Rivers | fast graphing library that allows for constructing

I have a dataframe (df) that contains datapoints along rivers, where each river basin (Basin ID) contains multiple rivers (River ID), and each river has points along its length (Length). It looks something like this (simplified):

In this example Basin 1 has three rivers, and Basin 2 has two rivers. The actual table has 600K rows and 12 other columns with other river data.

Objective: I want to extract the single longest river subset for each unique basin in df, so that I end up with something like this:

I assume that I would need to do something like

I need to define a recursive ML function composition that accepts two relations and returns the composition of the two relations. I need to use the thhisIsTheImageOf and createRelationFromImage function in my definition of composition.

Here is the code that is needed to be used to define the composition

...

I have a list that contains data for different rivers and want to extract only the name and flow rates and insert those in a dictionary, with the name as the key and a list of the flowrates as the values. To do this I made a another list of each row that was divided better and worked from there. However, when I run the code I encounter an error on append dictionary line that simply states KeyError: 'Avon', which I don't understand so I haven't been able to find a solution.

I would like to find all of the rivers within a certain bounding box. My final goal is subset them by name so I can choose which rivers to plot, but first I must know the names of the features in my extent! I'd prefer to use ggplot/tidyverse tools.

For example:

I'm using Python to merge 4 headerless CSV's into one output file.

Each CSV has a unique number in the first column as shown in the 2 example CSV files below:

1.csv

A thief steals from you and gets a 20 meter head start. You could easily catch him on open ground, since you can run twice as fast and so catch up 5 meters for every 10 meters you travel. Once the theif reaches a time warp he is trying to get back to , you cant catch him and there are a bunch of rivers in the path: see Figure. There are N places (marked by black lines) shallow enough to cross, but you'll get 5 meters further behind for every 10 meters of river you cross. Assume the thief is too terrified to consider moving back towards the left side of the map. Given the starting and ending locations, and the positions of (both ends of) the N crossings, i want an algorithm to find whether it is possible for the thief to escape, i.e., to reach the end location before you catch up. i am struggling on how to approach this problem .. any ideas are highly appreciated ..thanks

figure

Creating procedural terrain I cannot get an evaporation matrix and find why my algorithm is wrong. How my program works:

• A user loads a map chunk.
• If it exists it loads its data.
• If it doesn't exist, it creates the chunk and returns the generated data.

Creation of chunk steps:

• Create a heightfield (Diamond Square algorithm).
• Create [0,3] rivers (and more chunks if river needs to continue on a different chunk).
• Calculate evaporation.

When generating the terrain I use the following constants:

I want to generate a R markdown html document with plots and it should be possible to jump to a certain plot by search-function (in my example there are 3 plots and I want to jump in the html-doc to the plot, where the main is "rivers"). I think, the problem is, that main and axis labels of a plot are grafical elements, like the plot itself, and not text. So the search-function doesn't work.

Of course it would be possible to add manually text before each plot, but as all my plots are generated with a for-loop, I don_t know how to do it.

is there a possibilty to include text-output in this kind of for-loop or are there other ideas, how the main or axis labels of a plot can be suitable for search-function?

thanks in advance!

I want to use purrr::map_* functions to extract info from multiple models involving linear regression method. I am first creating some random dataset. The dataset has three dependent variables, and one independent variable.