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[{"path":"/articles/Simple REDCap workflow.html","id":"working-with-data-from-redcap","dir":"Articles","previous_headings":"","what":"Working with data from REDCap","title":"Simple REDCap workflow","text":"REDCap excellent tool clinical data acquisition storage. widely used world wide standard tool medical research data Denmark. case analysing data directly whole storing data REDCap, API functionality convenient secure, storing (possibly) sensitive data permanently computer. using “events” “repeated instruments” functionality REDCap, data storage format little confusing work , accustomed tidy data. library(REDCapR) provides simple way working data, however, package just exports data standard format.","code":""},{"path":"/articles/Simple REDCap workflow.html","id":"examples","dir":"Articles","previous_headings":"Working with data from REDCap","what":"Examples","title":"Simple REDCap workflow","text":"","code":"library(REDCapR) # Returns the variables \"record_id\" and \"age\". ds_some_vars <- redcap_read( redcap_uri = \"https://redcap.au.dk/api/\", # This is the address for projects at the AU-server token = \"YOUR_API_KEY_GOES_HERE\", fields = c(\"record_id\", \"age\") )$data # Return only records with IDs of 1 and 4 ds_some_rows <- redcap_read( redcap_uri = \"https://redcap.au.dk/api/\", # This is the address for projects at the AU-server token = \"YOUR_API_KEY_GOES_HERE\", records = c(1, 4) )$data"},{"path":"/articles/Toolbox.html","id":"a-toolbox","dir":"Articles","previous_headings":"","what":"A toolbox","title":"Toolbox","text":"toolbox small workshop mix old, worn, well proven tools newcomers. package seen something like . tried collect tools functions packages use regularly addition functions written fill use cases, able find solutions elsewhere.","code":""},{"path":"/articles/Toolbox.html","id":"cpr-intro","dir":"Articles","previous_headings":"A toolbox","what":"CPR manipulations","title":"Toolbox","text":"Note , handled, CPR numbers (social security numbers) handled care considered highly sensitive data. CPR number structured DDMMYY-XXXX, 1st X designating decade birth, last X designate binary gender (biological sex) dependent even/uneven female/male, last digits used modulus calculation verify validity CPR number. Foreigners unidentified persons given temporary CPR numbers including letters. information can found cpr.dk. Note, CPR numbers used examples publicly known non-organic.","code":""},{"path":"/articles/Toolbox.html","id":"age_calc","dir":"Articles","previous_headings":"A toolbox > CPR manipulations","what":"age_calc()","title":"Toolbox","text":"age_calc() function created learning exercise functions similarly lubridate::time_length().","code":"(age <- age_calc(as.Date(\"1945-10-23\"),as.Date(\"2018-09-30\"))) #> [1] 72.93699 trunc(age) #> [1] 72"},{"path":"/articles/Toolbox.html","id":"cpr_check","dir":"Articles","previous_headings":"A toolbox > CPR manipulations","what":"cpr_check()","title":"Toolbox","text":"Checks validity CPR numbers according modulus 11 rule. Note due limitations possible available CPR numbers, rule apply CPR numbers 2007. Including CPR numbers letters gives warning NA, can checked modulus 11 function. used care, see message.","code":"cpr_check(c(\"2310450637\", \"010190-2000\", \"010115-4000\",\"300450-1030\",\"010150-4021\", \"010150-4AA1\")) #> OBS: as per 2007 not all valid CPR numbers apply to modulus 11 rule. #> See the vignette 'Toolbox' #> Warning in matrix(as.numeric(unlist(strsplit(cpr_short, \"\"))), nrow = 10): NAs #> introduced by coercion #> [1] TRUE FALSE FALSE FALSE FALSE NA"},{"path":"/articles/Toolbox.html","id":"cpr_dob","dir":"Articles","previous_headings":"A toolbox > CPR manipulations","what":"cpr_dob()","title":"Toolbox","text":"Extracts date birth (DOB) CPR number. Accounts decade birth. See earlier.","code":"cpr_dob(c(\"2310450637\", \"010190-2000\", \"010115-4000\",\"300450-1030\",\"010150-4021\")) #> [1] \"23-10-1945\" \"01-01-1990\" \"01-01-2015\" \"30-04-1950\" \"01-01-1950\""},{"path":"/articles/Toolbox.html","id":"cpr_female","dir":"Articles","previous_headings":"A toolbox > CPR manipulations","what":"cpr_female()","title":"Toolbox","text":"Gives logical vector whether female gender last digit CPR.","code":"cpr_female(stRoke::cprs[,1]) #> [1] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> [13] TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE #> [25] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE #> [37] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE #> [49] FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE #> [61] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE #> [73] TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE #> [85] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE #> [97] TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE #> [109] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE #> [121] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE #> [145] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE #> [157] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE #> [169] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE #> [181] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE #> [193] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE"},{"path":[]},{"path":"/articles/Toolbox.html","id":"ci_plot","dir":"Articles","previous_headings":"A toolbox > Plotting","what":"ci_plot()","title":"Toolbox","text":"Plots odds ratios 95 % confidence intervals. Performs binary logistic regression outcome factors two (2) levels ordinal logistic regression outcome factors two levels. Mind relevant assumptions. Outputs ggplot element manipulation.","code":"data(talos) talos[,\"mrs_1\"]<-factor(talos[,\"mrs_1\"],ordered=TRUE) ci_plot(ds = talos, x = \"rtreat\", y = \"mrs_1\", vars = c(\"hypertension\",\"diabetes\")) #> Waiting for profiling to be done..."},{"path":"/articles/Toolbox.html","id":"generic_stroke","dir":"Articles","previous_headings":"A toolbox > Plotting","what":"generic_stroke()","title":"Toolbox","text":"learning purposes. Uses annonymized data TALOS trial output Table 1 (gtsummary::tbl_summary()), plotting -called grotta-bars based mRS scores (rankinPlot::grottaBar()) ordinal logistic regression model plot (stRoke::ci_plot()).","code":"generic_stroke(stRoke::talos, \"rtreat\", \"mrs_6\", variables = c(\"hypertension\",\"diabetes\",\"civil\")) #> Waiting for profiling to be done... #> $`Table 1` #> <div id=\"ncpwthmtsm\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\"> #> <style>html { #> font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif; #> } #> #> #ncpwthmtsm .gt_table { #> display: table; #> border-collapse: collapse; #> margin-left: auto; #> margin-right: auto; #> color: #333333; #> font-size: 16px; #> font-weight: normal; #> font-style: normal; #> background-color: #FFFFFF; #> width: auto; #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #A8A8A8; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #A8A8A8; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_heading { #> background-color: #FFFFFF; #> text-align: center; #> border-bottom-color: #FFFFFF; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_caption { #> padding-top: 4px; #> padding-bottom: 4px; #> } #> #> #ncpwthmtsm .gt_title { #> color: #333333; #> font-size: 125%; #> font-weight: initial; #> padding-top: 4px; #> padding-bottom: 4px; #> padding-left: 5px; #> padding-right: 5px; #> border-bottom-color: #FFFFFF; #> border-bottom-width: 0; #> } #> #> #ncpwthmtsm .gt_subtitle { #> color: #333333; #> font-size: 85%; #> font-weight: initial; #> padding-top: 0; #> padding-bottom: 6px; #> padding-left: 5px; #> padding-right: 5px; #> border-top-color: #FFFFFF; #> border-top-width: 0; #> } #> #> #ncpwthmtsm .gt_bottom_border { #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_col_headings { #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_col_heading { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: normal; #> text-transform: inherit; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> vertical-align: bottom; #> padding-top: 5px; #> padding-bottom: 6px; #> padding-left: 5px; #> padding-right: 5px; #> overflow-x: hidden; #> } #> #> #ncpwthmtsm .gt_column_spanner_outer { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: normal; #> text-transform: inherit; #> padding-top: 0; #> padding-bottom: 0; #> padding-left: 4px; #> padding-right: 4px; #> } #> #> #ncpwthmtsm .gt_column_spanner_outer:first-child { #> padding-left: 0; #> } #> #> #ncpwthmtsm .gt_column_spanner_outer:last-child { #> padding-right: 0; #> } #> #> #ncpwthmtsm .gt_column_spanner { #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> vertical-align: bottom; #> padding-top: 5px; #> padding-bottom: 5px; #> overflow-x: hidden; #> display: inline-block; #> width: 100%; #> } #> #> #ncpwthmtsm .gt_group_heading { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> text-transform: inherit; #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> vertical-align: middle; #> text-align: left; #> } #> #> #ncpwthmtsm .gt_empty_group_heading { #> padding: 0.5px; #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> vertical-align: middle; #> } #> #> #ncpwthmtsm .gt_from_md > :first-child { #> margin-top: 0; #> } #> #> #ncpwthmtsm .gt_from_md > :last-child { #> margin-bottom: 0; #> } #> #> #ncpwthmtsm .gt_row { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> margin: 10px; #> border-top-style: solid; #> border-top-width: 1px; #> border-top-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> vertical-align: middle; #> overflow-x: hidden; #> } #> #> #ncpwthmtsm .gt_stub { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> text-transform: inherit; #> border-right-style: solid; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #ncpwthmtsm .gt_stub_row_group { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> text-transform: inherit; #> border-right-style: solid; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> padding-left: 5px; #> padding-right: 5px; #> vertical-align: top; #> } #> #> #ncpwthmtsm .gt_row_group_first td { #> border-top-width: 2px; #> } #> #> #ncpwthmtsm .gt_summary_row { #> color: #333333; #> background-color: #FFFFFF; #> text-transform: inherit; #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #ncpwthmtsm .gt_first_summary_row { #> border-top-style: solid; #> border-top-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_first_summary_row.thick { #> border-top-width: 2px; #> } #> #> #ncpwthmtsm .gt_last_summary_row { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_grand_summary_row { #> color: #333333; #> background-color: #FFFFFF; #> text-transform: inherit; #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #ncpwthmtsm .gt_first_grand_summary_row { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> border-top-style: double; #> border-top-width: 6px; #> border-top-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_striped { #> background-color: rgba(128, 128, 128, 0.05); #> } #> #> #ncpwthmtsm .gt_table_body { #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_footnotes { #> color: #333333; #> background-color: #FFFFFF; #> border-bottom-style: none; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_footnote { #> margin: 0px; #> font-size: 90%; #> padding-left: 4px; #> padding-right: 4px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #ncpwthmtsm .gt_sourcenotes { #> color: #333333; #> background-color: #FFFFFF; #> border-bottom-style: none; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> } #> #> #ncpwthmtsm .gt_sourcenote { #> font-size: 90%; #> padding-top: 4px; #> padding-bottom: 4px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #ncpwthmtsm .gt_left { #> text-align: left; #> } #> #> #ncpwthmtsm .gt_center { #> text-align: center; #> } #> #> #ncpwthmtsm .gt_right { #> text-align: right; #> font-variant-numeric: tabular-nums; #> } #> #> #ncpwthmtsm .gt_font_normal { #> font-weight: normal; #> } #> #> #ncpwthmtsm .gt_font_bold { #> font-weight: bold; #> } #> #> #ncpwthmtsm .gt_font_italic { #> font-style: italic; #> } #> #> #ncpwthmtsm .gt_super { #> font-size: 65%; #> } #> #> #ncpwthmtsm .gt_footnote_marks { #> font-style: italic; #> font-weight: normal; #> font-size: 75%; #> vertical-align: 0.4em; #> } #> #> #ncpwthmtsm .gt_asterisk { #> font-size: 100%; #> vertical-align: 0; #> } #> #> #ncpwthmtsm .gt_indent_1 { #> text-indent: 5px; #> } #> #> #ncpwthmtsm .gt_indent_2 { #> text-indent: 10px; #> } #> #> #ncpwthmtsm .gt_indent_3 { #> text-indent: 15px; #> } #> #> #ncpwthmtsm .gt_indent_4 { #> text-indent: 20px; #> } #> #> #ncpwthmtsm .gt_indent_5 { #> text-indent: 25px; #> } #> <\/style> #> <table class=\"gt_table\"> #> #> <thead class=\"gt_col_headings\"> #> <tr> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic<\/strong><\/th> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 200&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall<\/strong>, N = 200<sup class=\"gt_footnote_marks\">1<\/sup><\/th> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Active&lt;/strong&gt;, N = 79&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Active<\/strong>, N = 79<sup class=\"gt_footnote_marks\">1<\/sup><\/th> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Placebo&lt;/strong&gt;, N = 121&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Placebo<\/strong>, N = 121<sup class=\"gt_footnote_marks\">1<\/sup><\/th> #> <\/tr> #> <\/thead> #> <tbody class=\"gt_table_body\"> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">hypertension<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">101 (50%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">38 (48%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">63 (52%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">diabetes<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">23 (12%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">9 (11%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">14 (12%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">civil<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\"><\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\"><\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\"><\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    alone<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">59 (30%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">22 (28%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">37 (31%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    partner<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">141 (70%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">57 (72%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">84 (69%)<\/td><\/tr> #> <\/tbody> #> #> <tfoot class=\"gt_footnotes\"> #> <tr> #> <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1<\/sup> n (%)<\/td> #> <\/tr> #> <\/tfoot> #> <\/table> #> <\/div> #> #> $`Figure 1` #> #> $`Figure 2`"},{"path":"/articles/Toolbox.html","id":"index_plot","dir":"Articles","previous_headings":"A toolbox > Plotting","what":"index_plot()","title":"Toolbox","text":"Used plotting scores multi dimensional patient test.","code":"index_plot(stRoke::score[score$event==\"A\",])"},{"path":"/articles/Toolbox.html","id":"read_redcap_tables","dir":"Articles","previous_headings":"A toolbox > Plotting","what":"read_redcap_tables()","title":"Toolbox","text":"Reads REDCap data accounting events repeated instruments. Based library(REDCapR) library(agdamsbo/REDCapRITS). Please see vignette REDCap.","code":""},{"path":"/articles/redcap.html","id":"initial-note","dir":"Articles","previous_headings":"","what":"Initial note","title":"Simple REDCap workflow","text":"Please refer book “neuRo-group” thorough guide using R REDCap together data analysis, also notes advices build REDCap database. Note, book work progress goal “completion”.","code":""},{"path":"/articles/redcap.html","id":"working-with-data-from-redcap","dir":"Articles","previous_headings":"","what":"Working with data from REDCap","title":"Simple REDCap workflow","text":"REDCap excellent tool clinical data acquisition storage. widely used world wide standard tool medical research data Denmark. case analysing data directly whole storing data REDCap, API functionality convenient secure, storing (possibly) sensitive data permanently computer. using “events” “repeated instruments” functionality REDCap, data storage format little confusing work , accustomed tidy data. library(REDCapR) provides simple way working data, however, package just exports data standard format.","code":""},{"path":"/articles/redcap.html","id":"examples","dir":"Articles","previous_headings":"Working with data from REDCap","what":"Examples","title":"Simple REDCap workflow","text":"added","code":"library(REDCapR) # Returns the variables \"record_id\" and \"age\". ds_some_vars <- redcap_read( redcap_uri = \"https://redcap.au.dk/api/\", # This is the address for projects at the AU-server token = \"YOUR_API_KEY_GOES_HERE\", fields = c(\"record_id\", \"age\") )$data # Return only records with IDs of 1 and 4 ds_some_rows <- redcap_read( redcap_uri = \"https://redcap.au.dk/api/\", # This is the address for projects at the AU-server token = \"YOUR_API_KEY_GOES_HERE\", records = c(1, 4) )$data"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Andreas Gammelgaard Damsbo. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Damsbo (2023). stRoke: Clinical Stroke Research Toolbox. https://agdamsbo.github.io/stRoke/, https://github.com/agdamsbo/stRoke, https://agdamsbo.github.io/neuRo-group/.","code":"@Manual{, title = {stRoke: Clinical Stroke Research Toolbox}, author = {Andreas Gammelgaard Damsbo}, year = {2023}, note = {https://agdamsbo.github.io/stRoke/, https://github.com/agdamsbo/stRoke, https://agdamsbo.github.io/neuRo-group/}, }"},{"path":"/index.html","id":"stroke-package-","dir":"","previous_headings":"","what":"Clinical Stroke Research Toolbox","title":"Clinical Stroke Research Toolbox","text":"R-toolbox custom functions convenient data management analysis clinical health research teaching. package mainly collected personal use, use beyond encouraged. package migrated functions agdamsbo/daDoctoR-package, new functions added.","code":""},{"path":"/index.html","id":"a-few-notes","dir":"","previous_headings":"","what":"A few notes","title":"Clinical Stroke Research Toolbox","text":"function generic_stroke()can used data(talos) custom data set illustrate classic analysis mRS outcome stroke trials. documenting testing package, used OpenAI’s chatgpt gpttools. chatgpt interesting tool, way perfect, helps tedious tasks. gpttools gptstudio interesting implementations R RStudio.","code":""},{"path":"/index.html","id":"source","dir":"","previous_headings":"","what":"Source","title":"Clinical Stroke Research Toolbox","text":"package shared GitHub, welcome contribute opening issue pull request.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Clinical Stroke Research Toolbox","text":"package can installed GitHub:","code":"remotes::install_github(\"agdamsbo/stRoke\")"},{"path":"/reference/age_calc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate age in years, months, or days — age_calc","title":"Calculate age in years, months, or days — age_calc","text":"Calculate age years, months, days","code":""},{"path":"/reference/age_calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate age in years, months, or days — age_calc","text":"","code":"age_calc(dob, enddate = Sys.Date(), units = \"years\", precise = TRUE)"},{"path":"/reference/age_calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate age in years, months, or days — age_calc","text":"dob Date birth enddate End date age calculation (default Sys.Date()) units Units age calculation (default \"years\"). Can c(\"days\", \"months\", \"years\") precise Option calculate age precisely (default TRUE)","code":""},{"path":"/reference/age_calc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate age in years, months, or days — age_calc","text":"numeric vector length 1","code":""},{"path":"/reference/age_calc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate age in years, months, or days — age_calc","text":"Becker, J.P. (2020). eeptools: R Package Teaching Learning Ecology Evolutionary Biology. Journal Statistical Software, 93(2), 1-27. doi: 10.18637/jss.v093.i02","code":""},{"path":[]},{"path":"/reference/age_calc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate age in years, months, or days — age_calc","text":"","code":"trunc(age_calc(as.Date(\"1945-10-23\"),as.Date(\"2018-09-30\"))) #> [1] 72"},{"path":"/reference/ci_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence interval plot with point estimate — ci_plot","title":"Confidence interval plot with point estimate — ci_plot","text":"Horizontal forest plot point estimate confidence intervals. Includes dichotomous olr, depending number levels \"x\". Title axis labels can added ggplot afterwards.","code":""},{"path":"/reference/ci_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence interval plot with point estimate — ci_plot","text":"","code":"ci_plot(ds, x, y, vars = NULL, dec = 3, lbls = NULL, title = NULL)"},{"path":"/reference/ci_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence interval plot with point estimate — ci_plot","text":"ds data set x text string main exposure variable y text string outcome variable vars variables multivariate analysis. dec Decimals labels lbls Labels variable names title Plot title. Can specified later.","code":""},{"path":"/reference/ci_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence interval plot with point estimate — ci_plot","text":"ggplot element","code":""},{"path":"/reference/ci_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence interval plot with point estimate — ci_plot","text":"","code":"data(talos) talos[,\"mrs_1\"]<-factor(talos[,\"mrs_1\"],ordered=TRUE) ci_plot(ds = talos, x = \"rtreat\", y = \"mrs_1\", vars = c(\"hypertension\",\"diabetes\")) #> Waiting for profiling to be done..."},{"path":"/reference/cpr_check.html","id":null,"dir":"Reference","previous_headings":"","what":"CPR check — cpr_check","title":"CPR check — cpr_check","text":"Checking validity cpr number. Vectorised.","code":""},{"path":"/reference/cpr_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CPR check — cpr_check","text":"","code":"cpr_check(cpr)"},{"path":"/reference/cpr_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CPR check — cpr_check","text":"cpr cpr-numbers ddmmyy\"-.\"xxxx ddmmyyxxxx. Also mixed formatting. Vector data frame column.","code":""},{"path":"/reference/cpr_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CPR check — cpr_check","text":"Logical vector cpr validity","code":""},{"path":"/reference/cpr_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CPR check — cpr_check","text":"","code":"fsd<-c(\"2310450637\", \"010190-2000\", \"010115-4000\",\"300450-1030\",\"010150-4021\") cpr_check(\"2310450637\") #> OBS: as per 2007 not all valid CPR numbers apply to modulus 11 rule. #> See the vignette 'Toolbox' #> [1] TRUE cpr_check(fsd) #> OBS: as per 2007 not all valid CPR numbers apply to modulus 11 rule. #> See the vignette 'Toolbox' #> [1] TRUE FALSE FALSE FALSE FALSE all(cpr_check(fsd)) #> OBS: as per 2007 not all valid CPR numbers apply to modulus 11 rule. #> See the vignette 'Toolbox' #> [1] FALSE"},{"path":"/reference/cpr_dob.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracting date of birth from CPR — cpr_dob","title":"Extracting date of birth from CPR — cpr_dob","text":"easy calculation. handle cprs letters (interim cpr)","code":""},{"path":"/reference/cpr_dob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracting date of birth from CPR — cpr_dob","text":"","code":"cpr_dob(cpr, format = \"%d-%m-%Y\")"},{"path":"/reference/cpr_dob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracting date of birth from CPR — cpr_dob","text":"cpr cpr-numbers ddmmyy\"-.\"xxxx ddmmyyxxxx. Also mixed formatting. Vector data frame column. format character string dob date format. Default \"%d-%m-%Y\".","code":""},{"path":"/reference/cpr_dob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extracting date of birth from CPR — cpr_dob","text":"character vector","code":""},{"path":"/reference/cpr_dob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extracting date of birth from CPR — cpr_dob","text":"","code":"cpr_dob(\"231045-0637\") #> [1] \"23-10-1945\" fsd<-c(\"2310450637\", \"010190-2000\", \"010115-4000\",\"300450-1030\",\"010150-4021\") cpr_dob(fsd) #> [1] \"23-10-1945\" \"01-01-1990\" \"01-01-2015\" \"30-04-1950\" \"01-01-1950\""},{"path":"/reference/cpr_female.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine female sex from CPR — cpr_female","title":"Determine female sex from CPR — cpr_female","text":"Just checking last number string equal .","code":""},{"path":"/reference/cpr_female.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine female sex from CPR — cpr_female","text":"","code":"cpr_female(cpr)"},{"path":"/reference/cpr_female.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine female sex from CPR — cpr_female","text":"cpr Vector. cpr-numbers ddmmyy\"-.\"xxxx ddmmyyxxxx. Also mixed formatting. Vector data frame column.","code":""},{"path":"/reference/cpr_female.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine female sex from CPR — cpr_female","text":"Logical vector","code":""},{"path":"/reference/cpr_female.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine female sex from CPR — cpr_female","text":"","code":"cpr_female(stRoke::cprs[,1]) #> [1] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> [13] TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE #> [25] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE #> [37] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE #> [49] FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE #> [61] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE #> [73] TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE #> [85] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE #> [97] TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE #> [109] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE #> [121] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE #> [145] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE #> [157] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE #> [169] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE #> [181] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE #> [193] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE"},{"path":"/reference/cprs.html","id":null,"dir":"Reference","previous_headings":"","what":"Data frame of 200 cpr numbers — cprs","title":"Data frame of 200 cpr numbers — cprs","text":"just repeated sample 8 synthesized cpr-numbers testing purposes.","code":""},{"path":"/reference/cprs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data frame of 200 cpr numbers — cprs","text":"","code":"data(cprs)"},{"path":"/reference/cprs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data frame of 200 cpr numbers — cprs","text":"data frame 200 rows 1 variable: cpr Mixed format cpr-numbers, characters","code":""},{"path":"/reference/cprs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data frame of 200 cpr numbers — cprs","text":"https://da.wikipedia.org/wiki/231045-0637/","code":""},{"path":"/reference/files_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter files in a folder — files_filter","title":"Filter files in a folder — files_filter","text":"function filters files folder based provided filter.","code":""},{"path":"/reference/files_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter files in a folder — files_filter","text":"","code":"files_filter(folder.path, filter.by, full.names = TRUE)"},{"path":"/reference/files_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter files in a folder — files_filter","text":"folder.path character. Path folder filtered filter.character. Filter applied files full.names logical. Whether return full file names ","code":""},{"path":"/reference/files_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter files in a folder — files_filter","text":"character vector. Filtered files","code":""},{"path":"/reference/files_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter files in a folder — files_filter","text":"","code":"# Gives path to files/folders with \"tests\" in the name in the working directory files_filter(getwd(),\"tests\") #> character(0)"},{"path":"/reference/generic_stroke.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic stroke study outcome — generic_stroke","title":"Generic stroke study outcome — generic_stroke","text":"Includes table 1, grotta bars ordinal logistic regression plot. Please just use function illustration purposes. dos: modify grottaBar include function.","code":""},{"path":"/reference/generic_stroke.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic stroke study outcome — generic_stroke","text":"","code":"generic_stroke(df, group, score, strata = NULL, variables = NULL)"},{"path":"/reference/generic_stroke.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic stroke study outcome — generic_stroke","text":"df Data set data frame group Variable group score Outcome measure variable strata Optional variable stratify variables String variable names include adjusted OLR-analysis","code":""},{"path":"/reference/generic_stroke.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic stroke study outcome — generic_stroke","text":"Returns list three elements","code":""},{"path":"/reference/generic_stroke.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generic stroke study outcome — generic_stroke","text":"","code":"generic_stroke(stRoke::talos, \"rtreat\", \"mrs_6\", variables = c(\"hypertension\",\"diabetes\",\"civil\")) #> Waiting for profiling to be done... #> $`Table 1` #> <div id=\"otptyzevaf\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\"> #> <style>html { #> font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif; 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#> } #> #> #otptyzevaf .gt_grand_summary_row { #> color: #333333; #> background-color: #FFFFFF; #> text-transform: inherit; #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #otptyzevaf .gt_first_grand_summary_row { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> border-top-style: double; #> border-top-width: 6px; #> border-top-color: #D3D3D3; #> } #> #> #otptyzevaf .gt_striped { #> background-color: rgba(128, 128, 128, 0.05); #> } #> #> #otptyzevaf .gt_table_body { #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> } #> #> #otptyzevaf .gt_footnotes { #> color: #333333; #> background-color: #FFFFFF; #> border-bottom-style: none; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> } #> #> #otptyzevaf .gt_footnote { #> margin: 0px; #> font-size: 90%; #> padding-left: 4px; #> padding-right: 4px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #otptyzevaf .gt_sourcenotes { #> color: #333333; #> background-color: #FFFFFF; #> border-bottom-style: none; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> } #> #> #otptyzevaf .gt_sourcenote { #> font-size: 90%; #> padding-top: 4px; #> padding-bottom: 4px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #otptyzevaf .gt_left { #> text-align: left; #> } #> #> #otptyzevaf .gt_center { #> text-align: center; #> } #> #> #otptyzevaf .gt_right { #> text-align: right; #> font-variant-numeric: tabular-nums; #> } #> #> #otptyzevaf .gt_font_normal { #> font-weight: normal; #> } #> #> #otptyzevaf .gt_font_bold { #> font-weight: bold; #> } #> #> #otptyzevaf .gt_font_italic { #> font-style: italic; #> } #> #> #otptyzevaf .gt_super { #> font-size: 65%; #> } #> #> #otptyzevaf .gt_footnote_marks { #> font-style: italic; #> font-weight: normal; #> font-size: 75%; #> vertical-align: 0.4em; #> } #> #> #otptyzevaf .gt_asterisk { #> font-size: 100%; #> vertical-align: 0; #> } #> #> #otptyzevaf .gt_indent_1 { #> text-indent: 5px; #> } #> #> #otptyzevaf .gt_indent_2 { #> text-indent: 10px; #> } #> #> #otptyzevaf .gt_indent_3 { #> text-indent: 15px; #> } #> #> #otptyzevaf .gt_indent_4 { #> text-indent: 20px; #> } #> #> #otptyzevaf .gt_indent_5 { #> text-indent: 25px; #> } #> <\/style> #> <table class=\"gt_table\"> #> #> <thead class=\"gt_col_headings\"> #> <tr> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic<\/strong><\/th> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 200&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall<\/strong>, N = 200<sup class=\"gt_footnote_marks\">1<\/sup><\/th> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Active&lt;/strong&gt;, N = 79&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Active<\/strong>, N = 79<sup class=\"gt_footnote_marks\">1<\/sup><\/th> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Placebo&lt;/strong&gt;, N = 121&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Placebo<\/strong>, N = 121<sup class=\"gt_footnote_marks\">1<\/sup><\/th> #> <\/tr> #> <\/thead> #> <tbody class=\"gt_table_body\"> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">hypertension<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">101 (50%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">38 (48%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">63 (52%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">diabetes<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">23 (12%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">9 (11%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">14 (12%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">civil<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\"><\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\"><\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\"><\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    alone<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">59 (30%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">22 (28%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">37 (31%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    partner<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">141 (70%)<\/td> #> <td headers=\"stat_1\" class=\"gt_row gt_center\">57 (72%)<\/td> #> <td headers=\"stat_2\" class=\"gt_row gt_center\">84 (69%)<\/td><\/tr> #> <\/tbody> #> #> <tfoot class=\"gt_footnotes\"> #> <tr> #> <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1<\/sup> n (%)<\/td> #> <\/tr> #> <\/tfoot> #> <\/table> #> <\/div> #> #> $`Figure 1` #> #> $`Figure 2` #>"},{"path":"/reference/index_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot multidimensional cognitive test scores — index_plot","title":"Plot multidimensional cognitive test scores — index_plot","text":"Plot index scores five dimensional cognitive testing. Includes option facet.","code":""},{"path":"/reference/index_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot multidimensional cognitive test scores — index_plot","text":"","code":"index_plot( ds, id = \"id\", sub_plot = \"_is\", scores = c(\"_is\", \"_lo\", \"_up\", \"_per\"), dom_names = c(\"immediate\", \"visuospatial\", \"verbal\", \"attention\", \"delayed\", \"total\"), facet.by = NULL )"},{"path":"/reference/index_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot multidimensional cognitive test scores — index_plot","text":"ds complete data frame id colname id column. Base colouring sub_plot main outcome scores variable plot scores variables subset plotting. follow standard naming (changed) dom_names domain names axis naming facet.variable base facet_grid ","code":""},{"path":"/reference/index_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot multidimensional cognitive test scores — index_plot","text":"ggplot element","code":""},{"path":"/reference/index_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot multidimensional cognitive test scores — index_plot","text":"","code":"index_plot(stRoke::score[score$event==\"A\",])"},{"path":"/reference/label_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for labels in gtsummary — label_select","title":"Helper function for labels in gtsummary — label_select","text":"Function select labels list label pairs (format: age~\"Age\"). Alternative use attributes, eg library(Hmisc).","code":""},{"path":"/reference/label_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for labels in gtsummary — label_select","text":"","code":"label_select(lst, vec)"},{"path":"/reference/label_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for labels in gtsummary — label_select","text":"lst List variables labels (format: age~\"Age\") vec Vector variables subset list","code":""},{"path":"/reference/label_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for labels in gtsummary — label_select","text":"List labels ordered like vec, formatted like lst","code":""},{"path":"/reference/label_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for labels in gtsummary — label_select","text":"","code":"vars<-c(\"hypertension\", \"diabetes\", \"mrs_1\") labels_all<-list(rtreat~\"Trial treatment\", civil~\"Cohabitation\", diabetes~\"Known diabetes\", hypertension~\"Known hypertension\", mrs_1~\"One month mRS\", mrs_6~\"Six months mRS\", '[Intercept]'~\"Intercept\") stRoke::talos[vars] |> gtsummary::tbl_summary(label = label_select(labels_all,vars)) #> <div id=\"twqogyttqy\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\"> #> <style>html { #> font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif; #> } #> #> #twqogyttqy .gt_table { #> display: table; #> border-collapse: collapse; #> margin-left: auto; #> margin-right: auto; #> color: #333333; #> font-size: 16px; #> font-weight: normal; #> font-style: normal; #> background-color: #FFFFFF; #> width: auto; #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #A8A8A8; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #A8A8A8; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_heading { #> background-color: #FFFFFF; #> text-align: center; #> border-bottom-color: #FFFFFF; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_caption { #> padding-top: 4px; #> padding-bottom: 4px; #> } #> #> #twqogyttqy .gt_title { #> color: #333333; #> font-size: 125%; #> font-weight: initial; #> padding-top: 4px; #> padding-bottom: 4px; #> padding-left: 5px; #> padding-right: 5px; #> border-bottom-color: #FFFFFF; #> border-bottom-width: 0; #> } #> #> #twqogyttqy .gt_subtitle { #> color: #333333; #> font-size: 85%; #> font-weight: initial; #> padding-top: 0; #> padding-bottom: 6px; #> padding-left: 5px; #> padding-right: 5px; #> border-top-color: #FFFFFF; #> border-top-width: 0; #> } #> #> #twqogyttqy .gt_bottom_border { #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_col_headings { #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_col_heading { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: normal; #> text-transform: inherit; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> vertical-align: bottom; #> padding-top: 5px; #> padding-bottom: 6px; #> padding-left: 5px; #> padding-right: 5px; #> overflow-x: hidden; #> } #> #> #twqogyttqy .gt_column_spanner_outer { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: normal; #> text-transform: inherit; #> padding-top: 0; #> padding-bottom: 0; #> padding-left: 4px; #> padding-right: 4px; #> } #> #> #twqogyttqy .gt_column_spanner_outer:first-child { #> padding-left: 0; #> } #> #> #twqogyttqy .gt_column_spanner_outer:last-child { #> padding-right: 0; #> } #> #> #twqogyttqy .gt_column_spanner { #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> vertical-align: bottom; #> padding-top: 5px; #> padding-bottom: 5px; #> overflow-x: hidden; #> display: inline-block; #> width: 100%; #> } #> #> #twqogyttqy .gt_group_heading { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> text-transform: inherit; #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> vertical-align: middle; #> text-align: left; #> } #> #> #twqogyttqy .gt_empty_group_heading { #> padding: 0.5px; #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> vertical-align: middle; #> } #> #> #twqogyttqy .gt_from_md > :first-child { #> margin-top: 0; #> } #> #> #twqogyttqy .gt_from_md > :last-child { #> margin-bottom: 0; #> } #> #> #twqogyttqy .gt_row { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> margin: 10px; #> border-top-style: solid; #> border-top-width: 1px; #> border-top-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 1px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 1px; #> border-right-color: #D3D3D3; #> vertical-align: middle; #> overflow-x: hidden; #> } #> #> #twqogyttqy .gt_stub { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> text-transform: inherit; #> border-right-style: solid; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #twqogyttqy .gt_stub_row_group { #> color: #333333; #> background-color: #FFFFFF; #> font-size: 100%; #> font-weight: initial; #> text-transform: inherit; #> border-right-style: solid; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> padding-left: 5px; #> padding-right: 5px; #> vertical-align: top; #> } #> #> #twqogyttqy .gt_row_group_first td { #> border-top-width: 2px; #> } #> #> #twqogyttqy .gt_summary_row { #> color: #333333; #> background-color: #FFFFFF; #> text-transform: inherit; #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #twqogyttqy .gt_first_summary_row { #> border-top-style: solid; #> border-top-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_first_summary_row.thick { #> border-top-width: 2px; #> } #> #> #twqogyttqy .gt_last_summary_row { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_grand_summary_row { #> color: #333333; #> background-color: #FFFFFF; #> text-transform: inherit; #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #twqogyttqy .gt_first_grand_summary_row { #> padding-top: 8px; #> padding-bottom: 8px; #> padding-left: 5px; #> padding-right: 5px; #> border-top-style: double; #> border-top-width: 6px; #> border-top-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_striped { #> background-color: rgba(128, 128, 128, 0.05); #> } #> #> #twqogyttqy .gt_table_body { #> border-top-style: solid; #> border-top-width: 2px; #> border-top-color: #D3D3D3; #> border-bottom-style: solid; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_footnotes { #> color: #333333; #> background-color: #FFFFFF; #> border-bottom-style: none; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_footnote { #> margin: 0px; #> font-size: 90%; #> padding-left: 4px; #> padding-right: 4px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #twqogyttqy .gt_sourcenotes { #> color: #333333; #> background-color: #FFFFFF; #> border-bottom-style: none; #> border-bottom-width: 2px; #> border-bottom-color: #D3D3D3; #> border-left-style: none; #> border-left-width: 2px; #> border-left-color: #D3D3D3; #> border-right-style: none; #> border-right-width: 2px; #> border-right-color: #D3D3D3; #> } #> #> #twqogyttqy .gt_sourcenote { #> font-size: 90%; #> padding-top: 4px; #> padding-bottom: 4px; #> padding-left: 5px; #> padding-right: 5px; #> } #> #> #twqogyttqy .gt_left { #> text-align: left; #> } #> #> #twqogyttqy .gt_center { #> text-align: center; #> } #> #> #twqogyttqy .gt_right { #> text-align: right; #> font-variant-numeric: tabular-nums; #> } #> #> #twqogyttqy .gt_font_normal { #> font-weight: normal; #> } #> #> #twqogyttqy .gt_font_bold { #> font-weight: bold; #> } #> #> #twqogyttqy .gt_font_italic { #> font-style: italic; #> } #> #> #twqogyttqy .gt_super { #> font-size: 65%; #> } #> #> #twqogyttqy .gt_footnote_marks { #> font-style: italic; #> font-weight: normal; #> font-size: 75%; #> vertical-align: 0.4em; #> } #> #> #twqogyttqy .gt_asterisk { #> font-size: 100%; #> vertical-align: 0; #> } #> #> #twqogyttqy .gt_indent_1 { #> text-indent: 5px; #> } #> #> #twqogyttqy .gt_indent_2 { #> text-indent: 10px; #> } #> #> #twqogyttqy .gt_indent_3 { #> text-indent: 15px; #> } #> #> #twqogyttqy .gt_indent_4 { #> text-indent: 20px; #> } #> #> #twqogyttqy .gt_indent_5 { #> text-indent: 25px; #> } #> <\/style> #> <table class=\"gt_table\"> #> #> <thead class=\"gt_col_headings\"> #> <tr> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic<\/strong><\/th> #> <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;N = 200&lt;/strong&gt;&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>N = 200<\/strong><sup class=\"gt_footnote_marks\">1<\/sup><\/th> #> <\/tr> #> <\/thead> #> <tbody class=\"gt_table_body\"> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">Known hypertension<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">101 (50%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">Known diabetes<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">23 (12%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">One month mRS<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\"><\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    0<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">31 (16%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    1<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">74 (37%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    2<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">64 (32%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    3<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">16 (8.0%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    4<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">10 (5.0%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    5<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.5%)<\/td><\/tr> #> <tr><td headers=\"label\" class=\"gt_row gt_left\">    6<\/td> #> <td headers=\"stat_0\" class=\"gt_row gt_center\">4 (2.0%)<\/td><\/tr> #> <\/tbody> #> #> <tfoot class=\"gt_footnotes\"> #> <tr> #> <td class=\"gt_footnote\" colspan=\"2\"><sup class=\"gt_footnote_marks\">1<\/sup> n (%)<\/td> #> <\/tr> #> <\/tfoot> #> <\/table> #> <\/div>"},{"path":"/reference/plot_olr.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest plot from ordinal logistic regression. — plot_olr","title":"Forest plot from ordinal logistic regression. — plot_olr","text":"Heavily inspired https://www.r-bloggers.com/plotting-odds-ratios-aka--forrestplot--ggplot2/","code":""},{"path":"/reference/plot_olr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest plot from ordinal logistic regression. — plot_olr","text":"","code":"plot_olr( x, title = NULL, dec = 3, lbls = NULL, hori = \"OR (95 % CI)\", vert = \"Variables\", short = FALSE, input = c(\"model\", \"df\") )"},{"path":"/reference/plot_olr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest plot from ordinal logistic regression. — plot_olr","text":"x input data. title plot title dec decimals labels lbls labels variable names. Careful, right order checked automatically! hori labels horizontal axis (y axis plot rotated) vert labels horizontal axis (x axis plot rotated) short flag half number ticks horizontal axis. input can either \"model\", olr model (polr()), \"df\", data frame three columns , lower CI upper CI.","code":""},{"path":"/reference/plot_olr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest plot from ordinal logistic regression. — plot_olr","text":"gg object","code":""},{"path":"/reference/plot_olr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest plot from ordinal logistic regression. — plot_olr","text":"","code":"iris$ord<-factor(sample(1:3,size=nrow(iris),replace=TRUE),ordered=TRUE) lm <- MASS::polr(ord~., data=iris, Hess=TRUE, method=\"logistic\") plot_olr(lm, input=\"model\") #> Waiting for profiling to be done..."},{"path":"/reference/plot_ord_odds.html","id":null,"dir":"Reference","previous_headings":"","what":"Forrest plot from ordinal logistic regression. — plot_ord_odds","title":"Forrest plot from ordinal logistic regression. — plot_ord_odds","text":"Heavily inspired https://www.r-bloggers.com/plotting-odds-ratios-aka--forrestplot--ggplot2/","code":""},{"path":"/reference/plot_ord_odds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forrest plot from ordinal logistic regression. — plot_ord_odds","text":"","code":"plot_ord_odds( x, title = NULL, dec = 3, lbls = NULL, hori = \"OR (95 % CI)\", vert = \"Variables\", short = FALSE, input = c(\"model\", \"df\") )"},{"path":"/reference/plot_ord_odds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forrest plot from ordinal logistic regression. — plot_ord_odds","text":"x input data. title plot title dec decimals labels lbls labels variable names. Carefull, right order checked automatically! hori labels horizontal axis (y axis plot rotated) vert labels horizontal axis (x axis plot rotated) short flag half number ticks horizontal axis. input can either \"model\", olr model (polr()), \"df\", dataframe whith three columns , lower CI upper CI.","code":""},{"path":"/reference/plot_ord_odds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forrest plot from ordinal logistic regression. — plot_ord_odds","text":"gg object","code":""},{"path":"/reference/quantile_cut.html","id":null,"dir":"Reference","previous_headings":"","what":"Easy function for splitting numeric variable in quantiles — quantile_cut","title":"Easy function for splitting numeric variable in quantiles — quantile_cut","text":"Using base/stats functions cut() quantile().","code":""},{"path":"/reference/quantile_cut.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Easy function for splitting numeric variable in quantiles — quantile_cut","text":"","code":"quantile_cut( x, groups, y = NULL, na.rm = TRUE, group.names = NULL, ordered.f = FALSE, inc.outs = FALSE, detail.list = FALSE )"},{"path":"/reference/quantile_cut.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Easy function for splitting numeric variable in quantiles — quantile_cut","text":"x Variable cut. groups Number groups. y alternative vector draw quantile cuts . Limits within x. Default NULL. na.rm Remove NA's. Default TRUE. group.names Names groups split . Default NULL, giving intervals names. ordered.f Set resulting vector ordered. Default FALSE. inc.outs Flag include min(x) max(x) boarders case y!=NULL. detail.list flag include details ","code":""},{"path":"/reference/quantile_cut.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Easy function for splitting numeric variable in quantiles — quantile_cut","text":"vector list vector details (length 2)","code":""},{"path":"/reference/quantile_cut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Easy function for splitting numeric variable in quantiles — quantile_cut","text":"","code":"aa <- as.numeric(sample(1:1000,2000,replace = TRUE)) x <- 1:450 y <- 6:750 summary(quantile_cut(aa,groups=4,detail.list=FALSE)) ## Cuts quartiles #> [1,246] (246,500] (500,760] (760,1e+03] #> 504 496 502 498"},{"path":"/reference/read_redcap_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Download REDCap data — read_redcap_data","title":"Download REDCap data — read_redcap_data","text":"Wrapper function using REDCapR::redcap_read REDCapRITS::REDCap_split including clean-. Handles","code":""},{"path":"/reference/read_redcap_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download REDCap data — read_redcap_data","text":"","code":"read_redcap_data( uri, token, records = NULL, fields = NULL, events = NULL, forms = NULL, generics = c(\"record_id\", \"redcap_event_name\", \"redcap_repeat_instrument\", \"redcap_repeat_instance\") )"},{"path":"/reference/read_redcap_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download REDCap data — read_redcap_data","text":"uri REDCap database uri token API token records records download fields fields download events events download forms forms download generics vector auto-generated generic variable names ignore discarding empty rows","code":""},{"path":"/reference/read_redcap_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download REDCap data — read_redcap_data","text":"list instruments","code":""},{"path":"/reference/read_redcap_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download REDCap data — read_redcap_data","text":"","code":"# Examples will be provided later"},{"path":"/reference/read_redcap_tables.html","id":null,"dir":"Reference","previous_headings":"","what":"Download REDCap data — read_redcap_tables","title":"Download REDCap data — read_redcap_tables","text":"Wrapper function using REDCapR::redcap_read REDCapRITS::REDCap_split including clean-. Handles","code":""},{"path":"/reference/read_redcap_tables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download REDCap data — read_redcap_tables","text":"","code":"read_redcap_tables( uri, token, records = NULL, fields = NULL, events = NULL, forms = NULL, generics = c(\"record_id\", \"redcap_event_name\", \"redcap_repeat_instrument\", \"redcap_repeat_instance\") )"},{"path":"/reference/read_redcap_tables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download REDCap data — read_redcap_tables","text":"uri REDCap database uri token API token records records download fields fields download events events download forms forms download generics vector auto-generated generic variable names ignore discarding empty rows","code":""},{"path":"/reference/read_redcap_tables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download REDCap data — read_redcap_tables","text":"list instruments","code":""},{"path":"/reference/read_redcap_tables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download REDCap data — read_redcap_tables","text":"","code":"# Examples will be provided later"},{"path":"/reference/score.html","id":null,"dir":"Reference","previous_headings":"","what":"Data frame with sample data of cognitive testing score — score","title":"Data frame with sample data of cognitive testing score — score","text":"Contains non-identifiable organic trial data five-dimensional cognitive test.","code":""},{"path":"/reference/score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data frame with sample data of cognitive testing score — score","text":"","code":"data(score)"},{"path":"/reference/score.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data frame with sample data of cognitive testing score — score","text":"data frame 20 rows 26 variables: id id event event a_is domain index score b_is domain b index score c_is domain c index score d_is domain d index score e_is domain e index score i_is total index score a_lo domain lower ci b_lo domain b lower ci c_lo domain c lower ci d_lo domain d lower ci e_lo domain e lower ci i_lo total lower ci a_up domain upper ci b_up domain b upper ci c_up domain c upper ci d_up domain d upper ci e_up domain e upper ci i_up total upper ci a_per domain percentile b_per domain b percentile c_per domain c percentile d_per domain d percentile e_per domain e percentile i_per total percentile","code":""},{"path":"/reference/source_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Source Lines from a File — source_lines","title":"Source Lines from a File — source_lines","text":"Sources specific lines file","code":""},{"path":"/reference/source_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Source Lines from a File — source_lines","text":"","code":"source_lines(file, lines, ...)"},{"path":"/reference/source_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Source Lines from a File — source_lines","text":"file character string giving path file sourced. lines numeric vector line numbers sourced. ... Additional arguments passed source.","code":""},{"path":"/reference/source_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Source Lines from a File — source_lines","text":"result source.","code":""},{"path":[]},{"path":"/reference/source_lines.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Source Lines from a File — source_lines","text":"","code":"test_file <- tempfile(fileext = \".R\") writeLines(c(\"# Line 1\", \"2+2\", \"# Line 3\"), test_file) source_lines(test_file, 1:2, echo=TRUE) #> #> > 2 + 2 #> [1] 4"},{"path":"/reference/stRoke-package.html","id":null,"dir":"Reference","previous_headings":"","what":"stRoke: Clinical Stroke Research Toolbox — stRoke-package","title":"stRoke: Clinical Stroke Research Toolbox — stRoke-package","text":"R-toolbox custom functions convenient data management analysis clinical health research teaching. package mainly collected personal use, use beyond encouraged. package migrated functions agdamsbo/daDoctoR-package, new functions added.","code":""},{"path":[]},{"path":"/reference/stRoke-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"stRoke: Clinical Stroke Research Toolbox — stRoke-package","text":"Maintainer: Andreas Gammelgaard Damsbo agdamsbo@clin.au.dk (ORCID)","code":""},{"path":"/reference/talos.html","id":null,"dir":"Reference","previous_headings":"","what":"Data frame with sample of TALOS data — talos","title":"Data frame with sample of TALOS data — talos","text":"Contains non-identifiable data TALOS trial","code":""},{"path":"/reference/talos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data frame with sample of TALOS data — talos","text":"","code":"data(talos)"},{"path":"/reference/talos.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data frame with sample of TALOS data — talos","text":"data frame 200 rows 6 variables: rtreat Randomisation mrs_1 Modified Rankin scale score follow-mrs_6 Modified Rankin scale score end study hypertension Known hypertension diabetes Known diabetes civil Cohabitation status","code":""},{"path":"/reference/talos.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data frame with sample of TALOS data — talos","text":"https://doi.org/10.1161/STROKEAHA.117.020067/","code":""},{"path":[]},{"path":"/news/index.html","id":"functions-0-23-1-2","dir":"Changelog","previous_headings":"","what":"Functions:","title":"stRoke 0.23.1.2","text":"cpr_dob() now includes format=. minor updates.","code":""},{"path":"/news/index.html","id":"documentation-0-23-1-2","dir":"Changelog","previous_headings":"","what":"Documentation:","title":"stRoke 0.23.1.2","text":"Two new vignettes Taking last steps documenting releasing CRAN","code":""},{"path":"/news/index.html","id":"other-0-23-1-2","dir":"Changelog","previous_headings":"","what":"Other:","title":"stRoke 0.23.1.2","text":"New hex logo","code":""},{"path":[]},{"path":"/news/index.html","id":"functions-0-23-1-1","dir":"Changelog","previous_headings":"","what":"Functions:","title":"stRoke 0.23.1.1","text":"age_calc() now also outputs numeric vector units=“days”, character vector difftime() .","code":""},{"path":"/news/index.html","id":"documentation-0-23-1-1","dir":"Changelog","previous_headings":"","what":"Documentation:","title":"stRoke 0.23.1.1","text":"Added NEWS.md file track changes package. Added codecov Added tests help gpttools","code":""}]
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