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ui.R
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library(shinythemes)
shinyUI(fluidPage(theme = shinytheme("journal"),
titlePanel("Twitter Affinity Score Explorer"),
sidebarLayout(
sidebarPanel(
fileInput('affinity', 'Choose AFFINITY file to upload',
accept = c(
'text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv',
'.tsv'
)
),
fileInput('matrix', 'Choose MATRIX file to upload',
accept = c(
'text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv',
'.tsv'
)
),
sliderInput("maxPercentage", "Max. percentage", 50, 100, value = 99, step = 0.5),
sliderInput("percentage", "Min. percentage", 0, 50, value = 20, step = 0.5),
sliderInput("numberFactor", "Num. of components/clusters", 0, 10, value = 3, step = 1),
sliderInput("minAffinity", "Min. affinity", 0, 100, value = 2, step = 1),
sliderInput("treshold", "Min. treshold", 0, 20, value = 0, step = 1),
sliderInput("minCorrelation", "Significant treshold", 0, 5, value = 0.3, step = 0.1),
selectInput("binarydistance", "Methods for distance",
choices = list(
"Jaccard index" = 1,
"Simple matching coefficient of Sokal & Michener " = 2,
"Sokal & Sneath" = 3,
"Rogers & Tanimoto" = 4,
"Dice" = 5,
"Hamann coefficient" = 6,
"Ochiai" = 7,
"Sokal & Sneath" = 8,
"Phi of Pearson" = 9,
"S2 coefficient of Gower & Legendre" = 10
), selected = 1),
selectInput("hclustmethods", "Methods for hclust",
choices = list(
"ward" = "ward",
"single" = "single",
"complete" = "complete",
"average" = "average",
"mcquitty" = "mcquitty",
"median" = "median",
"centroid" = "centroid"
), selected = "ward"),
tags$hr(),
checkboxInput('header', 'Header', TRUE),
tags$hr(),
p('If you want a sample .csv or .tsv file to upload,',
'you can first download the sample',
a(href = 'mtcars.csv', 'mtcars.csv'), 'or',
a(href = 'pressure.tsv', 'pressure.tsv'),
'files, and then tresholdy uploading them.'
)
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Overview", tableOutput('contents'), dataTableOutput('tableAffinity')),
tabPanel("HClust", imageOutput('dendrogram')),
tabPanel("MDS", imageOutput('mds')),
tabPanel("3DHclust", plotOutput('threed')),
tabPanel("FA - Diagram", plotOutput('fact')),
tabPanel("FA - Table", verbatimTextOutput('tableFact')),
tabPanel("Network", plotOutput('graph')),
tabPanel("Bayes LCA", plotOutput('BayesLoadCorelation'), dataTableOutput('tableBayesLoad'), verbatimTextOutput('tableBayes'))
)
)
)
))