Hello!

This is my very first blog post using an R Markdown file within blogdown. I’m going to demonstrate how we can include the following in a single post:

1. R code
2. A plot
3. An image (via markdown syntax)
4. An image (via knitr::include_graphics)
5. A relative link to another section in my site
6. A demo using a dataset that I have read in

1) Iris data set

Let’s make a plot using the iris data set in ggplot2.

library(ggplot2)

# Dataset
head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

scatter <- ggplot(data=iris, aes(x= Sepal.Length, y = Sepal.Width))
scatter + geom_point(aes(color=Species, shape=Species)) +
  xlab("Sepal Length") + ylab("Sepal Width")

Using markdown syntax ![Photo by Valentin Lacoste on Unsplash](valentin-lacoste-1675701-unsplash.jpg) to generate the image below:

I’m still wondering how to get the caption to show up when I embed images in this way.

We can also accomplish this with a code chunk, setting the following parameter out.width="500px", fig.align='center'

knitr::include_graphics("valentin-lacoste-1675701-unsplash.jpg")

I can also include some relative links to, for example, tell you to check out this other section of my site!

The structure of relative links is a bit confusing. When specifying the location of the page you want to get to, the reference point is the directory that your post lives in (and NOT the project root directory).

This is a bit weird, so the fastest way to figure it out is probably by trial-and-error, but I’m a visual learner, so I had to diagram this out:

6) Reading in a data set

Sometimes its nice to call in iris and other times, you just wanna go your own way. Here’s what it looks like to read in a .csv file that I have stored in the same directory as this blog post. This file has information about behavior or rhesus macaque monkeys.

library(dplyr)
library('DT')

behavior <- readr::read_csv("aggression_behavior.csv")

## Rows: 214 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): focal, compound, infant?
## dbl (8): birthyr, obyear, rank, attack_all, chase_all, threatcontact_all, th...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

dplyr::glimpse(behavior)

## Rows: 214
## Columns: 11
## $ focal               <chr> "bs9", "gk3", "se8", "qq8", "vd8", "qv5", "sr8", "…
## $ compound            <chr> "a1", "a1", "a1", "a1", "a1", "a1", "a1", "a1", "a…
## $ birthyr             <dbl> 2003, 1991, 2001, 2001, 2001, 1996, 2001, 2001, 19…
## $ obyear              <dbl> 2008, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 20…
## $ rank                <dbl> 3, 1, 1, 3, 3, 3, 2, 2, 1, 2, 1, 2, 2, 2, 3, 2, 3,…
## $ `infant?`           <chr> "N", "Y", "N", "N", "Y", "Y", "N", "Y", "N", "N", …
## $ attack_all          <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, …
## $ chase_all           <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, …
## $ threatcontact_all   <dbl> 0.000000, 0.000000, 0.000000, 2.232060, 0.997506, …
## $ threatnocontact_all <dbl> 0.000000, 0.000000, 0.000000, 2.976080, 1.995010, …
## $ agg_in              <dbl> 0.000000, 0.000000, 0.000000, 5.208140, 2.992516, …

And now, let’s use the DT package to make this into a table that’s easier to manage.

datatable(behavior)