Load and view a real-world dataset in RStudio

• Calculate “Measure of Frequency” metrics

• Calculate “Measure of Central Tendency” metrics

• Calculate “Measure of Dispersion” metrics

• Use R’s in-built functions for additional data quality metrics

• Create a custom R function to calculate descriptive statistics on any given dataset

R script used with accompanying data frame 'plot_character' that is within the project to calculate summary statistics and structural equation modelling.

INTRODUCTION As part of its responsibilities, the BC Ministry of Environment monitors water quality in the province’s streams, rivers, and lakes. Often, it is necessary to compile statistics involving concentrations of contaminants or other compounds. Quite often the instruments used cannot measure concentrations below certain values. These observations are called non-detects or less thans. However, non-detects pose a difficulty when it is necessary to compute statistical measurements such as the mean, the median, and the standard deviation for a data set. The way non-detects are handled can affect the quality of any statistics generated. Non-detects, or censored data are found in many fields such as medicine, engineering, biology, and environmetrics. In such fields, it is often the case that the measurements of interest are below some threshold. Dealing with non-detects is a significant issue and statistical tools using survival or reliability methods have been developed. Basically, there are three approaches for treating data containing censored values: 1. substitution, which gives poor results and therefore, is not recommended in the literature; 2. maximum likelihood estimation, which requires an assumption of some distributional form; and 3. and nonparametric methods which assess the shape of the data based on observed percentiles rather than a strict distributional form. This document provides guidance on how to record censor data, and on when and how to use certain analysis methods when the percentage of censored observations is less than 50%. The methods presented in this document are:1. substitution; 2. Kaplan-Meier, as part of nonparametric methods; 3. lognormal model based on maximum likelihood estimation; 4. and robust regression on order statistics, which is a semiparametric method. Statistical software suitable for survival or reliability analysis is available for dealing with censored data. This software has been widely used in medical and engineering environments. In this document, methods are illustrated with both R and JMP software packages, when possible. JMP often requires some intermediate steps to obtain summary statistics with most of the methods described in this document. R, with the NADA package is usually straightforward. The package NADA was developed specifically for computing statistics with non-detects in environmental data based on Helsel (2005b). The data used to illustrate the methods described for computing summary statistics for non-detects are either simulated or based on information acquired from the B.C. Ministry of Environment. This document is strongly based on the book Nondetects And Data Analysis written by Dennis R. Helsel in 2005 (Helsel, 2005b).

STAD-R is a set of R programs that performs descriptive statistics, in order to make boxplots and histograms. STAD-R was designed because is necessary before than the thing, check if the dataset have the same number of repetitions, blocks, genotypes, environments, if we have missing values, where and how many, review the distributions and outliers, because is important to be sure that the dataset is complete and have the correct structure for do and other kind of analysis.

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

Note: Sample size is 4,351 respondents in 146 neighborhoods.

• Automated parametric analysis workflow built using R Studio.
• Demonstrates core statistical analysis methods on numerical datasets.
• Includes step-by-step R scripts for performing t-tests, ANOVA, and summary statistics.
• Provides visual outputs such as boxplots and distribution plots for better interpretation.
• Designed for students, researchers, and data analysts learning statistical automation in R.
• Useful for understanding reproducible research workflows in data analysis.
• Dataset helps in teaching how to automate statistical pipelines using R programming.

Sample summary statistics for subjects’ real life and experimental financial outcomes, demographic characteristics, and measures of cognitive and affect measures.

GWAS summary statistics for multivariate GWAS model extension of cognitive and noncognitive skills. From: 'Malanchini, M., Allegrini, A. G., Nivard, M. G., Biroli, P., Rimfeld, K., Cheesman, R., ... & Plomin, R. (2023). Genetic contributions of noncognitive skills to academic development. Research Square.' Columns: SNP = rsID, CHR = chromosome, BP = position, MAF = minor allele frequency (1000 Genomes Phase 3), A1 = effect allele, A2 = other allele, BETA = estimate of the SNP effect, SE = standard error of BETA, Z = Z-statistic, PVAL = p-value.

Summary statistics of variables used in analyses (N = 81,674).

Summary statistics at each timepoint of data collection for participants with complete data (n = 229).

Summary statistics of temporal trend analysis (coefficient and R square) for socio- demographic and ecological variables, (p<0.05).

Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques. Module M.2 introduces basic functions in R, as well as in its packages tidyverse and rstatix, for estimating descriptive statistics.

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

Dataset

This dataset was created by Rajdeep Kaur Bajwa

Contents

Summary statistics generated for the manuscript entitled "Epigenome-wide association study of lung function in Latino children and youth with asthma" Our aim was to identify DNA methylation signals associated with lung function in Latino youth with asthma and validate previous epigenetic signals from non-Latino populations. For that, we performed multiple epigenome-wide association studies (EWAS) of lung function measurements analyzing whole blood from 250 Puerto Rican (PR) and 148 Mexican American (MEX) youth with asthma from the Genes-Environment and Admixture in Latino Americans (GALA II) study. The following measurements were evaluated Pre- and post- albuterol administration: Forced expiratory volume in one second (FEV1.Meas), forced vital capacity (FVC.Meas) and their ratio (FEV1.FVC.Meas). DNA methylation was profiled with the Infinium EPIC BeadChip or the Infinium HumanMethylation450 BeadChip array (Illumina, San Diego, CA, USA). The association of methylation beta-values and raw PFT values (in liters) was tested by robust linear regressions with correction for age, sex, height, the first three genotype principal components (PCs), in utero maternal smoking exposure, the first six ReFACTor components, and batch, when appropriate, via limma R package. The results for individuals of the same ethnic subgroup were meta-analyzed using fixed- or random-effects models, based on Cochran's Q p-value. Version 1 is deprecated. The EWAS result files (*.txt) contains: RSID: CpG name. STUDY: Number of sets of individuals included in the meta-analysis. BETA_meta: Coefficient of the regression. SEBETA_meta: Standard error of the coefficient of the regression. PVALUE_meta: P-value for the association. PVALUE_Q: Cochran's Q p-value. Model: Fixed-effect (FE) or Random-effects (RE2) model. PVALUE_meta_adj: False discovery rate (Benjamini & Hochberg method).

This data set contains two files both of which contain R objects.

chr19_snpdata_hm3only.RDS : A data frame with snp information

evd_list_chr19_hm3.RDS : A list of eigen decomposition of the SNP correlation matrix spanning chromosome 19

These data contain only SNPs in both 1k Genomes and HapMap3. Correlation matrices were estimated using LD Shrink. These data were built for use with the causeSims R package found here: https://github.com/jean997/causeSims

Abstract \r

\r The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.\r \r \r \r There are 4 csv files here:\r \r BAWAP_P_annual_BA_SYB_GLO.csv\r \r Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.\r \r Source data: annual BILO rainfall on \\wron\Project\BA\BA_N_Sydney\Working\li036_Lingtao_LI\Grids\BILO_Rain_Ann\\r \r \r \r P_PET_monthly_BA_SYB_GLO.csv\r \r long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month\r \r \r \r Climatology_Trend_BA_SYB_GLO.csv\r \r Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend\r \r \r \r Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv\r \r Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). All data used in this analysis came directly from James Risbey, CMAR, Hobart. As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).\r \r

Dataset History \r

\r Dataset was created from various BILO source data, including Monthly BILO rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET (calculated by Randall Donohue), Correlation coefficient data from James Risbey\r \r

Dataset Citation \r

\r Bioregional Assessment Programme (XXXX) SYD ALL climate data statistics summary. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/b0a6ccf1-395d-430e-adf1-5068f8371dea.\r \r

Dataset Ancestors \r

\r * Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012\r \r

Descriptive statistics (Pearson’s r, mean and standard deviations).

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.