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}
}