Futures are Rust’s way of expressing asynchronous computations, but even after reading the documentation, it can be hard to figure out how all the pieces of futures (and tokio) fit together. While this may not matter too much if you’re just using futures, it becomes a stumbling block once you want to implement asynchronous primitives yourself. The highly anticipated async and await keywords that are coming down the pike promise to greatly reduce the friction of writing and using asynchronous code, but add even more to the mystery of how this stuff all works.
NOTE: The YouTube transcoding process seems to have introduce audio lag on all the non-1080p versions. I’ll report it to YT, but if it’s bothering you, the 1080p version works fine :)
In this stream, we tackle all of this head on. We go through what futures are, how they work, how they are used to represent things like asynchronous I/O (like networking), how they are executed, and how they are evolving as they are being adopted into the standard library. We also discuss async/await, and how they affect futures, and how Rust’s proposed pinning types act as the glue to make async/await work.
This is pretty dense material, but hopefully serves as a good and thorough overview of futures in Rust. If you want to know how it all works, this stream probably has you covered! To help you navigate, here are the major transition points:
0:00:00 - Introduction
0:05:57 - What are futures?
0:16:39 - How are futures executed?
0:26:10 - Super weird audio problems
0:29:09 - All good again! (you didn’t miss anything)
0:50:04 - How do you integrate futures with I/O?
1:36:11 - Driving futures with Tokio
1:49:28 - Multi-threaded future execution
2:15:00 - Stream and Sink
2:17:11 - (brief aside about tokio-io-pool)
2:24:07 - Putting futures in the standard library
2:30:00 - Arbitrary self types
2:33:46 - What is async/await?
2:46:58 - Why is async/await hard?
2:53:34 - Self-referential data-types
2:58:48 - Pinning!
3:50:40 - Q&A
The tokio documentation push is being organized at https://github.com/tokio-rs/doc-push .
#react #fetch-api #async #await #async-await
In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.
If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.
[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)Packages into Memory
#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials
A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!
In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2
#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials
What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:
Common symptoms of messy data include data that contain:
In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:
As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!
Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package
readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse
readr package function
read_csv() is the function to use (we’ll cover that later).
Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:
The Brooklyn Excel file
Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the
readxlpackage. We specify the function argument
skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:
library(readxl) # Load Excel files brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)
Note we saved this dataset with the variable name
brooklyn for future use.
The tidyverse offers a user-friendly way to view this data with the
glimpse() function that is part of the
tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:
Once the package is installed, load it to memory:
tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:
glimpse(brooklyn) ## Observations: 20,185 ## Variables: 21 ## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "… ## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA… ## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 … ## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "… ## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6… ## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,… ## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T… ## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112… ## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3… ## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22… ## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1… ## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "… ## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, … ## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…
glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.
#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials
In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.
On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.
But when it comes to comparing blog post performance, things get a bit trickier.
For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).
But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!
My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.
However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.
But then, I learned R, and realized that there was a much better way.
In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!
To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.
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You’ll also need the
stringr packages installed — which, as a reminder, you can do with the
Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:
post_url: the page path of the blog post
post_date: the date the post was published (formatted m/d/yy)
category: the blog category the post was published in (optional)
title: the title of the blog post (optional)
Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!
For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.
#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials