Thanks and suggestions for improvement
Posted: Sat Feb 17, 2018 8:42 am
Dear all
I would like to thank the Jamovi developers for such a useful tool. I've been able to (relatively) easily convert a CRAN library to include a Jamovi module. Hopefully this will make statistical discovery a bit easier to non-experts. And hopefully Jamovi can be promoted a bit more to this target group.
I had some suggestions for improvement:
I would like to thank the Jamovi developers for such a useful tool. I've been able to (relatively) easily convert a CRAN library to include a Jamovi module. Hopefully this will make statistical discovery a bit easier to non-experts. And hopefully Jamovi can be promoted a bit more to this target group.
I had some suggestions for improvement:
- (1) My current version (0.8.2.2 Mac OS) fails to load the Jamovi store for a "SSL: CERTIFICATE_VERIFY_FAILED" error. I hope this was just a build oversight, and will be an easy fix.
- (2) Could .rda files be an import option?
- (3) Documentation - could this be a wiki instead so people could contribute? I think GitHub has a wiki function for projects
- (4) Documentation - could there be a page with specifications to be aware of? My example is that I had to specify in my library it was built for R 3.3 (not 3.4) as the Mac version is kept at 3.3. The reason why is here: https://github.com/jamovi/jamovi/issues ... -361148721 and this is quite useful information to know
- (5) Documentation - some documentation to describe adding jamovi functions to a pre-existing module
- (6) As (5) but having a create() function, with a pre-existing switch
- (7) Documentation - making it clearer how the jamovi "columns" actually are internally handled. The examples seem to indicate how Jamovi expects a function to work is function( x~y , data ) so that the formula becomes e.g t.test(formula, self$data). My function handles data as function( list1 , list2 ) so that the data is not specified. I did get around the issue by doing this:
so that I called the columns by data[[column_name]].Code: Select all
# read the option values into shorter variable names method1 <- self$options$method1 method2 <- self$options$method2 # get the data data <- self$data # convert to appropriate type data[[method1]] <- jmvcore::toNumeric(data[[method1]]) data[[method2]] <- jmvcore::toNumeric(data[[method2]]) # calculate the results results <- blandr.statistics( data[[method1]] , data[[method2]] )