tests.Rmd

---
title: "Testing the ViParc dataset"
csl: the-american-naturalist.csl
output:
  html_document:
    theme: cerulean
    toc: yes
  pdf_document:
    toc: yes
<!-- bibliography: references.bib -->
editor_options: 
  chunk_output_type: console
---

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REFERENCES:
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<style type="text/css">
.main-container {
  max-width: 1370px;
  margin-left: auto;
  margin-right: auto;
}
</style>

```{r general_options, include = FALSE}
knitr::knit_hooks$set(
  margin = function(before, options, envir) {
    if (before) par(mgp = c(1.5, .5, 0), bty = "n", plt = c(.105, .97, .13, .97))
    else NULL
  },
  prompt = function(before, options, envir) {
    options(prompt = if (options$engine %in% c("sh", "bash")) "$ " else "> ")
  })

knitr::opts_chunk$set(margin = TRUE, prompt = TRUE, comment = "",
                      collapse = TRUE, cache = FALSE, autodep = TRUE,
                      dev.args = list(pointsize = 11), fig.height = 3.5,
                      fig.width = 4.24725, fig.retina = 2, fig.align = "center")

options(width = 137)
```

## Packages

Installing required packages:

```{r}
required <- c("dplyr", "magrittr", "tibble")
to_install <- which(! required %in% row.names(installed.packages()))
if (length(to_install) > 0) install.packages(to_install)
```

Loading `magrittr`:

```{r}
library(magrittr)
```

## Utilitary functions

A utilitary function that completes a `table` vector with integer names, making
it start at 0 and progress by step of 1:

```{r}
complete_vector <- function(x) {
  the_names <- as.integer(names(x))
  new_names <- as.character(0:max(the_names))
  out <- setNames(rep(0, length(new_names)), new_names)
  out[names(x)] <- x
  out
}
```

## Loading the dataset

Loading the dataset:

```{r}
download.file("https://raw.github.com/viparc/prophylactic/master/data/viparc.csv", "viparc.csv")
viparc <- "viparc.csv" %>%
  read.csv(stringsAsFactors = FALSE) %>% 
  tibble::as_tibble()
file.remove("viparc.csv")
```

## Farms, cycles and weeks

Number of farms:

```{r}
length(unique(viparc$USUBJID))
```

The total number of cycles:

```{r}
viparc %>%
  dplyr::select(USUBJID, FLOCKSEQUENCE) %>%
  unique() %>%
  nrow()
```

The total number of weeks:

```{r}
nrow(viparc)
```

The distribution of the number of cycles per farm:

```{r}
viparc %>%
  dplyr::select(USUBJID, FLOCKSEQUENCE) %>%
  unique() %>%
  dplyr::group_by(USUBJID) %>%
  dplyr::tally() %>%
  dplyr::ungroup() %$%
  table(n)
```

The distribution of the number of weeks per cycle:

```{r}
viparc %>%
  dplyr::group_by(USUBJID, FLOCKSEQUENCE) %>%
  dplyr::tally() %>% 
  dplyr::ungroup() %$%
  table(n) %>% 
  complete_vector() %>% 
  print() %T>% 
  barplot()
```

Checking for missing weeks:

```{r}
viparc %>%
  dplyr::group_by(USUBJID, FLOCKSEQUENCE) %>%
  dplyr::arrange(WEEK) %>%
  dplyr::summarise(d = length(unique(diff(WEEK)))) %>%
  dplyr::ungroup() %>%
  dplyr::filter(d > 1)
```

## Drugs

The drugs:

```{r}
ab <- dplyr::select(viparc, dplyr::matches("^[a-z]", FALSE))
```

The names of drugs, sorted alphabetically:

```{r}
ab %>% 
  names() %>% 
  sort()
```

The drugs ordered by usage:

```{r}
sort(colSums(ab), TRUE)
```

The same in proportion of the number of weeks:

```{r}
round(100 * sort(colSums(ab), TRUE) / nrow(viparc))
```

A histogram of the drug usage:

```{r}
barplot(sort(colSums(ab), TRUE))
```

The number of drugs per week:

```{r}
table(rowSums(ab))
```

In proportion of the total number of weeks:

```{r}
round(100 * table(rowSums(ab)) / nrow(viparc))
```