用于分析对调查中不同排名问题的回答之间关系的代码

Question

我的目标是找到更简单的代码，它可以概括，显示对两个调查问题的回答之间的关系。在 MWE 中，一个问题要求受访者从 1 到 8 对八个营销选择进行排名，另一个问题要求他们对从 1 到 9 的九个属性选择进行排名。排名越高表明受访者更喜欢该选择。这是数据框。

structure(list(Email = c("a", "b", "c", "d", "e", "f", "g", "h", 
"i"), Ads = c(2, 1, 1, 1, 1, 2, 1, 1, 1), Alumni = c(3, 2, 2, 
3, 2, 3, 2, 2, 2), Articles = c(6, 4, 3, 2, 3, 4, 3, 3, 3), Referrals = c(4, 
3, 4, 8, 7, 8, 8, 6, 4), Speeches = c(7, 7, 6, 7, 4, 7, 4, 5, 
5), Updates = c(8, 6, 6, 5, 5, 5, 5, 7, 6), Visits = c(5, 8, 
7, 6, 6, 6, 6, 4, 8), `Business Savvy` = c(10, 6, 10, 10, 4, 
4, 6, 8, 9), Communication = c(4, 3, 8, 3, 3, 9, 7, 6, 7), Experience = c(7, 
7, 7, 9, 2, 8, 5, 9, 5), Innovation = c(2, 1, 4, 2, 1, 2, 2, 
1, 1), Nearby = c(3, 2, 2, 1, 5, 3, 3, 2, 2), Personal = c(8, 
10, 6, 8, 6, 10, 4, 3, 3), Rates = c(9, 5, 9, 6, 9, 7, 10, 5, 
4), `Staffing Model` = c(6, 8, 5, 5, 7, 5, 8, 7, 8), `Total Cost` = c(5, 
4, 3, 7, 8, 6, 9, 4, 6)), row.names = c(NA, -9L), class = c("tbl_df", 
"tbl", "data.frame"))

如果数字排名不能用于我计算关系（correlations）的解决方案，请指正。

希望它们可以被使用，我得出了以下单调的代码，我希望它计算每个方法选择与每个属性选择的相关矩阵。

library(psych)

dataframe2 <- psych::corr.test(dataframe[  , c(2, 9:17)])[[1]][1:10]  # the first method vs all attributes
dataframe3 <- psych::corr.test(dataframe[  , c(3, 9:17)])[[1]][1:10]  # the 2nd method vs all attributes and so on
dataframe4 <- psych::corr.test(dataframe[  , c(4, 9:17)])[[1]][1:10]  
dataframe5 <- psych::corr.test(dataframe[  , c(5, 9:17)])[[1]][1:10]
dataframe6 <- psych::corr.test(dataframe[  , c(6, 9:17)])[[1]][1:10]  
dataframe7 <- psych::corr.test(dataframe[  , c(7, 9:17)])[[1]][1:10]
dataframe8 <- psych::corr.test(dataframe[  , c(8, 9:17)])[[1]][1:10]

# create a dataframe from the rbinded rows
bind <- data.frame(rbind(dataframe2, dataframe3, dataframe4, dataframe5, dataframe6, dataframe7, dataframe8))

重命名行和列：

colnames(bind) <- c("Sel", colnames(dataframe[9:17]))
rownames(bind) <- colnames(dataframe[2:8])

如何更有效地完成上述任务？

顺便说一句，绑定数据框还允许使用 DataExplorer 包生成热图。

library(DataExplorer)

DataExplorer::plot_correlation(bind)

Answer 1

[总结]

在我们的讨论范围内，有两种获取相关数据的方法。

使用stats::cor，即cor(subset(dataframe, select = -Email))
使用psych::corr.test，即corr.test(subset(dataframe, select = -Email))[[1]]

然后您可以使用所需的行和列对相关矩阵进行子集化。

为了使用DataExplorer::plot_correlation，你可以简单地做plot_correlation(dataframe, type = "c")。注意：输出热图将包括所有列的相关性，因此您可以忽略不感兴趣的列。

[原答案]

## Create data
dataframe <- structure(
  list(
    Email = c("a", "b", "c", "d", "e", "f", "g", "h",  "i"),
    Ads = c(2, 1, 1, 1, 1, 2, 1, 1, 1),
    Alumni = c(3, 2, 2, 3, 2, 3, 2, 2, 2),
    Articles = c(6, 4, 3, 2, 3, 4, 3, 3, 3),
    Referrals = c(4, 3, 4, 8, 7, 8, 8, 6, 4),
    Speeches = c(7, 7, 6, 7, 4, 7, 4, 5, 5),
    Updates = c(8, 6, 6, 5, 5, 5, 5, 7, 6),
    Visits = c(5, 8, 7, 6, 6, 6, 6, 4, 8),
    `Business Savvy` = c(10, 6, 10, 10, 4, 4, 6, 8, 9),
    Communication = c(4, 3, 8, 3, 3, 9, 7, 6, 7),
    Experience = c(7, 7, 7, 9, 2, 8, 5, 9, 5),
    Innovation = c(2, 1, 4, 2, 1, 2, 2, 1, 1),
    Nearby = c(3, 2, 2, 1, 5, 3, 3, 2, 2),
    Personal = c(8, 10, 6, 8, 6, 10, 4, 3, 3),
    Rates = c(9, 5, 9, 6, 9, 7, 10, 5, 4),
    `Staffing Model` = c(6, 8, 5, 5, 7, 5, 8, 7, 8),
    `Total Cost` = c(5, 4, 3, 7, 8, 6, 9, 4, 6)
  ),
  row.names = c(NA, -9L),
  class = c("tbl_df", "tbl", "data.frame")
)

严格按照您的示例，我们可以执行以下操作：

## Calculate correlation
df2 <- subset(dataframe, select = -Email)
marketing_selections <- names(df2)[1:7]
attribute_selections <- names(df2)[8:16]
corr_matrix <- psych::corr.test(df2)[[1]]
bind <- subset(corr_matrix,
               subset = rownames(corr_matrix) %in% marketing_selections,
               select = attribute_selections)
DataExplorer::plot_correlation(bind)

警告

然而，这是你真正想要的吗？ psych::corr.test生成相关矩阵，DataExplorer::plot_correlation再次计算相关。就像是相关性的相关性。

用于分析对调查中不同排名问题的回答之间关系的代码

Code to analyze relationships between responses to different ranking questions on a survey

r

survey

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correlation