The antidiabetic properties and effects of selected food extracts (bitter kola, bitter leaf, ginger & okro) on STZ-induced Sprague-Dawley rats on a high-fat diet
Luiz
2024-07-23
Load the packages
library(ggplot2)
library(reshape2)
library(dplyr)
library(car)
library(tidyr)
library(ggpubr)Load the data
## ID Treatment W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 F1
## 1 Head Distilled_water 228 234 244 252 264 276 280 308 286 302 300 302 8.6
## 2 Neck Distilled_water 244 240 242 256 292 276 276 280 274 278 280 274 35.0
## 3 Back Distilled_water 284 286 292 306 318 322 324 330 346 350 345 342 9.1
## 4 Tail Distilled_water 290 306 318 322 322 330 334 346 344 344 354 354 8.2
## 5 No Mark Distilled_water 256 268 278 290 292 300 302 310 318 322 326 324 8.3
## 6 Head Bitter_ Kola 272 294 300 310 320 328 260 270 264 278 278 274 7.9
## F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 Lee BMI
## 1 9.0 6.4 6.6 7.2 7.3 7.0 7.3 7.5 7.0 8.1 6.5 319 0.68
## 2 23.4 25.9 22.6 23.4 26.1 24.5 23.8 26.9 28.2 28.0 22.1 325 0.69
## 3 9.4 6.7 6.7 7.3 7.4 8.8 6.5 8.2 7.9 8.0 5.8 325 0.74
## 4 6.9 7.1 7.0 7.2 8.2 8.2 8.4 8.4 7.2 7.9 5.6 322 0.73
## 5 7.4 9.2 6.8 6.7 6.8 7.2 6.8 8.0 6.9 7.5 5.9 327 0.73
## 6 7.8 8.0 6.7 7.8 7.3 6.7 6.5 6.2 6.3 6.0 8.1 300 0.45Visualize weight distribution for treatments in weeks
# Define the shape palette for different treatments
shape_palette <- c(16, 17, 18, 19, 15, 0, 1, 2, 5, 6)
# Define the weeks columns to plot
weight_columns <- colnames(extract_data)[grepl("^W\\d+$", colnames(extract_data))]
# Initialize an empty list to store the plots
plots <- list()
# Loop through each week column and create plots
for (weight in weight_columns) {
plot <- ggplot(extract_data, aes_string(x = "ID", y = weight, col = "Treatment", shape = "Treatment", fill = "Treatment")) +
geom_point(size = 2) +
scale_shape_manual(values = shape_palette) +
theme_minimal() +
labs(title = paste("Weight in", weight), x = "ID", y = "Weight") +
theme(axis.text.x = element_text(angle = 40, hjust = 1)) # Optional: rotate x-axis labels for better readability
# Add the plot to the list
plots[[weight]] <- plot
}## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.print(plots)## $W1## Warning: Removed 1 row containing missing values or values outside the scale range
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## $W2## Warning: Removed 1 row containing missing values or values outside the scale range
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## $W4## Warning: Removed 3 rows containing missing values or values outside the scale range
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## $W5## Warning: Removed 3 rows containing missing values or values outside the scale range
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## $W7## Warning: Removed 5 rows containing missing values or values outside the scale range
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## $W8## Warning: Removed 5 rows containing missing values or values outside the scale range
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## $W9## Warning: Removed 5 rows containing missing values or values outside the scale range
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## $W10## Warning: Removed 5 rows containing missing values or values outside the scale range
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## $W11## Warning: Removed 5 rows containing missing values or values outside the scale range
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## $W12## Warning: Removed 5 rows containing missing values or values outside the scale range
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#boxplot
# Reshape the data for plotting
long_data <- melt(extract_data[, 1:14], id.vars = c("ID", "Treatment"), variable.name = "Week", value.name = "Weight")
# Calculate the median weight for each combination of ID and Treatment
median_weights <- long_data %>%
group_by(ID, Treatment) %>%
summarise(median_weight = median(Weight, na.rm = TRUE)) %>%
ungroup()## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.# Add the median_weight to the original dataframe
long_data <- long_data %>%
left_join(median_weights, by = c("ID", "Treatment"))
# Ensure unique treatment levels within each ID for correct factor reordering
long_data <- long_data %>%
group_by(ID) %>%
mutate(Treatment = factor(Treatment, levels = unique(Treatment[order(median_weight)]))) %>%
ungroup()
# Create the boxplot
ggplot(long_data, aes(x = ID, y = Weight, fill = Treatment)) +
geom_boxplot() +
scale_fill_hue(direction = 1) +
theme_minimal() +
labs(title = "Weight Distribution by Treatment", x = "ID", y = "Weight") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))## Warning: Removed 43 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
Calculate average weight across 12 weeks
# Calculate the mean weight across the weeks for each row
extract_data <- extract_data %>%
rowwise() %>%
mutate(mean_weight = mean(c_across(W1:W7), na.rm = TRUE))
# View the dataframe with the mean weight column added
head(extract_data)## # A tibble: 6 × 29
## # Rowwise:
## ID Treatment W1 W2 W3 W4 W5 W6 W7 W8 W9 W10
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 Head Distilled… 228 234 244 252 264 276 280 308 286 302
## 2 Neck Distilled… 244 240 242 256 292 276 276 280 274 278
## 3 Back Distilled… 284 286 292 306 318 322 324 330 346 350
## 4 Tail Distilled… 290 306 318 322 322 330 334 346 344 344
## 5 No Mark Distilled… 256 268 278 290 292 300 302 310 318 322
## 6 Head Bitter_ K… 272 294 300 310 320 328 260 270 264 278
## # ℹ 17 more variables: W11 <int>, W12 <int>, F1 <dbl>, F2 <dbl>, F3 <dbl>,
## # F4 <dbl>, F5 <dbl>, F6 <dbl>, F7 <dbl>, F8 <dbl>, F9 <dbl>, F10 <dbl>,
## # F11 <dbl>, F12 <dbl>, Lee <int>, BMI <dbl>, mean_weight <dbl>shape_palette <- c(16, 17, 18, 19, 15, 0, 1, 2, 5, 6)
# Plot
ggplot(extract_data, aes(x = ID, y = mean_weight, col = Treatment, shape = Treatment, fill = Treatment)) +
geom_point(size = 3) +
scale_shape_manual(values = shape_palette)+
theme_minimal() +
labs(title = "Mean Weight Distribution in across the weeks by Treatment", x = "ID", y = "Mean Weight")+
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
Weight Changes Over Time by Treatment
# Reshape the data to long format
long_data <- gather(extract_data, key = "Week", value = "Weight", W1:W12)
# Ensure Week is treated as a factor and ordered correctly
long_data$Week <- factor(long_data$Week, levels = paste0("W", 1:12))
# Define the shape palette
shape_palette <- c(16, 17, 18, 19, 15, 0, 1, 2, 5, 6)
# Get unique IDs
unique_ids <- unique(long_data$ID)
# Loop through each ID to generate and save separate plots
for (id in unique_ids) {
plot <- ggplot(long_data[long_data$ID == id, ], aes(x = Week, y = Weight, col = Treatment, group = interaction(ID, Treatment), shape = Treatment)) +
geom_point(size = 2) +
geom_line(linewidth = 0.4) +
scale_shape_manual(values = shape_palette) +
theme_minimal() +
labs(title = paste("Weight Changes Over Time by Treatment for", id), x = "Week", y = "Weight") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))
# Print the plot
print(plot)
}
## Warning: Removed 43 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 43 rows containing missing values or values outside the scale range
## (`geom_line()`).
FBS Changes Over Time by Treatment
# Reshape the data for plotting
FBS_long_data <- gather(extract_data, key = "Week", value = "FBS", F1:F12)
# Ensure Week is treated as a factor and ordered correctly
FBS_long_data$Week <- factor(FBS_long_data$Week, levels = paste0("F", 1:12))
# Define the shape palette
shape_palette <- c(16, 17, 18, 19, 15, 0, 1, 2, 5, 6)
# Get unique IDs
FBS_unique_ids <- unique(FBS_long_data$ID)
# Create the plot
ggplot(FBS_long_data) +
aes(x = Week, y = FBS, colour = Treatment, group = interaction(Treatment, ID)) +
geom_jitter(size = 1.5) +
geom_line(aes(group = interaction(Treatment, ID)), linewidth = 0.4) +
scale_color_hue(direction = 1) +
theme_minimal() +
labs(title = "FBS Changes Over Time by Treatment", x = "Week", y = "FBS") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
plot.title = element_text(size = 14, face = "bold", hjust = 0.5)) +
facet_wrap(vars(ID), scales = "free")## Warning: Removed 45 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 45 rows containing missing values or values outside the scale range
## (`geom_line()`).
# Loop through each ID to generate and save separate plots
for (id in FBS_unique_ids) {
plot <- ggplot(FBS_long_data[FBS_long_data$ID == id, ], aes(x = Week, y = FBS, col = Treatment, group = interaction(ID, Treatment), shape = Treatment)) +
geom_point(size = 2) +
geom_line(linewidth = 0.4) +
scale_shape_manual(values = shape_palette) +
theme_minimal() +
labs(title = paste("FBS Changes Over Time by Treatment for", id), x = "Week", y = "FBS") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))
# Print the plot
print(plot)
}
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 1 row containing missing values or values outside the scale range
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## Warning: Removed 44 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 44 rows containing missing values or values outside the scale range
## (`geom_line()`).