**NOTE: This small package is a Stat545b project, primarily for the purpose of learning function & package development.

timeseries

Lifecycle: experimental License: MIT

The goal of timeseries is to speed up the initial stages of data exploration by providing a quick template for visualizing temporal patterns in data set.

Installation

devtools::install_github("nicolalove/timeseries", build_vignettes = TRUE, ref = "v0.2.0")

Package Development

As per the specifications of the rubric for Exercise 1.1, I will now explain how I created each part of my R package using the devtools and usethis packages.

  1. I initialized the package project by loading library(devtools) in the RStudio console & used the create_package function to create the timeseries package.

  2. In a new script under the timeseries package R-Project, I manually coded the function qtseries, which I developed in assignment 1-b of this course. I used the use_r() function to save qtseries as an .R file in the subdirectory of the timeseries package, committed the changes and ran the load_all() function to make the qtseries function available. Finally, I used the Check button under the Build tab to ensure that everything was running smoothly so far (I did this several times after each numbered step to ensure that the small changes hadn’t caused any errors).

  3. I then updated the description file with the relevant information, and specified a license using use_mit_license() function. I made sure to update the description file with the dependencies I use in the function, examples and tests using the use_package() and use_pipe() functions.

  4. I used roxygen2comment style and tags using @ to specify the document the qtseries function, mostly following along with the recorded first Stat545a lecture on R package development. I looked up some examples for writing functions using ggplot2 as a dependency, and found that the ggplot2 developers recommend using the @importFrom tag if the new function uses many lines of ggplot functions. This was especially helpful as using the :: call for every line of ggplot functions was getting messy and made qtseries difficult to read. Once I specified all the relevant documentation, I used the document() function to save the roxygen2 inputs. After running another check(), I installed the timeseries package in my library & restarted my R session via install().

  5. I implemented some formal tests for the qtseries function by using use_testthat() to add the testthat package to the Description file, and used use_test("qtseries") to specifically write a test script for qtseries function. These tests were developed in assignment 1-b of this course, so I just copied and pasted the tests in the test-qtseries.R script. I then checked the tests using the test() function, as well as using the Check button under the Build tab to ensure everything was running smoothly. I also ran the load_all() function again after this step.

  6. I created a ReadMe.Rmd file using the use_readme_rmd() function, and added a couple badges using use_lifecycle_badge("experimental") and manually writing the link for the MIT license badge. Once I am finished editing this Readme document I will render it using the build_readme() function.

  7. I built a vignette with use_vignette(), added the relevant information and examples, and then rendered the document using build_vignette().

  8. Steps 2-7 were taken for the qstacktseries function.

Basic Example

Building a time series plot with a dummy data set

testdf <- data.frame(year = rep(seq(from = 1980, to= 1990, 1), 3),
                       rainfall = c(rnorm(11, 100, 30), rnorm(11, 70, 10),rnorm(11, 20, 20)),
                       temp = c(rnorm(11, 70, 30), rnorm(11, 100, 10),rnorm(11, 45, 20)),
                       daysofsun = c(NA, NA, rnorm(9, 280, 50), rnorm(11, 300, 30), NA, rnorm(10, 340, 15)),
                       region = c(rep("A",11), rep("B", 11), rep("C", 11)))
library(timeseries)
qtseries(testdf, year, temp, region, verbose = TRUE)
#> Plotting time series...Done labelling plot!...Done applying default theme aesthetics!

qstacktseries(testdf,year, temp, region, verbose = TRUE)
#> Plotting time series...Done labelling plot!...Done applying default theme aesthetics!