<b>stringi</b>: Fast and Portable Character String Processing in <i>R</i>

Gągolewski, Marek

doi:10.18637/jss.v103.i02

Cited by 62 publications

(41 citation statements)

References 13 publications

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“…and the packages dplyr (Wickham et al, 2018), ggplot2 (Wickham, 2016), lmerTest (Kuznetsova et al, 2017), psych (Revelle, 2018), lme4 (Bates et al, 2015), effects (Fox & Weisberg), simr (Green & MacLeod, 2016), WebPower (Zhang & Mai, 2022), lsr (Navarro, 2015), and stringi (Gagolewski, 2022) to create letter matrices and finally analyze the data. The entire analysis script as well as the script for generating the letter matrices was preregistered in advance (https://osf.io/yuh78).…”

Section: Methodsmentioning

confidence: 99%

Falsifying the Insufficient Adjustment Model: No Evidence for Unidirectional Adjustment From Anchors

Röseler¹,

Incerti²,

Rebholz³

et al. 2023

Preprint

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After considering a more or less random number (i.e., an anchor), people’s subsequent estimates are biased toward that number. Such anchoring phenomena have been explained via an adjustment process that ends too early. We present a formalized version of the insufficient adjustment model, which captures the idea that decreasing the time that people have to adjust from anchors draws their estimates closer to the anchors. In four independent studies (N = 898), we could not confirm this effect of time on anchoring. Moreover, anchoring effects vanished in the two studies that deviated from classical paradigms by using a visual scale or a two-alternative forced-choice paradigm to allow faster responses. Although we propose that the current version of the insufficient adjustment model should be discarded, we believe that adjustment models hold the most potential for the future of anchoring research, and we make suggestions for what these might look like.

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Section: Methodsmentioning

confidence: 99%

Falsifying the Insufficient Adjustment Model: No Evidence for Unidirectional Adjustment From Anchors

Röseler¹,

Incerti²,

Rebholz³

et al. 2023

Preprint

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“…The Lens is an open-access platform archiving hundreds of millions of scholarly articles. Search results were exported to ris format and combined using custom code (RStudio, v2021.09.0, build 351, RStudio PBC) drawing on the following packages (Synthesisr, Data.table, Tidyverse, Expss, Revtools 17 , dplyr, stringi 18 ), to filter by publication year (>2019); remove exact and fuzzy duplicate matches of title and doi; remove systematic reviews and meta-analyses and then identify relevant articles by abstract keyword search (Supplementary Material B - Combine Screening Code) . The screened results were imported into Rayyan 19 for manual screening (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Incidence and prognostic factors of knee extension deficits following anterior cruciate ligament reconstruction: A systematic review and meta-analysis of randomised controlled trials

Ektas¹,

Scholes²,

Harrison-Brown³

et al. 2020

Preprint

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Background and aimsKnee extension deficits complicate recovery from ACL injury and reconstruction, however the incidence of knee extension loss is not well defined. The aim of this review was to identify the incidence of loss of extension (LOE) following ACL rupture and reconstruction, explore the definitions of knee extension deficits reported and identify prognostic factors affecting LOE incidence.Methods and analysisA systematic search was conducted in Medline, Cochrane Library and PEDro for studies in publication up to September 2019, with no restrictions on publication year. References were screened and assessed for inclusion using predetermined eligibility criteria. Randomised controlled trials (RCTs) that quantified knee angle, loss of extension or incidence of extension deficit were included for quality assessment and data extraction. Statistical summaries were generated and meta-analyses performed in two parts to examine: (i) the probability of a datapoint being zero incidence compared to a non-zero incidence, and (ii) the relationship between the predictors and non-zero LOE incidence.ResultsA sample of 8594 papers were retrieved using the search criteria, with 48 studies meeting eligibility criteria. Pooled results from 4065 participants were included for analysis, with 3965 participants who had undergone ACLR. The proportion of included studies judged at an overall low risk of bias was small (6%). The analysis revealed median LOE incidence of 15.9% (IQR 1.4 - 46.5) at a median follow up from treatment of 4.9 months (IQR 1.9 - 24). Median LOE incidence was 23 % (IQR 8.4 - 50.0) for the subset of studies reporting up to 12 months of follow up. The observed group and study were the most important predictors for whether a datapoint reported an incidence of extension deficit. Time to follow up (P < 0.001) and graft type (P = 0.02) were found to have a significant influence on non-zero LOE incidence (%).ConclusionsThis review examined the definitions for the measurement and interpretation of postoperative knee extension, and established the trajectory of knee extension deficit after ACL injury and reconstruction. While factors associated with loss of extension were identified, the trajectory of knee extension deficits were difficult to infer due to discrepancies in measurement techniques and patient variation. Clinicians should expect up to 1 in 3 patients to present postoperatively with loss of extension of at least 3 degrees, which may partially resolve over time. Future work should focus on the development of a standardised framework for postoperative measurement and reporting of LOE.

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“…Then, passing the nrows argument we can indicate the number of rows to fetch. In [29] it is noted that effective processing of character strings is needed at various stages of data analysis pipelines: from data cleansing and preparation, through information extraction, to report generation; compare, e.g., [81] and [16]. Pattern searching, string collation and sorting, normalisation, transliteration, and formatting are ubiquitous in text mining, natural language processing, and bioinformatics.…”

Section: File Connections (*)mentioning

confidence: 99%

“…In the following subsections, we review the most essential elements of the regex syntax as we did in [29]. One general introduction to regexes is [25].…”

Section: Matching Individual Charactersmentioning

confidence: 99%

Minimalist Data Wrangling with Python

Gągolewski¹

2023

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This open-access textbook is a non-profit project. It is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Please spread the word about it.Product and company names mentioned herein may be the trademarks of their respective owners. Rather than use a trademark symbol with every occurrence of a trademarked name, the names are used in an editorial fashion to the benefit of the trademark owner, with no intention of infringement of the trademark.Weird is the world we live in, but the following had to be written.Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is provided without warranty, either express or implied. The author will of course not be held liable for any damages caused or alleged to be caused directly or indirectly by this book.Anyway, any bug reports/corrections/feature requests are welcome. To make this textbook even better, please file them at https://github.com/gagolews/datawranglingpy. Typeset with XeL A T E X. Please be understanding: it was an algorithmic process, hence the results are ∈ [good enough, perfect).

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stringi: Fast and Portable Character String Processing in R

Cited by 62 publications

References 13 publications

Falsifying the Insufficient Adjustment Model: No Evidence for Unidirectional Adjustment From Anchors

Falsifying the Insufficient Adjustment Model: No Evidence for Unidirectional Adjustment From Anchors

Incidence and prognostic factors of knee extension deficits following anterior cruciate ligament reconstruction: A systematic review and meta-analysis of randomised controlled trials

Minimalist Data Wrangling with Python

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