inst/ShinyApp/ui.R
In openefsa/abstractScreening: Screening of Abstracts
shinyUI(
navbarPage("Automatic abstract screening",
tabPanel("Selecting Data", titlePanel("Selecting the data"),
fluidRow(sidebarPanel(
radioButtons("type", label = "Abstract or Full Text Screening", choices = list("Abstract", "Full text"),selected = "Abstract"),
radioButtons("sep", label = "Select delimiter", choices = list("Comma", "Tab"),selected = "Comma"),
fileInput('file','Upload file with abstract and indicator of relevance'),
conditionalPanel(
condition = "input.type == 'Full text'",
fileInput("files_full", "Choose pdf files of the full texts", multiple=TRUE, accept = c(".pdf",".PDF"))),
selectInput("selection", "Choose an input space:",
choices = c("TDM","TFIDF","bigram","trigram","topics")),
conditionalPanel(
condition = "input.selection == 'topics'",
numericInput("nr_topics", label="Select number of topics", 30, min = 10, max = 60)),
sliderInput("remove_percentage","Select percentage of data you want to keep after removing sparse terms", min = 80, max = 100, value = 95),
sliderInput("train_percentage","Select percentage of data you want to use in training set", min = 1, max = 100, value = 50),
h5("Show some output of the uploaded data"),
checkboxInput('show', 'Show first lines of the uploaded data and final input space', value = FALSE, width = NULL),
conditionalPanel(
condition = "input.selection == 'topics'",
checkboxInput('show2', 'Show info on topics', value = FALSE, width = NULL)
),
checkboxInput('make_wordcloud', 'Create a word cloud', value = FALSE, width = NULL),
uiOutput("wordcloud_options")),
mainPanel(h1("Summaries of the uploaded data can be presented here"),
#uiOutput("relevance_title"),
#verbatimTextOutput("relevance1"),
plotOutput("relevance_plot"),
uiOutput("summary1_title"),
DT::dataTableOutput("summary"),
uiOutput("summary2_title"),
tableOutput("info_topics"),
#tableOutput("summary_topics"),
uiOutput("wordcloud_title"),
plotOutput("plot")))),
tabPanel("Ensemble Classifier", titlePanel("Building an ensemble"),
sidebarPanel(
checkboxInput("SVM_Linear", "Include SVM Linear", value=FALSE),
uiOutput("SVM_Linear_Imbalance") ,
uiOutput("SVM_Linear_Grid"),
checkboxInput("SVM_Polynomial", "Include SVM Polynomial", value=FALSE),
uiOutput("SVM_Poly_Imbalance") ,
uiOutput("SVM_Poly_Grid"),
checkboxInput("SVM_Radial", "Include SVM Radial", value=FALSE),
uiOutput("SVM_Radial_Imbalance") ,
uiOutput("SVM_Radial_Grid"),
checkboxInput("GBM", "Include GBM", value=FALSE),
uiOutput("GBM_Imbalance") ,
uiOutput("GBM_Grid"),
checkboxInput("NN", "Include NN", value=FALSE),
uiOutput("NN_Imbalance") ,
uiOutput("NN_Grid"),
checkboxInput("RF", "Include RF", value=FALSE),
uiOutput("RF_Imbalance") ,
actionButton("build_ensemble", "Build classifiers") ,
actionButton("build_ensemble2", "Construct ensemble"),
uiOutput("Generate_Rdata")
),
mainPanel(h1("Overview of performance measures for the individual classifiers"),
DT::dataTableOutput("mytable"),
h1("Plot of the performance of the individual models in the ensemble"),
plotOutput("plot_ensemble_comparison"),
# h1("Performance of the total ensemble (all classifiers)"),
# verbatimTextOutput("fitted_ensemble3"),
h1("Performance of the selected ensemble (selected classifiers in table)"),
verbatimTextOutput('x4')
)),
tabPanel("Predictions on New Data", titlePanel("Determine relevance of new abstracts"),
fluidRow(
sidebarPanel(
fileInput('f1', 'Upload previous model in .Rdata format', accept=c('.RData, .Rds')),
fileInput('new_data','Upload your new data here'),
downloadButton('downloadData', 'Download predictions')
),
mainPanel(
uiOutput("title_ensemble1"),
verbatimTextOutput('datastr'),
uiOutput("title_ensemble2"),
plotOutput("plot_uploaded_ensemble"),
verbatimTextOutput('performance_newdata')))),
navbarMenu("Other options",
# tabPanel("Full text screening", titlePanel("Upload the pdf files of the full texts"),
# fluidRow(sidebarPanel( fileInput("files_full", "Choose pdf files of the full texts", multiple=TRUE, accept = c(".pdf",".PDF"))
#
# ),
# mainPanel(h1("Summaries of the uploaded data can be presented here"),
# verbatimTextOutput("test_full")
# ))),
#
# "----",
"Individual Classifiers",
tabPanel("SVM", titlePanel("SVM Classifier"),
sidebarPanel(radioButtons("kernel", label = "Select kernel", choices = list("Linear", "Polynomial","Radial"),selected = "Linear"),
radioButtons("imbalanced_solution", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
#checkboxInput('smote', 'Perform SMOTE sampling (in case of imbalanced data)', value = FALSE, width = NULL),
checkboxInput('tune_svm', 'Would you like to tune parameters? (if not selected, default values are used)', value = FALSE, width = NULL),
uiOutput("tuning_method_svm"),
uiOutput("tuning_method_options_svm"),
uiOutput("tuning_metric_svm"),
actionButton("build", "Build classifier") ,
checkboxInput("show_overall_summary", "Summarize performance measures for distinct SVM options", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_svm2"),
tableOutput("overall_summary")
)),
tabPanel("Gradient Boosting", titlePanel("Gradient Boosting Machine Classifier"),
sidebarPanel(
radioButtons("imbalanced_solution_gbm", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
#checkboxInput('smote_gbm', 'Perform SMOTE sampling (in case of imbalanced data)', value = FALSE, width = NULL),
checkboxInput('tune_gbm', 'Would you like to tune parameters? (if not selected, default values are used)', value = FALSE, width = NULL),
uiOutput("tuning_method_gbm"),
uiOutput("tuning_method_options_gbm"),
uiOutput("tuning_metric_gbm"),
actionButton("build_gbm", "Build classifier") ,
checkboxInput("show_overall_summary_gbm", "Summarize performance measures", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_gbm"),
tableOutput("overall_summary_gbm")
)),
tabPanel("Random Forest", titlePanel("Random Forest Classifier"),
sidebarPanel(
radioButtons("imbalanced_solution_rf", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
actionButton("build_rf", "Build classifier") ,
checkboxInput("show_overall_summary_rf", "Summarize performance measures", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_rf"),
tableOutput("overall_summary_rf")
)),
tabPanel("Neural Network", titlePanel("Neural Network Classifier"),
sidebarPanel(
radioButtons("imbalanced_solution_nn", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
checkboxInput('tune_nn', 'Would you like to tune parameters? (if not selected, default values are used)', value = FALSE, width = NULL),
uiOutput("tuning_method_nn"),
uiOutput("tuning_method_options_nn"),
uiOutput("tuning_metric_nn"),
actionButton("build_nn", "Build classifier") ,
checkboxInput("show_overall_summary_nn", "Summarize performance measures", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_nn"),
tableOutput("overall_summary_nn")
)),
"----",
"Data extraction tools",
tabPanel("Data extraction", titlePanel("Data extraction"),
sidebarPanel(
textInput("words_selection", 'Which word do you want to search?', value = "outcome", width = NULL, placeholder = NULL),
numericInput("length_sentence", 'How many characters should be shown before and after the querried word?', value = 100),
actionButton("extracts_words", "Extract sentences")
),
mainPanel(
uiOutput("highlightedtext")
)),
tabPanel(a("Press here to go to the demo version of EXACT software", href="http://exactdemo.iit.nrc.ca/intro.php",target="_blank"))
))
)
openefsa/abstractScreening documentation built on May 12, 2019, 10:33 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
shinyUI(
navbarPage("Automatic abstract screening",
tabPanel("Selecting Data", titlePanel("Selecting the data"),
fluidRow(sidebarPanel(
radioButtons("type", label = "Abstract or Full Text Screening", choices = list("Abstract", "Full text"),selected = "Abstract"),
radioButtons("sep", label = "Select delimiter", choices = list("Comma", "Tab"),selected = "Comma"),
fileInput('file','Upload file with abstract and indicator of relevance'),
conditionalPanel(
condition = "input.type == 'Full text'",
fileInput("files_full", "Choose pdf files of the full texts", multiple=TRUE, accept = c(".pdf",".PDF"))),
selectInput("selection", "Choose an input space:",
choices = c("TDM","TFIDF","bigram","trigram","topics")),
conditionalPanel(
condition = "input.selection == 'topics'",
numericInput("nr_topics", label="Select number of topics", 30, min = 10, max = 60)),
sliderInput("remove_percentage","Select percentage of data you want to keep after removing sparse terms", min = 80, max = 100, value = 95),
sliderInput("train_percentage","Select percentage of data you want to use in training set", min = 1, max = 100, value = 50),
h5("Show some output of the uploaded data"),
checkboxInput('show', 'Show first lines of the uploaded data and final input space', value = FALSE, width = NULL),
conditionalPanel(
condition = "input.selection == 'topics'",
checkboxInput('show2', 'Show info on topics', value = FALSE, width = NULL)
),
checkboxInput('make_wordcloud', 'Create a word cloud', value = FALSE, width = NULL),
uiOutput("wordcloud_options")),
mainPanel(h1("Summaries of the uploaded data can be presented here"),
#uiOutput("relevance_title"),
#verbatimTextOutput("relevance1"),
plotOutput("relevance_plot"),
uiOutput("summary1_title"),
DT::dataTableOutput("summary"),
uiOutput("summary2_title"),
tableOutput("info_topics"),
#tableOutput("summary_topics"),
uiOutput("wordcloud_title"),
plotOutput("plot")))),
tabPanel("Ensemble Classifier", titlePanel("Building an ensemble"),
sidebarPanel(
checkboxInput("SVM_Linear", "Include SVM Linear", value=FALSE),
uiOutput("SVM_Linear_Imbalance") ,
uiOutput("SVM_Linear_Grid"),
checkboxInput("SVM_Polynomial", "Include SVM Polynomial", value=FALSE),
uiOutput("SVM_Poly_Imbalance") ,
uiOutput("SVM_Poly_Grid"),
checkboxInput("SVM_Radial", "Include SVM Radial", value=FALSE),
uiOutput("SVM_Radial_Imbalance") ,
uiOutput("SVM_Radial_Grid"),
checkboxInput("GBM", "Include GBM", value=FALSE),
uiOutput("GBM_Imbalance") ,
uiOutput("GBM_Grid"),
checkboxInput("NN", "Include NN", value=FALSE),
uiOutput("NN_Imbalance") ,
uiOutput("NN_Grid"),
checkboxInput("RF", "Include RF", value=FALSE),
uiOutput("RF_Imbalance") ,
actionButton("build_ensemble", "Build classifiers") ,
actionButton("build_ensemble2", "Construct ensemble"),
uiOutput("Generate_Rdata")
),
mainPanel(h1("Overview of performance measures for the individual classifiers"),
DT::dataTableOutput("mytable"),
h1("Plot of the performance of the individual models in the ensemble"),
plotOutput("plot_ensemble_comparison"),
# h1("Performance of the total ensemble (all classifiers)"),
# verbatimTextOutput("fitted_ensemble3"),
h1("Performance of the selected ensemble (selected classifiers in table)"),
verbatimTextOutput('x4')
)),
tabPanel("Predictions on New Data", titlePanel("Determine relevance of new abstracts"),
fluidRow(
sidebarPanel(
fileInput('f1', 'Upload previous model in .Rdata format', accept=c('.RData, .Rds')),
fileInput('new_data','Upload your new data here'),
downloadButton('downloadData', 'Download predictions')
),
mainPanel(
uiOutput("title_ensemble1"),
verbatimTextOutput('datastr'),
uiOutput("title_ensemble2"),
plotOutput("plot_uploaded_ensemble"),
verbatimTextOutput('performance_newdata')))),
navbarMenu("Other options",
# tabPanel("Full text screening", titlePanel("Upload the pdf files of the full texts"),
# fluidRow(sidebarPanel( fileInput("files_full", "Choose pdf files of the full texts", multiple=TRUE, accept = c(".pdf",".PDF"))
#
# ),
# mainPanel(h1("Summaries of the uploaded data can be presented here"),
# verbatimTextOutput("test_full")
# ))),
#
# "----",
"Individual Classifiers",
tabPanel("SVM", titlePanel("SVM Classifier"),
sidebarPanel(radioButtons("kernel", label = "Select kernel", choices = list("Linear", "Polynomial","Radial"),selected = "Linear"),
radioButtons("imbalanced_solution", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
#checkboxInput('smote', 'Perform SMOTE sampling (in case of imbalanced data)', value = FALSE, width = NULL),
checkboxInput('tune_svm', 'Would you like to tune parameters? (if not selected, default values are used)', value = FALSE, width = NULL),
uiOutput("tuning_method_svm"),
uiOutput("tuning_method_options_svm"),
uiOutput("tuning_metric_svm"),
actionButton("build", "Build classifier") ,
checkboxInput("show_overall_summary", "Summarize performance measures for distinct SVM options", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_svm2"),
tableOutput("overall_summary")
)),
tabPanel("Gradient Boosting", titlePanel("Gradient Boosting Machine Classifier"),
sidebarPanel(
radioButtons("imbalanced_solution_gbm", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
#checkboxInput('smote_gbm', 'Perform SMOTE sampling (in case of imbalanced data)', value = FALSE, width = NULL),
checkboxInput('tune_gbm', 'Would you like to tune parameters? (if not selected, default values are used)', value = FALSE, width = NULL),
uiOutput("tuning_method_gbm"),
uiOutput("tuning_method_options_gbm"),
uiOutput("tuning_metric_gbm"),
actionButton("build_gbm", "Build classifier") ,
checkboxInput("show_overall_summary_gbm", "Summarize performance measures", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_gbm"),
tableOutput("overall_summary_gbm")
)),
tabPanel("Random Forest", titlePanel("Random Forest Classifier"),
sidebarPanel(
radioButtons("imbalanced_solution_rf", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
actionButton("build_rf", "Build classifier") ,
checkboxInput("show_overall_summary_rf", "Summarize performance measures", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_rf"),
tableOutput("overall_summary_rf")
)),
tabPanel("Neural Network", titlePanel("Neural Network Classifier"),
sidebarPanel(
radioButtons("imbalanced_solution_nn", label = "Remedial measure for imbalanced dataset", choices = list("None", "SMOTE","ROSE"),selected = "None"),
checkboxInput('tune_nn', 'Would you like to tune parameters? (if not selected, default values are used)', value = FALSE, width = NULL),
uiOutput("tuning_method_nn"),
uiOutput("tuning_method_options_nn"),
uiOutput("tuning_metric_nn"),
actionButton("build_nn", "Build classifier") ,
checkboxInput("show_overall_summary_nn", "Summarize performance measures", value = FALSE, width = NULL)
),
mainPanel(h1("Performance on the test set"),
verbatimTextOutput("fitted_nn"),
tableOutput("overall_summary_nn")
)),
"----",
"Data extraction tools",
tabPanel("Data extraction", titlePanel("Data extraction"),
sidebarPanel(
textInput("words_selection", 'Which word do you want to search?', value = "outcome", width = NULL, placeholder = NULL),
numericInput("length_sentence", 'How many characters should be shown before and after the querried word?', value = 100),
actionButton("extracts_words", "Extract sentences")
),
mainPanel(
uiOutput("highlightedtext")
)),
tabPanel(a("Press here to go to the demo version of EXACT software", href="http://exactdemo.iit.nrc.ca/intro.php",target="_blank"))
))
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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