Showing 3 of total 3 results (show query)
ropensci
daiquiri:Data Quality Reporting for Temporal Datasets
Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).
Maintained by T. Phuong Quan. Last updated 7 months ago.
data-qualityinitial-data-analysisreproducible-researchtemporal-datatime-series
36 stars 5.94 score 12 scripts
inzightvit
iNZightTools:Tools for 'iNZight'
Provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding.
Maintained by Tom Elliott. Last updated 3 months ago.
1 stars 5.16 score 18 scripts 2 dependents
lmiratrix
simITS:Analysis via Simulation of Interrupted Time Series (ITS) Data
Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) <arXiv:2002.05746>. This package provides methods for fitting ITS models with lagged outcomes and variables to account for temporal dependencies. It then conducts inference via simulation, simulating a set of plausible counterfactual post-policy series to compare to the observed post-policy series. This package also provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. This work partially funded by Arnold Ventures in collaboration with MDRC.
Maintained by Luke Miratrix. Last updated 2 years ago.
2 stars 4.30 score