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IAPETUS_talk.Rmd
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---
title: "Introduction to Rmarkdown"
subtitle: "https://bradduthie.shinyapps.io/IAPETUS_talk/#1"
author: "Brad Duthie"
date: "22 November 2018"
output:
ioslides_presentation:
logo: images/IAPETUSnewlogo.png
widescreen: yes
fig_width: 5
fig_height: 5
runtime: shiny
---
<style type="text/css">
body p {
color: #000000;
}
ul {
color: black;
}
ol {
color: black;
}
a {
color: darkblue;
text-decoration: none
}
.fifty {
font-size: 50%;
}
.seventy {
font-size: 70%;
}
slides > slide.backdrop {
background: none !important;
background-color: white !important;
}
slides > slide.title-slide hgroup h1 {
color: black;
}
slides > slide.title-slide p {
color: black;
}
slides > slide.light > hgroup h2 {
color: black;
}
slides > slide.light > hgroup h1 {
color: black;
}
ul {
color: black;
}
ol {
color: black;
}
p {
color: black
}
h1, h2, h3, h4, h5, h6, h7, h8, p {
color: black;
}
.checkbox-inline, .radio-inline {
margin-right: 90px;
}
.forceBreak { -webkit-column-break-after: always; break-after: column; }
</style>
```{r, echo = FALSE}
initialise_inds <- function(N = 200){
ID <- matrix(data = 0, nrow = N, ncol = 30);
ID[,1] <- 1:N;
ID[,2] <- rbinom(n = N, size = 1, prob = 0.5);
ID[,3] <- sample(x = 1:10, size = N, replace = TRUE);
ID[,4] <- sample(x = 1:10, size = N, replace = TRUE);
ID[,5] <- rep(x = 0, times = N);
ID[,6] <- rep(x = -1, times = N);
ID[,7] <- rep(x = -1, times = N);
ID[,8] <- rep(x = 0, times = N);
ID[,9] <- rep(x = -1, times = N);
ID[,10] <- rep(x = 0, times = N);
return(ID);
}
initialise_Rmat <- function(ID){
rows <- dim(ID)[1];
Rmat <- matrix(data = 0, nrow = rows, ncol = rows);
diag(Rmat) <- 1;
Rmat <- cbind(ID[,1], Rmat);
return(Rmat);
}
Rbuild <- function(Rmat, ID, Rmat_new){
inds <- dim(ID)[1];
for(i in 1:inds){
if(ID[i, 6] %in% Rmat[,1] == FALSE){
MomP <- -1;
}else{
MomP <- which(Rmat[,1] == ID[i, 6]);
}
if(ID[i, 7] %in% Rmat[,1] == FALSE){
DadP <- -1;
}else{
DadP <- which(Rmat[,1] == ID[i, 7]);
}
# Inbreeding coefficient
if(MomP < 0 | DadP < 0){
Rmat_new[i, i+1] <- 1;
}else{
Rmat_new[i, i+1] <- 1 + Rmat[MomP, DadP+1];
}
# Kinship coefficient
j <- 1;
while(j < i){
if(ID[j, 6] %in% Rmat[,1] == FALSE){
Om <- -1;
}else{
Om <- which(Rmat[,1] == ID[j, 6]);
}
if(ID[j, 7] %in% Rmat[,1] == FALSE){
Od <- -1;
}else{
Od <- which(Rmat[,1] == ID[j, 7]);
}
if(ID[i,5] == 0 & ID[j,5] == 0){
if(Om < 0 | MomP < 0){
w <- 0;
}else{
if(MomP == Om){
w <- 0.5 * Rmat[MomP, Om + 1];
}else{
w <- Rmat[MomP, Om + 1];
}
}
if(Od < 0 | MomP < 0){
y <- 0;
}else{
y <- Rmat[MomP, Od + 1];
}
if(Om < 0 | DadP < 0){
x <- 0;
}else{
x <- Rmat[DadP, Om + 1];
}
if(Od < 0 | DadP < 0){
z <- 0;
}else{
if(DadP == Od){
z <- 0.5 * Rmat[DadP, Od + 1];
}else{
z <- Rmat[DadP, Od + 1];
}
}
}
rMmuP <- 0.5 * (w + y);
rDmuP <- 0.5 * (x + z);
rval <- 0.5 * (rMmuP + rDmuP);
Rmat_new[i, j+1] <- rval;
Rmat_new[j, i+1] <- rval;
j <- j + 1;
}
}
for(i in 1:inds){
for(j in 1:inds){
if(ID[i, 6] == ID[j, 1]){
if(ID[j, 6] == -1){
Rmat_new[i, j+1] = 0.25;
Rmat_new[j, i+1] = 0.25;
}
}
if(ID[i, 7] == ID[j, 1]){
if(ID[j, 7] == -1){
Rmat_new[i, j+1] = 0.25;
Rmat_new[j, i+1] = 0.25;
}
}
}
}
return(Rmat_new);
}
mateselect <- function(ID, Rmat_new){
fem <- ID[ID[,2] == 0 & ID[,5] == 0,];
mal <- ID[ID[,2] == 1 & ID[,5] == 0,];
for(i in 1:dim(fem)[1]){
strat <- sum(fem[i,11:20]);
vals <- rep(0, dim(mal)[1]);
fpos <- which(Rmat_new[,1] == i);
for(j in 1:length(vals)){
mpos <- which(Rmat_new[,1] == j);
kval <- Rmat_new[i, j+1];
vals[j] <- kval * strat;
}
if(strat < 0){
weight <- 1 / (1 + (-1 * vals));
probs <- weight / sum(weight);
}else{
weight <- 1 + vals;
probs <- weight / sum(weight);
}
mate <- sample(x = 1:dim(mal)[1], size = 1, pr = probs);
mateID <- mal[mate,1];
matepos <- which(ID[,1] == mateID);
ownID <- fem[i, 1];
ownpos <- which(ID[,1] == ownID);
ID[ownpos, 9] <- mateID;
ID[matepos, 9] <- ownID;
}
return(ID);
}
make_offspring <- function(ID, Rmat_new, nn = 6){
start_ID <- max(ID[,1]) + 1;
for(i in 1:dim(ID)[1]){
if(ID[i, 2] == 0 & ID[i, 9] > 0 & ID[i, 5] == 0){
mum <- ID[i,];
dadpos <- which(ID[,1] == ID[i, 9]);
dad <- ID[dadpos,];
offs <- matrix(data = 0, nrow = nn, ncol = dim(ID)[2]);
offs[,1] <- start_ID:(start_ID+nn-1);
start_ID <- start_ID + nn;
offs[,2] <- rbinom(n = nn, size = 1, prob = 0.5);
offs[,3] <- mum[3];
offs[,4] <- mum[4];
offs[,6] <- mum[1];
offs[,7] <- dad[1];
mrpos <- which(Rmat_new[,1] == mum[1]);
drpos <- which(Rmat_new[,1] == dad[1]);
offs[,8] <- Rmat_new[mrpos, drpos+1];
offs[,11:30] <- mum[11:30];
dum_vec <- rbinom(n = length(offs[,11:30]), size = 1, prob = 0.5);
dummy <- matrix(data = dum_vec, nrow = dim(offs)[1]);
dummy[dummy > 0] <- NA;
}
}
}
produce_offs <- function(ID, Rmat_new, nn){
# NOTE: This toy model has unrealistic recombination between parents!
mums <- sum(ID[,2] == 0 & ID[, 5] == 0 & ID[, 9] != 0);
offs <- matrix(data = 0, nrow = mums * nn, ncol = dim(ID)[2]);
start <- 1;
for(i in 1:dim(ID)[1]){ # Unrealistic recombination saves some time.
if(ID[i,2] == 0 & ID[i, 5] == 0 & ID[i, 9] != 0){
her_off <- matrix(data = ID[i,], nrow = nn, ncol = dim(ID)[2],
byrow = TRUE);
her_off[,2] <- rbinom(n = nn, size = 1, prob = 0.5);
her_off[,6] <- ID[i,1];
her_off[,7] <- ID[i,9];
mum_p <- which(Rmat_new[,1] == ID[i,1]);
dad_p <- which(Rmat_new[,1] == ID[i,9]);
her_off[,8] <- Rmat_new[mum_p, dad_p + 1];
her_off[,9] <- -1;
dad_o <- which(ID[,1] == ID[i,9]);
dad_overlay <- matrix(data = ID[dad_o,], nrow = nn,
ncol = dim(ID)[2], byrow = TRUE);
to_vec <- rbinom(n = nn*dim(ID)[2], size = 1, prob = 0.5);
to_overlay <- matrix(data = to_vec, nrow = nn);
to_overlay[,1:10] <- 1;
her_off[to_overlay < 1] <- NA;
her_off[is.na(her_off)] <- dad_overlay[is.na(her_off)];
offs[start:(start+(nn-1)), 1:dim(ID)[2]] <- her_off;
start <- start + nn;
}
}
max_ID <- max(ID[,1]);
offs[,1] <- (max_ID + 1):(max_ID + dim(offs)[1]);
return(offs);
}
mortality <- function(offs, beta){
depression <- exp(-1 * beta * offs[,8]);
alive <- rbinom(n = dim(offs)[1], size = 1, prob = depression);
offs[alive < 1,5] <- -1;
offs <- offs[offs[,5] > -1,];
return(offs);
}
mutation <- function(offs, mu){
mutations <- rbinom(n = 20*dim(offs)[1], size = 1, prob = mu);
tot_mut <- sum(mutations);
mut_mat <- matrix(data = mutations, nrow = dim(offs)[1]);
mu_eff <- rnorm(n = tot_mut, mean = 0, sd = 2);
mut_mat[mut_mat > 0] <- mu_eff;
offs[,11:30] <- offs[,11:30] + mut_mat;
return(offs);
}
retain <- function(ID, offs){
retainers <- rep(x = 0, times = dim(ID)[1]);
for(i in 1:dim(ID)[1]){
ID_num <- ID[i, 1];
ismum <- sum(offs[,6] == ID_num);
isdad <- sum(offs[,7] == ID_num);
ispar <- ismum + isdad;
if(ispar < 1){
retainers[i] <- 1;
}
}
to_remove <- which(retainers == 1);
ID <- ID[-to_remove,];
return(ID);
}
get_stats <- function(ID){
mean_inbr <- mean(ID[,8]);
mean_aval <- mean(ID[,11:20]);
sd_aval <- sd(ID[,11:20]);
mean_nval <- mean(ID[,21:30]);
sd_nval <- sd(ID[,21:30]);
stat_vec <- c(mean_inbr, mean_aval, sd_aval, mean_nval, sd_nval);
return(stat_vec);
}
immigration <- function(ID, imm, IDst){
max_ID <- max(ID[,1]);
immigrants <- initialise_inds(N = imm);
avalues <- rnorm(n = 10 * imm, mean = IDst[2], sd = IDst[3]);
nvalues <- rnorm(n = 10 * imm, mean = IDst[4], sd = IDst[5]);
a_mat <- matrix(data = avalues, nrow = imm);
n_mat <- matrix(data = nvalues, nrow = imm);
immigrants[,2] <- 1;
immigrants[,11:20] <- a_mat;
immigrants[,21:30] <- n_mat;
imm_IDs <- (max_ID + 1):(max_ID + imm);
immigrants[,1] <- imm_IDs;
ID <- rbind(ID, immigrants);
return(ID);
}
new_gen <- function(ID, Rmat, cost = 0, imm = 5, beta = 1,
mu = 0.01, Kf = 100, Km = 100, nn = 6){
N <- dim(ID)[1];
death <- rep(x = 0, times = N);
females <- sum(ID[,2] == 0 & ID[,5] <= 1);
males <- sum(ID[,2] == 1 & ID[,5] <= 1);
strat <- apply(X = ID[,11:20], MARGIN = 1, FUN = sum);
strat_c <- abs(strat) * cost;
strat_d <- rbinom(n = N, size = 1, prob = strat_c);
death <- death + strat_d;
death <- death + (ID[,5] > 1);
ID[,5] <- -1 * death;
ID <- ID[order(ID[,2], decreasing = FALSE),];
ID <- ID[order(ID[,5], decreasing = FALSE),];
livef <- sum(ID[,2] == 0 & ID[,5] < 2 & ID[,5] > -1);
livem <- sum(ID[,2] == 1 & ID[,5] < 2 & ID[,5] > -1);
if(livef > Kf){
rm <- livef - Kf;
rows <- which(ID[,2] == 0 & ID[,5] < 2 & ID[,5] > -1);
ID[rows[1:rm],5] <- -1;
}
if(livem > Km){
rm <- livem - Km;
rows <- which(ID[,2] == 1 & ID[,5] < 2 & ID[,5] > -1);
ID[rows[1:rm],5] <- -1;
}
ID <- rbind(ID[ID[,5] >= 0,], ID[ID[,5] < 0,]);
Rmat_new <- initialise_Rmat(ID);
Rmat_new <- Rbuild(Rmat, ID, Rmat_new);
ID <- mateselect(ID, Rmat_new);
offs <- produce_offs(ID, Rmat_new, nn);
offs <- mortality(offs, beta);
offs <- mutation(offs, mu);
IDst <- get_stats(ID);
lgn <- ID[ID[,5] == 0,];
ID[ID[,5] >= 0, 5] <- -1;
ID <- retain(ID, offs);
ID <- rbind(ID, offs);
ID <- immigration(ID, imm, IDst);
results <- list(ID = ID, Rmat = Rmat_new, stats = IDst, last_gen = lgn);
return(results);
}
mate_connect <- function(ID){
living <- ID[ID[,5] == 0,];
fem_count <- sum(living[,2] == 0);
mal_count <- sum(living[,2] == 1);
livsor <- living[order(living[,2]),];
livsor[,3] <- 1:dim(livsor)[1];
pointpairs <- matrix(data = 0, nrow = dim(livsor)[1], ncol = 8);
pointpairs[,1] <- livsor[,1];
for(i in 1:dim(livsor)[1]){
if(livsor[i, 2] == 0){
femID <- livsor[i, 1];
malID <- livsor[i, 9];
femps <- i;
malps <- which(livsor[,1] == malID);
pointpairs[i, 2] <- malID;
}
}
pointpairs[,3] <- 1:dim(pointpairs)[1];
distr <- seq(from = 0, to = 2*pi, length.out = dim(pointpairs)[1]);
for(i in 1:dim(pointpairs)[1]){
xplace <- 2 * cos(distr[pointpairs[i, 3]]);
yplace <- 2 * sin(distr[pointpairs[i, 3]]);
pointpairs[i, 5] <- xplace;
pointpairs[i, 6] <- yplace;
}
for(i in 1:dim(pointpairs)[1]){
if(i <= fem_count){
matepos <- which(pointpairs[,1] == pointpairs[i, 2]);
pointpairs[i, 4] <- matepos;
pointpairs[i, 7] <- pointpairs[matepos, 5];
pointpairs[i, 8] <- pointpairs[matepos, 6];
}
}
return(pointpairs);
}
```
<!--- --------------------------------------------------------------------------
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--------------------------------------------------------------------------- --->
## Timeline for this afternoon
| Time | Activity |
| ---------------- | ------------------------------------------------------- |
| 14:00 -- 14:30 | Presentation introducing Rmarkdown |
| 14:30 -- 15:00 | Getting started with knitr and references |
| 15:00 -- 15:20 | Integrating R analysis |
| 15:20 -- 16:00 | Self-guided writing with Rmarkdown |
| 16:00 -- 16:30 | Tea break |
| 16:30 -- 17:00 | Creating presentations |
| 17:00 -- 18:00 | Adding shiny to Rmarkdown |
## Objectives for today
<hr>
1. **Understand the features of Rmarkdown and why using it to write scientific documents may be useful**
2. **Create an Rmarkdown file and assemble it into an HTML, PDF, or DOCX document using knitr in Rstudio**
3. **Apply basic integration of R code into Rmarkdown to analyse data and plot results in output**
4. **Be able to navigate to the [accompanying Rmarkdown notes](https://stirlingcodingclub.github.io/Manuscripts_in_Rmarkdown/Rmarkdown_notes.html) and make use of them for additional tools**
5. **Learn how to create presentations in Rmarkdown**
6. **Learn how to use shiny within presentations**
<hr>
# Introducing Rmarkdown and Shiny
## Where did Rmarkdown come from?
| Microsoft Word (1983) | $\LaTeX{}$ (1980) |
| ------------------------------ | --------------------------------- |
| - Used in the life sciences | - Used in maths and physics |
| - What you see is what you get | - Edit files in [plain text](https://en.wikipedia.org/wiki/LaTeX) (code) |
| - Proprietary software | - Free software |
| - **Low learning curve** | - **High learning curve** |
| - **No analysis integration** | - **No analysis integration** |
<hr>
**Rmarkdown** (2012) is free software with a **relatively low learning curve** in which authors write in plain text and can easily integrate R analyses, citations, and tables or figures.
## Why is Rmarkdown worth learning?
<hr>
- Learning is a relatively low additional time investment if already invested in R
- Produces high quality [HTML](https://stirlingcodingclub.github.io/Manuscripts_in_Rmarkdown/ms_history/ms_final.html), [PDF](https://github.com/StirlingCodingClub/Manuscripts_in_Rmarkdown/blob/224e0f3673aece576d5c859f5409b6c9b68a5565/ms.pdf), and [DOCX](https://github.com/StirlingCodingClub/Manuscripts_in_Rmarkdown/raw/224e0f3673aece576d5c859f5409b6c9b68a5565/ms.docx) documents with the push of a button from an [Rmd file](https://github.com/StirlingCodingClub/Manuscripts_in_Rmarkdown/blob/master/ms.Rmd) in Rstudio
- Removes the need to format citations manually (with BibTeX)
- Allows users to insert images and equations seamlessly
- **Complete integration of data analysis and manuscript** (no copy-pasting when values or figures change)
- Integration of [shiny](https://shiny.rstudio.com/) into Rmarkdown documents and slides
<hr>
**You do not need to learn everything at once for Rmarkdown to be useful. If you get stuck or cannot figure out how to do something, you can always knit a DOCX and work from there.**
## The Bumpus sparrow data set
<div class="columns-2">
[Hermon Bumpus](http://www.brown.edu/Administration/News_Bureau/Databases/Encyclopedia/search.php?serial=B0700), a professor of comparative zoology collected some house sparrows (*Passer domesticus*) after an especially severe snowstorm in Providence, Rhode Island.
Some birds lived, others died. Bumpus collected data to test natural selection in action. He published a [paper and measurements](https://www.fieldmuseum.org/science/blog/hermon-bumpus-and-house-sparrows) of 136 sparrows.
[These data](https://www.fieldmuseum.org/sites/default/files/plowther/2014/09/24/bumpus.txt) are now a classic data set in biology, and have been analysed multiple times.
**Image:** [Luc Viatour](https://commons.wikimedia.org/wiki/File:Male_House_Sparrow_open_beak.jpg)
## Simple integration of R data on slides {.smaller}
<div class="columns-2">
Code below reads in the [Bumpus sparrow](https://github.com/StirlingCodingClub/Manuscripts_in_Rmarkdown/blob/4d13f87c4c4d954a06230b0af559d49fadcb65ad/IAPETUS_talk.Rmd#L489) data set.
```{r}
dat <- read.csv(file = "data/Bumpus_data.csv");
```
Mean sparrow length is `r round(mean(dat$totlen), digits = 2)`. This number is printed with the code ([see line](https://github.com/StirlingCodingClub/Manuscripts_in_Rmarkdown/blob/4d13f87c4c4d954a06230b0af559d49fadcb65ad/IAPETUS_talk.Rmd#L499)):
```{r, eval = FALSE}
`r round(mean(dat$totlen), digits = 2)`
```
Code can be used to generate a plot.
```{r}
par(mar = c(5, 5, 1, 1));
# The code below produces the right histogram
hist(x = dat$totlen,
main = "",
xlab = "Sparrow Length (mm)",
ylab = "Frequency",
cex.axis = 1.5,
cex.lab = 1.5,
col = "blue");
```
</div>
## {.smaller}
```{r, echo = FALSE}
library(knitr);
kable(dat[1:10,]);
```
## {.smaller}
```{r, echo=FALSE}
OffATr <- function(r, m, mmin=1, Beta=1, c=1){
return( (1/2)*(1+r) * (1 - exp(-c*(m - mmin - Beta*r)) ));
}
# Below is fmd for analysis and plotting
OffATrd <- function(r, m, mmin=1, Beta=1, c=1){
return( 0.5 * (1 + r) * (exp(-c * (m - mmin - Beta * r)) * c) );
# Also can simplify: return( (c/2)*(1+r)*exp(-c*(m-mmin-Beta*r)) );
} # We'll need the above to find m^{*} and \gamma^{*}
# To find m^{*}, can use the function below
findm <- function(low.guess,high.guess,rval,mmin=1,Beta=1,c=1){
fm <- function(m,r=rval){
OffATrd(r=rval, mmin=mmin, Beta=Beta, m=m, c=c)*(0-m) +
OffATr(r=rval, mmin=mmin, Beta=Beta, m=m, c=c);
}
lg <- fm(m=low.guess, r=rval);
hg <- fm(m=high.guess,r=rval);
if(lg > 0){
u <- low.guess;
l <- high.guess;
}else{
u <- high.guess;
l <- low.guess;
}
if((fm(l) > 0 & fm(u) > 0) | (fm(l) < 0 & fm(u) < 0)){
return("Value of m is outside the range");
}else{
check <- 1;
mguess <- 0.5 * (l+u);
i <- 0;
while(abs(check) > 0.000001 & i < 1000000){
check <- fm(r=rval, m=mguess);
if(check > 0){
u <- mguess;
mguess <- 0.5*(l+mguess);
}else{
l <- mguess;
mguess <- 0.5*(u+mguess);
}
i <- i+1;
}
return(mguess);
}
} # Running the below returns the estimate
fmpl <- expression(zeta[off] == frac(1,2)*(1+r)*(1-e^{(-c*(m-m[min]-beta*r))}));
inputPanel(
sliderInput("rval", label = "Relatedness", width="100%",
min = 0.0, max = 1, value = 0, step = 0.05),
sliderInput("cval", label = "PI curve", width="100%",
min = 0.0, max = 3, value = 1, step=0.2),
sliderInput("mmin", label = "Minimum m value", width="100%",
min = 0.0, max = 2, value = 1, step = 0.1),
sliderInput("Beta", label = "Inbreeding depression",
width="100%", min = 0.0, max = 5, value = 0, step = 0.2)
)
renderPlot({
cc <- as.numeric(input$cval);
mm <- as.numeric(input$mmin);
bb <- as.numeric(input$Beta);
rr <- as.numeric(input$rval);
rmm <- findm(low.guess=0,high.guess=6,rval=rr,mmin=mm,Beta=bb,c=cc);
mzt <- OffATr(r=rr, m=rmm, mmin=mm, Beta=bb, c=cc);
rmg <- mzt / rmm;
PI <- seq(from=0,to=4,by=0.01);
All <- OffATr(r=rr, m=PI, mmin=mm, Beta=bb, c=cc);
par(mar=c(5,5,2,2));
plot(PI,All,type="l",lwd=4,ylim=c(0,1),
xlab=expression(paste("Parental investment (",italic(m),")")),
ylab=expression(paste("IBD alleles in offspring (",zeta[off],")")),
cex.lab=1.5,cex.axis=1.5);
abline(h=0,lty="dotted",lwd=0.8);
abline(a=0,b=rmg,lty="solid", lwd=2,col="blue");
arrows(x0=rmm,y0=mzt/2,x1=rmm,y1=0,length = 0.15,angle=30,code=2,lwd=3,col="blue");
text(x=1,y=0.8,labels=fmpl,cex=2);
text(x=rmm,y=0.5*mzt+0.07,labels=expression(paste(italic(m)^"*")),col="blue",cex=2);
text(x=0.5,y=rmg*0.5+0.07,labels=expression(paste(italic(gamma^"*"))),
srt=rmg,cex=2,col="blue");
})
```
## {.fifty}
```{r, echo = FALSE, fig.width = 4}
inputPanel(
sliderInput("Imm", label = "Immigrants", width="100%",
min = 0.0, max = 10, value = 0, step = 1),
sliderInput("ID", label = "Inbreeding Depression", width="100%",
min = 0.0, max = 10, value = 0, step = 0.5),
sliderInput("cost", label = "Strategy Cost", width="100%",
min = 0.0, max = 0.1, value = 0, step = 0.01),
sliderInput("mu", label = "Mutation Rate", width="100%",
min = 0.0, max = 0.1, value = 0, step = 0.01),
actionButton("mate", "New Generation"),
actionButton("reset", "Reset")
)
gen <- reactiveValues();
iter <- reactiveValues();
mtt <- reactiveValues();
gen$dat <- list(ID = NULL, Rmat = NULL, lgn = NULL);
iter$all <- 0;
iter$nue <- 0;
iter$kva <- 0;
iter$mkv <- 0;
mtt$dat <- NULL;
Ds <- observeEvent(input$mate, {
if( is.null(gen$dat$ID) == TRUE | is.null(gen$dat$ID) == TRUE){
gen$dat$ID <- initialise_inds(N = 80);
gen$dat$Rmat <- initialise_Rmat(gen$dat$ID);
}else{
gen$dat <- new_gen(ID = gen$dat$ID, Rmat = gen$dat$Rmat,
Kf = 40, Km = 40, beta = input$ID,
imm = input$Imm, cost = input$cost,
mu = input$mu);
iter$all <- c(iter$all, mean(gen$dat$ID[,11:20]));
iter$nue <- c(iter$nue, mean(gen$dat$ID[,21:30]));
kvalues <- gen$dat$Rmat;
kvalues <- kvalues[,-1];
if(dim(kvalues)[1] > 80){
kvalues <- kvalues[1:80, 1:80];
}
diag(kvalues) <- NA ;#diag(kvalues) * 0.5;
iter$kva <- kvalues;
iter$mkv <- c(iter$mkv, mean(kvalues, na.rm = TRUE));
mtID <- gen$dat$last_gen;
mtt$dat <- mate_connect(mtID);
}
})
rs <- observeEvent(input$reset, {
gen$dat <- list(ID = NULL, Rmat = NULL);
iter$all <- 0;
iter$nue <- 0;
iter$kva <- 0;
iter$mkv <- 0;
})
renderPlot({
par(bg = 'transparent');
malle <- iter$all;
mneut <- iter$nue
mkval <- iter$mkv
gens <- 1:length(iter$all);
if(length(gens) < 6){
par(mfrow = c(1, 2));
if(length(gens) > 1){
dat <- mtt$dat;
femrow <- 1:sum(dat[,2] > 0);
malrow <- (sum(dat[,2] > 0) + 1):dim(dat)[1];
plot(x = dat[, 5], y = dat[, 6], pch = 15, col = "blue", cex = 1.25,
xlim = c(-2, 2), ylim = c(-2, 2), xaxt = "n", yaxt = "n", xlab = "",
ylab = "", type = "n", bty = "n");
text(x = -2, y = -2, pos = 4, col = "red", labels = "Males", cex = 1.75);
text(x = -2, y = 2, pos = 4, col = "blue", labels = "Females", cex = 1.75);
arrows(x0 = dat[femrow,5], x1 = dat[femrow,7], y0 = dat[femrow,6],
y1 = dat[femrow,8], length = 0, lwd = 2);
points(x = dat[femrow, 5], y = dat[femrow, 6], pch = 15, col = "blue", cex = 1.25);
points(x = dat[malrow, 5], y = dat[malrow, 6], pch = 16, col = "red", cex = 1.25);
plot(x = 0:5, y = 0:5, type = "n", ylim = c(0, 1), cex.axis = 1.5,
cex.lab = 1.5, xlab = "Generation", ylab = "Mean Kinship",
bg = "transparent");
points(x = gens, y = mkval, pch = 20, cex = 1.5, type = "b",
lwd = 2, col = "black");
}else{
plot.new();
}
}else{
par(mfrow = c(1, 2));
dat <- mtt$dat;
femrow <- 1:sum(dat[,2] > 0);
malrow <- (sum(dat[,2] > 0) + 1):dim(dat)[1];
plot(x = dat[, 5], y = dat[, 6], pch = 15, col = "blue", cex = 1.25,
xlim = c(-2, 2), ylim = c(-2, 2), xaxt = "n", yaxt = "n", xlab = "",
ylab = "", type = "n", bty = "n");
arrows(x0 = dat[femrow,5], x1 = dat[femrow,7], y0 = dat[femrow,6],
y1 = dat[femrow,8], length = 0, lwd = 2);
text(x = -2, y = -2, pos = 4, col = "red", labels = "Males", cex = 1.75);
text(x = -2, y = 2, pos = 4, col = "blue", labels = "Females", cex = 1.75);
points(x = dat[femrow, 5], y = dat[femrow, 6], pch = 15, col = "blue", cex = 1.25);
points(x = dat[malrow, 5], y = dat[malrow, 6], pch = 15, col = "red", cex = 1.25);
x0 <- length(gens) - 5;
x1 <- length(gens) + 0;
plot(x = x0:x1, y = 0:5, type = "n", ylim = c(0, 1), cex.axis = 1.5,
cex.lab = 1.5, xlab = "Generation", ylab = "Mean Kinship",
bg = "transparent");
points(x = gens, y = mkval, pch = 20, cex = 1.5, type = "b",
lwd = 2, col = "black");
}
})
```
## {.fifty}
```{r, echo = FALSE, fig.width = 4}
inputPanel(
sliderInput("Imm3", label = "Immigrants", width="100%",
min = 0.0, max = 10, value = 0, step = 1),
sliderInput("ID3", label = "Inbreeding Depression", width="100%",
min = 0.0, max = 10, value = 0, step = 0.5),
sliderInput("cost3", label = "Strategy Cost", width="100%",
min = 0.0, max = 0.1, value = 0, step = 0.01),
sliderInput("mu3", label = "Mutation Rate", width="100%",
min = 0.0, max = 0.1, value = 0, step = 0.01),
actionButton("mate3", "New Generation"),
actionButton("reset3", "Reset")
)
gen <- reactiveValues();
iter <- reactiveValues();
gen$dat <- list(ID = NULL, Rmat = NULL);
iter$all <- 0;
iter$nue <- 0;
iter$kva <- 0;
iter$mkv <- 0;
Ds <- observeEvent(input$mate3, {
if( is.null(gen$dat$ID) == TRUE | is.null(gen$dat$ID) == TRUE){
gen$dat$ID <- initialise_inds(N = 80);
gen$dat$Rmat <- initialise_Rmat(gen$dat$ID);
}else{
gen$dat <- new_gen(ID = gen$dat$ID, Rmat = gen$dat$Rmat,
Kf = 40, Km = 40, beta = input$ID3,
imm = input$Imm3, cost = input$cost3,
mu = input$mu3);
iter$all <- c(iter$all, mean(gen$dat$ID[,11:20]));
iter$nue <- c(iter$nue, mean(gen$dat$ID[,21:30]));
kvalues <- gen$dat$Rmat;
kvalues <- kvalues[,-1];
if(dim(kvalues)[1] > 80){
kvalues <- kvalues[1:80, 1:80];
}
diag(kvalues) <- NA ;#diag(kvalues) * 0.5;
iter$kva <- kvalues;
iter$mkv <- c(iter$mkv, mean(kvalues, na.rm = TRUE));
}
})
rs <- observeEvent(input$reset3, {
gen$dat <- list(ID = NULL, Rmat = NULL);
iter$all <- 0;
iter$nue <- 0;
iter$kva <- 0;
iter$mkv <- 0;
})
renderPlot({
par(bg = 'transparent');
malle <- iter$all;
mneut <- iter$nue
mkval <- iter$mkv
gens <- 1:length(iter$all);
if(length(gens) < 6){
par(mfrow = c(1, 2));
if(length(gens) > 0){
par(mar = c(7, 5, 5, 5));
hist(iter$kva, xlim = c(0, 1), ylim = c(0, 20), freq = FALSE, col = "black",
yaxt = "n", ylab = "", main = "", xlab = "",
breaks = seq(from = 0, to = 1, by = 0.05), cex.axis = 1.5);
title(xlab = "Kinship (k)", line = 4, cex.axis = 1.5, cex.lab = 1.5);
box(lwd = 3);
plot(x = 0:5, y = 0:5, type = "n", ylim = c(0, 1), cex.axis = 1.5,
cex.lab = 1.5, xlab = "Generation", ylab = "Mean Kinship",
bg = "transparent");
points(x = gens, y = mkval, pch = 20, cex = 1.5, type = "b",
lwd = 2, col = "black");
}else{
plot.new();
}
}else{
x0 <- length(gens) - 5;
x1 <- length(gens) + 0;
par(mfrow = c(1, 2), mar = c(7, 5, 5, 5));
hist(iter$kva, xlim = c(0, 1), ylim = c(0, 20), freq = FALSE, col = "black",
yaxt = "n", ylab = "", main = "", xlab = "",
breaks = seq(from = 0, to = 1, by = 0.05), cex.axis = 1.5);
title(xlab = "Kinship (k)", line = 4, cex.axis = 1.5, cex.lab = 1.5);
box(lwd = 3);
plot(x = x0:x1, y = 0:5, type = "n", ylim = c(0, 1), cex.axis = 1.5,
cex.lab = 1.5, xlab = "Generation", ylab = "Mean Kinship",
bg = "transparent");
points(x = gens, y = mkval, pch = 20, cex = 1.5, type = "b",
lwd = 2, col = "black");
}
})
```
## Simulated kinships vs. Mandarte song sparrows {.fifty}
<div class = "columns-2">
<br>
```{r, echo = FALSE, fig.width = 4}
renderPlot({
par(bg = 'transparent');
malle <- iter$all;
mneut <- iter$nue
mkval <- iter$mkv
gens <- 1:length(iter$all);
par(mar = c(7, 5, 5, 5));
hist(iter$kva, xlim = c(0, 1), ylim = c(0, 20), freq = FALSE, col = "black",
yaxt = "n", ylab = "", main = "", xlab = "",
breaks = seq(from = 0, to = 1, by = 0.05), cex.axis = 1.5);
title(xlab = "Kinship (k)", line = 4, cex.axis = 1.5, cex.lab = 1.5);
box(lwd = 3);
})
```
</div>
## {.fifty}
```{r, echo = FALSE, fig.width = 4}
inputPanel(
sliderInput("Imm2", label = "Immigrants", width="100%",
min = 0.0, max = 10, value = 2, step = 1),
sliderInput("ID2", label = "Inbreeding Depression", width="100%",
min = 0.0, max = 10, value = 8, step = 0.5),
sliderInput("cost2", label = "Strategy Cost", width="100%",
min = 0.0, max = 0.1, value = 0, step = 0.01),
sliderInput("mu2", label = "Mutation Rate", width="100%",
min = 0.0, max = 0.1, value = 0, step = 0.01),
actionButton("mate2", "New Generation"),
actionButton("reset2", "Reset")
)
gen <- reactiveValues();
iter <- reactiveValues();
gen$dat <- list(ID = NULL, Rmat = NULL);
iter$all <- 0;
iter$nue <- 0;
iter$kva <- 0;
iter$mkv <- 0;
Ds <- observeEvent(input$mate2, {
if( is.null(gen$dat$ID) == TRUE | is.null(gen$dat$ID) == TRUE){
gen$dat$ID <- initialise_inds(N = 80);
gen$dat$Rmat <- initialise_Rmat(gen$dat$ID);
}else{
gen$dat <- new_gen(ID = gen$dat$ID, Rmat = gen$dat$Rmat,
Kf = 40, Km = 40, beta = input$ID2,
imm = input$Imm2, cost = input$cost2,
mu = input$mu2);
iter$all <- c(iter$all, mean(gen$dat$ID[,11:20]));
iter$nue <- c(iter$nue, mean(gen$dat$ID[,21:30]));
kvalues <- gen$dat$Rmat;
kvalues <- kvalues[,-1];
if(dim(kvalues)[1] > 80){
kvalues <- kvalues[1:80, 1:80];
}
diag(kvalues) <- NA ;#diag(kvalues) * 0.5;
iter$kva <- kvalues;
iter$mkv <- c(iter$mkv, mean(kvalues, na.rm = TRUE));
}
})
rs <- observeEvent(input$reset2, {
gen$dat <- list(ID = NULL, Rmat = NULL);
iter$all <- 0;
iter$nue <- 0;
iter$kva <- 0;
iter$mkv <- 0;
})
renderPlot({
par(bg = 'transparent');
malle <- iter$all;
mneut <- iter$nue
gens <- 1:length(iter$all);
if(length(gens) < 6){
par(mfrow = c(1, 2));
plot(x = 0:5, y = 0:5, type = "n", ylim = c(-1, 1), cex.axis = 1.5,
cex.lab = 1.5, xlab = "Generation", ylab = "Inbreeding strategy",
bg = "transparent");
abline(h = 0, lty = "dotted", lwd = 0.8);
polygon(x=c(-0.125, 2.65, 2.65, -0.125), y=c(0.825, 0.825, 1.1, 1.1),
border=NA, col="black");
polygon(x=c(-0.125, 2.65, 2.65, -0.125), y=c(-0.825, -0.825, -1.1, -1.1),
border=NA, col="black");
abline(v = -0.125, lwd = 4);
text(x = 0, y = 0.95, labels = "Inbreeding Preference", cex = 1.5,
col = "white", pos = 4);
text(x = 0, y = -0.95, labels = "Inbreeding Avoidance", cex = 1.5,
col = "white", pos = 4);
if(length(gens) > 0){
points(x = gens, y = malle, pch = 20, cex = 1.5, type = "b",
lwd = 2, col = "black");
par(mar = c(7, 5, 5, 5));
hist(iter$kva, xlim = c(0, 1), ylim = c(0, 20), freq = FALSE, col = "black",
yaxt = "n", ylab = "", main = "", xlab = "",
breaks = seq(from = 0, to = 1, by = 0.05), cex.axis = 1.5);
title(xlab = "Kinship (k)", line = 4, cex.axis = 1.5, cex.lab = 1.5);
box(lwd = 3);
}
}else{
x0 <- length(gens) - 5;
x1 <- length(gens) + 0;
par(mfrow = c(1, 2));
plot(x = x0:x1, y = 0:5, type = "n", ylim = c(-1, 1), cex.axis = 1.5,
cex.lab = 1.5, xlab = "Generation", ylab = "Inbreeding strategy",
bg = "transparent");
abline(h = 0, lty = "dotted", lwd = 0.8);
polygon(x=c(x0-0.125, x0+2.65, x0+2.65, x0-0.125),
y=c(0.825, 0.825, 1.1, 1.1), border=NA, col="black");
polygon(x=c(x0-0.125, x0+2.65, x0+2.65, x0-0.125),
y=c(-0.825, -0.825, -1.1, -1.1), border=NA, col="black");
abline(v = x0-0.125, lwd = 4);
text(x = x0, y = 0.95, labels = "Inbreeding Preference", cex = 1.5,
col = "white", pos = 4);
text(x = x0, y = -0.95, labels = "Inbreeding Avoidance", cex = 1.5,
col = "white", pos = 4);
points(x = gens, y = malle, pch = 20, cex = 1.5, type = "b",
lwd = 2, col = "black");
par(mar = c(7, 5, 5, 5));
hist(iter$kva, xlim = c(0, 1), ylim = c(0, 20), freq = FALSE, col = "black",
yaxt = "n", ylab = "", main = "", xlab = "",
breaks = seq(from = 0, to = 1, by = 0.05), cex.axis = 1.5);
title(xlab = "Kinship (k)", line = 4, cex.axis = 1.5, cex.lab = 1.5);
box(lwd = 3);
}
})
```
# Getting started with knitr and references
## Create a new Rmarkdown document {.smaller}
<!---
<pre><code>```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ```</code></pre>
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**In Rstudio**: <u>F</u>ile $\to$ New <u>F</u>ile $\to$ R <u>M</u>arkdown...