inst/App/ui.R
In nicocriscuolo/OliveR: Multivariate analysis of data and geographic display of results
ui <- fluidPage(
fluidRow(
column(width = 12,
p(h3(strong(em("OliveR")), strong("0.1.0"), "- A statistical software for multivariate data analysis and geographic display of results", align = "center"))
)
),
br(),
fluidRow(
column(width = 2,
actionButton(inputId = "import_data", label = h4(icon(name = "upload"), "Import the data"), width = "100%"),
br(),
br(),
conditionalPanel(condition = "input.import_data > 0",
radioButtons(inputId = "analysis_type", label = h5(icon(name = "book"), "Type of data"), choices = list("Real data" = 1, "Genetic data" = 2), selected = 1),
conditionalPanel(condition = "input.analysis_type == 1",
fileInput(inputId = "Dataframe", label = h5(icon(name = "file"), "Input file (.csv)"), accept = c(".csv"))
),
conditionalPanel(condition = "input.analysis_type == 2",
fileInput(inputId = "Dataframe_G", label = h5(icon(name = "file"), "Input file (.csv)"), accept = c(".csv"))
)
),
conditionalPanel(condition = "output.fileUploaded_Normal | output.fileUploaded_Genetic",
h5(icon(name = "folder-open"), "Geographic files directory"),
shinyDirButton(id = "shape_folder", label = "Select..", title = "Choose a directory"),
verbatimTextOutput(outputId = "shape_folder_datapath"),
conditionalPanel(condition = "output.shape_folder_datapath != 0",
selectInput(inputId = "shapefiles", label = h5(icon(name = "list"), "Number of shapefiles"), choices = list("-----" = 0, "1" = 1, "2" = 2, "3" = 3), selected = 0)
),
conditionalPanel(condition = "input.shapefiles != 0",
textInput(inputId = "shapefile_name_1", label = h5(icon(name = "file-text"), "Name of shapefile n° 1"), value = ""),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
textInput(inputId = "shapefile_name_2", label = h5(icon(name = "file-text"), "Name of shapefile n° 2"), value = "")
),
conditionalPanel(condition = "input.shapefiles == 3",
textInput(inputId = "shapefile_name_3", label = h5(icon(name = "file-text"), "Name of shapefile n° 3"), value = "")
)
),
br(),
conditionalPanel(condition = "input.shapefile_name_1 || input.shapefile_name_2 || input.shapefile_name_3",
actionButton(inputId = "Start_analysis", label = h4(icon(name = "check"), "Start the analysis"), width = "100%")
)
)
#helpText(strong("Note:"), "The file to be imported must be in .csv format and the first 4 columns must contain default variables. The first, whose name must be 'Sample_ID', must contain the sample names, while the second one, to be renamed 'Label', is a categorical variable and contains the membership groups of each statistical unit. Finally, the last two must contain UTM geographic coordinates and the headers must be 'UTM_Est' and 'UTM_Nord' respectively. The next columns of the file must contain the genetic loci with the two alleles separated by '/'; indicates missing values with one single 'NA'.")
), # parentesi di chiusura pannello input comandi (colonna larghezza 2)
conditionalPanel(condition = "input.Start_analysis == 0",
column(width = 10,
plotOutput(outputId = "wordcloud", height = "600px", width = "1000px")
)
),
conditionalPanel(condition = "input.Start_analysis > 0",
column(width = 10,
conditionalPanel(condition = "input.analysis_type == 1",
tabsetPanel(id = "panels_principal_analysis", type = "pills",
tabPanel(title = h4(icon(name = "table"), "Dataset"),
br(),
tabsetPanel(type = "pills",
tabPanel(title = "Table",
br(),
fluidRow(
column(width = 12,
DT::dataTableOutput(outputId = "filetable_A")
)
)
),
tabPanel(title = "Summary",
br(),
fluidRow(
column(width = 12,
verbatimTextOutput (outputId = "summary_data")
)
)
),
tabPanel(title = "Stats",
br(),
fluidRow(
column(width = 12,
DT::dataTableOutput(outputId = "stats")
)
)
),
tabPanel(title = "Images",
br(),
fluidRow(
column(width = 3, selectInput(inputId = "sample_name", label = h5("Name of the sample"), choices = "")
)
),
br(),
fluidRow(
column(width = 4,
shinyDirButton(id = "image_1_folder", label = "Select the image folder n° 1", title = "Choose a directory"),
br(),
br(),
imageOutput(outputId = "image_1")
),
column(width = 4,
shinyDirButton(id = "image_2_folder", label = "Select the image folder n° 2", title = "Choose a directory"),
br(),
br(),
imageOutput(outputId = "image_2")
),
column(width = 4,
shinyDirButton(id = "image_3_folder", label = "Select the image folder n° 3", title = "Choose a directory"),
br(),
br(),
imageOutput(outputId = "image_3")
)
),
br(),
br()
),
tabPanel(title = "Subset and merge",
br(),
h4(strong("Subset of the data")),
h5("Select the variable of the principal dataset"),
fluidRow(
column(width = 4, selectInput(inputId = "Subset_Variable", label = NULL, choices = "", multiple = TRUE)
),
column(width = 2, downloadButton(outputId = "download", label = "Download Subset")
)
),
br(),
h4(strong("Merge the data")),
h5("Choose the dataset to merge"),
fluidRow(
column(width = 4,
fileInput(inputId = "Dataframe_M", label = NULL, accept = c(".csv"))
),
column(width = 2,
conditionalPanel(condition = "output.fileUploaded_Merge",
downloadButton(outputId = "download_M", label = "Download Merge")
)
)
)
)
)
),
tabPanel(title = h4(icon(name = "bar-chart"), "Plot"),
br(),
fluidRow(
column(width = 2, selectInput(inputId = "Variable_1", label = h5("X Variable"), choices = "")
),
column(width = 2, selectInput(inputId = "Variable_2", label = h5("Y Variable"), choices = "")
),
conditionalPanel(condition = "input.Variable_1 != 'Sample_ID' && input.Variable_1 != 'Label' && input.Variable_2 != 'Sample_ID' && input.Variable_2 != 'Label'",
column(width = 2, selectInput(inputId = "Variable_3", label = h5("Z Variable"), choices = "")
)
),
conditionalPanel(condition = "input.Variable_1 != 'Sample_ID' && input.Variable_2 != 'Sample_ID' && input.Variable_1 != 'Label' && input.Variable_2 != 'Label' && input.Variable_3 == '-----'",
column(width = 2, h5("Pearson coefficient"), verbatimTextOutput(outputId = "pearson")
)
)
),
conditionalPanel(condition = "input.Variable_1 == 'UTM_Est' && input.Variable_2 == 'UTM_Nord'",
fluidRow(
column(width = 2,
colourpicker::colourInput(inputId = "Colours_1", label = h5("Shapefile n° 1"), value = "#D6FFD6", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_1", label = "Polygon name"),
br(),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_2", label = h5("Shapefile n° 2"), value = "#E0F7FF", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2", label = "Polygon name")
),
br(),
conditionalPanel(condition = "input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_3", label = h5("Shapefile n° 3"), value = "#FFFFF0", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_3", label = "Polygon name")
),
br(),
br(),
checkboxInput(inputId = "google_1", label = h5(icon(name = "map"), "Google Maps"), width = "100%"),
br(),
conditionalPanel(condition = "input.google_1 == true",
textInput(inputId = "API_key_1",
label = h5("Google Maps API key"),
value = "")
)
),
column(width = 10,
conditionalPanel(condition = "input.google_1 == true && input.API_key_1",
google_mapOutput(outputId = "google_map_1", height = "650px")
),
conditionalPanel(condition = "input.google_1 == false",
plotlyOutput(outputId = "geoplot_1", width = "930px", height = "820px"), align = "right"
)
)
)
),
conditionalPanel(condition = "input.Variable_1 != 'UTM_Est' || input.Variable_2 != 'UTM_Nord'",
plotlyOutput(outputId = "plot", height = "600px")
)
),
tabPanel(title = h4(icon(name = "line-chart"), "Linear Models"),
br(),
tabsetPanel(type = "pills",
tabPanel(title = "ANOVA",
br(),
fluidRow(
column(width = 2, selectInput(inputId = "Variable_Y", label = h5("Y Variable"), choices = "")
)
),
fluidRow(
column(width = 7, h4(strong("Levene's Test")),
verbatimTextOutput(outputId = "levene")
),
column(width = 5, h4(strong("ANOVA Test")),
verbatimTextOutput(outputId = "anova")
)
),
fluidRow(
column(width = 12, plotlyOutput(outputId = "anova_plot", height = "600px")
)
),
br(),
fluidRow(
column(width = 12, h4(strong("Post Hoc Test")),
verbatimTextOutput(outputId = "bonferroni")
)
)
)
)
),
tabPanel(title = h4(icon(name = "arrows"), "PCA"),
br(),
fluidRow(
column(width = 12,
radioButtons(inputId = "cov_cor",
label = h5("Matrix"),
choices = c("Correlation", "Covariance"),
selected = "Correlation"),
p("If your dataset contains one or more ",
tags$b("costant variables",
.noWS = "outside"),
", use the covariance matrix to compute the PCA.",
style = "color: gray;")
),
br(),
conditionalPanel(condition = "input.panels_PCA == 'panel_Screeplot'",
column(width = 1, checkboxInput(inputId = "choose_variance", label = "Variance explained", value = FALSE)
)
),
conditionalPanel(condition = "input.panels_PCA == 'panel_Screeplot' && input.choose_variance == false",
column(width = 2, selectInput(inputId = "PCs", label = h5("Number of PCs"), choices = "")
)
),
conditionalPanel(condition = "input.panels_PCA == 'panel_Loadings' || input.panels_PCA == 'panel_PCs_Plot'",
column(width = 2, selectInput(inputId = "PCs_B", label = h5("Number of PCs"), choices = "")
)
)
),
tabsetPanel(id = "panels_PCA", type = "pills",
tabPanel(title = "Summary", value = "panel_Summary",
br(),
fluidRow(
column(width = 12,
verbatimTextOutput (outputId = "summary_PCA")
)
)
),
tabPanel(title = "Screeplot", value = "panel_Screeplot",
br(),
fluidRow(
column(width = 10, plotlyOutput(outputId = "screeplot", height = "600px")
),
conditionalPanel(condition = "input.choose_variance == true",
column(width = 2, checkboxGroupInput(inputId = "Box_Var", label = h5("Variance explained"), choices = list("70%" = 1, "75%" = 2, "80%" = 3, "85%" = 4, "90%" = 5, "95%" = 6), selected = NULL)
)
)
)
),
tabPanel(title = "Loadings", value = "panel_Loadings",
br(),
fluidRow(
column(width = 12, plotlyOutput (outputId = "loadings", height = "600px")
)
)
),
tabPanel(title = "PCs Plot", value = "panel_PCs_Plot",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "PCs_plot", width = "100%", height = "600px")
)
)
)
)
),
tabPanel(title = h4(icon(name = "th-large"), "Cluster Analysis"),
br(),
fluidRow(
column(width = 1, checkboxInput(inputId = "Perform_PCA", label = "PCA", value = FALSE)
),
conditionalPanel(condition = "input.Perform_PCA == true",
column(width = 1, radioButtons(inputId = "cov_cor_2", label = h5("Matrix"), choices = c("Correlation", "Covariance"), selected = "Correlation")
),
column(width = 2, selectInput(inputId = "PCs_2", label = h5("Number of PCs"), choices = "")
)
),
column(width = 2, selectInput(inputId = "Cluster_Method", label = h5("Partitioning methods"), choices = list("K-Means" = "kmeans", "PAM" = "pam", "Clara" = "clara", "Fanny" = "fanny"), selected = "K-Means")
),
conditionalPanel(condition = "input.panels_cluster_analysis == 'panel_Table' || input.panels_cluster_analysis == 'panel_Silhouette' || input.panels_cluster_analysis == 'panel_Geoplot'",
column(width = 2, numericInput(inputId = "Cluster_Count", label = h5("Cluster count"), value = 3, min = 2, max = 9)
)
),
conditionalPanel(condition = "input.panels_cluster_analysis == 'panel_Table' || input.panels_cluster_analysis == 'panel_Silhouette' || input.panels_cluster_analysis == 'panel_Geoplot'",
column(width = 2, h5("Table of cluster"), downloadButton(outputId = "download_Cluster", label = "Download")
)
),
column(width = 12,
p("If your dataset contains one or more ",
tags$b("costant variables",
.noWS = "outside"),
", use the covariance matrix to compute the PCA.",
style = "color: gray;"))
),
tabsetPanel(id = "panels_cluster_analysis", type = "pills",
tabPanel(title = "WSS Plot",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "wss", height = "600px")
)
)
),
tabPanel(title = "Gap Statistic",
br(),
fluidRow(
conditionalPanel(condition = "input.Perform_PCA == true && input.PCs_2 == '1'",
column(width = 12, textOutput(outputId = "error_gap_statistics"),
tags$head(tags$style("#error_gap_statistics{color: red;}"))
)
),
conditionalPanel(condition = "input.Perform_PCA == false || input.Perform_PCA == true && input.PCs_2 != '1'",
column(width = 12, plotlyOutput(outputId = "gap_statistics", height = "600px")
)
)
)
),
tabPanel(title = "Table Plot", value = "panel_Table",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "tabplot", height = "550px", width = "100%")
)
)
),
tabPanel(title = "Silhouette Plot", value = "panel_Silhouette",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "silplot", height = "550px")
)
)
),
tabPanel(title = "Geoplot", value = "panel_Geoplot",
br(),
fluidRow(
column(width = 2,
colourpicker::colourInput(inputId = "Colours_2_1", label = h5("Shapefile n° 1"), value = "#D6FFD6", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2_1", label = "Polygon name"),
br(),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_2_2", label = h5("Shapefile n° 2"), value = "#E0F7FF", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2_2", label = "Polygon name")
),
br(),
conditionalPanel(condition = "input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_2_3", label = h5("Shapefile n° 3"), value = "#FFFFF0", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2_3", label = "Polygon name")
),
br(),
br(),
h5("Adjusted Rand Index"),
verbatimTextOutput(outputId = "adjusted_rand_index"),
br(),
br(),
checkboxInput(inputId = "google_2",
label = h5(icon(name = "map"), "Google Maps"),
width = "100%"),
br(),
conditionalPanel(condition = "input.google_2 == true",
textInput(inputId = "API_key_2",
label = h5("Google Maps API key"),
value = "")
)
),
column(width = 10,
conditionalPanel(condition = "input.google_2 == true && input.API_key_2",
google_mapOutput(outputId = "google_map_2", height = "650px")
),
conditionalPanel(condition = "input.google_2 == false",
plotlyOutput(outputId = "geoplot_2", width = "930px", height = "820px"), align = "right"
)
)
)
)
)
)
)
), # parentesi chiusura CONDITIONAL PANEL con input.analysis_type == 1
############################ GENETIC DATA
conditionalPanel(condition = "input.analysis_type == 2",
tabsetPanel(id = "panels_genetic_analysis", type = "pills",
tabPanel(title = h4("Dataset"),
br(),
#conditionalPanel(condition = "output.fileUploaded_Genetic",
tabsetPanel(type = "pills",
tabPanel(title = "Table",
br(),
fluidRow(
column(width = 12, DT::dataTableOutput(outputId = "filetable_G")
)
)
)
)
#)
),
tabPanel(title = h4("Mantel Test"),
br(),
fluidRow(
column(width = 1, radioButtons(inputId = "na.method.IBD", label = h5("NA value"), choices = list("Zero" = "zero", "Mean" = "mean"))
),
column(width = 1, radioButtons(inputId = "distance_IBD", label = h5("Distance"), choices = list("Binary" = "binary", "Geometric" = "geometric"))
),
conditionalPanel(condition = "input.distance_IBD == 'binary'",
column(width = 2, selectInput(inputId = "dist_binary.IBD", label = h5("Similarity coefficient"), choices = list("Jaccard Index" = 1, "Sokal & Michener" = 2, "Sokal & Sneath" = 3, "Rogers & Tanimoto" = 4, "Dice & Sorensen" = 5, "Hamann coefficient" = 6, "Ochiai" = 7, "Phi of Pearson" = 9, "S2 Coefficent" = 10), selected = 1)
)
),
column(width = 2, selectInput(inputId = "dist_measure.IBD", label = h5("Distance measure"), choices = list("Euclidean" = "euclidean", "Maximum" = "maximum", "Manhattan" = "manhattan", "Canberra" = "canberra", "Minkowski" = "minkowski"))
),
conditionalPanel(condition = "input.isolation_by_distance == false & output.fileUploaded_Normal",
column(width = 2, selectInput(inputId = "dist_measure_REAL.IBD", label = h5("Distance measure (Real)"), choices = list("Euclidean" = "euclidean", "Maximum" = "maximum", "Manhattan" = "manhattan", "Canberra" = "canberra", "Minkowski" = "minkowski"))
)
),
column(width = 2, checkboxInput(inputId = "isolation_by_distance", label = "Isolation by distance", value = TRUE)
)
),
fluidRow(
column(width = 12, plotOutput(outputId = "IBD", height = "600px")
)
)
),
tabPanel(title = h4("Cluster Analysis"),
br(),
fluidRow(
column(width = 1, radioButtons(inputId = "na.method", label = h5("NA value"), choices = list("Zero" = "zero", "Mean" = "mean"))
),
column(width = 1, radioButtons(inputId = "distance", label = h5("Distance"), choices = list("Binary" = "binary", "Geometric" = "geometric"))
),
conditionalPanel(condition = "input.distance == 'binary'",
column(width = 2, selectInput(inputId = "dist_binary", label = h5("Similarity coefficient"), choices = list("Jaccard Index" = 1, "Sokal & Michener" = 2, "Sokal & Sneath" = 3, "Rogers & Tanimoto" = 4, "Dice & Sorensen" = 5, "Hamann coefficient" = 6, "Ochiai" = 7, "Phi of Pearson" = 9, "S2 Coefficent" = 10), selected = 1)
)
),
conditionalPanel(condition = "input.distance == 'geometric'",
column(width = 2, selectInput(inputId = "dist_geometric", label = h5("Geometric distance"), choices = list("Euclidean" = "euclidean", "Maximum" = "maximum", "Manhattan" = "manhattan", "Canberra" = "canberra", "Minkowski" = "minkowski"))
)
),
column(width = 2, selectInput(inputId = "dendro_method", label = h5("Method"), choices = list("Complete" = "complete", "Single" = "single", "Ward" = "ward.D", "Ward - 2" = "ward.D2", "UPGMA" = "average", "WPGMA" = "mcquitty", "Median" = "median", "UPGMC" = "centroid"))
),
conditionalPanel(condition = "input.panels_cluster_analysis_G == 'panel_tree' || input.panels_cluster_analysis_G == 'panel_heatmap'||input.panels_cluster_analysis_G == 'panel_silhouette_G' || input.panels_cluster_analysis_G == 'panel_geoplot_G'",
column(width = 2, numericInput(inputId = "Cluster_Count_2", label = h5("Cluster count"), value = 1, min = 1, max = 9)
)
),
conditionalPanel(condition = "input.panels_cluster_analysis_G == 'panel_tree'",
column(
width = 2,
radioButtons(inputId = "tree",
label = h5("Tree type"),
choices = list("Dendrogram" = "dendrogram",
"Cladogram" = "cladogram" #, "Phylo" = "phylo"
)
)
)
)
),
tabsetPanel(id = "panels_cluster_analysis_G", type = "pills",
tabPanel(title = "Heatmap", value = "panel_heatmap",
br(),
fluidRow(
column(width = 12, align = "center", plotlyOutput(outputId = "heatmap", height = "800px")
)
)
),
tabPanel(title = "Tree", value = "panel_tree",
br(),
fluidRow(
column(width = 12,
plotOutput(outputId = "dendrogram",
height = "700px"
)
)
)
),
tabPanel(title = "Cutree", value = "panel_cutree",
br(),
fluidRow(
column(width = 12, align = "center", plotlyOutput(outputId = "cutree", height = "600px")
)
)
),
tabPanel(title = "Silhouette Plot", value = "panel_silhouette_G",
br(),
fluidRow(
column(width = 12, plotOutput(outputId = "siltabplot_G", height ="550px")
)
)
),
tabPanel(title = "Geoplot", value = "panel_geoplot_G",
br(),
fluidRow(
column(width = 2,
colourpicker::colourInput(inputId = "Colours_G_1", label = h5("Shapefile n° 1"), value = "#D6FFD6", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_G_1", label = "Polygon name"),
br(),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_G_2", label = h5("Shapefile n° 2"), value = "#E0F7FF", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_G_2", label = "Polygon name")
),
br(),
conditionalPanel(condition = "input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_G_3", label = h5("Shapefile n° 3"), value = "#FFFFF0", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_G_3", label = "Polygon name")
),
br(),
br(),
checkboxInput(inputId = "google_G",
label = h5(icon(name = "map"), "Google Maps"),
width = "100%"),
br(),
conditionalPanel(condition = "input.google_G == true",
textInput(inputId = "API_key_G",
label = h5("Google Maps API key"),
value = "")
)
),
column(width = 10,
conditionalPanel(condition = "input.google_G == false",
plotlyOutput(outputId = "geoplot_G",
width = "900px",
height = "820px"),
align = "center"
),
conditionalPanel(condition = "input.google_G == true && input.API_key_G",
google_mapOutput(outputId = "google_map_G", height = "650px")
)
)
)
)
)
)
)
)
) # parentesi di chiusura pannello output principali (colonna larghezza 10)
)
), # parentesi chiusura fluidRow contenente pannello input comandi e pannello output
br(),
fluidRow(
column(width = 4, offset = 8,
textOutput("current_time")
)
)
)
nicocriscuolo/OliveR documentation built on Dec. 4, 2020, 9:05 p.m.
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ui <- fluidPage(
fluidRow(
column(width = 12,
p(h3(strong(em("OliveR")), strong("0.1.0"), "- A statistical software for multivariate data analysis and geographic display of results", align = "center"))
)
),
br(),
fluidRow(
column(width = 2,
actionButton(inputId = "import_data", label = h4(icon(name = "upload"), "Import the data"), width = "100%"),
br(),
br(),
conditionalPanel(condition = "input.import_data > 0",
radioButtons(inputId = "analysis_type", label = h5(icon(name = "book"), "Type of data"), choices = list("Real data" = 1, "Genetic data" = 2), selected = 1),
conditionalPanel(condition = "input.analysis_type == 1",
fileInput(inputId = "Dataframe", label = h5(icon(name = "file"), "Input file (.csv)"), accept = c(".csv"))
),
conditionalPanel(condition = "input.analysis_type == 2",
fileInput(inputId = "Dataframe_G", label = h5(icon(name = "file"), "Input file (.csv)"), accept = c(".csv"))
)
),
conditionalPanel(condition = "output.fileUploaded_Normal | output.fileUploaded_Genetic",
h5(icon(name = "folder-open"), "Geographic files directory"),
shinyDirButton(id = "shape_folder", label = "Select..", title = "Choose a directory"),
verbatimTextOutput(outputId = "shape_folder_datapath"),
conditionalPanel(condition = "output.shape_folder_datapath != 0",
selectInput(inputId = "shapefiles", label = h5(icon(name = "list"), "Number of shapefiles"), choices = list("-----" = 0, "1" = 1, "2" = 2, "3" = 3), selected = 0)
),
conditionalPanel(condition = "input.shapefiles != 0",
textInput(inputId = "shapefile_name_1", label = h5(icon(name = "file-text"), "Name of shapefile n° 1"), value = ""),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
textInput(inputId = "shapefile_name_2", label = h5(icon(name = "file-text"), "Name of shapefile n° 2"), value = "")
),
conditionalPanel(condition = "input.shapefiles == 3",
textInput(inputId = "shapefile_name_3", label = h5(icon(name = "file-text"), "Name of shapefile n° 3"), value = "")
)
),
br(),
conditionalPanel(condition = "input.shapefile_name_1 || input.shapefile_name_2 || input.shapefile_name_3",
actionButton(inputId = "Start_analysis", label = h4(icon(name = "check"), "Start the analysis"), width = "100%")
)
)
#helpText(strong("Note:"), "The file to be imported must be in .csv format and the first 4 columns must contain default variables. The first, whose name must be 'Sample_ID', must contain the sample names, while the second one, to be renamed 'Label', is a categorical variable and contains the membership groups of each statistical unit. Finally, the last two must contain UTM geographic coordinates and the headers must be 'UTM_Est' and 'UTM_Nord' respectively. The next columns of the file must contain the genetic loci with the two alleles separated by '/'; indicates missing values with one single 'NA'.")
), # parentesi di chiusura pannello input comandi (colonna larghezza 2)
conditionalPanel(condition = "input.Start_analysis == 0",
column(width = 10,
plotOutput(outputId = "wordcloud", height = "600px", width = "1000px")
)
),
conditionalPanel(condition = "input.Start_analysis > 0",
column(width = 10,
conditionalPanel(condition = "input.analysis_type == 1",
tabsetPanel(id = "panels_principal_analysis", type = "pills",
tabPanel(title = h4(icon(name = "table"), "Dataset"),
br(),
tabsetPanel(type = "pills",
tabPanel(title = "Table",
br(),
fluidRow(
column(width = 12,
DT::dataTableOutput(outputId = "filetable_A")
)
)
),
tabPanel(title = "Summary",
br(),
fluidRow(
column(width = 12,
verbatimTextOutput (outputId = "summary_data")
)
)
),
tabPanel(title = "Stats",
br(),
fluidRow(
column(width = 12,
DT::dataTableOutput(outputId = "stats")
)
)
),
tabPanel(title = "Images",
br(),
fluidRow(
column(width = 3, selectInput(inputId = "sample_name", label = h5("Name of the sample"), choices = "")
)
),
br(),
fluidRow(
column(width = 4,
shinyDirButton(id = "image_1_folder", label = "Select the image folder n° 1", title = "Choose a directory"),
br(),
br(),
imageOutput(outputId = "image_1")
),
column(width = 4,
shinyDirButton(id = "image_2_folder", label = "Select the image folder n° 2", title = "Choose a directory"),
br(),
br(),
imageOutput(outputId = "image_2")
),
column(width = 4,
shinyDirButton(id = "image_3_folder", label = "Select the image folder n° 3", title = "Choose a directory"),
br(),
br(),
imageOutput(outputId = "image_3")
)
),
br(),
br()
),
tabPanel(title = "Subset and merge",
br(),
h4(strong("Subset of the data")),
h5("Select the variable of the principal dataset"),
fluidRow(
column(width = 4, selectInput(inputId = "Subset_Variable", label = NULL, choices = "", multiple = TRUE)
),
column(width = 2, downloadButton(outputId = "download", label = "Download Subset")
)
),
br(),
h4(strong("Merge the data")),
h5("Choose the dataset to merge"),
fluidRow(
column(width = 4,
fileInput(inputId = "Dataframe_M", label = NULL, accept = c(".csv"))
),
column(width = 2,
conditionalPanel(condition = "output.fileUploaded_Merge",
downloadButton(outputId = "download_M", label = "Download Merge")
)
)
)
)
)
),
tabPanel(title = h4(icon(name = "bar-chart"), "Plot"),
br(),
fluidRow(
column(width = 2, selectInput(inputId = "Variable_1", label = h5("X Variable"), choices = "")
),
column(width = 2, selectInput(inputId = "Variable_2", label = h5("Y Variable"), choices = "")
),
conditionalPanel(condition = "input.Variable_1 != 'Sample_ID' && input.Variable_1 != 'Label' && input.Variable_2 != 'Sample_ID' && input.Variable_2 != 'Label'",
column(width = 2, selectInput(inputId = "Variable_3", label = h5("Z Variable"), choices = "")
)
),
conditionalPanel(condition = "input.Variable_1 != 'Sample_ID' && input.Variable_2 != 'Sample_ID' && input.Variable_1 != 'Label' && input.Variable_2 != 'Label' && input.Variable_3 == '-----'",
column(width = 2, h5("Pearson coefficient"), verbatimTextOutput(outputId = "pearson")
)
)
),
conditionalPanel(condition = "input.Variable_1 == 'UTM_Est' && input.Variable_2 == 'UTM_Nord'",
fluidRow(
column(width = 2,
colourpicker::colourInput(inputId = "Colours_1", label = h5("Shapefile n° 1"), value = "#D6FFD6", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_1", label = "Polygon name"),
br(),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_2", label = h5("Shapefile n° 2"), value = "#E0F7FF", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2", label = "Polygon name")
),
br(),
conditionalPanel(condition = "input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_3", label = h5("Shapefile n° 3"), value = "#FFFFF0", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_3", label = "Polygon name")
),
br(),
br(),
checkboxInput(inputId = "google_1", label = h5(icon(name = "map"), "Google Maps"), width = "100%"),
br(),
conditionalPanel(condition = "input.google_1 == true",
textInput(inputId = "API_key_1",
label = h5("Google Maps API key"),
value = "")
)
),
column(width = 10,
conditionalPanel(condition = "input.google_1 == true && input.API_key_1",
google_mapOutput(outputId = "google_map_1", height = "650px")
),
conditionalPanel(condition = "input.google_1 == false",
plotlyOutput(outputId = "geoplot_1", width = "930px", height = "820px"), align = "right"
)
)
)
),
conditionalPanel(condition = "input.Variable_1 != 'UTM_Est' || input.Variable_2 != 'UTM_Nord'",
plotlyOutput(outputId = "plot", height = "600px")
)
),
tabPanel(title = h4(icon(name = "line-chart"), "Linear Models"),
br(),
tabsetPanel(type = "pills",
tabPanel(title = "ANOVA",
br(),
fluidRow(
column(width = 2, selectInput(inputId = "Variable_Y", label = h5("Y Variable"), choices = "")
)
),
fluidRow(
column(width = 7, h4(strong("Levene's Test")),
verbatimTextOutput(outputId = "levene")
),
column(width = 5, h4(strong("ANOVA Test")),
verbatimTextOutput(outputId = "anova")
)
),
fluidRow(
column(width = 12, plotlyOutput(outputId = "anova_plot", height = "600px")
)
),
br(),
fluidRow(
column(width = 12, h4(strong("Post Hoc Test")),
verbatimTextOutput(outputId = "bonferroni")
)
)
)
)
),
tabPanel(title = h4(icon(name = "arrows"), "PCA"),
br(),
fluidRow(
column(width = 12,
radioButtons(inputId = "cov_cor",
label = h5("Matrix"),
choices = c("Correlation", "Covariance"),
selected = "Correlation"),
p("If your dataset contains one or more ",
tags$b("costant variables",
.noWS = "outside"),
", use the covariance matrix to compute the PCA.",
style = "color: gray;")
),
br(),
conditionalPanel(condition = "input.panels_PCA == 'panel_Screeplot'",
column(width = 1, checkboxInput(inputId = "choose_variance", label = "Variance explained", value = FALSE)
)
),
conditionalPanel(condition = "input.panels_PCA == 'panel_Screeplot' && input.choose_variance == false",
column(width = 2, selectInput(inputId = "PCs", label = h5("Number of PCs"), choices = "")
)
),
conditionalPanel(condition = "input.panels_PCA == 'panel_Loadings' || input.panels_PCA == 'panel_PCs_Plot'",
column(width = 2, selectInput(inputId = "PCs_B", label = h5("Number of PCs"), choices = "")
)
)
),
tabsetPanel(id = "panels_PCA", type = "pills",
tabPanel(title = "Summary", value = "panel_Summary",
br(),
fluidRow(
column(width = 12,
verbatimTextOutput (outputId = "summary_PCA")
)
)
),
tabPanel(title = "Screeplot", value = "panel_Screeplot",
br(),
fluidRow(
column(width = 10, plotlyOutput(outputId = "screeplot", height = "600px")
),
conditionalPanel(condition = "input.choose_variance == true",
column(width = 2, checkboxGroupInput(inputId = "Box_Var", label = h5("Variance explained"), choices = list("70%" = 1, "75%" = 2, "80%" = 3, "85%" = 4, "90%" = 5, "95%" = 6), selected = NULL)
)
)
)
),
tabPanel(title = "Loadings", value = "panel_Loadings",
br(),
fluidRow(
column(width = 12, plotlyOutput (outputId = "loadings", height = "600px")
)
)
),
tabPanel(title = "PCs Plot", value = "panel_PCs_Plot",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "PCs_plot", width = "100%", height = "600px")
)
)
)
)
),
tabPanel(title = h4(icon(name = "th-large"), "Cluster Analysis"),
br(),
fluidRow(
column(width = 1, checkboxInput(inputId = "Perform_PCA", label = "PCA", value = FALSE)
),
conditionalPanel(condition = "input.Perform_PCA == true",
column(width = 1, radioButtons(inputId = "cov_cor_2", label = h5("Matrix"), choices = c("Correlation", "Covariance"), selected = "Correlation")
),
column(width = 2, selectInput(inputId = "PCs_2", label = h5("Number of PCs"), choices = "")
)
),
column(width = 2, selectInput(inputId = "Cluster_Method", label = h5("Partitioning methods"), choices = list("K-Means" = "kmeans", "PAM" = "pam", "Clara" = "clara", "Fanny" = "fanny"), selected = "K-Means")
),
conditionalPanel(condition = "input.panels_cluster_analysis == 'panel_Table' || input.panels_cluster_analysis == 'panel_Silhouette' || input.panels_cluster_analysis == 'panel_Geoplot'",
column(width = 2, numericInput(inputId = "Cluster_Count", label = h5("Cluster count"), value = 3, min = 2, max = 9)
)
),
conditionalPanel(condition = "input.panels_cluster_analysis == 'panel_Table' || input.panels_cluster_analysis == 'panel_Silhouette' || input.panels_cluster_analysis == 'panel_Geoplot'",
column(width = 2, h5("Table of cluster"), downloadButton(outputId = "download_Cluster", label = "Download")
)
),
column(width = 12,
p("If your dataset contains one or more ",
tags$b("costant variables",
.noWS = "outside"),
", use the covariance matrix to compute the PCA.",
style = "color: gray;"))
),
tabsetPanel(id = "panels_cluster_analysis", type = "pills",
tabPanel(title = "WSS Plot",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "wss", height = "600px")
)
)
),
tabPanel(title = "Gap Statistic",
br(),
fluidRow(
conditionalPanel(condition = "input.Perform_PCA == true && input.PCs_2 == '1'",
column(width = 12, textOutput(outputId = "error_gap_statistics"),
tags$head(tags$style("#error_gap_statistics{color: red;}"))
)
),
conditionalPanel(condition = "input.Perform_PCA == false || input.Perform_PCA == true && input.PCs_2 != '1'",
column(width = 12, plotlyOutput(outputId = "gap_statistics", height = "600px")
)
)
)
),
tabPanel(title = "Table Plot", value = "panel_Table",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "tabplot", height = "550px", width = "100%")
)
)
),
tabPanel(title = "Silhouette Plot", value = "panel_Silhouette",
br(),
fluidRow(
column(width = 12, plotlyOutput(outputId = "silplot", height = "550px")
)
)
),
tabPanel(title = "Geoplot", value = "panel_Geoplot",
br(),
fluidRow(
column(width = 2,
colourpicker::colourInput(inputId = "Colours_2_1", label = h5("Shapefile n° 1"), value = "#D6FFD6", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2_1", label = "Polygon name"),
br(),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_2_2", label = h5("Shapefile n° 2"), value = "#E0F7FF", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2_2", label = "Polygon name")
),
br(),
conditionalPanel(condition = "input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_2_3", label = h5("Shapefile n° 3"), value = "#FFFFF0", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_2_3", label = "Polygon name")
),
br(),
br(),
h5("Adjusted Rand Index"),
verbatimTextOutput(outputId = "adjusted_rand_index"),
br(),
br(),
checkboxInput(inputId = "google_2",
label = h5(icon(name = "map"), "Google Maps"),
width = "100%"),
br(),
conditionalPanel(condition = "input.google_2 == true",
textInput(inputId = "API_key_2",
label = h5("Google Maps API key"),
value = "")
)
),
column(width = 10,
conditionalPanel(condition = "input.google_2 == true && input.API_key_2",
google_mapOutput(outputId = "google_map_2", height = "650px")
),
conditionalPanel(condition = "input.google_2 == false",
plotlyOutput(outputId = "geoplot_2", width = "930px", height = "820px"), align = "right"
)
)
)
)
)
)
)
), # parentesi chiusura CONDITIONAL PANEL con input.analysis_type == 1
############################ GENETIC DATA
conditionalPanel(condition = "input.analysis_type == 2",
tabsetPanel(id = "panels_genetic_analysis", type = "pills",
tabPanel(title = h4("Dataset"),
br(),
#conditionalPanel(condition = "output.fileUploaded_Genetic",
tabsetPanel(type = "pills",
tabPanel(title = "Table",
br(),
fluidRow(
column(width = 12, DT::dataTableOutput(outputId = "filetable_G")
)
)
)
)
#)
),
tabPanel(title = h4("Mantel Test"),
br(),
fluidRow(
column(width = 1, radioButtons(inputId = "na.method.IBD", label = h5("NA value"), choices = list("Zero" = "zero", "Mean" = "mean"))
),
column(width = 1, radioButtons(inputId = "distance_IBD", label = h5("Distance"), choices = list("Binary" = "binary", "Geometric" = "geometric"))
),
conditionalPanel(condition = "input.distance_IBD == 'binary'",
column(width = 2, selectInput(inputId = "dist_binary.IBD", label = h5("Similarity coefficient"), choices = list("Jaccard Index" = 1, "Sokal & Michener" = 2, "Sokal & Sneath" = 3, "Rogers & Tanimoto" = 4, "Dice & Sorensen" = 5, "Hamann coefficient" = 6, "Ochiai" = 7, "Phi of Pearson" = 9, "S2 Coefficent" = 10), selected = 1)
)
),
column(width = 2, selectInput(inputId = "dist_measure.IBD", label = h5("Distance measure"), choices = list("Euclidean" = "euclidean", "Maximum" = "maximum", "Manhattan" = "manhattan", "Canberra" = "canberra", "Minkowski" = "minkowski"))
),
conditionalPanel(condition = "input.isolation_by_distance == false & output.fileUploaded_Normal",
column(width = 2, selectInput(inputId = "dist_measure_REAL.IBD", label = h5("Distance measure (Real)"), choices = list("Euclidean" = "euclidean", "Maximum" = "maximum", "Manhattan" = "manhattan", "Canberra" = "canberra", "Minkowski" = "minkowski"))
)
),
column(width = 2, checkboxInput(inputId = "isolation_by_distance", label = "Isolation by distance", value = TRUE)
)
),
fluidRow(
column(width = 12, plotOutput(outputId = "IBD", height = "600px")
)
)
),
tabPanel(title = h4("Cluster Analysis"),
br(),
fluidRow(
column(width = 1, radioButtons(inputId = "na.method", label = h5("NA value"), choices = list("Zero" = "zero", "Mean" = "mean"))
),
column(width = 1, radioButtons(inputId = "distance", label = h5("Distance"), choices = list("Binary" = "binary", "Geometric" = "geometric"))
),
conditionalPanel(condition = "input.distance == 'binary'",
column(width = 2, selectInput(inputId = "dist_binary", label = h5("Similarity coefficient"), choices = list("Jaccard Index" = 1, "Sokal & Michener" = 2, "Sokal & Sneath" = 3, "Rogers & Tanimoto" = 4, "Dice & Sorensen" = 5, "Hamann coefficient" = 6, "Ochiai" = 7, "Phi of Pearson" = 9, "S2 Coefficent" = 10), selected = 1)
)
),
conditionalPanel(condition = "input.distance == 'geometric'",
column(width = 2, selectInput(inputId = "dist_geometric", label = h5("Geometric distance"), choices = list("Euclidean" = "euclidean", "Maximum" = "maximum", "Manhattan" = "manhattan", "Canberra" = "canberra", "Minkowski" = "minkowski"))
)
),
column(width = 2, selectInput(inputId = "dendro_method", label = h5("Method"), choices = list("Complete" = "complete", "Single" = "single", "Ward" = "ward.D", "Ward - 2" = "ward.D2", "UPGMA" = "average", "WPGMA" = "mcquitty", "Median" = "median", "UPGMC" = "centroid"))
),
conditionalPanel(condition = "input.panels_cluster_analysis_G == 'panel_tree' || input.panels_cluster_analysis_G == 'panel_heatmap'||input.panels_cluster_analysis_G == 'panel_silhouette_G' || input.panels_cluster_analysis_G == 'panel_geoplot_G'",
column(width = 2, numericInput(inputId = "Cluster_Count_2", label = h5("Cluster count"), value = 1, min = 1, max = 9)
)
),
conditionalPanel(condition = "input.panels_cluster_analysis_G == 'panel_tree'",
column(
width = 2,
radioButtons(inputId = "tree",
label = h5("Tree type"),
choices = list("Dendrogram" = "dendrogram",
"Cladogram" = "cladogram" #, "Phylo" = "phylo"
)
)
)
)
),
tabsetPanel(id = "panels_cluster_analysis_G", type = "pills",
tabPanel(title = "Heatmap", value = "panel_heatmap",
br(),
fluidRow(
column(width = 12, align = "center", plotlyOutput(outputId = "heatmap", height = "800px")
)
)
),
tabPanel(title = "Tree", value = "panel_tree",
br(),
fluidRow(
column(width = 12,
plotOutput(outputId = "dendrogram",
height = "700px"
)
)
)
),
tabPanel(title = "Cutree", value = "panel_cutree",
br(),
fluidRow(
column(width = 12, align = "center", plotlyOutput(outputId = "cutree", height = "600px")
)
)
),
tabPanel(title = "Silhouette Plot", value = "panel_silhouette_G",
br(),
fluidRow(
column(width = 12, plotOutput(outputId = "siltabplot_G", height ="550px")
)
)
),
tabPanel(title = "Geoplot", value = "panel_geoplot_G",
br(),
fluidRow(
column(width = 2,
colourpicker::colourInput(inputId = "Colours_G_1", label = h5("Shapefile n° 1"), value = "#D6FFD6", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_G_1", label = "Polygon name"),
br(),
conditionalPanel(condition = "input.shapefiles == 2 | input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_G_2", label = h5("Shapefile n° 2"), value = "#E0F7FF", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_G_2", label = "Polygon name")
),
br(),
conditionalPanel(condition = "input.shapefiles == 3",
colourpicker::colourInput(inputId = "Colours_G_3", label = h5("Shapefile n° 3"), value = "#FFFFF0", allowTransparent = TRUE),
checkboxInput(inputId = "polygon_name_G_3", label = "Polygon name")
),
br(),
br(),
checkboxInput(inputId = "google_G",
label = h5(icon(name = "map"), "Google Maps"),
width = "100%"),
br(),
conditionalPanel(condition = "input.google_G == true",
textInput(inputId = "API_key_G",
label = h5("Google Maps API key"),
value = "")
)
),
column(width = 10,
conditionalPanel(condition = "input.google_G == false",
plotlyOutput(outputId = "geoplot_G",
width = "900px",
height = "820px"),
align = "center"
),
conditionalPanel(condition = "input.google_G == true && input.API_key_G",
google_mapOutput(outputId = "google_map_G", height = "650px")
)
)
)
)
)
)
)
)
) # parentesi di chiusura pannello output principali (colonna larghezza 10)
)
), # parentesi chiusura fluidRow contenente pannello input comandi e pannello output
br(),
fluidRow(
column(width = 4, offset = 8,
textOutput("current_time")
)
)
)
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