Package 'WAACHShelp' reference manual

Title:	Various helper functions to aid reproducibility in WAACHS analysis-related tasks
Description:	Data analysis requirements for the WAACHS project often involves a range of complex and nuanced functions. This is conflated by different analysts working across similar tasks at different points in time. This (local) package aims to ameliorate this with a set of functions applicable to all analysts (who use R)---aiding reproducibility.
Authors:	Zac Dempsey [aut, cre]
Maintainer:	Zac Dempsey <[email protected]>
License:	MIT + file LICENSE
Version:	1.5.3
Built:	2026-05-28 08:29:05 UTC
Source:	https://github.com/The-Kids-Biostats/WAACHShelp

Add rows to labelled data set.

Description

This function was deprecated because it was no longer required by analysts.

Usage

add_rows2(..., id = NULL)
add_rows2(..., id = NULL)

Arguments

...

Other parameters to parse to function.

id

Default NULL

Details

Almost identical to the original dplyr::add_row but also looks for format.sas attribute which haven provides when loading as SAS dataset.

Created by PV (2023).

Value

SAS labelled dataframe

Custodial data flagging function

Description

This function flags whether a carer record (custodial, per its name) exists when a child is of a certain age.

Usage

aducust_flag(
  data,
  dobmap,
  carer_map,
  flag_name = "carer_aducust",
  child_id_var = "rootnum",
  carer_id_var = "carer_rootnum",
  data_start_date = "ReceptionDate",
  data_end_date = "DischargeDate",
  dobmap_dob_var = "dob",
  child_start_age = 0,
  child_end_age = 18,
  carer_summary = FALSE,
  any_carer_summary = FALSE
)
aducust_flag(
  data,
  dobmap,
  carer_map,
  flag_name = "carer_aducust",
  child_id_var = "rootnum",
  carer_id_var = "carer_rootnum",
  data_start_date = "ReceptionDate",
  data_end_date = "DischargeDate",
  dobmap_dob_var = "dob",
  child_start_age = 0,
  child_end_age = 18,
  carer_summary = FALSE,
  any_carer_summary = FALSE
)

Arguments

data

Input dataset (carer aducust).

dobmap

DOBmap file at the child level.

carer_map

Mapping file with columns "child ID", "carer ID". Can have multiple rows per child (e.g., one per carer 1, carer 2, NEWBMID).

flag_name

Name of flagging variable to return. Default "carer_aducust".

child_id_var

Variable denoting "child ID". Must exist and be called the same thing in dobmap and carer_map. Default "rootnum".

carer_id_var

Variable denoting "carer ID". Must exist in carer_map. Default "carer_rootnum".

data_start_date

Start date to consider in data. Corresponds to aducust start date. Default "ReceptionDate".

data_end_date

End date to consider in data. Corresponds to aducust end date. Default "DischargeDate".

dobmap_dob_var

Date of birth (DOB) variable in dobmap. Default "dob".

child_start_age

Numeric. Start (minimum) age (years) to consider for flagging (default 0).

child_end_age

Numeric. End (maximum) age (years) to consider for flagging (default 18).

carer_summary

Collapse aducust flags within carer (i.e., for each carer_id_var). Default FALSE.

any_carer_summary

Collapse aducust flags across carers. Default FALSE.

Details

While it is designed for use with flagging carer custodial records, it can be applied in many other circumstances where flagging of a carer (or otherwise) record exists when a child (or otherwise) is of a certain age.

For more details, see the vignette.

Value

Flagged dataframe.

Note

If carer_summary is TRUE, then we are flagging whether a specific carer has any aducust record.
Therefore, we are assessing whether a specific carer has any aducust records.
If any_carer_summary is TRUE, then we are flagging whether any carer (if multiple) have any aducust records.
If any_carer_summary is TRUE, carer_summary will be ignored.

Examples

## Not run: 
# Example 1: Basic use
aducust_flag(data = carer_aducust %>% rename(carer_rootnum = root),
             dobmap = dobmap,
             carer_map = child_carer_map,
             child_id_var = "NEWUID",
             carer_id_var = "carer_rootnum",
             carer_summary = FALSE,
             any_carer_summary = TRUE
)

## End(Not run)
## Not run: 
# Example 1: Basic use
aducust_flag(data = carer_aducust %>% rename(carer_rootnum = root),
             dobmap = dobmap,
             carer_map = child_carer_map,
             child_id_var = "NEWUID",
             carer_id_var = "carer_rootnum",
             carer_summary = FALSE,
             any_carer_summary = TRUE
)

## End(Not run)

Education flagging function

Description

This function serves to flag whether an individual is expected to have education data for every year within a "visibility window" based on their date of birth. The fact that "staggered" (June/July) years were introduced in Western Australia in 1997 has been built into the function.

Usage

check_att(
  dob,
  visibility_min = 2008,
  visibility_max = 2014,
  show_expectation = TRUE
)
check_att(
  dob,
  visibility_min = 2008,
  visibility_max = 2014,
  show_expectation = TRUE
)

Arguments

dob

Date of individual. Must be class "Date".

visibility_min

Minimum year in visibility window. Default 2008.

visibility_max

Maximum year in visibility window. Default 2014.

show_expectation

Logical. Show flagged dataframe with expected flag per year in visibility window. Default TRUE.

Value

Flagged dataframe.

Examples

check_att(dob = as.Date("2000-05-30"))
check_att(dob = as.Date("2000-05-30"))

Create Template for Quarto Markdown

Description

This function creates a HTML or Word template in the current working directory using a specified Quarto extension. It copies the template files to the ⁠_extensions/⁠ directory and generates a new Quarto markdown (.qmd) file.

Usage

create_markdown(file_name = NULL, directory = "reports", ext_name = "html")
create_markdown(file_name = NULL, directory = "reports", ext_name = "html")

Arguments

file_name

A string. The name of the new Quarto markdown (.qmd) file. This must be provided.

directory

A string. The name of the directory to plate the files. Default is NULL. Requires user specification

ext_name

A string. The extension type to create. Default "html" (alternatives: "word").

Details

Adapted from create_template from https://github.com/The-Kids-Biostats/thekidsbiostats.

The function first checks whether a ⁠_extensions/⁠ directory exists in the current working directory. If not, it creates one. It then copies the necessary extension files from the package's internal data to the ⁠_extensions/⁠ directory. Finally, it creates a new Quarto markdown file based on the extension template.

By default, the reports folder will be selected to house the report.

For more details, see the vignette.

Note

The function assumes that the package WAACHShelp contains the necessary extension files under ⁠ext_qmd/_extensions/⁠.

Examples

## Not run: 
create_markdown(file_name = "my_doc", ext_name = "word")

## End(Not run)

## Not run: 
create_markdown(file_name = "my_doc", ext_name = "word")

## End(Not run)

Create a new project structure with extensions

Description

Adapted from create_project from https://github.com/The-Kids-Biostats/thekidsbiostats.

Usage

create_project(
  project_name = "standard",
  data = TRUE,
  reports = TRUE,
  output = TRUE,
  documentation = TRUE,
  other_folders = NULL,
  R = TRUE
)
create_project(
  project_name = "standard",
  data = TRUE,
  reports = TRUE,
  output = TRUE,
  documentation = TRUE,
  other_folders = NULL,
  R = TRUE
)

Arguments

project_name

String. Default "standard". Name of R Project object to be created.

data

Logical. If TRUE, a data directory will be created in the selected folder. Defaults to TRUE.

reports

Logical. If TRUE, a reports directory will be created in the selected folder. Defaults to TRUE.

output

Logical. If TRUE, an output direction will be created in the selected folder. Defaults to TRUE.

documentation

Logical. If TRUE, a documentation directory will be created in the selected folder. Defaults to TRUE.

other_folders

Vector of strings that contain any other folders that should also be created. Elements should be unique. Default NULL.

R

Logical. If TRUE, an R directory will be created in the selected folder. Defaults to TRUE.

Details

This function creates a directory structure for a new project based on a specified extension. It can also create additional folders such as data, reports, output and documentation. The function copies specific files and folders from the chosen extension to the project directory.

For more details, see the vignette.

Note

An interactive window will appear prompting the user to select the folder where the project structure should be created. The default is the current working directory.

Examples

## Not run: 
create_project(project_name = "investigation_x", other_folders = c("folder1", "folder2"))

## End(Not run)

## Not run: 
create_project(project_name = "investigation_x", other_folders = c("folder1", "folder2"))

## End(Not run)

ICD Classification Data set

Description

Data set specifying the ICD (9 & 10) codes for different events in the morbidity data set.

Usage

data(icd_dat)
data(icd_dat)

Format

A data frame where rows correspond to an event and columns correspond to the variable name, morbidity search parameters (diagnosis/ediag, ecode) and ICD code breakdown

num: Counter variable representing the number of rows associated with any given var
classification: Classification type
var: Variable name
broad_type: Type of diagnosis field this ICD code corresponds to (diagnosis & ediag = "diag_ediag", ecode = "ecode")
letter: Letter of ICD code (purely numeric is empty string "")
lower: Lower bound of numeric element of ICD code
upper: Upper bound of numeric element of ICD code

...

Source

Generated internally by package

ICD flagging function

Description

This function serves to add flags to an input data set (morbidity at this stage) pursuant to ICD codes. Flags can be added for general categories based on pre-established ICD codes (e.g., any mental health contact, any substance-related contact, any alcohol/tobacco-related contact etc.) or add a custom set of ICD codes. The file with these pre-established ICD codes are saved as an .RData file and are trivial to change.

Usage

icd_morb_flag(
  data,
  dobmap = NULL,
  flag_category,
  flag_other_varname,
  diag_type,
  diag_type_custom_vars = NULL,
  diag_type_custom_params,
  under_age = FALSE,
  age = 18,
  person_summary = FALSE,
  id_var = "rootnum",
  morb_date_var = "subadm",
  dobmap_dob_var = "dob",
  dobmap_other_vars = NULL
)
icd_morb_flag(
  data,
  dobmap = NULL,
  flag_category,
  flag_other_varname,
  diag_type,
  diag_type_custom_vars = NULL,
  diag_type_custom_params,
  under_age = FALSE,
  age = 18,
  person_summary = FALSE,
  id_var = "rootnum",
  morb_date_var = "subadm",
  dobmap_dob_var = "dob",
  dobmap_other_vars = NULL
)

Arguments

data

Input dataset (morbidity).

dobmap

DOBmap file corresponding to input dataset.

flag_category

Type of flag to generate. Takes values from reference file (e.g., MH_morb, Sub_morb, etc.) or "Other" for custom ICD specification and flagging.

flag_other_varname

Flag variable name (specified only When flag_category == "Other"). Input as character string.

diag_type

Diagnosis type. Select from "principal diagnosis", (all) "additional diagnoses", "external cause of injury", "custom".

diag_type_custom_vars

Variables to search across when diag_type == "custom".

diag_type_custom_params

Search parameters to search across when diag_type == "custom". Must be a list where the keys are the variable names and values are the inputs to WAACHShelp::val_filt. Can also be a list of lists where multiple ICD can be searched across for a single variable. See examples for specification.

under_age

Return additional variables corresponding to when participant was strictly under age y.o.. Uses DOBmap DOB and subadm morbidity admission date. Variables have suffix "_under{age}".

age

Numeric. Age (years) to consider for the under_age variable (default 18).

person_summary

Summarise results at a person-level.

id_var

Joining (ID) variable consistent between data and dobmap. Default rootnum.

morb_date_var

Hospital morbidity date variable in data. Default "subadm".

dobmap_dob_var

Date of birth (DOB) variable in dobmap. Default "dob".

dobmap_other_vars

Other variables to carry across from DOBmap file when joining to data. Default NULL. Can be a vector of strings.

Details

For more details, see the vignette.

Value

Flagged dataframe.

Examples

## Not run: 
# Example 1: Basic use
## Create any mental health or substance-related morbidity flag, "MH_morb"
## Searches "principal diagnosis", "additional diagnoses", "external cause of injury".
## Create additional flag for whether admission occurred when under 18 years of age
icd_morb_flag(
  data = morb,
  dobmap = dob,
  flag_category = "MH_morb",
  under_age = T,
  age = 18,
  dobmap_other_vars = c("xyz123", "abc456") # Also join `xyz123`, `abc456` from DOBmap
  )

# Example 2: Basic use
## Create any substance-related morbidity flag, "Sub_morb"
icd_morb_flag(data = morb,
              flag_category = "Sub_morb" # Create any MH contact flag
              )

# Example 3: Search  *principal diagnosis* and *first additional diagnosis*
# for a custom set of ICD codes
## Call this variable "test_var"
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = "custom",
              diag_type_custom_vars = c("diagnosis", "ediag20"),
              diag_type_custom_params = list("diagnosis" = list("letter" = "F",
                                                                "lower" = 0,
                                                                "upper" = 99.9999),
                                             "ediag20" = list("letter" = "",
                                                              "lower" = 0,
                                                              "upper" = 99.9999)),
              flag_other_varname = "test_var"
              )

# Example 4:
# Search only across primary diagnosis and
# (all) additional diagnosis fields for a custom set of ICD codes.
## Call this variable "test_var2"
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = c("principal diagnosis", "additional diagnoses"),
              diag_type_custom_params = list("principal diagnosis" = list("letter" = "F",
                                                                          "lower" = 0,
                                                                          "upper" = 99.9999),
                                             "additional diagnoses" = list("letter" = "",
                                                                           "lower" = 0,
                                                                           "upper" = 99.9999)),
              flag_other_varname = "test_var2"
              )

# Example 5: Search across (all) additional diagnosis fields and another random field, `dagger`
## Call this variable "test_var3"
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = c("custom", "additional diagnoses"),
              diag_type_custom_vars = "dagger",
              diag_type_custom_params = list("dagger" = list("letter" = "F",
                                                             "lower" = 0,
                                                             "upper" = 99.9999)),
              flag_other_varname = "test_var3"
              )

# Example 6: Searching across multiple ICD code types within a variable
## Call this variable "test_var4" -- replicating MH_morb flag
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = c("additional diagnoses",
                            "additional diagnoses",
                            "external cause of injury"),
              flag_other_varname = "test_var3",
              diag_type_custom_params =
              list("principal diagnosis" = list(list("letter" = "F",
                                                     "lower" = 0,
                                                     "upper" = 99.9999),
                                                list("letter" = "",
                                                     "lower" = 290,
                                                     "upper" = 319.9999)),
                    "additional diagnoses" = list(list("letter" = "F",
                                                       "lower" = 0,
                                                       "upper" = 99.9999),
                                                  list("letter" = "",
                                                       "lower" = 290,
                                                       "upper" = 319.9999)),
                    "external cause of injury" = list(list("letter" = "E",
                                                           "lower" = 950,
                                                           "upper" = 959.9999),
                                                      list("letter" = "X",
                                                           "lower" = 60,
                                                           "upper" = 84.9999))))

## End(Not run)
## Not run: 
# Example 1: Basic use
## Create any mental health or substance-related morbidity flag, "MH_morb"
## Searches "principal diagnosis", "additional diagnoses", "external cause of injury".
## Create additional flag for whether admission occurred when under 18 years of age
icd_morb_flag(
  data = morb,
  dobmap = dob,
  flag_category = "MH_morb",
  under_age = T,
  age = 18,
  dobmap_other_vars = c("xyz123", "abc456") # Also join `xyz123`, `abc456` from DOBmap
  )

# Example 2: Basic use
## Create any substance-related morbidity flag, "Sub_morb"
icd_morb_flag(data = morb,
              flag_category = "Sub_morb" # Create any MH contact flag
              )

# Example 3: Search  *principal diagnosis* and *first additional diagnosis*
# for a custom set of ICD codes
## Call this variable "test_var"
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = "custom",
              diag_type_custom_vars = c("diagnosis", "ediag20"),
              diag_type_custom_params = list("diagnosis" = list("letter" = "F",
                                                                "lower" = 0,
                                                                "upper" = 99.9999),
                                             "ediag20" = list("letter" = "",
                                                              "lower" = 0,
                                                              "upper" = 99.9999)),
              flag_other_varname = "test_var"
              )

# Example 4:
# Search only across primary diagnosis and
# (all) additional diagnosis fields for a custom set of ICD codes.
## Call this variable "test_var2"
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = c("principal diagnosis", "additional diagnoses"),
              diag_type_custom_params = list("principal diagnosis" = list("letter" = "F",
                                                                          "lower" = 0,
                                                                          "upper" = 99.9999),
                                             "additional diagnoses" = list("letter" = "",
                                                                           "lower" = 0,
                                                                           "upper" = 99.9999)),
              flag_other_varname = "test_var2"
              )

# Example 5: Search across (all) additional diagnosis fields and another random field, `dagger`
## Call this variable "test_var3"
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = c("custom", "additional diagnoses"),
              diag_type_custom_vars = "dagger",
              diag_type_custom_params = list("dagger" = list("letter" = "F",
                                                             "lower" = 0,
                                                             "upper" = 99.9999)),
              flag_other_varname = "test_var3"
              )

# Example 6: Searching across multiple ICD code types within a variable
## Call this variable "test_var4" -- replicating MH_morb flag
icd_morb_flag(data = morb,
              flag_category = "Other",
              diag_type = c("additional diagnoses",
                            "additional diagnoses",
                            "external cause of injury"),
              flag_other_varname = "test_var3",
              diag_type_custom_params =
              list("principal diagnosis" = list(list("letter" = "F",
                                                     "lower" = 0,
                                                     "upper" = 99.9999),
                                                list("letter" = "",
                                                     "lower" = 290,
                                                     "upper" = 319.9999)),
                    "additional diagnoses" = list(list("letter" = "F",
                                                       "lower" = 0,
                                                       "upper" = 99.9999),
                                                  list("letter" = "",
                                                       "lower" = 290,
                                                       "upper" = 319.9999)),
                    "external cause of injury" = list(list("letter" = "E",
                                                           "lower" = 950,
                                                           "upper" = 959.9999),
                                                      list("letter" = "X",
                                                           "lower" = 60,
                                                           "upper" = 84.9999))))

## End(Not run)

Summarise a dataframe to a person level

Description

In cases where we have a long data frame (i.e., multiple rows of data per participant) with a flag against each record (e.g., variable x = "Yes"/"No"), this function will collapse this dataframe to a participant level.

Usage

person_summary(data, flag_category, flag_category_val = "Yes", grouping_var)
person_summary(data, flag_category, flag_category_val = "Yes", grouping_var)

Arguments

data

Input dataframe.

flag_category

Name of the variable to perform classification on.

flag_category_val

String value of the flag_category variable the flag will be judged against. Defaults to "Yes".

grouping_var

Grouping ID variable that identifies potentially multiple records per participant.

Details

The collapsing is based on the value of flag_category_val and a grouping variable (participant ID) grouping_var.

Specifically, records will be collapsed such that:

If any record(s) for a participant is equal to flag_category_val, then return "Yes".
If all record(s) for a participant are not equal to flag_category_val, then return "No".

Examples

## Not run: 
person_summary(data = dat,
               flag_category = "variable_x",
               flag_category_val = "Yes",
               grouping_var = "record_id"
               )

## End(Not run)

## Not run: 
person_summary(data = dat,
               flag_category = "variable_x",
               flag_category_val = "Yes",
               grouping_var = "record_id"
               )

## End(Not run)

Compile metadata from dataframe

Description

This function was deprecated because it was no longer required by analysts.

Usage

proc_contents(df)
proc_contents(df)

Arguments

df

Dataframe to input.

Details

This little function pulls out the labels and formats from a dataframe and compiles this metadata as a dataframe.

Imitates the "proc contents" function of SAS.

Created by PV (2023).

Value

Dataframe

Examples

proc_contents(iris)

proc_contents(iris)

HTML summary table

Description

This function was deprecated because it was no longer required by analysts.

Usage

proc_freq(var1, data = NULL, sort = NULL, min.frq = 0)
proc_freq(var1, data = NULL, sort = NULL, min.frq = 0)

Arguments

var1

Name of the variable

data

Dataset that contains var1

sort

Optional argument which can take on "asc" or "desc" to indicate the type of sort required.

min.frq

Minimum frequency

Details

Renders a HTML table ready for plonking into a HTML or word document.

Renders output similar to the "proc freq" function of SAS.

Created by PV (2023).

Value

Dataframe

Examples

test = iris
proc_freq(Species, test)
test[1:4, "Species"] <- NA
proc_freq(Species, test)

test = iris
proc_freq(Species, test)
test[1:4, "Species"] <- NA
proc_freq(Species, test)

Save dataframe

Description

General function to save a dataset in usable formats across different platforms. Specifically, will return .csv, .sas7bdat, and .RDS files.

Usage

save_waachs(dataframe, path, filename)
save_waachs(dataframe, path, filename)

Arguments

dataframe

Input dataset to save.

path

Path to save file to.

filename

Name of the file to save.

Value

Saved object

Summary tabling function

Description

General tabling function to standardise output and formatting across analysts and projects. Based on gtsummary::tbl_summary

Usage

sum_tab(data, ...)
sum_tab(data, ...)

Arguments

data

Input dataset.

...

Any other argument relevant to gtsummary::tbl_summary.

Value

Summary table.

Summary function reminiscent of Stata's "tabset".

Description

This function was deprecated because it was no longer required by analysts.

Usage

sumfun(x, na.rm = TRUE, ...)
sumfun(x, na.rm = TRUE, ...)

Arguments

x

Vector from input dataframe to summarise.

na.rm

(Default TRUE) to remove NA (missing) observations from summary.

...

Any other arguments parsed into component base R functions.

Details

Created by PV (2023).

Value

Summary table with n (length), miss (number of missing observations), mean, sd (standard deviation), med (median), q25 (first quartile), q75 (third quartile), min (minimum value), max (maximum value).

Examples

sumfun(iris$Sepal.Length)
sumfun(iris$Sepal.Length)

Apply WAACHS theme to ggplot2 plots

Description

This function applies a custom theme to ggplot2 plots, incorporating colours to align with the project's visual identity.

Usage

theme_waachs(
  base_size = 12,
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)
theme_waachs(
  base_size = 12,
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

Arguments

base_size

Base font size

base_line_size

Base line size (default base_size/22)

base_rect_size

Base rectangle size (default base_size/22)

Details

The function determines the operating system and selects appropriate font names for Windows or other systems. It also adjusts colour scales.

Value

A list of ggplot2 theme elements and scale adjustments.

Examples


ggplot(mtcars,
       aes(x = mpg, y = wt, col = factor(cyl))) +
  geom_point() +
  theme_waachs()

ggplot(mtcars,
       aes(x = mpg, y = wt, col = factor(cyl))) +
  geom_point() +
  theme_waachs()

WAACHS transpose `gtsummary::tbl_summary` table

Description

Quick helper function to easily transpose gtsummary::tbl_summary tables. Best used in conjunction with WAACHShelp::waachs_table.

Usage

transpose_tblsum(tbl, ...)
transpose_tblsum(tbl, ...)

Arguments

tbl

tbl_summary input (of class c("tbl_summary", "gtsummary")).

...

Other parameters (not currently in use).

Value

data.frame with transposed summary table.

Examples

mtcars %>%
  mutate(cyl = as_factor(cyl)) %>%
  select(cyl, mpg, disp, hp, wt, am) %>%
  tbl_summary(by = cyl) %>%
  modify_header(label ~ "cyl") %>% # Re-label "Characteristic" by stratification variable ("cy")
  transpose_tblsum() %>%
  waachs_table()

mtcars %>%
  mutate(cyl = as_factor(cyl)) %>%
  select(cyl, mpg, disp, hp, wt, am) %>%
  tbl_summary(by = cyl) %>%
  modify_header(label ~ "cyl") %>% # Re-label "Characteristic" by stratification variable ("cy")
  transpose_tblsum() %>%
  waachs_table()

HTML summary table

Description

This function was deprecated because it was no longer required by analysts.

Usage

twoway(var1, var2, data = NULL, var2lab = NULL)
twoway(var1, var2, data = NULL, var2lab = NULL)

Arguments

var1

Vector of first variable

var2

Vector of second variable

data

Dataset containing var1 and var2

var2lab

Label for var2.

Details

Two-way table similar to the "proc freq" function of SAS with two variables

Created by PV (2023).

Value

Two-way table

Basic ICD code function

Description

This function is already used by the team, and filters alphanumeric ICD-9 and ICD-10 codes pursuant to requirements.

Usage

val_filt(input_vec, letter, lower, upper)
val_filt(input_vec, letter, lower, upper)

Arguments

input_vec

Vector of all admissible ICD codes to filter on.

letter

Letter to base filtration on (if purely numeric use empty string "")

lower

Lower bound on the numeric element of the ICD code (includes numerics >=lower).

upper

Upper bound on the numeric element of the ICD code (includes numerics <=upper).

Value

Vector with filtered ICD codes.

Examples

# Filter ICD codes in val3 to those between F10 and F10.9 (inclusive).
## Not run: 
val_filt(val3, "F", 10.0, 10.9)

## End(Not run)

# Filter ICD codes in val3 to those between F10 and F10.9 (inclusive).
## Not run: 
val_filt(val3, "F", 10.0, 10.9)

## End(Not run)

WAACHS colour palette

Description

Function to return discrete or continuous colour palette based on WAACHS logo colours.

Usage

waachs_palette(
  type = "discrete",
  n,
  visualisation = F,
  bias = 2,
  interpolate = "spline",
  ...
)
waachs_palette(
  type = "discrete",
  n,
  visualisation = F,
  bias = 2,
  interpolate = "spline",
  ...
)

Arguments

type

Type of colour palette to render (values "discrete", "continuous").

n

Number of colours to generate in palette (if type == "continuous").

visualisation

(Default TRUE). Return visualisation of the colour spectrum to aid in decision making.

bias

A positive number representing the spacing between colours at the high end. Parsed to grDevices::colorRamp (default 2).

interpolate

Interpolation algorithm to parse to grDevices::colorRamp (default "spline").

...

Miscellaneous arguments to parse to grDevices::colorRampPalette.

Value

Vector with colour hex codes. If type == "continuous" returns vector of length n. If visualisation == TRUE returns a list containing colour palette vector and ggplot2 visualisation of colour spectrum.

Examples

colours <- waachs_palette(type = "continuous",
                          n = 100,
                          visualisation = TRUE) # Render colour palette
print(colours$palette) # Print colour palette
print(colours$plot)    # Print plot

colours <- waachs_palette(type = "continuous",
                          n = 100,
                          visualisation = TRUE) # Render colour palette
print(colours$palette) # Print colour palette
print(colours$plot)    # Print plot

WAACHS table formatting function.

Description

Function to apply consistent formatting to summary tables rendered using R. Works with functions from the gtsummary package, or dataframes.

Usage

waachs_table(
  x,
  font.size = 10,
  font.size.header = 11,
  line.spacing = 1.5,
  padding = 2.5,
  body_bg_col = "#FEF0D8",
  header_bg_col = "#89A1AD",
  header_text_col = "black",
  highlight = NULL,
  highlight_darken = 0.3,
  font_family = "Barlow",
  ...
)
waachs_table(
  x,
  font.size = 10,
  font.size.header = 11,
  line.spacing = 1.5,
  padding = 2.5,
  body_bg_col = "#FEF0D8",
  header_bg_col = "#89A1AD",
  header_text_col = "black",
  highlight = NULL,
  highlight_darken = 0.3,
  font_family = "Barlow",
  ...
)

Arguments

x

A table, typically a data.frame, tibble, or output from gtsummary.

font.size

The font size for text in the body of the table, defaults to 8 (passed through to set_flextable_defaults).

The font size for text in the header of the table, defaults to 10.

line.spacing

Line spacing for the table, defaults to 1.5 (passed through to set_flextable_defaults).

padding

Padding around all four sides of the text within the cell, defaults to 2 (passed through to set_flextable_defaults).

body_bg_col

Body background colour (default WAACHS cream).

header_bg_col

Header background colour (default WAACHS blue).

header_text_col

Header text colour (default black).

highlight

A numeric vector specifying rows to highlight.

highlight_darken

A numeric value specifying the amount by which body_bg_col should be "darkened" (tinted) (default 0.3).

font_family

Font family for plot (default Barlow).

...

Other arguments parsed to flextable::set_flextable_defaults.

Details

Inspired and based on thekidsbiostats::thekids_table().

Examples

head(mtcars) %>%
  waachs_table()

head(mtcars) %>%
  waachs_table()

Package 'WAACHShelp'

Help Index

Add rows to labelled data set.

Description

Usage

Arguments

Details

Value

Custodial data flagging function

Description

Usage

Arguments

Details

Value

Note

Examples

Education flagging function

Description

Usage

Arguments

Value

Examples

Create Template for Quarto Markdown

Description

Usage

Arguments

Details

Note

Examples

Create a new project structure with extensions

Description

Usage

Arguments

Details

Note

Examples

ICD Classification Data set

Description

Usage

Format

Source

ICD flagging function

Description

Usage

Arguments

Details

Value

Examples

Summarise a dataframe to a person level

Description

Usage

Arguments

Details

Examples

Compile metadata from dataframe

Description

Usage

Arguments

Details

Value

Examples

HTML summary table

Description

Usage

Arguments

Details

Value

Examples

Save dataframe

Description

Usage

Arguments

Value

Summary tabling function

Description

Usage

Arguments

Value

Summary function reminiscent of Stata's "tabset".

Description

WAACHS transpose `gtsummary::tbl_summary` table