Package 'certetoolbox'

Title: A Certe R Package for Miscellaneous Functions
Description: A Certe R Package for miscellaneous functions that do not fit a dedicated package. This package also mitigates the 'vctrs' package by allowing numeric-character coercions. This package is part of the 'certedata' universe.
Authors: Erwin E. A. Hassing [aut, cre], Matthijs S. Berends [aut], Certe Medical Diagnostics & Advice Foundation [cph, fnd]
Maintainer: Erwin E. A. Hassing <[email protected]>
License: GPL-2
Version: 1.15.1
Built: 2024-11-22 10:26:14 UTC
Source: https://github.com/certe-medical-epidemiology/certetoolbox

Help Index

Create Excel Workbook Object

Description

The as_excel() function relies on the openxlsx2 package for creating an Excel Workbook object in R. These objects can be saved using save_excel() or export_xlsx().

Usage

as_excel(
  ...,
  sheet_names = NULL,
  autofilter = TRUE,
  autowidth = TRUE,
  widths = NULL,
  rows_zebra = TRUE,
  cols_zebra = FALSE,
  freeze_top_row = TRUE,
  digits = 2,
  align = "center",
  table_style = "TableStyleMedium2",
  creator = Sys.info()["user"],
  department = read_secret("department.name"),
  project_number = project_get_current_id(ask = FALSE)
)

save_excel(xl, filename = NULL, overwrite = FALSE)

Arguments

...

data sets, use named items for multiple tabs (see Examples)
sheet_names

sheet names
autofilter

create autofilter on columns in first row. This can also be a vector with the same length as ....
autowidth

automatically adjust columns widths. This can also be a vector with the same length as ....
widths

width of columns, must be length 1 or ncol() of the data set. If set, overrides autowidth.
rows_zebra

create banded rows. This can also be a vector with the same length as ....
cols_zebra

create banded columns. This can also be a vector with the same length as ....
freeze_top_row

freeze the first row of the sheet. This can also be a vector with the same length as ....
digits

number of digits for numeric values (integer values will always be rounded to whole numbers), defaults to 2
align

horizontal alignment of text
table_style

style(s) for each table, see below. This can also be a vector with the same length as ....
creator

name of the creator of the workbook
department

name of the department of the workbook
project_number

project number, to add project ID as the subject of the workbook
xl

Excel object, as created with as_excel() (or manually with the openxlsx2 package)
filename

file location to save Excel document to, defaults to a random filename in the current folder
overwrite

overwrite existing file

Supported Table Styles

For the argument table_style, use one or more of these table styles as character input. The default is TableStyleMedium2.

tablestyles.png

Examples

# creates a Workbook object
xl <- as_excel("this is a sheet" = mtcars,
               "another sheet" = anscombe)
xl

# then save it with save_excel() or export_xlsx()

Force Time as UTC

Description

Force Time as UTC

Usage

as.UTC(x, ...)

## S3 method for class 'data.frame'
as.UTC(x, ...)

## S3 method for class 'POSIXct'
as.UTC(x, ...)

## Default S3 method:
as.UTC(x, ...)

Arguments

x

a vector of datetime values
...

not used at the moment

Examples

Sys.time()
as.UTC(Sys.time())

Automatically Transform Data Set

Description

This function transforms a data.frame by guessing the right data classes and applying them, using readr::parse_guess() and cleaner functions such as cleaner::clean_Date().

Usage

auto_transform(
  x,
  datenames = "en",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ".",
  big.mark = "",
  timezone = "",
  na = c("", "NULL", "NA", "<NA>"),
  snake_case = FALSE,
  ...
)

Arguments

x

a data.frame
datenames

language of the date names, such as weekdays and months
dateformat

expected date format, will be coerced with format_datetime()
timeformat

expected time format, will be coerced with format_datetime()
decimal.mark

separator for decimal numbers
big.mark

separator for thousands
timezone

expected time zone
na

values to interpret as NA
snake_case

apply snake case to the column names
...

not used as the time, allows for future extension

Download CBS-data

Description

Download data from CBS Open data Statline.

Usage

cbs_topics()

cbs_search(topic, max_print = 25)

cbs_download(identifier, clean_cols = TRUE)

cbs_moreinfo(identifier)

Arguments

topic

topics to search for
max_print

maximum number of subjects to print
identifier

tracking number (1 to max_print) in cbs_search()
clean_cols

Clean column names.

Details

cbs_topics() retrieves all topics.

cbs_search() searches for a specific subject.

cbs_download() downloads tables. Input has to be a CBS Identifier (printed in red in cbs_search()), or a tracking number of cbs_search(), or the result of cbs_search().

cbs_moreinfo() gives a detailed explanation for the table. Input can also be a dataset downloaded with cbs_download().

Examples

## Not run: 
cbs_search("Inwoners")

x <- cbs_download(2) # 2nd hit of cbs_search()
str(x)

cbs_moreinfo(x)
cbs_moreinfo(2)

## End(Not run)

Paste Character Vectors Together

Description

The concat() function is at default identical to paste(c(...), sep = "", collapse = "").

The collapse() function is at default identical to paste(x, sep = "", collapse = "").

Usage

concat(..., sep = "")

collapse(x, sep = "")

Arguments

..., x

element(s) to be pasted together, can also be vectors
sep

separator character, will also be used for collapsing

Examples

concat("a", "b", "c")

concat(c("a", "b"), "c")
collapse(c("a", "b"), "c")

concat(letters[1:5], "-")
collapse(letters[1:5], "-")

Create a Crosstab

Description

Transform a data set into an n x m table, e.g. to be used in certestats::confusion_matrix().

Usage

crosstab(
  df,
  identifier,
  compare,
  outcome,
  positive = "^pos.*",
  negative = "^neg.*",
  ...,
  na.rm = TRUE,
  ignore_case = TRUE
)

Arguments

df

a data.frame
identifier

a column name to use as identifier, such as a patient ID or an order ID
compare

a column name for the two axes of the table: the labels between the outcomes must be compared
outcome

a column name containing the outcome values to compare
positive

a regex to match the values in outcome that must be considered as the Positive class, use FALSE to not use a Positive class
negative

a regex to match the values in outcome that must be considered as the Negative class, use FALSE to not use a Negative class
...

manual regexes for classes if not using positive and negative, such as ⁠Class1 = "c1", Class2 = "c2", Class3 = "c3"⁠
na.rm

a logical to indicate whether empty values must be removed before forming the table
ignore_case

a logical to indicate whether the case in the values of positive, negative and ... must be ignored

Examples

df <- data.frame(
  order_nr = sort(rep(LETTERS[1:20], 2)),
  test_type = rep(c("Culture", "PCR"), 20),
  result = sample(c("pos", "neg"),
                  size = 40,
                  replace = TRUE,
                  prob = c(0.3, 0.9))
)
head(df)

out <- df |> crosstab(order_nr, test_type, result)
out


df$result <- gsub("pos", "#p", df$result)
df$result <- gsub("neg", "#n", df$result)
head(df)
# gives a warning that pattern matching failed:
df |> crosstab(order_nr, test_type, result)

# define the pattern yourself in such case:
df |> crosstab(order_nr, test_type, result,
               positive = "#p",
               negative = "#n")
                             
                             
# defining classes manually, can be more than 2:
df |> crosstab(order_nr, test_type, result,
               ClassA = "#p", Hello = "#n")
                             
if ("certestats" %in% rownames(utils::installed.packages())) {
  certestats::confusion_matrix(out)
}

Dates around Today

Description

These are convenience functions to get certain dates relatively to today.

Usage

yesterday(ref = today())

tomorrow(ref = today())

week(ref = today())

year(ref = today())

last_week(ref = today(), only_start_end = FALSE)

this_week(ref = today(), only_start_end = FALSE)

next_week(ref = today(), only_start_end = FALSE)

last_month(ref = today(), only_start_end = FALSE)

this_month(ref = today(), only_start_end = FALSE)

next_month(ref = today(), only_start_end = FALSE)

last_quarter(ref = today(), only_start_end = FALSE)

this_quarter(ref = today(), only_start_end = FALSE)

next_quarter(ref = today(), only_start_end = FALSE)

last_year(ref = today(), only_start_end = FALSE)

this_year(ref = today(), only_start_end = FALSE)

next_year(ref = today(), only_start_end = FALSE)

last_n_years(n, ref = end_of_last_year(), only_start_end = FALSE)

last_5_years(ref = end_of_last_year(), only_start_end = FALSE)

last_10_years(ref = end_of_last_year(), only_start_end = FALSE)

last_n_months(n, ref = end_of_last_month(), only_start_end = FALSE)

last_3_months(ref = end_of_last_month(), only_start_end = FALSE)

last_6_months(ref = end_of_last_month(), only_start_end = FALSE)

year_to_date(ref = today(), only_start_end = FALSE)

year_since_date(ref = today(), only_start_end = FALSE)

start_of_last_week(ref = today(), day = 1)

end_of_last_week(ref = today(), day = 7)

start_of_this_week(ref = today(), day = 1)

end_of_this_week(ref = today(), day = 7)

start_of_last_month(ref = today())

end_of_last_month(ref = today())

start_of_this_month(ref = today())

end_of_this_month(ref = today())

start_of_next_month(ref = today())

end_of_next_month(ref = today())

start_of_last_quarter(ref = today())

end_of_last_quarter(ref = today())

start_of_this_quarter(ref = today())

end_of_this_quarter(ref = today())

start_of_next_quarter(ref = today())

end_of_next_quarter(ref = today())

start_of_last_year(ref = today())

end_of_last_year(ref = today())

start_of_this_year(ref = today())

end_of_this_year(ref = today())

start_of_next_year(ref = today())

end_of_next_year(ref = today())

nth_monday(ref = today(), n = 1)

nth_tuesday(ref = today(), n = 1)

nth_wednesday(ref = today(), n = 1)

nth_thursday(ref = today(), n = 1)

nth_friday(ref = today(), n = 1)

nth_saturday(ref = today(), n = 1)

nth_sunday(ref = today(), n = 1)

week2date(wk, yr = year(today()), day = 1)

week2resp_season(wk, remove_outside_season = FALSE)

Arguments

ref

reference date (defaults to today)
only_start_end

logical to indicate whether only the first and last value of the resulting vector should be returned
n

relative number of weeks
day

day to return (0 are 7 are Sunday, 1 is Monday, etc.)
wk

week to search for
yr

year to search for, defaults to current year
remove_outside_season

a logical to remove week numbers in the range 21-39

Details

All functions return a vector of dates, except for yesterday(), today(), tomorrow(), week2date(), and the ⁠start_of_*()⁠, ⁠end_of_*()⁠ and ⁠nth_*()⁠ functions; these return 1 date.

Week ranges always start on Mondays and end on Sundays.

year() always returns an integer.

The last_n_years(), last_5_years() and last_10_years() functions have their reference date set to end_of_last_year() at default.

The last_n_months(), last_3_months() and last_6_months() functions have their reference date set to end_of_last_month() at default.

week2resp_season() transforms week numbers to an ordered factor, in a range 40-53, 1:39 (or, if remove_outside_season = TRUE, 40-53, 1:20). This function is useful for plotting.

Examples

today()
today() %in% this_month()

next_week()
next_week(only_start_end = TRUE)

# 2nd Monday of last month:
last_month() |> nth_monday(2)

# last_*_years() will have 1 Jan to 31 Dec at default:
last_5_years(only_start_end = TRUE)
last_5_years(today(), only_start_end = TRUE)

last_3_months(only_start_end = TRUE)

year_to_date(only_start_end = TRUE)

## Not run: 

  # great for certedb functions:
  certedb::get_diver_data(last_5_years(),
                          Bepaling == "ACBDE")

## End(Not run)

df <- data.frame(date = sample(seq.Date(start_of_last_year(),
                                        end_of_this_year(),
                                        by = "day"),
                               size = 500))
df$time <- as.POSIXct(paste(df$date, "12:00:00"))

library(dplyr, warn.conflicts = FALSE)

# these are equal:
df |>
  filter(date |> between(start_of_last_week(),
                          end_of_last_week()))
df |>
  filter(date %in% last_week())

# but this does not work:
df |>
  filter(time %in% last_week())

# so be sure to transform times to dates in certain filters
df |>
  filter(as.Date(time) %in% last_week())

Export Data Sets and Plots

Description

These functions can be used to export data sets and plots. They invisibly return the object itself again, allowing for usage in pipes (except for the plot-exporting functions export_pdf(), export_png() and export_html()). The functions work closely together with the certeprojects package to support Microsoft Planner project numbers.

Usage

export(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  fn = NULL,
  ...
)

export_rds(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  ...
)

export_xlsx(
  ...,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  sheet_names = NULL,
  autofilter = TRUE,
  rows_zebra = TRUE,
  cols_zebra = FALSE,
  freeze_top_row = TRUE,
  table_style = "TableStyleMedium2",
  align = "center"
)

export_excel(
  ...,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  sheet_names = NULL,
  autofilter = TRUE,
  rows_zebra = TRUE,
  cols_zebra = FALSE,
  freeze_top_row = TRUE,
  table_style = "TableStyleMedium2",
  align = "center"
)

export_csv(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  na = "",
  ...
)

export_csv2(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  na = "",
  ...
)

export_tsv(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  na = "",
  ...
)

export_txt(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  sep = "\t",
  na = "",
  ...
)

export_sav(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  ...
)

export_spss(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  ...
)

export_feather(
  object,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  ...
)

export_pdf(
  plot,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  size = "A5",
  portrait = FALSE,
  ...
)

export_png(
  plot,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  width = 1000,
  height = 800,
  dpi = NULL,
  ...
)

export_html(
  plot,
  filename = NULL,
  project_number = project_get_current_id(ask = FALSE),
  overwrite = NULL,
  ...
)

export_clipboard(
  object,
  sep = "\t",
  na = "",
  header = TRUE,
  quote = FALSE,
  decimal.mark = dec_mark(),
  ...
)

export_teams(
  object,
  filename = NULL,
  full_teams_path = NULL,
  account = connect_teams(),
  ...
)

Arguments

object, plot

the R object to export
filename

the full path of the exported file
project_number

a Microsoft Planner project number
overwrite

a logical value to indicate if an existing file must be overwritten. In interactive mode, this will be asked if the file exists. In non-interactive mode, this has a special default behaviour: the original file will be copied to filename_datetime.ext before overwriting the file. Exporting with existing files is always non-destructive: if exporting fails, the original, existing file will not be altered.
fn

a manual export function, such as haven::write_sas to write SAS files. This function has to have the object as first argument and the future file location as second argument.
...

arguments passed on to methods
sheet_names

sheet names
autofilter

create autofilter on columns in first row. This can also be a vector with the same length as ....
rows_zebra

create banded rows. This can also be a vector with the same length as ....
cols_zebra

create banded columns. This can also be a vector with the same length as ....
freeze_top_row

freeze the first row of the sheet. This can also be a vector with the same length as ....
table_style

style(s) for each table, see below. This can also be a vector with the same length as ....
align

horizontal alignment of text
na

replacement character for empty values (default: "")
sep

separator for values in a row (default: tab)
size

paper size, defaults to A5. Can be A0 to A7.
portrait

portrait mode, defaults to FALSE (i.e., landscape mode)
width

required width of the PNG file in pixels
height

required height of the PNG file in pixels
dpi

plot resolution, defaults to DPI set in showtext package
header

(for export_clipboard()) use column names as header (default: TRUE)
quote

(for export_clipboard()) use quotation marks (default: FALSE)
decimal.mark

(for export_clipboard()) character to use for decimal numbers, defaults to dec_mark()
full_teams_path

path in Teams to export object to. Can be left blank to use interactive folder picking mode in the console.
account

a Teams account from Azure or an AzureAuth Microsoft 365 token, e.g. retrieved with certeprojects::connect_teams()

Details

The export() function can export to any file format, also with a manually set export function when passed on to the fn argument. This function fn has to have the object as first argument and the future file location as second argument. If fn is left blank, the ⁠export_*⁠ function will be used based on the filename.

RDS files as created using export_rds() are compatible with R3 and R4.

The export_xlsx() and export_excel() functions use save_excel(as_excel(...)) internally. IMPORTANT: these two functions can accept more than one data.frame. When naming the data sets, the names will become sheet names in the resulting Excel file. For a complete visual overview of supported table styles, see as_excel(). If the last value in ... is a character of length 1 and filename is NULL, this value is assumed to be the filename.

For export_csv(), export_csv2() and export_tsv(), files will be saved in UTF-8 encoding and NA values will be exported as "" at default. Like other ⁠*.csv⁠ and ⁠*.csv2⁠ functions, csv is comma (⁠,⁠) separated and csv2 is semicolon (⁠;⁠) separated.

The export_txt() function exports to a tab-separated file.

Exporting to an SPSS file using export_sav() or export_spss() requires the haven package to be installed.

Exporting to a Feather file using export_feather() requires the arrow package to be installed. Apache Feather provides efficient binary columnar serialization for data sets, enabling easy sharing data across data analysis languages (such as between Python and R).

Exporting to a PDF file using export_pdf() requires the ggplot2 package to be installed. If the filename is left blank in export_pdf(), export_png() or export_html(), the title of plot will be used if it's available and the certeplot2 package is installed, and a timestamp otherwise. NOTE: All export functions invisibly return object again, but the plotting functions invisibly return the file path

Exporting to a PNG file using export_png() requires the ggplot2 and showtext packages to be installed.

Exporting to an HTML file using export_html() requires the ggplot2 and htmltools packages to be installed. The arguments put in ... will be passed on to plotly::layout() if plot is not yet a Plotly object (but rather a ggplot2 object), which of course then requires the plotly package to be installed as well.

Exporting to the clipboard using export_clipboard() requires the clipr package to be installed. The function allows any object (also other than data.frames) to be exported to the clipboard and is only limited to the available amount of RAM memory.

Exporting to Microsoft Teams using export_teams() requires the AzureGraph package to be installed. The function allows any object (also other than data.frames) to be exported to any Team channel. The filename set in filename will determine the exported file type and defaults to an RDS file.

See Also

import()

Examples

library(dplyr, warn.conflicts = FALSE)

# export to two files: 'whole_file.rds' and 'first_ten_rows.xlsx'
starwars |>
  export_rds("whole_file") |>
  slice(1:10) |>
  export_xlsx("first_ten_rows")
  
# the above is equal to:
# starwars |>
#   export("whole_file.rds") |>
#   slice(1:10) |>
#   export("first_ten_rows.xlsx")


# Apache's Feather format is column-based
# and allow for cross-language specific and fast file reading
starwars |> export_feather()
import("starwars.feather",
       col_select = starts_with("h")) |> 
  head()
  

# (cleanup)
file.remove("whole_file.rds")
file.remove("first_ten_rows.xlsx")
file.remove("starwars.feather")

## Not run: 

# ---- Microsoft Teams support -------------------------------------------

# IMPORTING

# import from Teams by picking a folder interactively from any Team
x <- import_teams()

# to NOT pick a Teams folder (e.g. in non-interactive mode), set `full_teams_path`
x <- import_teams(full_teams_path = "MyTeam/MyChannel/MyFolder/MyFile.xlsx")


# EXPORTING

# export to Teams by picking a folder interactively from any Team
mtcars |> export_teams()

# the default is RDS, but you can set `filename` to specify yourself
mtcars |> export_teams("mtcars.xlsx")

# to NOT pick a Teams folder (e.g. in non-interactive mode), set `full_teams_path`
mtcars |> export_teams("mtcars.xlsx", full_teams_path = "MyTeam/MyChannel/MyFolder")
mtcars |> export_teams(full_teams_path = "MyTeam/MyChannel/MyFolder")


## End(Not run)

Create Random Identifier

Description

This function creates unique identifier (IDs) using sample().

Usage

generate_identifier(id_length = 6, n = 1, chars = c(0:9, letters[1:6]))

Arguments

id_length

character length of ID
n

number of IDs to generate
chars

characters to use for generation, defaults to hexadecimal characters (0-9 and a-f)

Examples

generate_identifier(8)
generate_identifier(6, 3)

Hospitalname

Description

Hospitalname and/or location, with support for all hospitals in Northern Netherlands, including Meppel, Hardenberg and Zwolle.

Usage

hospital_name(x, format = "{naamkort}, {plaats}")

Arguments

x

text to be transformed
format

default is "{naamkort}, {plaats}". Attributes like x to be returned in 'glue'-format (in curly brackets).

Examples

hospital_name(c("MCL", "MCL", "Martini"))
hospital_name(c("Antonius", "WZA", "Martini"), format = "{naam} te {plaats}")

# special case for GGD
hospital_name(c("Martini", "GGD Groningen", "GGD Drenthe"), format = "{naam}")
hospital_name(c("Martini", "GGD Groningen", "GGD Drenthe"), format = "{naamkort}")
hospital_name("ggd friesland", "{naam}")

Import Data Sets

Description

These functions can be used to import data, from local or remote paths, or from the internet. They work closely with the certeprojects package to support Microsoft Planner project numbers. To support row names and older R versions, ⁠import_*()⁠ functions return plain data.frames, not e.g. tibbles.

Usage

import(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  auto_transform = TRUE,
  ...
)

import_rds(filename, project_number = project_get_current_id(ask = FALSE), ...)

import_xlsx(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  sheet = 1,
  range = NULL,
  auto_transform = TRUE,
  datenames = "nl",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = dec_mark(),
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0,
  ...
)

import_excel(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  sheet = 1,
  range = NULL,
  auto_transform = TRUE,
  datenames = "nl",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = dec_mark(),
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0,
  ...
)

import_csv(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  auto_transform = TRUE,
  datenames = "nl",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ".",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0,
  ...
)

import_csv2(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  auto_transform = TRUE,
  datenames = "nl",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ",",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0,
  ...
)

import_tsv(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  auto_transform = TRUE,
  datenames = "nl",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ".",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0,
  ...
)

import_txt(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  auto_transform = TRUE,
  sep = "\t",
  datenames = "nl",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ",",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0,
  ...
)

import_sav(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  auto_transform = TRUE,
  datenames = "en",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ".",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  ...
)

import_spss(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  auto_transform = TRUE,
  datenames = "en",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ".",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  ...
)

import_feather(
  filename,
  project_number = project_get_current_id(ask = FALSE),
  col_select = everything(),
  ...
)

import_clipboard(
  sep = "\t",
  header = TRUE,
  startrow = 1,
  auto_transform = TRUE,
  datenames = "nl",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = dec_mark(),
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  ...
)

import_mail_attachment(
  search = "hasattachment:yes",
  search_subject = NULL,
  search_from = NULL,
  search_when = NULL,
  search_attachment = NULL,
  folder = certemail::get_inbox_name(account = account),
  n = 5,
  sort = "received desc",
  account = certemail::connect_outlook(),
  auto_transform = TRUE,
  sep = ",",
  ...
)

import_url(
  url,
  auto_transform = TRUE,
  sep = ",",
  datenames = "en",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ".",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0,
  ...
)

import_teams(
  full_teams_path = NULL,
  account = connect_teams(),
  auto_transform = TRUE,
  sep = ",",
  datenames = "en",
  dateformat = "yyyy-mm-dd",
  timeformat = "HH:MM",
  decimal.mark = ".",
  big.mark = "",
  timezone = "UTC",
  na = c("", "NULL", "NA", "<NA>"),
  skip = 0
)

Arguments

filename

the full path of the file to be imported, will be parsed to a character, can also be a remote location (from http/https/ftp/ssh, GitHub/GitLab)
project_number

a Microsoft Planner project number
auto_transform

transform the imported data with auto_transform()
...

arguments passed on to methods
sheet

Excel sheet to import, defaults to first sheet
range

a cell range to read from, allows typical Excel ranges such as "B3:D87" and "Budget!B2:G14"
datenames

language of the date names, such as weekdays and months
dateformat

expected date format, will be coerced with format_datetime()
timeformat

expected time format, will be coerced with format_datetime()
decimal.mark

separator for decimal numbers
big.mark

separator for thousands
timezone

expected time zone
na

values to interpret as NA
skip

number of first rows to skip
sep

character to separate values in a row
col_select

columns to select, supports the tidyselect language)
header

use first row as header
startrow

first row to start importing
search

an ODATA filter, ignores sort and defaults to search only mails with attachments
search_subject

a character, equal to search = "subject:(search_subject)", case-insensitive
search_from

a character, equal to search = "from:(search_from)", case-insensitive
search_when

a Date vector of size 1 or 2, equal to search = "received:date1..date2", see Examples
search_attachment

a character to use a regular expression for attachment file names
folder

email folder name to search in, defaults to Inbox of the current user by calling get_inbox_name()
n

maximum number of emails to search
sort

initial sorting
account

a Teams account from Azure or an AzureAuth Microsoft 365 token, e.g. retrieved with certeprojects::connect_teams()
url

remote location of any data set, can also be a (non-raw) GitHub/GitLab link
full_teams_path

a full path in Teams, including the Team name and the channel name. Leave blank to use interactive mode, which allows file/folder picking from a list in the console.

Details

Importing any unlisted filetype using import() requires the rio package to be installed.

Importing an Excel file using import_xlsx() or import_excel() requires the readxl package to be installed.

Importing an SPSS file using import_sav() or import_spss() requires the haven package to be installed.

Importing a Feather file using import_feather() requires the arrow package to be installed. Apache Feather provides efficient binary columnar serialization for data sets, enabling easy sharing data across data analysis languages (such as between Python and R). Use the col_select argument (which supports the tidyselect language) for specific data selection to improve importing speed.

Importing the clipboard using import_clipboard() requires the clipr package to be installed.

Importing mail attachments using import_mail_attachment() requires the certemail package to be installed. It calls download_mail_attachment() internally and saves the attachment to a temporary folder. For all folder names, run: sapply(certemail::connect_outlook()$list_folders(), function(x) x$properties$displayName).

The import_url() function tries to download the file first, after which it will be imported using the appropriate ⁠import_*()⁠ function.

The import_teams() function uses certeprojects::teams_download_file() to provide an interactive way to select a file in any Team, to download the file, and to import the file using the appropriate ⁠import_*()⁠ function.

See Also

export()

Examples

export_csv(iris)
import_csv("iris") |> head()

# the above is equal to:
# export(iris, "iris.csv")
# import("iris.csv") |> head()


# row names are also supported
export_csv(mtcars)
import_csv("mtcars") |> head()


# Apache's Feather format is column-based
# and allow for specific and fast file reading
library(dplyr, warn.conflicts = FALSE)
starwars |> export_feather()
import("starwars.feather",
       col_select = starts_with("h")) |> 
  head()
  

# (cleanup)
file.remove("iris.csv")
file.remove("mtcars.csv")
file.remove("starwars.feather")

## Not run: 

# ---- Microsoft Teams support -------------------------------------------

# IMPORTING

# import from Teams by picking a folder interactively from any Team
x <- import_teams()

# to NOT pick a Teams folder (e.g. in non-interactive mode), set `full_teams_path`
x <- import_teams(full_teams_path = "MyTeam/MyChannel/MyFolder/MyFile.xlsx")


# EXPORTING

# export to Teams by picking a folder interactively from any Team
mtcars |> export_teams()

# the default is RDS, but you can set `filename` to specify yourself
mtcars |> export_teams("mtcars.xlsx")

# to NOT pick a Teams folder (e.g. in non-interactive mode), set `full_teams_path`
mtcars |> export_teams("mtcars.xlsx", full_teams_path = "MyTeam/MyChannel/MyFolder")
mtcars |> export_teams(full_teams_path = "MyTeam/MyChannel/MyFolder")


## End(Not run)

Vectorised Pattern Matching with Keyboard Shortcut

Description

Convenient wrapper around grepl() to match a pattern: x %like% pattern. It always returns a logical vector and is always case-insensitive (use x %like_case% pattern for case-sensitive matching). Also, pattern can be as long as x to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.

Usage

like(x, pattern, ignore.case = TRUE)

x %like% pattern

x %unlike% pattern

x %like_case% pattern

x %unlike_case% pattern

Arguments

x

a character vector where matches are sought, or an object which can be coerced by as.character() to a character vector.
pattern

a character vector containing regular expressions (or a character string for fixed = TRUE) to be matched in the given character vector. Coerced by as.character() to a character string if possible.
ignore.case

if FALSE, the pattern matching is case sensitive and if TRUE, case is ignored during matching.

Details

These like() and ⁠%like%⁠/⁠%unlike%⁠ functions:

  • Are case-insensitive (use ⁠%like_case%⁠/⁠%unlike_case%⁠ for case-sensitive matching)

  • Support multiple patterns

  • Check if pattern is a valid regular expression and sets fixed = TRUE if not, to greatly improve speed (vectorised over pattern)

  • Always use compatibility with Perl unless fixed = TRUE, to greatly improve speed

Using RStudio? The ⁠%like%⁠/⁠%unlike%⁠ functions can also be directly inserted in your code from the Addins menu and can have its own keyboard shortcut like Shift+Ctrl+L or Shift+Cmd+L (see menu Tools > ⁠Modify Keyboard Shortcuts...⁠). If you keep pressing your shortcut, the inserted text will be iterated over ⁠%like%⁠ -> ⁠%unlike%⁠ -> ⁠%like_case%⁠ -> ⁠%unlike_case%⁠.

Value

A logical vector

Source

Idea from the like function from the data.table package, although altered as explained in Details.

See Also

grepl()

Examples

a <- "This is a test"
b <- "TEST"
a %like% b
b %like% a

# also supports multiple patterns
a <- c("Test case", "Something different", "Yet another thing")
b <- c(     "case",           "diff",      "yet")
a %like% b
a %unlike% b

a[1] %like% b
a %like% b[1]

Microorganisms Code from GLIMS10

Description

This function is analogous to all ⁠mo_*⁠ functions of the AMR package, see AMR::mo_property().

Usage

mo_glims(
  x,
  language = AMR::get_AMR_locale(),
  keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
  ...
)

Arguments

x

any character (vector) that can be coerced to a valid microorganism code with as.mo(). Can be left blank for auto-guessing the column containing microorganism codes if used in a data set, see Examples.
language

language to translate text like "no growth", which defaults to the system language (see get_AMR_locale())
keep_synonyms

a logical to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is FALSE, which will return a note if old taxonomic names were processed. The default can be set with the package option AMR_keep_synonyms, i.e. options(AMR_keep_synonyms = TRUE) or options(AMR_keep_synonyms = FALSE).
...

other arguments passed on to as.mo(), such as 'minimum_matching_score', 'ignore_pattern', and 'remove_from_input'

Examples

mo_glims("E. coli")

library(dplyr, warn.conflicts = FALSE)
data.frame(mo = c("ESCCOL", "Staph aureus")) |>
  mutate(glims = mo_glims())
  
# even works for non-existing entries in AMR package
mo_glims("Streptococcus mitis/oralis")
AMR::as.mo("Streptococcus mitis/oralis")
AMR::mo_genus("Streptococcus mitis/oralis")
AMR::mo_gramstain("Streptococcus mitis/oralis")

P-symbol format as asterisk

Description

P-symbol format as asterisk

Usage

p_symbol(p, emptychar = " ")

Arguments

p

numeric value between 0 and 1
emptychar

sign to be displayed for 0.1 < p < 1.0

Check Privacy of Plain Files

Description

Checks if files contain privacy sensitive data and moves them to a 'vault folder' if this is the case.

Usage

privacy_check(
  path = getwd(),
  vault = paste0(path, "/vault"),
  log_file = paste0(vault, "/_privacy_log.txt"),
  suspicious_cols = "(zip.*code|postcode|bsn|geboortedatum)"
)

Arguments

path

path to check
vault

path to vault
log_file

path to log file where details will be written to
suspicious_cols

regular expression to match suspicious column names

Read Certe Secret From File

Description

This function reads from a local or remote YAML file, as set in the environmental variable "secrets_file".

Usage

read_secret(property, file = Sys.getenv("secrets_file"))

Arguments

property

the property to read, case-sensitive
file

either a character string naming a file or a connection open for writing

Details

In the secrets file, the property name and value have to be separated with a colon (:), as is intended in YAML files.

The default value for file is the environmental variable "secrets_file".

The file will be read using read_yaml(), which allows almost any local path or remote connection (such as websites).

Examples

# for this example, create a temporary 'secrets' file
my_secrets_file <- tempfile(fileext = ".yaml")
Sys.setenv(secrets_file = my_secrets_file)
writeLines(c("tenant_id: 8fb3c03060e02e89",
             "default_users: user_1"),
           my_secrets_file)

read_secret("tenant_id")
read_secret("default_users")

Return Reference Directory

Description

Returns the relative reference directory for non-projects.

Usage

ref_dir(sub = "")

Arguments

sub

relative subfolder or file

Details

This function returns the absolute path using tools::file_path_as_absolute().

Temporarily Remember Objects

Description

Can be used in dplyr-syntax to remember values and objects for later use. Objects are (temporarily) stored in the certetoolbox package environment.

Usage

remember(.data, ...)

recall(x = NULL, delete = TRUE)

Arguments

.data

data.frame
...

value(s) to be remembered
x

value to be recalled
delete

a logical to indicate whether the delete value after recalling

Details

values can be saved with remember() and recalled (and deleted) with recall().

Examples

library(dplyr, warn.conflicts = FALSE)

x <- mtcars %>% remember(nrow(.)) 
recall()
recall() # value removed
x <- mtcars %>% remember(n = nrow(.)) 
recall(n)
recall(n) # value removed

## Not run: 
 tbl %>%
   filter(...) %>%
   remember(rows = nrow(.)) %>%
   group_by(...) %>%
   summarise(...) %>%
   plot2(title = "Test",
         subtitle = paste("n =", recall(rows)))

## End(Not run)

Human-readable File Size

Description

Formats bytes into human-readable units, from "kB" (10^3) to "YB" (10^23).

Usage

size_humanreadable(bytes, decimals = 1, decimal.mark = dec_mark())

Arguments

bytes

number of bytes
decimals

precision, not used for bytes and kilobytes
decimal.mark

decimal mark to use, defaults to dec_mark()

Details

If using object.size() on an object, this function is equal to using format2() to format the object size.

Examples

size_humanreadable(c(12, 1234, 123456, 12345678))

size_humanreadable(1024 ^ c(0:4))

Format Data Set as Flextable

Description

Format a data.frame as flextable() with Certe style, bold headers and Dutch number formats. This function can also transform existing flextable and gtsummary objects to allow the formatting provided in this tbl_flextable() function.

Usage

tbl_flextable(
  x,
  row.names = rownames(x),
  row.names.bold = TRUE,
  rows.italic = NULL,
  rows.bold = NULL,
  rows.height = NULL,
  rows.fill = NULL,
  rows.zebra = TRUE,
  row.total = FALSE,
  row.total.name = "Totaal",
  row.total.function = sum,
  row.total.widths = NULL,
  row.total.bold = TRUE,
  row.extra.header = list(values = NULL, widths = 1),
  row.extra.footer = list(values = NULL, widths = 1),
  column.names = colnames(x),
  column.names.bold = TRUE,
  columns.width = NULL,
  columns.percent = NULL,
  columns.italic = NULL,
  columns.bold = NULL,
  columns.fill = NULL,
  columns.zebra = FALSE,
  column.total = FALSE,
  column.total.name = "Totaal",
  column.total.function = sum,
  column.total.bold = TRUE,
  align = "c",
  align.part = "all",
  caption = "",
  na = "",
  logicals = c("X", ""),
  round.numbers = 2,
  round.percent = 1,
  format.dates = "d mmm yyyy",
  decimal.mark = dec_mark(),
  big.mark = big_mark(),
  font.family = "Source Sans Pro",
  font.size = 9,
  font.size.header = font.size + 1,
  values.colour = NULL,
  values.fill = NULL,
  values.bold = NULL,
  values.italic = NULL,
  autofit = is.null(columns.width) & is.null(rows.height),
  autofit.fullpage = TRUE,
  autofit.fullpage.width = 16,
  vline = NULL,
  vline.part = c("body", "footer"),
  theme = current_markdown_colour(),
  colours = list(rows.fill.even = paste0(theme, "6"), rows.fill.odd = paste0(theme, "5"),
    columns.fill = paste0(theme, "5"), values.fill = paste0(theme, "3"), values.colour =
    theme, vline.colour = theme, hline.colour = theme, header.fill = theme, header.colour
    = "white", vline.header.colour = "white"),
  split.across.pages = NROW(x) > 37,
  print = !interactive(),
  ...
)

## S3 method for class 'certetoolbox_flextable'
print(x, use_knitr = !is_latex_output(), ...)

Arguments

x

a data.frame or a flextable object or a gtsummary object
row.names

row names to be displayed. Will be 1:nrow(x) if set to TRUE, but can be a vector of values.
row.names.bold

display row names in bold
rows.italic

column indexes of rows in italics
rows.bold

column indexes of rows in bold
rows.height

height of the rows in centimetres
rows.fill

the column indices of rows to be shaded
rows.zebra

banded rows in the body - equivalent to rows.fill = seq(2, nrow(x), 2)
row.total

add a row total (at the bottom of the table)
row.total.name

name of the row total
row.total.function

function used to calculate all numeric values per column (non-numeric columns are skipped)
row.total.widths

cell width in row total
row.total.bold

bold formatting of row total
row.extra.header

an extra header to be displayed above the table
row.extra.footer

an extra footer to show below the table
column.names

column names to be displayed. Can also be a named vector where the names are existing columns, or indices of columns. When this vector is smaller than ncol(x), only the first length(column.names) are replaced. When this vector is longer than ncol(x), all column names are replaced
column.names.bold

display column names in bold
columns.width

width of columns. For autofit.fullpage = TRUE, these are proportions to autofit.fullpage.width. For autofit.fullpage = FALSE, these are centimeters
columns.percent

display the column indices as percentages using format2() - example: columns.percent = c(2, 3)
columns.italic

column indices of columns to be displayed in italics
columns.bold

column indices of columns in bold
columns.fill

the column indices of rows to be shaded
columns.zebra

banded columns - equivalent to columns.fill = seq(2, ncol(x), 2)
column.total

adding a column total (to the right of the table)
column.total.name

name of the column total
column.total.function

function used to calculate all numeric values per row
column.total.bold

bold formatting of column total
align

default is "c", which aligns everything centrally. Use "r", "l", "c" and "j"/"u" (justify/align) to change alignment. Can be a vector or a character (like "lrrrcc")
align.part

part of the table where the alignment should take place ("all", "header", "body", "footer")
caption

table caption
na

text for missing values
logicals

vector with two values that replace TRUE and FALSE
round.numbers

number of decimal places to round up for numbers
round.percent

number of decimal places to round to when using columns.percent
format.dates

see format2()
decimal.mark

decimal separator, defaults to dec_mark()
big.mark

thousands separator, defaults to big_mark()
font.family

table font family
font.size

table font size
font.size.header

font size of header
values.colour, values.fill, values.bold, values.italic

values to be formatted
autofit

format table in width automatically. This will apply autofit().
autofit.fullpage

display table across width of page
autofit.fullpage.width

set number of centimetres to width of table
vline

indices of columns to have a vertical line to their right
vline.part

part of the table where the vertical lines should be placed ("all", "header", "body", "footer")
theme

a Certe colour theme, defaults to current_markdown_colour() which determines the Certe colour based on a markdown YAML header and defaults to "certeblauw". Can also be "certeroze", "certegroen", etc. This will set the list in colours and will be ignored if colours is set manually. Can be set to "white" for a clean look.
colours

a list with the following named character values: rows.fill.even, rows.fill.odd, columns.fill, values.fill, and values.colour. All values will be evaluated with colourpicker().
split.across.pages

a logical whether tables are allowed to split across page. This argument only has effect for PDF output.
print

forced printing (required in a for loop), default is TRUE in non-interactive sessions
...

not used
use_knitr

use the knitr package for printing. Ignored when in an interactive session. If FALSE, an internal certetoolbox function will be used to convert the LaTeX longtable that would print across multiple PDF pages. If in a non-interactive session where the output is non-LaTeX, the knitr package will always be used.

Details

Run tbl_markdown() on a flextable object to transform it into markdown for use in Quarto or R Markdown reports. If print = TRUE in non-interactive sessions (Quarto or R Markdown), the flextable object will also be printed in markdown.

The value for theme is dependent on whether a colour is set in the markdown YAML header. Otherwise, use theme to set a Certe colour theme, defaults to "certeblauw": 

# from the example below
tbl_flextable(df)

flextableblauw.png

tbl_flextable(df, theme = "certeroze")

flextableroze.png

tbl_flextable(df, theme = "certegeel")

flextablegeel.png

tbl_flextable(df, theme = "certegroen", vline = c(2:3))

flextablegroen.png

tbl_flextable(
  df,
  theme = "certelila",
  row.total = TRUE,
  row.total.function = median,
  round.numbers = 4,
  row.extra.header = list(values = LETTERS[1:5])
)

flextablelila.png

Value

flextable object

See Also

flextable()

Examples

## Not run: 

# generate a data.frame
df <- data.frame(text = LETTERS[1:10],
                 `decimal numbers` = runif(10, 0, 10),
                 `whole numbers` = as.integer(runif(10, 0, 10)),
                 `logical values` = as.logical(round(runif(10, 0, 1))),
                 dates = today() - runif(10, 200, 2000),
                 stringsAsFactors = FALSE)

# default
tbl_flextable(df)      # dataset has no row names
tbl_flextable(mtcars)  # dataset has row names

# print in markdown
df |> 
  tbl_flextable() |> 
  tbl_markdown()
  
# transform a gtsummary to a flextable
iris |>
  tbl_gtsummary(Species, add_p = TRUE) |>
  tbl_flextable()
  
# extra formatting
tbl_flextable(df,
              logicals = c("X", "-"),     # replaces TRUE en FALSE
              values.colour = "X",
              values.fill = "X",
              row.names = "S. aureus",
              columns.italic = 1,
              format.dates = "ddd dd-mm-yy",
              round.numbers = 3)

# row totals
tbl_flextable(df,
              row.total = TRUE,           # add row total
              row.total.function = max,   # instead of sum()
              row.total.name = "Maximum", # also works with dates
              columns.percent = 2,        # 2nd column as percentages
              round.percent = 0)          # rounding percentages

# column names
tbl_flextable(df,
              column.names = c("1" = "Column 1",
                               "2" = "Column 2",
                               dates = "DATES!"))
tbl_flextable(df,
              column.names = LETTERS)

# vertical lines, alignment and row names
tbl_flextable(df,
              align = "lrrcc", # also works: c("l", "r", "r", "c", "c")
              font.size = 12,
              vline = c(2, 4),
              vline.part = "all",
              row.names = paste("Experiment", 1:10))

# width of cells and table
tbl_flextable(data.frame(test1 = "A", test2 = "B"),
              vline = 1,
              autofit.fullpage.width = 16, # default values in cm
              columns.width = c(1, 3))     # ratio; cells become 4 and 12 cm

tbl_flextable(data.frame(test1 = "A", test2 = "B"),
              vline = 1,
              autofit.fullpage = FALSE,    # no fullpage autofit
              columns.width = c(1, 3))     # cells become 1 and 3 cm
              
# adding extra header or footer
tbl_flextable(data.frame(test1 = "A", test2 = "B"),
              row.extra.header = list(values = c("Header", "Header"),
                                      widths = c(1, 1)),
              row.extra.footer = list(values = c("Footer", "Footer"),
                                      widths = c(1, 1)))

## End(Not run)

Summarise Table as gtsummary

Description

Summarise a data.frame as gtsummary with Dutch defaults. These objects are based on the gt package by RStudio. To provide Certe style and compatibility with MS Word, use tbl_flextable() to transform the gtsummary object.

Usage

tbl_gtsummary(
  x,
  by = NULL,
  label = NULL,
  digits = 1,
  ...,
  language = "nl",
  column1_name = "Eigenschap",
  add_n = FALSE,
  add_p = FALSE,
  add_ci = FALSE,
  add_overall = FALSE,
  decimal.mark = dec_mark(),
  big.mark = big_mark()
)

Arguments

x

a data.frame
by

A column name (quoted or unquoted) in data. Summary statistics will be calculated separately for each level of the by variable (e.g. by = trt). If NULL, summary statistics are calculated using all observations. To stratify a table by two or more variables, use tbl_strata()
label

List of formulas specifying variables labels, e.g. list(age ~ "Age", stage ~ "Path T Stage"). If a variable's label is not specified here, the label attribute (attr(data$age, "label")) is used. If attribute label is NULL, the variable name will be used.
digits

List of formulas specifying the number of decimal places to round summary statistics. If not specified, tbl_summary guesses an appropriate number of decimals to round statistics. When multiple statistics are displayed for a single variable, supply a vector rather than an integer. For example, if the statistic being calculated is "{mean} ({sd})" and you want the mean rounded to 1 decimal place, and the SD to 2 use digits = list(age ~ c(1, 2)). User may also pass a styling function: digits = age ~ style_sigfig
...

Arguments passed on to gtsummary::tbl_summary()
language

the language to use, defaults to Dutch
column1_name

name to use for the first column
add_n

add the overall N using gtsummary::add_n()
add_p

add the p values gtsummary::add_p() (tests will be determined automatcally)
add_ci

add the confidence interval using gtsummary::add_ci()
add_overall

add the overall statistics using gtsummary::add_overall()
decimal.mark

decimal separator, defaults to dec_mark()
big.mark

thousands separator, defaults to big_mark()

Details

tbl_gtsummary() creates a summary table with gtsummary::tbl_summary(), to which different extra columns can be added e.g. with add_p = TRUE and add_overall = TRUE.

Examples

# These examples default to the Dutch language

iris |>
  tbl_gtsummary()

iris |> 
  tbl_gtsummary(Species, add_p = TRUE)
  
iris |> 
  tbl_gtsummary(Species, add_n = TRUE)
  
# support strata by providing 
iris2 <- iris
iris2$Category <- sample(LETTERS[1:2], size = 150, replace = TRUE)
head(iris2)

iris2 |> 
  tbl_gtsummary(c(Category, Species))

# transform to flextable 
# (formats to Certe style and allows rendering to Word)
iris |> 
  tbl_gtsummary(Species) |> 
  tbl_flextable()

Print Table as Markdown, LaTeX of HTML

Description

Prints a data.frame as Markdown, LaTeX or HTML using knitr::kable(), with bold headers and Dutch number formats.

Usage

tbl_markdown(
  x,
  row.names = rownames(x),
  column.names = colnames(x),
  align = NULL,
  padding = 2,
  caption = "",
  na = "",
  type = "markdown",
  format.dates = "dd-mm-yyyy",
  decimal.mark = dec_mark(),
  big.mark = big_mark(),
  logicals = c("X", ""),
  columns.percent = NA,
  column.names.bold = TRUE,
  round.numbers = 2,
  round.percent = 1,
  newlines.leading = 0,
  newlines.trailing = 2,
  print = TRUE
)

Arguments

x

a data.frame or a flextable object or a gtsummary object
row.names

row names to be displayed
column.names

column names to be displayed
align

alignment of columns (default: numbers to the right, others to the left)
padding

extra cell padding
caption

caption of table
na

text for missing values (default: "")
type

type of formatting the table - valid options are "latex", "html", "markdown", "pandoc" and "rst"
format.dates

formatting of dates, will be evaluated with format2()
decimal.mark

decimal separator, defaults to dec_mark()
big.mark

thousands separator, defaults to big_mark()
logicals

vector with two values that replace TRUE and FALSE
columns.percent

display the column indices as percentages using format2() - example: columns.percent = c(2, 3)
column.names.bold

display column names in bold
round.numbers

number of decimal places to round up for numbers
round.percent

number of decimal places to round to when using columns.percent
newlines.leading

number of white lines to print before the table
newlines.trailing

number of white lines to print after the table
print

only useful when input is a Flextable: force printing

Details

When in an R Markdown rapport a table is printed using this function, column headers only print well if newlines.leading >= 2, or by manually using cat("\\n\\n") before printing the table.

Value

character

See Also

knitr::kable()

Examples

tbl_markdown(mtcars[1:6, 1:6], padding = 1)

Update Data Set Based on Row Numbers

Description

Update a data.frame using specific integers for row numbers or a vectorised filter. Also supports dplyr groups. see Examples.

Usage

## S3 method for class 'data.frame'
update(object, rows, ...)

Arguments

object

a data.frame
rows

row numbers or a logical vector
...

arguments passed on to mutate()

Examples

iris |> 
  update(3:4, Species = c("A", "B")) |> 
  head()
  
iris |> 
  update(Species == "setosa" & Sepal.Length > 5,
         Species = "something else") |> 
  head()
  
if (require("dplyr")) {

  # also supports dplyr groups:
  iris |> 
    group_by(Species) |>
    # update every 2nd to 4th row in group
    update(2:4, Species = "test") |> 
    # groups will be updated automatically
    count()

}