2f:T435,For this assignment, you will explore impacts of calcium addition on sugar maple seedlings in two watersheds at Hubbard Brook Experimental Forest using data from Juice and Fahey (2019). Sugar maple (_Acer saccharum_) growth can be inhibited by soil acidification from acid rain, which leaches calcium (a nutrient important for plant growth) from soils. Learn more: [Decades of acid rain is causing loss of valuable Northeast sugar maples, Cornell researchers warn](https://news.cornell.edu/stories/2006/05/acid-rain-causing-decline-sugar-maples-say-researchers). Stephanie Juice, Tim Fahey and colleagues at Hubbard Brook Long Term Ecological Research (LTER) site (Vermont, USA) measured “general sugar maple germinant health by height, leaf area, biomass, and chlorophyll content” (Juice and Fahey 2019) for seedlings in untreated and calcium-treated watersheds to investigate the impact of soil calcium addition on sugar maple seedling growth. In this assignment, you will explore the sugar maple data to gain insights about the impact of watershed calcium addition.30:[{"id":"84da9367be5328b6e4363a169e1504c723c3a6ba1b5510d2df1d2fe49d8922497c2e02c5ef4dad9258da9faea5455dbca05e7d6cea741f78f23c449adab29750","href":"https://static.observableusercontent.com/files/84da9367be5328b6e4363a169e1504c723c3a6ba1b5510d2df1d2fe49d8922497c2e02c5ef4dad9258da9faea5455dbca05e7d6cea741f78f23c449adab29750","name":"w1_acsa_seed_physical.txt","type":"text/plain","lastModified":1690731211994.375,"size":33070}] 2d:{"title":"Assignment (Data science workflows in JavaScript Workshop Series)","cells":[{"id":0,"value":"# Assignment (Data science workflows in JavaScript Workshop Series)","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"## Logistics\n\n- **Fork this notebook** to make your own copy. Complete tasks below in your fork.\n- When you are ready to submit, ensure that your fork visibility is **Public unlisted** so that we can see your work. To update the setting, go to the Share button, the under Access choose “Can view (unlisted)” for Public visibility. Make sure to Save changes.\n- Submit assignment by sharing the URL in [this Google Form](https://forms.gle/cYLwZBsyvikVwgH59) by end of day **Friday, August 4th 2023**\n- Ask questions in the Observable Community Slack #learn-javascript channel.\n- Things in yellow are where you’re prompted to add content for the assignment.","pinned":false,"hidden":false,"mode":"md"},{"id":27,"value":"## About the data","pinned":false,"hidden":false,"mode":"md"},{"id":29,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":33,"value":"**Data source:** Juice, S. and T. Fahey. 2019. Health and mycorrhizal colonization response of sugar maple (Acer saccharum) seedlings to calcium addition in Watershed 1 at the Hubbard Brook Experimental Forest ver 3. Environmental Data Initiative. https://doi.org/10.6073/pasta/0ade53ede9a916a36962799b2407097e","pinned":false,"hidden":false,"mode":"md"},{"id":43,"value":"## Task 1: Access the data","pinned":false,"hidden":false,"mode":"md"},{"id":45,"value":"Access the data from the [this data package on the Environmental Data Initiative](https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-hbr.157.3). You will only need one file: **w1_acsa_seed_physical** (found in the Resources section on that page). You can either read in the remote file directly using the URL, or download the file and attach it in your notebook. Note that while this is a text file (.txt), values are comma-separated, so you can still use the CSV methods we used in the workshop.","pinned":false,"hidden":false,"mode":"md"},{"id":58,"value":"Below this cell, add a new JavaScript cell below, then add code to load data from the file above as `maples`.","pinned":false,"hidden":false,"mode":"md"},{"id":452,"value":"maples = FileAttachment(\"w1_acsa_seed_physical.txt\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs"},{"id":66,"value":"---\n## Task 2: Take a look at the data in a Data table cell","pinned":false,"hidden":false,"mode":"md"},{"id":74,"value":"Below, visualize the `maples` data in a Data table cell. Familiarize yourself with the variables. Here are variable summaries (for the variables you’ll use in this assignment) from the [data package metadata](https://portal.edirepository.org/nis/metadataviewer?packageid=knb-lter-hbr.157.3): \n\n- **Year**: year measured\n- **Watershed**: study watershed (Reference = untreated; W1 = Calcium treated)\n- **Elevation**: categorical description of watershed elevation\n- **Transect**: study transect\n- **Sample**: sugar maple seedling sample number\n- **StemLength**: seedling stem length (millimeters)\n- **Leaf1Area**: area of first leaf sampled from seedling (cm²)\n- **Leaf2Area**: area of second leaf sampled from seedling (cm²)\n- **LeafDryMass**: dry mass of both leaves (grams)\n- **StemDryMass**: dry mass of seedling stem (grams)\n- **CorrectedLeafArea**: total leaf area for both sampled leaves (Leaf1Area + Leaf2Area), corrected for the area removed for chlorophyll analysis (cm²)","pinned":false,"hidden":false,"mode":"md"},{"id":93,"value":"Below this cell, add a new Data table cell below, selecting maples as the data source:","pinned":false,"hidden":false,"mode":"md"},{"id":456,"value":"maples","pinned":true,"hidden":false,"mode":"ojs"},{"id":97,"value":"---\n## Task 3: Data wrangling","pinned":false,"hidden":false,"mode":"md"},{"id":99,"value":"Make a simplified version of the `maples` data that only includes observations from 2003. Using the method(s) of your choice (e.g. Array methods, Arquero, SQL, Data table cell, any other tools you prefer, or a combination!), create a simplified version of the `maples` data updated as follows:\n\n- Only keep (and rename) variables:\n - Year (rename to **year**)\n - Watershed (rename to **watershed**)\n - StemLength (rename to **stemLength**)\n - LeafDryMass (rename to **leafMass**)\n - StemDryMass (rename to **stemMass**)\n - CorrectedLeafArea (rename to **leafArea**)\n- Only keep observations from 2003","pinned":false,"hidden":false,"mode":"md"},{"id":105,"value":"Below this cell, add a JavaScript, SQL, or Data Table cell to wrangle the data as described above, naming your new subset `maples2003`.","pinned":false,"hidden":false,"mode":"md"},{"id":461,"value":"maples2003 = maples.filter(d => d.Year === 2003)\n.map(d => ({year: d.Year, watershed: d.Watershed, stemLength: d.StemLength, leafMass: d.LeafDryMass, stemMass: d.StemDryMass, leafArea: d.CorrectedLeafArea}))","pinned":true,"hidden":false,"mode":"ojs"},{"id":470,"value":"maples2003","pinned":true,"hidden":false,"mode":"ojs"},{"id":127,"value":"---\n## Task 4: Exploratory data visualization","pinned":false,"hidden":false,"mode":"md"},{"id":129,"value":"In the area below, create three exploratory data visualizations for the 2003 maple seedlings. Where prompted, add a Markdown cell with a 1-sentences summary of a takeaway or insight gleaned from the visualization.\n\n**Note:** Throughout the rest of the assignment, you will use the 2003 subset `maples2003` you created above.","pinned":false,"hidden":false,"mode":"md"},{"id":131,"value":"Below this cell, use the **Chart cell** to create histogram of leafArea for seedlings recorded in 2003. Facet (vertically) by watershed to compare the distributions in the untreated (Reference) and calcium treated (W1) watersheds. ","pinned":false,"hidden":false,"mode":"md"},{"id":474,"value":"Plot.plot({\n color: {\n legend: true\n },\n marks: [\n Plot.auto(maples2003, {x: \"leafArea\", fy: \"watershed\", color: \"watershed\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":490,"value":"_(Note: I \"ejected\" the Chart cell to JS so that I could add a mean reference line. Result below.)_","pinned":false,"hidden":false,"mode":"md"},{"id":487,"value":"Plot.plot({\n color: { legend: true },\n marks: [\n Plot.frame({ strokeOpacity: 0.1 }),\n Plot.rectY(\n maples2003,\n Plot.binX(\n { y: \"count\" },\n { fy: \"watershed\", x: \"leafArea\", fill: \"watershed\", tip: true }\n )\n ),\n Plot.ruleX(\n maples2003,\n Plot.groupZ({ x: \"mean\" }, { fy: \"watershed\", x: \"leafArea\", stroke: \"red\", tip: true })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":210,"value":"Below this cell, add a new Markdown cell, then describe and compare the distributions for leaf areas in untreated and calcium treated watersheds based on the histograms.","pinned":false,"hidden":false,"mode":"md"},{"id":485,"value":"The untreated leaves display a skewed right distribution, with an average value just over 20.\nThe leaves from calcium treated watersheds display a larger spread in area size, with an average value just over 30 and some outliers reaching 60.","pinned":false,"hidden":false,"mode":"md"},{"id":181,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":183,"value":"Below this cell, insert a **Plot snippet from the Add cell menu to create a Scatterplot** of **stemLength** and **stemMass**, with fill color mapped to **watershed**. Then, visualize the linear trends by adding a `linearRegressionY` mark atop the scatterplot mark, with stroke color mapped to **watershed**.","pinned":false,"hidden":false,"mode":"md"},{"id":497,"value":"Plot.plot({\n color: { legend: true },\n marks: [\n Plot.dot(maples2003, {x: \"stemLength\", y: \"stemMass\", fill: \"watershed\", tip: true}),\n //Plot.linearRegressionY(maples2003, {x: \"stemLength\", y: \"stemMass\"}),\n Plot.linearRegressionY(maples2003, {x: \"stemLength\", y: \"stemMass\", stroke: \"watershed\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":219,"value":"Below this cell, add a new Markdown cell, then describe and compare the relationship between stem length and mass in untreated and calcium treated watersheds.","pinned":false,"hidden":false,"mode":"md"},{"id":509,"value":"The relationship between stem length and mass is similar for the two groups, with the calcium treated regression line exhibiting a higher constant term and larger error band (likely due to the presence of outliers with more variability).","pinned":false,"hidden":false,"mode":"md"},{"id":198,"value":"---\nBelow this cell, add a JavaScript cell to import PlotMatrix from [Fil’s Autoplot matrix notebook](https://observablehq.com/@observablehq/autoplot-matrix). Then in a second JavaScript cell, use PlotMatrix to create an interactive matrix of charts to visualize the different variable relationships and distributions.","pinned":false,"hidden":false,"mode":"md"},{"id":519,"value":"data = maples2003","pinned":true,"hidden":false,"mode":"ojs"},{"id":512,"value":"import {PlotMatrix} with {data} from \"@observablehq/autoplot-matrix\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":526,"value":"PlotMatrix(data)","pinned":true,"hidden":false,"mode":"ojs"},{"id":204,"value":"---\n## Task 5: Analysis / modeling","pinned":false,"hidden":false,"mode":"md"},{"id":228,"value":"**Choose one** of the following to explore differences and patterns in sugar maple morphology in untreated and calcium-treated watersheds: \n\n- **Option 1:** Perform simple linear regression on stem dimensions (stemLength versus stemMass) for the untreated and calcium-amended watershed. For the purposes of this assignment, you can do these separately (e.g. create separate subsets for Reference and W1 watersheds, then use simple linear regression to estimate parameters for each). \n\n- **Option 2:** Perform k-means clustering with two clusters to see if, based on the four quantitative variables in `maples2003`, the seedlings are largely within separate clusters based on watershed. Add a visualization or table to describe the outcomes of cluster analysis.\n \n- **Option 3:** Perform principal component analysis using the four quantitative variables in `maples2003`, and create a biplot showing both observation scores (points) on PC1 and PC2, overlayed by the variable vectors (arrows).\n","pinned":false,"hidden":false,"mode":"md"},{"id":237,"value":"Below this cell, add any cells necessary to perform the analysis you chose from the options above.","pinned":false,"hidden":false,"mode":"md"},{"id":528,"value":"ML = require(\"https://www.lactame.com/lib/ml/6.0.0/ml.min.js\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":531,"value":"import {scale, asMatrix} from \"@chrispahm/hierarchical-clustering\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":533,"value":"// Create a scaled version of the numeric variables\nmaples2003Scaled = scale(maples2003.map(({ year, watershed, ...rest }) => rest))","pinned":true,"hidden":false,"mode":"ojs"},{"id":543,"value":"// Convert to an array of arrays, just containing the values (no keys):\nmaples2003Array = maples2003Scaled.map(Object.values)","pinned":true,"hidden":false,"mode":"ojs"},{"id":545,"value":"// Perform principal component analysis:\nmaples2003PCA = new ML.PCA(maples2003Array) // Already scaled above - otherwise can add {scale: true} here! ","pinned":true,"hidden":false,"mode":"ojs"},{"id":554,"value":"// Get variance explained by each PC:\nvariancePC = maples2003PCA.getExplainedVariance()","pinned":true,"hidden":false,"mode":"ojs"},{"id":556,"value":"// Get cumulative variance explained: \ncumulativeVariance = maples2003PCA.getCumulativeVariance()","pinned":true,"hidden":false,"mode":"ojs"},{"id":558,"value":"This tells us that ${(cumulativeVariance[1] * 100).toFixed(1)}% of variance in the data is explained by the first two principal components.","pinned":false,"hidden":false,"mode":"md"},{"id":562,"value":"loadings = maples2003PCA\n .getEigenvectors()\n .data.map((d, i) => ({\n PC1: d[0],\n PC2: d[1],\n Variable: Object.keys(maples2003Scaled[0])[i]\n }))","pinned":true,"hidden":false,"mode":"ojs"},{"id":565,"value":"scoresCombined = maples2003PCA.predict(maples2003Array).data.map((d,i) => ({watershed: maples2003[i].watershed,\n //year: maples2003[i].year,\n PC1: d[0],\n PC2: d[1]}))","pinned":true,"hidden":false,"mode":"ojs"},{"id":568,"value":"scalingFactor = 5","pinned":true,"hidden":false,"mode":"ojs"},{"id":570,"value":"Plot.plot({\n marks: [\n Plot.dot(scoresCombined, { x: \"PC1\", y: \"PC2\", fill: \"watershed\", r: 5, tip: true }),\n Plot.arrow(loadings, {\n x1: 0, x2: d => d.PC1 * scalingFactor, y1: 0, y2: (d) => d.PC2 * scalingFactor\n }),\n Plot.text(loadings, {\n x: (d) => d.PC1 * scalingFactor, y: (d) => d.PC2 * scalingFactor,\n text: \"Variable\",\n dy: -5,\n dx: 30,\n fill: \"black\",\n stroke: \"white\"\n })\n ],\n color: { legend: true }\n}) ","pinned":true,"hidden":false,"mode":"ojs"},{"id":242,"value":"--- \n","pinned":false,"hidden":false,"mode":"md"},{"id":244,"value":"Below this cell, add a new Markdown cell and in 1-2 sentences summarize the findings from your analysis for Task 5. Then, in 1-2 sentences based on **all** of the exploration you have done in this assignment, share your preliminary thoughts on if and how calcium addition impacts sugar maple seedlings. ","pinned":false,"hidden":false,"mode":"md"},{"id":535,"value":"For task 5, I went with Option 3, principal component analysis (PCA) using the four quantitative variables in maples2003. Based on the angles of the vectors displayed in the biplot, I believe we can conclude that leafArea and stemMass as well as leafMass and leafArea are positively correlated. We should also be able to conclude that leafMass and stemLength are not likely to be correlated.\n\nCombining these results with the preliminary exploration we carried on, I believe that calcium addition in watersheds leads to larger and heavier leaves. These could correlate with healthier trees and thus larger productivity in maple syrup extraction, which would indicate an economic advantage to treated watersheds.","pinned":false,"hidden":false,"mode":"md"},{"id":246,"value":"---\n## Congratulations! Submit your assignment.\n\nOnce you’re done with the assignment, make sure to: \n\n- Update your notebook settings (click the Share button) so that Public access is “View (unlisted)”\n- Submit the URL to your notebook in [this Google form](https://forms.gle/cYLwZBsyvikVwgH59) by midnight on August 4, 2023\n- Await your course certificate, which we’ll send out in mid-August! \n","pinned":false,"hidden":false,"mode":"md"},{"id":262,"value":"_**Thank you** for joining us for the Data Science Workflows in JavaScript workshop series! We look forward to seeing you in our upcoming courses and workshops, and in our Community Slack!_ ","pinned":false,"hidden":false,"mode":"md"}],"files":"$W30","stdlib":"1","titling":"auto"}