These data files are used in the notes 'INTRODUCTION TO STATISTICS USING R', which may be downloaded from my academia.edu profile: https://cyi.academia.edu/EfthymiaNikita/Books

For any suggestions/amendments, please contact me at: e.nikita@cyi.ac.cy

ABSTRACT Meta-analysis is an adequate statistical technique to combine results from different studies, and its use has been growing in the medical field. Thus, not only knowing how to interpret meta-analysis, but also knowing how to perform one, is fundamental today. Therefore, the objective of this article is to present the basic concepts and serve as a guide for conducting a meta-analysis using R and RStudio software. For this, the reader has access to the basic commands in the R and RStudio software, necessary for conducting a meta-analysis. The advantage of R is that it is a free software. For a better understanding of the commands, two examples were presented in a practical way, in addition to revising some basic concepts of this statistical technique. It is assumed that the data necessary for the meta-analysis has already been collected, that is, the description of methodologies for systematic review is not a discussed subject. Finally, it is worth remembering that there are many other techniques used in meta-analyses that were not addressed in this work. However, with the two examples used, the article already enables the reader to proceed with good and robust meta-analyses. Level of Evidence V, Expert Opinion.

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

MAPE and PB statistics for IBFI compared with other imputation methods (mean, median, mode, PMM, and Hotdeck) for 20% missingness of type MAR and all parameters tested (RN, TH, TC, RH, and PR).

R code and tutorial for downloading and processing agrometeorological data from API client sources. Last update on March 18, 2022.

Time series analysis of climate data using R

This resource expands on the Data Nugget "Trees and bushes, Home Sweet Home for Warblers". The analyses and graphing is done in R and there are questions that ask about the management implications.

This dataset is about books. It has 1 row and is filtered where the book is Political analysis using R. It features 7 columns including author, publication date, language, and book publisher.

Retrieving soil raster data from POLARIS using the XPolaris R-package.

The data consists of twenty pedigrees simulated using R package pedSimulate with different combinations of females selection (random, positively or negatively based on own phenotype or parent (genetic) average), additive genetic variance (10 vs. 20), and proportion of males selected (10% vs. 20%). The code used to simulate and analyze the data is available at "JupyterNotebook.html", and the corresponding Jupyter notebook (JupyterNotebook.ipynb) and R Markdown (JupyterNotebook.Rmd) files.

Model fit statistics for the proposed approach and as reported in the original study by Kassis et al. (n = 377).

In this MANUAL FOR VISIBILITY GRAPHS MODELING USING R-STUDIO We will first present basic notions that will allow the understanding of the mapping process, then we'll show the computational idea. Finally, let's work with the R scripts inside the RStudio, exploring pseudo-random series, Brownian motion series, periodic series, series of fibonacci and series of audio signals. We'll show you: 1) how to generate time series in RS Studio and later turn them into visibility graphs. 2) how to import time series allocated in a directory, turning them into visibility graphs. 3) how to visualize networks using three types of algorithms, followed by calculation and visualization of the main properties of complex networks. About the codes included The 3 codes included generates visibility graphs of series generated by RStudio functions. This code also calculates some metrics for complex networks, generates the graph plot and its degree distribution, shows the plot of the series and its histogram.

Associations between selected transition and predictors (n = 377).

Code accompanying the publication "Strategies to access web-enabled urban spatial data for socioeconomic research using R functions". Since the introduction of the World Wide Web in the 1990s, available information for research purposes has increased exponentially leading to a significant proliferation of web-based research. Nowadays it is common the use of internet-based databases which are obtained by either primary data online surveys or secondary official and non-official registers. However, information disposal varies depending on data category and country and specifically, the collection of microdata at low geographical level for urban analysis can be a challenge. The most common difficulties when working with secondary web-based data can be grouped into two categories: accessibility and availability problems. Accessibility problems are present when the data publication in the servers blocks or delays the download process, which becomes a tedious reiterative task that can produce errors in the construction of big databases. Availability problems usually arise when the official agencies restrict access to the information for statistical confidentiality reasons. In order to overcome some of these problems, this paper presents different strategies based on URL parsing, PDF text extraction and web scraping. A set of functions, which are available under a GPL-2 license, have been built in the R package specially to extract and organize databases at the municipality level (NUTS 5) in Spain on population, unemployment, vehicle fleet and firm.

This dataset is about book subjects. It has 5 rows and is filtered where the books is Applying the Rasch model in social sciences using R and BlueSky statistics. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Crown and stem rust are major diseases of perennial ryegrass (Lolium perenne L.). Plant breeders and pathologists often rate rust severity in the field using the modified Cobb scale, but this method is subjective and labor intensive. A novel, open-source system using ImageJ and R was developed to quantify pustule number and area using digital images collected from spaced plants in the field. The computer-processing pipeline included development of training data for prediction of pixel identity using random forest and noise reduction spatial processing. Raters and the computer scored rust severity on plant images of varying complexity including whole-plant (WP), five-leaf (FL), and single-leaf (SL) image series. Computer accuracy was determined using the SL, while the FL series gave insight into the true value of WP severity. Rater ability was assessed using a panel of nine scientists with varying levels of disease rating experience. Results showed rater perceptions of crown rust severity were very consistent across images, but agreement on severity values for a given image were low. Rater consistency for stem rust severity was low and FL scores were not strongly correlated with WP scores (r = 0.36, P = 0.03), indicating low rater accuracy. The computer-processing pipeline was able to accurately discriminate, count, and quantify crown and stem rust pustules on leaf samples. Correlations between computer and the median rater score for crown rust were excellent (r > 0.90, P < 0.001) for all image series. Similar to raters, there was a lack of correlation between WP and FL series (r = 0.20, not significant) indicating that this technique is limited to leaf or stem samples for stem rust and not applicable to WP. However, the computer-processing pipeline shows promise in replacing visual rating of WP for crown rust.

Dataset

This dataset was created by Mussa H

Contents

Dataset

This dataset was created by Medou Neine

Contents

This is a repository for codes and datasets for the open-access paper in Linguistik Indonesia, the flagship journal for the Linguistic Society of Indonesia (Masyarakat Linguistik Indonesia [MLI]) (cf. the link in the references below).

This module allows students to frame mistakes and frustrations during their first introduction to R in terms of improved learning and growth mindset.