计算传播学期中
李璐思李心语
2022-11-05
##由于计算机算力不足和频繁报错，不得不在Excel中进行数据预处理，处理数据包括： ##1.提取State为NY的数据 2.保留product/tags/Submitted via/Company response to consumer/Timely response列，其余删去 ##3.将上列数据中的元素分别转换为数值 4.删去存在空白的列
library(readxl)## Warning: 程辑包'readxl'是用R版本4.2.2 来建造的#调用函数library()显示库中有哪些包dataset<-read.csv("E:/计算传播期中/Consumer Complaints NA处理.csv")
#导入文件1cor=cor(dataset, use='everything', method='pearson')
print(cor)## Product Tags Submitted.via
## Product 1.00000000 0.047581681 0.06630745
## Tags 0.04758168 1.000000000 0.08389851
## Submitted.via 0.06630745 0.083898506 1.00000000
## Company.response.to.consumer -0.06044528 0.007167132 0.01289726
## Timely.response. 0.03118129 -0.006513342 0.00433958
## Company.response.to.consumer Timely.response.
## Product -0.060445276 0.031181287
## Tags 0.007167132 -0.006513342
## Submitted.via 0.012897256 0.004339580
## Company.response.to.consumer 1.000000000 0.126896918
## Timely.response. 0.126896918 1.000000000##使用协方差分析的统计方法，判断以下五个变量的相关性关系
##协方差分析的结果在-1-1之间，数据越接近∣1∣，说明二者相关性越强
##以下结果说明，5个元素之间的相关关系很弱library(corrplot)## Warning: 程辑包'corrplot'是用R版本4.2.2 来建造的## corrplot 0.92 loaded##运行程序，做协方差可视化图
##颜色越深，圆圈越大，说明二者相关性越强
corrplot(cor)
#作主成分分析
##主成分分析是一种常用的数据分析方法。通过线性变换将原始数据变换为一组各维度线性无关的表示，可用于提取数据的主要特征分量，常用于高维数据的降维。PCA是将已存在的特征进行压缩，降维完毕后的特征不是原本的特征矩阵中的任何一个特征，而是通过某些方式组合起来的新特征。
princomp1.pr <- princomp(dataset,cor = TRUE)#显示分析结果，loadings（载荷）
summary(princomp1.pr,loadings = TRUE)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.0663839 1.0622274 0.9915718 0.9559504 0.9151189
## Proportion of Variance 0.2274349 0.2256654 0.1966429 0.1827682 0.1674885
## Cumulative Proportion 0.2274349 0.4531003 0.6497433 0.8325115 1.0000000
##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Product 0.535 0.146 0.692 0.111 0.449
## Tags 0.492 0.278 -0.483 0.662
## Submitted.via 0.516 0.341 -0.243 -0.728 -0.169
## Company.response.to.consumer -0.385 0.614 -0.241 0.644
## Timely.response. -0.241 0.639 0.413 0.126 -0.588#画出主成分的碎石图,主成分特征值的大小构成的陡坡图
screeplot(princomp1.pr,type = "lines")
#画出数据关于前两个主成分的散点图和原坐标在主成分下的方向(比如，倾向第一主成分，可选择4、9、8等编号。箭头代表xi在主成分下的方向)
biplot(princomp1.pr)
dataset<-read.csv("E:/计算传播期中/Consumer Complaints ciyun.csv")
#调用默认包R以读取csv格式的文件library(jiebaR,jiebaRD) ## Warning: 程辑包'jiebaR'是用R版本4.2.2 来建造的## 载入需要的程辑包：jiebaRD## Warning: 程辑包'jiebaRD'是用R版本4.2.2 来建造的library(wordcloud2)## Warning: 程辑包'wordcloud2'是用R版本4.2.2 来建造的#运行上述程序包news <- read.csv("E:/计算传播期中/Consumer Complaints ciyun.csv")
#导入csv文件text<-news$x
#提取文本所在列（考虑到数据分析和运行的成本，在Excel中对数据进行预处理，仅保留comsumer complaints narrative所在列并命名为xmixseg<-worker("mix")
#建立模型分词
a<-segment(text,mixseg)
#开始分词stopwords <- read.csv("E:/计算传播期中/停用词表.txt",sep = "\t",skip=0,header=TRUE,comment.char="",check.names=FALSE)
#通过stopwords函数调用停用词表，sep="\t"来读取使用制表符来分割行内数据的文件，skip 读取数据前跳过的行的数目为0，class(stopwords)## [1] "data.frame"#调用class函数显示停用词类型
stopwords<- as.vector(stopwords[ ,1])
#停用词即当前列词removewords<-function(target_words,stop_words){
target_words=target_words[target_words %in% stop_words==FALSE]
return(target_words)
}
#定义函数removwords，去除停用词wordResult<-removewords(a,stopwords)
#输出去除停用词后的分词结果freq<-table(wordResult)
#词频统计
freq## wordResult
## 0 0.0
## 79 8
## 0.00 0.125
## 106 1
## 0.25 0.27
## 4 1
## 00 00.00
## 10 1
## 000 000.00
## 22 1
## 02 0945
## 1 1
## 1 1-
## 400 8
## 1-2 1-3
## 9 1
## 1. 1.00
## 107 71
## 1.025 1.144
## 1 1
## 1.4 1.41
## 3 1
## 1.45 1.5
## 1 4
## 1.625 1.9
## 1 1
## 10 10-
## 222 19
## 10-12 10-14
## 2 1
## 10-15 10-20
## 2 1
## 10. 10.00
## 9 66
## 10.875 100
## 5 59
## 100.00 1000
## 165 1
## 1000.00 10000.00
## 226 87
## 100000.00 1001
## 24 1
## 1005 1005.9
## 1 1
## 1024 1024.36
## 4 1
## 1024.38 1024.39
## 8 1
## 1024.40 1024.41
## 1 9
## 1026.13 1026.36
## 8 1
## 1026.39 1028
## 1 2
## 1031 105
## 2 2
## 107 1099
## 1 2
## 11 11.
## 35 6
## 11.00 11.03
## 8 1
## 11.25 11.3
## 1 1
## 11.5 11.625
## 1 1
## 11.75 11.85
## 1 1
## 11.9 11.99
## 2 1
## 110.00 1100.00
## 30 43
## 11000.00 110000.00
## 23 23
## 112 12
## 1 92
## 12- 12.
## 1 5
## 12.00 12.25
## 36 1
## 12.5 12.75
## 1 1
## 12.99 120
## 1 45
## 120.00 1200
## 62 1
## 1200.00 12000.00
## 54 28
## 120000.00 122
## 14 2
## 125 127
## 1 1
## 129 13
## 1 14
## 13. 13.00
## 2 20
## 13.12 130.00
## 1 49
## 1300.00 13000.00
## 76 18
## 130000.00 14
## 12 52
## 14- 14.
## 1 7
## 14.00 14.5
## 11 1
## 140 140.00
## 1 29
## 1400.00 14000.00
## 38 13
## 140000.00 145
## 8 1
## 1463 15
## 1 450
## 15- 15.
## 2 61
## 15.00 15.90
## 30 1
## 150 150.00
## 5 79
## 1500.00 15000.00
## 73 39
## 150000.00 16
## 7 252
## 16- 16.
## 2 59
## 16.00 16.667
## 8 1
## 160.00 1600.00
## 29 61
## 16000.00 160000.00
## 21 5
## 1639 1641
## 1 1
## 1681 1692
## 48 19
## 1692-1692 17
## 1 15
## 17.00 17.19
## 8 1
## 170.00 1700
## 31 2
## 1700.00 17000.00
## 58 13
## 170000.00 1746
## 21 1
## 1785.25 1785.31
## 8 4
## 18 18-20
## 62 1
## 18.00 18.1
## 8 1
## 18.15 18.25
## 1 1
## 180 180-
## 22 9
## 180.00 1800.00
## 33 42
## 18000.00 180000.00
## 11 8
## 1819 182
## 1 2
## 1842 19
## 2 10
## 19.00 19.24
## 24 1
## 19.49 190.00
## 1 31
## 1900.00 19000.00
## 36 27
## 190000.00 1950
## 7 1
## 1962 1969
## 2 1
## 1970 1970.
## 2 1
## 1974 1977
## 1 2
## 1983 1983.
## 1 2
## 1985 1986
## 1 1
## 1988. 1989.
## 1 1
## 1990 1990.
## 1 1
## 1992 1994.
## 4 1
## 1996 1997
## 1 2
## 1997. 1998
## 1 4
## 1999 2
## 2 641
## 2- 2-3
## 7 11
## 2-6 2.
## 1 101
## 2.00 2.2
## 35 2
## 2.5 2.50
## 10 1
## 2.6 2.625
## 2 2
## 2.74 2.75
## 1 2
## 2.99 20
## 1 114
## 20. 20.00
## 1 52
## 200 200.00
## 4 113
## 2000 2000.
## 6 1
## 2000.00 20000.00
## 130 36
## 200000.00 2001
## 15 6
## 2001- 2001.
## 1 7
## 2002 2002.
## 1 3
## 2003 2003.
## 10 3
## 2004 2004.
## 7 2
## 2005 2005.
## 4 1
## 2006 2006.
## 10 2
## 2007 2007.
## 14 7
## 2008 2008.
## 18 7
## 2009 2009.
## 23 17
## 2009.. 2010
## 1 30
## 2010. 2011
## 11 32
## 2011. 2012
## 15 20
## 2012. 2013
## 11 64
## 2013. 2014
## 17 119
## 2014. 2015
## 71 921
## 2015- 2015.
## 2 295
## 2015.. 20152.
## 1 1
## 2016 2016-
## 748 1
## 2016. 2016.-
## 229 1
## 2016.. 2017
## 1 1
## 2017. 2019
## 1 1
## 2022. 205.11
## 1 1
## 21 21.00
## 14 12
## 21.26 21.99
## 2 1
## 210 210.00
## 1 19
## 2100.00 21000.00
## 28 7
## 2105 215
## 2 1
## 22 22.00
## 6 5
## 22.24 22.99
## 1 1
## 220.00 2200
## 23 2
## 2200.00 22000.00
## 49 9
## 220000.00 2201
## 4 1
## 2205. 225
## 1 1
## 226.12 227
## 1 1
## 23 23.00
## 9 10
## 23.99 230.00
## 3 39
## 2300.00 23000.00
## 17 2
## 230000.00 24
## 14 80
## 24- 24-48
## 1 7
## 24.00 24.24
## 17 1
## 24.4 24.99
## 1 1
## 240 240.00
## 1 27
## 240.30-1 2400.00
## 1 30
## 24000.00 240000.00
## 10 8
## 241 245
## 1 6
## 25 25.00
## 42 99
## 25.24 25.49
## 2 1
## 250 250.00
## 1 76
## 2500.00 25000.00
## 52 33
## 250000.00 26
## 12 12
## 26.00 26.9
## 6 1
## 26.99 260
## 4 1
## 260.00 2600.00
## 13 24
## 26000.00 260000.00
## 4 3
## 2605 27
## 5 6
## 27.00 27.24
## 15 1
## 27.49 270.00
## 1 19
## 2700.00 27000.00
## 25 3
## 270000.00 275
## 5 1
## 275. 28
## 1 5
## 28.00 28.49
## 7 1
## 28.5 28.99
## 1 1
## 280.00 2800.00
## 9 29
## 28000.00 280000.00
## 1 4
## 2801. 29
## 1 4
## 29. 29.00
## 1 23
## 29.6 29.95
## 1 2
## 29.99 290.00
## 5 16
## 2900.00 29000.00
## 35 3
## 290000.00 3
## 2 515
## 3- 3-309
## 14 1
## 3-4 3-5
## 8 13
## 3-6 3.
## 1 75
## 3.00 3.09
## 19 1
## 3.25 3.3
## 3 1
## 3.375 3.4
## 4 1
## 3.5 3.50
## 8 1
## 3.6 3.625
## 2 1
## 3.75 3.750
## 6 4
## 3.819 3.828
## 2 1
## 3.875 3.99
## 4 1
## 30 30-
## 408 29
## 30-40 30-45
## 1 2
## 30-50 30-60
## 1 6
## 30.00 30.5
## 54 4
## 300 300.00
## 3 99
## 3000.00 30000.00
## 83 16
## 300000.00 31
## 4 19
## 31-60 31.00
## 2 4
## 310.00 3100.00
## 14 17
## 31000.00 310000.00
## 9 4
## 311 316
## 2 1
## 32 32.00
## 3 7
## 32.20 320
## 1 2
## 320.00 3200.00
## 22 23
## 32000.00 33
## 2 2
## 33.00 33.2
## 8 1
## 330.00 3300.00
## 20 15
## 33000.00 330000.00
## 16 2
## 34.00 340.
## 34 1
## 340.00 3400.00
## 21 22
## 34000.00 340000.00
## 5 1
## 343 35
## 1 18
## 35.00 350.00
## 85 30
## 3500.00 3500.21
## 28 1
## 35000.00 358
## 12 1
## 36 36.00
## 14 14
## 360 360.
## 25 3
## 360.00 3600.00
## 16 16
## 36000.00 360000.00
## 4 3
## 365 37
## 6 8
## 37.00 37.68
## 15 1
## 37.87 370.00
## 1 17
## 3700.00 37000.00
## 12 2
## 370000.00 38
## 3 3
## 38.00 380-
## 8 2
## 380.00 3800
## 14 1
## 3800.00 38000.00
## 11 6
## 380000.00 39
## 6 1
## 39.00 390.00
## 5 31
## 3900.00 39000.00
## 8 2
## 390000.00 4
## 2 223
## 4- 4-5
## 3 7
## 4-6 4-7
## 2 1
## 4-8 4.
## 1 53
## 4.0 4.00
## 2 18
## 4.025 4.12
## 1 1
## 4.125 4.15.
## 1 1
## 4.25 4.34
## 1 1
## 4.375 4.5
## 2 3
## 4.5- 4.625
## 1 1
## 4.75 4.8
## 1 1
## 4.875 40
## 1 48
## 40- 40.00
## 1 26
## 400 400-
## 4 1
## 400.00 4000.00
## 90 29
## 40000.00 400000.00
## 11 13
## 401 41
## 1 1
## 41.00 410.00
## 11 7
## 4100.00 41000.00
## 9 1
## 410000.00 42
## 1 4
## 42.00 420.00
## 12 12
## 4200.00 42000.00
## 7 3
## 420000.00 43
## 2 4
## 43.00 430.00
## 4 13
## 4300.00 43000.00
## 4 1
## 430000.00 433
## 2 1
## 44.00 440.00
## 2 17
## 4400.00 44000.00
## 6 5
## 45 45-90
## 53 2
## 45.00 450.00
## 6 30
## 4500.00 45000.00
## 13 6
## 46 46.00
## 4 1
## 460.00 4600.00
## 8 4
## 46000.00 460000.00
## 2 2
## 47 47.00
## 2 11
## 470.00 4700.00
## 12 6
## 47000.00 48
## 2 35
## 48- 48.00
## 1 1
## 480 480.00
## 2 15
## 4800.00 48000.00
## 6 6
## 480000.00 49
## 3 2
## 49.00 490.00
## 12 12
## 4900.00 49000.00
## 13 2
## 490000.00 5
## 2 311
## 5- 5-10
## 3 2
## 5-6 5-7
## 4 4
## 5. 5.00
## 34 54
## 5.025 5.25
## 1 1
## 5.4 5.5
## 1 2
## 5.54 5.74
## 1 4
## 5.75 5.75.
## 1 1
## 5.875 50
## 1 21
## 50.00 500
## 82 2
## 500.00 5000
## 172 9
## 5000.00 50000.00
## 70 47
## 500000.00 51.00
## 8 3
## 510.00 5100.00
## 13 4
## 51000.00 5101
## 1 1
## 52 52.00
## 4 5
## 520.00 5200
## 13 1
## 5200.00 52000.00
## 8 6
## 520000.00 53
## 3 3
## 53.00 530.00
## 1 7
## 5300.00 530000.00
## 1 2
## 54 54.00
## 1 2
## 540.00 5400.00
## 10 10
## 54000.00 540000.00
## 2 2
## 55 55.00
## 5 25
## 550.00 5500.00
## 18 5
## 55000.00 550000.00
## 2 1
## 551 56.00
## 1 6
## 560.00 5600.00
## 10 6
## 56000.00 560000.00
## 2 1
## 57 57.00
## 1 6
## 570.00 5700.00
## 4 6
## 57000.00 570000.00
## 2 2
## 58.00 580.00
## 4 16
## 5800.00 58000.00
## 6 2
## 59 59.00
## 1 15
## 590.00 5900.00
## 9 5
## 59000.00 590000.00
## 2 1
## 6 6-
## 260 3
## 6-12 6-7
## 1 1
## 6-8 6-9
## 14 3
## 6. 6.00
## 23 25
## 6.025 6.11.
## 1 2
## 6.125 6.375
## 5 2
## 6.5 6.54
## 4 2
## 6.60 6.640
## 1 1
## 6.74 6.75
## 2 1
## 6.75000 6.8
## 1 4
## 6.875 6.9
## 2 1
## 6.99 60
## 1 125
## 60- 60-120
## 2 1
## 60-70 60-90
## 1 3
## 60.00 600.00
## 34 41
## 6000.00 60000.00
## 39 13
## 600000.00 605
## 2 15
## 607 609
## 2 34
## 609. 61
## 4 1
## 61.00 610
## 7 2
## 610.00 6100.00
## 9 11
## 61000.00 610000.00
## 1 2
## 611 611.
## 38 5
## 616 617.
## 5 4
## 62 62.00
## 2 1
## 620.00 6200.00
## 20 3
## 62000.00 623
## 1 21
## 63.00 630.00
## 4 8
## 6300.00 63000.00
## 3 1
## 630000.00 64.00
## 1 5
## 640.00 6400.00
## 8 4
## 64000.00 640000.00
## 3 1
## 65.00 650.00
## 14 5
## 6500.00 65000.00
## 5 4
## 656 657
## 1 1
## 66 66.00
## 2 12
## 660.00 6600.00
## 9 6
## 66000.00 67.00
## 3 4
## 670.00 6700.00
## 6 3
## 67000.00 68
## 1 1
## 68.00 680.00
## 9 6
## 6800.00 69.00
## 4 5
## 690.00 6900.00
## 7 2
## 7 7-
## 198 42
## 7-10 7-8
## 15 1
## 7. 7.00
## 19 10
## 7.025 7.125
## 1 2
## 7.2 7.25
## 2 3
## 7.37 7.375
## 1 1
## 7.49 7.5
## 1 2
## 7.85 7.938
## 1 2
## 7.94 70
## 1 9
## 70.00 700
## 13 3
## 700.00 7000.00
## 37 23
## 70000.00 700000.00
## 4 3
## 71.00 710.00
## 1 3
## 7100.00 71000.00
## 3 1
## 710000.00 72
## 2 16
## 72.00 720.00
## 5 12
## 7200.00 72000.00
## 7 1
## 720000.00 73-10
## 1 1
## 73.00 730.00
## 5 8
## 7300.00 73000.00
## 7 1
## 74 74.00
## 2 7
## 740.00 7400.00
## 6 6
## 74000.00 75
## 3 12
## 75.00 750.00
## 33 27
## 7500.00 75000.00
## 12 5
## 76 76.00
## 1 3
## 760.00 7600.00
## 13 7
## 76000.00 77
## 2 1
## 77.00 770.00
## 5 9
## 7700.00 77000.00
## 2 1
## 78 78.00
## 4 9
## 780.00 7800.00
## 2 2
## 79 79.00
## 1 1
## 79.19 79.95
## 1 1
## 79.99 790.00
## 1 10
## 7900.00 8
## 1 103
## 8-10 8.
## 3 17
## 8.00 8.24
## 17 1
## 8.25 8.250
## 3 5
## 8.37 8.5
## 1 1
## 8.79 8.8
## 1 2
## 8.83 8.875
## 1 1
## 80 80.00
## 18 21
## 800 800.00
## 3 35
## 8000.00 80000.00
## 26 8
## 800000.00 803.
## 6 1
## 805 806
## 1 3
## 807 807.
## 5 2
## 809 809.
## 4 2
## 81.00 810.00
## 1 6
## 8100.00 82
## 3 2
## 82.00 820.00
## 7 15
## 8200.00 82000.00
## 1 1
## 820000.00 83.00
## 3 4
## 830.00 8300.00
## 10 2
## 83000.00 84
## 1 12
## 84- 84.00
## 12 3
## 840.00 8400.00
## 9 7
## 840000.00 85
## 1 1
## 85.00 850.00
## 8 14
## 8500.00 85000.00
## 9 4
## 86.00 860.00
## 4 8
## 8600.00 86000.00
## 2 1
## 87.00 870.00
## 3 4
## 8700.00 870000.00
## 2 2
## 88.00 880.00
## 2 3
## 8800.00 89.00
## 3 5
## 890.00 8900.00
## 7 4
## 9 9.
## 62 13
## 9.00 9.4
## 14 1
## 9.59 9.9
## 1 2
## 9.95 9.950
## 2 1
## 9.99 90
## 4 168
## 90- 90.00
## 1 8
## 900.00 9000.00
## 28 10
## 90000.00 909
## 5 1
## 91-508 91.00
## 2 2
## 910.00 9100.00
## 4 1
## 92 92.00
## 2 1
## 920.00 9200.00
## 5 2
## 93.00 930.00
## 1 7
## 9300.00 93000.00
## 2 1
## 94.00 940.00
## 5 5
## 95 95.00
## 4 22
## 950.00 9500.00
## 2 10
## 96 96.00
## 1 2
## 960 960.00
## 1 6
## 9600.00 96000.00
## 3 1
## 97.00 970.00
## 3 9
## 9700.00 98
## 1 2
## 98. 98.00
## 1 13
## 980.00 9800.00
## 8 7
## 987 99.00
## 1 10
## 99.4 990.00
## 1 9
## 9900.00 993
## 1 1
## A AA
## 902 7
## AAA aadvantage
## 2 1
## Aadvantage AAdvantage
## 3 44
## aand ab
## 1 1
## aback abalance
## 1 1
## abandon abandonded
## 3 1
## abandoned Abandoned
## 9 4
## abandonment abate
## 3 4
## abatement abbreviation
## 4 1
## abc aberration
## 1 1
## abet abetted
## 3 1
## abetting Abetting
## 1 1
## abhorrent abide
## 3 4
## ABIDE ABIDED
## 1 1
## abiding ABIDING
## 5 1
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## 3#查看词频统计结果wordcloud2(freq,shape='circle',backgroundColor='pink')#根据词频统计结果绘制词云，设置形状为原型，背景颜色为粉色