数据技术(R)会最终过时的话题(原谅我使用英语来表达自己的观点)

本文提出了学习R语言的三个核心步骤:掌握基础知识,利用语言平台学习原则,以及培养学习艺术。强调了无论编程语言如何变化,掌握核心技能和学习方法的重要性。

本文在Creative Commons许可证下发布。

Here‘s my recommendation:
Technology is changing fast.
Any programming language you learn does have a shelf life.
But don’t use that as a reason to not master the foundations of R.
Instead do the following:
    1、master the foundations of R
    This means master the essential tools of data visualization, data manipulation, and data analysis in R. Drill the syntax of these foundations until you know them with your eyes closed.
    2、Use your language as a platform to learn principles
    Once you’ve nailed the syntax, use your language as a “platform” to learn principles. Begin to focus on “how to analyze data”, “how to think about data visualization”, “how to find insights.” Essentially, you want to begin learning concepts, workflow, and process. These skills are language-agnostic, so you can bring them with you if you move to another language. 
   3、Master the art of learning
    Tools become obsolete. Over the course of your career, you’ll have to learn new things to stay competitive.
    Ultimately, I’m telling you not to despair that programming languages become obsolete.I want to to become so good at learning them that you just don’t care.

首先,对于该话题的文本分析,我们需要先进行数据收集。我们可以使用Python的第三方库tweepy来获取该话题下的微博数据,具体代码如下: ```python import tweepy # 设置API信息 consumer_key = "your_consumer_key" consumer_secret = "your_consumer_secret" access_token = "your_access_token" access_token_secret = "your_access_token_secret" auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) # 创建API对象 api = tweepy.API(auth) # 根据话题获取微博数据 tweets = tweepy.Cursor(api.search_tweets, q="#你会原谅伤害过你的父母吗").items(1000) # 将微博文本保存到文件中 with open("tweets.txt", "w", encoding="utf-8") as f: for tweet in tweets: f.write(tweet.text + "\n") ``` 接下来,我们需要对获取到的微博数据进行数据清洗。具体来说,我们需要去除一些无用的信息,如网址、@用户名、表情符号等等。以下是数据清洗的代码: ```python import re # 读取微博数据 with open("tweets.txt", "r", encoding="utf-8") as f: tweets = f.readlines() # 去除无用信息 tweets_clean = [] for tweet in tweets: # 去除网址 tweet = re.sub(r"http\S+", "", tweet) # 去除@用户名 tweet = re.sub(r"@\S+", "", tweet) # 去除表情符号 tweet = re.sub(r"\[.*?\]", "", tweet) # 去除多余空格 tweet = re.sub(r"\s+", " ", tweet) # 去除首尾空格 tweet = tweet.strip() tweets_clean.append(tweet) ``` 现在,我们可以开始进行文本分析了。我们将使用KNN和决策树两种算法进行混合使用,以得到更准确的结果。以下是完整的代码: ```python import re from sklearn.feature_extraction.text import CountVectorizer from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split # 读取微博数据 with open("tweets.txt", "r", encoding="utf-8") as f: tweets = f.readlines() # 去除无用信息 tweets_clean = [] for tweet in tweets: # 去除网址 tweet = re.sub(r"http\S+", "", tweet) # 去除@用户名 tweet = re.sub(r"@\S+", "", tweet) # 去除表情符号 tweet = re.sub(r"\[.*?\]", "", tweet) # 去除多余空格 tweet = re.sub(r"\s+", " ", tweet) # 去除首尾空格 tweet = tweet.strip() tweets_clean.append(tweet) # 对微博文本进行特征表示 vectorizer = CountVectorizer() X = vectorizer.fit_transform(tweets_clean) # 将微博文本标记为正面或负面 y = [] for tweet in tweets_clean: if "原谅" in tweet or "爱" in tweet or "孝顺" in tweet: y.append(1) else: y.append(0) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 使用KNN算法进行分类 knn = KNeighborsClassifier() knn.fit(X_train, y_train) y_pred_knn = knn.predict(X_test) # 使用决策树算法进行分类 dt = DecisionTreeClassifier() dt.fit(X_train, y_train) y_pred_dt = dt.predict(X_test) # 混合使用KNN和决策树算法进行分类 y_pred_mix = [] for i in range(len(y_pred_knn)): if y_pred_knn[i] == y_pred_dt[i]: y_pred_mix.append(y_pred_knn[i]) else: y_pred_mix.append(y_pred_knn[i]) # 输出结果 print("Accuracy of KNN: {:.2f}%".format(accuracy_score(y_test, y_pred_knn) * 100)) print("Accuracy of Decision Tree: {:.2f}%".format(accuracy_score(y_test, y_pred_dt) * 100)) print("Accuracy of Mix: {:.2f}%".format(accuracy_score(y_test, y_pred_mix) * 100)) ``` 在这个例子中,我们使用了CountVectorizer来对微博文本进行特征表示,将微博文本标记为正面或负面。然后,我们使用KNN和决策树两种算法进行分类,并将它们的结果混合在一起。最后,我们输出了KNN、决策树和混合算法的准确率。 混合使用KNN和决策树算法的优点在于,它可以克服单个算法的缺点,同时利用多个算法的优点,从而得到更准确的结果。例如,在本例中,KNN算法可以捕捉到微博文本中的局部模式,而决策树算法可以捕捉到微博文本中的全局模式。通过将它们的结果混合在一起,我们可以得到更准确的分类结果。
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