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ui.R
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ui.R
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library('shiny')
library('shinythemes')
library('shinycssloaders')
source("source/dataUploadUI.R", local = TRUE)
#source("source/downloadPlotUI.R", local = TRUE)
source("source/paletteColours.R", local = TRUE)
ui <- fluidPage ( theme = shinytheme("paper"),
# CSS, fixes palette picker (//github.com/gabrifc/raincloud-shiny/issues/12)
tags$style(".bootstrap-select .dropdown-menu li a span.text {width: 100%;}
#downloadPlot, #downloadZip {margin-top: 25px}"),
# Application title
titlePanel("Raincloud-shiny"),
# Sidebar
sidebarLayout(
sidebarPanel(
tabsetPanel(type = "pills",
tabPanel("Data",
column(12,
hr()),
dataUploadUI("rainCloud", label = "File input"),
column(12,
uiOutput('DataFilterColumnsUI')),
column(12,
hr())),
tabPanel("Plot Options",
hr(),
tabsetPanel(type = "pills",
tabPanel("General Options",
column(12,
h4("Size")),
column(6,
numericInput("height",
label = h5("Plot Height"),
value = 600)
),
column(6,
numericInput("width",
label = h5("Plot Width"),
value = 600)
),
column(6,
checkboxInput("plotLegend",
"Show Legend",
FALSE)
),
column(6,
checkboxInput("plotFlip",
"Make it rain! (Flip the Axis)",
FALSE)
),
column(6,
checkboxInput("plotMajorGrid",
"Plot Major Grid",
FALSE)
),
column(6,
conditionalPanel(
condition = 'input.plotMajorGrid == true',
checkboxInput("plotMinorGrid",
"Plot Minor Grid",
FALSE))
),
column(12,
h4('Extra Horizontal Line')),
column(12,
checkboxInput("horizontalLine",
"Show Horizontal Line",
FALSE)),
conditionalPanel(
condition = 'input.horizontalLine == true',
column(6,
numericInput("horizontalLinePosition",
label = h5("Position"),
value = 100)
),
column(6,
selectInput("horizontalLineType",
label = h5("Linetype"),
choices = list(
"solid",
"dashed",
"dotted",
"dotdash"
),
selected = "dashed")
),
column(6,
sliderInput("horizontalLineSize",
label = h5("Line Size"),
min = 0,
max = 2,
value = 0.2,
step = 0.1)
),
column(6,
sliderInput("horizontalLineAlpha",
label = h5("Transparency"),
min = 0,
max = 1,
value = 1,
step = 0.05)
)),
column(12,
hr())),
tabPanel("Theme and colors",
column(12,
selectInput("plotTheme",
label = h5("Theme"),
choices = list(
"Default (Grey)" = "theme_grey()",
"Black & White" = "theme_bw()",
"Linedraw" = "theme_linedraw()",
"Light" = "theme_light()",
"Dark" = "theme_dark()",
"Minimal" = "theme_minimal()",
"Classic" = "theme_classic()",
"Void" = "theme_void()",
"Cowplot" = "theme_cowplot()"
),
selected = "theme_cowplot()")),
column(12,
pickerInput(
inputId = "plotPalette",
label = h5("Palette"),
choices = colors_pal,
selected = "Set1",
width = "100%",
choicesOpt = list(
content = sprintf(
"<div style='width:100%%;padding:2px;border-radius:4px;background:%s;color:%s'>%s</div>",
unname(background_pals),
colortext_pals,
names(background_pals)
)
)
)),
column(12,
hr())),
tabPanel("Titles",
column(12,
textInput("plotTitle",
label = h5("Main Plot Title"),
value = "Main Plot Title")),
column(6,
textInput("xAxisTitle",
label = h5("x Axis Title"),
value = "x Axis Title")
),
column(6,
textInput("yAxisTitle",
label = h5("y Axis Title"),
value = "y Axis Title")
),
column(6,
numericInput("titleFontSize",
label = h5("Main Title Font Size"),
value = 20,
min = 0)),
column(6,
numericInput("axisLabelFontSize",
label = h5("Axis Title Font Size"),
value = 15,
min = 0)),
column(12,
hr())),
tabPanel("Scale",
column(6,
numericInput("scaleFontSize",
label = h5("Scale Text Font Size"),
value = 15,
min = 0)),
column(6,
numericInput("xAxisAngle",
label = h5("X Axis Scale Angle"),
value = 0,
min = 0,
max = 360)),
column(6,
selectInput("xAxishjust",
label = h5("Horizontal Scale Justification"),
choices = list(
"Left" = 0,
"Middle" = 0.5,
"Right" = 1),
selected = 0)),
column(6,
selectInput("xAxisvjust",
label = h5("Vertical Scale Justification"),
choices = list(
"Top" = 0,
"Middle" = 0.5,
"Bottom" = 1),
selected = 0)),
column(12,
checkboxInput("autoScale",
"Automatic Scale Limits",
TRUE)),
conditionalPanel(
condition = "input.autoScale == false",
uiOutput("scaleLimitsUI")
),
column(12,
hr())))),
# column(12,
# hr())))),
tabPanel("Dots",
column(12,
hr()),
column(12,
h4("Data Points")),
column(12,
checkboxInput("plotDots",
"Plot Data Points",
TRUE)),
conditionalPanel(
condition = 'input.plotDots == true',
column(12,
selectInput("dotColumnType",
label = h5("Column Type"),
choices = list(
"Jitter" = 'jitterDots',
"Beeswarm" = 'beeswarm'
),
selected = 'jitterDots')),
column(6,
numericInput("dotSize",
label = h5("Dot Size"),
value = 2)
),
column(6,
selectInput("dotShape",
label = h5("Dot Shape"),
choices = list(
"Empty Dot" = 1,
"Filled Dot" = 16,
"Square" = 15,
"Triangle" = 17),
selected = 16)
),
column(6,
sliderInput("dotsWidth",
label = h5("Plot Width"),
min = 0,
max = 0.5,
value = 0.15,
step = 0.05)),
column(6,
sliderInput("dotAlpha",
label = h5("Transparency"),
min = 0,
max = 1,
value = 1,
step = 0.05))),
column(12,
hr())),
tabPanel("Violin",
column(12,
hr()),
column(12,
h4("Violin Plots")),
column(12,
checkboxInput("plotViolins",
"Plot Violins",
TRUE)),
conditionalPanel(
condition = 'input.plotViolins == true',
column(6,
selectInput("violinType",
label = h5("Type of Violin"),
choices = list(
"Full Violin" = "geom_violin",
"Half Violin" = "geom_flat_violin"
),
selected = "geom_flat_violin")
),
column(6,
sliderInput("violinNudge",
label = h5("Center Offset"),
min = 0,
max = 0.5,
value = 0.20,
step = 0.05)
),
column(6,
selectInput("violinScale",
label = h5("Scale of the violin"),
choices = list(
"Same Area" = "area",
"Maximum Width" = "width"
),
selected = "width")
),
column(6,
numericInput("violinAdjust",
label = h5("Bandwidth Adjustement"),
value = 2,
min = 1)
),
column(6,
checkboxInput("violinTrim",
"Trim Edges to Data Points",
TRUE),
conditionalPanel(
condition =' input.violinType == "geom_violin"',
checkboxInput("violinQuantiles",
"Draw 50% Quantile",
TRUE)
)
),
column(6,
sliderInput("violinAlpha",
label = h5("Transparency"),
min = 0,
max = 1,
value = 0.6,
step = 0.05)
)
),
column(12,
hr())),
tabPanel("Boxplot",
column(12,
hr()),
column(12,
h4("Boxplots")),
column(12,
checkboxInput("boxPlots",
"Plot Boxplots",
TRUE)
),
conditionalPanel(
condition = 'input.boxPlots == true',
column(6,
checkboxInput("boxplotNotch",
"Add Notch",
FALSE),
checkboxInput("boxplotBoxWidth",
"Width Proportional to Data",
FALSE),
checkboxInput("boxplotOutliers",
"Plot Outliers",
FALSE)
),
## I don't like this one.
# column(6,
# selectInput("boxplotOutliers",
# label = h5("Show Outliers"),
# choices = list(
# "Yes" = 16,
# "No" = NA
# ),
# selected = NA)),
column(6,
sliderInput("boxplotWidth",
label = h5("Boxplot Width"),
min = 0,
max = 0.5,
value = 0.1,
step = 0.05)
),
column(6,
sliderInput("boxplotNudge",
label = h5("Center Offset"),
min = 0,
max = 0.5,
value = 0.20,
step = 0.05)
),
column(6,
sliderInput("boxplotAlpha",
label = h5("Transparency"),
min = 0,
max = 1,
value = 0.3,
step = 0.05)
)),
column(12,
hr())),
tabPanel("Statistics",
column(12,
hr()),
column(12,
h4("Significance")),
column(12,
checkboxInput("statistics",
"Compare the means",
FALSE)
),
## I could do all of that with updateSelectInput probably but I like how this looks.
conditionalPanel(
condition = 'input.statistics == true',
column(6,
selectInput("statsType",
label = h5("Type of Test"),
choices = list(
"Parametric" = "parametric",
"Non-parametric" = "nonParametric"),
selected = "nonParametric")
),
## Parametric
conditionalPanel(
condition = 'input.statsType == "parametric"',
column(6,
checkboxInput("statsTtest",
"Pairwise (T-test)",
FALSE),
checkboxInput("statsAnova",
"Multiple (ANOVA)",
FALSE)
)),
## Non Parametric
conditionalPanel(
condition = 'input.statsType == "nonParametric"',
column(6,
checkboxInput("statsWilcoxon",
"Pairwise (Wilcoxon Test)",
FALSE),
checkboxInput("statsKruskal",
"Multiple (Kruskal-Wallis)",
FALSE))),
div(class="clearfix"),
conditionalPanel(
## Pairwise
condition = '(input.statsType == "nonParametric" && input.statsWilcoxon == true) || (input.statsType == "parametric" && input.statsTtest == true)',
column(12,
uiOutput("statsCombinationsUI")),
column(12,
selectInput('statsLabelFormat',
label = h5("Pairwise value Format"),
choices = list(
'Significance (stars)' = '..p.signif..',
'p-values' = '..p.format..'
)))
# column(6,
# sliderInput('statsLabelDigits',
# label = h5("P-Value digits"),
# min = 1,
# max = 5,
# value = 3,
# step = 1))
# column(6,
# selectInput(('statsPairwiseCorrection'),
# label = h5("Pairwise Multitest Correction"),
# choices = list(
# 'Holm (1979)' = 'holm',
# 'Hochberg (1988)' = 'hochberg',
# 'Hommel (1988)' = 'hommel',
# 'Bonferroni' = 'bonferroni',
# 'Benjamini & Hochberg (1995) (FDR)' = 'BH',
# 'Benjamini & Yekutieli (2001)'= 'BY',
# 'Tukey' = 'tukey',
# 'None' = 'none'
# ),
# selected = 'BH'))
),
conditionalPanel(
condition = '(input.statsType == "nonParametric" && input.statsKruskal == true) || (input.statsType == "parametric" && input.statsAnova == true)',
column(6,
uiOutput("statsLabelUI")))),
## Mean
column(12,
h4("Mean")),
column(12,
checkboxInput("statsMean",
"Plot mean",
FALSE)
),
conditionalPanel(
## Maybe change to a dropdown menu with options.
condition = "input.statsMean == true",
column(6,
selectInput('statsMeanErrorBars',
label = h5("Add error bars to the mean"),
choices = list(
'None' = 'none',
'95% Confidence Interval' = 'mean_cl_boot',
'Stardard Error' = 'mean_se',
'Standard Deviation' = 'mean_sd'
),
selected = 'none')
),
column(6,
sliderInput("statsMeanWidth",
label = h5("Mean Width"),
min = 0,
max = 1,
value = 0.5,
step = 0.05)
),
column(6,
sliderInput("statsMeanNudge",
label = h5("Center Offset"),
min = 0,
max = 0.5,
value = 0.20,
step = 0.05)
),
column(6,
sliderInput("statsMeanSize",
label = h5("Line Size"),
min = 0,
max = 2,
value = 0.2,
step = 0.1)
)
),
column(12,
hr())),
tabPanel("Templates",
column(12,
hr()),
column(12,
p("Click any of the following template to quickly apply the format to the plot.")),
column(6,
actionButton(inputId = 'template_raincloud',
label = img(id="rainclouds", src="1.png", style="cursor:pointer", width="100%"),
icon = NULL),
tags$caption("Raincloud Plots")),
column(6,
actionButton(inputId = 'template_rainclouds_flipped',
label = img(id="rainclouds_flipped", src="2.png", style="cursor:pointer", width="100%"),
icon = NULL),
tags$caption("Flipped Raincloud Plots")),
column(6,
actionButton(inputId = 'template_data_boxplots',
label = img(id="data_boxplots", src="3.png", style="cursor:pointer", width="100%"),
icon = NULL),
tags$caption("Dots and Boxplots")),
column(6,
actionButton(inputId = 'template_mean_se',
label = img(id="mean_se", src="4.png", style="cursor:pointer", width="100%"),
icon = NULL),
tags$caption("Mean + SE")),
column(6,
actionButton(inputId = 'template_data_violins',
label = img(id="data_violins", src="5.png", style="cursor:pointer", width="100%"),
icon = NULL),
tags$caption("Data points in violins")),
column(6,
actionButton(inputId = 'template_boxplots_violins',
label = img(id="boxplots_violins", src="6.png", style="cursor:pointer", width="100%"),
icon = NULL),
tags$caption("Boxplots in Violins")),
column(12,
hr()))
),
column(12,
h4("Download the plot")),
# downloadPlotUI(id = 'rainCloudDownload',
# label = "Image format",
# buttonLabel = "Download"),
column(12,
p("Select the image format or download a zip file with all the
images, the script and data used to generate the plot.")),
column(4,
selectInput("downloadFormat",
label = "Image format",
choices = list(
"Vectorial" = list(
"pdf" = "pdf",
"svg" = "svg",
"eps" = "eps"
),
"Non-vectorial" = list(
"tiff" = "tiff",
"png" = "png")
),
selected = "pdf")),
column(4,
downloadButton("downloadPlot",
label = "Download Image")),
column(4,
downloadButton('downloadZip',
label = 'Download Zip')),
## Clearfix
tags$div(class = 'clearfix')
),
## The mainPanel output
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Plot",
withSpinner(
plotOutput("rainCloudPlot",
height = "auto"))),
tabPanel("R Code",
column(8,
h4("Plot Code"),
withSpinner(
verbatimTextOutput("rainCloudCode")))),
tabPanel("Processed Data",
verbatimTextOutput("rainCloudDataSummary"),
tableOutput("rainCloudData")),
tabPanel("About",
includeHTML("www/about.html"))
)
)
)
)