Not only is coding challenging, but it requires a steep learning curve to get acclimated and comfortable with. However, it is also an essential part of research nowadays - at the very least using a statistical software or data visualization tool. Here is a list of links to workshops, tutorials, YouTube videos, and more that I have found thus far in my graduate school research while trying to learn all these programs and languages. I hope these may be of help to you. 

Introduction to Coding

• Software Carpentry designs workshops specifically for teaching research computing: https://software-carpentry.org/
• Data Carpentry designs workshops for data-driven research: https://datacarpentry.org/

Introduction to R
Online Courses/Workshops

• RStudio resources for beginners: https://education.rstudio.com/learn/beginner/
• Learn R, in R! An interactive tutorial - I HIGHLY RECOMMEND: https://swirlstats.com/
• StatQuest for all things statistical analyses AND DNA/RNA sequencing! What a pro!  https://statquest.org/video-index/
• Coursera free online course on R programming: https://www.coursera.org/learn/r-programming
• A fun R advent calendar, "25 Days of R" for an intro: https://kiirstio.wixsite.com/kowen/post/the-25-days-of-christmas-an-r-advent-calendar
• Harvard Introduction to R Workshop: http://tutorials.iq.harvard.edu/R/Rintro/Rintro.html
• Rmarkdown tutorial: https://ourcodingclub.github.io/tutorials/rmarkdown/
• Interactive tutorial called "learnr" 
  • filter( ): https://learnr-examples.shinyapps.io/ex-data-filter/
  • summarise( ): https://learnr-examples.shinyapps.io/ex-data-summarise/#section-summarise-groups-with-summarise​

Free Books!

• Fundamentals of Data Visualization by Claus Wilke: https://serialmentor.com/dataviz/
• R for Data Science by Garrett Grolemund and Hadley Wickham: https://r4ds.had.co.nz/

Online Coding Club - Amazing resource with step-by-step R code for tidying data and data visualization!

• ​https://ourcodingclub.github.io/

DNA/RNA Sequencing 
Informative Articles

• How Next-Generation Sequencing works: https://www.illumina.com/science/technology/next-generation-sequencing/beginners/ngs-workflow.html
• A great overview to the experimental design, sample prep, and downstream analysis of RNA sequencing data: https://rnaseq.uoregon.edu/
  • Differential Expression software comparisons: https://rnaseq.uoregon.edu/#table5.2

Workshops

• Data Wrangling and Processing for Genomics - Data Carpentry workshop: https://datacarpentry.org/wrangling-genomics/
  • Requires knowledge of bash + use of terminal
  • Assessing read quality of FASTQ, trimming and filtering sequences, variant calling
• Harvard Chan Bioinformatics Core Differential Gene Expression Workshop: https://github.com/hbctraining/DGE_workshop/tree/master/lessons 
• RNA-sequencing data analysis workshop: https://github.com/hbctraining/Intro-to-rnaseq-hpc-O2
  • the shell, vim, quality assessment, alignment with STAR, generating counts matrix, multiqc, salmon
• Differential Expression analysis code: https://github.com/mistrm82/msu_ngs2015/blob/master/hands-on.Rmd
  • Comparison of DESeq2, EdgeR, and limma
  • YouTube tutorial that goes through the entire code with an explanation: https://www.youtube.com/watch?v=7UKMU5HK380
• Using awk (Unix tool) to analyze a transcriptome: http://reasoniamhere.com/2013/09/16/awk-gtf-how-to-analyze-a-transcriptome-like-a-pro-part-1/

YouTube Videos

• Beginner's Guide to RNA-Seq webinar: https://www.youtube.com/watch?v=8lAVfKbRK3I
• Canadian Bioinformatics Workshop on RNA Sequencing: https://www.youtube.com/watch?v=Ji9nFCYl7Bk
• RNA-seq results explained: https://www.youtube.com/watch?v=9Xo1HU_H828