ggplot中的李克特堆积条形图与前后测试

Question

我是 R 的新手和老师，所以感谢您的耐心等待。我在李克特堆积条形图上搜索了许多其他问题（this one is close，但不完全是我正在努力解决的问题）。我似乎找不到一个讨论如何将调查前和后测试的结果拉到同一个堆叠条形图中的内容。我已经阅读了 Hayley 的 R for Data Science 一书、GitHub 上的示例、R Companion Handbook 和 R Cookbook。作为初学者，仍然确实需要一些帮助。

我有一组 12 个学生问题，每个问题都有一个测试前和测试后的回答，等级为“非常同意”到“非常不同意”。

我的问题是：学生调查问卷的回答在考试前后有何变化？

我的数据原来显示为：

Student sex(F=0,M=1)  PreTestQ1   PostTestQ1
1       0              Agree      Disagree
2       0              Disagree   Agree
3       1              Agree      Agree
4       1              Disagree   Agree

首先，我将同意/不同意转换为数字数据（非常同意 = 1，非常不同意 = 4，没有中性选项），并使用以下方法从宽到长整理数据：

    # Set data frame as wide
msse_wide <- read_xls("ProcessDataMSSE.xls")
colnames(msse_wide) # Displays names of columns
head(msse_wide)


# Set data frame as long, after running wide code above
msse_long <- msse_wide %>%
  gather(question,obs_prepost, c(2:25)) # This pulls the columns from 2 to 25 (not including the "sex" column), test it out first as a precaution

# NOW MY DATA IS TIDY!!!! :)

我得到了：

    > msse_long
# A tibble: 1,824 x 3
     sex question obs_prepost
   <dbl> <chr>          <dbl>
 1     0 1Pre               3
 2     0 1Pre               3
 3     0 1Pre               2
 4     0 1Pre               3
 5     0 1Pre               2
 6     0 1Pre               3
 7     0 1Pre               3
 8     0 1Pre               2
 9     0 1Pre               2
10     0 1Pre               4
# … with 1,814 more rows

现在我想将“强烈同意 --- 强烈反对”回答的百分比可视化为堆叠条形图，使用百分比响应，并将测试前和测试后作为堆叠条形进行比较（因此，前后有 12 个问题，我将有 24 个堆叠条形图）。

最终目标类似于 R Companion 中的这个示例： Simple Stacked Bar Chart ...除了我被困在如何从我的数据中提取百分比，并在另一个之上比较前后测试。

Answer 1

像这样？：

数据：

msse_wide <- read.table(text='Student sex(F=0,M=1)  PreTestQ1   PostTestQ1
                              1       0              Agree      Disagree
                              2       0              Disagree   Agree
                              3       1              Agree      Agree
                              4       1              Disagree   Agree',
                        header=TRUE,
                        stringsAsFactors=FALSE)

使用dplyr、tidyr、ggplot2 和scales 的建议解决方案：

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
msse_wide %>% 
  pivot_longer(cols = -c(Student, sex.F.0.M.1.),
               names_to = "Test") %>% 
  group_by(Test, value) %>% 
  summarise(N = n()) %>%
  mutate(Pct = N / sum(N)) %>% 
  ggplot(aes(Test, Pct, fill = value)) +
    geom_bar(stat="identity") +
    scale_y_continuous(labels = percent)

编辑：

感谢@dc37 的评论：

添加

+ coord_flip()

上面的代码给出：

解释：

从 wide 形式的数据开始，我们使用 pivot_longer ，tidyrs 的后继 gather 来获得所需的结构。

然后我们按Test 和value（个人答案水平）分组，并使用 dplyr 的 n 函数通过统计每组中的案例进行总结。

然后我们改变（在这种情况下，创建）一个列，我们将每个 Test - value 组合的计数除以每个 Test 组的总和 (dplyr 现在只按第一组分组，Test)

最后我们使用ggplot2 绘制数据，scales 标记百分比轴。

Answer 2

我有一些似乎有效的方法，只是预处理和后处理出了问题。这是在基础 R 中。感谢您的帮助！

if(!require(psych)){install.packages("psych")}
if(!require(likert)){install.packages("likert")}
library(readxl)
setwd("MSSE 507 Capstone Data Analysis/")
read_xls("ProcessDataMSSE.xls")


Data = read_xls("ProcessDataMSSE.xls")

str(Data) # tbl_df, tbl, and data.frame classes

### Change Likert scores to factor and specify levels; factors because numeric values are ordinal

Data <- Data[, c(3:26)] # Get rid of the other columns! (Drop multiple columns) 

Data$`1Pre` <- factor(Data$`1Pre`,
                   levels = c("1", "2", "3", "4"),
                   ordered = TRUE)

Data$`1Post` = factor(Data$`1Post`,
                     levels = c("1", "2", "3", "4"),
                     ordered = TRUE)

Data$`2Pre` <- factor(Data$`2Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`2Post` = factor(Data$`2Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`3Pre` <- factor(Data$`3Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`3Post` = factor(Data$`3Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`4Pre` <- factor(Data$`4Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`4Post` = factor(Data$`4Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`5Pre` <- factor(Data$`5Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`5Post` = factor(Data$`5Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`6Pre` <- factor(Data$`6Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`6Post` = factor(Data$`6Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`7Pre` <- factor(Data$`7Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`7Post` = factor(Data$`7Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`8Pre` <- factor(Data$`8Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`8Post` = factor(Data$`8Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`9Pre` <- factor(Data$`9Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`9Post` = factor(Data$`9Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`10Pre` <- factor(Data$`10Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`10Post` = factor(Data$`10Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`11Pre` <- factor(Data$`11Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`11Post` = factor(Data$`11Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`12Pre` <- factor(Data$`12Pre`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data$`12Post` = factor(Data$`12Post`,
                      levels = c("1", "2", "3", "4"),
                      ordered = TRUE)

Data <- factor(Data,levels=Data[3:26])
Data
### Double check the data frame

library(psych) # Loads psych package

headTail(Data) # Displays last few and first few data

str(Data) # Shows structure of an object (observations and variables, etc.) - in this case, ordinal factors with 4 levels (1 through 4)

summary(Data) # Summary of the number of times you see a data point

Data$`1Pre` # This allows us to check how many data points are really there

str(Data)
### Remove unnecessary objects, removing the data frame in this case (we've converted that data frame into a table with the read.table function above)

library(likert)

Data <- as.data.frame(Data) # Makes the tibble a data frame

likert(Data) # This will give the percentage responses for each level and group

Result = likert(Data)

summary(Result) # This will give the mean and SD 


plot(Result,
     main = "Pre and Post Treatment Percentage Responses",
     ylab="Questions",
     type="bar")