Dryad | research prototype of the Dryad and DryadLINQ data

A .sql file is just plain text file with a different extension. So normally, you can open it with Notepad or any text editor such as Geany, Atom or Notepad++.

If you wish to execute the SQL script, in DBeaver, you first have to create the connection to the target database. Then, in the database navigator on the left, right-click on your database and select "SQL editor". A new empty SQL editor opens, linked to your database. Then click on "SQL Editor" in the top bar, and click on "import SQL script":

Navigate to the .sql file and select it.

Just in case, DBeaver also exists on Mac so if you need to use several OS it might be convenient to install DBeaver on all your computers and so keep the same tool everywhere.

Steps to follow to create a database from a .sql file

If you have an SQL script to create mySQL database, it will not run fine in a SQLite or any other database. For example, mySQL CREATE TABLE would mention something like ENGINE=InnoDB. SQLite will not understand this option. And it is generally true : SQL script are very similar from one database system to another, but not exactly identical. So unfortunately not interchangeable.

So to transfer your database from one computer (let's say the "source") to another (let's say the "target"), you need first to "dump" it i.e. to put all SQL instructions to create it into one file. As I understand you have already done it.

Then you will have to reinstall the same database engine (so mySQL in your case I guess) on the target computer. Then connect DBeaver (or whatever client : mySQL Workbench, Datagrip, even command line...) to this database and execute the script as shown above.

The SQL script is NOT the database, it is just the steps to create it. The database engine will read your SQL script and recreate your database accordingly.

Form an array and then permute the dimensions:

I don't have any problems with your View. In fact, with your input object, I am able to render the contents without issues.

Full Code:

The main issue is that the logistic curve you're plotting is approximately linear over the range of data you've got (this is generally true when the predicted probabilities are in the range from 0.3 to 0.7).

You can get standard errors on the plot by specifying se=TRUE in the geom_smooth() call ...

In the plot below I (1) used stat_sum() instead of geom_point() to visualize the overlapping points in the data set; (2) used fullrange=TRUE to get predictions over the full range of the plot (rather than just the range actually spanned by the data); (3) used expand_limits() to push the graph out to large age values, to illustrate that the prediction does look nonlinear if you extend it to low or high enough predicted probabilities (to get to high probabilities, you'd need to make age negative ...)