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< small class = "nav-text text-muted me-auto" data-bs-toggle = "tooltip" data-bs-placement = "bottom" title = "" > 25.8.1< / small >
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< li class = "active nav-item" > < a class = "nav-link" href = "../articles/FreesearchR.html" > Get started< / a > < / li >
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< img src = "../logo.png" class = "logo" alt = "" > < h1 > FreesearchR< / h1 >
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< small class = "dont-index" > Source: < a href = "https://github.com/agdamsbo/FreesearchR/blob/v25.8.1/vignettes/FreesearchR.Rmd" class = "external-link" > < code > vignettes/FreesearchR.Rmd< / code > < / a > < / small >
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< div class = "d-none name" > < code > FreesearchR.Rmd< / code > < / div >
< / div >
< div class = "section level2" >
< h2 id = "getting-started-with-freesearchr" > Getting started with < strong > < em > FreesearchR< / em > < / strong > < a class = "anchor" aria-label = "anchor" href = "#getting-started-with-freesearchr" > < / a >
< / h2 >
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< p > Below is a simple walk-trough and basic descriptions on the different
features of the < strong > < em > FreesearchR< / em > < / strong > app.< / p >
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< div class = "section level3" >
< h3 id = "launching" > Launching< a class = "anchor" aria-label = "anchor" href = "#launching" > < / a >
< / h3 >
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< p > The easiest way to get started is to launch < a href = "https://app.freesearchr.org/" class = "external-link" > the onlie version of the app (click
this link)< / a > . Please be aware not to upload sensitive data in this
version as data security can not be guaranteed in this online
environment. The app can easily be run from < em > R< / em > on your own
computer by running the code below (< a href = "https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine" > read
more on running locally here< / a > ):< / p >
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< div class = "sourceCode" id = "cb1" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > require< / a > < / span > < span class = "op" > (< / span > < span class = "st" > < a href = "https://pak.r-lib.org/" class = "external-link" > "pak"< / a > < / span > < span class = "op" > )< / span > < / span >
< span > < span class = "fu" > pak< / span > < span class = "fu" > ::< / span > < span class = "fu" > < a href = "https://pak.r-lib.org/reference/pak.html" class = "external-link" > pak< / a > < / span > < span class = "op" > (< / span > < span class = "st" > "agdamsbo/FreesearchR"< / span > < span class = "op" > )< / span > < / span >
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< span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://github.com/agdamsbo/FreesearchR" class = "external-link" > FreesearchR< / a > < / span > < span class = "op" > )< / span > < / span >
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< span > < span class = "fu" > FreesearchR< / span > < span class = "fu" > ::< / span > < span class = "fu" > < a href = "../reference/launch_FreesearchR.html" > launch_FreesearchR< / a > < / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < / span > < / code > < / pre > < / div >
< p > As a small note, a standalone Windows app version is on the drawing
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board as well, but no time frame is currently available.< / p >
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< / div >
< div class = "section level3" >
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< h3 id = "get-started" > Get started< a class = "anchor" aria-label = "anchor" href = "#get-started" > < / a >
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< / h3 >
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< p > Once in the app, get started by loading your data. You have three
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options available for importing data: file upload, REDCap server export
and local or sample data.< / p >
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< p > After choosing a data source nad importing data, you can preview the
basic data structure and missing observations, set a threshold to filter
data by completeness and further manually specify variables to include
for analyses.< / p >
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< div class = "section level4" >
< h4 id = "file-upload" > File upload< a class = "anchor" aria-label = "anchor" href = "#file-upload" > < / a >
< / h4 >
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< p > Several data file formats are supported for easy import (csv, txt,
xls(x), ods, rds, dta). If importing workbooks (xls(x) or ods), you are
prompted to specify sheet(s) to import. If choosing multiple sheets,
these are automatically merged by common variable(s), so please make
sure that key/ID variables are correctly named identically.< / p >
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< / div >
< div class = "section level4" >
< h4 id = "redcap-server-export" > REDCap server export< a class = "anchor" aria-label = "anchor" href = "#redcap-server-export" > < / a >
< / h4 >
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< p > Export data directly from a REDCap server. You need to first generate
an API-token (< a href = "https://confluence.research.cchmc.org/pages/viewpage.action?pageId=50987698" class = "external-link" > see
these instruction< / a > ) in REDCap. Make sure you have the necessary
rights to do so.< / p >
< p > Please don’
a keyring, as this may compromise data safety. Log in to your REDCap
server and retrieve the token when needed.< / p >
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< p > Type the correct web address of your REDCap server.< / p >
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< p > The module will validate the information and you can click
“Connect”.< / p >
< p > This will unfold options to preview your data dictionary (the main
database metadata), choose fields/variables to download as well as
filtering options.< / p >
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< / div >
< div class = "section level4" >
< h4 id = "local-or-sample-data" > Local or sample data< a class = "anchor" aria-label = "anchor" href = "#local-or-sample-data" > < / a >
< / h4 >
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< p > When opening the online hosted app, you can load some sample data to
try out the app. When running the app locally from < em > R< / em > on your
own computer, you will find all data frames loaded in your environment
here. This extends the possible uses of this app to allow for quick and
easy data insights and code generation.< / p >
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< / div >
< / div >
< div class = "section level3" >
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< h3 id = "prepare" > Prepare< a class = "anchor" aria-label = "anchor" href = "#prepare" > < / a >
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< / h3 >
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< p > This is the panel to prepare data for evaluation and analyses and get
a good overview of your data, check data is classed and formatted
correctly, perform simple modifications and filter data.< / p >
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< div class = "section level4" >
< h4 id = "summary" > Summary< a class = "anchor" aria-label = "anchor" href = "#summary" > < / a >
< / h4 >
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< p > Here, the data variables can be inspected with a simple visualisation
and a few key measures. Also, data filtering is available at two
levels:< / p >
< ul >
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< li > < p > Data type filtering allows to filter by variable < a href = "https://agdamsbo.github.io/FreesearchR/articles/data-types.html" > data
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type< / a > < / p > < / li >
< li > < p > Observations level filtering allow to filter data by
variable< / p > < / li >
< / ul >
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< / div >
< div class = "section level4" >
< h4 id = "modify" > Modify< a class = "anchor" aria-label = "anchor" href = "#modify" > < / a >
< / h4 >
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< p > Re-class, rename, and relabel variables. Subset data, create new
variables and reorder factor levels. Also, compare the modified dataset
to the original and restore the original data.< / p >
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< / div >
< / div >
< div class = "section level3" >
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< h3 id = "evaluate" > Evaluate< a class = "anchor" aria-label = "anchor" href = "#evaluate" > < / a >
< / h3 >
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< p > This panel allows for basic data evaluation.< / p >
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< div class = "section level4" >
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< h4 id = "characteristics" > Characteristics< a class = "anchor" aria-label = "anchor" href = "#characteristics" > < / a >
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< / h4 >
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< p > Create a classical baseline characteristics table with optional data
stratification and comparisons.< / p >
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< / div >
< div class = "section level4" >
< h4 id = "correlation-matrix" > Correlation matrix< a class = "anchor" aria-label = "anchor" href = "#correlation-matrix" > < / a >
< / h4 >
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< p > Visualise variable correlations and get suggestions to exclude highly
correlated variables.< / p >
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< / div >
< / div >
< div class = "section level3" >
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< h3 id = "visuals" > Visuals< a class = "anchor" aria-label = "anchor" href = "#visuals" > < / a >
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< / h3 >
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< p > There are a number of plotting options to visualise different aspects
of the data.< / p >
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< p > Below are the available plot types listed.< / p >
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< table class = "table" >
< colgroup >
< col width = "7%" >
< col width = "14%" >
< col width = "77%" >
< / colgroup >
< thead > < tr class = "header" >
< th align = "left" > Data type< / th >
< th align = "left" > Plot type< / th >
< th align = "left" > Description< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td align = "left" > continuous< / td >
< td align = "left" > Violin plot< / td >
< td align = "left" > A modern alternative to the classic boxplot to
visualise data distribution< / td >
< / tr >
< tr class = "even" >
< td align = "left" > continuous< / td >
< td align = "left" > Scatter plot< / td >
< td align = "left" > A classic way of showing the association between to
variables< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > continuous< / td >
< td align = "left" > Box plot< / td >
< td align = "left" > A classic way to plot data distribution by groups< / td >
< / tr >
< tr class = "even" >
< td align = "left" > dichotomous< / td >
< td align = "left" > Stacked horizontal bars< / td >
< td align = "left" > A classical way of visualising the distribution of an
ordinal scale like the modified Ranking Scale and known as Grotta
bars< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > dichotomous< / td >
< td align = "left" > Violin plot< / td >
< td align = "left" > A modern alternative to the classic boxplot to
visualise data distribution< / td >
< / tr >
< tr class = "even" >
< td align = "left" > dichotomous< / td >
< td align = "left" > Sankey plot< / td >
< td align = "left" > A way of visualising change between groups< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > dichotomous< / td >
< td align = "left" > Box plot< / td >
< td align = "left" > A classic way to plot data distribution by groups< / td >
< / tr >
< tr class = "even" >
< td align = "left" > dichotomous< / td >
< td align = "left" > Euler diagram< / td >
< td align = "left" > Generate area-proportional Euler diagrams to display
set relationships< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > categorical< / td >
< td align = "left" > Stacked horizontal bars< / td >
< td align = "left" > A classical way of visualising the distribution of an
ordinal scale like the modified Ranking Scale and known as Grotta
bars< / td >
< / tr >
< tr class = "even" >
< td align = "left" > categorical< / td >
< td align = "left" > Violin plot< / td >
< td align = "left" > A modern alternative to the classic boxplot to
visualise data distribution< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > categorical< / td >
< td align = "left" > Sankey plot< / td >
< td align = "left" > A way of visualising change between groups< / td >
< / tr >
< tr class = "even" >
< td align = "left" > categorical< / td >
< td align = "left" > Box plot< / td >
< td align = "left" > A classic way to plot data distribution by groups< / td >
< / tr >
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< tr class = "odd" >
< td align = "left" > categorical< / td >
< td align = "left" > Euler diagram< / td >
< td align = "left" > Generate area-proportional Euler diagrams to display
set relationships< / td >
< / tr >
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< / tbody >
< / table >
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< p > Export the plots directly from the sidebar with easily adjusted plot
dimensions for your next publication.< / p >
< p > Also copy the code to generate the plot in your own R-environment and
fine tune all the small details.< / p >
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< / div >
< div class = "section level3" >
< h3 id = "regression" > Regression< a class = "anchor" aria-label = "anchor" href = "#regression" > < / a >
< / h3 >
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< p > This section is only intended for very simple explorative analyses
and as a proof-of-concept for now. If you are doing complex regression
analyses you should probably just write the code yourself.< / p >
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< p > Below are the available regression types listed.< / p >
< table class = "table" >
< colgroup >
< col width = "16%" >
< col width = "46%" >
< col width = "15%" >
< col width = "21%" >
< / colgroup >
< thead > < tr class = "header" >
< th align = "left" > Data type< / th >
< th align = "left" > Regression model< / th >
< th align = "left" > Function< / th >
< th align = "left" > Study design< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td align = "left" > continuous< / td >
< td align = "left" > Linear regression model< / td >
< td align = "left" > stats::lm< / td >
< td align = "left" > cross-sectional< / td >
< / tr >
< tr class = "even" >
< td align = "left" > dichotomous< / td >
< td align = "left" > Logistic regression model< / td >
< td align = "left" > stats::glm< / td >
< td align = "left" > cross-sectional< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > categorical< / td >
< td align = "left" > Ordinal logistic regression model< / td >
< td align = "left" > MASS::polr< / td >
< td align = "left" > cross-sectional< / td >
< / tr >
< / tbody >
< / table >
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< div class = "section level4" >
< h4 id = "table" > Table< a class = "anchor" aria-label = "anchor" href = "#table" > < / a >
< / h4 >
< p > Generate simple regression models and get the results in a nice
table. This will also be included in the exported report.< / p >
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< p > This will generate a combined table with both univariate regression
model results for each included variable and a multivariate model with
all variables included for explorative analyses.< / p >
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< / div >
< div class = "section level4" >
< h4 id = "plots" > Plots< a class = "anchor" aria-label = "anchor" href = "#plots" > < / a >
< / h4 >
< p > Plot the coefficients from the regression models in a forest plot.
Choose which model(s) to include.< / p >
< / div >
< div class = "section level4" >
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< h4 id = "model-checks" > Model checks< a class = "anchor" aria-label = "anchor" href = "#model-checks" > < / a >
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< / h4 >
< p > Check model assumptions visually. Supported checks can be chosen.< / p >
< / div >
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< / div >
< div class = "section level3" >
< h3 id = "download" > Download< a class = "anchor" aria-label = "anchor" href = "#download" > < / a >
< / h3 >
< div class = "section level4" >
< h4 id = "report" > Report< a class = "anchor" aria-label = "anchor" href = "#report" > < / a >
< / h4 >
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< p > Download a nice report with baseline characteristics and regression
model results. Choose between MS Word or LibreOffice format.< / p >
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< / div >
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< h4 id = "data" > Data< a class = "anchor" aria-label = "anchor" href = "#data" > < / a >
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< p > Export the modified dataset in different formats.< / p >
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< h4 id = "code" > Code< a class = "anchor" aria-label = "anchor" href = "#code" > < / a >
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< p > See all the code snippets from the different steps in your data
evaluation.< / p >
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< p > Site built with < a href = "https://pkgdown.r-lib.org/" class = "external-link" > pkgdown< / a > 2.1.3.< / p >
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< p > Developed by Andreas Gammelgaard Damsbo. Provided without < strong > any warranty< / strong > .< / p >
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