In your related thread you were stuck at finding the data to use with the code snippet.
This tutorial guides the reader through the analysis of an empirical data set. The data are gene expression measurements from livers of female mouse of a specific F2 intercross. For a detailed description of the data and the biological implications we refer the reader to Ghazalpour et al (2006), Integrating Genetics and Network Analysis to Characterize Genes Related to Mouse Weight (link to paper; link to additional information). We note that the data set contains 3600 measured expression profiles. These were filtered from the original over 20,000 profiles by keeping only the most variant and most connected probes. In addition to the expression data, several physiological quantitative traits were measured for the mice. Please download the following
and unzip them in a folder of your choice, preferably a new folder created specifically for this tutorial. Note the name of the folder; when you start an R session, the first command should be to change the R working directory into this folder. [Instructions]
(Website unavailable).
If you totally missed this, it's understandable why there's been trouble. The same with the installation issues. It is also possible that there is a complete lack of understanding on even the most basic things needed to know to be able to get past the immediate mechanics on to the substance of the analysis that the R based tools will be used to undertake. If that is the case, some preparatory time getting familiar with using R before getting into the tutorial will provide lasting benefit.
There are too many free online introductions to R to be able to list here—nevermind that they seem to grow faster than they can be connected. For the visually oriented there are probably many YouTube choices and online courses as well. Two, I recommend
R For Data Science for beginners to intermediate
Introduction to Data Science for graduate-level biosciences beginners and progressing to intermediate-level applications in machine learning.
Both use the RStudio interface to illustrate the content. Despite their more advanced topics, both have strong chapters on the basics. If neither of these prove helpful, the next step would be to seek on-line or in-person individually tutoring. The community here, which is a volunteer effort, assumes that users have the fundamentals but need help with some of the hurdles that can come up in the ordinary course of using RStudio and R that experience shows are often hard for everyone to get over. There's also the opportunity to ask questions that go beyond the programming aspects into the more theoretical, such as how to interpret collinearity in regression modeling. Beginners often hesitate to ask these questions in communities such as Stackoverflow, which can elicit answers that have the potential to seem harsh.
As you progress and come back with questions here, be sure to include enough information to get people willing to give answers on the same page with you by including a a reprex (see the FAQ).