In IBAHCM/RPiR: Reproducible Programming in R

Generating reports from R

What are you going to do?

• Read and write well-documented and well-structured code
• Use this to generate clear and well-documented results/reports

What are you going to do?

• Read and write well-documented and well-structured code
• Use this to generate clear and well-documented results/reports

Header information

#' ---
#' title: "Comparing a simple growth model with a birth-death model"
#' author: "Richard Reeve"
### <b>
#' date: '`r format(Sys.Date(), "%B %d %Y")`'
### </b>
#' output: html_document
#' ---
#'

Mixed text and code

```{R, eval = FALSE}

'

' Load in the functions that do the work

library(RPiR) source("0104-step-growth.R") source("0105-step-birth-death.R")

```{R, echo = FALSE}
#'
#' Load in the functions that do the work
library(RPiR)
source(system.file("dummy_project/0104-step-growth.R", package = "RPiR"))
source(system.file("dummy_project/0105-step-birth-death.R", package = "RPiR"))

Formatted text -- lists and maths

#' We are going to compare two population dynamics models:
#'
#' 1. A simple growth model
### <b>
#'    $$N(t + 1) = \lambda \times N(t) + N(t)$$
### </b>
#' 2. A birth death model
### <b>
#'    $$N(t + 1) = b \times N(t) - d \times N(t) + N(t)$$
### </b>
#'

(note the blank #' line before the numbered list)

Descriptive text vs code comments

#' First we set up the simulation parameters for every experiment.

### <b>
## Set the birth and death rates
### </b>
birth.rate <- 0.2
death.rate <- 0.1

### <b>
## Starting population size
### </b>
initial.count <- 1

Formatted text -- headers

```{R, echo = FALSE}

And setting times

start.time <- 0 end.time <- 100

the timesteps that the simulation will run through

timesteps <- seq(from = start.time + 1, to = end.time)

```{R}
### <b>
#'
#' ## Run the full $birth, death$ simulation
### </b>

## Set up the population starting size (at the first timestep)
population.df1 <- data.frame(count = initial.count)

(note the blank #' line again before the section header)

Plotting and chunk options

```{R, echo = FALSE}

Set up the population starting size (at the first timestep)

population.df1 <- data.frame(count = initial.count)

Now we loop through the time itself (starting at the second timestep)

for (new.time in timesteps) { updated.population <- step_deterministic_birth_death(latest = tail(population.df1, 1), birth.rate = birth.rate, death.rate = death.rate) population.df1 <- rbind(population.df1, updated.population) } population.df1$time <- c(start.time, timesteps)

```{R, fig.width=4, fig.height=4, fig.align="center", fig.cap="Fig. 1: Birth-death model", .smaller}
#+ initial, fig.width=5, fig.height=5, fig.align="center", fig.cap="Fig. 1: Birth-death model"
plot_populations(population.df1)

More chunk options for output {.smaller}

You can make it obvious what the R output is by default:

a <- 5 + 3
a

Or you can remove the (slightly odd) comments on output: ```{R, comment = ""}

+ comment=""

b <- 4 + 4 b

Or you can run code directly inside your text by using:
```r
#' What is b - a? `r b - a`.

What is b - a? r b - a.

Further details

See at https://rmarkdown.rstudio.com/docs/articles/rmarkdown.html#cheatsheets or on Moodle for a cheatsheet and reference document with further options.

IBAHCM/RPiR documentation built on Jan. 12, 2023, 7:41 p.m.