diff --git a/articles/toolbox.html b/articles/toolbox.html
index 6957f60..3a9414a 100644
--- a/articles/toolbox.html
+++ b/articles/toolbox.html
@@ -223,23 +223,23 @@ model plot (with stRoke::ci_plot()
variables = c("hypertension", "diabetes", "civil"))
#> Waiting for profiling to be done...
#> $`Table 1`
-#> <div id="cldmfkdgmg" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-#> <style>#cldmfkdgmg table {
+#> <div id="bdpixpkpcq" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+#> <style>#bdpixpkpcq table {
#> font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
#> -webkit-font-smoothing: antialiased;
#> -moz-osx-font-smoothing: grayscale;
#> }
#>
-#> #cldmfkdgmg thead, #cldmfkdgmg tbody, #cldmfkdgmg tfoot, #cldmfkdgmg tr, #cldmfkdgmg td, #cldmfkdgmg th {
+#> #bdpixpkpcq thead, #bdpixpkpcq tbody, #bdpixpkpcq tfoot, #bdpixpkpcq tr, #bdpixpkpcq td, #bdpixpkpcq th {
#> border-style: none;
#> }
#>
-#> #cldmfkdgmg p {
+#> #bdpixpkpcq p {
#> margin: 0;
#> padding: 0;
#> }
#>
-#> #cldmfkdgmg .gt_table {
+#> #bdpixpkpcq .gt_table {
#> display: table;
#> border-collapse: collapse;
#> line-height: normal;
@@ -265,12 +265,12 @@ model plot (with stRoke::ci_plot()
#> border-left-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_caption {
+#> #bdpixpkpcq .gt_caption {
#> padding-top: 4px;
#> padding-bottom: 4px;
#> }
#>
-#> #cldmfkdgmg .gt_title {
+#> #bdpixpkpcq .gt_title {
#> color: #333333;
#> font-size: 125%;
#> font-weight: initial;
@@ -282,7 +282,7 @@ model plot (with stRoke::ci_plot()
#> border-bottom-width: 0;
#> }
#>
-#> #cldmfkdgmg .gt_subtitle {
+#> #bdpixpkpcq .gt_subtitle {
#> color: #333333;
#> font-size: 85%;
#> font-weight: initial;
@@ -294,7 +294,7 @@ model plot (with stRoke::ci_plot()
#> border-top-width: 0;
#> }
#>
-#> #cldmfkdgmg .gt_heading {
+#> #bdpixpkpcq .gt_heading {
#> background-color: #FFFFFF;
#> text-align: center;
#> border-bottom-color: #FFFFFF;
@@ -306,13 +306,13 @@ model plot (with stRoke::ci_plot()
#> border-right-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_bottom_border {
+#> #bdpixpkpcq .gt_bottom_border {
#> border-bottom-style: solid;
#> border-bottom-width: 2px;
#> border-bottom-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_col_headings {
+#> #bdpixpkpcq .gt_col_headings {
#> border-top-style: solid;
#> border-top-width: 2px;
#> border-top-color: #D3D3D3;
@@ -327,7 +327,7 @@ model plot (with stRoke::ci_plot()
#> border-right-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_col_heading {
+#> #bdpixpkpcq .gt_col_heading {
#> color: #333333;
#> background-color: #FFFFFF;
#> font-size: 100%;
@@ -347,7 +347,7 @@ model plot (with stRoke::ci_plot()
#> overflow-x: hidden;
#> }
#>
-#> #cldmfkdgmg .gt_column_spanner_outer {
+#> #bdpixpkpcq .gt_column_spanner_outer {
#> color: #333333;
#> background-color: #FFFFFF;
#> font-size: 100%;
@@ -359,15 +359,15 @@ model plot (with stRoke::ci_plot()
#> padding-right: 4px;
#> }
#>
-#> #cldmfkdgmg .gt_column_spanner_outer:first-child {
+#> #bdpixpkpcq .gt_column_spanner_outer:first-child {
#> padding-left: 0;
#> }
#>
-#> #cldmfkdgmg .gt_column_spanner_outer:last-child {
+#> #bdpixpkpcq .gt_column_spanner_outer:last-child {
#> padding-right: 0;
#> }
#>
-#> #cldmfkdgmg .gt_column_spanner {
+#> #bdpixpkpcq .gt_column_spanner {
#> border-bottom-style: solid;
#> border-bottom-width: 2px;
#> border-bottom-color: #D3D3D3;
@@ -379,11 +379,11 @@ model plot (with stRoke::ci_plot()
#> width: 100%;
#> }
#>
-#> #cldmfkdgmg .gt_spanner_row {
+#> #bdpixpkpcq .gt_spanner_row {
#> border-bottom-style: hidden;
#> }
#>
-#> #cldmfkdgmg .gt_group_heading {
+#> #bdpixpkpcq .gt_group_heading {
#> padding-top: 8px;
#> padding-bottom: 8px;
#> padding-left: 5px;
@@ -409,7 +409,7 @@ model plot (with stRoke::ci_plot()
#> text-align: left;
#> }
#>
-#> #cldmfkdgmg .gt_empty_group_heading {
+#> #bdpixpkpcq .gt_empty_group_heading {
#> padding: 0.5px;
#> color: #333333;
#> background-color: #FFFFFF;
@@ -424,15 +424,15 @@ model plot (with stRoke::ci_plot()
#> vertical-align: middle;
#> }
#>
-#> #cldmfkdgmg .gt_from_md > :first-child {
+#> #bdpixpkpcq .gt_from_md > :first-child {
#> margin-top: 0;
#> }
#>
-#> #cldmfkdgmg .gt_from_md > :last-child {
+#> #bdpixpkpcq .gt_from_md > :last-child {
#> margin-bottom: 0;
#> }
#>
-#> #cldmfkdgmg .gt_row {
+#> #bdpixpkpcq .gt_row {
#> padding-top: 8px;
#> padding-bottom: 8px;
#> padding-left: 5px;
@@ -451,7 +451,7 @@ model plot (with stRoke::ci_plot()
#> overflow-x: hidden;
#> }
#>
-#> #cldmfkdgmg .gt_stub {
+#> #bdpixpkpcq .gt_stub {
#> color: #333333;
#> background-color: #FFFFFF;
#> font-size: 100%;
@@ -464,7 +464,7 @@ model plot (with stRoke::ci_plot()
#> padding-right: 5px;
#> }
#>
-#> #cldmfkdgmg .gt_stub_row_group {
+#> #bdpixpkpcq .gt_stub_row_group {
#> color: #333333;
#> background-color: #FFFFFF;
#> font-size: 100%;
@@ -478,15 +478,15 @@ model plot (with stRoke::ci_plot()
#> vertical-align: top;
#> }
#>
-#> #cldmfkdgmg .gt_row_group_first td {
+#> #bdpixpkpcq .gt_row_group_first td {
#> border-top-width: 2px;
#> }
#>
-#> #cldmfkdgmg .gt_row_group_first th {
+#> #bdpixpkpcq .gt_row_group_first th {
#> border-top-width: 2px;
#> }
#>
-#> #cldmfkdgmg .gt_summary_row {
+#> #bdpixpkpcq .gt_summary_row {
#> color: #333333;
#> background-color: #FFFFFF;
#> text-transform: inherit;
@@ -496,16 +496,16 @@ model plot (with stRoke::ci_plot()
#> padding-right: 5px;
#> }
#>
-#> #cldmfkdgmg .gt_first_summary_row {
+#> #bdpixpkpcq .gt_first_summary_row {
#> border-top-style: solid;
#> border-top-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_first_summary_row.thick {
+#> #bdpixpkpcq .gt_first_summary_row.thick {
#> border-top-width: 2px;
#> }
#>
-#> #cldmfkdgmg .gt_last_summary_row {
+#> #bdpixpkpcq .gt_last_summary_row {
#> padding-top: 8px;
#> padding-bottom: 8px;
#> padding-left: 5px;
@@ -515,7 +515,7 @@ model plot (with stRoke::ci_plot()
#> border-bottom-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_grand_summary_row {
+#> #bdpixpkpcq .gt_grand_summary_row {
#> color: #333333;
#> background-color: #FFFFFF;
#> text-transform: inherit;
@@ -525,7 +525,7 @@ model plot (with stRoke::ci_plot()
#> padding-right: 5px;
#> }
#>
-#> #cldmfkdgmg .gt_first_grand_summary_row {
+#> #bdpixpkpcq .gt_first_grand_summary_row {
#> padding-top: 8px;
#> padding-bottom: 8px;
#> padding-left: 5px;
@@ -535,7 +535,7 @@ model plot (with stRoke::ci_plot()
#> border-top-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_last_grand_summary_row_top {
+#> #bdpixpkpcq .gt_last_grand_summary_row_top {
#> padding-top: 8px;
#> padding-bottom: 8px;
#> padding-left: 5px;
@@ -545,11 +545,11 @@ model plot (with stRoke::ci_plot()
#> border-bottom-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_striped {
+#> #bdpixpkpcq .gt_striped {
#> background-color: rgba(128, 128, 128, 0.05);
#> }
#>
-#> #cldmfkdgmg .gt_table_body {
+#> #bdpixpkpcq .gt_table_body {
#> border-top-style: solid;
#> border-top-width: 2px;
#> border-top-color: #D3D3D3;
@@ -558,7 +558,7 @@ model plot (with stRoke::ci_plot()
#> border-bottom-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_footnotes {
+#> #bdpixpkpcq .gt_footnotes {
#> color: #333333;
#> background-color: #FFFFFF;
#> border-bottom-style: none;
@@ -572,7 +572,7 @@ model plot (with stRoke::ci_plot()
#> border-right-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_footnote {
+#> #bdpixpkpcq .gt_footnote {
#> margin: 0px;
#> font-size: 90%;
#> padding-top: 4px;
@@ -581,7 +581,7 @@ model plot (with stRoke::ci_plot()
#> padding-right: 5px;
#> }
#>
-#> #cldmfkdgmg .gt_sourcenotes {
+#> #bdpixpkpcq .gt_sourcenotes {
#> color: #333333;
#> background-color: #FFFFFF;
#> border-bottom-style: none;
@@ -595,7 +595,7 @@ model plot (with stRoke::ci_plot()
#> border-right-color: #D3D3D3;
#> }
#>
-#> #cldmfkdgmg .gt_sourcenote {
+#> #bdpixpkpcq .gt_sourcenote {
#> font-size: 90%;
#> padding-top: 4px;
#> padding-bottom: 4px;
@@ -603,63 +603,63 @@ model plot (with stRoke::ci_plot()
#> padding-right: 5px;
#> }
#>
-#> #cldmfkdgmg .gt_left {
+#> #bdpixpkpcq .gt_left {
#> text-align: left;
#> }
#>
-#> #cldmfkdgmg .gt_center {
+#> #bdpixpkpcq .gt_center {
#> text-align: center;
#> }
#>
-#> #cldmfkdgmg .gt_right {
+#> #bdpixpkpcq .gt_right {
#> text-align: right;
#> font-variant-numeric: tabular-nums;
#> }
#>
-#> #cldmfkdgmg .gt_font_normal {
+#> #bdpixpkpcq .gt_font_normal {
#> font-weight: normal;
#> }
#>
-#> #cldmfkdgmg .gt_font_bold {
+#> #bdpixpkpcq .gt_font_bold {
#> font-weight: bold;
#> }
#>
-#> #cldmfkdgmg .gt_font_italic {
+#> #bdpixpkpcq .gt_font_italic {
#> font-style: italic;
#> }
#>
-#> #cldmfkdgmg .gt_super {
+#> #bdpixpkpcq .gt_super {
#> font-size: 65%;
#> }
#>
-#> #cldmfkdgmg .gt_footnote_marks {
+#> #bdpixpkpcq .gt_footnote_marks {
#> font-size: 75%;
#> vertical-align: 0.4em;
#> position: initial;
#> }
#>
-#> #cldmfkdgmg .gt_asterisk {
+#> #bdpixpkpcq .gt_asterisk {
#> font-size: 100%;
#> vertical-align: 0;
#> }
#>
-#> #cldmfkdgmg .gt_indent_1 {
+#> #bdpixpkpcq .gt_indent_1 {
#> text-indent: 5px;
#> }
#>
-#> #cldmfkdgmg .gt_indent_2 {
+#> #bdpixpkpcq .gt_indent_2 {
#> text-indent: 10px;
#> }
#>
-#> #cldmfkdgmg .gt_indent_3 {
+#> #bdpixpkpcq .gt_indent_3 {
#> text-indent: 15px;
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#> text-indent: 25px;
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#> </style>
diff --git a/pkgdown.yml b/pkgdown.yml
index 98f4244..94fedf9 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
articles:
ds2dd: ds2dd.html
toolbox: toolbox.html
-last_built: 2023-09-05T17:11Z
+last_built: 2023-09-05T17:59Z
urls:
reference: https://agdamsbo.github.io/stRoke/reference
article: https://agdamsbo.github.io/stRoke/articles
diff --git a/reference/color_plot-1.png b/reference/color_plot-1.png
index 8176318..eb89bb0 100644
Binary files a/reference/color_plot-1.png and b/reference/color_plot-1.png differ
diff --git a/reference/label_select.html b/reference/label_select.html
index 8313eee..c948a4d 100644
--- a/reference/label_select.html
+++ b/reference/label_select.html
@@ -94,15 +94,15 @@ Alternative is to use attributes, eg from label_select(labels_all,vars)
#> [[1]]
#> hypertension ~ "Known hypertension"
-#> <environment: 0x5557ae416448>
+#> <environment: 0x556388413c80>
#>
#> [[2]]
#> diabetes ~ "Known diabetes"
-#> <environment: 0x5557ae416448>
+#> <environment: 0x556388413c80>
#>
#> [[3]]
#> mrs_1 ~ "One month mRS"
-#> <environment: 0x5557ae416448>
+#> <environment: 0x556388413c80>
#>
## With gtsummary::tbl_summary()
diff --git a/reference/quantile_cut.html b/reference/quantile_cut.html
index fcca113..2f54f0a 100644
--- a/reference/quantile_cut.html
+++ b/reference/quantile_cut.html
@@ -120,7 +120,7 @@ as borders in case of y!=NULL.
x <- 1:450
y <- 6:750
summary(quantile_cut(aa,groups=4,detail.list=FALSE)) ## Cuts quartiles
-#> [1,240] (240,476] (476,738] (738,1e+03]
+#> [1,260] (260,500] (500,744] (744,1e+03]
#> 500 501 499 500
diff --git a/reference/write_ical.html b/reference/write_ical.html
index e936635..0609670 100644
--- a/reference/write_ical.html
+++ b/reference/write_ical.html
@@ -171,8 +171,8 @@ a time zone that is recognized by the user's OS.
#> # A tibble: 2 × 7
#> SUMMARY DTSTART DTEND UID LOCATION URL
#> <chr> <dttm> <dttm> <chr> <chr> <chr>
-#> 1 Conference 2020-02-10 12:00:00 2020-02-13 13:00:00 ical-e5ecc5… Confere… http…
-#> 2 Lunch 2020-02-11 10:00:00 2020-02-11 11:00:00 ical-8aa6c4… Confere… http…
+#> 1 Conference 2020-02-10 12:00:00 2020-02-13 13:00:00 ical-c1d368… Confere… http…
+#> 2 Lunch 2020-02-11 10:00:00 2020-02-11 11:00:00 ical-0cb59a… Confere… http…
#> # ℹ 1 more variable: DESCRIPTION <chr>
diff --git a/search.json b/search.json
index a8f296e..07bdba6 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://agdamsbo.github.io/stRoke/articles/ds2dd.html","id":"easy-data-set-to-data-base-workflow","dir":"Articles","previous_headings":"","what":"Easy data set to data base workflow","title":"ds2dd","text":"function can used simple tool creating data base metadata file REDCap (called DataDictionary) based given data set file.","code":""},{"path":"https://agdamsbo.github.io/stRoke/articles/ds2dd.html","id":"step-1---load-your-data-set","dir":"Articles","previous_headings":"Easy data set to data base workflow","what":"Step 1 - Load your data set","title":"ds2dd","text":"’ll use sample TALOS dataset included package.","code":"data(\"talos\") ds <- talos # As the data set lacks an ID column, one is added ds$id <- seq_len(nrow(ds))"},{"path":"https://agdamsbo.github.io/stRoke/articles/ds2dd.html","id":"step-2---create-the-datadictionary","dir":"Articles","previous_headings":"Easy data set to data base workflow","what":"Step 2 - Create the DataDictionary","title":"ds2dd","text":"Now additional specifications DataDictionary can made manually, can uploaded modified manually graphical user interface web page. function transform column names lower case substitute spaces underscores. output list DataDictionary vector new column names dataset fit meta data.","code":"datadictionary <- ds2dd(ds,record.id = \"id\",include.column.names = TRUE)"},{"path":"https://agdamsbo.github.io/stRoke/articles/ds2dd.html","id":"step-3---meta-data-upload","dir":"Articles","previous_headings":"Easy data set to data base workflow","what":"Step 3 - Meta data upload","title":"ds2dd","text":"Now DataDictionary can exported spreadsheet uploaded can uploaded using REDCapR package (projects “Development” status). Use one two approaches :","code":""},{"path":"https://agdamsbo.github.io/stRoke/articles/ds2dd.html","id":"manual-upload","dir":"Articles","previous_headings":"Easy data set to data base workflow > Step 3 - Meta data upload","what":"Manual upload","title":"ds2dd","text":"","code":"write.csv(datadictionary$DataDictionary,\"datadictionary.csv\")"},{"path":"https://agdamsbo.github.io/stRoke/articles/ds2dd.html","id":"upload-with-redcapr","dir":"Articles","previous_headings":"Easy data set to data base workflow > Step 3 - Meta data upload","what":"Upload with REDCapR","title":"ds2dd","text":"“REDCap R Handbook” written interfacing REDCap R using library(keyring)store credentials chapter 1.1.","code":"REDCapR::redcap_metadata_write( datadictionary$DataDictionary, redcap_uri = keyring::key_get(\"DB_URI\"), token = keyring::key_get(\"DB_TOKEN\") )"},{"path":"https://agdamsbo.github.io/stRoke/articles/ds2dd.html","id":"step-4---data-upload","dir":"Articles","previous_headings":"Easy data set to data base workflow","what":"Step 4 - Data upload","title":"ds2dd","text":"two options available data upload meta data upload: manual REDCapR. latter shown .","code":"# new column names are applied colnames(ds) <- datadictionary$`Column names` REDCapR::redcap_write( ds, redcap_uri = keyring::key_get(\"DB_URI\"), token = keyring::key_get(\"DB_TOKEN\") )"},{"path":"https://agdamsbo.github.io/stRoke/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. documenting testing package, used OpenAI’s chatgpt gpttools. chatgpt interesting tool, way perfect, helps tedious tasks. gpttools gptstudio interesting implementations R RStudio.","code":""},{"path":"https://agdamsbo.github.io/stRoke/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":"https://agdamsbo.github.io/stRoke/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":"https://agdamsbo.github.io/stRoke/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":"https://agdamsbo.github.io/stRoke/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":"https://agdamsbo.github.io/stRoke/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":"table(cpr_female(stRoke::cprs[, 1])) #> #> FALSE TRUE #> 98 102"},{"path":[]},{"path":"https://agdamsbo.github.io/stRoke/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":"https://agdamsbo.github.io/stRoke/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` #> #>