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Response
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IP
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Profiled on
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ArticleController :: show

Request

GET Parameters

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POST Parameters

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x-php-ob-level 
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Request Content

Request content not available (it was retrieved as a resource).

Response

Response Headers

Header Value
cache-control 
"no-cache, private"
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"text/html; charset=UTF-8"
date 
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Cookies

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Key Value
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Response Cookies

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Session 6

Session Metadata

Key Value
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Stateless check enabled
Usage
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    "function" => "run"
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    ]
    "function" => "require_once"
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]
Symfony\Component\Security\Core\Authentication\Token\Storage\UsageTrackingTokenStorage:41
Show trace 
[
  [
    "file" => "/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com/vendor/symfony/security-core/Authentication/Token/Storage/UsageTrackingTokenStorage.php"
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    "function" => "getMetadataBag"
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    ]
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]
Symfony\Component\Security\Http\Firewall\ContextListener:171
Show trace 
[
  [
    "file" => "/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com/vendor/symfony/security-http/Firewall/ContextListener.php"
    "line" => 171
    "function" => "remove"
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  [
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    "line" => 116
    "function" => "onKernelResponse"
    "class" => "Symfony\Component\Security\Http\Firewall\ContextListener"
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  [
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    "line" => 220
    "function" => "__invoke"
    "class" => "Symfony\Component\EventDispatcher\Debug\WrappedListener"
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  [
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    "function" => "callListeners"
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Flashes

Flashes

No flash messages were created.

Server Parameters

Server Parameters

Defined in .env

Key Value
APP_ENV 
"dev"
APP_SECRET 
"0a988e63f011514eaabfc650b599af4d"
CORS_ALLOW_ORIGIN 
"*"
DATABASE_URL 
"mysql://bbndb_rctuser:33F5W25z40or0f7@localhost:3306/rct_bbntimes"
GOOGLE_RECAPTCHA_SECRET_KEY 
"6LdV5fgpAAAAANxzTG8ZMfIjil1wu-1vrQvnUt-x"
GOOGLE_RECAPTCHA_SITE_KEY 
"6LdV5fgpAAAAAENKcn73MJAhQrbtQeqgyC4wDLMP"
MAILER_DSN 
"smtp://no-reply%40rct.dev.bbntimes.com:Bl6%26gLD48%26Of919@rct.dev.bbntimes.com:465"
MARIADB_PASSWORD 
"i!87pK&!85ezc8"

Defined as regular env variables

Key Value
APP_DEBUG 
"1"
BASE 
"/public"
CONTEXT_DOCUMENT_ROOT 
"/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com"
CONTEXT_PREFIX 
""
DOCUMENT_ROOT 
"/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com"
FCGI_ROLE 
"RESPONDER"
GATEWAY_INTERFACE 
"CGI/1.1"
HTTPS 
"on"
HTTP_ACCEPT 
"*/*"
HTTP_ACCEPT_ENCODING 
"gzip, br, zstd, deflate"
HTTP_CONNECTION 
"close"
HTTP_COOKIE 
"PHPSESSID=0k0ehbjg4hojkch84jdin87ogv"
HTTP_HOST 
"rct.dev.bbntimes.com"
HTTP_USER_AGENT 
"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)"
PASSENGER_COMPILE_NATIVE_SUPPORT_BINARY 
"0"
PASSENGER_DOWNLOAD_NATIVE_SUPPORT_BINARY 
"0"
PATH 
"/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin"
PHP_SELF 
"/public/index.php"
PP_CUSTOM_PHP_CGI_INDEX 
"plesk-php81-fastcgi"
PP_CUSTOM_PHP_INI 
"/var/www/vhosts/system/rct.dev.bbntimes.com/etc/php.ini"
QUERY_STRING 
""
REDIRECT_BASE 
"/public"
REDIRECT_HTTPS 
"on"
REDIRECT_PASSENGER_COMPILE_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_PASSENGER_DOWNLOAD_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_REDIRECT_HTTPS 
"on"
REDIRECT_REDIRECT_PASSENGER_COMPILE_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_REDIRECT_PASSENGER_DOWNLOAD_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_REDIRECT_SCRIPT_URI 
"https://rct.dev.bbntimes.com/technology/integrating-tableau-and-r-for-regression-analyses"
REDIRECT_REDIRECT_SCRIPT_URL 
"/technology/integrating-tableau-and-r-for-regression-analyses"
REDIRECT_REDIRECT_SSL_TLS_SNI 
"rct.dev.bbntimes.com"
REDIRECT_REDIRECT_STATUS 
"200"
REDIRECT_REDIRECT_UNIQUE_ID 
"Z7mS2ZDyWLT8YV1VjBpGZgAAAI8"
REDIRECT_SCRIPT_URI 
"https://rct.dev.bbntimes.com/technology/integrating-tableau-and-r-for-regression-analyses"
REDIRECT_SCRIPT_URL 
"/technology/integrating-tableau-and-r-for-regression-analyses"
REDIRECT_SSL_TLS_SNI 
"rct.dev.bbntimes.com"
REDIRECT_STATUS 
"200"
REDIRECT_UNIQUE_ID 
"Z7mS2ZDyWLT8YV1VjBpGZgAAAI8"
REDIRECT_URL 
"/public/technology/integrating-tableau-and-r-for-regression-analyses"
REMOTE_ADDR 
"18.119.113.14"
REMOTE_PORT 
"58964"
REQUEST_METHOD 
"GET"
REQUEST_SCHEME 
"https"
REQUEST_TIME 
1740215001
REQUEST_TIME_FLOAT 
1740215001.467
REQUEST_URI 
"/technology/integrating-tableau-and-r-for-regression-analyses"
SCRIPT_FILENAME 
"/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com/public/index.php"
SCRIPT_NAME 
"/public/index.php"
SCRIPT_URI 
"https://rct.dev.bbntimes.com/technology/integrating-tableau-and-r-for-regression-analyses"
SCRIPT_URL 
"/technology/integrating-tableau-and-r-for-regression-analyses"
SERVER_ADDR 
"5.196.1.209"
SERVER_ADMIN 
"[no address given]"
SERVER_NAME 
"rct.dev.bbntimes.com"
SERVER_PORT 
"443"
SERVER_PROTOCOL 
"HTTP/1.1"
SERVER_SIGNATURE 
"<address>Apache Server at rct.dev.bbntimes.com Port 443</address>\n"
SERVER_SOFTWARE 
"Apache"
SSL_TLS_SNI 
"rct.dev.bbntimes.com"
SYMFONY_DOTENV_VARS 
"APP_ENV,APP_SECRET,DATABASE_URL,MARIADB_PASSWORD,CORS_ALLOW_ORIGIN,MAILER_DSN,GOOGLE_RECAPTCHA_SECRET_KEY,GOOGLE_RECAPTCHA_SITE_KEY"
UNIQUE_ID 
"Z7mS2ZDyWLT8YV1VjBpGZgAAAI8"

Sub Requests 6

CookieController :: renderAnalysis (token = 730aa0)

Key Value
_controller 
"App\Controller\CookieController::renderAnalysis"
_format 
"html"
_locale 
"en"
_stopwatch_token 
"67f07d"

AppController :: renderHeader (token = 508dc1)

Key Value
_controller 
"App\Controller\AppController::renderHeader"
_format 
"html"
_locale 
"en"
_stopwatch_token 
"30e00e"
slug 
"home"

ArticleController :: trendingArticle (token = 27d263)

Key Value
_controller 
"App\Controller\ArticleController::trendingArticle"
_format 
"html"
_locale 
"en"
_stopwatch_token 
"827e71"
category 
"Technology"

ArticleController :: relatedArticles (token = ab3214)

Key Value
_controller 
"App\Controller\ArticleController::relatedArticles"
_format 
"html"
_locale 
"en"
_stopwatch_token 
"8a5443"
current_article 
App\Entity\Article {#1094
  -id: 7142
  -title: "Integrating Tableau and R in Regression Analysis: Why These Tools are even Better Together"
  -slug: "integrating-tableau-and-r-for-regression-analyses"
  -introtext: "<p id="54a7" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">Tableau has taken the data visualization world by storm, and for good reason. </span></p>\r\n"
  -content: """
    \r\n
    <p id="54a7" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">Beautiful and complex visualizations, dashboards, and reports can be created quickly and without any coding experience within its user-friendly interface. Tableau is particularly useful for the creation of interactive visualizations, as filters can be added to a single visualization or full dashboard with just a few clicks. However, Tableau is limited in its analytic capabilities. The calculated fields feature allows for simple measures such as means, sums, and date differences to be calculated, and Tableau has some built-in features for adding regression lines or identifying clustering. For any sort of advanced analyses, however, Tableau falls short.</span></p>\r\n
    <p id="7b38" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">R, on the other hand, was created by statisticians and therefore has extraordinary analytic capabilities. Between its built-in functions and those contained within the seemingly endless list of available packages, there’s little that you can’t do in R from an analytic standpoint. However, visualization in R presents more of a challenge. The development of packages like ggplot2 and plotly has significantly advanced R’s data visualization abilities, but these packages are still not as easy to use as Tableau’s interface. It takes a fair amount of coding ability to build a well-designed, interactive dashboard or visualization in R. Even for those with coding expertise, it is simply much more time-consuming to write R code to make one plot in a dashboard act as a filter for others, to add explanatory text, to restructure your data for a particular visualization (which often isn’t even necessary in Tableau), to resize graphs in a dashboard, to apply color, or to accomplish any number of other tasks which can be quickly accomplished in Tableau’s drag-and-drop environment.</span></p>\r\n
    <p id="f12e" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">Fortunately, Tableau is able to connect to R, enabling users to reap the benefits of both tools at once. To demonstrate this process and its usefulness, I will walk through a sample regression analysis conducted using R code and Tableau visualizations.</span></p>\r\n
    <h2><span style="color: #10228b;">Step 1: Set up a Connection between Tableau and R</span></h2>\r\n
    <p id="77e7" class="ha hb dp hc b ej im he hf em in hh hi hj io er hl hm ip eu ho hp iq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">In order for R and Tableau to be used together, a connection has to be set up using the “Rserve” package.</span></p>\r\n
    <p id="fa84" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">First, install the “<span style="color: #808080;"><a class="cm ir" style="color: #808080;" href="https://cran.r-project.org/web/packages/Rserve/Rserve.pdf" rel="noopener nofollow">Rserve</a></span>” package in R. The first code option is simply the standard code for installing R packages from CRAN. I am also including code for installing the “Rserve” package from RForge, as the CRAN install has frequently given me errors.<br /></span></p>\r\n
    <p class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;"><img style="color: #000000; background-color: inherit; font-family: inherit; font-size: 1rem;" src="/images/install.packages.png" alt="install.packages.png" width="531" height="116" /></span></p>\r\n
    <p><span style="color: #000000;"><br />Next, just load the newly-installed “Rserve” package:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/libraryRserve.png" alt="libraryRserve.png" width="531" height="44" /></span></p>\r\n
    <p><span style="color: #000000;"><br />The only other step that needs to be taken in R is to run the following command, which sets up a socket server and allows requests to be sent to R:</span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Rserveargs_--no-save.png" alt="Rserveargs_--no-save.png" width="531" height="45" /></span></p>\r\n
    <p id="f0fe" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;"><br />Rserve should now be working, so let’s switch over to Tableau.</span></p>\r\n
    <p id="0d00" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">Under “Help,” click “Settings and Performance” and then “Manage Analytics Extension Connection”:</span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Help.png" alt="Help.png" width="531" height="264" /></span></p>\r\n
    <p><span style="color: #000000;"><br />The following “Analytics Extension Connection” window should open. Select “Rserve” as the Analytics Extension, as well as “localhost” for server and “6311" for Port. Once that’s all entered, click the “Test Connection” button in the lower left corner of the window:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Analytics_Extention_Connection.png" alt="Analytics_Extention_Connection.png" width="531" height="618" /></span></p>\r\n
    <p><span style="color: #000000;"><br />After you click the Test Connection button, you should receive the following message indicating that your connection has been set up successfully:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Tableau.png" alt="Tableau.png" width="531" height="170" /></span></p>\r\n
    <p><span style="color: #000000;"><br />If you receive this message, then you’re good to go. We will be staying in Tableau for the rest of this tutorial, although we will need to keep R running to maintain the connection.</span></p>\r\n
    <h2 id="f4e8" class="ht hu dp cf hv hw hx hy hz ia ib ic id ie if ig ih ii ij ik il eh"><span style="color: #10228b;">Step 2: Run the Regression Analysis</span></h2>\r\n
    <p id="baed" class="ha hb dp hc b ej im he hf em in hh hi hj io er hl hm ip eu ho hp iq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">The data that I will be using in this tutorial come from NYC OpenData and represent inmates in custody in New York City. Included variables represent attributes such as mental health designation, race, gender, age, custody level, legal status, sealed status, security risk group membership, top charge, and infraction flag. The data are publicly available and are available for download&nbsp;<a class="cm ir" style="color: #000000;" href="https://data.cityofnewyork.us/Public-Safety/Daily-Inmates-In-Custody/7479-ugqb" rel="noopener nofollow">here</a>.</span></p>\r\n
    <p id="d0c6" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">I’m curious as to whether or not custody level (minimum, medium, or maximum) predicts whether or not an individual has a mental health designation, and will create a logistic regression model with mental health designation as my outcome and custody level as my predictor. Before setting up a connection to Tableau, I used the following R code to prepare these data and to write a csv file containing the cleaned data:</span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/ibrarytidyverse.png" alt="ibrarytidyverse.png" width="531" height="311" /></span></p>\r\n
    <p><span style="color: #000000;"><br />Once this file is connected to Tableau (in Tableau, click “Connect to Data,” select “Text File,” and navigate to your csv), we will use a calculated field to create our regression model. Select “Create Calculated Field” from the drop-down menu in the Data panel:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Create_Calculated_Field.png" alt="Create_Calculated_Field.png" width="531" height="697" /></span></p>\r\n
    <p id="72ce" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;"><br />I’ve named my calculated field “Regression.” Within the SCRIPT_REAL() calculation function, you can enter your code for analysis in R. If you’re familiar with using logistic regression models in R, then the code below will look extremely familiar. The important difference to get used to in Tableau is that instead of entering your variable names directly into your glm() function, “.arg” placeholders are used and the variables are specified below the glm() code (see Bradh Numeric and Custody Numeric). It’s a bit unintuitive to adjust to at first, so I recommend also running your analysis in R the first few times so that you can check your work.</span></p>\r\n
    <p id="8c0a" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">If your calculation can be completed, you will see “<em class="kh">The calculation is valid.</em>” in the bottom left corner. If you’re receiving this message, go ahead and hit the green “OK” button.</span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Regression.png" alt="Regression.png" width="531" height="262" /></span></p>\r\n
    <p><span style="color: #000000;"><br />You will now see your calculated field (indicated by the equals sign before the #) with your numeric table elements on the left-hand side of your screen.<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Age_-_Bradh_Numeric.png" alt="Age_-_Bradh_Numeric.png" width="531" height="376" /></span></p>\r\n
    <h2 id="f8ba" class="ht hu dp cf hv hw hx hy hz ia ib ic id ie if ig ih ii ij ik il eh"><span style="color: #10228b;">Step 3: Visualize and Explore your Regression Results</span></h2>\r\n
    <p id="150a" class="ha hb dp hc b ej im he hf em in hh hi hj io er hl hm ip eu ho hp iq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">First, let’s just visualize the fitted values for the three custody levels included in our analysis by dragging the “Custody Level” pill to Columns and the “Regression” pill to Rows. I’ve also added “Custody Level” under Color:</span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Custody_Level_and_Mental_Health.png" alt="Custody_Level_and_Mental_Health.png" width="531" height="397" /></span></p>\r\n
    <p id="6161" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;"><br />These fitted values are hovering around 0.5, indicating that custody level isn’t a great predictor of an individual’s mental health status. But here’s where Tableau really shines. Let’s say that we want to see how this relationship differs based on race and gender. In R, answering this question would involve adding race and gender to our regression model and figuring out fitted values for particular groups of people based upon the resulting coefficients. In Tableau, however, we can simply add these variables as filters to our visualization and interact with the product to see where the relationship is more interesting.</span></p>\r\n
    <p id="ae68" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">Drag the “Race” and “Gender” pills into the Filters box, as shown below:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Custody_Level.png" alt="Custody_Level.png" width="531" height="396" /></span></p>\r\n
    <p><span style="color: #000000;"><br />In order to interact with these filters, right-click on the filter pills and select “Show Filter.” You’ll see the filters appear on your screen:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Custody_Level_Designation.png" alt="Custody_Level_Designation.png" width="531" height="397" /></span></p>\r\n
    <p><span style="color: #000000;"><br />When all of the boxes are checked, the regression fitted values remain unchanged because the entire sample is still included in the analysis. By selecting certain boxes, however, it’s easy to instead see the relationship between custody level and mental health designation among Black women:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Race_B_and_F.png" alt="Race B and F" width="531" height="396" /></span></p>\r\n
    <p><span style="color: #000000;"><br />Or white women:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/White_Women.png" alt="White Women" width="531" height="396" /></span></p>\r\n
    <p><span style="color: #000000;"><br />Or Asian men:<br /><br /></span></p>\r\n
    <p><span style="color: #000000;"><img src="/images/Asian_Men.png" alt="Asian_Men.png" width="531" height="396" /></span></p>\r\n
    <p id="7261" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;"><br />These filters make it easy to see that certain sub-populations do have meaningful relationships between custody level and mental health designation, and that these relationships are very different among varying racial and gender groups.</span></p>\r\n
    <p id="465c" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">* Note: The data dictionary for this dataset does not provide descriptions of the included racial groups. I am therefore making assumptions about what the single-letter designations stand for. It is also unclear if the gender measure truly represents gender or if it’s really capturing sex.</span></p>\r\n
    <h2 id="55d9" class="ht hu dp cf hv hw hx hy hz ia ib ic id ie if ig ih ii ij ik il eh"><span style="color: #10228b;"><strong class="ay">Other Applications</strong></span></h2>\r\n
    <p id="080b" class="ha hb dp hc b ej im he hf em in hh hi hj io er hl hm ip eu ho hp iq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">In this example, we used a logistic regression analysis to answer an explanatory question (how does mental health designation differ by custody level, race, and gender?), and visualization served the primary purpose of exploring this relationship. Similar approaches could be taken with other analytic techniques such as k-means clustering, as Tableau’s visualization capabilities provide a useful means of exploring how your clusters change within different subsets of your data.</span></p>\r\n
    <p id="3a37" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">However, the integration of Tableau and R has so much potential beyond simple exploration. For example, integration of these tools is particularly useful for geospatial analyses given that Tableau truly excels at producing maps but lacks this advanced analytic ability. Additionally, these tools can be used together to better visualize predictive analyses such as linear regression or time-series analyses. Projects that require advanced analyses of really any sort, as well as complex and interactive visualizations and dashboards, would likely benefit from the combined capabilities of both Tableau and R.</span></p>\r\n
    <p id="06cd" class="ha hb dp hc b ej hd he hf em hg hh hi hj hk er hl hm hn eu ho hp hq ex hr hs dh eh" data-selectable-paragraph><span style="color: #000000;">The strengths of Tableau and R complement each other well, and learning how to use them together can maximize your efficiency while simultaneously enhancing your data visualization products.</span></p>
    """
  -user: Proxies\__CG__\App\Entity\User {#1092 …}
  -createdAt: DateTime @1602537098 {#1145
    date: 2020-10-12 21:11:38.0 UTC (+00:00)
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    date: 2020-10-12 22:31:41.0 UTC (+00:00)
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  -metakey: "Integrating Tableau and R for Regression Analyses"
  -metadesc: "Tableau has taken the data visualization world by storm, and for good reason."
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AppController :: renderFooter (token = 439165)

Key Value
_controller 
"App\Controller\AppController::renderFooter"
_format 
"html"
_locale 
"en"
_stopwatch_token 
"e24463"

CookieController :: renderCookie (token = ec713c)

Key Value
_controller 
"App\Controller\CookieController::renderCookie"
_format 
"html"
_locale 
"en"
_stopwatch_token 
"2e2fb8"