Kable rmarkdown force position8/30/2023 The final output is exported from it, perhaps to PDF or to HTML, but maybe most often the final output just is the. The master document may be passed around from person to person to be edited and updated. The outputs of data analyses-tables, figures-get cut and pasted in as well, or are kept alongside them. Citation and reference managers plug into those files. Changes to your work are tracked inside that file or files. A Word file or set of files is the most “real” thing in your project. Office solutions tend towards a cluster of tools where something like Microsoft Word is at the center of your work. There are basically two models for producing manuscripts. I am drawing here on Kieran Healy’s Plain Person’s Guide to Plain Text Science paper, which I would highly encoruage you to read. In order to understand the idea of R Markdown files, its important to have some understanding of how scientific manuscripts are produced. Calculating Theil’s H for a single state.The Most Important Rule: Check yourself before you wreck yourself.The binomial distribution as a data-generating process.Dichotomous Outcomes and The Binomial Distribution.The IID Violation and Robust Standard Errors.Interaction terms with two categorical variables.Interaction terms with multiple categories.Categorical and quantitative variables combined in a single model.Categorical variables with more than two categories.Including Categorical Variables as Predictors.How to read a table of regression results.Including more than two independent variables.Interpreting results in a multivariate OLS regression models.The Power of Controlling for Other Variables.How good is \(x\) as a predictor of \(y\)?.Adding an OLS regression line to a plot.Using the lm command to calculate OLS regression lines in R.The general procedure of hypothesis testing.Calculating the confidence interval for other sample statistics.Calculating the confidence interval for the sample mean.What can we do with the sampling distribution?.Central limit theorem and the normal distribution.The Concept of the Sampling Distribution.Scatterplot and Correlation Coefficient.Graphically examining differences in distributions.Percentiles and the Five Number Summary.Looking at the distribution of a quantitative variable.Looking at the distribution of a categorical variable.Observational Data, Experimental Thinking.Sepal.Length = cell_spec(Sepal.Length, color = ifelse(Sepal. This is a pretty common task in reports: For example coloring values % ![]() Head(iris)%>%Ĭolumn_spec(1, bold = TRUE, border_right = TRUE, color = "black", background = "lightgrey") %>%Īt this point you may be wondering: Can I set colors automatically? Yes, of course you can. The first and the 6th row have Sepal.Length > 5! We should color the entire row red! library(kableExtra) Kable_styling(position = "left", full_width = FALSE) %>%Ĭolumn_spec(1, bold = TRUE, border_right = TRUE, color = "black", background = "lightgrey") ![]() So let's make the first column bold, add a right border, color the text black and the background grey. Then you can pass formating arguments such as bold = TRUE, color = "black" or background ="grey". The first argument is the index of the rows or columns you want to format. You can format specific rows and columns with column_spec() or row_spec(). Kable_styling(font_size = 20, position = "left", full_width = FALSE) Let's increase the font size and position the table on the left. ![]() To wrap text around the table use position = "float_right". You can pass various arguments to kable_styling to influence the font and the position of the table. It works similar to ggplot2: You create a base table and then add formating layers with the pipe operator %>%. KableExtra is an awesome package that allows you to format and style your tables.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |