Part_1_Overview_and_missing_data.qmd

---
title: "Preoperative Atelectasis"
subtitle: "Part 1: Overview, selection criteria, and missing data"
author: "Javier Mancilla Galindo"
date: "`r Sys.Date()`"
execute: 
  echo: true
  warning: false
format:
  html:
    toc: true
    toc_float: true
    embed-resources: true
  pdf: 
    documentclass: scrartcl
editor: visual
---

# Setup

#### Packages used

```{r}
if (!require("pacman", quietly = TRUE)) {
  install.packages("pacman")
}

pacman::p_load(
  tidyverse, # Used for basic data handling and visualization.
  dataverse, # Retrieve dataset from the Harvard dataverse. 
  overviewR, # Used to assess missing data.
  table1, #Used to add labels to variables.
  report #Used to cite packages used in this session.   
)
```

```{r}
#| include: false  

# Create directories for sub-folders  
psfolder <- "../data/processed"
reports <- "../docs/reports"
dir.create(psfolder, showWarnings = FALSE)
dir.create(reports, showWarnings = FALSE)
```

##### Session and package dependencies

```{r}
#| echo: false  

# Credits chunk of code: Alex Bossers, Utrecht University (a.bossers@uu.nl)

# remove clutter
session <- sessionInfo()
session$BLAS <- NULL
session$LAPACK <- NULL
session$loadedOnly <- NULL
# write log file
writeLines(
  capture.output(print(session, locale = FALSE)),
  paste0("sessions/",lubridate::today(), "_session_Part_1.txt")
)

session
```

```{r}
#| include: false  

# Load dataset  
data <- get_dataframe_by_name(
  filename = "atelectasis_prevalence.tab",
  dataset = "10.7910/DVN/4JZZLB", 
  server = "dataverse.harvard.edu")

# Recode variables  
source("scripts/variable_names_raw.R", local = knitr::knit_global())
```

```{r}
#| include: false  

# Can be used to check the structure of the dataset after prior specification.
str(data)
```

# General overview

```{r}
summary(data)
```

### Exclude participants with CO-RADS ≥ 3:

Participants with higher probability of having a current diagnosis of COVID-19 are expected to have chest CT alterations due to COVID-19 pneumonia. Thus, will be excluded.

Number of patients according to CO-RADS:

```{r}
#| echo: false
summary(data$CORADS)
```

Number of participants in the dataset (to keep track of no. of exclusions).

```{r}
count(data)
```

```{r}
data <- data %>%
  filter(as.numeric(CORADS) < 3) %>%
  droplevels(data$CORADS)
```

```{r}
count(data)
```

### Exclude participants with prior COVID-19:

Since prior COVID-19 is considered a confounder and since there are only 3 participants with prior COVID-19 which would provide difficult to assess the role of prior COVID-19 in analyses, participants with prior COVID-19 were excluded from the analysis.

```{r}
count(data)
```

```{r}
data <- data %>%
  filter(prior_covid19 == "No") %>%
  droplevels(data$prior_covid19)
```

```{r}
count(data)
```

Will remove prior_covid19 column as it no longer provides information of a varying characteristic. Similarly, rapid_covid_test does not provide additional information.

```{r}
length(data)
```

```{r}
data <- data %>% select(-c(prior_covid19, rapid_covid_test))
```

```{r}
length(data)
```

# Missing data per variable

```{r}
#| echo: false
overview_na(data)
```

Missing data for PCR_covid and is explained since only patients who decided to have a test performed on their own will reported the result. The medical center did not require a negative PCR test at that time during the pandemic, reason why PCR tests were not systematically performed. As shown in earlier summary of variables, all available tests (n=3) were negative. This variable will not be analyzed further downstream:

```{r}
data <- data %>% select(-PCR_covid)
```

The variable atelectasis_location has missing data since those patients who did not have atelectasis naturally do not have a location recorded. Will assess if data are missing for those who had atelectasis:

```{r}
#| echo: false
data %>%
  filter(atelectasis == "Yes") %>%
  group_by(atelectasis_location) %>%
  overview_na()
```

There is no missing data for ***atelectasis_location*** after sub-setting only those who had atelectasis.

Lastly, I will subset all participants without the prior variable to further assess the extent of missing data for other variables:

```{r}
#| echo: false
data %>%
  select(-c(atelectasis_location)) %>%
  overview_na()
```

Missing data constitutes **less than 5%** for all remaining variables. Thus, will proceed with **complete-case analysis** without performing any data imputation procedure.

```{r}
#| include: false  

#Save dataset
write.csv(data, 
          file = paste0(psfolder,"/atelectasis_included.csv"),
          row.names=FALSE)
```

# Package references

```{r}
#| include: false
report::cite_packages(session)
```

-   Grolemund G, Wickham H (2011). “Dates and Times Made Easy with lubridate.” *Journal of Statistical Software*, *40*(3), 1-25. <https://www.jstatsoft.org/v40/i03/>.
-   Kuriwaki S, Beasley W, Leeper TJ (2023). *dataverse: R Client for Dataverse 4+ Repositories*. R package version 0.3.13.
-   Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B (2023). “Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.” *CRAN*. <https://easystats.github.io/report/>.
-   Meyer C, Hammerschmidt D (2023). *overviewR: Easily Extracting Information About Your Data*. R package version 0.0.13, <https://CRAN.R-project.org/package=overviewR>.
-   Müller K, Wickham H (2023). *tibble: Simple Data Frames*. R package version 3.2.1, <https://CRAN.R-project.org/package=tibble>.
-   R Core Team (2024). *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
-   Rich B (2023). *table1: Tables of Descriptive Statistics in HTML*. R package version 1.4.3, <https://CRAN.R-project.org/package=table1>.
-   Rinker TW, Kurkiewicz D (2018). *pacman: Package Management for R*. version 0.5.0, <http://github.com/trinker/pacman>.
-   Wickham H (2016). *ggplot2: Elegant Graphics for Data Analysis*. Springer-Verlag New York. ISBN 978-3-319-24277-4, <https://ggplot2.tidyverse.org>.
-   Wickham H (2023). *forcats: Tools for Working with Categorical Variables (Factors)*. R package version 1.0.0, <https://CRAN.R-project.org/package=forcats>.
-   Wickham H (2023). *stringr: Simple, Consistent Wrappers for Common String Operations*. R package version 1.5.1, <https://CRAN.R-project.org/package=stringr>.
-   Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” *Journal of Open Source Software*, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
-   Wickham H, François R, Henry L, Müller K, Vaughan D (2023). *dplyr: A Grammar of Data Manipulation*. R package version 1.1.4, <https://CRAN.R-project.org/package=dplyr>.
-   Wickham H, Henry L (2023). *purrr: Functional Programming Tools*. R package version 1.0.2, <https://CRAN.R-project.org/package=purrr>.
-   Wickham H, Hester J, Bryan J (2024). *readr: Read Rectangular Text Data*. R package version 2.1.5, <https://CRAN.R-project.org/package=readr>.
-   Wickham H, Vaughan D, Girlich M (2024). *tidyr: Tidy Messy Data*. R package version 1.3.1, <https://CRAN.R-project.org/package=tidyr>.

```{r}
#| include: false  

# Run this chunk if you wish to clear your environment and unload packages.

rm(data, session, psfolder, reports)
pacman::p_unload(negate = TRUE)

```