I added the float package in my YAML, but how do I use the ?įor figures, I could use the knitr option fig.pos = "H", but this didn't work for the table. For those who are familiar with LaTeX, hold_position uses and HOLD_position uses and the float package. If you find hold_position is not powerful enough to literally PIN your table in the exact position, you may want to use HOLD_position, which is a more powerful version of this feature. It doesn't place the table where I want it, so I want to try the stronger option in the kableExtra documentation: Kable_styling(latex_options = "hold_position") I used kableExtra to make the table and it looks like this: kable(site_info, "latex", caption = "Site Information", booktabs = T, align = "c") %>%Ĭollapse_rows(columns = 1, latex_hline = "major", valign = "middle")%>% Ggplot2 that provides a different way to create such plots.I am trying to get a table in an Rmarkdown pdf to stay in the right place. Don’t worry about the details! In fact, later in the book we will learn about an R package called Then we will use the symbols function to add symbols, the circles argument to set the sizes of the points, and the bg argument to set the colors. The argument type="n" tells R to do this. First we will create the axes, labels, etc. To create the scatter plot we will do two things. # "East Asia & Pacific (all income levels)" # "Sub-Saharan Africa (all income levels)" # "Europe & Central Asia (all income levels)" # "Latin America & Caribbean (all income levels)" # "Middle East & North Africa (all income levels)" Region # "Europe & Central Asia (all income levels)" Tools in R such as scripts with commented annotation and R Markdown facilitate this process. It’s much more effective to include the actual data sets and computer code that accomplished all the steps in the report, whether the report is an exercise or research paper. In principle this entire process could be detailed in a step-by-step guide using words however, in practice, it’s typically difficult or impossible to reproduce a full data analysis based on a written explanation. Reproducible research means the steps taken in a study can be replicated, from importing external data sets and wrangling them into formats for data analysis, to creating tables and figures using the analysis results. Ideally a scientific study will be reproducible, meaning that an independent group of researchers (or the original researchers) will be able to duplicate the study. Next consider a larger scientific endeavor. If we save a script file, we have the ingredients immediately available when we return to a portion of a project. In such cases we might have forgotten how we created a particularly nice graphic or how to replicate an analysis, with the result being several hours or days of work to rewrite the necessary code. Often we work on one part of an exercise or project for a few hours, then move on to something else, and then return to the original part a few days, months, or sometimes even years later. In addition to making the workflow more efficient, R scripts provide another great benefit. Although this all could be accomplished by typing and re-typing code lines at the R Console, it’s easier and more efficient to write the code in a script that can then be submitted to the R console either a line at a time, several lines at a time, or all together. Each of these iterations might require several lines of R code to create. For example, creating a figure effective at communicating results can involve trying out several different graphical representations, and then tens if not hundreds of iterations to fine-tune the chosen representation.
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