Data organization
Organ_weight <- inner_join(Organ, PigInfo, by = "PigID") %>%
mutate(Brain_perc = Brain_g/Nec_weight_kg/10) %>%
mutate(Liver_perc = Liver_g/Nec_weight_kg/10) %>%
mutate(Left_kidney_perc = Left_kidney_g/Nec_weight_kg/10) %>%
mutate(Right_kidney_perc = Right_kidney_g/Nec_weight_kg/10) %>%
mutate(Diet = factor(Diet, levels = c("ALAC", "WPI", "SF"))) %>%
select(Diet, SowID, Brain_g, Liver_g, Left_kidney_g, Right_kidney_g, Brain_perc, Liver_perc, Left_kidney_perc, Right_kidney_perc) %>%
pivot_longer(cols = c("Brain_g", "Liver_g", "Left_kidney_g", "Right_kidney_g", "Brain_perc", "Liver_perc", "Left_kidney_perc", "Right_kidney_perc"), names_to = "variable", values_to = "value") %>%
mutate(variable = factor(variable, levels = c("Brain_g", "Brain_perc", "Liver_g", "Liver_perc", "Left_kidney_g", "Left_kidney_perc", "Right_kidney_g", "Right_kidney_perc")))
# Compute sample sizes for each Diet
sample_sizes <- Organ_weight %>%
group_by(Diet, variable) %>%
summarise(n = n() / length(unique(variable)), .groups = 'drop')
Summary_stats <- Organ_weight %>%
group_by(Diet, variable) %>%
summarise(
Mean_SE = sprintf("%.2f ± %.2f", mean(value, na.rm = TRUE), sd(value, na.rm = TRUE) / sqrt(n())),
.groups = 'drop'
)
Summary_stats_wide <- Summary_stats %>% pivot_wider(names_from = Diet, values_from = Mean_SE)
# Modify column names to include sample sizes
names(Summary_stats_wide)[-1] <- paste(names(Summary_stats_wide)[-1],
"(n =",
sample_sizes$n[match(names(Summary_stats_wide)[-1], sample_sizes$Diet)],
")", sep = "")
Summary_stats_wide
## # A tibble: 8 × 4
## variable `ALAC(n =11)` `WPI(n =12)` `SF(n =6)`
## <fct> <chr> <chr> <chr>
## 1 Brain_g 40.18 ± 0.80 39.84 ± 1.07 43.63 ± 1.77
## 2 Brain_perc 1.46 ± 0.09 1.47 ± 0.06 0.92 ± 0.06
## 3 Liver_g 83.58 ± 5.31 75.08 ± 5.09 132.77 ± 5.90
## 4 Liver_perc 2.96 ± 0.11 2.71 ± 0.08 2.76 ± 0.08
## 5 Left_kidney_g 12.23 ± 1.05 13.78 ± 1.89 16.37 ± 0.88
## 6 Left_kidney_perc 0.43 ± 0.02 0.49 ± 0.05 0.34 ± 0.02
## 7 Right_kidney_g 11.88 ± 0.99 13.12 ± 1.57 15.92 ± 0.91
## 8 Right_kidney_perc 0.42 ± 0.02 0.47 ± 0.04 0.33 ± 0.02
# Conduct ANOVA and Tukey HSD for each value variable
TukeyHSD_results <- Organ_weight %>%
group_by(variable) %>%
tukey_hsd(value ~ Diet + SowID) %>%
filter(term == "Diet") %>%
add_significance(p.col = "p.adj",
cutpoints = c(0, 0.001, 0.01, 0.05, 1),
symbols = c("***", "**", "*", "ns")) %>%
select(variable, group1, group2, p.adj.signif) %>%
pivot_wider(names_from = c("group1", "group2"), names_sep = " vs ", values_from = "p.adj.signif")
left_join(Summary_stats_wide, TukeyHSD_results, by = "variable")
## # A tibble: 8 × 7
## variable `ALAC(n =11)` `WPI(n =12)` `SF(n =6)` `ALAC vs WPI` `ALAC vs SF`
## <fct> <chr> <chr> <chr> <chr> <chr>
## 1 Brain_g 40.18 ± 0.80 39.84 ± 1.07 43.63 ± 1… ns ns
## 2 Brain_perc 1.46 ± 0.09 1.47 ± 0.06 0.92 ± 0.… ns ***
## 3 Liver_g 83.58 ± 5.31 75.08 ± 5.09 132.77 ± … ns ***
## 4 Liver_perc 2.96 ± 0.11 2.71 ± 0.08 2.76 ± 0.… * ns
## 5 Left_kidney_g 12.23 ± 1.05 13.78 ± 1.89 16.37 ± 0… ns ns
## 6 Left_kidney_… 0.43 ± 0.02 0.49 ± 0.05 0.34 ± 0.… ns ns
## 7 Right_kidney… 11.88 ± 0.99 13.12 ± 1.57 15.92 ± 0… ns *
## 8 Right_kidney… 0.42 ± 0.02 0.47 ± 0.04 0.33 ± 0.… ns ns
## # ℹ 1 more variable: `WPI vs SF` <chr>
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] tidyr_1.3.0 rstatix_0.7.2 dplyr_1.1.3 readxl_1.4.3
##
## loaded via a namespace (and not attached):
## [1] rstudioapi_0.15.0 knitr_1.45 magrittr_2.0.3 tidyselect_1.2.0
## [5] R6_2.5.1 rlang_1.1.2 fastmap_1.1.1 carData_3.0-5
## [9] fansi_1.0.6 car_3.1-2 tools_4.2.2 broom_1.0.5
## [13] xfun_0.40 utf8_1.2.4 cli_3.6.2 withr_3.0.1
## [17] jquerylib_0.1.4 htmltools_0.5.6.1 abind_1.4-5 yaml_2.3.7
## [21] digest_0.6.33 tibble_3.2.1 lifecycle_1.0.4 purrr_1.0.2
## [25] sass_0.4.7 vctrs_0.6.5 glue_1.6.2 cachem_1.0.8
## [29] evaluate_1.0.1 rmarkdown_2.28 compiler_4.2.2 bslib_0.5.1
## [33] pillar_1.9.0 cellranger_1.1.0 backports_1.4.1 generics_0.1.3
## [37] jsonlite_1.8.8 pkgconfig_2.0.3