#Rmarkdown plot size code

To execute the entire file, click the arrow to the right of the ?Knit? button and select your desired output format (pdf, html or word). To run a chunk of R code, place your cursor anywhere in the chunk, click the ?Chunks? button and select ?Run Current Chunk?.

#Rmarkdown plot size code

The R code follows the same syntax in R markdown as in an R script file. These are required to show when a chunk begins and ends. Note the first and last lines of the chunk. In an R Markdown file you type the R code that you want in ?chunks? as follows: fevdata <- read.csv('',header=TRUE) When you click the Knit button in Rstudio, a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Ī quick references to the most commonly used R Markdown syntax can be found here: Īn extensive R Markdown cheatsheet can be found here: For more details on using R Markdown see. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. title: 'Example knitr/R Markdown document' author: 'Your name' date: '' output: pdf_document: default geometry: margin=0.75in - ``` # Load data into global environment.This is an R Markdown document.

15.13.3 Long-term follow-up of participants.

14.5 Clone an existing GitHub project to new RStudio project.

14.4 Create a new repository on GitHub and link to RStudio project.

14.3 Create a project in RStudio and commit a file.

14.2 Create an SSH RSA key and add to your GitHub account.

14.1 Setup Git on RStudio and associate with GitHub.

12.6.1 Running code and chunks, knitting.

12.5 The anatomy of a Notebook / R Markdown file.

12.3 What is the difference between a Notebook and an R Markdown file?.

11.8.1 Common solution: Multivariate Imputation by Chained Equations (mice).

11.7.1 Common solution: row-wise deletion.

11.6.1 For those who like an omnibus test.

11.6 Check for associations between missing and observed data.

11.5 Including missing data in demographics tables.

11.4 Look for patterns of missingness: missing_pattern().11.3 Identify missing values in each variable: missing_plot().11.2 Ensure your data are coded correctly: ff_glimpse().11.1.1 Missing completely at random (MCAR).10.11.1 Converting dates to survival time.10.8.6 Correlated groups of observations.

10.8.4 Testing for proportional hazards.10.8 Cox proportional hazards regression.9.7 Fitting logistic regression models with finalfit.9.6 Modelling strategy for binary outcomes.9.5 Fitting logistic regression models in base R.9.4.1 Linearity of continuous variables to the response.9.3 Data preparation and exploratory analysis.9.2.6 Effect modification and confounding.9.2.5 The fitted line and the logistic regression equation.8.13 Chi-squared / Fisher’s exact test using finalfit.8.11 Pearson’s chi-squared and Fisher’s exact tests.8.9 Change the order of values within a factor - fct_relevel().8.8 Group factor levels together - fct_collapse().

8.6 Should I convert a continuous variable to a categorical variable?.8 Working with categorical outcome variables.7.1.4 The fitted line and the linear equation.6.9.3 Non-parametric test for comparing more than two groups.6.9.2 Non-parametric test for comparing two groups.6.8.1 Pairwise testing and multiple comparisons.6.7 Compare the means of more than two groups.6.6 Compare the mean of one group: one sample t-tests.6 Working with continuous outcome variables.5.4.2 Annotation with a superscript and a variable.4.9.1 Worked example - three geoms together.3.10.5 Exercise - filter(), summarise(), pivot_wider().3.10.4 Exercise - mutate(), summarise().3.8.3 separate() a column into multiple columns.3.8.2 Pivot values from columns to rows (longer).3.8.1 Pivot values from rows into columns (wider).3.8 Reshaping data - long vs wide format.3.6 Common arithmetic functions - sum(), mean(), median(), etc.

3.4.1 Percentages formatting: percent().3.3 Aggregating: group_by(), summarise().2.9 Conditional calculations - if_else().2.1.2 Reading in the Global Burden of Disease example dataset.