Installation of packages | Load packages | Basic Use | Label position | Explode Pie | Explode Pie and Donuts | Explode Pie and Donuts independently | Customize start angle | Add title | Adjust the radius | Show Ratio by group | Doughnut plot | Summarized Data

Introduction | Installation | Make table from a data.frame | Use background and font color | Conditional Formatting | Use of color palette | Make a flextable from a ztable

What is survival analysis ? | Installation | Load package | Data melanoma | Survival analysis for whole group | Life table | Kaplan-Meier Plot | Cox-proportional hazard model | Hazard ratio plot : modelPlot() | Testing for proportional hazards | Disease-specific survival | Competing risk regression | Analysis of clustered data | 1. Frailty survival model | 2. Marginal approach

Package Installation | Linear Regression Models | Generalized Linear Models | Local Polynomial Regression Fitting | Play with predict3d()

Problem | Solution | 1. Identify a sample statistic. | 2. Select a confidence level. | 3. Find the margin of error. | Find standard deviation or standard error. | 4. Confidence interval of the proportion | Result of propCI() | Reference

Problem | Conditions | State the hypotheses | Analyze Sample Data | Standard deviation($s_d$) | standard error(SE) | Select a confidence level. | Degrees of freedom(DF) | Test statistics | 4. Interpret results. | Result of t.test | Result of meanCI() | Reference

Package "interpretCI" | Installation | Main functions | 1. meanCI(), propCI() | 2. plot() | 3.interpret() | Basic Usage | 1. Confidence interval of mean | 2. Mean difference in unpaired samples | 3. Mean differences in paired sample | 4. One-sided test | 5. Compare three or more groups | 6. Multiple pairs | 7. Split the data with group argument

Problem | Confidence interval of mean | Raw data | Sample statistics | Find the margin of error | Confidence interval of the mean | Plot | Result of meanCI() | Reference

Problem | Confidence interval of a sample proportion | Solution | 1. Identify a sample statistic. | 2. Select a confidence level. | 3. Find the margin of error. | Find standard deviation or standard error. | 4. Confidence interval of the proportion | Result of propCI() | Reference

Problem | Solution | Identify a sample statistic. | Standard deviation($s_d$) | standard error(SE) | Select a confidence level. | Degrees of freedom(DF) | Find critical value | Compute margin of error(ME): | Confidence level | Confidence interval of the mean difference | Plot | Result of meanCI() | Reference

Problem | Confidence interval of mean | Raw data | Identify sample statistics | Select a confidence level. | Find the margin of error | 1. Find standard error. | 2. Find the degree of freedom(df) | 3. Find the critical value | 4. Compute margin of error(ME) | 5. Specify confidence interval | Confidence interval of the mean difference | Plot | Result of meanCI() | Reference

Given Problem : r ifelse(two.sided,"Two","One")-Tailed Test | Hypothesis Test for a Mean | This approach consists of four steps: | 1. State the hypotheses | 2. Formulate an analysis plan | 3. Analyze sample data. | 4. Interpret results. | Result of meanCI() | Reference

Problem | Confidence interval of a sample proportion | Solution | This approach consists of four steps: | 1. State the hypotheses | 2. Formulate an analysis plan | 2. Select a confidence level. | 3. Analyze sample data | 4. Interpret results. | Result of propCI() | Reference

Given Problem : r ifelse(two.sided,"Two","One")-Tailed Test | Hypothesis test | This approach consists of four steps: | 1. State the hypotheses | 2. Formulate an analysis plan. | 3. Analyze sample data | 4. Interpret results. | Result of meanCI() | Reference

Problem | Solution | 1. State the hypotheses | 2. Formulate an analysis plan | 3. Analyze sample data | 4. Interpret results. | Result of propCI() | Reference

Installation | Load package | Main features | 1.Summarizing baseline characteristics : gaze() | For easy reproducible research : myft() | Summarizing baseline characteristics with two or more grouping variables | 2. For automatic selection of explanatory variables : autoReg() | Add univariate models to table and automatic selection of explanatory variables | Multiple imputation with mice() | Summarize regression model results in a plot : modelPlot()

Function "mytable" | Basic Usage | Explore a data.frame | Compress an object of class mytable | Delete Rows of an object of class mytable | Methods for categorical variables | For formatted numbers: addComma()

Function "mytable" | Basic Usage | Explore a data.frame | Use of labelled data | Choosing grouping variable(s) and row-variable(s) | Method for continuous variables | choice of variable : categorical or continuous variable - my way | Combining tables | For more beautiful output : myhtml | For more beautiful output : mylatex | Export to csv file : mycsv | Use of ztable | densityplot | Plot for odds ratios of a glm object | For automation of cox's proportional hazard model

Installation | Load package | Linear model with multiple variables | Selection of explanatory variable from univariable model | Stepwise backward elimination | Linear model with interaction between categorical variable | Missing data - automatic multiple imputation | Original data | Missed data | Multiple imputation

Loading package | Statistical tests for numeric variables | 1. Comparison of two groups | (1) Parametric method | (2) Non-parametric method | (3) Performs test for normality | 2. Comparison of three or more groups | Statistical tests for categorical variables | (1) Default method : chi-squared test with continuity correction | (2) Chi-squared test without continuity correction | (3) Fisher's exact test | (4) Test for trend in proportions | Make a combining table with two or more grouping variables | Missing data analysis

Introduction | Table Show | Merge two tables | Basic Use | data.frame | Tailoring zebra striping | Customize the caption and the font size | aov object | Linear model : 'lm' object | Analysis of Variance Table : 'anova' object | Generalized linear model ; 'glm' object | More 'aov' object | More 'lm' object | More 'glm' object | Principal Components Analysis : 'prcomp' object | Survival Analysis : 'coxph' object | Nonlinear Least Squares: 'nls' object | Maximum-likelihood Fitting of Univariate Distributions | Customize the zebra striping colors | Vertical striping | More tailoring zebra striping | Change the background color of all cells | Diagonal striping | All background colors | Place two or more ztables or figures side by side | mytable object from "moonBook" package | cbind.mytable object

editData | Install package | Usage: As an RStudio Add-in | Usage: As a regular function | Usage: As a shiny module

Linear regression Model | Simple linear regression model | Multiple regression model without interaction | Multiple regression model with interaction | Multiple regression model with two continuous predictor variables with or without interaction | Multiple regression model with three predictor variables | Logistic regression model | Multiple logistic regression model with two predictor variables | Model with interaction | Model without interaction | Multiple logistic regression model with two continuous predictor variables

Package installation | ggPoints() for interactive scatterplot with regression equation | ggRadar() for interactive radar chart | ggSpine() for an interactive spinogram | ggBar() for an interactive barplot | ggPair() for an interactive scatter plot with line plot | ggPieDonut() for a pie and donut plot | ggCLE() for a cleveland dot plot | Full version of this vignette

Installation | Introduction | Basic Table | Formatting the Table | Conditional Formatting | Make a Heatmap Table | Heatmap Table with desired palette | Heatmap Table with user-defined palette | Heatmap Table with non-numeric data | Selected Columnwise Heatmap Table | Selected Rowwise Heatmap Table

Introduction | Package Installation | Package Loading | Sample Data | Paragraph | mytable object | Plot | ggplot | R code | Two ggplots | Two plots | HTML Report | MS word document | MS Powerpoint document | pdf document

Introduction | Make The Powerpoint File with R plot/ggplot | Add a data.frame to the Powerpoint file | Add the result of R code to the Powerpoint file | Add the result of statistical analysis to the Powerpoint file | Adding the 2 plots/ggplots on a slide | Shiny app using package rrtable

Installation of packages | Load packages | Summarizing Frequencies | Ready for reproducible research | Frequency table for a continuous variable | Frequency table for two categorical variables | Numerical summary | Numerical summary of a vector | Numerical summary of a data.frame or a tibble | Use of dplyr::group_by() and dplyr::select() function to summarize | For reproducible research

Package Installation | Coverage of plot.htest() | For Chi-squared Test | For one sample t-test | Student t-test to compare means for two independent samples | F test to compare two variances | Use for Two Sample t-test for independence samples | Student t-test using pooled variance | Paired t-test | Options for t-test

Prerequisite | Install package | Usage: As an RStudio Add-in | Usage: As a regular function | Full vignette

Install package | Usage: As an RStudio Add-in | Usage: As a regular function

Motivation | For correlation analysis : cor, cor.test and lm | Solution ; Do not repeat yourself !! | Function "mycor" and Class "mycor" | Plot "mycor" object