python與R是當(dāng)前數(shù)據(jù)科學(xué)計(jì)算的兩大支柱狸涌，就我個(gè)人的使用經(jīng)驗(yàn)而言，R更直觀寥掐、簡(jiǎn)單和上手一些。很多專業(yè)的統(tǒng)計(jì)分析Python并沒有提供R中的對(duì)應(yīng)體磷蜀，而你想要使用Python做數(shù)據(jù)分析召耘，這時(shí)候就需要使用rpy2包來構(gòu)建這個(gè)橋梁了。

比如我最近遇到一個(gè)分析問題褐隆，Python無法計(jì)算非參數(shù)統(tǒng)計(jì)檢驗(yàn)wilcoxon test的置信區(qū)間污它，如果你仔細(xì)查看Python提供的非參數(shù)檢驗(yàn)，你會(huì)發(fā)現(xiàn)它使用的是正態(tài)逼近庶弃，這在樣本量大的時(shí)候（根據(jù)中心極限定理服從正態(tài)分布）當(dāng)然可以使用Python計(jì)算衫贬，當(dāng)如果你是小樣本，比如大多數(shù)生物醫(yī)學(xué)數(shù)據(jù)處理與分析中普遍樣本少的可憐歇攻。在Stack overflow上有人討論過并檢查統(tǒng)計(jì)檢驗(yàn)的p值固惯，結(jié)論是算檢驗(yàn)，R更靠譜些缴守，優(yōu)先采用葬毫。

言歸正傳，在銀行統(tǒng)計(jì)工作室rpy2使用示例一文中對(duì)rpy2包各方面的使用都有介紹屡穗，加rpy2官方文檔基本上可以掌握rpy2的使用贴捡，這里我提供這兩天實(shí)現(xiàn)的一個(gè)實(shí)例——從python中調(diào)用R的wilcox.test函數(shù)進(jìn)行非參數(shù)檢驗(yàn)，如果大家有這方面需求可以作為一個(gè)參考村砂。

代碼已經(jīng)封裝為一個(gè)函數(shù)烂斋，略寫了一下文檔。

# 推薦使用conda管理環(huán)境
# conda create --name test python=3.6
# source activate test
# conda install rpy2  # should add conda-forge channel
# reference link: <http://www.cnblogs.com/cloudtj/articles/6372200.html>, <https://rpy2.readthedocs.io/en/version_2.8.x/index.html>

def pyWilcox(x, y=None, alternative='two.sided', mu=0, paired=False, exact=None, correct=True, conf_interval=True, conf_level=0.95):
    ''' 
    Run wilcoxon test using wilcox.test in R stats package, default is 'two sided' test.
        return p value, statistical value and confidence interval in a dictory. 

    Arguments:
        
        x   
            numeric vector of data values. Non-finite (e.g., infinite or missing) values will be omitted.
        y   
            an optional numeric vector of data values: as with x non-finite values will be omitted.
        alternative 
            a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.
        mu  
            a number specifying an optional parameter used to form the null hypothesis. See ‘Details’.
        paired  
            a logical indicating whether you want a paired test.
        exact   
            a logical indicating whether an exact p-value should be computed.
        correct 
            a logical indicating whether to apply continuity correction in the normal approximation for the p-value.
        conf_interval   
            a logical indicating whether a confidence interval should be computed.
        conf_level  
            confidence level of the interval.

    Example:
        
        x = [i for i in range(0, 10)]
        y = [i for i in range(10, 20)]
        # two sample test
        pyWilcox(x, y)
        # one sample test
        pyWilcox(x)

        # output:
        #
        # {'p_value': [1.082508822446903e-05], 'statistic': [0.0], 'conf_interval': [-13.0, -7.0]}
        # {'p_value': [0.009151688852650072], 'statistic': [45.0], 'conf_interval': [2.500027475911944, 7.499972524088056]}
        

    Note:
        
        More information please run help('wilcox.test.default') in R console
    '''

    # 載入r對(duì)象
    from rpy2 import robjects
    # 載入導(dǎo)入包函數(shù)
    from rpy2.robjects.packages import importr
    # 將stats包導(dǎo)入為模塊
    stats = importr('stats')
# When one wants to create a vector from Python, either the class Vector or the convenience classes IntVector, FloatVector, BoolVector, StrVector can be used.

    # 將列表x轉(zhuǎn)換為r可識(shí)別數(shù)據(jù)對(duì)象
    x = robjects.FloatVector(x)
    
    # 將參數(shù)中的.替換為_础废，解決不兼容問題源祈， 來自rpy2文檔函數(shù)部分
    def iamfeelinglucky(func):
        def f(*args, **kwargs):
            d = {}
            for k, v in kwargs.items():
                d[k.replace('_', '.')] = v
            return func(**d)
        return f

    # 矯正參數(shù)名
    wilcox = iamfeelinglucky(stats.wilcox_test_default)

    # None類型似乎沒有相應(yīng)的函數(shù)，只能用條件語句進(jìn)行判斷
    if y != None:
        y = robjects.FloatVector(y)
        if exact != None:
            pr = wilcox(x = x, y = y, alternative = alternative, mu = mu, paired = paired, exact = exact, correct = correct, conf_int = conf_interval, conf_level = conf_level)
        else:
            pr = wilcox(x = x, y = y, alternative = alternative, mu = mu, paired = paired, conf_int = conf_interval, conf_level = conf_level)       
    else:
        if exact != None:
            pr = wilcox(x = x,  alternative = alternative, mu = mu, exact = exact, correct = correct, conf_int = conf_interval, conf_level = conf_level)
        else:
            pr = wilcox(x = x,  alternative = alternative, mu = mu, correct = correct, conf_int = conf_interval, conf_level = conf_level) 
           
    print(pr)
    res = list(pr)
    # 返回結(jié)果中需要的值構(gòu)建字典
    res = {"p_value":list(res[2]), "statistic":list(res[0]), "conf_interval":list(res[7])}   
    return(res)