Data organization
Fig <- PP.targeted.serum %>% inner_join(PigInfo, by = "PigID") %>%
select(PigID, Diet, TimePoint, Tryptophan) %>%
group_by(PigID) %>%
filter(n() == 4) %>% #Select those piglets with all 4 timepoints
ungroup() %>%
mutate(TimePoint = factor(TimePoint, levels = c("baseline", "30 min", "60 min", "120 min"))) %>%
mutate(Diet = factor(Diet, levels = c("ALAC", "WPI"))) %>%
mutate(PigID = factor(PigID)) %>%
ggplot(aes(x = TimePoint, y = Tryptophan, group = PigID, color = Diet)) +
geom_point(size = 1, position = position_dodge(width = 0.2)) +
geom_line(linewidth = 0.7, position = position_dodge(width = 0.2)) +
scale_color_manual(values = c("turquoise3", "purple")) +
labs(y = "Tryptophan (uM)", x = "") +
theme_classic2()
Fig

# for identifying pig ID
PP.targeted.serum %>% inner_join(PigInfo, by = "PigID") %>%
select(PigID, Diet, TimePoint, Tryptophan) %>%
group_by(PigID) %>%
filter(n() == 4) %>% #Select those piglets with all 4 timepoints
ungroup() %>%
mutate(TimePoint = factor(TimePoint, levels = c("baseline", "30 min", "60 min", "120 min"))) %>%
mutate(Diet = factor(Diet, levels = c("ALAC", "WPI"))) %>%
mutate(PigID = factor(PigID)) %>%
ggplot(aes(x = TimePoint, y = Tryptophan, group = PigID, color = PigID)) +
geom_point(size = 1, position = position_dodge(width = 0.2)) +
geom_line(linewidth = 0.7, position = position_dodge(width = 0.2)) +
labs(y = "Tryptophan (uM)", x = "") +
theme_classic2()

# Save the figure in pdf format:
ggsave(plot=Fig, height=4, width=5.5, dpi=300, filename="SI Figure 5/SI.Fig5.pdf", useDingbats=FALSE)
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] dplyr_1.1.3 ggpubr_0.6.0 ggplot2_3.5.1 readxl_1.4.3
##
## loaded via a namespace (and not attached):
## [1] highr_0.10 cellranger_1.1.0 bslib_0.5.1 compiler_4.2.2
## [5] pillar_1.9.0 jquerylib_0.1.4 tools_4.2.2 digest_0.6.33
## [9] jsonlite_1.8.8 evaluate_1.0.1 lifecycle_1.0.4 tibble_3.2.1
## [13] gtable_0.3.5 pkgconfig_2.0.3 rlang_1.1.2 cli_3.6.2
## [17] rstudioapi_0.15.0 yaml_2.3.7 xfun_0.40 fastmap_1.1.1
## [21] withr_3.0.1 knitr_1.45 systemfonts_1.0.5 generics_0.1.3
## [25] sass_0.4.7 vctrs_0.6.5 grid_4.2.2 tidyselect_1.2.0
## [29] glue_1.6.2 R6_2.5.1 textshaping_0.3.7 rstatix_0.7.2
## [33] fansi_1.0.6 rmarkdown_2.28 carData_3.0-5 farver_2.1.1
## [37] car_3.1-2 tidyr_1.3.0 purrr_1.0.2 magrittr_2.0.3
## [41] backports_1.4.1 scales_1.3.0 htmltools_0.5.6.1 abind_1.4-5
## [45] colorspace_2.1-0 ggsignif_0.6.4 ragg_1.2.6 labeling_0.4.3
## [49] utf8_1.2.4 munsell_0.5.1 broom_1.0.5 cachem_1.0.8