Clean up the environment

rm(list = ls())

Load necessary libraries for data reading, plotting, and statistical analysis

library(readxl)       # For reading Excel files
library(ggpubr)       # For creating publication-ready plots
library(dplyr)        # For data manipulation
library(ggh4x)        # For advanced faceting functions in ggplot2
library(rstatix)      # For statistical tests and adding significance markers
library(knitr)        # For displaying tibbles in a nice table format
library(kableExtra)   # For enhancing table formatting

Load data

# Load hormonal data
PP_hormones <- read_excel("Data/Data.xlsx", sheet = "PP_hormones")
Nec_hormones <- read_excel("Data/Data.xlsx", sheet = "Nec_hormones")

# Load pig information
PigInfo <- read_excel("Data/Data.xlsx", sheet = "PigInfo")

Data organization

PP.data <- PP_hormones %>% inner_join(PigInfo, by = "PigID") %>%
                             mutate(Day = "Day 11 or 12") %>%
                             select(Diet, Day, Cortisol, TimePoint)

Nec.data <- Nec_hormones %>% inner_join(PigInfo, by = "PigID") %>%
              mutate(Day = "Day 16") %>%
              mutate(TimePoint = "") %>%
              select(Diet, Day, Cortisol, TimePoint)
# Combine postprandial and necropsy data
All.data <- bind_rows(PP.data, Nec.data) %>% 
              mutate(Diet = factor(Diet, levels = c("ALAC", "WPI", "SF"))) %>% 
              mutate(TimePoint = factor(TimePoint, levels = c("baseline", "30 min", "60 min", "120 min")))

Plotting cortisol levels by diet and time point

Fig <- All.data %>% ggerrorplot(x = "TimePoint", y = "Cortisol", color = "Diet", 
                      palette = c("turquoise3","purple", "orange"),
                      na.rm = TRUE, xlab = "", 
                      ylab ="Cortisol (ug/L)",  add = "mean_se", position = position_dodge(0.6)) +
                    facet_nested(.~ Day , scales = "free", space = "free_x") + 
                    theme_classic() 

Fig

# Save the figure in pdf format
ggsave(plot=Fig, height=4, width=6, dpi=300, filename="Figure 6/Fig6C.pdf", useDingbats=FALSE)

Group difference evaluation

All.data %>% group_by(TimePoint) %>%
               wilcox_test(Cortisol ~ Diet, p.adjust.method = "none", paired = FALSE) %>% # Perform Wilcoxon test (Mann-Whitney U test)
               select(-p.adj) %>%
               add_significance(cutpoints = c(0, 0.05, 0.1,  1), symbols = c("*", "#", "ns")) %>%
               select(TimePoint, group1, group2, p, p.signif) %>%
               kable() %>% # Print the tibble in a nicely formatted table
               kable_styling(bootstrap_options = c("striped", "hover")) # Enhance table formatting with kableExtra
TimePoint group1 group2 p p.signif
baseline ALAC WPI 0.970 ns
30 min ALAC WPI 0.093 #
60 min ALAC WPI 0.009
120 min ALAC WPI 0.465 ns
NA ALAC WPI 0.089 #
NA ALAC SF 0.879 ns
NA WPI SF 0.049 
sessionInfo()
## R version 4.2.2 (2022-10-31)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] kableExtra_1.3.4 knitr_1.45       rstatix_0.7.2    ggh4x_0.2.6     
## [5] dplyr_1.1.3      ggpubr_0.6.0     ggplot2_3.5.1    readxl_1.4.3    
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0  xfun_0.40         bslib_0.5.1       purrr_1.0.2      
##  [5] carData_3.0-5     colorspace_2.1-0  vctrs_0.6.5       generics_0.1.3   
##  [9] viridisLite_0.4.2 htmltools_0.5.6.1 yaml_2.3.7        utf8_1.2.4       
## [13] rlang_1.1.2       jquerylib_0.1.4   pillar_1.9.0      glue_1.6.2       
## [17] withr_3.0.1       lifecycle_1.0.4   stringr_1.5.1     munsell_0.5.1    
## [21] ggsignif_0.6.4    gtable_0.3.5      cellranger_1.1.0  ragg_1.2.6       
## [25] rvest_1.0.3       evaluate_1.0.1    labeling_0.4.3    fastmap_1.1.1    
## [29] fansi_1.0.6       highr_0.10        broom_1.0.5       scales_1.3.0     
## [33] backports_1.4.1   cachem_1.0.8      webshot_0.5.5     jsonlite_1.8.8   
## [37] abind_1.4-5       farver_2.1.1      systemfonts_1.0.5 textshaping_0.3.7
## [41] digest_0.6.33     stringi_1.7.12    grid_4.2.2        cli_3.6.2        
## [45] tools_4.2.2       magrittr_2.0.3    sass_0.4.7        tibble_3.2.1     
## [49] tidyr_1.3.0       car_3.1-2         pkgconfig_2.0.3   xml2_1.3.5       
## [53] rmarkdown_2.28    svglite_2.1.2     httr_1.4.7        rstudioapi_0.15.0
## [57] R6_2.5.1          compiler_4.2.2