---
title: "Getting Started with EZEC"
author: "Zhian N. Kamvar"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with ezec}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo = FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE, tidy = FALSE)
knitr::opts_chunk$set(fig.align = "center", fig.show = 'asis', fig.height = 5,
                      fig.width = 5)
options(out.width = 100)
```

Welcome
=======

Introduction
------------

The package EZEC stands for Easy Effective Concentration. This package was 
designed to be a simple wrapper for the functions `drm()` and `ED()` in the 
[*drc*](http://cran.r-project.org/package=drc) package. It provides the function
`EC_table()` that will first fit a model to your data and then calculate
effective concentrations from that model over several samples arranged in a data
frame. In this tutorial, I will give a couple of short examples of how you can 
analyze your data. To get help with any functions in the package, simply type
`help("function.name")` and replace function.name with the name of the function
you want help for.

Credit
------

As this package is simply a wrapper to two functions from the *drc* package,
please cite the drc package and indicate the version number you used:

```{r}
packageVersion("drc")
citation("drc")
```


Data Format
===========

Your data must be in a format that encodes one observation per row. You can see
an example of this in the `dummydata` data set:


```{r dummydata}
library("ezec")
data("dummydata", package = "ezec")
head(dummydata) # the function head means "look at the top of the object"
```

You can see that this contains the columns `r paste(names(dummydata), collapse =
", ")`. The only important columns here are the **ID**, **dose**, and
**response** columns. 

To import your own data from csv text file, you should use the function
`read.table()` to import it into R. If you want to import data from an xlsx
file, you can use the function `read_excel()` from the *readxl* package.

Data Analysis
=============

You can analyze your data simply by running `EC_table()` with your data and
supplying a formula to describe what you want to analyze in the form of
`response_variable ~ explanitory_variable`. In our example of "dummydata", our
response variable is "response" and our explanitory variable is "dose".

```{r ec_table}
library("ezec")
data("dummydata", package = "ezec")
res <- EC_table(dummydata, form = response ~ dose)
print(res)
```

If you want to have all the plots plotted in one window, you can use the `par()`
function:

```{r ec_table_par, fig.width = 7, fig.height = 4}
par(mfrow = c(1, 2)) # set window to have 1 row and two columns
EC_table(dummydata, form = response ~ dose)
par(mfrow = c(1, 1)) # reset the window
```

Saving your results to a file
-----------------------------

If you want to save your results to a file that you can format for a manuscript,
you can use the `write.table()` functon:

```{r write, eval = FALSE}
write.table(res, file = "dummy_results.csv", row.names = FALSE, sep = ",")
```

Output: model summary
---------------------

You can also have the output be a summary of the model for each sample as a list

```{r}
EC_table(dummydata, form = response ~ dose, plot = FALSE, result = "summary")
```

Output: model
-------------

You can also choose to have the output be the model itself


```{r}
EC_table(dummydata, form = response ~ dose, plot = FALSE, result = "model")
```



