add instructions to author field for lab instructions

docs/labs/instructions/lab-1-instructions.html

@@ -6,6 +6,7 @@
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
+<meta name="author" content="Instructions">
 
 <title>Lab 1: Introduction to Quarto – Introduction to Statistical Computing in R</title>
 <style>
@@ -355,6 +356,12 @@ <h1 class="title">Lab 1: Introduction to Quarto</h1>
 
 <div class="quarto-title-meta">
 
+    <div>
+    <div class="quarto-title-meta-heading">Author</div>
+    <div class="quarto-title-meta-contents">
+             <p>Instructions </p>
+          </div>
+  </div>
 
 
 

docs/labs/instructions/lab-3-instructions.html

@@ -6,7 +6,7 @@
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
-<meta name="author" content="NAME">
+<meta name="author" content="Instructions">
 
 <title>Lab 3: Student Evaluations of Teaching – Introduction to Statistical Computing in R</title>
 <style>
@@ -4952,7 +4952,7 @@ <h2 id="toc-title">On this page</h2>
     <div>
     <div class="quarto-title-meta-heading">Author</div>
     <div class="quarto-title-meta-contents">
-             <p>NAME </p>
+             <p>Instructions </p>
           </div>
   </div>
 
@@ -5860,7 +5860,7 @@ <h2 class="anchored" data-anchor-id="rate-my-professor">Rate my Professor</h2>
 </script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class>
 <div class="sourceCode" id="cb13" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> &quot;Lab 3: Student Evaluations of Teaching&quot;</span></span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span><span class="co"> &quot;NAME&quot;</span></span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span><span class="co"> &quot;Instructions&quot;</span></span>
 <span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span><span class="co"> </span></span>
 <span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a><span class="co">  html:  </span></span>
 <span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a><span class="co">    embed-resources: true</span></span>

docs/search.json

@@ -2555,28 +2555,28 @@
     "href": "labs/instructions/lab-4-instuctions.html",
     "title": "Lab 4: Childcare Costs in California",
     "section": "",
-    "text": "Tip\n\n\n\n\n\nI advise you to focus particularly on:\n\nSetting chunk options carefully.\nMaking sure you don’t print out more output than you need.\nMaking sure you don’t assign more objects than necessary. Avoid “object junk” in your environment.\nMaking your code readable and nicely formatted.\nThinking through your desired result before writing any code.\nDownload starter .qmd file"
+    "text": "Tips for Success\n\n\n\n\n\nI advise you to focus particularly on:\n\nSetting chunk options carefully.\nMaking sure you don’t print out more output than you need.\nMaking sure you don’t assign more objects than necessary. Avoid “object junk” in your environment.\nMaking your code readable and nicely formatted.\nThinking through your desired result before writing any code."
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     "title": "Lab 4: Childcare Costs in California",
     "section": "The Data",
-    "text": "The Data\nIn this lab we’re going look at the median weekly cost of childcare in California. A detailed description of the data can be found here.\nThe data come to us from TidyTuesday.\n0. Load the appropriate libraries and the data.\n\n# load libraries\n\n\n# load data\nchildcare_costs <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/childcare_costs.csv')\n\nError in read_csv(\"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/childcare_costs.csv\"): could not find function \"read_csv\"\n\ncounties <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/counties.csv')\n\nError in read_csv(\"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/counties.csv\"): could not find function \"read_csv\"\n\n\n1. Briefly describe the data (~ 4 sentences). What information does it contain?"
+    "text": "The Data\nIn this lab we’re going look at the median weekly cost of childcare in California. A detailed description of the data can be found here.\nThe data come to us from TidyTuesday.\n0. Load the appropriate libraries and the data.\n\n# load packages\n\n\n# load data\nchildcare_costs <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/childcare_costs.csv')\n\ncounties <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/counties.csv')\n\n1. Briefly describe the dataset (~ 4 sentences). What information does it contain?"
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     "title": "Lab 4: Childcare Costs in California",
     "section": "California Childcare Costs",
-    "text": "California Childcare Costs\n2. Let’s focus only on California. Create a ca_childcare dataset containing (1) county information and (2) all information from the childcare_costs dataset.\na. Sketch a game plan for completing this task. You should do all of this within one pipeline\n\nb. Implement/code your game plan to create the dataset of childcare costs in California. Checkpoint: There are 58 counties in CA and 11 years in the dataset. Therefore, your new dataset should have 53 x 11 = 638 observations.\n\n# Code for Q2\n\n3. Using a function from the forcats package, complete the code below to create a new variable where each county is categorized into one of the 10 Census regions in California. Use the Region description (from the plot), not the Region number.\n\nca_childcare <- ca_childcare |> \n  mutate(county_name = str_remove(county_name, \" County\")) |>\n  ...\n# code for Q3\n\nError: The pipe operator requires a function call as RHS (<text>:3:3)\n\n\n\n\n\n\n\n\nTip\n\n\n\nI have provided you with code that eliminates the word “County” from each of the county names in your ca_childcare dataset. You should keep this line of code and pipe into the rest of your data manipulations.\nYou will learn about the str_remove() function from the stringr package next week!\n\n\n4. Let’s consider the median household income of each region, and how that income has changed over time. Create a table with ten rows, one for each region, and two columns, one for 2008 and one for 2018. The cells should contain the median of the median household income (expressed in 2018 dollars) of the region and the study_year. Order the rows by 2018 values.\n\n\n\n\n\n\nTip\n\n\n\nThis will require transforming your data! Sketch out what you want the data to look like before you begin to code. You should be starting with your California dataset that contains the regions.\n\n\n\n# code for Q4\n\n5. Which California region had the lowest median full-time median weekly price for center-based childcare for infants in 2018? Does this region correspond to the region with the lowest median income in 2018 that you found in Q4?\n\n\n\n\n\n\nWarning\n\n\n\nThe code should give me the EXACT answer. This means having the code output the exact row(s) and variable(s) necessary for providing the solution.\n\n\n\n# code for Q5\n\n6. The following plot shows, for all ten regions, the change over time of the full-time median price for center-based childcare for infants, toddlers, and preschoolers. Recreate the plot. You do not have to replicate the exact colors or theme, but your plot should have the same content, including the order of the facets and legend, reader-friendly labels, axes breaks, and a loess smoother.\n\n\n\n\n\n\nTip\n\n\n\nThis will require transforming your data! Sketch out what you want the data to look like before you begin to code. You should be starting with your California dataset that contains the regions.\nYou will also be required to use functions from forcats to change the labels and the ordering of your factor levels.\nRemember to avoid “object junk” in your environment!\n\n\n\n\n\nPlot to recreate\n\n\n\n# code for Q6"
+    "text": "California Childcare Costs\nLet’s start by focusing only on California.\n2. Create a ca_childcare dataset of childcare costs in California, containing (1) county information and (2) all information from the childcare_costs dataset. Hint: There are 58 counties in CA and 11 years in the dataset. Therefore, your new dataset should have 53 x 11 = 638 observations.\n3. Using a function from the forcats package, complete the code below to create a new variable where each county is categorized into one of the 10 Census regions in California. Use the Region description (from the plot), not the Region number.\n\nca_childcare <- ca_childcare |> \n  mutate(county_name = str_remove(county_name, \" County\")) |>\n  ...\n\n\n\n\n\n\n\nTip\n\n\n\nI have provided you with code that eliminates the word “County” from each of the county names in your ca_childcare dataset. You should keep this line of code and pipe into the rest of your data manipulations.\nYou will learn about the str_remove() function from the stringr package next week!\n\n\n4. Let’s consider the median household income of each region, and how that income has changed over time. Create a table with ten rows, one for each region, and two columns, one for 2008 and one for 2018. The cells should contain the median of the median household income (expressed in 2018 dollars) of the region and the study_year. Arrange the rows by 2018 values.\n\n\n\n\n\n\nTip\n\n\n\nThis will require transforming your data! Sketch out what you want the data to look like before you begin to code. You should be starting with your California dataset that contains the regions!\n\n\n5. Which California region had the lowest median full-time median weekly price for center-based childcare for infants in 2018? Does this region correspond to the region with the lowest median income in 2018 that you found in Q4?\n\n\n\n\n\n\nWarning\n\n\n\nThe code should give me the EXACT answer. This means having the code output the exact row(s) and variable(s) necessary for providing the solution.\n\n\n6. The following plot shows, for all ten regions, the change over time of the full-time median price for center-based childcare for infants, toddlers, and preschoolers. Recreate the plot. You do not have to replicate the exact colors or theme, but your plot should have the same content, including the order of the facets and legend, reader-friendly labels, axes breaks, and a loess smoother.\n\n\n\n\n\n\nTip\n\n\n\nThis will require transforming your data! Sketch out what you want the data to look like before you begin to code. You should be starting with your California dataset that contains the regions.\nYou will also be required to use functions from forcats to change the labels and the ordering of your factor levels.\nRemember to avoid “object junk” in your environment!\n\n\n\n\n\nPlot to recreate"
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     "title": "Lab 4: Childcare Costs in California",
     "section": "Median Household Income vs. Childcare Costs for Infants",
-    "text": "Median Household Income vs. Childcare Costs for Infants\n\n\n\n\n\n\nRefresher on Linear Regression\n\n\n\nWhile a second course in statistics is a pre-requisite for this class, here is a refresher on simple linear regression with a single predictor.\n\n\n7. Create a scatterplot showing the relationship between median household income (expressed in 2018 dollars) and the full-time median weekly price charged for center-based childcare for an infant in California. Overlay a linear regression line (lm) to show the trend.\n\n# plot for scatterplot\n\n8. Look up the documentation for lm() and fit a linear regression model to the relationship shown in your plot above (recall: \\(y = mx+b\\)). Identify the coefficient estimates from the model.\n\n# complete the code provided\nreg_mod1 <- lm()\n\nError in terms.formula(formula, data = data): argument is not a valid model\n\nsummary(reg_mod1)\n\nError in eval(expr, envir, enclos): object 'reg_mod1' not found\n\n\n9. Do you have evidence to conclude there is a relationship between the median household income and the median weekly cost of center-based childcare for infants in California? Cite values from your output for support."
+    "text": "Median Household Income vs. Childcare Costs for Infants\n\n\n\n\n\n\nRefresher on Linear Regression\n\n\n\nWhile a second course in statistics is a pre-requisite for this class, here is a refresher on simple linear regression with a single predictor.\n\n\n7. Create a scatterplot showing the relationship between median household income (expressed in 2018 dollars) and the full-time median weekly price charged for center-based childcare for an infant in California. Overlay a linear regression line (lm) to show the trend.\n8. Look up the documentation for lm() and fit a linear regression model to the relationship shown in your plot above.\n\n# complete the code provided\nreg_mod1 <- lm()\nsummary(reg_mod1)\n\n9. Using the output from summary(), write out the estimated regression line (recall: \\(y = mx + b\\)).\n10. Do you have evidence to conclude there is a relationship between the median household income and the median weekly cost of center-based childcare for infants in California? Cite values from your summary() output to support your claim!"
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@@ -3549,5 +3549,12 @@
     "title": "Scripts, Notebooks & Version Control",
     "section": "Consent to Participate",
     "text": "Consent to Participate\n\n\nIf you agree to participate…\n\nyou will be recorded once a week for 10-weeks while participating in pair programming to complete collaborative tasks.\nyou will complete surveys about your demographic information, your prior computing experiences, and your attitudes toward data science.\n\n\n\n\n\n\nYour participation in this research will not directly affect your course grade.\n\n\n\n\n\n\n\n\n\n\nConsent Form\n\n\nPlease complete the consent form (https://forms.gle/oax73hoe7uRSVLYw8) by Monday, 9/30."
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+    "title": "Lab 4: Childcare Costs in California",
+    "section": "Challenge 4: Open-Ended Analysis",
+    "text": "Challenge 4: Open-Ended Analysis\nInvestigate the full-time median price for childcare in a center-based setting versus the full-time median price for childcare in a family (in-home) setting in California. Pay close attention to the different developmental stages. Support your answer with one table of summary statistics and one visualization.\n\n\n\n\n\n\nTip\n\n\n\n\nAim for about 150-300 words.\n\nYou should (1) introduce the data and the topic you are investigating, (2) discuss your table and plot, and (3) conclude.\n\nThis should read like a mini-report. I recommend using #| code-fold: true for readability.\nDon’t just have a summary table or plot show up “out of the blue” – make sure to build up to them with text, describe what they show, and provide captions."
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