dtplyr | Data table backend for dplyr | Database library

I am trying to install the tidyverse package and not being able to do it. 1-tried via Packages -> Install 2-tried from console-> install.packages("tidyverse")

Gives errors as below and therefore not loading the lirary

I have a data.table as follows -

As of 18 Nov 2021, I've downloaded all the tar.gz files that tidyverse imports/depends/suggests. I'm working on a RedHat OS and, unfortunately, I don't have an Internet connection to this machine. I have all those files in a folder. Then I use this command

I'm building an R package in which I would like to use dtplyr to perform various bits of data manipulation. My issue is that dtplyr seems to only work if I import the whole of data.table (i.e. using the roxygen #' @import data.table). Without this I get errors like:

I’m creating a Shiny app that uses the caret package to do some SVM free-text analysis.

The app runs fine without any error in my computer. I’m using R x64 4.0.4 and R studio 1.3.1093

I’m deploying app to an internal enterprise server https://rconnect.xxxx.com/connect/#/apps/####

This app is deployed in the server and started.
But when I reach the line where I run the train function:

I have built a Shiny dashboard which is nearly complete.

I have multiple source files containing various functions, some which reference global variables which are defined in the main R code, without being passed them as arguments directly.

I have been building it and testing/debugging it by running segments of code, and manually running the shinyapp function to launch it. When I do this, the app works as expected. The functions from the source files can read the global variables.

However, when I clear the workspace and save the code and use the "Run App" feature to run the whole thing at once, it fails because the source file functions can no longer find the global variables, even though the code which creates these variables in the global environment comes before any code which calls these functions.

Note that if I've already run the section of code manually that creates the global variables and run the app without clearing the workspace, the functions are able to read them.

Does anyone know why this is, and how I can get around it?

In the reproducible example below, the global variable is given the value "Hello_World". The function test_function() simply returns the value of the global variable which is then used as a heading in the UI.

MAIN R CODE

I'm trying to create a dashboard in R Shiny. As part of this dashboard, I have a very large dataset which has a column added reactively and in turn three reactive subsets of this dataset are produced by filtering on different dates.

So far, I have achieved the above via dplyr and using filter and mutate functions. However, I've noticed that it seems to be these points in the code that is slowing it down. It takes approx 10 seconds to process each table when any of the reactive variables are changed which trigger these tables to update. So I'm looking to speed this up.

I understand dplyr is much slower than data.table, but it is on the other hand, easier to understand the syntax. I'm also aware the dtplyr package exists to translate the dplyr code into data.table syntax, but I'm having trouble getting it to work.

I've been looking at the documentation and some youtube demonstrations, and as I understand it, it seems in order to use dtplyr, I need to use lazy_dt function to convert the table and then apply the standard dplyr functions to that table and then use the as.data.table() or as.tibble() command to convert it back to a data table.

However, the code isn't working, even though I've basically followed the exact same syntax as in the demonstration.

Here is some example code using the libraries I have imported in my main code, and which uses the iris dataset. It produces the following error at the print and as.data.table commands:

I need to speed up code using data.table. I am getting stuck on how to reference variables that are being indexed from a vector.

data:

When trying to summarise columns by group using dtplyr, grouping seems to not be working. Since the group variable is an input of my function, I tried using group_by_ only to receive an error message.

Data: