Do you use R or Python to preprocess datasets for analyses? smallsets is an R package that transforms your R/Python preprocessing script into a Smallset Timeline, so that you can document and share your preprocessing decisions in a practical manner.

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. A short (3 min) and long (15 min) YouTube video provide an introduction to the project.

If you have questions about using smallsets or would like help building a Smallset Timeline, please email Lydia at [email protected].

After installing smallsets, run the snippet of code to build your first Smallset Timeline!

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 start s_data caption[Remove rows where C2
# is FALSE.]caption
s_data <- s_data[s_data$C2 == TRUE, ]

s_data$C6[is.na(s_data$C6)] <- mean(s_data$C6, na.rm = TRUE)
# smallsets snap s_data caption[Replace missing values in C6 and
# C8 with column means. Drop C7 because there are too many
# missing values.]caption
s_data$C8[is.na(s_data$C8)] <- mean(s_data$C8, na.rm = TRUE)
s_data$C7 <- NULL

s_data$C9 <- s_data$C3 + s_data$C4
# smallsets end s_data caption[Create a new column,
# C9, by summing C3 and C4.]caption

Please cite the Smallset Timeline paper if you use the smallsets software.

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{smallsets2022, 
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}
}