You can create a slide show broken up into sections by using the and heading tags you can also create a new slide without a header using a horizontal rule For example here is a simple slide show see Figure 4. You can add a subtitle to a slide or section by including text after the pipe character. For example:. Pressing Esc exits all of these modes.

R Markdown tips for authoring reproducible documents slide presentations web dashboards and more

See the sections below on Code Highlighting and Presenter Mode for additional detail on those modes. If you want to render bullets incrementally for some slides but not others you can ab use this syntax for blockquotes:. You can display the presentation using a wider form factor using the widescreen option. You can specify that smaller text be used with the smaller option.

You can also enable the smaller option on a slide-by-slide basis by adding the. You can customize the speed of slide transitions using transition option. This can be "default""slower""faster"or a numeric value with a number of seconds e. Slides can also have a. You can specify a background image for a slide using the attribute data-backgroundand use other attributes including data-background-sizedata-background-positionand data-background-repeat to tweak the style of the image.

You need to be familiar with CSS to fully understand these four attributes, and they correspond to the CSS properties backgroundbackground-sizebackground-positionand background-repeatrespectively. You can also target specific slides or classes of slides with custom CSS by adding IDs or classes to the slides headers within your document.

For example the following slide header:. The highlighted region will be displayed with a bold font. When you want to help the audience focus exclusively on the highlighted region press the h key and the rest of the code will fade away. You can add a logo to the presentation using the logo option by default, the logo will be displayed in a 85x85 pixel square. The logo graphic will be rescaled to 85x85 if necessary and added to the title slide.

A smaller icon version of the logo will be included in the bottom-left footer of each slide. The logo on the title page and the rectangular element containing it can be customised with CSS.

The ioslides template has an attractive default style for tables so you should not hesitate to add tables for presenting more complex sets of information. Pandoc Markdown supports several syntaxes for defining tables, which are described in the Pandoc Manual.

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You can center content on a slide by adding the. You can horizontally center content by enclosing it in a div tag with class centered. Note that content will flow across the columns so if you want to have an image on one side and text on the other you should make sure that the image has sufficient height to force the text to the other side of the slide.

You can color content using base color classes redbluegreenyellowand gray or variations of them, e. A separate presenter window can also be opened ideal for when you are presenting on one screen but have another screen that is private to you.

The window stays in sync with the main presentation window and also shows presenter notes and a thumbnail of the next slide. To enable presenter mode add? The presenter mode window will open and will always re-open with the presentation until it is disabled with:.

You can print an ioslides presentation from within browsers that have good support for print CSS as of this writing, Google Chrome has the best support. Printing maintains most of the visual styles of the HTML version of the presentation. Refer to Section 3. R Markdown: The Definitive Guide.The remark. The xaringan package has simplified several things compared to the official remark. Please note that remark.

You may use raw HTML when there is something you desire that is not supported by remark. For example, you can generate an HTML table via knitr::kable head iris'html'. Every new slide is created under a horizontal rule The content of the slide can be arbitrary, e. A slide can have a few properties, including class and background-imageetc. Properties are written in the beginning of a slide, e. For example, for a slide with the inverse class, you may define the CSS rules to render text in white on a dark background :.

Then include the CSS file say, my-style. Actually the style for the inverse class has been defined in the default theme of xaringanso you do not really need to define it again unless you want to override it. Other available class names are leftcenterand right for the horizontal alignment of all elements on a slide, and topmiddleand bottom for the vertical alignment. Background images can be set via the background-image property. The image can be either a local file or an online image. The path should be put inside urlwhich is the CSS syntax.

rmarkdown title slide

You can also set the background image size and position, e. All these properties require you to understand CSS. There is a special slide, the title slide, that is automatically generated from the YAML metadata of your Rmd document. It contains the title, subtitle, author, and date all are optional.

This slide has the classes inversecentermiddleand title-slide by default, which looks like the left image in Figure 7. If you do not like the default style, you may either customize the. You can also disable the automatic title slide via the seal option and create one manually by yourself:. You can assign classes to any elements on a slide, too. The syntax is.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have to include a note in my title slide declaring the source of my funding. I have not been able to reproduce this in Rmarkdown. I think this is the relevant part of the compilation log:. You could use an inline note. Another solution that allows more flexibility, but is restricted to the beamer output, would be to change the template.

Learn more. Asked 4 years, 8 months ago. Active 3 years, 5 months ago.

Viewed 13k times. MichaelChirico MichaelChirico Active Oldest Votes. I don't think so, pandoc parses both syntax as Note elements. For the record this sadly doesn't work on the. Does not work in general Rmarkdown documents either, as of November Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.To index Purpose (What is Correlation. The measurement scales used should be at least interval scales, but other correlation coefficients are available to handle other types of data.

Correlation coefficients can range from -1. The value of -1. A value of 0. The most widely-used type of correlation coefficient is Pearson r, also called linear or product- moment correlation. Simple Linear Correlation (Pearson r). Pearson correlation (hereafter called correlation), assumes that the two variables are measured on at least interval scales (see Elementary Concepts), and it determines the extent to which values of the two variables are "proportional" to each other.

The value of correlation (i. This line is called the regression line or least squares line, because it is determined such that the sum of the squared distances of all the data points from the line is the lowest possible. How to Interpret the Values of Correlations. As mentioned before, the correlation coefficient (r) represents the linear relationship between two variables. If the correlation coefficient is squared, then the resulting value (r2, the coefficient of determination) will represent the proportion of common variation in the two variables (i.

In order to evaluate the correlation between variables, it is important to know this "magnitude" or "strength" as well as the significance of the correlation. The significance level calculated for each correlation is a primary source of information about the reliability of the correlation. As explained before (see Elementary Concepts), the significance of a correlation coefficient of a particular magnitude will change depending on the size of the sample from which it was computed.

The test of significance is based on the assumption that the distribution of the residual values (i. However, Monte Carlo studies suggest that meeting those assumptions closely is not absolutely crucial if your sample size is not very small and when the departure from normality is not very large.

It is impossible to formulate precise recommendations based on those Monte- Carlo results, but many researchers follow a rule of thumb that if your sample size is 50 or more then serious biases are unlikely, and if your sample size is over 100 then you should not be concerned at all with the normality assumptions. Outliers are atypical (by definition), infrequent observations.

Because of the way in which the regression line is determined (especially the fact that it is based on minimizing not the sum of simple distances but the sum of squares of distances of data points from the line), outliers have a profound influence on the slope of the regression line and consequently on the value of the correlation coefficient.

A single outlier is capable of considerably changing the slope of the regression line and, consequently, the value of the correlation, as demonstrated in the following example. Note, that as shown on that illustration, just one outlier can be entirely responsible for a high value of the correlation that otherwise (without the outlier) would be close to zero.

Needless to say, one should never base important conclusions on the value of the correlation coefficient alone (i. Note that if the sample size is relatively small, then including or excluding specific data points that are not as clearly "outliers" as the one shown in the previous example may have a profound influence on the regression line (and the correlation coefficient).

Typically, we believe that outliers represent a random error that we would like to be able to control. Unfortunately, there is no widely accepted method to remove outliers automatically (however, see the next paragraph), thus what we are left with is to identify any outliers by examining a scatterplot of each important correlation.

Needless to say, outliers may not only artificially increase the value of a correlation coefficient, but they can also decrease the value of a "legitimate" correlation. See also Confidence Ellipse. Quantitative Approach to Outliers. Some researchers use quantitative methods to exclude outliers.Everything was clean, convenient and friendly. Really enjoyed the historic hotels. Easy to work with Bjarni.

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rmarkdown title slide