Diego P.F. Trindade -

Rstats posts

From Blogdown/Hugo to Quarto - deployed on Netlify

blogdown

quarto

netlify

I’m perhaps a bit late to the Quarto party, but, after some delay, I decided overcoming my laziness and started to migrate my old blogdown/hugo website to Quarto.

Diego P.F. Trindade

3 Statistical analysis in ecology with `broom`

tidymodels

broom

purrr

ecology

broom organizes the messy output we get from summary(model), giving us a tibble with different parameters present in our models. broom also supports working within lists, as we have seen in our previous posts. In short, when we need to perform several statistical analysis, with broom, we can nest the variables and run them all at once. To start, this package has three main functions tidy(), glance() and augment(). Each one gives us different parameters.

Diego P.F. Trindade

2 Data visualization with `ggplot2`

ggplot2

ecology

tidyverse

data visualization

Besides being able to clean and explore data in ecology, it is also important to know how to visualize and present the patterns you found. Here I’m gonna show very briefly how to create histograms, bar plots, box plots and scatter plots with ggplot2. Further, I’m going through how to combine different plots using cowplot. I will try to use the knowledge we gathered in our previous post, i.e. using dplyr and purrr functions.

Diego P.F. Trindade

1 Data wrangling in ecology with `tidyverse`

tidyverse

dplyr

biotime

data cleaning

The amount of data becoming available in ecology has been increasing rather fast in the recent years. Therefore, being able to wrangling, analyzing and visualizing this data, associated with a good scientific question, is timely for ecologists. Here, I will introduce some tidyverse packages and functions that can make this part of the job a bit easier.

Diego P.F. Trindade