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XYenChi is translating
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A beginner's guide to collecting and mapping Twitter data using R
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============================================================
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### Learn to use R's twitteR and leaflet packages, which allow you to map the location of tweets on any topic.
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Image by :
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[Jason Baker][14]. [CC BY-SA 4.0][15]. Source: [Cloud][16], [Globe][17]. Both [CC0][18].
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When I started learning R, I also needed to learn how to collect Twitter data and map it for research purposes. Despite the wealth of information on the internet about this topic, I found it difficult to understand what was involved in collecting and mapping Twitter data. Not only was I was a novice to R, but I was also unfamiliar with the technical terms in the various tutorials. Despite these barriers, I was successful! In this tutorial, I will break down how to collect Twitter data and display it on a map in a way that even novice coders can understand.
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Programming and development
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* [New Python content][1]
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* [Our latest JavaScript articles][2]
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* [Recent Perl posts][3]
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* [Red Hat Developers Blog][4]
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### Create the app
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If you don't have a Twitter account, the first thing you need to do is to [create one][19]. After that, go to [apps.twitter.com][20] to create an app that allows you to collect Twitter data. Don't worry, creating the app is extremely easy. The app you create will connect to the Twitter application program interface (API). Think of an API as an electronic personal assistant of sorts. You will be using the API to ask another program to do something for you. In this case, you will be connecting to the Twitter API and asking it to collect data. Just make sure you don't ask too much, because there is a [limit][21] on how many times you can request Twitter data.
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There are two APIs that you can use to collect tweets. If you want to do a one-time collection of tweets, then you'll use the **REST API**. If you want to do a continuous collection of tweets for a specific time period, you'll use the **streaming API**. In this tutorial, I'll focus on using the REST API.
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After you create your app, go to the **Keys and Access Tokens** tab. You will need the Consumer Key (API key), Consumer Secret (API secret), Access Token, and Access Token Secret to access your app in R.
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### Collect the Twitter data
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The next step is to open R and get ready to write code. For beginners, I recommend using [RStudio][22], the integrated development environment (IDE) for R. I find using RStudio helpful when I am troubleshooting or testing code. R has a package to access the REST API called **[twitteR][8]**.
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Open RStudio and create a new RScript. Once you have done this, you will need to install and load the **twitteR** package:
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```
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install.packages("twitteR")
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#installs TwitteR
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library (twitteR)
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#loads TwitteR
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```
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Once you've installed and loaded the **twitteR** package, you will need to enter the your app's API information from the section above:
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```
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api_key <- ""
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#in the quotes, put your API key
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api_secret <- ""
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#in the quotes, put your API secret token
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token <- ""
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#in the quotes, put your token
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token_secret <- ""
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#in the quotes, put your token secret
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```
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Next, connect to Twitter to access the API:
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```
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setup_twitter_oauth(api_key, api_secret, token, token_secret)
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```
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Let's try doing a Twitter search about community gardens and farmers markets:
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```
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tweets <- searchTwitter("community garden OR #communitygarden OR farmers market OR #farmersmarket", n = 200, lang = "en")
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```
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This code simply says to search for the first 200 tweets **(n = 200)** in English **(lang = "en")**, which contain the terms **community garden** or **farmers market**or any hashtag mentioning these terms.
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After you have done your Twitter search, save your results in a data frame:
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```
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tweets.df <-twListToDF(tweets)
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```
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To create a map with your tweets, you will need to export what you collected into a **.csv** file:
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```
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write.csv(tweets.df, "C:\Users\YourName\Documents\ApptoMap\tweets.csv")
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#an example of a file extension of the folder in which you want to save the .csv file.
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```
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Make sure you save your **R** code before running it and moving on to the next step.
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### Create the map
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Now that you have data, you can display it in a map. For this tutorial, we will make a basic app using the R package **[Leaflet][9]**, a popular JavaScript library for making interactive maps. Leaflet uses the [**magrittr**][23] pipe operator (**%>%**), which makes it easier to write code because the syntax is more natural. It might seem strange at first, but it does cut down on the amount of work you have to do when writing code.
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For the sake of clarity, open a new R script in RStudio and install these packages:
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```
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install.packages("leaflet")
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install.packages("maps")
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library(leaflet)
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library(maps)
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```
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Now you need a way for Leaflet to access your data:
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```
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read.csv("C:\Users\YourName\Documents\ApptoMap\tweets.csv", stringsAsFactors = FALSE)
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```
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**stringAsFactors = FALSE** means to keep the information as it is and not convert it into factors. (For information about factors, read the article ["stringsAsFactors: An unauthorized biography"][24], by Roger Peng.)
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It's time to make your Leaflet map. You are going to use the **OpenStreetMap**base map for your map:
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```
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m <- leaflet(mymap) %>% addTiles()
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```
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Let's add circles to the base map. For **lng** and **lat**, enter the name of the columns that contain the latitude and longitude of your tweets followed by **~**. The **~longitude** and **~latitude** refer to the name of the columns in your **.csv** file:
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```
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m %>% addCircles(lng = ~longitude, lat = ~latitude, popup = mymap$type, weight = 8, radius = 40, color = "#fb3004", stroke = TRUE, fillOpacity = 0.8)
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```
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Run your code. A web browser should pop up and display your map. Here is a map of the tweets that I collected in the previous section:
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### [leafletmap.jpg][6]
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Map of tweets by location, Leaflet and OpenStreetMap, [CC-BY-SA][5]
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<add here="" leafletmap.jpg=""></add>
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Although you might be surprised with the small number of tweets on the map, typically only 1% of tweets are geocoded. I collected a total of 366 tweets, but only 10 (around 3% of total tweets) were geocoded. If you are having trouble getting geocoded tweets, change your search terms to see if you get a better result.
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### Wrapping up
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For beginners, putting all the pieces together to create a Leaflet map from Twitter data can be overwhelming. This tutorial is based on my experiences doing this task, and I hope it makes the learning process easier for you.
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_Dorris Scott will present this topic in a workshop, [From App to Map: Collecting and Mapping Social Media Data using R][10], at the [We Rise][11] Women in Tech Conference ([#WeRiseTech][12]) June 23-24 in Atlanta._
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--------------------------------------------------------------------------------
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作者简介：
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Dorris Scott - Dorris Scott is a PhD student in geography at the University of Georgia. Her research emphases are in Geographic Information Systems (GIS), geographic data science, visualization, and public health. Her dissertation is on combining traditional and non-traditional data about Veteran’s Affairs hospitals in a GIS interface to help patients make more informed decisions regarding their healthcare.
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-----------------
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via: https://opensource.com/article/17/6/collecting-and-mapping-twitter-data-using-r
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作者：[Dorris Scott ][a]
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译者：[译者ID](https://github.com/译者ID)
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||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://opensource.com/users/dorrisscott
|
||||
[1]:https://opensource.com/tags/python?src=programming_resource_menu
|
||||
[2]:https://opensource.com/tags/javascript?src=programming_resource_menu
|
||||
[3]:https://opensource.com/tags/perl?src=programming_resource_menu
|
||||
[4]:https://developers.redhat.com/?intcmp=7016000000127cYAAQ&src=programming_resource_menu
|
||||
[5]:https://creativecommons.org/licenses/by-sa/2.0/
|
||||
[6]:https://opensource.com/file/356071
|
||||
[7]:https://opensource.com/article/17/6/collecting-and-mapping-twitter-data-using-r?rate=Rnu6Lf0Eqvepznw75VioNPWIaJQH39pZETBfu2ZI3P0
|
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[8]:https://cran.r-project.org/web/packages/twitteR/twitteR.pdf
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||||
[9]:https://rstudio.github.io/leaflet
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[10]:https://werise.tech/sessions/2017/4/16/from-app-to-map-collecting-and-mapping-social-media-data-using-r?rq=social%20mapping
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[11]:https://werise.tech/
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[12]:https://twitter.com/search?q=%23WeRiseTech&src=typd
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||||
[13]:https://opensource.com/user/145006/feed
|
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[14]:https://opensource.com/users/jason-baker
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[15]:https://creativecommons.org/licenses/by-sa/4.0/
|
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[16]:https://pixabay.com/en/clouds-sky-cloud-dark-clouds-1473311/
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[17]:https://pixabay.com/en/globe-planet-earth-world-1015311/
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[18]:https://creativecommons.org/publicdomain/zero/1.0/
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[19]:https://twitter.com/signup
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[20]:https://apps.twitter.com/
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[21]:https://dev.twitter.com/rest/public/rate-limiting
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[22]:https://www.rstudio.com/
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[23]:https://github.com/smbache/magrittr
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[24]:http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/
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[25]:https://opensource.com/users/dorrisscott
|
||||
@ -1,3 +1,4 @@
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XYenChi is Translating
|
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Integrate Ubuntu to Samba4 AD DC with SSSD and Realm – Part 15
|
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============================================================
|
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@ -0,0 +1,178 @@
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用 R 收集和映射推特数据的初学者向导
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============================================================
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### 学习使用 R's twitteR 和 leaflet 包, 你就可以把任何话题的推文定位画在地图上。
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Image by :
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[琼斯·贝克][14]. [CC BY-SA 4.0][15]. 来源: [Cloud][16], [Globe][17]. Both [CC0][18].
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当我开始学习 R ，出于研究的目的我也需要学习如何收集推特数据并对其进行映射。尽管网上关于这个话题的信息很多，但我发觉难以理解什么与收集并映射推特数据相关。我不仅是个 R 新手，而且对不同教程中技术关系不熟悉。尽管困难重重，我成功了！在这个教程里，我将以一种新手程序员都能看懂的方式攻略如何收集推特数据并将至展现在地图中。
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程序设计和开发
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* [新 Python 内容][1]
|
||||
|
||||
* [我们最新的 JavaScript 文章][2]
|
||||
|
||||
* [ Perl 近期投递][3]
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* [红帽子开发者博客][4]
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### 创建应用程序
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如果你没有推特帐号，首先你需要 [注册一个][19].然后, 到 [apps.twitter.com][20] 创建一个允许你收集推特数据应用程序。别担心，创建应用程序极其简单。你创建的应用程序会与推特应用程序接口（API）相连。 想象 API 是一个多功能电子个人助手。你可以使用 API 让其他程序帮你做事。这样一来，你可以接入推特 API 令其收集数据。只需确保不要请求太多，因为推特数据请求次数是有[限制][21] 的。
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收集推文有两个可用的 API 。你若想做一次性的推文收集，那么使用 **REST API**. 若是想在特定时间内持续收集，可以用 **streaming API**。教程中我主要使用 REST API.
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创建应用程序之后，前往 **Keys and Access Tokens** 标签。你需要 Consumer Key (API key), Consumer Secret (API secret), Access Token, 和 Access Token Secret 来在 R 中访问你的应用程序。
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### 收集推特数据
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下一步是打开 R 准备写代码。对于初学者，我推荐使用 [RStudio][22], R 的集成开发环境 (IDE) 。我发现 RStudio 在解决问题和测试代码时很实用。 R 有访问 REST API 的包叫 **[twitteR][8]**.
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打开 RStudio 并新建 RScript。做好这些之后，你需要安装和加载 **twitteR** 包:
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```
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install.packages("twitteR")
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#installs TwitteR
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library (twitteR)
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#loads TwitteR
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```
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安装并载入 **twitteR** 包之后，你得输入上文提及的应用程序的 API 信息：
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```
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api_key <- ""
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#in the quotes, put your API key
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api_secret <- ""
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#in the quotes, put your API secret token
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token <- ""
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#in the quotes, put your token
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token_secret <- ""
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#in the quotes, put your token secret
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```
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接下来，连接推特访问 API:
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```
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setup_twitter_oauth(api_key, api_secret, token, token_secret)
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```
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我们来试试有关社区花园和农夫市场的推特研究：
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```
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tweets <- searchTwitter("community garden OR #communitygarden OR farmers market OR #farmersmarket", n = 200, lang = "en")
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```
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代码意思是搜索前200篇 **(n = 200)**英文 **(lang = "en")**推文, 包括关键词 **community garden** 或 **farmers market**或任何提及这些关键词的话题标签。
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推特搜索完成之后，在数据框中保存你的结果：
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```
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tweets.df <-twListToDF(tweets)
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```
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为了用推文创建地图，你需要收集的导出为 **.csv** 文件:
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```
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write.csv(tweets.df, "C:\Users\YourName\Documents\ApptoMap\tweets.csv")
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#an example of a file extension of the folder in which you want to save the .csv file.
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```
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运行前确保 **R** 代码已保存然后继续进行下一步。.
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### 生成地图
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现在你有了可以展示在地图上的数据。在此教程中，我们将用一个 R 包 **[Leaflet][9]**做一个基本的应用程序，一个热门 JavaScript 库做交互式地图。 Leaflet 使用 [**magrittr**][23] 管道运算符 (**%>%**), 使得它易于写代码因为语法更加自然。刚接触可能有点奇怪，但它确实降低了写代码的工作量。
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为了清晰度，在 RStudio 打开一个新的 R 脚本安装这些包：
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```
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install.packages("leaflet")
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install.packages("maps")
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library(leaflet)
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library(maps)
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```
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现在需要一个路径让 Leaflet 访问你的数据：
|
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|
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```
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read.csv("C:\Users\YourName\Documents\ApptoMap\tweets.csv", stringsAsFactors = FALSE)
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```
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**stringAsFactors = FALSE** 意思是保留信息，不将它转化成 factors. (想了解 factors,读这篇文章["stringsAsFactors: An unauthorized biography"][24], by Roger Peng.)
|
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是时候制作你的 Leaflet 地图了。你将使用 **OpenStreetMap**基本地图来做你的地图：
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```
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m <- leaflet(mymap) %>% addTiles()
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```
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我们来给基本地图加个范围。对于 **lng** 和 **lat**, 添加列名包括推文的经纬度，前面加个**~**。 **~longitude** 和 **~latitude** 在你的 **.csv** 文件中与列名相关的：
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```
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m %>% addCircles(lng = ~longitude, lat = ~latitude, popup = mymap$type, weight = 8, radius = 40, color = "#fb3004", stroke = TRUE, fillOpacity = 0.8)
|
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```
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运行你的代码。一个网页浏览器将会弹出并展示你的地图。这是我前面收集的推文的地图：
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### [leafletmap.jpg][6]
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用定位、 Leaflet 和 OpenStreetMap 的推文地图, [CC-BY-SA][5]
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<add here="" leafletmap.jpg=""></add>
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虽然你可能会对地图上的图文数量如此之小感到惊奇，典型地，只有1%的推文被地理编码了。我收集了总数为366的推文，但只有10（大概总推文的3%）是被地理编码了的。如果为能得到地理编码过的推文困扰，改变搜索关键词看看能不能得到更好的结果。
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### 总结
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对于初学者，把所有碎片结合起来去从推特数据生成一个 Leaflet 地图可能很艰难。 这个教程基于我完成这个任务的经验，我希望它能让你的学习过程变得更轻松。
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|
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_Dorris Scott 将会在研讨会上提出这个话题, [从应用程序到地图: 用 R 收集映射社会媒体数据][10], at the [We Rise][11] Women in Tech Conference ([#WeRiseTech][12]) 六月 23-24 在亚特兰大。_
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
作者简介：
|
||||
|
||||
Dorris Scott - Dorris Scott 是佐治亚大学的地理学博士生。她的研究重心是地理信息系统 (GIS), 地理数据科学, 可视化和公共卫生。她的论文是在一个 GIS 系统接口联系退伍军人福利医院的传统和非传统数据，帮助病人为他们的健康状况作出更为明朗的决定。
|
||||
|
||||
|
||||
-----------------
|
||||
via: https://opensource.com/article/17/6/collecting-and-mapping-twitter-data-using-r
|
||||
|
||||
作者：[Dorris Scott ][a]
|
||||
译者：[XYenChi](https://github.com/XYenChi)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://opensource.com/users/dorrisscott
|
||||
[1]:https://opensource.com/tags/python?src=programming_resource_menu
|
||||
[2]:https://opensource.com/tags/javascript?src=programming_resource_menu
|
||||
[3]:https://opensource.com/tags/perl?src=programming_resource_menu
|
||||
[4]:https://developers.redhat.com/?intcmp=7016000000127cYAAQ&src=programming_resource_menu
|
||||
[5]:https://creativecommons.org/licenses/by-sa/2.0/
|
||||
[6]:https://opensource.com/file/356071
|
||||
[7]:https://opensource.com/article/17/6/collecting-and-mapping-twitter-data-using-r?rate=Rnu6Lf0Eqvepznw75VioNPWIaJQH39pZETBfu2ZI3P0
|
||||
[8]:https://cran.r-project.org/web/packages/twitteR/twitteR.pdf
|
||||
[9]:https://rstudio.github.io/leaflet
|
||||
[10]:https://werise.tech/sessions/2017/4/16/from-app-to-map-collecting-and-mapping-social-media-data-using-r?rq=social%20mapping
|
||||
[11]:https://werise.tech/
|
||||
[12]:https://twitter.com/search?q=%23WeRiseTech&src=typd
|
||||
[13]:https://opensource.com/user/145006/feed
|
||||
[14]:https://opensource.com/users/jason-baker
|
||||
[15]:https://creativecommons.org/licenses/by-sa/4.0/
|
||||
[16]:https://pixabay.com/en/clouds-sky-cloud-dark-clouds-1473311/
|
||||
[17]:https://pixabay.com/en/globe-planet-earth-world-1015311/
|
||||
[18]:https://creativecommons.org/publicdomain/zero/1.0/
|
||||
[19]:https://twitter.com/signup
|
||||
[20]:https://apps.twitter.com/
|
||||
[21]:https://dev.twitter.com/rest/public/rate-limiting
|
||||
[22]:https://www.rstudio.com/
|
||||
[23]:https://github.com/smbache/magrittr
|
||||
[24]:http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/
|
||||
[25]:https://opensource.com/users/dorrisscott
|
||||
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Reference in New Issue
Block a user