Rstudio

Manufactured by R Project for Statistical Computing

Sourced in Austria, United States

RStudio is an integrated development environment (IDE) for the R programming language. It provides a user-friendly interface for writing, running, and debugging R code. RStudio's core function is to facilitate the development and execution of R scripts, allowing users to interact with the R programming language and its vast ecosystem of packages and libraries.

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Lab products found in correlation

R version 3, by R Project for Statistical Computing (14 mentions) R version 4, by R Project for Statistical Computing (10 mentions) Sas software, by SAS Institute (6 mentions) R software, by R Project for Statistical Computing (5 mentions) Spss software, by IBM (4 mentions) Prism 9, by GraphPad (4 mentions) Prism 8, by GraphPad (3 mentions) Matlab, by MathWorks (3 mentions) Statistix, by SAS Institute (3 mentions) Spss statistic, by IBM (3 mentions) Spss 26, by IBM (3 mentions) Prism 6, by GraphPad (2 mentions) Stata, by StataCorp (2 mentions) Sigmaplot 11, by Grafiti LLC (2 mentions) R statistical software, by R Project for Statistical Computing (2 mentions) Stata version 13, by StataCorp (2 mentions) Version 7.0a, by GraphPad (2 mentions) R software package, by R Project for Statistical Computing (2 mentions) Stata 15, by StataCorp (2 mentions) Sas version 9, by SAS Institute (2 mentions)

352 protocols using rstudio

Validating Predictive Models Using Diverse Datasets

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We split our patient cohort into derivation (70% of observations) and validation (30%) datasets. Variable testing, model building, and model cross-validation occurred in the derivation set, while we reserved the validation dataset strictly for final model confirmation. We built the initial dataset using SAS 9.3 software (SAS Institute Inc. 2002–2010: Cary, NC, USA). We performed data management, variable generation, model building, and statistical analysis in STATA14 (StataCorp LP, 2015. Stata Statistical Software: Release 14. College Station, TX, USA) and R/R studio (R Core Team, 2016: R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria. www.R-project.org; RStudio, Inc., 2016: RStudio: Integrated Development for R: Boston, MA, USA). We performed model cross-validation using SAS software and developed graphs in Tableau 9.0 software (Tableau Software Inc., 2015: Seattle, WA, USA).

Linnen D.T., Kipnis P., Rondinelli J., Greene J.D., Liu V, & Escobar G.J. (2018). Risk Adjustment for Hospital Characteristics Reduces Unexplained Hospital Variation in Pressure Injury Risk in Adult Patients in an Integrated Healthcare Delivery System. Nursing research, 67(4), 314-323.

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Evaluating Peer-Mentor Inclusion Experience

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Analysis of the questions for the peer-mentors using a Likert scale were performed with RStudio [44 ] and the R software package ( [45 ] version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria). The scores of each question were calculated for the two years separately and for the total study. The Mann-Whitney U test was applied due to the abnormal distribution of the data. We considered a p-value < 0.05 as statistically significant.
The first-year students received the PGIS questionnaire. Although it is a validated questionnaire, we determined the internal consistency of the questions in our sample for the factors belonging and authenticity regarding mentors and peer students. The Internal consistency reliability, indicated by the Cronbach’s Alpha variable, was calculated using SPSS (IBM SPSS Statistics for Windows, version 26, IBM Corp., Armonk, N.Y., USA).
The answers to the PGIS-questions were then compared between the different indicated demographic variables in order to investigate whether the experienced inclusion differed per variable. These analyses were performed with RStudio [44 ] and the R software package ( [45 ] version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria). Non-normal distributed data were then compared with the Mann-Whitney U test. We considered a p-value < 0.05 as statistically significant.

van der Velden G.J., Meeuwsen J.A., Fox C.M., Stolte C, & Dilaver G. (2023). Peer-mentorship and first-year inclusion: building belonging in higher education. BMC Medical Education, 23, 833.

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Nomogram for Biochemical Disease-Free Survival

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For both models a nomogram and webtool were constructed using the optimism-corrected coefficients. Finally, for each model separately, three risk groups were identified on the basis of the 25^th and 75^th percentile of the linear predictor. The Kaplan-Meier method was used to display the biochemical disease-free survival curves for each risk group.
All statistical analyses were performed using R studio (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria, https://rstudio.com) and the survival, survminer, rms, pmsampsize, ggplot2, mice, psfmi, DynNom, and regplot packages [30] . Reporting was according to the TRIPOD statement [29] (link).

Willigenburg T., van Son M.J., van de Pol S.M., Eppinga W.S., Lagendijk J.J., de Boer H.C., Moerland M.A., van der Voort van Zyp J.R, & Peters M. (2021). Development and internal validation of multivariable prediction models for biochemical failure after MRI-guided focal salvage high-dose-rate brachytherapy for radiorecurrent prostate cancer. Clinical and Translational Radiation Oncology, 30, 7-14.

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Statistical Analysis of Genetic Variants

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Statistical analysis was performed using RStudio (http://www.R-project.org/). Unless otherwise indicated, an alpha level of 0.05 was used to determine statistical significance. For normally distributed data, as determined by non-significant p-values from the Shapiro–Wilk test, a one-way ANOVA model was used to test overall group differences, followed by a post hoc Tukey HSD test for between-group comparisons to identify differences among isoforms and between Ɛ4 vs non- Ɛ4 participants. Non-normally distributed data, as determined by significant p-values from the Shapiro–Wilk test, were analyzed using linear regression models. Model residuals were evaluated for normality and homoscedasticity. The method used to calculate correlation coefficients in all the scatterplots was Spearman. Finally, pairwise Wilcoxon rank sum tests were used to determine differences in non-normally distributed data for between-group comparison, and p-values were adjusted via the Bonferroni method for multiple comparisons.

Meuret C.J., Hu Y., Smadi S., Bantugan M.A., Xian H., Martinez A.E., Krauss R.M., Ma Q.L., Nedelkov D, & Yassine H.N. (2023). An association of CSF apolipoprotein E glycosylation and amyloid-beta 42 in individuals who carry the APOE4 allele. Alzheimer's Research & Therapy, 15, 96.

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Comprehensive Bioinformatic Analysis Pipeline

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A two-sided p-value of 0.05 was considered to indicate statistically significant results. Rstudio (www.r-project.org; version 4.2.1) was used to sort and observe the data (Packages: limma, edgeR, ggplot2, survminer, survival, RMS, randomForest, pROC, glmnet, heatmap, timeROC, via storyline, complot, ConsensusClusterPlus, forest plot, survival rock, beeswarm, edgeR, “TxDb.Hsapiens.UCSC.hg38,” “known gene,” “cluster profile,” “org.Hs.eg.DB,” “karyoploteR,” “GSVA,” “GSEABase,” “stringr,” “GEOquery,” “dplyr,” “ComplexHeatmap,” and “RColorBrewer”).

Lu J., Xu F., Rao C., Shen C., Jin J., Zhu Z., Wang C, & Li Q. (2022). Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology. Frontiers in Pharmacology, 13, 1076958.

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Retinal Cell Quantification and Morphometric Analysis

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Numbers of PKCα+ and nyx::mYFP+ cells/100 μm of retinal section were compared across samples, including the data from 3 DPI samples reported in McGinn et al. (2018 (link)), using a Kruskal-Wallis test, followed by a Conover's test for multiple comparisons of independent samples. For statistical analysis of morphometric data of individual BP neurons, data were imported into R Studio (ver 0.99.903) (R Project for Statistical Computing) using R (ver 3.3.1) for statistical analysis. ANOVAs and Kruskal-Wallis tests were used for parametric and most non-parametric data, respectively. Since none of the parametric data showed statistical significance (p < 0.05) as measured by a one-way ANOVA, no post-hoc tests were conducted. For any Kruskal-Wallis test outcome that had a p-value < 0.05, a post-hoc test was done using a Wilcoxon–Mann–Whitney test with a false discovery rate p-value adjustment. A generalized linear model with a Poisson distribution was applied for analysis of connectivity patterns, and any comparisons having p-values < 0.05 were considered significant. Sample sizes were n = 3 for quantifications in cryosections (with 12 contralateral controls for PKCα+ and nyx::mYFP+ BP counting); Supplementary Table 1 contains the numbers of neurons subjected to morphometric analyses.

McGinn T.E., Galicia C.A., Leoni D.C., Partington N., Mitchell D.M, & Stenkamp D.L. (2019). Rewiring the Regenerated Zebrafish Retina: Reemergence of Bipolar Neurons and Cone-Bipolar Circuitry Following an Inner Retinal Lesion. Frontiers in Cell and Developmental Biology, 7, 95.

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Predictive Model for Severe aSAH Outcomes

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Quantitative data are expressed as the mean ± standard deviation and were analyzed by unpaired Student’s t-test or the Mann–Whitney U test. Pearson χ2 or Fisher’s exact test was used to categorizing categorical variables reported as counts. Univariate and multivariate logistic regression analysis was performed to explore the independent risk factors for the 6-month unfavorable clinical outcome. A receiver operating characteristic (ROC) curve and areas under the curve (AUC) was used to analyze the accuracy of different predictive models. Statistical analysis was performed using SPSS version 18 software (IBM, Armonk, NY, USA). P < 0.05 was considered statistically significant. A nomogram was used to visualize the predictive model using RStudio (R software version 4.0.2). Nomogram discrimination was assessed using the C-index to calculate sensitivity and specificity for prediction at each cut-off value. The C-index represents the AUC of which value assigned 0.5 and 1.0 indicates zero and perfect capability to predict the good prognosis rate of severe aSAH patients. The calibration curve was determined using the Hosmer–Lemeshow test and plotted by RStudio. The decision curve analysis (DCA) was then used to evaluate the clinical net benefit of the novel predictive model.

Huang H.Y., Yuan B., Chen S.J., Han Y.L., Zhang X., Yu Q, & Wu Q. (2022). A novel nomogram model for clinical outcomes of severe subarachnoid hemorrhage patients. Frontiers in Neuroscience, 16, 1041548.

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Quantitative Analysis of GRBV Titers

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Statistical analyses were performed on the titer of GRBV in infected grapevines and viruliferous insects according to the calculated expression fold change values (

2^{- Δ Δ Ct}

) as determined via qPCR. Analyses of variance (ANOVA) and Welch’s t-tests were performed in the RStudio program (the R Project for Statistical Computing). The significance level was set at

α

= 0.05.

Flasco M.T., Hoyle V., Cieniewicz E.J., Loeb G., McLane H., Perry K, & Fuchs M.F. (2023). The Three-Cornered Alfalfa Hopper, Spissistilus festinus, Is a Vector of Grapevine Red Blotch Virus in Vineyards. Viruses, 15(4), 927.

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Examining Age-Related Changes in Risk-Taking

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To evaluate the relationship between age and performance variables and risk-taking, we implemented linear mixed-effects models (R version 3.5.2 via RStudio version 1.1.463; https://www.r-project.org/), including random intercepts estimated for each participant in order to account for the longitudinal aspect of the dataset. We tested linear and inverse (age⁻¹) functional forms of age and selected the model with the lowest Akaike Information Criterion (AIC), indicating best model fit. In the case that the AIC for each model was equivalent, we characterized developmental changes with an inverse age function, as we hypothesize that development is characterized by an initial period of rapid growth followed by stabilization into adulthood, and previous studies have shown that inverse age provides a better fit for developmental changes through adolescence (Luna et al., 2004 (link); Murty et al., 2018 (link); Ordaz et al., 2013 (link); Simmonds et al., 2017 (link)). Sex and visit number were included as covariates, to ensure the robustness of the associations. We tested for sex by age interactions, and the interaction terms were removed from the final model if not significant.

Parr A.C., Calabro F., Larsen B., Tervo-Clemmens B., Elliot S., Foran W., Olafsson V, & Luna B. (2021). Dopamine-related striatal neurophysiology is associated with specialization of frontostriatal reward circuitry through adolescence. Progress in neurobiology, 201, 101997.

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EEG Abnormalities Analysis Protocol

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R (version 4.0.0) in RStudio (version 1.2.5042; R Project for Statistical Computing) was used for statistical analysis to demonstrate within-group correlation for the PDR values. Descriptive statistics were calculated from each individual’s maximum PDR (Hz) at every EEG recording date. For each EEG, a binary “yes” or “no” categorical value was recorded for background slowing, presence of epileptiform activity, photoparoxysmal responses, seizure history, and overall abnormal EEG designation. These values were determined by the EEG rater who originally summarized the results as well as the reviewing epileptologist. A binomial proportion of individuals who received a “yes” to each of the abnormalities on at least one EEG date was calculated.

Poffenberger C.N., Inati S., Tayebi N., Stubblefield B., Ryan E., Schiffmann R., Sidransky E, & Lopez G. (2020). EEG abnormalities in patients with chronic neuronopathic Gaucher disease: A retrospective review. Molecular genetics and metabolism, 131(3), 358-363.

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