R-net Pc Programmer

[Zoraida]

Aproperly programmed complex rehab power wheelchair can make a world of difference to a user's mobility, independence, and ability to access their environment. The R-Net control system has many parameters that affects the overall performance of QUICKIE power wheelchair, which every technician should know. This one-hour technical webinar will go through the most common programming parameters that can be accessed simply by using an R-Net joystick with a display (no programmer needed), which is commonly known as OBP (On-Board Programming).

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When we talk about the .NET world the CLR is what everything we do depends on.What is the minimum knowledge of CLR a .NET programmer must have to be a good programmer?Can you give me one/many you think is/are the most important subjects:GC?, AppDomain?, Threads?, Processes?, Assemblies/Fusion?

Update: I noticed from some of comments that my question was not clear to some. When I say CLR I don't mean .Net Framework. It is NOT about memorizing .NET libraries, it is rather to understand how does the execution environment (in which those libraries live on runtime) work.

My question was directly inspired by John Robbins the author of "Debugging Applications for Microsoft .NET" book (which I recommend) and colleague of here cited Jeffrey Richter at Wintellect. In one of introductory chapters he is saying that "...any .NET programmer should know what is probing and how assemblies are loaded into runtime". Do you think there are other such things?

A great programmer cannot be measured by the quantity of things he knows about the CLR. Sure it's a nice beginning, but he must also know OOP/D/A and a lot of other things like Design Patterns, Best Practices, O/RM concepts etc.

I would rather hire a "great Java developer" with great general knowledge and experience in Java for a .Net job then a "master" in .Net that have little experience and thinks O/RM is a stock ticker and stored procedures are a great way to "abstract away the database"...

I've seen professional teachers in .Net completely fail in doing really simple things without breaking their backs due to lack of "general knowledge" while they at the same time "know everything" there is to know about .Net and the CLR...

Jon's answer seems to be pretty complete to me (plus delegates) but I think what fundamentally seperates a good programmer from an average one is answering the why questions rather than the how. It's great to know how garbage collections works and how value types and reference types work, but it's a whole other level to understand when to use a value type vs. reference type. It's the difference between speaking in a language vs. giving a speech in a language (it's all about how we apply the knowledge we have and how we arrive at those decisions).

Jon's answer is good. Those are all fairly basic but important areas that a lot of developers do not have a good understanding of. I think knowing the difference between value and reference types ties in to a basic understanding of how the GC in .NET behaves, but, more importantly, a good understanding of the Dispose pattern is important.

The rest of the areas you mention are either very deep knowledge about the CLR itself or more advanced concepts that aren't widely used (yet). [.NET 4.0 will start to change some of that with the introduction of the parallel extensions and MEF.]

I've been writing R code several hours a day for the last 10 years and consider myself high intermediate/advanced. I learned python (python 2) years ago but I've used it so little over the last 5 years that my proficiency has atrophied to essentially zero.

What are some good resources for quickly building a good base proficiency with Python (python 3) for an advanced R programmer? I specifically want some focus on using pandas and want to learn in the context of using Jupyter, with some emphasis on using notebooks, its features, etc. I don't need or want fundamentals common to every language ("here's what a for loop does; here's how if/else works, etc"). I'm open to many formats - datacamp-style interactive courses, Coursera, or even a book.

I find that Tom Augspurger's blog has a lot of great posts that likely really make sense to R programmers, e.g., he wrote something called stitch that is like knitr, he has a Modern Pandas series that I think is really good. Some cool things on dask as well. I also like that he's very friendly about R and generally interested in what happens in the data wrangling ecosystem. He also contributes to pandas.

The R language is used by data scientists and programmers for statistical computing. In part because of the increasing amounts of data collected by software systems, and the need to analyze that data, R is one of the fastest-growing technologies among my colleagues who use C#. A familiarity with R can be a valuable addition to your technical skill set.

In R, the dot (.) character is often used rather than the underscore (_) character to create variable and function names that are easier to read. The result of calling the chisq.test function is quite a bit of text:

This file is supposed to represent flower data with the color of the flower, the length and width of the petal, and the growth rate. The idea is to predict Rate values (in the last column) from the Color, Length and Width values. After a comment statement, the first three R commands in the linear regression analysis are:

The second command loads the data into memory in a table object named data. Notice that R uses named parameters. The header parameter tells if the first line is header information (TRUE, or T in shortened form) or not (FALSE or F). R is case-sensitive and you can usually use either the (

The print function displays the data table in memory. The print function has many optional parameters. Notice that the output in Figure 1 displays data item indices starting at 1. For array, matrix and object indices, R is a 1-based language, rather than 0-based like the C# language.

In words, to make a prediction using the model, you calculate a linear sum of products of the Estimate values multiplied by their corresponding X values. The Intercept value is a constant not associated with any variable. When you have categorical explanatory variables, one of the values is dropped (blue in this case).

The multiple R-squared value (0.9927) is the percentage of variation in the dependent variable explained by the linear combination of the independent variables. Put slightly differently, R-squared is a value between 0 and 1 where higher values mean a better predictive model. Here, the R-squared value is extremely high, indicating Color, Length and Width can predict Rate very accurately. The F-statistic, adjusted R-squared value, and p-value are other measures of model fit.

One of the points of this example is that when programming using R, your biggest challenge by far is understanding the statistics behind the language functions. Most people learn R in an incremental way, by adding knowledge of one technique at a time, as needed to answer some specific question. A C# analogy would be learning about the various collection objects in the Collections.Generic namespace, such as the Hashtable and Queue classes. Most developers learn about one data structure at a time rather than trying to memorize information about all the classes before using any of them in a project.

The type of chi-square test in Figure 1 is often called a test for uniform distribution because it tests if the observed data all have equal counts; that is, if the data is distributed uniformly. There are several other kinds of chi-square tests, including one called a chi-square test of independence.

Notice in the first chi-square dice example, the input parameter is a vector, but in the second sex-affiliation example, the input is a matrix. The chisq.test function has a total of seven parameters, one required (a vector or a matrix), followed by six optional named parameters.

Like many C# methods in the Microsoft .NET Framework namespaces, most R functions are heavily overloaded. In C#, overloading is usually implemented using multiple methods with the same name but with different parameters. The use of generics is also a form of overloading. In R, overloading is implemented using a single function with many optional named parameters.

As a C# programmer, when I want to make a graph of some program output data, I typically run my program, copy the output data, Ctrl+V paste that data into Notepad to remove weird control characters, copy that data, paste it into Excel, and then create a graph using Excel. This is kind of hacky, but it works fine in most situations.

The first command shows how to define a function in R using the function keyword. The built-in R function named outer creates a matrix of values using vectors x and y, and a function definition f. The result is a 25 x 25 matrix where the value in each cell is the value of function f that corresponds to x and y.

The persp function has a lot of optional, named parameters. The col parameter is the color, or colors, to use. Parameters phi and theta set the viewing angle (left and right, and up and down) of the graph. Parameter ticktype controls how values on the x, y and z axes are displayed. Parameters r and d control the perceived eye distance to the graph, and the perceived 3D effect. The parameter named shade controls simulated shading from a virtual light source. The parameter named expand controls the ratio of the height and width of the graph. The persp function has many more parameters, but the ones used here will be sufficient in most situations.

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