If your goal is to become proficient in R as efficiently as possible in order to import, organize, analyze, and plot data for your project, then I also recommend datacamp.com, which offers succinct online videos and frequent online exercises to practice what you have learned. The fundamental R courses emphasize the Tidyverse packages, which are cohesive as Eric Scott mentioned. New members in our research group follow this approach, and get up to speed (relatively) quickly.

Have a look here: https://r-dir.com/

Hi, it may be useful to check some free online tutorial like datacamp.com

What statistical package do you currently use? Robert A. Muenchen has very interesting training manuals on R for Stata/SPSS/SAS Users. I hope you'll find them very useful.

Check out http://www.cookbook-r.com/

"The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data."

https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf

I'd recommend starting with the Tidyverse and learning so-called "base R" later, when you need it. Briefly, the "Tidyverse" is a collection of packages that all have the same underlying philosophies and therefore work together and consistently. One of those philosophies is that code should be readable by humans, which makes it great for beginners. R for Data Science is a great (free, online) book to start with. (http://r4ds.had.co.nz/index.html.)

ModernDive is another good place to start (http://moderndive.com/)

I respectfully disagree to Eric Scott 's recommendation. The tidyverse is just not to "learn R from the basics". It's nice for data scientists and high-throughput analysts, but it hides very fundamental ways how R ticks. To efficiently solve your problems with R, you should anyway know both - base R and the tidyverse. Experts disagree if it is better for students to learn base R first and then tidyverse or the other way around. From experience with own students I am of the opinion that - in the long run! - having good skills in base R first is more rewarding. This is similar to the contrast of ggplot and base graphics.

some links :

http://zoonek2.free.fr/UNIX/48_R/all.html

https://en.wikibooks.org/wiki/R_Programming

https://www.r-bloggers.com/an-r-meta-book/

for plot graph:

https://www.r-graph-gallery.com/

https://www.stat.auckland.ac.nz/~paul/RG2e/

If your goal is to become proficient in R as efficiently as possible in order to import, organize, analyze, and plot data for your project, then I also recommend datacamp.com, which offers succinct online videos and frequent online exercises to practice what you have learned. The fundamental R courses emphasize the Tidyverse packages, which are cohesive as Eric Scott mentioned. New members in our research group follow this approach, and get up to speed (relatively) quickly.