Rstudio interface

Data were analyzed using R programming environment (v.3.6.2, r-project.org via R-Studio interface v.1.2.5033, rstudio.com) and visualized using the ggplot2 package (v.3.3.3, r-project.org). Quantitative data are presented as the means ±  standard error of the mean (SEM). Statistical significance was assessed using unpaired, two-tailed Student’s t-test. p-values and the number of independent experiments (N) used for quantification and statistical analysis are indicated in the corresponding figures and figure legends. For normalized data, a two-tailed one sample t-test was used to determine significant difference from a hypothetical value of 1.0. For enrichment analysis of the manually curated list of protein location a Fischer-test was used. For enrichment analysis of protein location done using the Subcellular Barcode database a Hypergeometric test was used. Values were considered to be significant when p values were <0.05.

Linear least‐squares regressions were calculated for the validation data sets using Microsoft Excel (Microsoft, Redmond, Washington, USA). R software (R Core Team, 2022 ) with the RStudio interface (http://www.rstudio.com/) and the tidyverse (Wickham et al., 2019 ) and ggplot2 (Wickham, 2016 ) packages was used to analyze and visualize the data from the diversity panel study. We performed an ANOVA to test the effect of genotype, nitrogen source, and nitrogen treatment (nitrogen source and concentration) on the seedling rosette area and to determine the broad sense heritability of the trait.

Data cleaning and management were undertaken in Microsoft Excel (Microsoft Corp. Redmond, Washington, United States) and R version 3.3.0 (R Core Team) with RStudio interface (RStudio Team), facilitated by the ‘car’ [30 ] and ‘plyr’ [31 ] packages. All the analyses were conducted in RStudio.
A response was included in the analysis only if the participant answered at least one of the questions about the evaluation of the BCS of their cat. For Australian residential participants who provided their postcode, participants were classified as living in ‘urban’ or ‘rural’ areas by consulting information from a marketing website [32 ].
Two main analyses were conducted: the first examined the associations between the owners’ attitude towards feline O&O and the owner-reported BCS of their cats; the second investigated the risk factors for feline O&O and underweight by using multinomial logistic regression. The significance level was set at P<0.05 throughout this study unless indicated otherwise.

All variables assessed were non-parametric, according to the Shapiro-Wilk test for composite normality; thus, descriptive statistics are presented as medians (25–75 percentiles—IQRs). In order to identify dependencies between FeNO and other variables, non-parametric tests were applied: the Wilcoxon rank-sum test was used for qualitative variables and Kendall’s correlation was used for quantitative variables (such as the subjects’ age).
The Friedman test was used in order to compare values at three consecutive visits (baseline or follow-up, symptomatic and convalescent) for each patient. Post-hoc analysis was carried out using the Wilcoxon paired signed-rank test, along with the Bonferroni correction.
All tests were considered two-sided and statistical significance was defined as p < 0.05. Statistical analysis was performed with the R software for statistical computing, along with the RStudio interface (both open-source products).

Descriptive statistics were generated for baseline data to characterize and compare the intervention and control groups in terms of clinical and sociodemographic characteristics. Clinical data of interest, including tumor type and staging, type of treatment(s) and time between diagnosis and the end of primary treatment, were obtained from the medical records. Sociodemographic information about age, sex, educational level, living and work situation, as well as lifestyle data such as smoking behavior, alcohol consumption, and PA behavior prior to the cancer diagnosis were assessed with the T0 questionnaire. The questionnaire also included study-specific questions about patients’ use of the Internet and their level of computer skills. Data cleaning and analyses were performed in the R statistical package (3.3.1, [42 ]) using the Rstudio interface (Version 1.1453, 2009–2018, Rstudio Inc., Boston, MA, USA).

Based on prior unpublished pilot data, power analysis suggested that to minimize the 95% confidence interval [CI] to a width of 20% or less for sensitivity of RWMA in patients receiving coronary intervention/referral, 104 subjects would be required in each outcome group.
We conducted data analysis in R (R Foundation for Statistical Computing, Vienna, Austria) using the RStudio interface (RStudio Inc., Boston, MA). Demographic characteristics were tabulated by whether subjects received a coronary intervention and/or referral to CABG with differences evaluated by the t-test for continuous variables and chi-square test for categorical variables. We calculated diagnostic performance characteristics with the epiR package.

Our Brazilian δ²H_f isoscape was modeled using a Random Forest analysis approach. First, using the “Recursive Feature Elimination” method, we selected environmental variables that most contributed to the prediction of δ²H_f values. Then, using the selected variables, we ran the Random Forest analysis to predict the best-fitting isoscape.
The R script used here was adapted from Bataille et al. [16 (link)] and Sena-Souza et al. [15 (link)]. Statistical analyses were performed in R version 4.1.3 [45 ] as well as RStudio interface [47 ]. Packages used were ‘raster’ [48 ], ‘sf’ [49 (link)], ‘dismo’ [50 ], ‘caret’ [51 (link), 52 ], ‘rgdal’ [53 ], and ‘randomForest’ [54 ]. Plots and figures were built using package ‘ggplot2’ [55 ] and ‘pdp’ [56 ] in R, but also Inkscape [46 ] and Qgis [57 ] free software.