hiv_report.Rmd

---
title: "VRC01 Neutralization Report"
author: "David Benkeser"
date: "`r Sys.Date()`"
output: html_document
---
```{r, read-config, include = FALSE}
which_config <- Sys.getenv("which_config")
config_list <- config::get(
  config = which_config
)
```  


```{r, set-chunk-opts, echo = FALSE, eval = config_list$production}
library(knitr)
opts_chunk$set(
  echo = FALSE, warning = FALSE, message = FALSE
)
```

```{r, load-data}
here::i_am("hiv_report.Rmd")
data <- readRDS(
  file = here::here("output/data_clean.rds")
)
```

# Descriptive Analysis

## Tabular analysis

Some characteristics of the `r nrow(data)` viruses from the CATNAP database are displayed
in the table below.

```{r, table1}
table_one <- readRDS(
  file = here::here("output/table_one.rds")
)
table_one
```

## Graphical analysis

Below we report a scatter plot of antibody resistance as a function of 
number of PNGs in the glycan shield. 

```{r, plot-data}
#| fig.align = "center",
#| out.width = "600px"
knitr::include_graphics(
  here::here("output/scatterplot.png")
)

```

# Regression Analysis

## Primary model

```{r, fit-primary-model}
both_regression_tables_filename <- paste0(
  "both_regression_tables_config_",
  which_config,
  ".rds"
)
both_regression_tables <- readRDS(
  file = here::here("output", both_regression_tables_filename)
)
both_regression_tables$primary
```

## Secondary model

In this analysis, we generated a binary outcome where level 1 of the outcome
means the antibody resistance score was greater than `r config_list$cutpoint`.

```{r, secondary-model}
both_regression_tables$secondary
```