Error installing RODBC or ODBC on a Sagemaker Jupyter NoteBook Instance - r

Question

I have been trying to establish a connection to Teradata from a Sagemaker Jupyter Notebook instance. I was trying to do it the way I would through R Studio. But when ever I try to install the package in the instance I get an non-zero exit status error.
I have tried installing the following ways:
remotes::install_github()
or
devtools::install_github()
and also:
install.packages('odbc', repo="https://cran.rstudio.com/")
I tryed the same with RODBC, and I get the same warnings or errors.
Any ideas on how I can get around this problem?
Thanks in advance.
UPDATE:
If I run this line of code:
devtools::install_github("r-dbi/odbc")`
I get the following error (This is just an extract):
Error: Failed to install 'odbc' from GitHub:
System command error, exit status: 1, stdout + stderr (last 10 lines):
E> ** testing if installed package can be loaded from temporary location
E> Error: package or namespace load failed for ‘odbc’ in dyn.load(file, DLLpath = DLLpath, ...):
E> unable to load shared object '/tmp/RtmpBuMhbW/Rinst32846cdd20a9/00LOCK-odbc/00new/odbc/libs/odbc.so':
E> libodbc.so.2: cannot open shared object file: No such file or directory
E> Error: loading failed
E> Execution halted
E> ERROR: loading failed
E> * removing ‘/tmp/RtmpBuMhbW/Rinst32846cdd20a9/odbc’
E> -----------------------------------
E> ERROR: package installation failed
Traceback:

Answer 1

Alright, after hours of reading AWS/Sagemaker/Conda/R documentation I arrived at the conclusion that, I don't know how to explain the phenomena.
Nonetheless, I have found a workaround!
So we know how we can install packages through the Jupyter terminal or directly from a cell in the .ipynb file. Nonetheless, if you try to run those commands inside the R kernel just like you would on the terminal or in a Python kernel you would run into an error.
Also, for some people installing it in the terminal, or even on a Python kernel might just make conda install the package on a different environment. Yes, turns out there are a couple of environments on your Jupyter instance.
So, to get to the point, just like I said, you would use a conda install command, not a pip, sudo or yum command.
Make your life easier. Here is the code that you would need to run on your R Kernel cell, to install a package directly to the correct environment if, install.packages() and install_github() didn't work:
system(command = 'conda install -c r packageName --yes')
Example:
system(command = 'conda install -c r tidyverse --yes')
And that's it. After that you can call library() and resume your R workflow as you do.
Hope this helps guys!