In brittanymaldonado/maldonadopackage: Analyze monitoring data

knitr::opts_chunk$set(echo = TRUE)

First things first - install the package.

devtools::install_github("brittanymaldonado/maldonadopackage")

Now load the package.

library(maldonadopackage)

Function 1 - Formatting dates and creating "Year" and "Month" columns

Do you have a long-term dataset with many observations? Would you like to format your data in a way that allows easy visualization of year-to-year trends? Then this is the function for you! The dataset you input only needs to have a "Date" column in the following format: MM/DD/YYYY. This function is ideal for long-term datasets with observations across years or many observations per month. What do you get out of it? A date column reformatted with lubridate, a "Year" column that pulls the year for every observation, and a "Month" column that pulls the month for every observation.

library(ggplot2)
library(assertthat)
library(lubridate)
file_path <- system.file("extdata", "agp.csv", package="maldonadopackage")
agp <- read.csv(file_path)
agp_parsed <- parse_dates(agp)
head(agp_parsed)

That's great and all, but how do we use this? Now that the dates are formatted in this way, we can manipulate these data to observe trends.

Function 2 - Calculating biomass for each life stage of each species in the dataset

agp_biomass <- biomass(agp_parsed, Species, Stage, Year, Mass)
agp_biomass

This creates a small data frame that pretty easy to scroll through, but it would be better if we could visualize it. This third function is a streamlined approach to generating a plot for these data.

Function 3 - Generating a line plot showing biomass for each life stage of each species

plot_data(agp_biomass, Year, Biomass, Species)

brittanymaldonado/maldonadopackage documentation built on Dec. 31, 2020, 8:58 p.m.