Merge pull request #524 from ivelasq/master

rerender book

Author: ivelasq

_book/09-wt-aggregate-data.md

@@ -115,16 +115,16 @@ tibble(
 ## # A tibble: 10 x 3
 ##    student school test_score
 ##    <chr>   <chr>       <int>
-##  1 a       k              19
-##  2 b       l              61
-##  3 c       m              35
-##  4 d       n              81
-##  5 e       o             100
-##  6 f       k              26
-##  7 g       l              52
-##  8 h       m              40
-##  9 i       n              61
-## 10 j       o              14
+##  1 a       k              55
+##  2 b       l              85
+##  3 c       m              48
+##  4 d       n              12
+##  5 e       o              47
+##  6 f       k              65
+##  7 g       l              70
+##  8 h       m              94
+##  9 i       n              23
+## 10 j       o              49
 ```
 
 Aggregate data totals up a variable - the variable `test_score` in this case - to
@@ -147,11 +147,11 @@ tibble(
 ## # A tibble: 5 x 2
 ##   school mean_score
 ##   <chr>       <dbl>
-## 1 k              23
-## 2 l              27
-## 3 m              23
-## 4 n              48
-## 5 o              31
+## 1 k            37  
+## 2 l            15  
+## 3 m            40  
+## 4 n            34  
+## 5 o            60.5
 ```
 
 Notice here that this dataset no longer identifies individual students.

_book/10-wt-longitudinal-analysis_files/figure-html/fig10-8-1.png

@@ -517,7 +517,7 @@ sample(x = 1:10, size = 5)
 ```
 
 ```
-## [1] 3 6 1 2 4
+## [1] 1 7 8 9 5
 ```
 
 Passing `sample()` a vector of numbers and the size of the sample you want returns a random selection from the vector. Try changing the value of `x` and `size` to see how this works. 

_book/11-wt-text-analysis.md

@@ -132,20 +132,20 @@ An edgelist looks like the following, where the `sender` (sometimes called the "
 
 ```
 ## # A tibble: 12 x 2
-##    sender                     receiver          
-##    <chr>                      <chr>             
-##  1 Shigaya, Ivy               Warren, Alexandria
-##  2 Davila Rodriguez, Santiago Meyer, Amy        
-##  3 Davila Rodriguez, Santiago Steinbach, Ashlie 
-##  4 Chea, Paige                Meyer, Amy        
-##  5 Chea, Paige                Warren, Alexandria
-##  6 Chea, Paige                Comcowich, Bret   
-##  7 Sanchez, Jazmin            Steinbach, Ashlie 
-##  8 Sanchez, Jazmin            Fuhr, Gilberto    
-##  9 Sanchez, Jazmin            Comcowich, Bret   
-## 10 Gradeless, Laura           Parton, Alisha    
-## 11 Iron Cloud, Kristopher     Steinbach, Ashlie 
-## 12 Iron Cloud, Kristopher     Parton, Alisha
+##    sender            receiver              
+##    <chr>             <chr>                 
+##  1 Topaha, Draven    Brown, Issac          
+##  2 el-Munir, Dhaafir Lopez Almeida, Roxanna
+##  3 el-Munir, Dhaafir Mann, Jonathan        
+##  4 Hayes, Sky        Lopez Almeida, Roxanna
+##  5 Hayes, Sky        Brown, Issac          
+##  6 Hayes, Sky        el-Saadeh, Waleed     
+##  7 Warren, Amanda    Mann, Jonathan        
+##  8 Warren, Amanda    Barksdale, Eli        
+##  9 Warren, Amanda    el-Saadeh, Waleed     
+## 10 Gurung, Aysha     el-Naqvi, Labeeb      
+## 11 Meltzer, Cheyenne Mann, Jonathan        
+## 12 Meltzer, Cheyenne el-Naqvi, Labeeb
 ```
 
 In this edgelist, the `sender` column might identify someone who nominates another  (the receiver) as someone they go to for help. The sender might also identify someone who interacts with the receiver in other ways, like "liking" or "mentioning" their tweets. In the following steps, we will work to create an edgelist from the data from #tidytuesday on Twitter.

_book/12-wt-social-network-analysis.md

@@ -10,7 +10,7 @@ The power of doing data analysis with a programming language like R comes from t
 
 ### Working With Data Faster
 
-Data analysts who have have an efficient analytical process understand their clients' questions and participate by rapidly cycling through analysis and discussion. They quickly accumulate skill and experience because their routines facilitate many cycles of data analysis. Roger Peng and Elizabeth Matsui discuss epicycles of analysis in their book [The Art of Data Science](https://bookdown.org/rdpeng/artofdatascience/epicycles-of-analysis.html). In their book [R for Data Science](https://r4ds.had.co.nz/explore-intro.html), Garrett Grolemund and Hadley Wickham demonstrate a routine for data exploration. When the problem space is not clearly defined, as is often the case with education data analysis questions, the path to get from the initial question to analysis itself is full of detours and distractions. Having a routine that points you to the next immediate analytic step gets the analyst started quickly, and many quick starts results in a lot of data analyzed.
+Data analysts who have an efficient analytical process understand their clients' questions and participate by rapidly cycling through analysis and discussion. They quickly accumulate skill and experience because their routines facilitate many cycles of data analysis. Roger Peng and Elizabeth Matsui discuss epicycles of analysis in their book [The Art of Data Science](https://bookdown.org/rdpeng/artofdatascience/epicycles-of-analysis.html). In their book [R for Data Science](https://r4ds.had.co.nz/explore-intro.html), Garrett Grolemund and Hadley Wickham demonstrate a routine for data exploration. When the problem space is not clearly defined, as is often the case with education data analysis questions, the path to get from the initial question to analysis itself is full of detours and distractions. Having a routine that points you to the next immediate analytic step gets the analyst started quickly, and many quick starts results in a lot of data analyzed.
 
 But speed gives us more than just an accelerated flow of experience or the thrill of rapidly getting to the bottom of a teacher's data inquiry. It fuels the creativity required to understand problems in education and the imaginative solutions required to address them. Quickly analyzing data keeps the analytic momentum going at the speed needed to indulge organic exploration of the problem. Imagine an education consultant working with a school district to help them measure the effect of a new intervention on how well their students are learning math. During this process the superintendent presents the idea of comparing quiz scores at the schools in the district. The speed at which the consultant offers answers is important for the purposes of keeping the analytic conversation going. 
 
@@ -240,7 +240,7 @@ When an education client or coworker asks for help answering an analytic questio
  1. At what level is this question about, student, classroom, school, district, regional, state, or federal? 
  1. What can we learn by answering the analytic question at the current level, but also at the next level of scale up?
 
-If a teacher asks you to analyze the attendance pattern of one student, see what you learn by comparing to the the attendance pattern of the whole classroom or the whole school. If a superintendent of a school district asks you to analyze the behavior referrals of a school, analyze the behavior referrals of every school in the district. One of the many benefits of using programming languages like R to analyze data is that once you write code for one dataset, it can be used with many datasets with a relatively small amount of additional work. 
+If a teacher asks you to analyze the attendance pattern of one student, see what you learn by comparing to the attendance pattern of the whole classroom or the whole school. If a superintendent of a school district asks you to analyze the behavior referrals of a school, analyze the behavior referrals of every school in the district. One of the many benefits of using programming languages like R to analyze data is that once you write code for one dataset, it can be used with many datasets with a relatively small amount of additional work. 
 
 ### Look for Lots of Similarly Structured Data 
 
@@ -281,7 +281,7 @@ Here are some reflection questions and exercise to use to inspire connection in
 
 In his book *Feck Perfuction*, designer @victore2019 writes "Success goes to those who keep moving, to those who can practice, make mistakes, fail, and still progress. It all adds up. Like exercise for muscles, the more you learn, the more you develop, and the stronger your skills become" (p. 31). Doing data science is a skill and like all skills, repetition and mistakes are their fuel for learning. But what happens if you are the first person to do data science in your education workplace? When you have no data science mentors, analytics routines, or examples of past practice, it can feel aimless to say the least. The antidote to that aimlessness is daily practice. 
 
-Commit to writing code everyday. Even the the simplest three line scripts have a way of adding to your growing programming instincts. Train your ears to be radars for data projects that are usually done in a spreadsheet, then take them on and do them i R. Need the average amount of time a student with disabilities spends in speech and language sessions? Try it in R. Need to rename the columns in a student quiz dataset? Try it in R. The principal is hand assembling twelve classroom attendance sheets into one dataset? You get the picture. 
+Commit to writing code everyday. Even the simplest three line scripts have a way of adding to your growing programming instincts. Train your ears to be radars for data projects that are usually done in a spreadsheet, then take them on and do them in R. Need the average amount of time a student with disabilities spends in speech and language sessions? Try it in R. Need to rename the columns in a student quiz dataset? Try it in R. The principal is hand assembling twelve classroom attendance sheets into one dataset? You get the picture. 
 
 Now along the path of data science daily practice you may discover that your non-data science coworkers start kindly declining your offers for help. In my experience there is nothing mean happening here, but rather this is a response to imagining what it's like to do what you are offering to do using the more commonly found spreadsheet applications. As your programming and statistics skills progress, some of the tasks you offer to help with will be the kind that, if done in a spreadsheet app, are overwhelmingly difficult and time intensive. So in environments where programming is not used for data analysis, declining your offers of help are more perceived acts of kindness to you and probably not statements about the usefulness of your work. As frustrating as these situations might be, they are necessary experiences as an organization learns just how available speed and scale of data analysis are when you use programming as a tool. In fact, these are opportunities you should seize because they serve both as daily practice and as demonstrations of the speed and scale programming for data analysis provides. 
 

_book/15-data-science-in-your-job.md

@@ -214,7 +214,7 @@ Consider taking the additional time needed to help learners navigate
 minor issues and errors in their code: it can pay off in increased motivation on
 their part in the long-term.
 
-### Anticipate Ussues (and Sacrifice Accuracy for Clarity)
+### Anticipate Issues (and Sacrifice Accuracy for Clarity)
 
 Don't worry about being perfectly accurate early on, especially if doing so
 would lead to learners who are less interested in the topic you are teaching.

_book/16-teaching-data-science.md

@@ -468,11 +468,11 @@ sessionInfo()
 ## [13] glue_1.4.0       DBI_1.1.0        dbplyr_1.4.2     modelr_0.1.6    
 ## [17] readxl_1.3.1     lifecycle_0.2.0  munsell_0.5.0    gtable_0.3.0    
 ## [21] cellranger_1.1.0 rvest_0.3.5      evaluate_0.14    knitr_1.28      
-## [25] fansi_0.4.1      broom_0.5.5      Rcpp_1.0.4       backports_1.1.6 
+## [25] fansi_0.4.1      broom_0.5.5      Rcpp_1.0.4.6     backports_1.1.6 
 ## [29] scales_1.1.0     jsonlite_1.6.1   fs_1.4.1         hms_0.5.3       
 ## [33] digest_0.6.25    stringi_1.4.6    bookdown_0.18    grid_3.6.3      
 ## [37] cli_2.0.2        tools_3.6.3      magrittr_1.5     crayon_1.3.4    
-## [41] pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.3.0       reprex_0.3.0    
-## [45] lubridate_1.7.4  rstudioapi_0.11  assertthat_0.2.1 rmarkdown_2.1   
+## [41] pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.3.1       reprex_0.3.0    
+## [45] lubridate_1.7.8  rstudioapi_0.11  assertthat_0.2.1 rmarkdown_2.1   
 ## [49] httr_1.4.1       R6_2.4.1         nlme_3.1-145     compiler_3.6.3
 ```

_book/20-appendices.md

@@ -21,7 +21,7 @@
   <meta name="twitter:description" content="Bookdown for ‘Data Science in Education Using R’ by Emily A. Bovee, Ryan A. Estrellado, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez to be published by Routledge in 2020" />
 
 
-<meta name="author" content="Emily A. Bovee, Ryan A. Estrellado, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez" />
+<meta name="author" content="Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez" />
 
 
 
@@ -486,7 +486,7 @@
 <li class="chapter" data-level="16.4.1" data-path="c16.html"><a href="c16.html#provide-a-home-base-for-learners-to-access-resources-and-to-learn-more"><i class="fa fa-check"></i><b>16.4.1</b> Provide a Home Base for Learners to Access Resources (and to Learn More)</a></li>
 <li class="chapter" data-level="16.4.2" data-path="c16.html"><a href="c16.html#when-it-comes-to-writing-code-think-early-and-often"><i class="fa fa-check"></i><b>16.4.2</b> When it Comes to Writing Code, Think Early and Often</a></li>
 <li class="chapter" data-level="16.4.3" data-path="c16.html"><a href="c16.html#dont-touch-that-keyboard"><i class="fa fa-check"></i><b>16.4.3</b> Don’t Touch That Keyboard!</a></li>
-<li class="chapter" data-level="16.4.4" data-path="c16.html"><a href="c16.html#anticipate-ussues-and-sacrifice-accuracy-for-clarity"><i class="fa fa-check"></i><b>16.4.4</b> Anticipate Ussues (and Sacrifice Accuracy for Clarity)</a></li>
+<li class="chapter" data-level="16.4.4" data-path="c16.html"><a href="c16.html#anticipate-issues-and-sacrifice-accuracy-for-clarity"><i class="fa fa-check"></i><b>16.4.4</b> Anticipate Issues (and Sacrifice Accuracy for Clarity)</a></li>
 <li class="chapter" data-level="16.4.5" data-path="c16.html"><a href="c16.html#start-lessons-or-activities-with-visualizing-data"><i class="fa fa-check"></i><b>16.4.5</b> Start Lessons or Activities With Visualizing Data</a></li>
 <li class="chapter" data-level="16.4.6" data-path="c16.html"><a href="c16.html#consider-representation-and-inclusion-in-the-data-and-examples-you-use"><i class="fa fa-check"></i><b>16.4.6</b> Consider Representation and Inclusion in the Data and Examples You Use</a></li>
 <li class="chapter" data-level="16.4.7" data-path="c16.html"><a href="c16.html#draw-on-other-resources"><i class="fa fa-check"></i><b>16.4.7</b> Draw on Other Resources</a></li>

_book/_main.docx

@@ -21,7 +21,7 @@
   <meta name="twitter:description" content="Bookdown for ‘Data Science in Education Using R’ by Emily A. Bovee, Ryan A. Estrellado, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez to be published by Routledge in 2020" />
 
 
-<meta name="author" content="Emily A. Bovee, Ryan A. Estrellado, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez" />
+<meta name="author" content="Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez" />
 
 
 
@@ -486,7 +486,7 @@
 <li class="chapter" data-level="16.4.1" data-path="c16.html"><a href="c16.html#provide-a-home-base-for-learners-to-access-resources-and-to-learn-more"><i class="fa fa-check"></i><b>16.4.1</b> Provide a Home Base for Learners to Access Resources (and to Learn More)</a></li>
 <li class="chapter" data-level="16.4.2" data-path="c16.html"><a href="c16.html#when-it-comes-to-writing-code-think-early-and-often"><i class="fa fa-check"></i><b>16.4.2</b> When it Comes to Writing Code, Think Early and Often</a></li>
 <li class="chapter" data-level="16.4.3" data-path="c16.html"><a href="c16.html#dont-touch-that-keyboard"><i class="fa fa-check"></i><b>16.4.3</b> Don’t Touch That Keyboard!</a></li>
-<li class="chapter" data-level="16.4.4" data-path="c16.html"><a href="c16.html#anticipate-ussues-and-sacrifice-accuracy-for-clarity"><i class="fa fa-check"></i><b>16.4.4</b> Anticipate Ussues (and Sacrifice Accuracy for Clarity)</a></li>
+<li class="chapter" data-level="16.4.4" data-path="c16.html"><a href="c16.html#anticipate-issues-and-sacrifice-accuracy-for-clarity"><i class="fa fa-check"></i><b>16.4.4</b> Anticipate Issues (and Sacrifice Accuracy for Clarity)</a></li>
 <li class="chapter" data-level="16.4.5" data-path="c16.html"><a href="c16.html#start-lessons-or-activities-with-visualizing-data"><i class="fa fa-check"></i><b>16.4.5</b> Start Lessons or Activities With Visualizing Data</a></li>
 <li class="chapter" data-level="16.4.6" data-path="c16.html"><a href="c16.html#consider-representation-and-inclusion-in-the-data-and-examples-you-use"><i class="fa fa-check"></i><b>16.4.6</b> Consider Representation and Inclusion in the Data and Examples You Use</a></li>
 <li class="chapter" data-level="16.4.7" data-path="c16.html"><a href="c16.html#draw-on-other-resources"><i class="fa fa-check"></i><b>16.4.7</b> Draw on Other Resources</a></li>