R Notebook

library(tidyverse)
library(readxl)

autumn <- read_excel("autumn.xlsx", na = "not answered")

glimpse(autumn)

Rows: 20
Columns: 18
$ `Participant ID`                  <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
$ `Dog Type`                        <chr> "service", "service", "service", "service", "service", "service", "serice", "companion", "companion", "companion", "companion", "…
$ Gender                            <chr> "M", "M", "F", "F", "F", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M"
$ `Age Range`                       <chr> "50-59", "20-29", "50-59", "40-49", "50-59", "70-79", "40-49", "40-49", NA, "70-79", "30-39", "70-79", "60-69", "60-69", "40-49",…
$ `Program Start- Fill in question` <chr> "Current participant", "44154", "N/A", "Jan 2020 done with AKC certs but still participating in task training", "N/A", "STILL", "…
$ `1P`                              <dbl> 4, 5, 5, 4, 3, 4, 4, 3, 4, 4, 5, 3, 2, 4, 5, 4, 3, 5, 4, 4
$ `2P`                              <dbl> 3, 5, 5, 4, 4, 4, 4, 3, 4, 4, 5, 3, 2, 4, 5, 4, 3, 5, 4, 4
$ `3P`                              <dbl> 5, 5, 5, 5, 4, 5, 5, 4, 4, 5, 4, 4, 5, 5, 5, 5, 3, 5, 5, 5
$ `4P`                              <dbl> 3, 5, 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 5, 4, 5, 4, 4, 5, 4, 5
$ `5P`                              <dbl> 4, 5, 4, 5, 4, 4, 5, 4, 5, 4, 5, 3, 5, 5, 5, 4, 4, 5, 4, 5
$ `6P`                              <dbl> 4, NA, 5, 4, 4, 4, 5, 4, 4, 5, 4, 4, 5, 5, 5, 4, 3, 5, 4, 4
$ `1C`                              <dbl> 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 2, 3, 3, 2, 3, 2, 3, 4
$ `2C`                              <dbl> 2, 4, 5, 3, 4, 3, 3, 2, 2, 4, 4, 1, 1, 3, 3, 2, 3, 1, 2, 3
$ `3C`                              <dbl> 3, 4, 5, 3, 4, 5, 4, 3, 2, 3, 4, 2, 5, 4, 3, 2, 3, 1, 3, 2
$ `4C`                              <dbl> 2, 3, 4, 3, 3, 4, 3, 2, 2, 3, 4, 1, 3, 3, 3, 1, 3, 2, 3, 3
$ `5C`                              <dbl> 2, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 1, 4, 4, 3, 3, 3, 2, 3, 2
$ `6C`                              <dbl> 3, 4, 4, 3, 3, 3, 3, 3, 2, 3, 4, 1, 1, 3, 3, 2, 3, 2, 3, 2
$ `DTB Rate`                        <dbl> 5, 1, 3, 1, 2, 1, 1, 2, 1, 3, 3, 1, 1, 3, 1, 5, 2, 1, 4, 5

autumn <- autumn %>% 
  rename("PreStressful" = "1P") %>% 
  rename("PostStressful" = "1C")

summary(autumn)

 Participant ID    Dog Type            Gender           Age Range         Program Start- Fill in question  PreStressful        2P             3P             4P      
 Min.   : 1.00   Length:20          Length:20          Length:20          Length:20                       Min.   :2.00   Min.   :2.00   Min.   :3.00   Min.   :2.00  
 1st Qu.: 5.75   Class :character   Class :character   Class :character   Class :character                1st Qu.:3.75   1st Qu.:3.75   1st Qu.:4.00   1st Qu.:4.00  
 Median :10.50   Mode  :character   Mode  :character   Mode  :character   Mode  :character                Median :4.00   Median :4.00   Median :5.00   Median :4.00  
 Mean   :10.50                                                                                            Mean   :3.95   Mean   :3.95   Mean   :4.65   Mean   :4.05  
 3rd Qu.:15.25                                                                                            3rd Qu.:4.25   3rd Qu.:4.25   3rd Qu.:5.00   3rd Qu.:4.25  
 Max.   :20.00                                                                                            Max.   :5.00   Max.   :5.00   Max.   :5.00   Max.   :5.00  
                                                                                                                                                                     
       5P             6P        PostStressful       2C             3C             4C             5C             6C          DTB Rate  
 Min.   :3.00   Min.   :3.000   Min.   :2     Min.   :1.00   Min.   :1.00   Min.   :1.00   Min.   :1.00   Min.   :1.00   Min.   :1.0  
 1st Qu.:4.00   1st Qu.:4.000   1st Qu.:3     1st Qu.:2.00   1st Qu.:2.75   1st Qu.:2.00   1st Qu.:2.75   1st Qu.:2.00   1st Qu.:1.0  
 Median :4.50   Median :4.000   Median :3     Median :3.00   Median :3.00   Median :3.00   Median :3.00   Median :3.00   Median :2.0  
 Mean   :4.45   Mean   :4.316   Mean   :3     Mean   :2.75   Mean   :3.25   Mean   :2.75   Mean   :3.25   Mean   :2.75   Mean   :2.3  
 3rd Qu.:5.00   3rd Qu.:5.000   3rd Qu.:3     3rd Qu.:3.25   3rd Qu.:4.00   3rd Qu.:3.00   3rd Qu.:4.00   3rd Qu.:3.00   3rd Qu.:3.0  
 Max.   :5.00   Max.   :5.000   Max.   :4     Max.   :5.00   Max.   :5.00   Max.   :4.00   Max.   :5.00   Max.   :4.00   Max.   :5.0  
                NA's   :1                                                                                                             

autumn %>% 
  pivot_longer(cols = c(PreStressful, PostStressful), names_to = "Time", values_to = "Rating") %>% 
  ggplot(aes(x = Time, y = Rating)) +
  geom_boxplot(width = .5) +
  geom_jitter(width = .1) +
  labs(title = "Prior to enrollment, did you experience memories, thoughts, or images that reminded you of previous stressful military situation",
       x = "Time")

mean(autumn$PreStressful)

[1] 3.95

mean(autumn$PostStressful)

[1] 3

t.test(autumn$PreStressful, autumn$PostStressful, paired = T)

    Paired t-test

data:  autumn$PreStressful and autumn$PostStressful
t = 5.1461, df = 19, p-value = 5.742e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.5636176 1.3363824
sample estimates:
mean of the differences 
                   0.95