Title: Enhance the Ease of R Experience as an Emerging Researcher
Version: 0.1.0
Description: A toolkit of functions to help: i) effortlessly transform collected data into a publication ready format, ii) generate insightful visualizations from clinical data, iii) report summary statistics in a publication-ready format, iv) efficiently export, save and reload R objects within the framework of R projects.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: ggplot2, here, RColorBrewer, rstudioapi, scales, stats
Depends: R (≥ 2.10)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
LazyData: true
URL: https://dahhamalsoud.github.io/phdcocktail/, https://github.com/DahhamAlsoud/phdcocktail
BugReports: https://github.com/DahhamAlsoud/phdcocktail/issues
NeedsCompilation: no
Packaged: 2023-12-02 11:43:00 UTC; Dahham
Author: Dahham Alsoud ORCID iD [cre, aut, cph]
Maintainer: Dahham Alsoud <dahhamalsoud@gmail.com>
Repository: CRAN
Date/Publication: 2023-12-04 17:00:03 UTC

Get a safe name to export a file without overwriting

Description

Get a safe name to export a file without overwriting

Usage

get_safe_file_name(
  data,
  name = NULL,
  format = "xlsx",
  overwrite = FALSE,
  time_in_name = FALSE
)

Arguments

data

The object to be exported.

name

A desired name for the exported file. If no name is provided, the file will inherit the object's name.

format

The format of the exported file. Default is 'xlsx'.

overwrite

A logical to indicate whether preexisting files with identical names should be overwritten. Default is 'FALSE'.

time_in_name

A logical to indicate whether a timestamp should be included in the file's name.

Value

A safe name for exporting the file, as a "character string", and also indicated in a message.

Examples


if (FALSE) {
  library(phdcocktail)
  get_safe_file_name(mtcars)
}

Get a safe name to save current workspace without overwriting

Description

Get a safe name to save current workspace without overwriting

Usage

get_safe_workspace_name(name = "analysis", time_in_name = TRUE)

Arguments

name

A desired name for the saved workspace. If no name is provided, the name will be 'analysis'.

time_in_name

A logical to indicate whether a timestamp should be included in the workspace's name.

Value

A safe name for exporting the workspace, as a "character string", and also indicated in a message.

Examples


if (FALSE) {
  library(phdcocktail)
  get_safe_workspace_name()
}

Inflammatory Bowel Disease (IBD) datasets

Description

'ibd_data1' and 'ibd_data2' are two small datasets containing data collected from IBD patients, more specifically patients with Crohn's disease. 'ibd_data2' is a modified version of 'ibd_data1' by introducing missing and incorrect entries 'L11' into the column 'disease_location'.

Usage

ibd_data1

ibd_data2

Format

Two data frames with each 30 rows and six columns:

patientid

Patient ID

gender

Gender

disease_location

Disease location

disease_behaviour

Disease behaviour

crp_mg_l

C-reactive protein (mg/L)

calprotectin_ug_g

Faecal calprotectin (ug/g)

Source

Randomly generated data

Data dictionary for Inflammatory Bowel Disease (IBD) data

Description

A small, non-exhaustive list of variables that are commonly collected in IBD research. For each variable and its levels, if applicable, publications-ready labels are provided

Usage

ibd_data_dict

Format

A data frame with 53 rows and four columns:

variable

Variable name in the 'short', i.e. 'excel', form

variable_label

Variable name in the publication form

value

Value name in the 'short', i.e. 'excel', form

value_label

Value name in the publication form

Inflammatory Bowel Disease (IBD) outcomes

Description

A table containing proportions and percentages of IBD patients achieving clinical outcomes.

Usage

ibd_outcomes

Format

A data frame with eight rows and seven columns:

outcome

Outcome type

timepoint

Assessment timepoint

achieved

Number of patients who achieved the outcome

total

Total number of patients

proportion

Proportion of patients who achieved the outcome

percentage

Percentage of patients who achieved the outcome

percentage_labelled

Percentage of patients who achieved the outcome, suffixed with '%'

Identify the most recent saved R workspace

Description

Identify the most recent saved R workspace

Usage

identify_recent_workspace(folder = "output")

Arguments

folder

The folder in which the workspace need to be identified.

Value

The most recent saved workspace, as a "character string", and also indicated in a message.

Examples


library(phdcocktail)
if (FALSE) {
  identify_recent_workspace()
}

Plot % of outcomes as bars

Description

Plot % of outcomes as bars

Usage

plot_bars(
  data,
  outcome,
  proportion,
  percentage_labelled,
  achieved,
  total,
  x_axis_title = NULL,
  y_axis_title = "% Patients",
  legend_title = "Outcome",
  bar_fill = "Greys",
  grouping = NULL
)

Arguments

data

A data frame containing outcomes data.

outcome

Variable containing outcomes to be plotted.

proportion

Variable containing proportion of patients who achieved the outcome.

percentage_labelled

Variable containing percentage of patients who achieved the outcome, suffixed with '%' label.

achieved

Variable containing number of patients who achieved the outcome.

total

Variable containing total number of patients.

x_axis_title

Title of the x-axis.

y_axis_title

Title of the y-axis.

legend_title

Title of the legend.

bar_fill

Fill color of the bars.

grouping

Faceting variable.

Value

A bar plot of outcome percentages.

Examples


if (FALSE) {
library(phdcocktail)
data(ibd_outcomes, package = "phdcocktail")
plot_bars(ibd_outcomes)
  }

A custom print method for the 'quantiles_report' class

Description

A custom print method for the 'quantiles_report' class

Usage

## S3 method for class 'quantiles_report'
print(x, ...)

Arguments

x

A data frame of the class 'quantiles_report'.

...

Other argument that can be passed to 'print'.

Value

The function displays the content of the column 'report' in separate lines.

Examples


if (FALSE) {
library(phdcocktail)
summary_data <- report_quantiles(mtcars, summary_vrs = "mpg")
print(summary_data)
  }

Recode variables and their values based on a data dictionary

Description

Recode variables and their values based on a data dictionary

Usage

recode_vrs(data, data_dictionary, vrs = NULL, factor = FALSE)

Arguments

data

A data frame with raw data.

data_dictionary

A data dictionary containing labels for variables and their values.

vrs

A character vector specifying variables of which the values need to be recoded.

factor

A logical to indicate whether recoded variables need to be converted into ordered factors.

Value

The input data frame with recoded and labelled variables.

Examples


if (FALSE) {
  library(phdcocktail)
  data(ibd_data1, package = "phdcocktail")
  ibd_data_recoded <- recode_vrs(
    data = ibd_data1, data_dictionary = ibd_data_dict,
    vrs = c("disease_location", "disease_behaviour", "gender"), factor = TRUE
  )
}

Report median-quantiles summaries

Description

Report median-quantiles summaries

Usage

report_quantiles(data, summary_vrs, grouping_vrs = NULL)

Arguments

data

A data frame including numeric variables to be summarized.

summary_vrs

A character vector specifying the numeric variables to be summarized.

grouping_vrs

A character vector specifying the grouping variables, if any.

Value

A dataframe of the class 'quantiles_report', containing a 'report' column, which report the 'median (quartile 1-quartile 3)' combinations for each specified numeric variable, at each grouping key.

Examples


if (FALSE) {
library(phdcocktail)
summary_data <- report_quantiles(mtcars, summary_vrs = "mpg")
print(summary_data)
  }

Restart R session

Description

Restart R session

Usage

start_fresh()

Value

A clean R session

Examples


if (FALSE) {
library(phdcocktail)
start_fresh()
  }