Matrices are important mathematical objects, and they often describe networks of flows among nodes. The power of matrices lies in their ability to organize network-wide calculations, thereby simplifying the work of analysts who study entire systems.
But wouldn’t it be
nice if there were
an easy way to create R data frames whose entries were not numbers but
entire matrices? If that were possible, matrix algebra could be
performed on columns of similar matrices.
That’s the reason for matsindf. It provides functions to convert a
suitably-formatted
tidy data frame
into a data frame containing a column of matrices.
Furthermore, matsbyname is a sister package that
- provides matrix algebra functions that respect names of matrix rows
and columns (
dimnamesinR) to free the analyst from the task of aligning rows and columns of operands (matrices) passed to matrix algebra functions and - allows matrix algebra to be conducted within data frames using dplyr, tidyr, and other tidyverse functions.
When used together, matsindf and matsbyname allow analysts to wield
simultaneously the power of both matrix
mathematics and
tidyverse functional programming.
You can install matsindf from CRAN with:
install.packages("matsindf")You can install a recent development version of matsindf from github
with:
# install devtools if not already installed
# install.packages("devtools")
devtools::install_github("MatthewHeun/matsindf")
# To build vignettes locally, use
devtools::install_github("MatthewHeun/matsindf", build_vignettes = TRUE)The functions in this package were used in Heun et al. (2018).
Find more information, including vignettes and function documentation, at https://MatthewHeun.github.io/matsindf/.
Heun, Matthew Kuperus, Anne Owen, and Paul E. Brockway. 2018. “A Physical Supply-Use Table Framework for Energy Analysis on the Energy Conversion Chain.” Applied Energy 226 (September): 1134–62. https://doi.org/10.1016/j.apenergy.2018.05.109.