inst/shiny-examples/map.app/ui.R

In SPARTAAS: Statistical Pattern Recognition and daTing using Archaeological Artefacts assemblageS

library(shinythemes)
library(shinyjs)
library(leaflet)
#library(markdown)
#library(sp)           #for grid ...
library(cluster)      #for silhouette
library(ggplot2)
library(ape)          #for Moran.i
library(ks)           #for kde Hscv
library("shinyWidgets")
library(htmltools)

load(file = "./data/datarcheo.RData")
load(file = "./data/datacancer.RData")

#----------------------------------------------------------------------------------------------#
###########################                  - UI -                  ###########################
#----------------------------------------------------------------------------------------------#
ui <- fluidPage(useShinyjs(),style="padding-top: 150px;",theme = shinytheme("flatly"),
                tags$head(
                  tags$link(rel = "icon", type = "image/gif", href = "https://spartaas.gitpages.huma-num.fr/r-package/img/lambda.png"),
                  tags$title("mapclust")
                ),
                tags$head(tags$style(
                  type = "text/css",
                  ".leaflet-left {
                    bottom:30px;
                  }
                  .btn-default {
                    background-color:#5da3a8;
                    border-color:#5da3a8;
                  }
                  .dropdown-menu li a{
                    background-color:#fff;
                    border-color:#dce4ec;
                  }"
                )),
                #header
                absolutePanel(class = "panel panel-default",
                              style="z-index: 2000;padding: 8px; background: #ecf0f1; opacity: 1;border-bottom: 1px solid #2c3e50;",
                              top = 0, left = 0, right = 0,
                              fixed = TRUE,
                              titlePanel(h2("Divisive Hierarchical Clustering using Spatials Patches")),
                              div(span(strong("SPARTAAS | mapclust ")))
                ),
                #footer
                absolutePanel(style="z-index: 2000;padding: 0px; border-bottom: 0px solid #CCC; background: #fff;opacity: 1;",
                              bottom = 0, left = 0,
                              fixed = TRUE,
                              div(span(strong("SPARTAAS")),span("[Bellanger,Coulon,Husi]",style ="font-size:14px;"))
                ),
                #go top panel
                absolutePanel(class = "panel-hover",
                              style="opacity:0.8;z-index: 1000;padding: 8px; border-bottom: 1px solid #CCC; background: #FFFFEE;width:30px;",
                              bottom = 20, right = 0,
                              fixed = TRUE,
                              HTML("<a href=\"#top\"><span class=\"glyphicon glyphicon-chevron-up\"></span></a>")
                ),
                #-----------------------------------#
                #    NavBar
                #-----------------------------------#
                navbarPage("MapClust",id="navbar",
                           #-----------------------------------#
                           #    Home
                           #-----------------------------------#
                           tabPanel(value = "1",span(icon("home", lib = "glyphicon"),strong("Home")),style="max-width:1200px;",
                                    br(),
                                    sidebarLayout(
                                      #-----------------------------------#
                                      sidebarPanel(style="border-width:0px;border-color:#222222;",
                                                   h1("R Package:"),
                                                   br(),
                                                   HTML("<p>This method is part of the <a target='_blank' href='https://spartaas.gitpages.huma-num.fr/r-package/index.html'>SPARTAAS</a> package.</p>
                                                        <p>If you are interested you can install our R package available on the <a target='_blank' href='https://cran.r-project.org/package=SPARTAAS'>CRAN</a> and on <a target='_blank' href='https://github.com/arliph/SPARTAAS'>GitHub</a>.</p>

                                                        ")

                                      ),
                                      #-----------------------------------#
                                      mainPanel(
                                         HTML("
                                              <h3>Introduction</h3>
                                      <p>
                                              Hierarchical classification can also be considered by introducing constraints that allow, for example, geographical proximity to be taken into account.
                                              </p>
                                              <h4>Spatialpatches</h4>
                                              <p>
                                              The spatial distribution of data can be heterogeneous and present local aggregations or spatial patches. P. Petitgas proposed an algorithm to identify them in the context of fish population density (WOILLEZ et al., 2009). The parameters are the geographical coordinates (X,Y) and a variable of interest (var).
                                              </p>
                                              <p>
                                              The position of a patch is then determined by its center of gravity. The algorithm starts with the highest value of var and then considers each observation in decreasing order of value of var. The highest value initiates the first patch. Then, the observation considered is assigned to the nearest patch, provided that its distance from the center of gravity of the patch is smaller than the threshold distance dlim. Otherwise, the observation forms a new patch. The results on spatial patches are of course influenced by the choice of the dlim threshold and the location of the highest values of var.
                                              </p>
                                              <h4>MapClust</h4>
                                              <p>
                                              In order to better understand the spatial distribution of individuals, spatial patch construction is applied hierarchically top-down by varying the maximum acceptable distance (dlim) between observation points and the centre of gravity of a patch. This approach leads to a Hierarchical Top-down Classification (EVERITT et al., 2001) based at each step on the Spatialpatches algorithm: at each node (parent group) of the hierarchy we divide into two patches (child nodes). Spatialpatches has been developed to describe the spatial distribution patterns of a fish population based on density data, so the variable of interest is assumed to be positive. var can therefore be related to a positive count, frequency or real variable.
                                              </p>
                                              <p>
                                              In the case where the variable of interest var is real, means that it can take negative values, it was necessary to adapt our MapClust classification algorithm. We no longer work with the values of var but with the values of the probability density f associated with var. In the same way we were able to adapt the method to the case where var is multidimensional by working on a Kernel density estimate.
                                              </p>
                                              ")
                                      )
                                    )
                           ),
                           #-----------------------------------#
                           #    MapClust
                           #-----------------------------------#
                           tabPanel(value = "2",span(icon("map"),strong("MapClust")),style="max-width:1200px;",
                                    h1("MapClust"),
                                    sidebarLayout(
                                      sidebarPanel(width = 5,style="border-width:0px;border-color:#222222;",
                                                   wellPanel(width = 5, style="border-width:0px;",
                                                             selectInput("select", label = h4("Dataset:"),
                                                                         choices = list("datarcheo" = 1, "datacancer" = 2, "your data" = 3),
                                                                         selected = 1),
                                                             checkboxInput("Plabel","Print the list of names on the dendrogram.", value = FALSE),
                                                             checkboxInput("extenddend","Merge the leaves on the same level of the dendogram (activate before running the method: play button).", value = FALSE),
                                                             hr(style="border-color: #222222;"),
                                                             fluidRow(
                                                               column(width = 3,
                                                                      h4("Run:"),
                                                                      actionButton("Run",icon("play", lib = "glyphicon"))
                                                               ),
                                                               column(width = 6,
                                                                      h4("Import data:"),
                                                                      actionButton("UL",icon("import", lib = "glyphicon"))
                                                               ),
                                                               column(width = 1,
                                                                      h4("Export:"),
                                                                      dropdownButton(
                                                                        downloadButton("all.pdf","Download plots as pdf"),
                                                                        hr(style="border-color: #222222;"),
                                                                        #downloadButton("map.png", "Download map as png"),
                                                                        icon = icon("floppy-disk", lib = "glyphicon"), width = "220px",
                                                                        size = "sm",
                                                                        tooltip = tooltipOptions(title = "Download")
                                                                      )
                                                               )
                                                             )
                                                   ),hr(style="border-color: #222222;border-width:2px;"),
                                                   wellPanel(width = 5, style="border-width:0px;background:#dddddd;",
                                                             h4("Dendrogram"),
                                                             plotOutput("distPlot",click = "plot_click")
                                                   ),hr(style="border-color: #222222;border-width:2px;"),
                                                   wellPanel(width = 5, style="border-width:0px;background:#dddddd;",
                                                             h4("Partition evaluation"),
                                                             plotOutput("avgsilPlot"),
                                                             hr(style="border-color: #777777;"),
                                                             plotOutput("WSSPlot")
                                                   )
                                      ),
                                      mainPanel(width = 7,
                                                h4("Console"),
                                                hr(style="border-color: #222222;"),
                                                verbatimTextOutput("click_info"),
                                                h4("Map"),
                                                hr(style="border-color: #222222;"),
                                                leafletOutput("mymap",height = 500),br(),
                                                h4("Silhouette"),
                                                hr(style="border-color: #222222;"),
                                                plotOutput("silPlot",height = "auto"),
                                                h4("Summary"),
                                                hr(style="border-color: #222222;"),
                                                verbatimTextOutput("click_info2")
                                      )
                                    )
                           ),
                           #-----------------------------------#
                           #    Contact
                           #-----------------------------------#
                           tabPanel(value = "5",span(icon("envelope", lib = "glyphicon"),strong("Contact")),style="max-width:1200px;",
                                    HTML("<style>
                                       .carte{
                                       border-left-width: 4px;
                                       border-left-style:solid;
                                       border-color: #2ac0a2;
                                       padding-left: 10px;
                                       }
                                       </style>
                                       <h3>Authors:</h3>
                                       <hr>

                                       <div class=\"carte\"><h4>L. Bellanger</h4><h5>mail: &lt;lise.bellanger@univ-nantes.fr&gt;</h5></div>
                                       <div class=\"carte\"><h4>P. Husi</h4><h5>mail: &lt;philippe.husi@univ-tours.fr&gt;</h5></div><div class=\"carte\"><h4>A. Coulon</h4></div>

                                       <h3>Maintainer:</h3>
                                       <hr>
                                       <div class=\"carte\"><h4>A. Coulon</h4><h5>mail: &lt;arthur-coulon@outlook.fr&gt;</h5></div>
                                       ")
                                                ),
                           #-----------------------------------#
                           #    Wiki
                           #-----------------------------------#
                           tabPanel(value = "6",span(icon("question-sign", lib = "glyphicon"),strong("Wiki")),style="max-width:1200px;",
                                    HTML(
                                      "<style>body {text-align: justify}</style><h1>Get started with the MapClust application</h1>
                                      <h2>Table of content</h2>
                                      <div id=\"TOC\">
                                      <ul>
                                      <li><a href=\"#introduction\">Introduction</a><ul>
                                      <li><a href=\"#spatialpatches\">Spatialpatches</a></li>
                                      <li><a href=\"#mapclust\">MapClust</a></li>
                                      </ul></li>
                                      <li><a href=\"#data\">The App</a><ul>
                                      <li><a href=\"#first\">Run (first time)</a></li>
                                      <li><a href=\"#plabel\">Print label option</a></li>
                                      <li><a href=\"#import\">Import your data</a><ul>
                                      <li><a href=\"#csv\">CSV Format and write.table</a></li>
                                      <li><a href=\"#label\">Label</a></li>
                                      <li><a href=\"#header\">Header</a></li>
                                      <li><a href=\"#separator\">Separator</a></li>
                                      <li><a href=\"#quote\">Quote</a></li>
                                      <li><a href=\"#dec\">Decimal</a></li>
                                      <li><a href=\"#uni\">Univariate data</a></li>
                                      <li><a href=\"#multi\">Multivariate data</a></li>
                                      </ul></li>
                                      </ul></li>
                                      <li><a href=\"#eval\">Evaluation Plot</a><ul>
                                      <li><a href=\"#wssplot\">Within Sum of Square Plot</a></li>
                                      <li><a href=\"#avesilplot\">Average Silhouette Plot</a></li>
                                      </ul></li>
                                      </li>
                                      <li><a href=\"#output\">Output: select a partition</a><ul>
                                      <li><a href=\"#dendrogram\">Dendrogram</a></li>
                                      <li><a href=\"#map\">Map</a></li>
                                      <li><a href=\"#silhouette\">Silhouette</a></li>
                                      <li><a href=\"#summary\">Additionnal informations</a></li>
                                      </ul></li>
                                      <li><a href=\"#references\">References</a></li>
                                      </ul>
                                      </div>

<hr>
<h2 id=\"introduction\">Introduction</h2>
<hr>
<p>Hierarchical classification can also be considered by introducing constraints that allow, for example, geographical proximity to be taken into account.</p>
<h3 id=\"spatialpatches\">Spatialpatches</h3>
<p>The spatial distribution of data can be heterogeneous and present local aggregations or spatial patches. P. Petitgas proposed an algorithm to identify them in the context of fish population density (WOILLEZ et al., 2009). The parameters are the geographical coordinates (X,Y) and a variable of interest (var).</p>
<p>The position of a patch is then determined by its center of gravity. The algorithm starts with the highest value of var and then considers each observation in decreasing order of value of var. The highest value initiates the first patch. Then, the observation considered is assigned to the nearest patch, provided that its distance from the center of gravity of the patch is smaller than the threshold distance dlim. Otherwise, the observation forms a new patch. The results on spatial patches are of course influenced by the choice of the dlim threshold and the location of the highest values of var.</p>
<h3 id=\"mapclust\">MapClust</h3>
<p>In order to better understand the spatial distribution of individuals, spatial patch construction is applied hierarchically top-down by varying the maximum acceptable distance (dlim) between observation points and the centre of gravity of a patch. This approach leads to a Hierarchical Top-down Classification (EVERITT et al., 2001) based at each step on the Spatialpatches algorithm: at each node (parent group) of the hierarchy we divide into two patches (child nodes). Spatialpatches has been developed to describe the spatial distribution patterns of a fish population based on density data, so the variable of interest is assumed to be positive. var can therefore be related to a positive count, frequency or real variable.</p>
<p>In the case where the variable of interest var is real, means that it can take negative values, it was necessary to adapt our MapClust classification algorithm. We no longer work with the values of var but with the values of the probability density f associated with var. In the same way we were able to adapt the method to the case where var is multidimensional by working on a Kernel density estimate.</p>


<hr>
<h2 id=\"data\">The App</h2>
<hr>
<h3 id=\"first\">Run (first time)</h3>
<p>The function therefore requires as data geographical coordinates, latitude and longitude (<code>coord</code>) and the variable(s) of interest (<code>var</code>) in the same table.</p>
<p>For your first use, you can use our dataset. The first one is call datarcheo. Just select it in the MapClust tab (it will be selected by default) and run. You can also try the other dataset datacancer.</p>
<h3 id=\"plabel\">Print label option</h3>
<p>Located just below the dataset selection, it is used to define whether or not to display the labels on the dendrogram.</p>
<p><img src=\"GS/Plabel.png\">Select dataset and print label option</p>
<h3 id=\"import\">Import your data</h3>
<p>You can import your data. As they may contain only one or more variables of interest, you will need to adjust the parameters.</p>
<p>Let's have a look at the interface. There are three main parts. The import area with only one import button. The second part is more complex and has more options. This is the parameter section, where you need to configure the import tool according to your data. Finally, there is the preview area where you can see how your data should look and how your data is. There is still something left, the concordance indicator, which allows you to quickly check if your data matches what it should look like.</p>
<p><img src=\"GS/import.png\"></p>
<h4 id=\"csv\">CSV Format and write.table</h4>
<p><img src=\"GS/csv.png\"><br>A csv file.</p>
<p>It is a data.frame of 3 columns with headers separated by semicolons \";\".</p>
<p>The input format for importing data is the .csv format, but also supports the .txt format as a .csv file.</p>
<p>In R, you can export your data frame to a csv file using write.csv2 or write.table. In a csv you can choose a character to separate the columns. In the same way, you can define the character to indicate the decimal point.</p>
<code>
write.table(data,file=\"path/to/name_file.csv\",sep=\";\",dec=\".\",row.names=FALSE,quote=FALSE)
</code>
<p>In Excel you can save in csv format in order to import your data frame.</p>
<p>The import interface allows you to set these values using the 'header', 'decimal', 'separator' and 'quote' options.</p>
<p>To calculate exactly as you want, we need to know a few things. The first is the presence or absence of labels in your data. The label must be in the last column. We also need to know how many variables of interest you have in addition to the two coordinate variables.</p>
<p>If the setup doesn't match your real data frame, you can't import anything. You will have to change the data or parameters. If it was good you will see a green tick, if not you will see a red cross.</p>

<h4 id=\"label\">Label</h4>
<p>Yes or no option. Do you have labels in your data ? If you have labels, put them in the last colunm.</p>
<h4 id=\"header\">Header</h4>
<p>Yes or no option. Do you have headers on your colunms?</p>
<h4 id=\"separator\">Separator</h4>
<p>Select the character you want to use to separate the colunms.</p>
<h4 id=\"quote\">Quote</h4>
<p>Select the quotation marks to use on strings.</p>
<h4 id=\"dec\">Decimal</h4>
<p>Select the character to use to indicate the decimal point.</p>
<h4 id=\"uni\">Univariate data</h4>
<p>Move the slider to 'Univariate' and import your data. If you have labels, check the option, if not, uncheck Label.</p>
<p>Configure all the settings for the csv format. Which symbol to use to separate the columns, which decimal symbol to use, which quotes to use for the string, and whether to include a header in your columns.</p>
<p><img src=\"GS/importUni.png\"></p>
<h4 id=\"multi\">Multivariate data</h4>
<p>The only difference is that you have to set the slider to 'Multivariate'. When you do this, you have access to another slider. You must specify the number of variables of interest. Warning: You must not include the two coordinate variables.</p>
<p><img src=\"GS/importMulti.png\"></p>


<hr>
<h2 id=\"eval\">Evaluation Plot</h2>
<hr>
<p>It is essential to be able to evaluate the different partitions of the hierarchy in order to identify the most relevant one(s). In our case, the number k of classes in the partition to be retained is based on several indicators calculated for different values of K: the total within-class sum of squares (WSS) and the global average of silhouette widths.</p>
<p>When running the mapclust method, you have to make a choice. You must cut the dendrogram. This operation selects the partition. To compare all the possibilities, you can see the evaluation plots (WSSPlot and AveSilPlot). These two plots evaluate the relative quality of the partition.</p>
<h3 id=\"wssplot\">Within Sum of Square Plot (WSSPlot)</h3>
<p>This is the plot of the within group sum of squares against the number of clusters. The within group sum of squares decreases as the number of clusters increases. In this plot, the best partition is when adding one or more clusters doesn't decrease the WSS value. It's called the elbow method.</p>
<h4>Example:</h4>
<p><img style=\"width:600px;\" src=\"GS/WSS2.png\"></p>
<p>On this graph, we start by looking at the value for the lowest number of groups: 2. If I add a third group, we see that the WSS value will decrease (from 0.09 to 0.03). If I add another group, this value decreases again (from 0.03 to 0.01). After that, adding a group has no or only a small effect on the value. Adding a group is therefore not interesting, we will keep a partition with 4 groups.</p>
<h3 id=\"avesilplot\">Average silhouette Plot</h3>
<p>This graph shows the average silhouette width of each partition (ROUSSEEUW 1987). The silhouette width is a bounded index between -1 and 1, calculated for each observation. The closer the value is to 1, the better the observation is classified. We look for the average value for a partition that is closest to 1.</p>
<h4>Example:</h4>
<p><img style=\"width:600px;\" src=\"GS/AveSil2.png\"></p>
<p>On this graph we look for the maximum value. The best score corresponds to the division into 7 groups. Looking at the second best partition, we identify the one with 4 groups. Although the one with 7 groups is higher for this silhouette index, we will choose the partition in 4. We make this choice because the WSSPlot advised us the partition in 4.</p>


<hr>
<h2 id=\"output\">Output</h2>
<hr>
<h4>A partition is selected by clicking on the dendrogram at the desired height.</h4>
<p>You can change it at any tmie.</p>
<div id=\"dendrogram\" style=\"display:inline-block\">
  <img src=\"GS/dendro.png\">
  <img src=\"GS/dedroCut.png\">
</div><br>
<h3 id=\"map\">Map</h3>
<p><img src=\"GS/map.png\"></p><br>
<h3 id=\"silhouette\">Silhouette</h3>
<p><img src=\"GS/silhouette.png\"></p>
<p>This plot shows the silhouette index for each observation of the selected partition. The observations are sorted in descending order and by cluster.</p><br>
<h3 id=\"summary\">Additionnal informations</h3>
<p>Above the map you can see a small description of the cluster. You can find the average value of each variable.</p>
<p><img src=\"GS/Consol.png\"></p>
<p>Below the silhouette plot is a summary of the different values for each partition. You will find the dlim, the number of clusters, the WSS value, the average sil_widht and, if you are in the univariate case, the Moran index (MORAN, 1950) for each partition. The Moran index is a measure of spatial autocorrelation. It is calculated for each cluster of the partition and then the average is calculated to characterise the partition.</p>
<p><img src=\"GS/summary.png\"></p>


<hr>
<h2 id=\"references\">References</h2>
<hr>
<p>EVERITT, B.S., S. LANDAU et M. LEESE(2001). “Cluster Analysis”. In : London UK: Arnold.</p>
<p>MORAN, P. A. P. (1950). \"Notes on Continuous Stochastic Phenomena\". Biometrika. 37 (1): 17–23. <a href=\"https://doi.org/10.2307/2332142\" class=\"uri\">doi.org/10.2307/2332142</a>. JSTOR 2332142.</p>
<p>ROUSSEEUW, P.J. (1987). “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis”. In : J. Comput. Appl. Math. 20, 53–65. <a href=\"DOI:https://doi.org/10.1016/0377-0427(87)90125-7\" class=\"uri\">DOI:https://doi.org/10.1016/0377-0427(87)90125-7</a>.</p>
<p>WOILLEZ, M., J.RIVOIRARD et P.PETITGAS(2009). “Notes on survey-based spatial indicators for monitoring fish populations”. In : Aquat. Living Resour.22, p. 155–164.</p>

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                                    )
                                    )
                           )
                           )