This function returns the corresponding hex code discussed above. The function rgb() allows us to specify red, green and blue component with a number between 0 and 1. Where the RR is for red, GG for green and BB for blue and value ranges from 00 to FF.įor example, #FF0000 would be red and #00FF00 would be green similarly, #FFFFFF would be white and #000000 would be black. We define a color as a 6 hexadecimal digit number of the form #RRGGBB. Instead of using a color name, color can also be defined with a hexadecimal value. You can color your plot by indexing this vector.įor example, col=colors() is the same as col="yellow3". This returns a vector of all the color names in alphabetical order with the first element being white. "antiquewhite4" "aquamarine" "aquamarine1" "antiquewhite1" "antiquewhite2" "antiquewhite3" If you are unsure about your pipe chain or want to debug it, you can view the output after each step by highlighting your entire chain then clicking on “View Pipe Chain Steps” from the addins menu:įrom the addins menu, you can choose to either print the result to the console, or view the result in a new pane (as if you called the function View() after each step of the pipe).We use the following temp vector to create barplot throughout this section. Summarise(n = n(), price = mean(price)) %>%Īrrange(desc(color)) # A tibble: 7 × 3 Select(carat, cut, color, clarity, price) %>% ![]() ![]() This addin allows to print or view the output of your pipe chain after each step.įor instance, here is a chain with the dataset diamonds: library(tidyverse) Thanks to a reader of this article, I discovered the ViewPipeSteps addin. Legend.position = c(0.88, 0.22)) + theme(plot.caption = element_text(face = "italic")) +labs(title = "Sepal & petal length by species",įind more information about the theme() layer in this cheatsheet. Theme_minimal() + theme( = element_line(linetype = "blank"), Geom_point(shape = "circle", size = 1.5) + Click on the button Done and your code will be edited with your changes:Īes(x = Sepal.Length, y = Petal.Length, colour = Species) +.Customize your plot according to your needs.For other tips in R, see the article “ Tips and tricks in RStudio and R Markdown”. Beginners will have the possibility to use functions that they would not have used otherwise because the code is too complex, whereas advanced users may find them useful to speed up the writing of their code in some circumstances. I believe addins are worth trying for all R users. RStudio addins have the advantage that they allow you to execute complex and advanced code much more easily than if you would have to write it yourself. RStudio addins can be as simple as a function that inserts a commonly used snippet of code, and as complex as a Shiny application that accepts input from the user to draw a plot. By using the RStudio addins, RStudio will run the required code for you. So you could write code as you can import a dataset by writing code, but thanks to RStudio addins you can execute code without actually writing the necessary code. or you can import it by clicking on the “Import Dataset” button in the Environment pane, set the importing settings, then click on “Import”Ī RStudio addin is exactly like the Import Dataset button but for other common functionalities.import it by writing the code (thanks to the read.csv() function for instance).If it is still not clear, remember that for importing a dataset in RStudio, you have two options: In simpler words, when executing an addin (by clicking a button in the Addins menu), the corresponding code is executed without you having to write the code. ![]() What are RStudio addins? RStudio addins are extensions which provide a simple mechanism for executing advanced R functions from within RStudio. Since then, I am using these addins almost every time I use RStudio. Although I have been using RStudio for several years, I only recently discovered RStudio addins.
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