SignIn Survey Report

Asst. Prof. Inthraporn (Joop) Aranyanak, PhD

install.packages("readxl")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/User/AppData/Local/R/win-library/4.4’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/readxl_1.4.3.zip'
Content type 'application/zip' length 1202510 bytes (1.1 MB)
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The downloaded binary packages are in
    C:\Users\User\AppData\Local\Temp\RtmpgX9kRp\downloaded_packages

install.packages("openxlsx")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/User/AppData/Local/R/win-library/4.4’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/openxlsx_4.2.5.2.zip'
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The downloaded binary packages are in
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install.packages("ggplot2")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/User/AppData/Local/R/win-library/4.4’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/ggplot2_3.5.1.zip'
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The downloaded binary packages are in
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#install.packages("tidyverse")
install.packages("multcomp")

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https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/User/AppData/Local/R/win-library/4.4’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/multcomp_1.4-25.zip'
Content type 'application/zip' length 748772 bytes (731 KB)
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The downloaded binary packages are in
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install.packages("magick")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/User/AppData/Local/R/win-library/4.4’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/magick_2.8.3.zip'
Content type 'application/zip' length 24788347 bytes (23.6 MB)
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package ‘magick’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\User\AppData\Local\Temp\RtmpgX9kRp\downloaded_packages

Upload the file into the dataframe

library(readxl)
library(ggplot2)
library(tidyverse)
library(multcomp)
library(dplyr)
library(magick)

SignIn UI

data <- read_excel("C:\\Users\\User\\Desktop\\SignInSurvey.xlsx")
print(data)

summary(data)

       Id           Design1         Design2         Design3         Design4     
 Min.   : 1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:19.25   1st Qu.:4.000   1st Qu.:3.000   1st Qu.:4.000   1st Qu.:2.000  
 Median :37.50   Median :5.000   Median :4.000   Median :5.000   Median :4.000  
 Mean   :37.50   Mean   :4.932   Mean   :4.081   Mean   :4.838   Mean   :3.486  
 3rd Qu.:55.75   3rd Qu.:6.000   3rd Qu.:5.750   3rd Qu.:6.000   3rd Qu.:5.000  
 Max.   :74.00   Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  

# เปลี่ยนรูปแบบจาก wide เป็น narrow
data2 <- data %>%
  pivot_longer(cols = starts_with("Design"),
               names_to = "variable",      
               values_to = "value") %>%
  mutate(variable = as.factor(variable))
# print(data3)

#คำนวณ mean & SD เพื่อนำไปใช้ในการสร้าง Error Bar
summary_data <- data2 %>%
  group_by(variable) %>%
  summarize(
    mean_value = mean(value),
    se = sd(value) / sqrt(n()))

print(summary_data)

NA

#สร้างกราฟแท่งจาก Mean ของแต่ละแบบ พร้อมใส่ Error Bar

ggplot(summary_data, aes(x = variable, y = mean_value)) +
  geom_bar(stat = "identity", fill = "orange", width = 0.6) +
  geom_errorbar(aes(ymin = mean_value - se*1.96, ymax = mean_value + se*1.96), width = 0.2) +
  geom_text(aes(label = round(mean_value, 2), y = mean_value), vjust = -0.5, , hjust = -0.3, size = 3, color = "blue") +
  labs(title = "กราฟแสดงผลความพึงพอใจของแต่ละหน้าจอ SignIn", x = "รูปแบบหน้าจอ", y = "ระดับความพึงพอใจ")

# การวิเคราะห์ ANOVA เพื่อดูผลภาพรวมความต่างว่ามีนัยสำคัญหรือไม่
 anova_result <- aov(value ~ variable , data = data2)
 summary(anova_result)

             Df Sum Sq Mean Sq F value   Pr(>F)    
variable      3  103.2   34.39   11.78 2.64e-07 ***
Residuals   292  852.7    2.92                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

จากผลการวิเคราะห์ด้านบนสรุปได้ว่าค่า P Value< 0.05 จึงสรุปได้ว่าระดับความพึงพอใจของไอคอนเหล่านั้นมีความแตกต่างกันอย่างมีนัยสำคัญ

#แล้วมาเปรียบเทียบทีละคู่ว่าต่างกันอย่างมีนัยสำคัญหรือไม่
posthoc_result <- glht(anova_result, linfct = mcp(variable = "Tukey"))
 summary(posthoc_result)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = value ~ variable, data = data2)

Linear Hypotheses:
                       Estimate Std. Error t value Pr(>|t|)    
Design2 - Design1 == 0 -0.85135    0.28094  -3.030   0.0138 *  
Design3 - Design1 == 0 -0.09459    0.28094  -0.337   0.9868    
Design4 - Design1 == 0 -1.44595    0.28094  -5.147   <0.001 ***
Design3 - Design2 == 0  0.75676    0.28094   2.694   0.0371 *  
Design4 - Design2 == 0 -0.59459    0.28094  -2.116   0.1503    
Design4 - Design3 == 0 -1.35135    0.28094  -4.810   <0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

จากผลการวิเคราะห์ด้านบนที่นำแต่ละการออกแบบหน้าจอมาเปรียบเทียบกันจะเห็นได้ว่าการออกแบบหน้าจอลงชื่อเข้าใช้งานแบบที่ 1 กับแบบที่ 3 นั้นมีระดับผลความพึงพอใจเฉลี่ยไม่ต่างกันมากแล้วทั้งสองแบบมีค่าความแตกต่างทางสถิติที่ไม่ต่างกันอย่างมีนัยยะสำคัญ จึงสรุปได้ว่าเราสามารถใช้แบบใดแบบหนึ่งในการพัฒนาการออกแบบหน้าจอของระบบ