README.Rmd

---
output: github_document
#output: pdf_document
editor_options: 
  chunk_output_type: console
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r message=FALSE, warning=FALSE, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
devtools::load_all()
library(data.table)
```

# HRHRpal

The goal of HRHRpal is to ...

## Installation

You can install HRHRpal from the Folkehelseinstituttet repository via:

```{r gh-installation, eval = FALSE}
r <- getOption("repos")
r["fhi"] = "https://folkehelseinstituttet.github.io/drat/"
options(repos = r)

install.packages("HRHRpal")
```

## ProtectIdentifier

```{r, echo = FALSE}
data <- data.frame(id=c(
    1001,
    1001,
    1002,
    1001,
    1003,
    1003
    ))
data$weight <- c(41,42,43,44,45,46)
setDT(data)
```

This function takes a data.table (`data`) with a numerical identifier (`id`). 

```{r}
print(data)
```

We can then apply the `ProtectIdentifier` function to `data`.

```{r}
key <- ProtectIdentifier(data=data,identifier = "id", seed=4)
```

`ProtectIdentifier` works by obtaining a list of all unique values of the identifier

```{r echo=FALSE}
print(key[,"old"])
```

The rows are then sorted randomly:

```{r echo=FALSE}
setorder(key,new)
print(key[,"old"])
```

The rows are then numbered:

```{r echo=FALSE}
print(key)
```

The new values are then assigned to the original dataset:

```{r echo=FALSE}
print(data)
```