smallsets website:
lydialucchesi.github.io/smallsets/
Do you use R or Python to preprocess datasets for analyses? smallsets
is an R package (https://CRAN.R-project.org/package=smallsets) that
transforms the preprocessing code in your R, R Markdown, Python, or
Jupyter Notebook file into a Smallset Timeline. A Smallset Timeline is a
static, compact visualisation composed of small data snapshots of
different preprocessing steps. A full description of the Smallset
Timeline can be found in the paper Smallset Timelines: A Visual
Representation of Data Preprocessing
Decisions in the proceedings
of ACM FAccT ’22.
The smallsets user guide is available
here
and in the package in vignette("smallsets"). If you have questions or
would like help building a Smallset Timeline, please email
Lydia.
Download the smallsets cheatsheet (1-page PDF)
install.packages("smallsets")Run this snippet of code to build your first Smallset Timeline! It’s based on the synthetic dataset s_data, with 100 observations and eight variables (C1-C8), and the preprocessing script s_data_preprocess.R, discussed in the following section.
library(smallsets)
set.seed(145)
Smallset_Timeline(data = s_data,
code = system.file("s_data_preprocess.R", package = "smallsets"))
The Smallset Timeline above is based on the R preprocessing script
below, s_data_preprocess.R. Structured comments were added to it,
informing smallsets what to do.
# smallsets snap s_data caption[Remove rows where C2 is FALSE.]caption
s_data <- s_data[s_data$C2 == TRUE, ]
# smallsets snap +2 s_data caption[Replace missing values in C6 and C8 with
# column means. Drop C7 because there are too many missing values.]caption
s_data$C6[is.na(s_data$C6)] <- mean(s_data$C6, na.rm = TRUE)
s_data$C8[is.na(s_data$C8)] <- mean(s_data$C8, na.rm = TRUE)
s_data$C7 <- NULL
# smallsets snap +1 s_data caption[Create a new column, C9, by summing C3 and
# C4.]caption
s_data$C9 <- s_data$C3 + s_data$C4If you use the smallsets software, please cite the Smallset Timeline
paper.
Lydia R. Lucchesi, Petra M. Kuhnert, Jenny L. Davis, and Lexing Xie. 2022. Smallset Timelines: A Visual Representation of Data Preprocessing Decisions. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 1136–1153. https://doi.org/10.1145/3531146.3533175
@inproceedings{SmallsetTimelines,
author = {Lucchesi, Lydia R. and Kuhnert, Petra M. and Davis, Jenny L. and Xie, Lexing},
title = {Smallset Timelines: A Visual Representation of Data Preprocessing Decisions},
year = {2022},
isbn = {9781450393522},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3531146.3533175},
doi = {10.1145/3531146.3533175},
location = {Seoul, Republic of Korea},
series = {FAccT '22}
}