inst/shiny-examples/ShinyItemAnalysis/ui/Data.R
In kitdouble/ShinyIRT: Test and Item Analysis via Shiny
Data <- tabPanel("Data",
tabsetPanel(
#------------------------------------------------------------------------------------#
# DATA ####
#------------------------------------------------------------------------------------#
tabPanel("Data",
h3("Data"),
#------------------------------------------------------------------------------------#
# * Data ####
#------------------------------------------------------------------------------------#
p("For demonstration purposes, 20-item dataset", code("GMAT"), "from", code("difNLR"),"
R package is used. On this page, you may select one of five datasets offered by",
code("difNLR"), "and", code("ShinyItemAnalysis"), "packages or you may upload your own
dataset (see below). To return to demonstration dataset, refresh this page in your
browser", strong("(F5)"), "."),
tags$hr(),
#------------------------------------------------------------------------------------#
# * Training datasets ####
#------------------------------------------------------------------------------------#
h4("Training datasets"),
br(),
fluidRow(
column(3,
selectInput(inputId = "dataSelect",
label = "Select dataset",
choices = c("GMAT" = "GMAT_difNLR",
"GMAT2" = "GMAT2_difNLR",
"MSAT-B" = "MSATB_difNLR",
"Medical 100" = "dataMedical_ShinyItemAnalysis",
"HCI" = "HCI_ShinyItemAnalysis"),
selected = "GMAT_difNLR")),
column(9,
uiOutput("data_description"))),
tags$hr(),
#------------------------------------------------------------------------------------#
# * Upload your own datasets ####
#------------------------------------------------------------------------------------#
h4("Upload your own datasets"),
fluidRow(
box(width = 3,
fileInput(inputId = "data",
label = "Choose data (CSV file)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p("Main ", strong("data"), " file should contain responses of individual respondents (rows)
to given items (columns). Data need to be either binary or nominal (e.g. in ABCD format).
Header may contain item names, no row names should be included. In all data sets", strong("header"), "should
be either included or excluded. Columns of dataset are by default renamed to Item and number of particular column.
If you want to keep your own names, check box ", strong("Keep items names"), "below. Missing values in scored
dataset are by default evaluated as 0. If you want to keep them as missing, check box" , strong("Keep missing values"),
"below."),
p(strong("Note: "), "Analysis of ordinal (Likert scale) data is currently not supported. In case of ordinal data, you
may select 'nominal' and include key vector containing of maximum value for each item."))),
fluidRow(
box(width = 12,
column(2,
radioButtons(inputId = "data_type",
label = list("Type of data",
bsButton(inputId = "data_type_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "data_type_info",
title = "Info",
content = "Binary data are of 0-1 form, where 0 is incorrect answer and 1 is correct one. Nominal data may take e.g. ABCD form or Likert scale.",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
choices = c("Binary" = "binary",
"Nominal" = "nominal"),
selected = "nominal")
),
column(2,
radioButtons(inputId = "sep",
label = "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ",")),
column(2,
radioButtons(inputId = "quote",
label = "Quote",
choices = c("None" = "",
"Double Quote" = '"',
"Single Quote" = "'")),
selected = '"'),
column(3,
strong("Data specification"),
checkboxInput(inputId = "header",
label = list("Header",
bsButton(inputId = "header_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "header_info",
title = "Info",
content = "Header including item names should be included/excluded in all datasets.",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
value = TRUE),
checkboxInput(inputId = "itemnam",
label = list("Keep items names",
bsButton(inputId = "itemnam_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "itemnam_info",
title = "Info",
content = "Should item names be preserved?",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
value = FALSE)),
column(3,
strong("Missing values"),
checkboxInput(inputId = "missval",
label = list("Keep missing values",
bsButton(inputId = "missval_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "missval_info",
title = "Info",
content = "Should missing values be preserved?",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
value = FALSE),
conditionalPanel(
condition = "input.missval",
div(id = "inline-left",
textInput(inputId = "data_missingcoding",
label = list( bsButton(inputId = "data_missingcoding_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "data_missingcoding_info",
title = "Info",
content = "Enter encoding of missing values. Values should be seperated with comma, e.g. 9, 99, XXX. ",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
placeholder = "Missing values")),
div(id = "inline-left",
disabled(textInput(inputId = "data_NAcoding",
label = list(bsButton(inputId = "data_NAcoding_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "data_NAcoding_info",
title = "Info",
content = "Enter encoding of not administred values. Values should be seperated with comma, e.g. 9, 99, NA.",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
placeholder = "Not administred values")
)))))),
conditionalPanel(
condition = "input.data_type == 'nominal'",
fluidRow(
box(width = 3,
fileInput(inputId = "key",
label = "Choose key (CSV file)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p("For nominal data, it is necessary to upload ", strong("key"), "of correct answers."),
p(strong("Note: "), "In case of ordinal data, you are advised to include key vector containing of maximum value for each item."))
)
),
fluidRow(
box(width = 3,
fileInput(inputId = "groups",
label = "Choose group (optional)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p(strong("Group"), " is binary vector, where 0 represents reference group
and 1 represents focal group. Its length needs to be the same as number of individual
respondents in the main dataset. If the group is not provided then it won't be possible to run
DIF and DDF detection procedures in ", strong("DIF/Fairness"), " section. Missing values
are not supported for group membership vector and such cases/rows of the data should be removed."))
),
fluidRow(
box(width = 3,
fileInput(inputId = "criterion_variable",
label = "Choose criterion variable (optional)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p(strong("Criterion variable"), " is either discrete or continuous vector (e.g. future study
success or future GPA in case of admission tests) which should be predicted by the measurement.
Its length needs to be the same as number of individual respondents in the main dataset.
If the criterion variable is not provided then it wont be possible to run validity analysis in ",
strong("Predictive validity"), " section on ", strong("Validity"), " page."))
),
div(style = "vertical-align: top; float: right;",
actionButton(inputId = "submitButton",
label = "Upload data",
class = "btn btn-large btn-primary",
icon = icon("upload"),
width = "150px")),
div(style = "vertical-align: top; float: left;",
htmlOutput("checkDataText")),
br(),
br(),
br(),
br(),
div(style = "vertical-align: top; float: left;",
htmlOutput("checkDataColumns01Text")),
div(style = "vertical-align: top; float: right;",
uiOutput("renderdeleteButtonColumns01")),
br(),
div(style = "vertical-align: top; float: left;",
htmlOutput("checkGroupText")),
div(style = "vertical-align: top; float: right;",
uiOutput("renderdeleteButtonGroup")),
br(),
br()),
#------------------------------------------------------------------------------------#
# BASIC SUMMARY ####
#------------------------------------------------------------------------------------#
tabPanel("Basic summary",
#------------------------------------------------------------------------------------#
# * Data exploration ####
#------------------------------------------------------------------------------------#
h3("Basic summary"),
h4("Main dataset"),
textOutput("data_rawdata_dim"),
verbatimTextOutput("data_rawdata_summary"),
h4("Scored test"),
verbatimTextOutput("data_scoreddata_summary"),
h4("Group"),
verbatimTextOutput("data_group_summary"),
h4("Criterion variable"),
verbatimTextOutput("data_criterion_summary"),
br(),
br()
),
#------------------------------------------------------------------------------------#
# DATA EXPLORATION ####
#------------------------------------------------------------------------------------#
tabPanel("Data exploration",
#------------------------------------------------------------------------------------#
# * Data exploration ####
#------------------------------------------------------------------------------------#
h3("Data exploration"),
p("Here you can explore uploaded dataset. Rendering of tables can take some time."),
br(),
#------------------------------------------------------------------------------------#
# * Main dataset ####
#------------------------------------------------------------------------------------#
h4("Main dataset"),
DT::dataTableOutput('headdata'),
br(),
#------------------------------------------------------------------------------------#
# * Key ####
#------------------------------------------------------------------------------------#
h4("Key (correct answers)"),
DT::dataTableOutput('key'),
br(),
#------------------------------------------------------------------------------------#
# * Scored test ####
#------------------------------------------------------------------------------------#
h4("Scored test"),
DT::dataTableOutput('sc01'),
br(),
#------------------------------------------------------------------------------------#
# * Group vector ####
#------------------------------------------------------------------------------------#
h4("Group vector"),
DT::dataTableOutput('group'),
br(),
#------------------------------------------------------------------------------------#
# * Criterion variable vector ####
#------------------------------------------------------------------------------------#
h4("Criterion variable vector"),
DT::dataTableOutput('critvar'),
br(),
br())
))
kitdouble/ShinyIRT documentation built on May 3, 2019, 5:47 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Data <- tabPanel("Data",
tabsetPanel(
#------------------------------------------------------------------------------------#
# DATA ####
#------------------------------------------------------------------------------------#
tabPanel("Data",
h3("Data"),
#------------------------------------------------------------------------------------#
# * Data ####
#------------------------------------------------------------------------------------#
p("For demonstration purposes, 20-item dataset", code("GMAT"), "from", code("difNLR"),"
R package is used. On this page, you may select one of five datasets offered by",
code("difNLR"), "and", code("ShinyItemAnalysis"), "packages or you may upload your own
dataset (see below). To return to demonstration dataset, refresh this page in your
browser", strong("(F5)"), "."),
tags$hr(),
#------------------------------------------------------------------------------------#
# * Training datasets ####
#------------------------------------------------------------------------------------#
h4("Training datasets"),
br(),
fluidRow(
column(3,
selectInput(inputId = "dataSelect",
label = "Select dataset",
choices = c("GMAT" = "GMAT_difNLR",
"GMAT2" = "GMAT2_difNLR",
"MSAT-B" = "MSATB_difNLR",
"Medical 100" = "dataMedical_ShinyItemAnalysis",
"HCI" = "HCI_ShinyItemAnalysis"),
selected = "GMAT_difNLR")),
column(9,
uiOutput("data_description"))),
tags$hr(),
#------------------------------------------------------------------------------------#
# * Upload your own datasets ####
#------------------------------------------------------------------------------------#
h4("Upload your own datasets"),
fluidRow(
box(width = 3,
fileInput(inputId = "data",
label = "Choose data (CSV file)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p("Main ", strong("data"), " file should contain responses of individual respondents (rows)
to given items (columns). Data need to be either binary or nominal (e.g. in ABCD format).
Header may contain item names, no row names should be included. In all data sets", strong("header"), "should
be either included or excluded. Columns of dataset are by default renamed to Item and number of particular column.
If you want to keep your own names, check box ", strong("Keep items names"), "below. Missing values in scored
dataset are by default evaluated as 0. If you want to keep them as missing, check box" , strong("Keep missing values"),
"below."),
p(strong("Note: "), "Analysis of ordinal (Likert scale) data is currently not supported. In case of ordinal data, you
may select 'nominal' and include key vector containing of maximum value for each item."))),
fluidRow(
box(width = 12,
column(2,
radioButtons(inputId = "data_type",
label = list("Type of data",
bsButton(inputId = "data_type_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "data_type_info",
title = "Info",
content = "Binary data are of 0-1 form, where 0 is incorrect answer and 1 is correct one. Nominal data may take e.g. ABCD form or Likert scale.",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
choices = c("Binary" = "binary",
"Nominal" = "nominal"),
selected = "nominal")
),
column(2,
radioButtons(inputId = "sep",
label = "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ",")),
column(2,
radioButtons(inputId = "quote",
label = "Quote",
choices = c("None" = "",
"Double Quote" = '"',
"Single Quote" = "'")),
selected = '"'),
column(3,
strong("Data specification"),
checkboxInput(inputId = "header",
label = list("Header",
bsButton(inputId = "header_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "header_info",
title = "Info",
content = "Header including item names should be included/excluded in all datasets.",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
value = TRUE),
checkboxInput(inputId = "itemnam",
label = list("Keep items names",
bsButton(inputId = "itemnam_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "itemnam_info",
title = "Info",
content = "Should item names be preserved?",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
value = FALSE)),
column(3,
strong("Missing values"),
checkboxInput(inputId = "missval",
label = list("Keep missing values",
bsButton(inputId = "missval_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "missval_info",
title = "Info",
content = "Should missing values be preserved?",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
value = FALSE),
conditionalPanel(
condition = "input.missval",
div(id = "inline-left",
textInput(inputId = "data_missingcoding",
label = list( bsButton(inputId = "data_missingcoding_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "data_missingcoding_info",
title = "Info",
content = "Enter encoding of missing values. Values should be seperated with comma, e.g. 9, 99, XXX. ",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
placeholder = "Missing values")),
div(id = "inline-left",
disabled(textInput(inputId = "data_NAcoding",
label = list(bsButton(inputId = "data_NAcoding_info",
label = "",
icon = icon("info"),
style = "info",
size = "extra-small"),
bsPopover(id = "data_NAcoding_info",
title = "Info",
content = "Enter encoding of not administred values. Values should be seperated with comma, e.g. 9, 99, NA.",
placement = "right",
trigger = "hover",
options = list(container = "body"))),
placeholder = "Not administred values")
)))))),
conditionalPanel(
condition = "input.data_type == 'nominal'",
fluidRow(
box(width = 3,
fileInput(inputId = "key",
label = "Choose key (CSV file)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p("For nominal data, it is necessary to upload ", strong("key"), "of correct answers."),
p(strong("Note: "), "In case of ordinal data, you are advised to include key vector containing of maximum value for each item."))
)
),
fluidRow(
box(width = 3,
fileInput(inputId = "groups",
label = "Choose group (optional)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p(strong("Group"), " is binary vector, where 0 represents reference group
and 1 represents focal group. Its length needs to be the same as number of individual
respondents in the main dataset. If the group is not provided then it won't be possible to run
DIF and DDF detection procedures in ", strong("DIF/Fairness"), " section. Missing values
are not supported for group membership vector and such cases/rows of the data should be removed."))
),
fluidRow(
box(width = 3,
fileInput(inputId = "criterion_variable",
label = "Choose criterion variable (optional)",
accept = c("text/csv",
"text/comma-separated-values",
"text/tab-separated-values",
"text/plain",
".csv",
".tsv"))),
column(9,
p(strong("Criterion variable"), " is either discrete or continuous vector (e.g. future study
success or future GPA in case of admission tests) which should be predicted by the measurement.
Its length needs to be the same as number of individual respondents in the main dataset.
If the criterion variable is not provided then it wont be possible to run validity analysis in ",
strong("Predictive validity"), " section on ", strong("Validity"), " page."))
),
div(style = "vertical-align: top; float: right;",
actionButton(inputId = "submitButton",
label = "Upload data",
class = "btn btn-large btn-primary",
icon = icon("upload"),
width = "150px")),
div(style = "vertical-align: top; float: left;",
htmlOutput("checkDataText")),
br(),
br(),
br(),
br(),
div(style = "vertical-align: top; float: left;",
htmlOutput("checkDataColumns01Text")),
div(style = "vertical-align: top; float: right;",
uiOutput("renderdeleteButtonColumns01")),
br(),
div(style = "vertical-align: top; float: left;",
htmlOutput("checkGroupText")),
div(style = "vertical-align: top; float: right;",
uiOutput("renderdeleteButtonGroup")),
br(),
br()),
#------------------------------------------------------------------------------------#
# BASIC SUMMARY ####
#------------------------------------------------------------------------------------#
tabPanel("Basic summary",
#------------------------------------------------------------------------------------#
# * Data exploration ####
#------------------------------------------------------------------------------------#
h3("Basic summary"),
h4("Main dataset"),
textOutput("data_rawdata_dim"),
verbatimTextOutput("data_rawdata_summary"),
h4("Scored test"),
verbatimTextOutput("data_scoreddata_summary"),
h4("Group"),
verbatimTextOutput("data_group_summary"),
h4("Criterion variable"),
verbatimTextOutput("data_criterion_summary"),
br(),
br()
),
#------------------------------------------------------------------------------------#
# DATA EXPLORATION ####
#------------------------------------------------------------------------------------#
tabPanel("Data exploration",
#------------------------------------------------------------------------------------#
# * Data exploration ####
#------------------------------------------------------------------------------------#
h3("Data exploration"),
p("Here you can explore uploaded dataset. Rendering of tables can take some time."),
br(),
#------------------------------------------------------------------------------------#
# * Main dataset ####
#------------------------------------------------------------------------------------#
h4("Main dataset"),
DT::dataTableOutput('headdata'),
br(),
#------------------------------------------------------------------------------------#
# * Key ####
#------------------------------------------------------------------------------------#
h4("Key (correct answers)"),
DT::dataTableOutput('key'),
br(),
#------------------------------------------------------------------------------------#
# * Scored test ####
#------------------------------------------------------------------------------------#
h4("Scored test"),
DT::dataTableOutput('sc01'),
br(),
#------------------------------------------------------------------------------------#
# * Group vector ####
#------------------------------------------------------------------------------------#
h4("Group vector"),
DT::dataTableOutput('group'),
br(),
#------------------------------------------------------------------------------------#
# * Criterion variable vector ####
#------------------------------------------------------------------------------------#
h4("Criterion variable vector"),
DT::dataTableOutput('critvar'),
br(),
br())
))
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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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