Comparing Quality between PVTs

Published

August 21, 2025

Code 
library(tidyverse)

quality <- read.csv("C:/Users/Alexis Means/Documents/Project/Nutrition Sampling/R code/FRESH/processed.data/4.24.25_cleaned_quality.csv")

totalquality <- read.csv("C:/Users/Alexis Means/Documents/Project/Nutrition Sampling/R code/FRESH/raw.data/totalquality.csv")

plant <- read.csv("C:/Users/Alexis Means/Documents/Project/Nutrition Sampling/R code/Cleaning/processed.data/plant.csv")

plant <- plant %>% 
  select(-Spp, -CommonName, -Duration) %>% 
  rename(Spp = Code) %>% 
  select(Spp, Family, Genus, FG_New, Status)

totalquality <- totalquality %>% 
  rename(UniqueID = ID) %>% 
  select(Date, Transect, UniqueID)

d <- quality %>% 
  left_join(y = totalquality,
          by = "UniqueID") %>% 
  select(Date, Transect, Sample_ID, PVT, Spp, Pheno, Part, Description, DP, DE) %>%
  distinct(Sample_ID, .keep_all = TRUE) %>% 
  left_join(y = plant, 
            by = "Spp") %>% 
  mutate(Date = mdy(Date)) %>% 
  mutate(Season = if_else(Date < ymd("2024-06-01"), "SP", "SU")) %>% 
  select(-Date) %>% 
  mutate(Description = paste0(Description, "_", Season)) 

d <- d %>%
filter(DE != "#VALUE!", DP != "#VALUE!")

d <- d %>% 
  mutate(
    DE = as.numeric(DE),
    DP = as.numeric(DP),
    PVT = as.factor(PVT),
    Season = as.factor(Season),
    Spp = as.factor(Spp),
    Pheno = as.factor(Pheno),
    Part = as.factor(Part)
  ) 

d <- d %>%
  mutate(
    Pheno = factor(Pheno, levels = c("N", "B", "FL", "FR", "M", "C"))
  )
Code 
summary_df <- d %>%
  group_by(PVT, Spp, Pheno, Part, Season) %>%
  summarise(
    DE_mean = mean(DE, na.rm = TRUE),
    DP_mean = mean(DP, na.rm = TRUE),
    n = n(),
    .groups = "drop"
  )

View(summary_df)
Code 
season_comp <- summary_df %>%
  pivot_wider(
    names_from = Season,
    values_from = c(DE_mean, DP_mean)
  ) %>%
  mutate(
    DE_diff = DE_mean_SU - DE_mean_SP,
    DP_diff = DP_mean_SU - DP_mean_SP
  )

View(season_comp)

#write.csv(season_comp, "C:/Users/Alexis Means/Documents/Project/Nutrition Sampling/R code/quality/processed.data/season_comparison.csv")
Code 
pvt_comp <- summary_df %>%
  pivot_wider(
    names_from = PVT,
    values_from = c(DE_mean, DP_mean)
  )

View(pvt_comp)

#write.csv(pvt_comp, "C:/Users/Alexis Means/Documents/Project/Nutrition Sampling/R code/quality/processed.data/pvt_comparison.csv")
Code 
brte_fl <- d %>%
  filter(Spp == "BRTE", Pheno == "FL") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
brte_long <- brte_fl %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(brte_long, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "BRTE DE and DP by PVT and Season (FL)",
       y = "Value", x = "PVT") +
  theme_minimal()

Code 
brte_m <- d %>%
  filter(Spp == "BRTE", Pheno == "M") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
brte_m <- brte_m %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(brte_m, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "BRTE DE and DP by PVT and Season (M)",
       y = "Value", x = "PVT") +
  theme_minimal()

Code 
brte_c <- d %>%
  filter(Spp == "BRTE", Pheno == "C") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
brte_c <- brte_c %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(brte_c, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "BRTE DE and DP by PVT and Season (C)",
       y = "Value", x = "PVT") +
  theme_minimal()

Code 
pssp6_n <- d %>%
  filter(Spp == "PSSP6", Pheno == "N") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
pssp6_n <- pssp6_n %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(pssp6_n, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "PSSP6 DE and DP by PVT and Season (N)",
       y = "Value", x = "PVT") +
  theme_minimal()

Code 
pssp6_fl <- d %>%
  filter(Spp == "PSSP6", Pheno == "FL") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
pssp6_fl <- pssp6_fl %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(pssp6_fl, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "PSSP6 DE and DP by PVT and Season (FL)",
       y = "Value", x = "PVT") +
  theme_minimal()

Code 
pssp6_m <- d %>%
  filter(Spp == "PSSP6", Pheno == "M") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
pssp6_m <- pssp6_m %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(pssp6_m, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "PSSP6 DE and DP by PVT and Season (M)",
       y = "Value", x = "PVT") +
  theme_minimal()

Code 
amme_b <- d %>%
  filter(Spp == "AMME", Pheno == "B") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
amme_b <- amme_b %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(amme_b, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "AMME DE and DP by PVT and Season (B)",
       y = "Value", x = "PVT") +
  theme_minimal()

Code 
acth7_fl<- d %>%
  filter(Spp == "ACTH7", Pheno == "FL") %>%
  mutate(
    Season = factor(Season, levels = c("SP", "SU")),
    PVT = as.factor(PVT)
  )

# Pivot DE and DP into long format
acth7_fl <- acth7_fl %>%
  pivot_longer(
    cols = c(DE, DP),
    names_to = "Metric",
    values_to = "Value"
  )

# Plot with facets
ggplot(acth7_fl, aes(x = PVT, y = Value, color = Season)) +
  geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) +
  facet_wrap(~ Metric, scales = "free_y") +    # separate panels for DE and DP
  labs(title = "ACTH7 DE and DP by PVT and Season (FL)",
       y = "Value", x = "PVT") +
  theme_minimal()