one plot and if there’s time add styling code to another plot).\nLastly, save the final visualizations to the figs folder before collaborating on the lobster-report.qmd.\n\n\n\n\n\n\n\n\n\nQuestion 7\n\n\n\nCreate a multi-panel plot of lobster carapace length (SIZE_MM) using ggplot(), geom_histogram(), and facet_wrap(). Use the variable SITE in facet_wrap(). Use the object lobster_abundance.\n\n\n\nggplot(data = lobster_abundance, aes(x = SIZE_MM)) +\n geom_histogram() +\n facet_wrap(~SITE)\n\n\n\nPlots\n\n\n\n\n\n\n\n\n\nQuestion 8\n\n\n\nCreate a line graph of the number of total lobsters observed (y-axis) by year (x-axis) in the study, grouped by SITE.\n\n\nFirst, you’ll need to create a new dataset subset called lobsters_summarize:\n\nGroup the data by SITE AND YEAR\nCalculate the total number of lobsters observed using count()\n\n\nlobsters_summarize <- lobster_abundance %>% \n group_by(SITE, YEAR) %>% \n summarize(COUNT = n())\n\nNext, create a line graph using ggplot() and geom_line(). Use geom_point() to make the data points more distinct, but ultimately up to you if you want to use it or not. We also want SITE information on this graph, do this by specifying the variable in the color argument. Where should the color argument go? Inside or outside of aes()? Why or why not?\n\n# line plot\nggplot(data = lobsters_summarize, aes(x = YEAR, y = COUNT)) +\n geom_line(aes(color = SITE)) \n\n# line and point plot\nggplot(data = lobsters_summarize, aes(x = YEAR, y = COUNT)) +\n geom_point(aes(color = SITE)) +\n geom_line(aes(color = SITE)) \n\n\n\nPlots\n\n\n\n\n\n\n\n\n\n\nLine plot\n\n\n\n\n\n\n\nLine and point plot\n\n\n\n\n\n\n\n\n\n\n\n\n\nQuestion 9\n\n\n\n\nGo back to your visualization code and add some styling code (aka make your plots pretty!). Again, start with one plot and if there’s time add styling code to additional plots. Here’s a list of functions to help you get started (this is not an exhaustive list!) or revisit the data visualization lesson:\n\n\nlabs(): modifying axis, legend and plot labels\ntheme_(): add a complete theme to your plot (i.e. theme_light())\ntheme(): use to customize non-data components of a plot. We’ve listed out some parameters here, but run ?theme to see the full list (there’s a lot of customization you can do!)\n\naxis.title.y\npanel.background\nplot.background\npanel.grid.major.*\ntext\n\nscale_*_date(): use with dates and update breaks, limits, and labels\nscale_*_continuous(): use with continuous variables and update breaks, limits, and labels\nscale_*_discrete(): use with discrete variables and update breaks, limits, and labels\nscales package: use this within the above scale functions and you can do things like add percents to axes labels\ngeom_() within a geom function you can modify:\n\nfill: updates fill colors (e.g. column, density, violin, & boxplot interior fill color)\ncolor: updates point & border line colors (generally)\nshape: update point style\nalpha: update transparency (0 = transparent, 1 = opaque)\nsize: point size or line width\nlinetype: update the line type (e.g. “dotted”, “dashed”, “dotdash”, etc.)\n\n\n\nOnce you’re happy with how your plot looks, assign it to an object, and save it to the figs directory using ggsave()\n\n\n\n\n\n\n\n\n\nSave your work and use Git\n\n\n\nDon’t forget the Git workflow! After you’ve completed the exercises or reached a significant stopping point, use the workflow: Stage (add) -> Commit -> Pull -> Push\n\n\n\n\n\n\n\n\n\n\nSetup\n\n\n\n\nStay in the Quarto Document collaborator-analysis.qmd and create a new section with a level 2 header and title it “Exercise: Data Visualization”\n\nStructure of the data visualization exercises:\n\nQuestions 7-9 will have you create the necessary subsets to create the data visualizations, as well as the basic code to create a visualization.\nQuestion 10, return to the data visualization code you’ve written and add styling code to it. For this exercise, only add styling code to the visualization you want to include in the lobster-report.qmd (start with just one plot and if there’s time add styling code to another plot).\nLastly, save the final visualizations to the figs folder before collaborating on the lobster-report.qmd.\n\n\n\n\n\n\n\n\n\nQuestion 7\n\n\n\nCreate a multi-panel plot of lobster commercial traps (TRAPS) grouped by year, using ggplot(), geom_histogram(), and facet_wrap(). Use the variable YEAR in facet_wrap(). Use the object lobster_traps.\n\n\n\nggplot(data = lobster_traps, aes(x = TRAPS)) +\n geom_histogram() +\n facet_wrap( ~ YEAR)\n\n\n\nPlots\n\n\n\n\n\n\n\n\n\nQuestion 8\n\n\n\nCreate a line graph of the number of total lobster commercial traps observed (y-axis) by year (x-axis) in the study, grouped by SITE.\n\n\nFirst, you’ll need to create a new dataset subset called lobsters_traps_summarize:\n\nGroup the data by SITE AND YEAR\nCalculate the total number of lobster commercial traps observed using sum(). Look up sum() if you need to. Call the new column TOTAL_TRAPS. Don’t forget about NAs here!\n\n\nlobsters_traps_summarize <- lobster_traps %>% \n group_by(SITE, YEAR) %>% \n summarize(TOTAL_TRAPS = sum(TRAPS, na.rm = TRUE))\n\nNext, create a line graph using ggplot() and geom_line(). Use geom_point() to make the data points more distinct, but ultimately up to you if you want to use it or not. We also want SITE information on this graph, do this by specifying the variable in the color argument. Where should the color argument go? Inside or outside of aes()? Why or why not?\n\n# line plot\nggplot(data = lobsters_traps_summarize, aes(x = YEAR, y = TOTAL_TRAPS)) +\n geom_line(aes(color = SITE))\n\n# line and point plot\nggplot(data = lobsters_traps_summarize, aes(x = YEAR, y = TOTAL_TRAPS)) +\n geom_point(aes(color = SITE)) +\n geom_line(aes(color = SITE))\n\n\n\nPlots\n\n\n\n\n\n\n\n\n\n\nLine plot\n\n\n\n\n\n\n\nLine and point plot\n\n\n\n\n\n\n\n\n\n\n\n\nQuestion 9\n\n\n\nCreate a bar graph that shows the amount of high and low fishing pressure of lobster commercial traps at each SITE from 2019-2021. Note: The high and low fishing pressure metrics are completely made up and are not based on any known facts.\n\n\nFirst, you’ll need to create a new dataset subset called lobster_traps_fishing_pressure:\n\nfilter() for the years 2019, 2020, and 2021\nAdd a new column called FISHING_PRESSURE that contains the values “high” or “low”. A “high” fishing pressure has exactly or more than 8 traps, and a “low” fishing pressure has less than 8 traps. Use mutate() and if_else(). Check your output\nCalculate the number of “high” and “low” observations using group() and summarize(). Check your output\nRemove the NA values from the subsetted data. Hint: check out drop_na(). Check your output\n\n\nlobster_traps_fishing_pressure <- lobster_traps %>% \n filter(YEAR %in% c(2019, 2020, 2021)) %>%\n mutate(FISHING_PRESSURE = if_else(TRAPS >= 8, true = \"high\", false = \"low\")) %>%\n group_by(SITE, FISHING_PRESSURE) %>%\n summarize(COUNT = n()) %>%\n drop_na()\n\nNext, create a bar graph using ggplot() and geom_bar(). Note that geom_bar() automatically creates a stacked bar chart. Try using the argument position = \"dodge\" to make the bars side by side. Pick which bar position you like best.\n\n# bar plot\nggplot(data = lobster_traps_fishing_pressure, aes(x = SITE, y = COUNT, fill = FISHING_PRESSURE)) +\n geom_col()\n\n# dodged bar plot\nggplot(data = lobster_traps_fishing_pressure, aes(x = SITE, y = COUNT, fill = FISHING_PRESSURE)) +\n geom_col(position = \"dodge\")\n\n\n\nPlots\n\n\n\n\n\n\n\n\n\n\nBar plot\n\n\n\n\n\n\n\nDodged bar plot\n\n\n\n\n\n\n\n\n\n\n\n\nQuestion 10\n\n\n\n\nGo back to your visualization code and add some styling code (aka make your plots pretty!). Again, start with one plot and if there’s time add styling code to additional plots. Here’s a list of functions to help you get started (this is not an exhaustive list!) or revisit the data visualization lesson:\n\n\nlabs(): modifying axis, legend and plot labels\ntheme_(): add a complete theme to your plot (i.e. theme_light())\ntheme(): use to customize non-data components of a plot. We’ve listed out some parameters here, but run ?theme to see the full list (there’s a lot of customization you can do!)\n\naxis.title.y\npanel.background\nplot.background\npanel.grid.major.*\ntext\n\nscale_*_date(): use with dates and update breaks, limits, and labels\nscale_*_continuous(): use with continuous variables and update breaks, limits, and labels\nscale_*_discrete(): use with discrete variables and update breaks, limits, and labels\nscales package: use this within the above scale functions and you can do things like add percents to axes labels\ngeom_() within a geom function you can modify:\n\nfill: updates fill colors (e.g. column, density, violin, & boxplot interior fill color)\ncolor: updates point & border line colors (generally)\nshape: update point style\nalpha: update transparency (0 = transparent, 1 = opaque)\nsize: point size or line width\nlinetype: update the line type (e.g. “dotted”, “dashed”, “dotdash”, etc.)\n\n\n\nOnce you’re happy with how your plot looks, assign it to an object, and save it to the figs directory using ggsave()\n\n\n\n\n\n\n\n\n\nSave your work and use Git\n\n\n\nDon’t forget the Git workflow! After you’ve completed the exercises or reached a significant stopping point, use the workflow: Stage (add) -> Commit -> Pull -> Push\n\n\n\n\n\n\n\n14.5.2 Collaborate on a report and publish using GitHub pages\nThe final step! Time to work together again. Collaborate with your partner in lobster-report.qmd to create a report to publish to GitHub pages.\n\n\n\n\n\n\nCode Review\n\n\n\nAs you’re working on the lobster-report.qmd you will be conducting two types of code reviews: (1) pair programming and (2) lightweight code review.\n\nPair programming is where two people develop code together at the same workstation. One person is the “driver” and one person is the “navigator”. The driver writes the code while the navigator observes the code being typed, points out any immediate quick fixes, and will also Google / troubleshoot if errors occur. Both the Owner and the Collaborator should experience both roles, so switch halfway through or at a meaningful stopping point.\nA lightweight code review is brief and you will be giving feedback on code readability and code logic as you’re adding Owner and Collaborator code from their respective analysis.qmds to the lobster-report.qmd. Think of it as a walk through of your the code for the data visualizations you plan to include in the report (this includes the code you wrote to create the subset for the plot and the code to create the plot) and give quick feedback.\n\n\n\nMake sure your Quarto Document is well organized and includes the following elements:\n\ncitation of the data\nbrief summary of the abstract (i.e. 1-2 sentences) from the EDI Portal\nOwner analysis and visualizations (you choose which plots you want to include)\n\nadd alternative text to your plots\nplots can be added either with the data visualization code or with Markdown syntax - it’s up to you if you want to include the code or not.\n\nCollaborator analysis and visualizations (you choose which plots you want to include)\n\nadd alternative text to your plots\nplots can be added either with the data visualization code or with Markdown syntax - it’s up to you if you want to include the code or not.\n\n\nFinally, publish on GitHub pages (from Owner’s repository). Refer back to Chapter 11 for steps on how to publish using GitHub pages.", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#convert-missing-values-using-mutate-and-na_if", "href": "session_14.html#convert-missing-values-using-mutate-and-na_if", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "14.2 Convert missing values using mutate() and na_if()", "text": "14.2 Convert missing values using mutate() and na_if()\n\n\n\n\n\n\nQuestion 1\n\n\n\nThe variable SIZE_MM uses -99999 as the code for missing values (see metadata or use unique()). This has the potential to cause conflicts with our analyses, so let’s convert -99999 to an NA value. Do this using mutate() and na_if(). Look up the help page to see how to use na_if(). Check your output data using unique().\n\n\n\nlobster_abundance <- lobster_abundance %>% \n mutate(SIZE_MM = na_if(SIZE_MM, -99999))", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#filter-practice", "href": "session_14.html#filter-practice", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "14.3 filter() practice", "text": "14.3 filter() practice\n\n\n\n\n\n\nQuestion 2\n\n\n\nCreate and store a subset that does NOT include observations from Naples Reef (NAPL). Check your output data frame to ensure that NAPL is NOT in the data frame.\n\n\n\nnot_napl <- lobster_abundance %>% \n filter(SITE != \"NAPL\")\n\n\n\n\n\n\n\nQuestion 3\n\n\n\nCreate and store a subset with lobsters at Arroyo Quemado (AQUE) AND with a carapace length greater than 70 mm. Check your output.\n\n\n\naque_70mm <- lobster_abundance %>% \n filter(SITE == \"AQUE\" & SIZE_MM >= 70)\n\n\n\n\n\n\n\nSave your work and use Git\n\n\n\nDon’t forget the Git workflow! After you’ve completed the exercises or reached a significant stopping point, use the workflow: Stage (add) -> Commit -> Pull -> Push", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#convert-missing-values-using-mutate-and-na_if-1", "href": "session_14.html#convert-missing-values-using-mutate-and-na_if-1", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "14.4 Convert missing values using mutate() and na_if()", "text": "14.4 Convert missing values using mutate() and na_if()\n\n\n\n\n\n\nQuestion 1\n\n\n\nThe variable TRAPS uses -99999 as the code for missing values (see metadata or use unique()). This has the potential to cause conflicts with our analyses, so let’s convert -99999 to an NA value. Do this using mutate() and na_if(). Look up the help page to see how to use na_if(). Check your output data using unique().\n\n\n\nlobster_traps <- lobster_traps %>% \n mutate(TRAPS = na_if(TRAPS, -99999))", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#filter-practice-1", "href": "session_14.html#filter-practice-1", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "14.5 filter() practice", "text": "14.5 filter() practice\n\n\n\n\n\n\nQuestion 2\n\n\n\nCreate and store a subset that does NOT include observations from Naples Reef (NAPL). Check your output data frame to ensure that NAPL is NOT in the data frame.\n\n\n\nnot_napl <- lobster_traps %>% \n filter(SITE != \"NAPL\")\n\n\n\n\n\n\n\nQuestion 3\n\n\n\nCreate and store a subset with lobsters at Carpinteria Reef (CARP) AND number of commercial trap floats is greater than 20. Check your output.\n\n\n\ncarp_20_traps <- lobster_traps %>% \n filter(SITE == \"CARP\" & TRAPS > 20)\n\n\n\n\n\n\n\nSave your work and use Git\n\n\n\nDon’t forget the Git workflow! After you’ve completed the exercises or reached a significant stopping point, use the workflow: Stage (add) -> Commit -> Pull -> Push", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { "objectID": "session_14.html#bonus-add-marine-protected-area-mpa-designation-to-the-data", "href": "session_14.html#bonus-add-marine-protected-area-mpa-designation-to-the-data", - "title": "14  Practice Session II", + "title": "14  Practice Session: Collaborative Report", "section": "14.6 Bonus: Add marine protected area (MPA) designation to the data", "text": "14.6 Bonus: Add marine protected area (MPA) designation to the data\nThe sites IVEE and NAPL are marine protected areas (MPAs). Add this designation to your data set using a new function called case_when(). Then create some new plots using this new variable. Does it change how you think about the data? What new plots or analysis can you do with this new variable?\n\nLobster Abundance & Size DataLobster Trap Buoy Counts Data\n\n\nUse the object lobster_abundance and add a new column called DESIGNATION that contains “MPA” if the site is IVEE or NAPL, and “not MPA” for all other values.\n\nlobster_mpa <- lobster_abundance %>% \n mutate(DESIGNATION = case_when(\n SITE %in% c(\"IVEE\", \"NAPL\") ~ \"MPA\",\n SITE %in% c(\"AQUE\", \"CARP\", \"MOHK\") ~ \"not MPA\"\n ))\n\n\n\nUse the object lobster_traps and add a new column called DESIGNATION that contains “MPA” if the site is IVEE or NAPL, and “not MPA” for all other values.\n\nlobster_traps_mpa <- lobster_traps %>%\n mutate(DESIGNATION = case_when(\n SITE %in% c(\"IVEE\", \"NAPL\") ~ \"MPA\",\n SITE %in% c(\"AQUE\", \"CARP\", \"MOHK\") ~ \"not MPA\"\n ))\n\n\n\n\n\n\n\n\nLTER, Santa Barbara Coastal, Daniel C Reed, and Robert J Miller. 2022. “SBC LTER: Reef: Abundance, Size and Fishing Effort for California Spiny Lobster (Panulirus Interruptus), Ongoing Since 2012.” Environmental Data Initiative. https://doi.org/10.6073/PASTA/25AA371650A671BAFAD64DD25A39EE18.", "crumbs": [ - "14  Practice Session II" + "14  Practice Session: Collaborative Report" ] }, { @@ -1599,6 +1599,66 @@ "17  Writing Functions" ] }, + { + "objectID": "session_18.html", + "href": "session_18.html", + "title": "18  Practice Session: Functions", + "section": "", + "text": "Learning Objectives", + "crumbs": [ + "18  Practice Session: Functions" + ] + }, + { + "objectID": "session_18.html#learning-objectives", + "href": "session_18.html#learning-objectives", + "title": "18  Practice Session: Functions", + "section": "", + "text": "Practice creating functions\nPractice testing functions\nPractice adding error messages to functions \n\n\n\n\n\n\n\nAcknowledgements\n\n\n\nThese exercises are adapted from Allison Horst’s EDS 221: Scientific Programming Essentials Course for the Bren School’s Master of Environmental Data Science program.", + "crumbs": [ + "18  Practice Session: Functions" + ] + }, + { + "objectID": "session_18.html#getting-ready", + "href": "session_18.html#getting-ready", + "title": "18  Practice Session: Functions", + "section": "18.1 Getting Ready", + "text": "18.1 Getting Ready\n\n\n\n\n\n\nSetup\n\n\n\n\nMake sure you’re in the right project (training_{USERNAME}) and use the Git workflow by Pulling to check for any changes in the remote repository (aka repository on GitHub).\nCreate a new Quarto Document.\n\nTitle it “R Practice: Functions”.\nSave the file and name it “r-practice-functions”.\n\nOrganize your Quarto Document and folders in a meaningful way. Organization is personal - so this is up to you! Consider the different ways we’ve organized previous files using: headers, bold text, naming code chunks, comments in code chunks. Consider what directories (folders) we’ve used or talked about to organize these files. What is most important is organizing and documenting the file so that your future self (or if you share this file with others!) understands it as well as your current self does right now.\nUse the Git workflow. After you’ve set up your project and uploaded your data go through the workflow: Stage (add) -> Commit -> Pull -> Push", + "crumbs": [ + "18  Practice Session: Functions" + ] + }, + { + "objectID": "session_18.html#r-functions-warm-up", + "href": "session_18.html#r-functions-warm-up", + "title": "18  Practice Session: Functions", + "section": "18.2 R Functions Warm Up", + "text": "18.2 R Functions Warm Up\nWe’re going to start by creating some simple functions. Recall that the anatomy of a function is the same for all functions and each one contains:\n\na function name,\narguments that allow a user to specify inputs,\nand body of commands and outputs enclosed within a set of curly braces {}\n\n\n\n\n\n\n\nQuestion 1\n\n\n\nCreate a function called double_it() that doubles any value input value. Then try out the function, are the values returned what you expect?\n\n\n\n\nAnswer\n# create function #\ndouble_it <- function(x) {\n print(2 * x)\n}\n\n# try it out #\n# explicit notation\ndouble_it(x = 24)\n\n# non explicit notation\ndouble_it(24)\n\n\n\n\n\n\n\n\nQuestion 2\n\n\n\nWrite a function called exclaim_age() that returns the statement “I am ___ years old!”, where the blank is entered by the user as argument age.\nThen try out the function, are the values returned what you expect?\n\n\n\n\nAnswer\n# write function #\nexclaim_age <- function(age) {\n print(paste(\"I am\", age, \"years old!\"))\n}\n\n# try it out #\n# explicit notation\nexclaim_age(age = 12)\n\n# non explicit notation\nexclaim_age(12)", + "crumbs": [ + "18  Practice Session: Functions" + ] + }, + { + "objectID": "session_18.html#functions-with-conditionals", + "href": "session_18.html#functions-with-conditionals", + "title": "18  Practice Session: Functions", + "section": "18.3 Functions with Conditionals", + "text": "18.3 Functions with Conditionals\n\n\n\n\n\n\nQuestion 3\n\n\n\nConsider the function called find_max():\n\nTalk to your neighbor about what this function does and what you expect the output would be.\nRun the function with some values. Is it running how you expect?\nRun the function again, but this time use the function in an arithmetic expression. Is the output what you expect?\n\n\n\n\n# \"find_max() function\" #\nfind_max <- function(value_1, value_2) {\n \n if (value_1 > value_2) {\n return(value_1)\n }\n else if (value_2 > value_1) {\n return(value_2)\n }\n}\n\n\n\nAnswer Part C\n# example using `find_max()` in an arithmetic expression #\n# we expect the answer to be 20\n5 * find_max(4, 2)", + "crumbs": [ + "18  Practice Session: Functions" + ] + }, + { + "objectID": "session_18.html#adding-error-or-warning-messages", + "href": "session_18.html#adding-error-or-warning-messages", + "title": "18  Practice Session: Functions", + "section": "18.4 Adding Error or Warning Messages", + "text": "18.4 Adding Error or Warning Messages\n\n\n\n\n\n\nQuestion 4 Setup\n\n\n\nLet’s continue to test the find_max() function and make sure it runs appropriately in the following function calls in Questions 4a, 4b, and 4c.\n\n\n\n\n\n\n\n\nQuestion 4a\n\n\n\nRun find_max(4, 2, 5) in the Console.\nWhat happens? What kind of message appears? Is it sufficient? If not, consider adding a warning or error message using warning() or stop(). Remember, use ?function to access the Help page. Add additional logic to the function, as needed.\n\n\n\n\nAnswer 4a\n\nWhen you run find_max(4, 2, 5), the following error message appears:\nError in find_max(4, 2, 5) : unused argument (5)\nThis is an error message that is automatically created by R since our function only requires two parameters. This is a sufficient error message.\n\n\n\n\n\n\n\nQuestion 4b\n\n\n\nRun find_max(4, 4) in the Console.\nWhat happens? What kind of message appears? Is it sufficient? If not, consider adding a warning or error message using warning() or stop(). Remember, use ?function to access the Help page. Add additional logic to the function, as needed.\n\n\n\n\nHint 4b\n\nWhen you run find_max(4, 4), no message appears and the function is sent to the Console, but no value is either returned or printed.\nTo account for this scenario, add an if() statement to the beginning of the function, and then use either warning() or stop().\n\n\n\nAnswer 4b Code\n# `find_max()` function with error message\nfind_max <- function(value_1, value_2) {\n \n if (value_1 == value_2) {\n stop(\"Values must be different from each other.\")\n }\n \n if (value_1 > value_2) {\n return(value_1)\n }\n else if (value_2 > value_1) {\n return(value_2)\n }\n}\n\n# try it out #\n# does the message appear as you expected?\nfind_max(4, 4)\n\n\n\n\n\n\n\n\nQuestion 4c\n\n\n\nRun find_max(4, \"cow\") in the Console.\nWhat happens? What kind of message appears? Is it sufficient? If not, consider adding a warning or error message using warning() or stop(). Remember, use ?function to access the Help page. Add additional logic to the function, as needed.\n\n\n\n\nHint 4c\n\nWhen you run find_max(4, \"cow\"), the function runs as is and returns the value “cow”. This is not expected because these two values aren’t necessarily comparable since they’re different data types.\nHowever, find_max() doesn’t know any better since this scenario hasn’t been defined in the body of the function yet.\nTo account for this scenario, add additional logic that checks the class of each argument before the function continues to execute.\nThe logical operator for OR is |. The not-equal-to operator is !=.\n\n\n\nAnswer 4c Code\n# `find_max()` function with error messages and checks\nfind_max <- function(value_1, value_2) {\n \n # `|` is the logical OR operator\n # `!=` is the not-equal-to operator\n if (is.numeric(value_1) != TRUE | is.numeric(value_2) != TRUE) {\n # alt expression: is.numeric(value_1) == FALSE | is.numeric(value_2) == FALSE\n stop(\"Value must be a numeric type.\")\n }\n \n if (value_1 == value_2) {\n stop(\"Values must be different from each other.\")\n }\n \n if (value_1 > value_2) {\n return(value_1)\n }\n else if (value_2 > value_1) {\n return(value_2)\n }\n}\n\n# try it out #\n# does the message appear as you expected?\nfind_max(4, \"cow\")\nfind_max(\"cow\", 4)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSave and apply the Git and GitHub Workflow\n\n\n\nSave your work and use the Git workflow:\nStage (add) -> Commit -> Pull -> Push", + "crumbs": [ + "18  Practice Session: Functions" + ] + }, { "objectID": "session_21.html", "href": "session_21.html", diff --git a/2024-10-coreR/session_01.html b/2024-10-coreR/session_01.html index 18bd182a..3579be23 100644 --- a/2024-10-coreR/session_01.html +++ b/2024-10-coreR/session_01.html @@ -212,7 +212,7 @@

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