Long format? Reshaping?

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by TMK » Mon Oct 21, 2019 5:05 pm

Dear all,

thank you for creating jamovi!!! I am just starting to use it and I have heard that it supports data in long format. However, I used a perfectly fine long format dataset and it doesn't seem to give out the right descriptive statistics. So my simple question is? Does jamovi support long format? If so, in which analyses? can one restructure data from wide to long and vice versa? If not, when is this coming. Data handling techniques such as reshaping are probably what we need the most. So this question is really pressing for us. Thank you very much. Dear wishes, Till

ps I couldn't find a similar topic with the search function so I created a new topic
TMK
 
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by jonathon » Mon Oct 21, 2019 11:54 pm

hi,

sorry, no data shaping at present. the only analyses that support long format are the ones in GAMLj sorry. you can use the filters to remove duplicate rows, but i appreciate that's a bit of a hack.

probably won't be added very soon either, sorry. it's a big job.

jonathon
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by TMK » Fri Dec 06, 2019 10:27 am

Hi Jonathon,

thank you so much for your reply, it's such a great spirit that you actually answer to the community, I really like the idea of Jamovi and hope that it will be further promoted!!! I totally understand it is a big job, however, it would be so worthwhile to think about implementing it sooner rather than later imho, as features such as those from the tidyverse, for data wrangling would be so meaningful for experimental psychologists (and for others too I assume). We spend most of our data time with preparing datasets to make them ready for analyses. Wide to long reshaping, aggregating, choosing rows and columns, all this stuff would be amazing to have it in Jamovi for students to access more easily than R. It would be so great if students could take over the job of dataset preparation more easily, and if they could do so in Jamovi as Jamovi is our choice that we teach them when they want to avoid R.
Thank you for your consideration
All the best
Till
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