国庆节的最后一天，你收到了澳大利亚某气象局的offer，在你上班的第一天，老板Jason把你叫到了办公室，布置了一个神秘任务。原来，Jason正在闷声发大财，默默的在一年内收集了澳大利亚Canberra当地2007年一整年的天气数据。作为Data Maniac的荣誉学员，你需要：

Q1：

我们已经在后台加载了这个名为 ‘weather’ 的数据。用你这几节课学到的方法，你需要快速的了解数据结构（观测值/变量个数，变量性质等）；

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Q2：

你发现这个数据集中的’RainTomorrow’变量很有意思，你想到这个变量是否和‘Sunshine’以及‘Rainfall’这两个变量是否有某种不可告人的关系，为了更好得分析，你需要用课上学到的dplyr包中的某种方法只留下以下4个变量，‘Date’，‘Rainfall’，‘Sunshine’, ‘RainTomorrow’。并且将其命名为weather_cut；

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Q3：为了更好的了解‘RainTomorrow‘和’Sunshine‘两个变量的关系，用tidyr包的spread函数将’RainTomorrow‘的两个值’Yes’和‘No’变成两个变量，填充在这两个新变量下的是原来变量’Sunshine‘的值。如果成功，你可以看到新的数据集的前六行为。讲这个新的数据集命名为weather_forecast;

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Q4: 分析完毕后，你想把weather_forecast恢复成原样，于是你需要用tidyr包的gather函数把它变回去，结果的前六行如下：

Date Rainfall No Yes 1 2007-11-01 0.0 NA 6.3 2 2007-11-02 3.6 NA 9.7 3 2007-11-03 3.6 NA 3.3 4 2007-11-04 39.8 NA 9.1 5 2007-11-05 2.8 10.6 NA 6 2007-11-06 0.0 8.2 NA

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Q5：你发现weather_forecast数据集中有很多的NA，你需要用课上学习的方法（any(is.na())找到‘Yes‘和’No’两个变量中确认是否有NA，并且用summary函数找到这两个变量中的NA的数量；

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Q6.

确认NA后，你决定用平均数去填充”Yes”和“No”中的所有变量；

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Q7.

解决了NA的问题以后，你需要用hist函数，plot函数以及boxplot函数找到weather_forecast数据集的‘No’变量中的异常值，感受下哪个函数最适合发现异常值。

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