| Type: | Package |
| Title: | Curated Datasets and Tools for Epidemiological Data Analysis |
| Version: | 0.1.1 |
| Maintainer: | Natàlia Pallarès <npallares@igtp.cat> |
| Description: | Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, 'DIVINE' makes the process more efficient, transparent, and reproducible. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| URL: | https://bruigtp.github.io/DIVINE/ |
| BugReports: | https://github.com/bruigtp/DIVINE/issues |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 4.1) |
| LazyData: | true |
| Imports: | dplyr, fmsb, ggplot2, gtsummary, haven, openxlsx, plotly,purrr, rlang, scales, stringr, tibble, tidyselect |
| NeedsCompilation: | no |
| Packaged: | 2025-12-11 07:29:02 UTC; jcarmezim |
| Author: | Natàlia Pallarès [aut, cre], João Carmezim [aut], Pau Satorra [aut], Lucia Blanc [aut], Cristian Tebé [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-12-11 07:50:02 UTC |
DIVINE's table on laboratory data
Description
Information on laboratory data of patients included in the DIVINE cohort. Data was collected at hospital admission.
Usage
data(analytics)Format
A data frame with 5813 rows and 9 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- analytics_available:
Is there an analytic available for this patient?
- total_leukocytes:
Total leukocytes (mil/mm³)
- hemoglobin:
Hemoglobin (g/dl)
- total_lymphocytes:
Total lymphocytes (mil/mm³)
- d_dimer:
D-dimer (µg/L)
- c_reactive_protein:
C-reactive protein (mg/L)
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's table on information about comorbidities
Description
Information about comorbidities of patients included in the DIVINE cohort. Data was collected at hospital admission.
Usage
data(comorbidities)Format
A data frame with 5813 rows and 37 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- sociofunctional:
A factor with levels
Lives with a spouse of similar age,Lives with a spouse with some degree of dependency,Lives with a non-family caregiver,Lives with family. The caregiver is not their spouse,Lives with family without physical dependency,Lives alone and has no children or they are far away,Lives alone and has nearby children. Sociofunctional status- frailty:
A factor with levels
No,PCCandMACA. Is the patient a chronic complex patient (PCC) or a patient with advanced chronic disease (MACA)?- barthel_score:
Punctuation in the Barthel scale used to measure performance in activities of daily living
- weight:
Weight (kg)
- height:
Height (cm)
- body_mass_index:
Body mass index computed as
\frac{\mbox{weight (kg)}}{\mbox{height (m)}^2}- dm:
A factor with levels
NoandYes. Diabetes mellitus Type 2- type_dm:
A factor with levels
With target organ involvementandWithout complications. For patients with diabetes mellitus type 2, type of disease- chronic_lung_disease:
A factor with levels
NoandYes. Chronic lung disease (including COPD, asthma and obstructive sleep apnea, among others)- chronic_kidney_disease:
A factor with levels
NoandYes. Severe chronic kidney disease- mild_kidney_disease:
A factor with levels
NoandYes. Mild kidney disease- renal_therapy:
A factor with levels
NoandYes. Is the patient currently receiving renal replacement therapy?- heart_disease:
A factor with levels
NoandYes. Heart failure- coronary_disease:
A factor with levels
NoandYes. Coronary heart disease- myocardial_infarction:
A factor with levels
NoandYes. Has the patient ever had a heart attack?- hematologic_neo:
A factor with levels
NoandYes. Haematological neoplasia- hematologic_neo_type:
A factor with levels
Leukemia,LymphomaandMyeloma. For patients with Haematological neoplasia, type of disease.- non_metastatic_neo:
A factor with levels
NoandYes. Non-Metastatic Neoplasia- metastatic_neo:
A factor with levels
NoandYes. Metastatic Neoplasia- stroke_tia:
A factor with levels
NoandYes. Has the patient ever had a stroke or a transient ischemic attack?- peripheral_vasculopathy:
A factor with levels
NoandYes. Peripheral artery disease- dementia:
A factor with levels
NoandYes. Dementia- mild_liver_disease:
A factor with levels
NoandYes. Mild liver disease- severe_liver_disease:
A factor with levels
NoandYes. Severe liver disease- connective_tissue_disease:
A factor with levels
NoandYes. Connective tissue disease- peptic_ulcer:
A factor with levels
NoandYes. Peptic ulcer- hemiplegia:
A factor with levels
NoandYes. Hemiplegia- hiv:
A factor with levels
NoandYes. Human immunodeficiency virus- charlson_index:
Value of the Charlson Comorbidity Index. This index predicts the ten-year mortality for a patient given the information of their comorbid conditions
- hypertension:
A factor with levels
NoandYes. Hypertension- dyslipidemia:
A factor with levels
NoandYes. Dyslipidemia- depression:
A factor with levels
NoandYes. Depression- ceiling:
A factor with levels
Oxygen mask(non-rebreather oxygen mask),HFNC or NIMV(high-flow nasal cannula or non-invasive mechanical ventilation) andIMV and ICU admission(invasive mechanical ventilation and acces to intensive care unit). Therapeutic ceiling of care assigned to the patient- ceiling_dico:
A factor with the dichotomization of the variable ceiling in two levels
No(IMV and ICU admission) andYes(Oxygen maskandHFNC or NIMV)
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's table on complications data
Description
Information on complications data of patients included in the DIVINE cohort. Data was collected during hospitalization.
Usage
data(complications)Format
A data frame with 5813 rows and 9 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- comp:
A factor with levels
NoandYes. Did the patient experiment a complication while hospitalised?- kidney_failure:
A factor with levels
NoandYes. Did the patient experiment kidney failure during hospital admission?- mental_status_change:
A factor with levels
NoandYes. Did the patient experiment a change in its mental status during hospital admission?- nosocomial_infection:
A factor with levels
NoandYes. Did the patient experiment a nosocomial infection during hospital admission?- comp_cardiac:
A factor with levels
NoandYes. Did the patient experiment a cardiac complication during hospital admission? Cardiac complications included heart failure and acute coronary event.- comp_respiratory:
A factor with levels
NoandYes. Did the patient experiment a respiratory complication during hospital admission? Respiratory complications included acute respiratory failure, venous thromboembolism, and pneumonia.
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's table on treatments previous to hospital admission
Description
Information on previous treatments for patients included in the DIVINE cohort. Data was collected at hospital admission.
Usage
data(concomitant_medication)Format
A data frame with 5813 rows and 11 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- statins_pre:
A factor with levels
NoandYes. Previous treatment with statins- cortis_pre:
A factor with levels
NoandYes. Previous treatment with corticosteroids- acei_pre:
A factor with levels
NoandYes. Previous treatment with angiotensin-converting enzyme (ACE) inhibitors- ara2_pre:
A factor with levels
NoandYes. Previous treatment with angiotensin II receptor antagonists (ARA-II)- cortis_systemic_pre:
A factor with levels
NoandYes. Routine treatment with systemic corticosteroids- cortis_inhaled_pre:
A factor with levels
NoandYes. Routine treatment with inhaled corticosteroids- anticoagulants_pre:
A factor with levels
NoandYes. Previous treatment with anticoagulants- immunosuppre_pre:
A factor with levels
NoandYes. Previous treatment with immunosuppressants
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
Data Overview Function
Description
This function provides a comprehensive overview of a data frame, including itsdimensions, variable types, missing values count and a preview of the first few rows.
Usage
data_overview(data, preview_rows = 6)Arguments
data | A data frame. The dataset for which you want an overview. |
preview_rows | Integer. The number of rows to display in the preview. Default is 6. |
Details
The function is useful for quickly inspecting the structure of a data frame andidentifying any missing values or general characteristics of the data. It also allowsusers to customize how many rows they want to preview from the dataset.
Value
A list containing the following components:
dimensions | A vector of two elements: the number of rows and columns in the data. |
variable_types | A named vector with the class of each variable (column) in the data. |
missing_values | A named vector with the count of missing values (NA) for each variable. |
preview | A data frame showing the first |
Examples
# Example usage with a simple data framedata <- data.frame( Age = c(25, 30, NA, 22, 35), Height = c(175, 160, 180, NA, 165), Gender = c("Male", "Female", "Female", "Male", "Male"))overview <- data_overview(data, preview_rows = 4)print(overview)# Example usage with the default preview size (6 rows)overview_default <- data_overview(data)print(overview_default)DIVINE's demographic table
Description
Demographic data of patients included in the DIVINE cohort. Data was collected at hospital admission.
Usage
data(demographic)Format
A data frame with 5813 rows and 8 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- sex:
A factor with levels
MaleandFemale. Sex at birth- age:
Age at hospital admission
- smoker:
A factor with levels
Ex-smoker,NoandYes. Smoking status- alcohol:
A factor with levels
NoandYes. Consumption of alcohol- residence_center:
A factor with levels
NoandYes. Is the patient currently living in a long-term facility?
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's table on closure data
Description
Information on closure data of patients included in the DIVINE cohort. Data was collected at the end of hospitalization.
Usage
data(end_followup)Format
A data frame with 5813 rows and 8 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- clinical_stability_days:
Days from hospital admission to clinical stability
- exitus:
A factor with levels
NoandYes. Did the patient die during hospital admission?- exitus_days:
Days from hospital admission to exitus
- discharge:
A factor with levels
NoandYes. Was the patient discharge from the hospital?- discharge_days:
Days from hospital admission to discharge
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
Export Data to Various Formats
Description
Export a dataframe or tibble to multiple file formats. Ifformat is NULL (default),the format will be inferred from the file extension ofpath. Ifformat is providedand the extension inpath does not match, the function will update the path touse the extension that corresponds toformat and warn the user.
Usage
export_data(data = NULL, path = NULL, format = NULL)Arguments
data | A dataframe or tibble to export. |
path | A character string specifying the file path for the exported file. |
format | Optional character string specifying the export format. Supported formats:"xlsx", "csv", "rds", "txt", "sav", "dta", "sas7bdat" (alias "xpt"). If NULL (default),the function infers the format from the |
Details
Supported formats and their functionality are provided via the package dependencies:
xlsx: Uses
openxlsxfor Excel exports.csv: Base R functionality.
rds: Base R functionality.
txt: Base R functionality with tab-separated values.
sav: Uses
havenfor SPSS exports.dta: Uses
havenfor Stata exports.sas7bdat: Uses
havenfor SAS exports.
Value
This function does not return a value. It writes the data to the specified file path and displays a success message upon completion.
Examples
## Not run: df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))# Infer format from path extension (no format argument)export_data(df, path = "example.xlsx")export_data(df, path = "example.csv")# Explicit format (function will ensure path extension matches)export_data(df, format = "csv", path = "example") # adds .csvexport_data(df, format = "rds", path = "example.rds")## End(Not run)DIVINE's table on icu data
Description
Information on ICU data of patients included in the DIVINE cohort. Data was collected during hospitalization.
Usage
data(icu)Format
A data frame with 5813 rows and 14 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- icu:
A factor with levels
NoandYes. Was the patient admitted to the ICU?- icu_enter_days:
Days from hospital admission to ICU admission.
- icu_exit_days:
Days from hospital admission to ICU discharge.
- vent_mec:
A factor with levels
NoandYes. Did the patient received invasive mechanical ventilation?- vent_mec_start_days:
Days from hospital admission to start of invasive mechanical ventilation.
- vent_mec_end_days:
Days from hospital admission to end of invasive mechanical ventilation.
- vent_mec_no_inv:
A factor with levels
NoandYes. Did the patient received non-invasive mechanical ventilation?- vent_mec_no_inv_start_days:
Days from hospital admission to start of non-invasive mechanical ventilation.
- vent_mec_no_inv_end_days:
Days from hospital admission to end of non-invasive mechanical ventilation.
- sev_pneum
A factor with levels
NoandYes. Did the patient required a sustained supply of oxygen therapy greater than FiO2 of 35% to maintain oxygen saturation above 95%?- sev_pneum_days
Days from hospital admission to development of severe pneumonia.
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
Replace Missing Values
Description
Replace missing values (NA) in a data.frame with a specified value or method (such as mean, median, mode, constant, or custom function),applying imputation column-wise.
Usage
impute_missing( data, method = list(dplyr::where(is.numeric) ~ "mean", dplyr::where(is.character) ~ "mode", dplyr::where(is.factor) ~ "mode"), filter_by = NULL, drop_all_na = FALSE, verbose = TRUE)Arguments
data | A data frame. The dataset in which missing values should be imputed. |
method | A list of one-sided formulas of the form
The default is |
filter_by | Character vector of column names. If provided, only rows that haveall specified columns non-NA are kept (appliedbefore imputation). |
drop_all_na | Logical; if |
verbose | Logical; if |
Details
You can remove rows that are entirelyNA before imputation usingdrop_all_na, or filter rows based on specific variables usingfilter_by.
The
methodargument usestidyselect helpers. For example,where(is.numeric()) ~ "median"imputes all numeric columns by their medians."mode"works for numeric, character and factor columns.When imputing factors with a character constant, the constant is added as a new level if needed.
When passing a custom function, it should return at least one value; if multiple values are returned, only the first is used (with a warning).
Value
A tibble with missing values replaced according to the provided specifications.
Examples
# Impute all numeric columns by their means:impute_missing(icu)# Impute numeric columns by median:impute_missing( icu, method = list(where(is.numeric) ~ "median"))# Keep only rows where both "vent_mec_no_inv" and "vent_mec" are non-missing:impute_missing( icu, filter_by = c("vent_mec_no_inv", "vent_mec"))DIVINE's table on antibiotics received during hospitalization
Description
Information on antibiotics received for patients included in the DIVINE cohort. Data was collected during hospitalization.
Usage
data(inhosp_antibiotics)Format
A data frame with 5813 rows and 17 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- any_antibiotic:
A factor with levels
NoandYes. Did the patient receive treatment with antibiotics during hospital admission?- amoxicillin:
A factor with levels
NoandYes. Treatment with amoxicillin- amoxicillin_clavulanic_acid:
A factor with levels
NoandYes. Treatment with amoxicillin and clavulanic acid- azithromycin:
A factor with levels
NoandYes. Treatment with azithromycin- ceftriaxone:
A factor with levels
NoandYes. Treatment with ceftriaxone- ciprofloxacin:
A factor with levels
NoandYes. Treatment with ciprofloxacin- cotrimoxazole:
A factor with levels
NoandYes. Treatment with cotrimoxazole- levofloxacin:
A factor with levels
NoandYes. Treatment with levofloxacin- linezolid:
A factor with levels
NoandYes. Treatment with linezolid- meropenem:
A factor with levels
NoandYes. Treatment with meropenem- piperacillin:
A factor with levels
NoandYes. Treatment with piperacillin- piperacillin_tazobactam:
A factor with levels
NoandYes. Treatment with piperacillin+tazobactam- teicoplanin:
A factor with levels
NoandYes. Treatment with teicoplanin- other_antibiotic:
A factor with levels
NoandYes. Treatment with another antibiotic
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's table on antivirals received during hospitalization
Description
Information on antivirals for patients included in the DIVINE cohort. Data was collected during hospitalization.
Usage
data(inhosp_antivirals)Format
A data frame with 5813 rows and 10 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- any_antiviral:
A factor with levels
NoandYes. Did the patient receive treatment with antivirals during hospital admission?- hydroxychloroquine:
A factor with levels
NoandYes. Treatment with hydroxychloroquine- interferon_b:
A factor with levels
NoandYes. Treatment with interferon beta- kaletra_ritonavir_lopinavir:
A factor with levels
NoandYes. Treatment with kaletra/ritonavir-lopinavir- remdesivir:
A factor with levels
NoandYes. Treatment with remdesivir- tocilizumab:
A factor with levels
NoandYes. Treatment with tocilizumab- other_antiviral:
A factor with levels
NoandYes. Treatment with another antiviral
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's table on other treatments received during hospitalization.
Description
Information on other treatments for patients included in the DIVINE cohort. Data was collected during hospitalization.
Usage
data(inhosp_other_treatments)Format
A data frame with 5813 rows and 6 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- corticosteroids:
A factor with levels
NoandYes. Treatment with corticosteroids- lmwh:
A factor with levels
NoandYes. Treatment with low-molecular-weight heparin (LMWH)- oral_anticoagulants:
A factor with levels
NoandYes. Treatment with oral anticoagulants
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
Multi-Dataset Join Utility
Description
This function performs a sequential join of multiple datasets by a specified key column.
Usage
multi_join( datasets, key = c("record_id", "covid_wave", "center"), join_type = "left")Arguments
datasets | A list of data frames to be joined. |
key | A character string representing the key column to join by. Defaults to "record_id". |
join_type | A character string specifying the type of join. Options are "left", "right", "inner", or "full". |
Value
A single data frame containing the joined datasets.
Examples
multi_join( list(analytics, comorbidities), join_type = "left")multi_join( list(analytics, comorbidities), key = c("record_id", "covid_wave", "center"), join_type = "left")multi_plot: Flexible Static or Interactive Plotting of Variables
Description
Generate a variety of plots—histogram, density, boxplot, barplot, violin, scatter,heatmap, or spider (radar)—either as static ggplot2 objects or interactive Plotly widgets.
Usage
multi_plot( data, x = NULL, y = NULL, plot_type = NULL, interactive = FALSE, fill_color = "steelblue", color = "black", bin_width = NULL, group = NULL, facet = NULL, radar = NULL, radar_color = "steelblue", radar_labels = NULL, radar_cex = 1, radar_ref_lev = "Yes", title = NULL, x_lab = NULL, y_lab = NULL, legend_position = "right", axis_text_angle = 0, axis_text_size = 12, title_size = 14, theme_custom = ggplot2::theme_minimal())Arguments
data | A data frame or tibble containing your data. |
x | Character; name of the variable for x-axis (required for all plot types except spider). |
y | Character; name of the variable for y-axis (required for boxplot, violin, scatter, and heatmap). |
plot_type | Character; one of |
interactive | Logical; if |
fill_color | Character; fill color for non-grouped geoms (default |
color | Character; outline/line color (default |
bin_width | Numeric; bin width for histograms. If |
group | Character; name of grouping variable (optional). |
facet | Character; name of variable to facet by (optional). |
radar | Character vector; names of exactly 5 variables for spider plot (only for |
radar_color | Character or vector; border/fill color for spider chart (only for |
radar_labels | Character or vector; names of the variables for spider chart (only for |
radar_cex | Numeric; font size for variable labels in the spider chart (only for |
radar_ref_lev | Character; reference level for factors included in the spider chart (only for |
title | Character; plot title (optional). |
x_lab | Character; x-axis label (defaults to |
y_lab | Character; y-axis label (defaults to |
legend_position | Character; one of |
axis_text_angle | Numeric; rotation angle (degrees) for x-axis tick labels (default |
axis_text_size | Numeric; size of axis text in pts (default |
title_size | Numeric; size of plot title text in pts (default |
theme_custom | A ggplot2 theme object (default |
Details
Histogram: requires
x; usesgeom_histogram(). Use for continuous numeric variables only.Density: requires
x; usesgeom_density(). It should be numeric.Boxplot/Violin: require both
xandy; automatically groups byxor bygroupif provided, with dynamic dodge width.Barplot: requires
x; counts occurrences. Use for categorical variables only.Scatter: requires both
xandy; usesgeom_point(). Both variables must be numeric.Heatmap: requires both
xandy. Both variables must be categorical.Spider: requires
radar(vector of variables); usesfmsb::radarchart(), static only.
Value
Aggplot object (ifinteractive = FALSE orplot_type = "spider")or aplotly object (ifinteractive = TRUE).
Examples
multi_plot(icu, x = "icu_enter_days", y = "vent_mec_start_days", plot_type = "scatter", color = "darkred", title = "ICU exit vs MV days")multi_plot( comorbidities, radar = c("hypertension", "dyslipidemia", "depression", "mild_kidney_disease", "dm"), radar_color = "steelblue", radar_ref_lev = "Yes", plot_type = "spider")DIVINE's table on severity scores at hospital admission
Description
Information on severity scores at hospital admission for patients included in the DIVINE cohort. Data was collected at hospital admission.
Usage
data(scores)Format
A data frame with 5813 rows and 10 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- psi:
Pneumonia severity index (PSI) at hospital admission
- group_psi:
A factor with levels
1,2,3, and4. PSI group- curb65:
CURB65 score at hospital admission
- group_curb65:
A factor with levels
1,2, and3. CURB65 group- mulbsta:
MULBSTA score at hospital admission
- group_mulbsta:
A factor with levels
Low-riskandHigh-risk. MULBSTA group- rox_index:
ROX index at hospital admission
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
Create Summary Table
Description
This function generates a summary table using thegtsummary package.It allows customization of the reported statistics for continuous variables and categorical variables.Users can optionally include p-values for group comparisons and managethe reporting of missing values.
Usage
stats_table( data, vars = NULL, var_labels = NULL, by = NULL, statistic_type = "mean_sd", pvalue = FALSE, test_method = NULL, include_na = TRUE)Arguments
data | A data frame containing the dataset. |
vars | A character vector of variable names to include in the summary. If NULL (default), all variables are included. |
var_labels | A list of labels to replace variable names in the table. |
by | A character string specifying a grouping variable. If NULL (default), no grouping is applied. |
statistic_type | A character string specifying the type of statistic to reportfor continuous variables. Options are:
|
pvalue | A logical value indicating whether to include p-values in the summary. Defaults to FALSE. |
test_method | Optional. Only used if |
include_na | A logical value indicating whether to include rows with missing values in the output. Defaults to TRUE. |
Value
A gtsummary table object.
Examples
# Mean ± SD summarystats_table( vital_signs, vars = c("temperature", "saturation"), by = "supporto2", statistic_type = "mean_sd")# Both mean ± SD and median [Q1; Q3]stats_table( vital_signs, statistic_type = "both", include_na = FALSE)# Add p-value with default testsstats_table( vital_signs, vars = c("temperature", "saturation"), by = "supporto2", pvalue = TRUE)# Add p-value and define methodstats_table( vital_signs, vars = c("temperature", "saturation"), by = "supporto2", pvalue = TRUE, test_method = list(temperature ~ "t.test"))DIVINE's symptoms table
Description
Information on COVID-19 associated symptoms of patients included in the DIVINE cohort. Data was collected at hospital admission.
Usage
data(symptoms)Format
A data frame with 5813 rows and 24 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- symptoms_days:
Days from symptoms onset to hospitalization
- rhinorrhea:
A factor with levels
NoandYes. Rhinorrhea- anosmia:
A factor with levels
NoandYes. Anosmia- ageusia:
A factor with levels
NoandYes. Ageusia- arthromyalgia:
A factor with levels
NoandYes. Arthromyalgia- odynophagia:
A factor with levels
NoandYes. Odynophagia- fever:
A factor with levels
NoandYes. Fever- cough:
A factor with levels
NoandYes. Cough- dyspnea:
A factor with levels
NoandYes. Dyspnoea- expectoration:
A factor with levels
NoandYes. Expectoration- diarrhea:
A factor with levels
NoandYes. Diarrhea- vomit:
A factor with levels
NoandYes. Vomiting- nausea:
A factor with levels
NoandYes. Nausea- asthenia:
A factor with levels
NoandYes. Asthenia- anorexia:
A factor with levels
NoandYes. Anorexia- cephal:
A factor with levels
NoandYes. Headache- chest_pain:
A factor with levels
NoandYes. Chest pain- abdominal_pain:
A factor with levels
NoandYes. Abdominal pain- confusional_syndrome:
A factor with levels
NoandYes. Confusional syndrome- shock_admission:
A factor with levels
NoandYes. Shock on admission- bacterial_infection:
A factor with levels
NoandYes. Bacterial infection
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's vaccine table
Description
Information on COVID-19 vaccines of patients included in the DIVINE cohort. Data was collected at hospital admission and it is available for waves 3 and 5 (patients were not yet vaccinated in waves 1 and 2).
Usage
data(vaccine)Format
A data frame with 5813 rows and 6 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- vaccine:
A factor with levels
No,YesandNot applicable(for patients included in waves before vaccination started). Is the patient vaccinated for COVID-19?- complete_vaccine:
A factor with levels
No,Partial,CompleteandNot applicable(for patients included in waves before vaccination started). Is the patient partially vaccinated (one dose of two-dose vaccines), completely vaccinated (one dose for one-dose vaccines or two doses for two-dose vaccines) or not vaccinated at all?- immune_vaccine:
A factor with levels
No immunity,Partial immunity,Total immunityandNot applicable(for patients included in waves before vaccination started). Defines the level of immunity of the patient: not vaccinated (No immunity), vaccinated with only one dose for two-dose vaccines (Partial immunity), vaccinated with two doses but less than 7 days have passed since the second dose (Partial immunity) or vaccinated with all the doses and more than 7 days have passed since the second dose (Total immunity)
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w
DIVINE's table on vital signs
Description
Information on vital signs of patients included in the DIVINE cohort. Data was collected at hospital admission.
Usage
data(vital_signs)Format
A data frame with 5813 rows and 13 columns
- record_id:
Identifier of each record. This information does not match the real data.
- covid_wave:
A factor with levels
Wave 1,Wave 2,Wave 3, andWave 5. COVID-19 wave.- center:
A factor with levels
Hospital A,Hospital B,Hospital C,Hospital D, andHospital E. Center of admission- temperature:
Human body temperature (ºC)
- fio2_contributed:
Fraction of inspired oxygen (%)
- syst_blood_press:
Systolic blood pressure (mmHg)
- diast_blood_press:
Diastolic blood pressure (mmHg)
- saturation:
Oxygen saturation (%)
- cardiac_freq:
Heart rate (bpm)
- supporto2:
A factor with levels
NoandYes. Oxygen Support- normal_radio:
A factor with levels
NoandYes. Normal X-ray- pleural_effusion:
A factor with levels
NoandYes. Pleural effusion- saturation_fio2:
Oxygen Saturation to FiO2 Ratio
References
Pallarès, N., Tebé, C., Abelenda-Alonso, G., Rombauts, A., Oriol, I., Simonetti, A. F., Rodríguez-Molinero, A., Izquierdo, E., Díaz-Brito, V., Molist, G., Gómez Melis, G., Carratalà, J., Videla, S., & MetroSud and Divine study groups (2023). Characteristics and Outcomes by Ceiling of Care of Subjects Hospitalized with COVID-19 During Four Waves of the Pandemic in a Metropolitan Area: A Multicenter Cohort Study. Infectious diseases and therapy, 12(1), 273–289. https://doi.org/10.1007/s40121-022-00705-w