Tidyverse

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Tidyverse is a collection of R packages designed to work together for data science tasks. It provides a consistent set of functions and data structures for manipulating, visualizing, and modeling data. Tidyverse includes packages such as ggplot2 for data visualization, dplyr for data transformation, and tidyr for data tidying. The core function of Tidyverse is to provide a cohesive and efficient ecosystem for data analysis and exploration.

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Lab products found in correlation

Survminer, by R Project for Statistical Computing (2 mentions) Ggplot2, by R Project for Statistical Computing (1 mentions) Matplotlib, by R Project for Statistical Computing (1 mentions) R version 3, by R Project for Statistical Computing (1 mentions)

3 protocols using tidyverse

Hospital Characteristics and Adjusted Overuse Rates

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We used multiple linear regression to report the adjusted composite overuse means for each hospital characteristic level, adjusted for the other hospital characteristics.²¹ We made post-hoc pairwise comparisons of hospital characteristics with Tukey P value and CI adjustment. A P value of 0.05 was used to indicate significance, and all tests were 2-sided. For the cluster comparison, we compared the proportions of each hospital characteristic within each cluster against its proportion in the entire cohort of hospitals. Because this difference in proportions is largely affected by sample size, we also calculated the Cohen h value and reported results where h was greater than 0.2.²²Claims analysis was performed using SAS Enterprise, version 7.15 HF8 (SAS Institute) on the CMS Virtual Research Data Center, and statistical analyses were performed from July 1, 2020, to December 20, 2020, using Python programming, version 3.7 and R, version 4.0.0 (using the tidyverse, ggplot2, ggridges, and matplotlib packages; R Foundation).^{23 ,24 ,25 (link),26 ,27 (link)} The hospital normalized rates, characteristics, and clusters output are available for reference.³⁵

Chalmers K., Smith P., Garber J., Gopinath V., Brownlee S., Schwartz A.L., Elshaug A.G, & Saini V. (2021). Assessment of Overuse of Medical Tests and Treatments at US Hospitals Using Medicare Claims. JAMA Network Open, 4(4), e218075.

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Statistical Analysis of Biomedical Research

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Data are presented as the mean (standard deviation) unless otherwise noted. Data were analyzed for parametric distribution. Parametric correlations of dichotomous variables were compared using Student’s t test and ANOVA for continuous variables with more than 3 variables. Nonparametric categorical variables between groups were compared using the Kruskal–Wallis test, whereas the Mann–Whitney U test was conducted for continuous variables. Ordinal associations between variables were analyzed using Somers’ D, Kendall’s tau-b, and Kendall’s tau-c tests. Categorical associations between variables were analyzed using chi-squared-based measures of association, including Phi, Creamer’s V, and McNemar’s Test. Statistical analyses were performed using R version 3.5.3 and the packages tidyverse, ROCR survival, and survminer (R Foundation for Statistical Computing, Vienna, Austria). p values < 0.05 were considered statistically significant.

Sander M, & Schmieger H. (2001). Method for Host-Independent Detection of Generalized Transducing Bacteriophages in Natural Habitats. Applied and Environmental Microbiology, 67(4), 1490-1493.

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Comprehensive Statistical Analysis of Research Data

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Data are presented as the mean (standard deviation) unless otherwise noted. Data were analyzed for parametric distribution. Parametric correlation of dichotomous variables was compared using Student's t test and ANOVA for 3 or more continuous variables. Nonparametric categorical variables between groups were compared using the Kruskal-Wallis test, whereas the Mann-Whitney U test was conducted for continuous variables. Statistical analyses were performed using R version 3.5.3 and the packages tidyverse and survminer (R Foundation for Statistical Computing, Vienna, Austria). p values<0.05 were considered statistically significant. Additionally, both parametric and nonparametric statistical analyses were performed using SPSS version 27 (IBM Technologies).

Azari F., Zhang K., Kennedy G., Bou-Samra P., Chang A., Nadeem B., Chang A., Galandarova A., Ibrahimli A., Karimov Z., Din A., Kucharczuk J., Doraid J., Pechet T., Delikatny E, & Singhal S. (2023). Prospective validation of tumor folate receptor expression density with the association of pafolacianine fluorescence during intraoperative molecular imaging-guided lung cancer resections. European journal of nuclear medicine and molecular imaging, 50(8).

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