Plots of pre and post training values

Tyler Sagendorf

01 May, 2024

This article generates plots of post- and pre-training measures of body mass, body composition, and VO\(_2\)max (Fig. 1B, C; Extended Data Fig. 1A–C).

library(MotrpacRatTraining6moWAT) # plot_pre_post
library(MotrpacRatTraining6moWATData)
library(dplyr)
library(ggplot2)

save_plots <- dir.exists(paths = file.path("..", "..", "plots"))

x <- PHENO_WAT %>%
  filter(omics_analysis) %>%
  rename(group = timepoint) %>%
  mutate(age = "6M")

y <- rename(PHENO_WAT_STATS, group = timepoint)

Total Body Mass

# Body mass (measured on NMR day)
p_wt <- plot_pre_post(x, 
                      pre = "pre_weight", 
                      post = "post_weight",
                      stats = filter(y, response == "NMR Weight")) +
  facet_grid(~ sex) +
  labs(y = "Total Body Mass (g)")

p_wt

ggsave(file.path("..", "..", "plots", "pre_post_NMR_body_mass.pdf"), p_wt,
       height = 1.7, width = 2.2, family = "ArialMT")

NMR Body Fat

# NMR body fat
p_fat <- plot_pre_post(x, pre = "pre_fat", post = "post_fat",
                       stats = filter(y, response == "NMR Fat Mass")) +
  facet_grid(~ sex) +
  labs(y = "Body Fat (g)")

p_fat

ggsave(file.path("..", "..", "plots", "pre_post_NMR_fat.pdf"), p_fat,
       height = 1.7, width = 2.2, family = "ArialMT")

NMR % Fat

# NMR % fat
p_pct_fat <- plot_pre_post(x, pre = "pre_fat_pct", post = "post_fat_pct",
                           stats = filter(y, response == "NMR % Fat")) +
  facet_grid(~ sex) +
  labs(y = "% Body Fat")

p_pct_fat

ggsave(file.path("..", "..", "plots", "pre_post_NMR_pct_fat.pdf"), p_pct_fat,
       height = 1.7, width = 2.2, family = "ArialMT")

Relative VO\(_2\)max

# VO2max (mL/kg/min)
p_vo2 <- plot_pre_post(
  x, pre = "pre_vo2max_ml_kg_min",
  post = "post_vo2max_ml_kg_min",
  stats = filter(y, response == "VO2max (relative to body mass)") %>%
    slice(rep(1:n(), times = 2)) %>%
    mutate(sex = rep(c("Female", "Male"), each = 3))) +
  facet_grid(~ sex) +
  labs(y = latex2exp::TeX("VO$_2$max (mL/kg/min)"))

p_vo2

ggsave(file.path("..", "..", "plots", "pre_post_relative_VO2max.pdf"), p_vo2,
       height = 1.7, width = 2.2, family = "ArialMT")

VO\(_2\)max (relative to lean mass)

# VO2max (relative to lean mass)
p_vo2_lean <- plot_pre_post(
  x, pre = "pre_vo2max_ml_kg_lean_min",
  post = "post_vo2max_ml_kg_lean_min",
  stats = filter(y, response == "VO2max (relative to lean mass)")) +
  facet_grid(~ sex) +
  labs(y = latex2exp::TeX("VO$_2$max (mL/kg lean/min)")) +
  coord_cartesian(ylim = c(100, 165))

p_vo2_lean

ggsave(file.path("..", "..", "plots", "pre_post_lean_relative_VO2max.pdf"), p_vo2_lean,
       height = 1.7, width = 2.2, family = "ArialMT")