exploreBooks/exploreData.html at master · notesofdabbler/exploreBooks · GitHub

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<!DOCTYPE html>
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</head>

<body>
<pre><code class="r">options(width = 400)

#
#
#  Explore the R related books scraped from Springer site
#  Specific questions considered:
#  - Find the most downloaded books
#  - Contruct a network of the R related books based on recommended books with each book and explore the network
#        +  Find any disconnected components of the graph
#        +  Find books that have largest degree (most recommended)
#
#  Note: Some tables are wide and only part of them are showing. You can scroll
#  to the right to see the remaining part of the table

# load libraries
library(dplyr)
library(tidyr)
library(ggplot2)
library(igraph)
library(networkD3)

# load data
load(file = &quot;bookinfodf.Rda&quot;) # info on books
load(file = &quot;recobookinfodf.Rda&quot;) # info on recommended books

head(data.frame(bookinfodf))
</code></pre>

<pre><code>##                                                                                                                                    title                                                                      authors                series pubyr price1 price1curr price2 price2curr citations downloads                                          link id
## 1            Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry                                                                   Oliver Som                  none  2012  89.00        USD 119.00        USD         1      3357 http://www.springer.com/us/book/9783834934918  1
## 2 Geochemical Modelling of Igneous Processes – Principles And Recipes in R Language - Bringing the Power of R to a Geochemical Community Vojtěch Janoušek;Jean-François Moyen;Hervé Martin;Vojtěch Erban;Colin Farrow Springer Geochemistry  2015  69.99        USD  99.00        USD         0      2279 http://www.springer.com/us/book/9783662467916  2
## 3                                                                               Beginning R - An Introduction to Statistical Programming                                                                   Larry Pace                  none  2012  29.99        USD  39.99        USD         0     79426 http://www.springer.com/us/book/9781430245544  3
## 4                                                                                                                                 Pro R                                                                    Larry Pace                  none  2016  29.99        USD  49.99        USD         0         0 http://www.springer.com/us/book/9781484200926  4
## 5                                                                               Beginning R - An Introduction to Statistical Programming                                                      Larry Pace;Joshua Wiley                  none  2015  29.99        USD  39.99        USD         0      3441 http://www.springer.com/us/book/9781484203743  5
## 6                                                                                                R Recipes - A Problem-Solution Approach                                                                   Larry Pace                  none  2014  29.99        USD  39.99        USD         1     12156 http://www.springer.com/us/book/9781484201312  6
</code></pre>

<pre><code class="r">head(data.frame(recobookinfodf))
</code></pre>

<pre><code>##                                                                                                                         title                                                               recobooks                                 recobooklinks rnk id
## 1 Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry                                                     Electronic Commerce http://www.springer.com/us/book/9783319100906   1  1
## 2 Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry                                        Customer Relationship Management http://www.springer.com/us/book/9783642201097   2  1
## 3 Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry                                      Multiple Attribute Decision Making http://www.springer.com/us/book/9783540105589   3  1
## 4 Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry                            Sustainability and Human Resource Management http://www.springer.com/us/book/9783642375231   4  1
## 5 Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry Let&#39;s Get Engaged! Crossing the Threshold of Marketing’s Engagement Era http://www.springer.com/us/book/9783319118147   5  1
## 6 Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry                                        Linear and Nonlinear Programming http://www.springer.com/us/book/9783319188416   6  1
</code></pre>

<pre><code class="r">paste0(&quot;Number of books = &quot;,nrow(bookinfodf))
</code></pre>

<pre><code>## [1] &quot;Number of books = 154&quot;
</code></pre>

<pre><code class="r"># Remove titles not related to R
# Criteria (1) R appears in the beginning or end of title or in the middle with spaces around it
# Criteria (2) Series is related to statistics
RinTitle = grepl(&quot;^R .*$|^.* R$| R &quot;,bookinfodf$title)

# Series in the book list
seriesList = unique(bookinfodf$series)
statsSeries = seriesList[grep(&quot;Statistics&quot;,seriesList)]
statsSeries
</code></pre>

<pre><code>##  [1] &quot;Statistics and Computing&quot;                  &quot;Springer Texts in Statistics&quot;              &quot;SpringerBriefs in Statistics&quot;              &quot;Springer Series in Statistics&quot;             &quot;Statistics for Biology and Health&quot;         &quot;Statistics for Industry and Technology&quot;    &quot;Lecture Notes in Statistics&quot;               &quot;Lecture Notes in Statistics - Proceedings&quot; &quot;ICSA Book Series in Statistics&quot;
## [10] &quot;Environmental and Ecological Statistics&quot;
</code></pre>

<pre><code class="r">seriesOfInterest = c(&quot;Use R!&quot;,statsSeries) # statsSeries doesn&#39;t have Use R, so added manually

# keep only R or stats relevant books
incbook = RinTitle | (bookinfodf$series %in% seriesOfInterest)
bookinfodf_filt = bookinfodf[incbook,]
recobookinfodf_filt = inner_join(recobookinfodf,bookinfodf_filt[,&quot;id&quot;],by = &quot;id&quot;)

paste0(&quot;Number of books after filtering non R books = &quot;,nrow(bookinfodf_filt))
</code></pre>

<pre><code>## [1] &quot;Number of books after filtering non R books = 120&quot;
</code></pre>

<pre><code class="r"># check dropped books
data.frame(bookinfodf[!incbook,&quot;title&quot;])
</code></pre>

<pre><code>##                                                                                                                                                                                title
## 1                                                        Innovation without R&amp;D - Heterogeneous Innovation Patterns of Non-R&amp;D-Performing Firms in the German Manufacturing Industry
## 2                                                                                                                                                                     R&amp;D Management
## 3                                                                                                                                            F. R. Leavis  - The Creative University
## 4                                                                      Open Innovation in R&amp;D Departments - An Analysis of Employees’ Intention to Exchange Knowledge in OI-Projects
## 5                                                                                      R-Matrix Theory of Atomic Collisions - Application to Atomic, Molecular and Optical Processes
## 6                                                                                                                  A Glimpse at Hilbert Space Operators - Paul R. Halmos in Memoriam
## 7                                                                                                        Innovation and IT in an International Context - R&amp;D strategy and operations
## 8                                                                                                           The Public Sector R&amp;D Enterprise - A New Approach to Portfolio Valuation
## 9                                                                                                                   (R)Evolution - Organizations and the Dynamics of the Environment
## 10                                                                                                                                  The Geography of Networks and R&amp;D Collaborations
## 11                                                                                                                                   Vacuum-Assisted Breast Biopsy with Mammotome(R)
## 12                                                                                                                                                 Space groups (156) P3m1-(148) R-3
## 13                                                                                               Symmetry: Representation Theory and Its Applications - In Honor of Nolan R. Wallach
## 14                                                                                       Optimization of Pharmaceutical R&amp;D Programs and Portfolios - Design and Investment Strategy
## 15                                                                                                                       Cloud Computing - Challenges, Limitations and R&amp;D Solutions
## 16                                                                                                                             Space groups (148) R-3-(141)I41/amd - Structure Types
## 17                                                        Eurocode-Compliant Seismic Analysis and Design of R/C Buildings - Concepts, Commentary and Worked Examples with Flowcharts
## 18                                                                                The Keys of Middle-earth - Discovering Medieval Literature Through the Fiction of J. R. R. Tolkien
## 19                                                                                                The New Health Bioeconomy - R&amp;D Policy and Innovation for the Twenty-First Century
## 20                                                                                                  Advanced Spark for Professionals - Analytics for Data Driven Enterprises and R&amp;D
## 21                                        Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning - Volume 2: HVAC&amp;R Component and Energy System
## 22                                                                                             Bending the Arc of Innovation - Public Support of R&amp;D in Small, Entrepreneurial Firms
## 23                                                                                      Acoustics, Information, and Communication - Memorial Volume in Honor of Manfred R. Schroeder
## 24                                                                                    Analysis of the Electric Dipole Moment in the R-parity Violating Supersymmetric Standard Model
## 25                                                                                                   Improving the Efficiency of R&amp;D and the Market Diffusion of Energy Technologies
## 26 Creating R&amp;D Incentives for Medicines for Neglected Diseases - An Economic Analysis of Parallel Imports, Patents, and Alternative Mechanisms to Stimulate Pharmaceutical Research
## 27                                                         Institutional Economics and the Theory of Social Value: Essays in Honor of Marc R. Tool - Essays in Honor of Marc R. Tool
## 28                                                                                                                                               Space groups (166) R-3m – (160) R3m
## 29                                                                Multiple Stars across the H-R Diagram - Proceedings of the ESO Workshop held in Garching, Germany, 12-15 July 2005
## 30                                                                                                                                               Space groups (173) P63 – (166) R-3m
## 31                                                                                                                                                  R-Trees: Theory and Applications
## 32                                                                                                            Digital (R)Evolution in Radiology - Bridging the Future of Health Care
## 33                                                                                 The Liquid Crystal Display Story - 50 Years of Liquid Crystal R&amp;D that lead The Way to the Future
## 34                                                                                Persistence Pays - U.S. Agricultural Productivity Growth and the Benefits from Public R&amp;D Spending
</code></pre>

<pre><code class="r"># sort books based on number of downloads and show top 50 downloaded books
downloads_srt = bookinfodf_filt %&gt;% arrange(desc(downloads)) %&gt;% select(title,downloads)
#data.frame(downloads_srt[1:50,])
</code></pre>

<pre><code class="r">p = ggplot(data = downloads_srt[1:50,]) + geom_point(aes(x = reorder(title,downloads),y = downloads),size = 5) +
  geom_segment(aes(x = reorder(title,downloads), y = 0, xend = reorder(title,downloads),yend = downloads)) +
  xlab(&quot;&quot;) +
  theme_bw() + coord_flip()
p
</code></pre>

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" alt="plot of chunk unnamed-chunk-2"/> </p>

<pre><code class="r"># Number of books published by year
#table(bookinfodf_filt$pubyr)

booksbyyr = bookinfodf_filt %&gt;% group_by(pubyr) %&gt;% summarize(numbooks = n())
</code></pre>

<pre><code class="r">p = ggplot(data = booksbyyr) + geom_bar(aes(x = factor(pubyr),y = numbooks),stat = &quot;identity&quot;,fill = &quot;grey&quot;) +
   xlab(&quot;&quot;) + ylab(&quot;# books&quot;) + theme_bw()
p
</code></pre>

<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAFoCAYAAAC/jUTwAAAEDWlDQ1BJQ0MgUHJvZmlsZQAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VVBg/m8AAC3oSURBVHgB7d0JkFxF/cDx32Y3MYEkHCEGgkZCoWUIwQQCcogQiisEWIFgJFCBGAShVASxggQR/KMcIoeCIhYiyJmywIVwh4Qb4oEpCJdYxkgEgxBM2Fyb3X3/9+uqNzW7md2dedPd0535vqrJzL55r/vXn+68+c27piFJJ2FCAAEEEEAAAQTqSKBfHbWVpiKAAAIIIIAAAkaABIiBgAACCCCAAAJ1J0ACVHddToMRQAABBBBAgASIMYAAAggggAACdSdAAlR3XU6DEUAAAQQQQIAEiDGAAAIIIIAAAnUnQAJUd11OgxFAAAEEEECABIgxgAACCCCAAAJ1J0ACVHddToMRQAABBBBAgASIMYAAAggggAACdSdAAlR3XU6DEUAAAQQQQIAEiDGAAAIIIIAAAnUnQAJUd11OgxFAAAEEEECgKXSC//3vfzJ//nzp189PrtbR0SGNjY2hs2wSX5Ik0tnZGWXsmG/Snc5nYO6ceJMKMN+ExPkMzJ0Tb1KBb/NRo0bJxIkTN4mjnBnBJ0Ctra0yZswYGTt2bDntqXqZ1atXy9ChQ6sux3cBmvysXbtWBg8e7LvqquuL2XzNmjUyZMiQqg18F4C5b3GRWM31y5Vuhxnn/sYM5uVb33vvvbkTID+7VcpvC0sigAACCCCAAALOBUiAnBNTAQIIIIAAAgiEJkACFFqPEA8CCCCAAAIIOBcgAXJOTAUIIIAAAgggEJoACVBoPUI8CCCAAAIIIOBcgATIOTEVIIAAAggggEBoAiRAofUI8SCAAAIIIICAcwESIOfEVIAAAggggAACoQmQAIXWI8SDAAIIIIAAAs4FSICcE1MBAggggAACCIQmQAIUWo8QDwIIIIAAAgg4FyABck5MBQgggAACCCAQmkDwP4YaGhjxIIAAAiELtLS0hBxeIbbm5ubCa14gUAsB9gDVQp06EUAAAQQQQKCmAiRANeWncgQQQAABBBCohQAJUC3UqRMBBBBAAAEEaipAAlRTfipHAAEEEEAAgVoIkADVQp06EUAAAQQQQKCmAiRANeWncgQQQAABBBCohQAJUC3UqRMBBBBAAAEEaipAAlRTfipHAAEEEEAAgVoIkADVQp06EUAAAQQQQKCmAiRANeWncgQQQAABBBCohQAJUC3UqRMBBBBAAAEEaipAAlRTfipHAAEEEEAAgVoIkADVQp06EUAAAQQQQKCmAiRANeWncgQQQAABBBCohUBTLSqlTgQQQCB0gZaWltBDNPE1NzdHEWc5QWJejhLL2BJgD5AtScpBAAEEEEAAgWgESICi6SoCRQABBBBAAAFbAiRAtiQpBwEEEEAAAQSiESABiqarCBQBBBBAAAEEbAmQANmSpBwEEEAAAQQQiEaABCiariJQBBBAAAEEELAlEPxl8EmSSHt7u7S1tdlqc6/l+Kyr10AqfLOzs9OrU4Xh9bp4zOYdHR3exmaviBW+Gau57+1Bhaw1WdzXttF242KNWx1cxx7zOPe9bdHPvrxT8AmQNqypqUn69++ft40VrdfY2OitrooC62NhHQQbN26MMnbM++hcB2/HbB5r7A660RTpa9toO/5Y41YH17FrAhTrOPcdd0NDQ+6hGXwClDUue87d0jJX1Hp81VVmSGUtlsUdc+xlNTSghTLr7Dmg0PoMJRsvfS4Y2AJZ3DGau6KM1SLWuLUffcSudfiox/a49B13NUacA2S79ykPAQQQQAABBIIXIAEKvosIEAEEEEAAAQRsC5AA2RalPAQQQAABBBAIXoAEKPguIkAEEEAAAQQQsC1AAmRblPIQQAABBBBAIHgBEqDgu4gAEUAAAQQQQMC2AAmQbVHKQwABBBBAAIHgBUiAgu8iAkQAAQQQQAAB2wIkQLZFKQ8BBBBAAAEEghcgAQq+iwgQAQQQQAABBGwLkADZFqU8BBBAAAEEEAhegAQo+C4iQAQQQAABBBCwLUACZFuU8hBAAAEEEEAgeAESoOC7iAARQAABBBBAwLYACZBtUcpDAAEEEEAAgeAFSICC7yICRAABBBBAAAHbAiRAtkUpDwEEEEAAAQSCFyABCr6LCBABBBBAAAEEbAuQANkWpTwEEEAAAQQQCF6ABCj4LiJABBBAAAEEELAtQAJkW5TyEEAAAQQQQCB4ARKg4LuIABFAAAEEEEDAtgAJkG1RykMAAQQQQACB4AVIgILvIgJEAAEEEEAAAdsCJEC2RSkPAQQQQAABBIIXIAEKvosIEAEEEEAAAQRsC5AA2RalPAQQQAABBBAIXoAEKPguIkAEEEAAAQQQsC1AAmRblPIQQAABBBBAIHgBEqDgu4gAEUAAAQQQQMC2AAmQbVHKQwABBBBAAIHgBbwkQBdccIE8+OCDBYzLLrtM9t13X/M49thjC/N5gQACCCCAAAII+BBocllJa2urnHXWWTJ//nzZc889C1VpMjRv3jwZPHiwNDQ0FObzAgEEEEAAAQQQ8CHgNAFauXKlTJ8+XUaNGlVoS2dnp+h8TYL09bRp0wrv6YuOjg5pb28vzNu4cWPhNS8QQAABBBBAAAEbAk4TIE189PHss88WYl2xYoUMHz7cPJYsWSJTpkyRBQsWFN6/9NJL5eKLLy78vffee8v1118vq1evLsxz+WL9+vUui3dWdpIk0tbWZpJKZ5U4Kjh2c7WPbcI8th7rOV5f28aeI8j3Tqxxa2tdx67blA0bNgjblr7Hlu40yTs5TYBKBbXDDjvIU089Zd6aPHmyzJ07V9555x0ZOXKkmXfhhRfK7NmzC6vqe+vWrZOhQ4cW5rl+4bMuW23RvWlr1641hxVtldlTOS0tLT29FdT85uZmp/Go+Zo1a2TIkCFO63FVeKzjPGZzF30ZYz+qQ6xx+4hdEx89hYRtS9//YxobG/teqIclvJwEXVz30qVLZcaMGWaW7rHQTh4xYkRhEW3MwIEDC48BAwYU3uMFAggggAACCCBgQ8D7HqDRo0fLsGHDZOrUqfLWW2+ZvT3VZHA2ECgDAQQQQAABBOpLwEsCpOf1FE/XXHON6DkITU1N5lH8Hq8RQAABBBBAAAHXAl4SoFKN0MNcTAgggAACCCCAQC0EvJ8DVItGUicCCCCAAAIIIFAsQAJUrMFrBBBAAAEEEKgLARKguuhmGokAAggggAACxQIkQMUavEYAAQQQQACBuhAgAaqLbqaRCCCAAAIIIFAsQAJUrMFrBBBAAAEEEKgLARKguuhmGokAAggggAACxQIkQMUavEYAAQQQQACBuhAgAaqLbqaRCCCAAAIIIFAsULM7QRcHUY+v+UX1euz1+msz47z++pwWIxCLAHuAYukp4kQAAQQQQAABawIkQNYoKQgBBBBAAAEEYhEgAYqlp4gTAQQQQAABBKwJkABZo6QgBBBAAAEEEIhFgAQolp4iTgQQQAABBBCwJkACZI2SghBAAAEEEEAgFgESoFh6ijgRQAABBBBAwJoACZA1SgpCAAEEEEAAgVgESIBi6SniRAABBBBAAAFrAiRA1igpCAEEEEAAAQRiESABiqWniBMBBBBAAAEErAmQAFmjpCAEEEAAAQQQiEWABCiWniJOBBBAAAEEELAmQAJkjZKCEEAAAQQQQCAWARKgWHqKOBFAAAEEEEDAmgAJkDVKCkIAAQQQQACBWARIgGLpKeJEAAEEEEAAAWsCJEDWKCkIAQQQQAABBGIRIAGKpaeIEwEEEEAAAQSsCZAAWaOkIAQQQAABBBCIRaAp9ECTJDEhZs+u49V6fNXlui02yo/ZwnXsWfnZsw1vX2VozDHG7conZotYY481bh2DrmPX8rOHqzHvqlzfcVfTF8EnQNpJ7e3tsnHjRlf91aXcjo4Ob3V1qTjQP3y5u2i+69g7Ozsl1vESa9wuxomW6XqsuIo75tgx73lU6Id6rP9Hfce9WSdADQ0N0tTUJAMGDOh5tFh8x2ddFsN2VpQvdxcNcB27JkC6EXddjwsbxnlX1Rj7MGtBrLHHGre6u45dP9Tb2tqc15ONIZvPvrct/frlP5Mn/5o2xSgLAQQQQAABBBDwKEAC5BGbqhBAAAEEEEAgDAESoDD6gSgQQAABBBBAwKMACZBHbKpCAAEEEEAAgTAESIDC6AeiQAABBBBAAAGPAiRAHrGpCgEEEEAAAQTCECABCqMfiAIBBBBAAAEEPAqQAHnEpioEEEAAAQQQCEOABCiMfiAKBBBAAAEEEPAoQALkEZuqEEAAAQQQQCAMARKgMPqBKBBAAAEEEEDAowAJkEdsqkIAAQQQQACBMARIgMLoB6JAAAEEEEAAAY8CJEAesakKAQQQQAABBMIQIAEKox+IAgEEEEAAAQQ8CpAAecSmKgQQQAABBBAIQ4AEKIx+IAoEEEAAAQQQ8ChAAuQRm6oQQAABBBBAIAwBEqAw+oEoEEAAAQQQQMCjAAmQR2yqQgABBBBAAIEwBEiAwugHokAAAQQQQAABjwIkQB6xqQoBBBBAAAEEwhDInQD985//NC147bXX5Morr5QlS5aE0SKiQAABBBBAAAEE+hDIlQCdd955cuqpp8qGDRvk4IMPlueee06mTJkib7/9dh/V8TYCCCCAAAIIIFB7gVwJ0D333CMPPPCAPPHEE7LNNttIS0uLnHTSSTJ//vzat4gIEEAAAQQQQACBPgQqToCSJJH29nYZPHiw3H///XL88cebKtra2mTIkCF9VMfbCCCAAAIIIIBA7QWaKg2hoaFB9tlnHzn66KPl2WefNYe/br75Zrnttttkzpw5lRbH8ggggAACCCCAgHeBivcAaYR33HGHTJ061RzyGjt2rIwcOVJeeuklczjMewuoEAEEEEAAAQQQqFAgVwL0pz/9yZwEPXHiRFPd5MmT5eGHH5ZHH320wupZHAEEEEAAAQQQ8C+QKwE655xzzPk/Gq5e+XX44YfLT37yExk9erT/FlAjAggggAACCCBQoUCuBGju3Lly7rnnyuzZs2X8+PEyYcIEefnll+Uzn/lMhdWzOAIIIIAAAggg4F+g4pOgNcRddtlFHnvsMZk0aZI58VmTISYEEEAAAQQQQCAWgbL3AC1atMjs4dG9PPo44ogjZN26dXLBBRcU5j/yyCMl263LPPjgg4X3NHk68sgj5ZBDDpE33nijMJ8XCCCAAAIIIICAD4Gy9wDtuuuuctddd/Ua084779zl/dbWVjnrrLPM1WJ77rmnee+jjz4ye40ef/xxWbZsmZx++uny9NNPd1mPPxBAAAEEEEAAAZcCZSdAepPDLInRgDo7O00CozdA/PSnPy39+m26M2nlypUyffp0GTVqVKENusdHzxnaeuutzWPVqlWiZQwYMMAss2LFCnn33XcLy2vCtO222xb+5gUCCCCAAAIIIFCtQNkJUHFFb731lrkP0KuvvipbbrmluTP01VdfLWeccUbxYibx0eRHb5iYTcuXL++S0Ggi9P7775t7Cekyd999t1x77bXZ4jJu3Dj5/ve/L6tXry7Mc/li/fr1LouPrmxf7i5gXMeud0XX5F2fXU8LFy50XYWV8vW8wBgn12PFpUmssccat/al69h1m6K/telj22J7bPr+DO3o6MjdhFwJkB620jtBL1iwwOzFefHFF+WEE06QAw44QPRQWW/TVlttJXpoLJv0PKLtttsu+1POPvts88hmaMKke4mGDh2azXL+7LMu542psoKYLVzHrntB16xZw0/AFI0x1+ZFVVl9GWvcihBr7LHG7cNcEx/9nIz156V89m1jY2PubcGmx63KKOq1116Tiy66SIYNGyZa+f777y8zZ86UZ555ps+19c7RixcvNpmtJjb6IZId/upzZRZAAAEEEEAAAQQsCORKgHbbbTeTxBTXrycyF+/JKX6v+PWIESPML8cfddRR5iqwK664ovhtXiOAAAIIIIAAAs4Fch0CO/PMM+XQQw+Vgw46yJznoz+BoefyHHPMMSUDvvTSS7vM1/VnzZolTU1NJU+e7rIwfyCAAAIIIIAAApYFcu0B0h9CfeKJJ2SvvfYyh6/OP/98c6Jz//79yw5PD3uVunKs7AJYEAEEEEAAAQQQyCmQaw+Q1rXHHnuYc4B6uww+Z0yshgACCCCAAAIIOBXItQdIL4PXe/no/X/23ntvc6b6r371K6eBUjgCCCCAAAIIIGBLIFcClF0Grzct1Jsd6k9bXHLJJaJXhzEhgAACCCCAAAKhC+Q6BKaJjp74nF2+XnwZfF/3AQodhPgQQAABBBBAYPMXyLUHqJrL4Dd/UlqIAAIIIIAAAqEL5NoDVOll8KEjEB8CCCCAAAII1JdArj1A2WXwY8aMEf0djjyXwdcXM61FAAEEEEAAgZAEcu0Bev31180Pn7700ksyePBg0V94HzRokJx44okhtY1YEEAAAQQQQACBkgK5EqBTTz3V3PX5oYcekm222Uaee+45Ofnkk81l8RMnTixZETMRQAABBBBAAIFQBCo+BKa/Urt06VKZM2eO6O966ZVgkyZNktNOO00WLVoUSruIAwEEEEAAAQQQ6FGg4gSooaFB9ttvP7nlllvM+T9a8tq1a+X555+XAw44oMeKeAMBBBBAAAEEEAhFoOwESPfu6EnP+vjrX/8qejPE4cOHy+67725+CHXx4sXywQcfhNIu4kAAAQQQQAABBHoUKPscIE18br311h4L0jd22WWXXt/nTQQQQAABBBBAIASBshOgoUOHmt/9CiFoYkAAAQQQQAABBKoRKPsQWDWVsC4CCCCAAAIIIBCSAAlQSL1BLAgggAACCCDgRSBXAnTVVVcVgit+XZjJCwQQQAABBBBAIGCBshOgO++8U8aNGyennHKK/OhHP5IXXnhB1q1bJzfccEPAzSM0BBBAAAEEEEBgU4GyE6Dp06ebOz7PnDlTGhsb5brrrpPx48fL8uXLzU0Qb7zxRlm2bNmmNTAHAQQQQAABBBAITKDsBOjdd9+V1tZWOeigg2TIkCFy9913y5tvvik77rijTJs2TT788EN55ZVXAmse4SCAAAIIIIAAApsKlH0ZvP7gqf78xXvvvSeaDF1yySWy1157SXt7uxx66KHmsWnxzEEAAQQQQAABBMITKHsPkP7el/7che710TtAjxo1SubNmycrV640P4J63HHHycKFC8NrIREhgAACCCCAAALdBMreA5Stp+f/7LTTTqLnAulDD3stWLDAPA8cODBbjGcEEEAAAQQQQCBYgYoTIG3JM888U2hQ9nqPPfYozOMFAggggAACCCAQskDZh8BCbgSxIYAAAggggAAClQiQAFWixbIIIIAAAgggsFkIkABtFt1IIxBAAAEEEECgEgESoEq0WBYBBBBAAAEENgsBEqDNohtpBAIIIIAAAghUIkACVIkWyyKAAAIIIIDAZiGQ6zJ4ny1PkkT00dnZ6aVan3V5aVCVlfhyrzLMkqu7jl3LZ7x0pXdt3rU2e3/FGrcKxBp7rHH7MM+2KzEaZbHb+9/Ze0laX94p+ARIG6aDoKOjI28bK1rPZ10VBVajhX25u2ie69h1rDBeuvaca/Outdn7K9a4VSDW2GON24e5fqjrI0ajmLaJwSdADQ0N5tfn+/fvb29r10tJeqdrX3X1EkYwb8Vs4Tp2/Y/e1tbGeCkara7Ni6qy+jLWuBUh1thjjduHuSY/sX4W+Y5bc4S8E+cA5ZVjPQQQQAABBBCIVoAEKNquI3AEEEAAAQQQyCtAApRXjvUQQAABBBBAIFoBEqBou47AEUAAAQQQQCCvAAlQXjnWQwABBBBAAIFoBUiAou06AkcAAQQQQACBvAIkQHnlWA8BBBBAAAEEohUgAYq26wgcAQQQQAABBPIKkADllWM9BBBAAAEEEIhWgAQo2q4jcAQQQAABBBDIK0AClFeO9RBAAAEEEEAgWgESoGi7jsARQAABBBBAIK8ACVBeOdZDAAEEEEAAgWgFSICi7ToCRwABBBBAAIG8AiRAeeVYDwEEEEAAAQSiFSABirbrCBwBBBBAAAEE8gqQAOWVYz0EEEAAAQQQiFaABCjariNwBBBAAAEEEMgrQAKUV471EEAAAQQQQCBaARKgaLuOwBFAAAEEEEAgrwAJUF451kMAAQQQQACBaAVIgKLtOgJHAAEEEEAAgbwCJEB55VgPAQQQQAABBKIVIAGKtusIHAEEEEAAAQTyCpAA5ZVjPQQQQAABBBCIVoAEKNquI3AEEEAAAQQQyCtAApRXjvUQQAABBBBAIFoBEqBou47AEUAAAQQQQCCvAAlQXjnWQwABBBBAAIFoBUiAou06AkcAAQQQQACBvAIkQHnlWA8BBBBAAAEEohUgAYq26wgcAQQQQAABBPIKNOVdsZr1LrvsMrn//vtNEdtvv73cd9991RTHuggggAACCCCAQEUCNUmAHnzwQZk3b54MHjxYGhoaKgqYhRFAAAEEEEAAgWoFvCdAnZ2dsnLlStEkSF9PmzatSxuWLFkir7zySmGeJkjjxo2TJEkK81y+0Hp81eWyHbbKjtnCdexZ+dmzLfOYy4nVIta4dazEGnuscfswV5vsEdv2wHfc1Ywj7wnQihUrZPjw4eahyc6UKVNkwYIFhT5++eWX5fbbby/8/alPfUp22WUXaW1tLcxz+WLDhg3e6nLZDltl+3K3FW9xOd1jLx5nxcuF9vrggw8OLaSy4+luXvaKNV4w1riVLdbYY43bh7l+qOtnUYxHSHx/huqOlLyT9wRohx12kKeeesrEO3nyZJk7d6688847MnLkSDNv+vTpoo9sWr58uaxatUqGDBmSzXL6rAPPV11OG2Kp8JgtYo091rh1yMUae6xxY25pQ1dhMa7Hi34OafLjup4Km13W4r4/QxsbG8uKq9RC3q8CW7p0qcyYMcPE0tbWZr69jBgxolRszEMAAQQQQAABBJwIeN8DNHr0aBk2bJhMnTpV3nrrLZk9e7ZUk8E5UaFQBBBAAAEEENisBbwnQKp5zTXXyPr166Wpqck8NmthGocAAggggAACwQnUJAFShYEDBwaHQUAIIIAAAgggUB8C3s8Bqg9WWokAAggggAACIQuQAIXcO8SGAAIIIIAAAk4ESICcsFIoAggggAACCIQsQAIUcu8QGwIIIIAAAgg4ESABcsJKoQgggAACCCAQsgAJUMi9Q2wIIIAAAggg4ESABMgJK4UigAACCCCAQMgCJEAh9w6xIYAAAggggIATARIgJ6wUigACCCCAAAIhC9TsTtAhoxAbAggggAAC5Qq0tLSUu2hNl2tubq5p/aFVzh6g0HqEeBBAAAEEEEDAuQAJkHNiKkAAAQQQQACB0ARIgELrEeJBAAEEEEAAAecCJEDOiakAAQQQQAABBEITIAEKrUeIBwEEEEAAAQScC5AAOSemAgQQQAABBBAITYAEKLQeIR4EEEAAAQQQcC5AAuScmAoQQAABBBBAIDQBEqDQeoR4EEAAAQQQQMC5AAmQc2IqQAABBBBAAIHQBEiAQusR4kEAAQQQQAAB5wIkQM6JqQABBBBAAAEEQhMgAQqtR4gHAQQQQAABBJwLkAA5J6YCBBBAAAEEEAhNgAQotB4hHgQQQAABBBBwLkAC5JyYChBAAAEEEEAgNAESoNB6hHgQQAABBBBAwLkACZBzYipAAAEEEEAAgdAESIBC6xHiQQABBBBAAAHnAiRAzompAAEEEEAAAQRCE2gKLaDu8SRJIp2dndLR0dH9LSd/+6zLSQMsF+rL3XLYprhYY481bkWPNfZY48bc/Ff3/k+s48VH3L4/QzVHyDsFnwBlDaumkVkZ5T77rKvcmGq1XMwWscYea9w6RmONPda4Ma/NljHW8eIrbl/1VNv7wSdADQ0N0q9fP2lq8hOqz7qq7Twf6/tyd9GWWGOPNW7tw1hjjzVuzF1sOfouM9bx4iNu35+hmiPknTgHKK8c6yGAAAIIIIBAtAIkQNF2HYEjgAACCCCAQF4BEqC8cqyHAAIIIIAAAtEKkABF23UEjgACCCCAAAJ5BUiA8sqxHgIIIIAAAghEK+Dn0iqHPC0tLQ5Lt1d0c3OzvcIoCQEEEEAAAQSqEmAPUFV8rIwAAggggAACMQqQAMXYa8SMAAIIIIAAAlUJkABVxcfKCCCAAAIIIBCjAAlQjL1GzAgggAACCCBQlQAJUFV8rIwAAggggAACMQqQAMXYa8SMAAIIIIAAAlUJkABVxcfKCCCAAAIIIBCjAAlQjL1GzAgggAACCCBQlQAJUFV8rIwAAggggAACMQqQAMXYa8SMAAIIIIAAAlUJkABVxcfKCCCAAAIIIBCjAAlQjL1GzAgggAACCCBQlQAJUFV8rIwAAggggAACMQqQAMXYa8SMAAIIIIAAAlUJNFW1NisjgAACCCCAQLQCLS0tUcTe3NxsPU72AFknpUAEEEAAAQQQCF2ABCj0HiI+BBBAAAEEELAuQAJknZQCEUAAAQQQQCB0ARKg0HuI+BBAAAEEEEDAugAJkHVSCkQAAQQQQACB0AVIgELvIeJDAAEEEEAAAesCJEDWSSkQAQQQQAABBEIXIAEKvYeIDwEEEEAAAQSsC5AAWSelQAQQQAABBBAIXYAEKPQeIj4EEEAAAQQQsC5AAmSdlAIRQAABBBBAIHSBmiRAjz32mBx55JFyyCGHyBtvvBG6EfEhgAACCCCAwGYm4P3HUD/66COZM2eOPP7447Js2TI5/fTT5emnn97MWGkOAggggAACCIQs4D0B0j0+EyZMkK233to8Vq1aJW1tbTJgwADj9OSTT8qCBQsKZkOHDjV7ijZs2FCYF+OLWOOPNW4dI7HGHmvcmNdmyxTreIk1bsZ5WOO8s7Mzd0ANSTrlXjvHivfdd58sWrRILr/8crP2gQceKHfddZeMHDnS/K2Hxx599NFCydtuu61MmTJFPvvZzxbmuXyxZs0a2XLLLV1W4aRsHQS6QRk0aJCT8l0WGrP5+vXrZYsttnDJ46TsWM11c7Vu3TrMnYyK0oViXtrF5VzMy9edN2+eTJ06tfwVipb0vgdoq622ktbW1kIIujHbbrvtCn8fdthhoo9sWr58ueheooEDB2aznD7r3ihfddlsiCZA+ogx9pjNOzo6MLc5kPsoS8d4e3s75n042XxbP4wxtynad1mY922ULdGvX/5TmfOvmdVe4fPYsWNl8eLFoh2siY1u0LLDXxUWxeIIIIAAAggggEAuAe97gEaMGCEnnXSSHHXUUfLee+/JFVdckStwVkIAAQQQQAABBPIKeE+ANNAzzzxTZs2aJU1NTVLN7qu8jWY9BBBAAAEEEKhvgZokQErOYa/6Hni0HgEEEEAAgVoKeD8HqJaNpW4EEEAAAQQQQEAFSIAYBwgggAACCCBQdwIkQHXX5TQYAQQQQAABBEiAGAMIIIAAAgggUHcCJEB11+U0GAEEEEAAAQRIgBgDCCCAAAIIIFB3AiRAddflNBgBBBBAAAEEanYfoEroX3rpJdHfBPMx6Q+KfuxjH/NRldU69KdFNm7cGOX9lWI2198xi3G8YG71v19ZhcVqro2LNfZY48a8rP9SZiH9Pca8k/dfg88TqH6w+5omTJggt912m4wbN85XlVbqufvuu0V/Fff222+3Up7PQvbcc0/5zW9+I5/73Od8Vlt1XXPnzpU//OEPcuedd1Zdlu8C9tprL7nppptEx3tM0+9//3vRh4732KbPf/7z8stf/lL22GOPqEK/9957jbeO99imffbZR66//nqZOHFiVKHrdkW35TrWY5v23Xdfue6662Tvvff2EnpjY2PuX5SIYg9Q//79vUBqJWvWrDGYPuu00TjdA7R+/XqJLW7MbfR+5WUwzis3q3YNNW9oaIju/2jM25a1a9diXu3ArXD9devWRWPOOUAVdi6LI4AAAggggED8AiRA8fchLUAAAQQQQACBCgUaL06nCtfZrBfXc39222236E5sHTZsmDm3YMcdd4yuf9R87NixMnDgwKhiV3M9hyZGcx3j+ojNfNttt412nMdu/olPfCKq/58aLOb+u0y35eo+aNAg/5VXWGMUJ0FX2CYWRwABBBBAAAEEehXgEFivPLyJAAIIIIAAApujAAnQ5tirtAkBBBBAAAEEehWomwTovffekzPPPFOOOuooueCCC0RvYKfTY489JkceeaQccsgh8sYbb5h5//jHP+T444+Xgw46SPQeGDrNnDlT9J4S2eMHP/iBme/jH73nybHHHivTpk2Tp59+2lSp8Z9xxhnyxS9+UYpP47r22mvl4IMPlunTp8tHH31kln399dfl5JNPluOOO87ct8ZHzFpHteadnZ1y5ZVXyhFHHGHaqpf5+5oqMdeYHnjgAbnwwgsL4ZUaQ4U3Hb6oxFzD0HF0+OGHy4cfftglqu7t6fKmoz/+/Oc/m3Hb3Nxs7gul1fQ0zvW97jGWWl+X8zHp/0Hdtpx++umybNkyU2WpbYu+Ucr8oosuMtuhr371q+ZWHD5i1jpKmVVinsX54x//WG6++ebsTy/P1ZqfcMIJoves0cf//d//eYlZK6nW/L///a/Z5ut4+8UvfuEtbq2oGvOf//znhc9P/Rw9+uijvcZesrL0Hg91Mc2ePTu56667TFu/973vJelNppLVq1cn6Q2yknTjnyxevDg54IADzPvpB26S3n06ef/995P0JFfzfrpRSNIP4CS9x0EyadKk5LnnnvPipjGkgyVJ72hq4klvGpikiUFy2WWXJddcc02icX3lK19J0o1t8vzzzydTp05N0htHJulgS9JEz8So8/7yl7+YMtKbDZo2+Ai+WvP58+cnJ510UtLe3p787ne/S77zne/4CNs4l2uuAV1xxRXJ6NGjk2984xuF+EqNocKbDl9UYv63v/0tSRPoZJtttjFtzsIq1Z7sPZfPX/jCF5J//etfpr/1/1iaSJQc5xpDqRhLre8y3qzshx9+OPn6179u/nz00UeT0047rcdtSynzxx9/PDnrrLPM+vr/+qqrrsqKdv5cyqzUtkUDKWWu83W7k14IkPz0pz/VP71M1Zqn92RK0hv1mW26btd1m+lrqtY8/SJrPn90u5h+0U3ShMhL6NWaa7xqrQ/9bNLxVOupbvYAnX322ZImAiYJ1LPT//3vf5s9PnoVz9Zbb23uQrxq1Srz7ezdd981V/foVT7pYJU//vGP5uZl+pMHesdizV7322+/kgml7ZmDBw+WlpYW8xMXesWOxqZ7Rp555hn58pe/bOLSbzILFy6UZ5991uwpampqMt+kdZ5O2r4XX3xRXn31VfNzGfq+j6la86eeekomT54seqfP9ANR0qTTR9hSibkGpOOk+zexUmPIR/CVmP/nP/+RG2+8UXbdddcuoZVqT5cFHP2hd9T+5Cc/afpbbxio8ZUa51p9qRhLre8o1C7F6h6EdGNu5vW1bSllrnufb7jhBtHtT/pFTHxebVXKrBLz1tZW+eEPfyjnnXdeFxPXf1Rr/sorr8j2229v7syt2xndxviaqjHXn3148803zdWb99xzj/z617+W7bbbzkvo1ZqrsX6G6hGJF154wfuYKYVUNwnQDjvsIPrB/+STT5rbi+vhI/19Mb2sNps0Ufj73//e5dLg9NuxpHthzCI6+PQQ03e/+91sFefPOmA+/vGPm8RsxowZcv7555v/rMWxZzEWz9O2fPDBByY+PfSV7i0yh5H0cF+/fn66vVpz3UWq/8H1Q+Hqq682h9Scg6cVVGKu8cyaNcuMrSw2PZxUfHl51j/Z+y6fyzXXMZ3u8ZQxY8ZsEk739myygKMZmvzolO4BETXTW+kXj+lix1IxllrfUahdit1qq61k6NCh5kvVt771Lbnkkku6xK0L6//H3sx1mW9/+9vy8ssvl+wTfd/FVMqsEvNzzz1XLk4P/2255ZYuwuuxzGrN9TCSxqyXa99xxx0mieuxMstvVGO+YsUK0Yf+pIqOJ01K0j0rliMsXVy15lmpl19+uaR7qr19DmX1lnr280lYquYazHvkkUdM8vLQQw+JdqY+9BtMNuktvHfaaSfR26dnk96+XhMQnXQ9PedGN8Q+J41LkxjdG/XNb37TVF0cexZj8Txtw/Dhw81/jvTQjOg3Ht2Tpecn6DceX1M15vp7VXPmzDG/WXXooYeavQO+4i7XvFQ8Q4YM6XEMlVre9rxyzH19a6y0bZo86DjVb8k6FY/pbJz3Vmb39Xtb1uZ7S5cuNecA6W8g6bgtjlvr0fHUl/ktt9wi6WF60f+vPqfuZsWx92a+YMEC0R+q1nNadG+z7qHNzqP0EX815nr+jI4x3fv2s5/9THRvis8pr7kmbXoEQBMgTbb1MyE9VcBb6NWYa5CatC1ZskQOO+wwbzH3VlHdJEDpcXbRzFNPTMxuXKc3bNK9C+lxSLP7WQfWFltsYW7gpHtPdH567kzhG5kmQJqI+Jw0pi996Uty4oknyjnnnFOoWg/dLVq0yPytuxN33313c9iu+zxdYMSIEYVdvFn7CgU5fFGtuW5Y9URuPbyke618/VhqJeal+HRPox4KKTWGSi1vc1655gMGDLBZrZWy9ENBT+LWE9Cz+EqN854qK7V+T8vanP/OO++Yw+u//e1vzRckLbvUtiVrU/e6NenRD2GddC9z9oWr+3Iu/i5lVq65flnUPUD6hTDbrvTURtuxV2uuCY/+SKpOute/+2Fg2/EWl1eNuSanupc3PX/VFKkOuvfRx1Stucb4xBNPmJP99RB3EFOtT0LyVX+6qz/Zeeedk/Hjx5uHnuinU/rhmqSHhczJ0HrSrU7pYbIkvTImOfDAA7ucqKV/64mZPqf0P2qSblwKcWv8K1euTN5+++1ET4ZLz5FJ0ivWzInRepJZeqVbkh46StJvoUl6HooJNf2mY06OTs8VStI9SN7Cr9Y83Ytl2qcn+mmb0nMkvMReiXkWkJ7IWnwSdE9jKFve1XMl5lkM+++/f5eToHV+9/Zky7p6Ts/JS9LEMUkT+cJY1xP7S43zLIbiGHtaP1vW5bP+n0uTlkLc+v9Rp1LbliyOYvN0L0uSXuWZpOf0mXGuF2T4mHoyK9e8OMabbrrJ60nQ1Zqne/6TY445xmwX0zvRm4tEitvj6rUN8/RK4OSUU04xn1tf+9rXzLbfVbzF5VZrrmWlV08nt956a3GxNX3NnaDTNFQv+9Rv7d3PjUnPVu9yLkcQGWu3IHTXevdbjpeal44ySa8kC6Y9lZjr4Tz9hhnKVMq3p9hCGkM9mfcUe0jzKzEPKe5KzGMe57Ga6ykQetFDKFMl4zyk8VLJOA/FWuMgAQqpN4gFAQQQQAABBLwI1M05QF40qQQBBBBAAAEEohAgAYqimwgSAQQQQAABBGwKkADZ1KQsBBBAAAEEEIhCgAQoim4iSAQQQAABBBCwKUACZFOTshBAAAEEEEAgCgESoCi6iSARQAABBBBAwKYACZBNTcpCAAEEEEAAgSgESICi6CaCRAABBBBAAAGbAiRANjUpCwEEEEAAAQSiECABiqKbCBIBBBBAAAEEbAqQANnUpCwEEEAAAQQQiEKABCiKbiJIBBBAAAEEELApQAJkU5OyEEAAAQQQQCAKARKgKLqJIBFAAAEEEEDApgAJkE1NykIAAQQQQACBKARIgKLoJoJEAAEEEEAAAZsCJEA2NSkLAQQQQAABBKIQIAGKopsIEgEEEEAAAQRsCpAA2dSkLAQQQAABBBCIQoAEKIpuIkgEEEAAAQQQsCnw/2W9TxOPjBiSAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-3"/> </p>

<pre><code class="r"># Upcoming books to be published in 2016 and beyond
data.frame(bookinfodf[bookinfodf$pubyr &gt;= 2016,]) # looks like still non-R books are there in the list
</code></pre>

<pre><code>##                                                                                                                        title                                                                                              authors                                              series pubyr price1 price1curr price2 price2curr citations downloads                                          link  id
## 1                                                                                                                     Pro R                                                                                            Larry Pace                                                none  2016  29.99        USD  49.99        USD         0         0 http://www.springer.com/us/book/9781484200926   4
## 2                                                                                    F. R. Leavis  - The Creative University                                                                                     Steven Cranfield         SpringerBriefs on Key Thinkers in Education  2016  39.99        USD  54.99        USD         0       124 http://www.springer.com/us/book/9783319259833  34
## 3                                                                    Principal Component and Correspondence Analyses Using R                                                                              Hervé Abdi;Derek Beaton                        SpringerBriefs in Statistics  2017  39.99        USD  54.99        USD         0         0 http://www.springer.com/us/book/9783319092553  85
## 4 Eurocode-Compliant Seismic Analysis and Design of R/C Buildings - Concepts, Commentary and Worked Examples with Flowcharts Ioannis Avramidis;Asimina Athanatopoulou;Konstantinos Morfidis;Anastasios Sextos;Agathoklis Giaralis Geotechnical, Geological and Earthquake Engineering  2016 139.00        USD 179.00        USD         0       125 http://www.springer.com/us/book/9783319252698 119
## 5                                                                            Learning Analytics in R with SNA, LSA, and MPIA                                                                                        Fridolin Wild                                                none  2016  99.00        USD 129.00        USD         0         0 http://www.springer.com/us/book/9783319287898 124
## 6                                         The New Health Bioeconomy - R&amp;D Policy and Innovation for the Twenty-First Century                                                                                         James Mittra                                                none  2016  79.99        USD 105.00        USD         0         0 http://www.springer.com/us/book/9781137436283 126
## 7                                              Data Wrangling - Munging in R with SQL and MongoDB for Financial Applications                                                                    Patrick Houlihan;Alexander Moreno                                                none  2016  29.99        USD  49.99        USD         0         0 http://www.springer.com/us/book/9781484206126 129
## 8                                           Advanced Spark for Professionals - Analytics for Data Driven Enterprises and R&amp;D                                                                                       Henning Dekant                                                none  2016  29.99        USD  49.99        USD         0         0 http://www.springer.com/us/book/9781484214626 130
## 9                                                                               ggplot2 - Elegant Graphics for Data Analysis                                                                        Hadley Wickham;Carson Sievert                                              Use R!  2016  44.99        USD  59.99        USD         0         0 http://www.springer.com/us/book/9783319242750 151
</code></pre>

<pre><code class="r">##  exploring book recommendations

# books appearing multiple times due to different editions
dupbooks = unique(bookinfodf_filt$title[duplicated(bookinfodf_filt[,c(&quot;title&quot;)])])
data.frame(bookinfodf_filt[bookinfodf_filt$title %in% dupbooks,])
</code></pre>

<pre><code>##                                                      title                       authors series pubyr price1 price1curr price2 price2curr citations downloads                                          link  id
## 1 Beginning R - An Introduction to Statistical Programming                    Larry Pace   none  2012  29.99        USD  39.99        USD         0     79426 http://www.springer.com/us/book/9781430245544   3
## 2 Beginning R - An Introduction to Statistical Programming       Larry Pace;Joshua Wiley   none  2015  29.99        USD  39.99        USD         0      3441 http://www.springer.com/us/book/9781484203743   5
## 3             ggplot2 - Elegant Graphics for Data Analysis Hadley Wickham;Carson Sievert Use R!  2016  44.99        USD  59.99        USD         0         0 http://www.springer.com/us/book/9783319242750 151
## 4             ggplot2 - Elegant Graphics for Data Analysis                Hadley Wickham Use R!  2009  49.99        USD  69.95        USD       456    109832 http://www.springer.com/us/book/9780387981406 153
</code></pre>

<pre><code class="r"># remove the books with latest edition for simplicity
bookinfodf_filt2 = bookinfodf_filt[!(bookinfodf_filt$id %in% c(5,151)),]


# create a wt that is 9 - rank so that higher lower rank corresponds to higher wt
recobookinfodf_filt$wt = 9 - recobookinfodf_filt$rnk
#unique(recobookinfodf_filt[,c(&quot;wt&quot;,&quot;rnk&quot;)])

recobookinfodf_filt2 = recobookinfodf_filt[!duplicated(recobookinfodf_filt[,c(&quot;title&quot;,&quot;recobooks&quot;)]),]

# keep only recommended books that are part of the original R book list
# here it is done by merging recobooks with original book list based on recobooks field
recobookinfodf_filt2 = inner_join(recobookinfodf_filt,bookinfodf_filt2[,c(&quot;id&quot;)],by = c(&quot;id&quot;))
names(recobookinfodf_filt2)[names(recobookinfodf_filt2) == &quot;id&quot;] = &quot;titleid&quot;
names(recobookinfodf_filt2)[names(recobookinfodf_filt2) == &quot;title&quot;] = &quot;titlefrom&quot;
recobookinfodf_filt2 = inner_join(recobookinfodf_filt2,bookinfodf_filt2[,c(&quot;id&quot;,&quot;link&quot;,&quot;title&quot;)],by = c(&quot;recobooklinks&quot; = &quot;link&quot;))
names(recobookinfodf_filt2)[names(recobookinfodf_filt2) == &quot;id&quot;] = &quot;recoid&quot;
names(recobookinfodf_filt2)[names(recobookinfodf_filt2) == &quot;title&quot;] = &quot;titleto&quot;


# Create an undirected graph based on books &lt;-&gt; recobooks links
edgelist = recobookinfodf_filt2[,c(&quot;titlefrom&quot;,&quot;titleto&quot;,&quot;wt&quot;)]
names(edgelist) = c(&quot;from&quot;,&quot;to&quot;,&quot;weights&quot;)
head(edgelist,10)
</code></pre>

<pre><code>## Source: local data frame [10 x 3]
##
##                                                                            from                                            to weights
##                                                                           (chr)                                         (chr)   (dbl)
## 1                      Beginning R - An Introduction to Statistical Programming               Introductory Time Series with R       5
## 2                      Beginning R - An Introduction to Statistical Programming                    Modern Optimization with R       4
## 3                      Beginning R - An Introduction to Statistical Programming Time Series Analysis - With Applications in R       1
## 4                                                                  R by Example        R for Marketing Research and Analytics       8
## 5                                                                  R by Example  ggplot2 - Elegant Graphics for Data Analysis       6
## 6                                                                  R by Example                    Political Analysis Using R       5
## 7                                                                  R by Example                       A Beginner&#39;s Guide to R       3
## 8  Biostatistics with R - An Introduction to Statistics Through Biological Data        R for Marketing Research and Analytics       8
## 9  Biostatistics with R - An Introduction to Statistics Through Biological Data  ggplot2 - Elegant Graphics for Data Analysis       6
## 10 Biostatistics with R - An Introduction to Statistics Through Biological Data                    Political Analysis Using R       5
</code></pre>

<pre><code class="r">g = graph_from_data_frame(edgelist,vertices = bookinfodf_filt2$title,directed = FALSE)

# Find disconnected components
comps = clusters(g)
paste0(&quot;Number of disconnected components = &quot;,length(comps$csize))
</code></pre>

<pre><code>## [1] &quot;Number of disconnected components = 24&quot;
</code></pre>

<pre><code class="r">paste0(&quot;Number of titles in each component&quot;)
</code></pre>

<pre><code>## [1] &quot;Number of titles in each component&quot;
</code></pre>

<pre><code class="r">comps$csize
</code></pre>

<pre><code>##  [1]  1 17  1  1 65  9  1  1  3  4  1  1  1  2  1  1  1  1  1  1  1  1  1  1
</code></pre>

<pre><code class="r"># Find the titles that appear as one off components
names(comps$membership)[comps$csize[comps$membership] == 1]
</code></pre>

<pre><code>##  [1] &quot;Geochemical Modelling of Igneous Processes – Principles And Recipes in R Language - Bringing the Power of R to a Geochemical Community&quot; &quot;Pro R &quot;
##  [3] &quot;R Recipes - A Problem-Solution Approach&quot;                                                                                                &quot;R Quick Syntax Reference&quot;
##  [5] &quot;Using R for Statistics&quot;                                                                                                                 &quot;Beginning Data Science with R&quot;
##  [7] &quot;Qualitative Comparative Analysis with R - A User’s Guide&quot;                                                                               &quot;Introduction to Image Processing Using R - Learning by Examples&quot;
##  [9] &quot;Assessing Schools for Generation R (Responsibility) - A Guide for Legislation and School Policy in Science Education&quot;                   &quot;Computational Finance - An Introductory Course with R&quot;
## [11] &quot;Benchmarking with DEA, SFA, and R&quot;                                                                                                      &quot;Pro Data Visualization using R and JavaScript&quot;
## [13] &quot;Guide to Programming and Algorithms Using R&quot;                                                                                            &quot;Computer Simulation and Data Analysis in Molecular Biology and Biophysics - An Introduction Using R&quot;
## [15] &quot;Learning Analytics in R with SNA, LSA, and MPIA&quot;                                                                                        &quot;Data Wrangling - Munging in R with SQL and MongoDB for Financial Applications&quot;
## [17] &quot;Spatial Database for GPS Wildlife Tracking Data - A Practical Guide to Creating a Data Management System with PostgreSQL/PostGIS and R&quot; &quot;Computational Biology - A Practical Introduction to BioData Processing and Analysis with Linux, MySQL, and R&quot;
</code></pre>

<pre><code class="r"># Other connected components with few vertices
names(comps$membership)[comps$csize[comps$membership] == 3] # finance
</code></pre>

<pre><code>## [1] &quot;Quantitative Trading with R - Understanding Mathematical and Computational Tools from a Quant’s Perspective&quot; &quot;Modern Actuarial Risk Theory - Using R&quot;                                                                      &quot;Analyzing Financial Data and Implementing Financial Models Using R&quot;
</code></pre>

<pre><code class="r">names(comps$membership)[comps$csize[comps$membership] == 4] # econometrics
</code></pre>

<pre><code>## [1] &quot;A Primer for Spatial Econometrics - With Applications in R&quot;                           &quot;An Introduction to R for Quantitative Economics - Graphing, Simulating and Computing&quot; &quot;Applied Econometrics with R&quot;                                                          &quot;Analysis of Integrated and Cointegrated Time Series with R&quot;
</code></pre>

<pre><code class="r">names(comps$membership)[comps$csize[comps$membership] == 9] # ecology, chemometrics etc.
</code></pre>

<pre><code>## [1] &quot;Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences&quot; &quot;Analysis of Phylogenetics and Evolution with R&quot;                                             &quot;Morphometrics with R&quot;                                                                       &quot;Applied Statistical Genetics with R - For Population-based Association Studies&quot;
## [5] &quot;A Practical Guide to Ecological Modelling - Using R as a Simulation Platform&quot;               &quot;A Primer of Ecology with R&quot;                                                                 &quot;A Guide to QTL Mapping with R/qtl&quot;                                                          &quot;Mixed Effects Models and Extensions in Ecology with R&quot;
## [9] &quot;Bioconductor Case Studies&quot;
</code></pre>

<pre><code class="r">names(comps$membership)[comps$csize[comps$membership] == 17]
</code></pre>

<pre><code>##  [1] &quot;Beginning R - An Introduction to Statistical Programming&quot;                                 &quot;Quality Control with R - An ISO Standards Approach&quot;                                       &quot;Modern Optimization with R&quot;                                                               &quot;Introducing Monte Carlo Methods with R&quot;
##  [5] &quot;A User’s Guide to Network Analysis in R&quot;                                                  &quot;R Through Excel - A Spreadsheet Interface for Statistics, Data Analysis, and Graphics&quot;    &quot;Bayesian Computation with R&quot;                                                              &quot;Multivariate Nonparametric Methods with R - An approach based on spatial signs and ranks&quot;
##  [9] &quot;Introductory Time Series with R&quot;                                                          &quot;Data Manipulation with R&quot;                                                                 &quot;Nonlinear Regression with R&quot;                                                              &quot;Introductory Statistics with R&quot;
## [13] &quot;Time Series Analysis - With Applications in R&quot;                                            &quot;Software for Data Analysis - Programming with R&quot;                                          &quot;Lattice - Multivariate Data Visualization with R&quot;                                         &quot;Wavelet Methods in Statistics with R&quot;
## [17] &quot;A Modern Approach to Regression with R&quot;
</code></pre>

<pre><code class="r"># find degree distribition
table(degree(g))
</code></pre>

<pre><code>##
##  0  1  2  3  4  5  6  7  8 10 11 13 14 15 17 19 50 51 56
## 18  9  4  5 47  4  3  4  5  4  1  2  4  2  1  1  2  1  1
</code></pre>

<pre><code class="r"># find titles with highest degree
# doesn&#39;t seem to correlate well with downloads
degree_df = data.frame(title = names(degree(g)),degree = degree(g))
degree_df = inner_join(degree_df,bookinfodf_filt2[,c(&quot;title&quot;,&quot;downloads&quot;)],by = &quot;title&quot;)
</code></pre>

<pre><code>## Warning in inner_join_impl(x, y, by$x, by$y): joining factor and character vector, coercing into character vector
</code></pre>

<pre><code class="r">degree_df %&gt;% arrange(desc(degree)) %&gt;% head(20)
</code></pre>

<pre><code>##                                                                           title degree downloads
## 1                                                       A Beginner&#39;s Guide to R     56     93308
## 2                                  ggplot2 - Elegant Graphics for Data Analysis     51    109832
## 3                                                    Political Analysis Using R     50      4232
## 4                                        R for Marketing Research and Analytics     50     16868
## 5                                               Introductory Time Series with R     19    100013
## 6                                                      Data Manipulation with R     17     54722
## 7                                                   Bayesian Computation with R     15     59948
## 8                                                   Nonlinear Regression with R     15     37563
## 9                                                          Meta-Analysis with R     14      5503
## 10                                                 Dynamic Linear Models with R     14     17242
## 11                                Time Series Analysis - With Applications in R     14    133288
## 12                                   Functional Data Analysis with R and MATLAB     14     62537
## 13                                                   Modern Optimization with R     13     16126
## 14                                          Analyzing Compositional Data with R     13     25018
## 15 Humanities Data in R - Exploring Networks, Geospatial Data, Images, and Text     11      4850
## 16                                          Solving Differential Equations in R     10     40248
## 17                                 Multistate Analysis of Life Histories with R     10     13141
## 18                              Software for Data Analysis - Programming with R     10    106786
## 19                                         Wavelet Methods in Statistics with R     10     19496
## 20             Introduction to Probability Simulation and Gibbs Sampling with R      8     37809
</code></pre>

<pre><code class="r">#
# for example: R for Marketing Research and Analytics has degree 50 i.e being recommended
# along with several other books. Not sure how Springer reco system works
#
recobookinfodf_filt2[grepl(&quot;Marketing Research&quot;,recobookinfodf_filt2$titleto),c(&quot;titlefrom&quot;,&quot;titleto&quot;)]
</code></pre>

<pre><code>## Source: local data frame [44 x 2]
##
##                                                                       titlefrom                                titleto
##                                                                           (chr)                                  (chr)
## 1                                                                  R by Example R for Marketing Research and Analytics
## 2  Biostatistics with R - An Introduction to Statistics Through Biological Data R for Marketing Research and Analytics
## 3                                                      Numerical Ecology with R R for Marketing Research and Analytics
## 4                                                          Meta-Analysis with R R for Marketing Research and Analytics
## 5                                                       Graphical Models with R R for Marketing Research and Analytics
## 6                 Bayesian Networks in R - with Applications in Systems Biology R for Marketing Research and Analytics
## 7           Six Sigma with  R - Statistical Engineering for Process Improvement R for Marketing Research and Analytics
## 8                                                    Political Analysis Using R R for Marketing Research and Analytics
## 9                                     Forest Analytics with R - An Introduction R for Marketing Research and Analytics
## 10        The R Software - Fundamentals of Programming and Statistical Analysis R for Marketing Research and Analytics
## ..                                                                          ...                                    ...
</code></pre>

<pre><code class="r"># plot the network with networkD3
# code adapted from https://christophergandrud.github.io/networkD3/
pltNodes = data.frame(name = bookinfodf_filt2$title, group = 1,size = 1)
pltNodes$name = as.character(pltNodes$name)
pltNodes$group = comps$membership[pltNodes$name] # group is the connected component number
pltNodes$id = seq(0,nrow(pltNodes) - 1) # networkD3 mentioned something about starting index with 0

pltLinks = recobookinfodf_filt2[,c(&quot;titlefrom&quot;,&quot;titleto&quot;,&quot;wt&quot;)]
pltLinks = inner_join(pltLinks,pltNodes[,c(&quot;id&quot;,&quot;name&quot;)],by = c(&quot;titlefrom&quot; = &quot;name&quot;))
names(pltLinks)[names(pltLinks) == &quot;id&quot;] = &quot;source&quot;

pltLinks = inner_join(pltLinks,pltNodes[,c(&quot;id&quot;,&quot;name&quot;)],by = c(&quot;titleto&quot; = &quot;name&quot;))
names(pltLinks)[names(pltLinks) == &quot;id&quot;] = &quot;target&quot;


forceNetwork(Links = pltLinks, Nodes = pltNodes,
             Source = &quot;source&quot;, Target = &quot;target&quot;,
             Value = &quot;wt&quot;, NodeID = &quot;name&quot;,
             Group = &quot;group&quot;, opacity = 0.8,zoom = TRUE,fontSize = 20)
</code></pre>

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Using R&quot;,&quot;A Primer for Spatial Econometrics - With Applications in R&quot;,&quot;Statistical Analysis of Network Data with R&quot;,&quot;A User’s Guide to Network Analysis in R&quot;,&quot;An Introduction to Applied Multivariate Analysis with R&quot;,&quot;R Through Excel - A Spreadsheet Interface for Statistics, Data Analysis, and Graphics&quot;,&quot;A Tiny Handbook of R&quot;,&quot;Primer to Analysis of Genomic Data Using R&quot;,&quot;Beginning Data Science with R&quot;,&quot;Competing Risks and Multistate Models with R&quot;,&quot;Bayesian Computation with R&quot;,&quot;Multistate Analysis of Life Histories with R&quot;,&quot;Qualitative Comparative Analysis with R - A User’s Guide&quot;,&quot;R for SAS and SPSS Users&quot;,&quot;Applied Bayesian Statistics - With R and OpenBUGS Examples&quot;,&quot;Introduction to Image Processing Using R - Learning by Examples&quot;,&quot;Functional and Phylogenetic Ecology in R&quot;,&quot;Vector Generalized Linear and Additive Models - With an Implementation in R&quot;,&quot;Applied Multivariate Statistics with R&quot;,&quot;Multivariate Statistical Quality Control Using R&quot;,&quot;Contingency Table Analysis - Methods and Implementation Using R&quot;,&quot;Morphometrics with R&quot;,&quot;Linear Mixed-Effects Models Using R - A Step-by-Step Approach&quot;,&quot;Production and Efficiency Analysis with R&quot;,&quot;Assessing Schools for Generation R (Responsibility) - A Guide for Legislation and School Policy in Science Education&quot;,&quot;XML and Web Technologies for Data Sciences with R&quot;,&quot;Computational Finance - An Introductory Course with R&quot;,&quot;Multivariate Nonparametric Methods with R - An approach based on spatial signs and ranks&quot;,&quot;Benchmarking with DEA, SFA, and R&quot;,&quot;A Beginner&#39;s Guide to R&quot;,&quot;Introduction to Probability Simulation and Gibbs Sampling with R&quot;,&quot;Applied Statistical Genetics with R - For Population-based Association Studies&quot;,&quot;Dynamic Linear Models with R&quot;,&quot;Introductory Time Series with R&quot;,&quot;Principal Component and Correspondence Analyses Using R&quot;,&quot;Data Manipulation with R&quot;,&quot;An Introduction to R for Quantitative Economics - Graphing, Simulating and Computing&quot;,&quot;Nonlinear Regression with R&quot;,&quot;Applied Econometrics with R&quot;,&quot;EnvStats - An R Package for Environmental Statistics&quot;,&quot;Statistical Analysis of Financial Data in R&quot;,&quot;Statistical Analysis of Climate Series - Analyzing, Plotting, Modeling, and Predicting with R&quot;,&quot;Two-Way Analysis of Variance - Statistical Tests and Graphics Using R&quot;,&quot;Text Analysis with R for Students of Literature&quot;,&quot;Statistical Analysis and Data Display - An Intermediate Course with Examples in R&quot;,&quot;Advances in Social Science Research Using R&quot;,&quot;Pro Data Visualization using R and JavaScript&quot;,&quot;Guide to Programming and Algorithms Using R&quot;,&quot;Introductory Statistics with R&quot;,&quot;Time Series Analysis - With Applications in R&quot;,&quot;A Practical Guide to Ecological Modelling - Using R as a Simulation Platform&quot;,&quot;Permutation Tests for Stochastic Ordering and ANOVA - Theory and Applications with R&quot;,&quot;Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R - Order-Restricted Analysis of Microarray Data&quot;,&quot;Software for Data Analysis - Programming with R&quot;,&quot;Simulation and Inference for Stochastic Differential Equations - With R Examples&quot;,&quot;A Primer of Ecology with R&quot;,&quot;Functional Data Analysis with R and MATLAB&quot;,&quot;Computer Simulation and Data Analysis in Molecular Biology and Biophysics - An Introduction Using R&quot;,&quot;Modeling Binary Correlated Responses using SAS, SPSS and R&quot;,&quot;Analyzing Financial Data and Implementing Financial Models Using R&quot;,&quot;Introduction to Data Analysis and Graphical Presentation in Biostatistics with R - Statistics in the Large&quot;,&quot;Lattice - Multivariate Data Visualization with R&quot;,&quot;Wavelet Methods in Statistics with R&quot;,&quot;A Modern Approach to Regression with R&quot;,&quot;Learning Analytics in R with SNA, LSA, and MPIA&quot;,&quot;Multivariate Methods of Representing Relations in R for Prioritization Purposes - Selective Scaling, Comparative Clustering, Collective Criteria and Sequenced Sets&quot;,&quot;A Guide to QTL Mapping with R/qtl&quot;,&quot;Interactive and Dynamic Graphics for Data Analysis - With R and GGobi&quot;,&quot;Data Wrangling - Munging in R with SQL and MongoDB for Financial Applications&quot;,&quot;Analysis of Integrated and Cointegrated Time Series with R&quot;,&quot;Statistical Methods for Environmental Epidemiology with R - A Case Study in Air Pollution and Health&quot;,&quot;Mixed Effects Models and Extensions in Ecology with R&quot;,&quot;Applied Statistics Using SPSS, STATISTICA, MATLAB and R&quot;,&quot;Spatial Database for GPS Wildlife Tracking Data - A Practical Guide to Creating a Data Management System with PostgreSQL/PostGIS and R&quot;,&quot;Computational Biology - A Practical Introduction to BioData Processing and Analysis with Linux, MySQL, and R&quot;,&quot;Business Analytics for Managers&quot;,&quot;ggplot2 - Elegant Graphics for Data Analysis&quot;,&quot;Bioconductor Case Studies&quot;],&quot;group&quot;:[1,2,3,4,5,5,5,6,5,2,5,5,2,5,5,5,5,5,5,5,5,5,5,5,7,5,5,8,5,9,5,5,5,5,5,2,5,5,5,6,9,10,5,2,5,2,5,5,11,5,2,5,12,5,5,13,5,5,5,5,5,6,5,14,15,5,16,2,17,5,5,6,5,2,5,2,10,2,10,5,5,5,5,5,5,5,18,19,2,2,6,5,5,2,5,6,5,20,14,9,5,2,2,2,21,5,6,5,22,10,5,6,5,23,24,5,5,6]},&quot;options&quot;:{&quot;NodeID&quot;:&quot;name&quot;,&quot;Group&quot;:&quot;group&quot;,&quot;colourScale&quot;:&quot;d3.scale.category20()&quot;,&quot;fontSize&quot;:20,&quot;fontFamily&quot;:&quot;serif&quot;,&quot;clickTextSize&quot;:50,&quot;linkDistance&quot;:50,&quot;linkWidth&quot;:&quot;function(d) { return Math.sqrt(d.value); }&quot;,&quot;charge&quot;:-120,&quot;linkColour&quot;:&quot;#666&quot;,&quot;opacity&quot;:0.8,&quot;zoom&quot;:true,&quot;legend&quot;:false,&quot;nodesize&quot;:false,&quot;radiusCalculation&quot;:&quot; Math.sqrt(d.nodesize)+6&quot;,&quot;bounded&quot;:false,&quot;opacityNoHover&quot;:0,&quot;clickAction&quot;:null}},&quot;evals&quot;:[]}</script>&lt;!&ndash;/html_preserve&ndash;&gt;</p>

<pre><code class="r"># This analysis was done in RStudio 0.99.489
sessionInfo()
</code></pre>

<pre><code>## R version 3.2.3 (2015-12-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.11.1 (El Capitan)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base
##
## other attached packages:
##  [1] networkD3_0.2.8 igraph_1.0.1    ggplot2_1.0.1   tidyr_0.3.1     knitr_1.11      stringr_1.0.0   dplyr_0.4.3     rmarkdown_0.8.1 rvest_0.3.1     xml2_0.1.2
##
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.2      highr_0.5.1      formatR_1.2.1    plyr_1.8.3       tools_3.2.3      digest_0.6.8     jsonlite_0.9.19  evaluate_0.8     gtable_0.1.2     DBI_0.3.1        yaml_2.1.13      curl_0.9.4       parallel_3.2.3   proto_0.3-10     httr_1.0.0       htmlwidgets_0.5  grid_3.2.3       R6_2.1.1         XML_3.98-1.3     reshape2_1.4.1   selectr_0.2-3    magrittr_1.5     scales_0.3.0
## [24] htmltools_0.2.6  MASS_7.3-45      assertthat_0.1   mime_0.4         colorspace_1.2-6 labeling_0.3     stringi_1.0-1    lazyeval_0.1.10  munsell_0.4.2    markdown_0.7.7
</code></pre>

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