Installing our R development environment on Ubuntu 20.04
This article is originally published at https://laranikalranalytics.blogspot.com/
Step 1: Install R, Here the link with instructions: How to instal R on Ubuntu 20.04
Adding the steps I followed because sometimes the links become unavailable:
Add GPG key:
Add repository:
Update repositories:
Install R:
R studio download link ***Select package Ubuntu 18/Debian 10 (64-bit deb package).
*In this case I am showing version 1.4.1106, please adjust to the version you are installing.
After downloading rstudio installation file: rstudio-1.4.1106-amd64.deb, it is a good practice(in some cases needed) to import RStudio's public code-signing key prior to installation, please visit this page for more detail: RStudio Public Key
Installing needed package:
Once veryfied, to install the rstudio downloaded package, open a terminal window and from the folder where you downloaded rstudio, execute:
$ sudo dpkg -i rstudio-1.4.1106-amd64.deb
Showing my output because I got some errors:
So, I ran it: $ sudo apt --fix-broken install
Running again the install and it worked just fine:
Step 3: Install a series of Ubuntu packages needed before installing R packages:
Open a terminal window and run all the next installation commands:
The other important thing to do before installing R packages is to install Java and set it as default, for this installation I am using Java 8.
This is a good reference article about: How To Install Java 8 on Ubuntu 20.04/18.04/16.04
On the same terminal above, run R: #R
Now continue to the R packages installation:
tidyverse -> Opinionated collection of R packages designed for data science.
install.packages( "tidyverse", dependencies = TRUE )
data.table -> Fast manipulation of large datasets.
install.packages( "data.table", dependencies = TRUE )
sqldf -> Run SQL instructions on your datasets.
install.packages( "sqldf", dependencies = TRUE )
stringdist -> Computes string distances, very useful when creating clusters of catalog descriptions.
install.packages( "stringdist", dependencies = TRUE )
RODBC -> Database access.
install.packages( "RODBC", dependencies = TRUE )
xts -> Non regular time series package
install.packages( "xts", dependencies = TRUE )
dygraphs -> Nice graphs for R
install.packages( "dygraphs", dependencies = TRUE )
openxlsx -> Read, Write and Edit XLSX Files
install.packages( "openxlsx", dependencies = TRUE )
lubridate -> Dates handling.
install.packages( "lubridate", dependencies = TRUE )
forecast -> ARIMA and forecast package
install.packages( "forecast", dependencies = TRUE )
mailR -> Send email from R
install.packages( "mailR", dependencies = TRUE )
gbm -> gbm ( Gradient Boosting Machine )algorithm for R
install.packages( "gbm", dependencies = TRUE )
gbm -> xgboost algorithm for R
install.packages( "xgboost", dependencies = TRUE )
aTSA -> Time Series Analysis
install.packages( "aTSA", dependencies = TRUE )
rattle -> Tab-oriented user interface that is similar to Microsoft Office's ribbon interface. It makes getting started with data mining in R very easy.
install.packages( "rattle", dependencies = TRUE )
Rcmdr -> R Commander. A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
install.packages( "Rcmdr", dependencies = TRUE )
itsmr -> Time Series Analysis Using the Innovations Algorithm. Provides functions for modeling and forecasting time series data.
install.packages( "itsmr", dependencies = TRUE )
stlplus -> A new implementation of STL. Allows for NA values, local quadratic smoothing, post-trend smoothing, and endpoint blending. The usage is very similar to that of R's built-in stl().
install.packages( "stlplus", dependencies = TRUE )
TSA -> Useful to compute data seasonality.
install.packages( "TSA", dependencies = TRUE )
Enjoy it!!!.
Carlos Kassab
https://www.linkedin.com/in/carlos-kassab-48b40743/
More information about R:
https://www.r-bloggers.com
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This article is originally published at https://laranikalranalytics.blogspot.com/
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