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Merge pull request #52 from pydemull/dev
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.github/workflows/R-CMD-check.yaml

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matrix:
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config:
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- {os: windows-latest, r: 'release'}
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- {os: ubuntu-latest, r: 'oldrel-1'}
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env:
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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

DESCRIPTION

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@@ -12,35 +12,35 @@ Description: A tool to analyse 'ActiGraph' accelerometer data and to implement
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License: GPL (>= 3)
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Imports:
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assertthat,
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dbplyr (>= 2.1.1),
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dplyr (>= 1.1.0),
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flextable (>= 0.8.2),
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forcats (>= 0.5.2),
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ggplot2 (>= 3.4.0),
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golem (>= 0.3.4),
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hms (>= 1.1.2),
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lubridate (>= 1.8.0),
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magrittr (>= 2.0.3),
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modelr (>= 0.1.9),
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patchwork (>= 1.1.2),
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dbplyr,
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dplyr,
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flextable,
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forcats,
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ggplot2,
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golem,
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hms,
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lubridate,
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magrittr,
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modelr,
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patchwork,
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PhysicalActivity,
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plyr (>= 1.8.7),
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reactable (>= 0.3.0),
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rmarkdown (>= 2.16),
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plyr,
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reactable,
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rmarkdown,
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RSQLite,
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shiny (>= 1.7.2),
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shinycssloaders (>= 1.0.0),
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shinydashboard (>= 0.7.2),
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shinydashboardPlus (>= 2.0.3),
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shinyFeedback (>= 0.4.0),
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shinyjs (>= 2.1.0),
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stringr (>= 1.4.1),
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tidyr (>= 1.2.1),
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shiny,
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shinycssloaders,
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shinydashboard,
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shinydashboardPlus,
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shinyFeedback,
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shinyjs,
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stringr,
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tidyr,
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zoo
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Encoding: UTF-8
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LazyData: true
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Roxygen: list(markdown = TRUE)
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RoxygenNote: 7.2.3
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RoxygenNote: 7.3.2
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URL: https://pydemull.github.io/activAnalyzer/, https://github.com/pydemull/activAnalyzer
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BugReports: https://github.com/pydemull/activAnalyzer/issues
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Suggests:

R/app_server.R

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period_info_12$corr_mets() >= 0,
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period_info_13$corr_mets() >= 0,
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period_info_14$corr_mets() >= 0,
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period_info_15$corr_mets() >= 0,
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period_info_15$corr_mets() >= 0
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)
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# Thanks to https://stackoverflow.com/questions/29661269/increment-by-1-for-every-change-in-column
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# for the code block below
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df_with_computed_metrics$intensity_category <- as.factor(df_with_computed_metrics$intensity_category)
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df_with_computed_metrics$intensity_category_num <- as.numeric(as.character(forcats::fct_recode(df_with_computed_metrics$intensity_category , "0" = "Nonwear", "1" = "SED", "2" = "LPA", "3" = "MVPA")))
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df_with_computed_metrics$intensity_category_num <- as.numeric(as.character(forcats::fct_recode(df_with_computed_metrics$intensity_category, "0" = "Nonwear", "1" = "SED", "2" = "LPA", "3" = "MVPA")))
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df_with_computed_metrics$bout <- cumsum(c(1, as.numeric(diff(df_with_computed_metrics$intensity_category_num))!= 0))
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}
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output$int_dist_analysis_fig1 <- renderPlot({
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results_list()$results_by_day$p_band
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}, width = "auto", height = function(){round(nlevels(as.factor(results_list()$df_with_computed_metrics$date))/3, 0) * 370}
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, res = 100)
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}, width = "auto", height = function(){round(nlevels(as.factor(results_list()$df_with_computed_metrics$date))/3, 0) * 370}, res = 100)
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# Plotting intensity distribution analysis (2)
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output$int_dist_analysis_fig1bis <- renderPlot({
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results_list()$results_by_day$p_log
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}, width = "auto", height = function(){round(nlevels(as.factor(results_list()$df_with_computed_metrics$date))/3, 0) * 370}
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, res = 100)
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}, width = "auto", height = function(){round(nlevels(as.factor(results_list()$df_with_computed_metrics$date))/3, 0) * 370}, res = 100)
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# Showing results summarized over valid days in a table (means)
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output$results_summary_int_dist_means <- reactable::renderReactable({

R/globals.R

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"bin_label",
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"lm",
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"days",
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"."
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".",
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".x"
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))

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