.Rhistory

# Summary for Pre_Score and Post_score
summary(data[c("Pre_Score", "Post_score")])
# Using psych package for detailed stats
library(psych)
describe(data[c("Pre_Score", "Post_score")])
cross_tab <- table(data$Gender, data$Hospital_Unit)
print(cross_tab)
# Paired t-test
t_test <- t.test(data$Pre_Score, data$Post_score, paired = TRUE)
print(t_test)
# Mean improvement
mean_improvement <- mean(data$Post_score - data$Pre_Score)
print(mean_improvement)
# ANOVA for Post_score by Gender
anova_post <- aov(Post_score ~ Gender, data = data)
summary(anova_post)
# Summary table for scores
score_summary <- data.frame(
Statistic = c("Mean", "Median", "Standard Deviation"),
Pre_Score = c(mean(data$Pre_Score), median(data$Pre_Score), sd(data$Pre_Score)),
Post_Score = c(mean(data$Post_score), median(data$Post_score), sd(data$Post_score))
)
print(score_summary)
# Frequency table for Gender
gender_freq <- table(data$Gender)
gender_freq_df <- as.data.frame(gender_freq)
colnames(gender_freq_df) <- c("Gender", "Count")
print(gender_freq_df)
library(gt)
# Create a nicely formatted table for scores
gt(score_summary)
library(ggplot2)
ggplot(data, aes(x = factor(1), y = Pre_Score)) +
geom_boxplot(fill = "skyblue", alpha = 0.7) +
labs(title = "Pre-Training Scores", x = "", y = "Score") +
theme_minimal()
ggplot(data, aes(x = factor(1), y = Post_score)) +
geom_boxplot(fill = "lightgreen", alpha = 0.7) +
labs(title = "Post-Training Scores", x = "", y = "Score") +
theme_minimal()
ggplot(data, aes(x = Improvement)) +
geom_histogram(binwidth = 5, fill = "steelblue", alpha = 0.7) +
labs(title = "Improvement in Scores", x = "Improvement", y = "Frequency") +
theme_minimal()
#
#
library(gtsummary)
# Create a summary table for all variables
summary_table <- data %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{mean} ({sd})",  # Mean and standard deviation for numeric variables
all_categorical() ~ "{n} ({p}%)"    # Count and percentage for categorical variables
),
digits = all_continuous() ~ 1, # Set decimal places
label = list(
Designation_A ~ "Designation",
Gender ~ "Gender",
Age ~ "Age Group",
Years_Work_exp ~ "Work Experience",
Hospital_Unit ~ "Hospital Unit",
Years_Education ~ "Years of Education",
Desire_learn_ECG ~ "Desire to Learn ECG",
Previous_edcu_ECG ~ "Previous Education on ECG",
Pre_Score ~ "Pre-training Score",
Post_score ~ "Post-training Score"
)
) %>%
modify_header(label ~ "**Variable**") %>% # Change column header
bold_labels()
# Print the summary table
summary_table
# Load necessary libraries
library(dplyr)
library(gtsummary)
# Conduct paired t-test
paired_ttest <- t.test(data$Pre_Score, data$Post_score, paired = TRUE)
# Summarize t-test results
ttest_summary <- tibble(
Statistic = c("Mean Difference", "t-Statistic", "p-Value"),
Value = c(
mean(data$Post_score - data$Pre_Score),  # Mean difference
paired_ttest$statistic,                 # t-statistic
paired_ttest$p.value                    # p-value
)
)
# Convert summary into a gtsummary table
ttest_table <- ttest_summary %>%
gt::gt() %>%
gt::tab_header(
title = "Paired t-Test Results",
subtitle = "Comparison of Pre-Training and Post-Training Scores"
)
# Print the table
ttest_table
str(data)
# Load necessary libraries
library(dplyr)
# Initialize the table
summary_table <- data.frame(
Measure = c(
"Female", "Age (range)", "Undergraduate", "Overall score (range)",
"Rate (1)", "Rhythm (2,3,4,9)", "Axis (5)",
"Conduction abnormality (6)", "Clinical cases (7)",
"Spot diagnosis (8)", "STEMI territories (10)"
),
Before = c(
paste0(round(mean(data$Pre_Score), 1), " (", min(data$Pre_Score), "-", max(data$Pre_Score), ")"),
"55.5% (71/128)", "81.1% (107/132)", "4.2/10 (0-9)",
"55.6% (79/141)", "35.5% (200/564)", "58.2% (82/141)",
"29.8% (42/141)", "9.95% (14/141)", "51.1% (72/141)",
"75.2% (106/141)"
),
After = c(
paste0(round(mean(data$Post_score), 1), " (", min(data$Post_score), "-", max(data$Post_score), ")"),
"57.5% (73/127)", "78% (103/132)", "5.0/10 (0-9)",
"84.9% (118/139)", "41.4% (230/556)", "71.9% (100/139)",
"29.5% (41/139)", "17.3% (24/139)", "36.7% (51/139)",
"87.1% (121/139)"
),
`Odds Ratio (95% CI)` = c(
"-", "-", "-", "-",
"4.4 (2.5-7.8)", "1.3 (1.0-1.6)", "1.8 (1.1-3.0)",
"0.99 (0.6-1.6)", "1.9 (0.9-3.8)", "0.56 (0.3-0.9)",
"2.2 (1.2-4.1)"
),
`P value` = c(
"-", "-", "-", "-",
"<0.001", "0.04", "0.02",
"0.96", "0.08", "0.02", "0.01"
),
`Paired T-test (P value)` = c(
"-", "-", "-", "<0.001", "-", "-",
"-", "-", "-", "-", "-"
)
)
# View the table
print(summary_table)
names(data)
# Load necessary library
library(dplyr)
# Create the summary table
summary_table <- data.frame(
Measure = c(
"Gender (Female)",
"Age (range)",
"Years of Work Experience",
"Desire to Learn ECG",
"Previous ECG Education",
"Pre_Score (range)",
"Post_Score (range)"
),
Before = c(
paste0(round(sum(data$Gender == "Female") / nrow(data) * 100, 1), "% (",
sum(data$Gender == "Female"), "/", nrow(data), ")"),
paste0(mean(as.numeric(as.character(data$Age)), na.rm = TRUE), " (",
min(as.numeric(as.character(data$Age)), na.rm = TRUE), "-",
max(as.numeric(as.character(data$Age)), na.rm = TRUE), ")"),
paste0(round(mean(as.numeric(as.character(data$Years_Work_exp)), na.rm = TRUE), 1), " years"),
paste0(round(sum(data$Desire_learn_ECG == "Yes") / nrow(data) * 100, 1), "% (",
sum(data$Desire_learn_ECG == "Yes"), "/", nrow(data), ")"),
paste0(round(sum(data$Previous_edcu_ECG == "Yes") / nrow(data) * 100, 1), "% (",
sum(data$Previous_edcu_ECG == "Yes"), "/", nrow(data), ")"),
paste0(mean(data$Pre_Score, na.rm = TRUE), " (", min(data$Pre_Score, na.rm = TRUE), "-",
max(data$Pre_Score, na.rm = TRUE), ")"),
"-"
),
After = c(
paste0(round(sum(data$Gender == "Female") / nrow(data) * 100, 1), "% (",
sum(data$Gender == "Female"), "/", nrow(data), ")"),
paste0(mean(as.numeric(as.character(data$Age)), na.rm = TRUE), " (",
min(as.numeric(as.character(data$Age)), na.rm = TRUE), "-",
max(as.numeric(as.character(data$Age)), na.rm = TRUE), ")"),
paste0(round(mean(as.numeric(as.character(data$Years_Work_exp)), na.rm = TRUE), 1), " years"),
paste0(round(sum(data$Desire_learn_ECG == "Yes") / nrow(data) * 100, 1), "% (",
sum(data$Desire_learn_ECG == "Yes"), "/", nrow(data), ")"),
paste0(round(sum(data$Previous_edcu_ECG == "Yes") / nrow(data) * 100, 1), "% (",
sum(data$Previous_edcu_ECG == "Yes"), "/", nrow(data), ")"),
"-",
paste0(mean(data$Post_score, na.rm = TRUE), " (", min(data$Post_score, na.rm = TRUE), "-",
max(data$Post_score, na.rm = TRUE), ")")
),
`Odds Ratio (95% CI)` = c(
"-", "-", "-", "-", "-", "-", "-"
),
`P value` = c(
"-", "-", "-", "-", "-", "<0.001", "<0.001"
),
`Paired T-test (P value)` = c(
"-", "-", "-", "-", "-", "<0.001", "<0.001"
)
)
# Print the table
print(summary_table)
# Save the table to a CSV file (optional)
write.csv(summary_table, "summary_table.csv", row.names = FALSE)
View(data)
View(data)
# Remove the first column
df <- df[, -1]
# Remove the first column
data <- data[, -1]
names(data)
# Paired t-test
t.test(data$Pre_Score, data$Post_score, paired = TRUE)
# Wilcoxon test for Pre_Score by Designation_A
wilcox.test(Pre_Score ~ Designation_A, data = data)
# Wilcoxon test for Post_score by Designation_A
wilcox.test(Post_score ~ Designation_A, data = data)
library(ggplot2)
# Reshape data for ggplot
library(tidyr)
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Boxplot comparison
ggplot(long_data, aes(x = Score_Type, y = Score, fill = as.factor(Designation_A))) +
geom_boxplot() +
labs(fill = "Designation_A", x = "Score Type", y = "Score") +
theme_minimal()
# Load required libraries
library(ggplot2)
library(tidyr)
library(ggpubr)
# Reshape data for ggplot
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Boxplot with P-values
ggplot(long_data, aes(x = Score_Type, y = Score, fill = as.factor(Designation_A))) +
geom_boxplot() +
stat_compare_means(aes(group = Designation_A), method = "t.test") + # Adds p-values
labs(
fill = "Designation_A",
x = "Score Type",
y = "Score",
title = "Comparison of Pre-Score and Post-Score by Designation"
) +
theme_minimal()
# Create boxplots for each grouping variable
grouping_vars <- c("Gender", "Age", "Years_Work_exp", "Hospital_Unit", "Years_Education")
for (var in grouping_vars) {
# Dynamic ggplot code for each variable
p <- ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(aes_string(group = var), method = "t.test") + # Adds p-values
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste("Comparison of Pre-Score and Post-Score by", var)
) +
theme_minimal()
# Print each plot
print(p)
}
# Load required libraries
library(ggplot2)
library(tidyr)
library(ggpubr)
library(patchwork) # For combining plots
# Reshape data for ggplot
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Define grouping variables
grouping_vars <- c("Gender", "Age", "Years_Work_exp", "Hospital_Unit", "Years_Education")
# Initialize an empty plot object
combined_plot <- NULL
# Loop through each grouping variable and create plots
for (var in grouping_vars) {
p <- ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(aes_string(group = var), method = "t.test") + # Adds p-values
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste("Comparison by", var)
) +
theme_minimal()
# Add each plot to combined_plot
combined_plot <- if (is.null(combined_plot)) p else combined_plot + p
}
# Combine all plots into a grid
combined_plot <- combined_plot + plot_layout(ncol = 2) # Arrange in 2 columns
print(combined_plot)
library(cowplot)
# Create list of plots
plot_list <- lapply(grouping_vars, function(var) {
ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(aes_string(group = var), method = "t.test") +
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste("Comparison by", var)
) +
theme_minimal()
})
# Combine plots in a single frame
combined_plot <- plot_grid(plotlist = plot_list, ncol = 2)
print(combined_plot)
# Load required libraries
library(ggplot2)
library(tidyr)
library(ggpubr)
library(patchwork) # For combining plots
# Reshape data for ggplot
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Define grouping variables
grouping_vars <- c("Gender", "Age", "Years_Work_exp", "Hospital_Unit", "Years_Education")
# Initialize an empty plot object
combined_plot <- NULL
# Loop through each grouping variable and create plots
for (var in grouping_vars) {
p <- ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(
aes_string(group = var),
method = "t.test",
label.y = max(long_data$Score) + 5 # Position p-values above the boxes
) +
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste("Comparison by", var)
) +
theme_minimal()
# Add each plot to combined_plot
combined_plot <- if (is.null(combined_plot)) p else combined_plot + p
}
# Combine all plots into a grid
combined_plot <- combined_plot + plot_layout(ncol = 2) # Arrange in 2 columns
print(combined_plot)
# Combine all plots into a grid
combined_plot <- combined_plot + plot_layout(ncol = 2) # Arrange in 2 columns
print(combined_plot)
stat_compare_means(
aes_string(group = var),
method = "t.test",
label.y = 100 # Set a fixed y-coordinate
)
# Load required libraries
library(ggplot2)
library(tidyr)
library(ggpubr)
library(patchwork) # For combining plots
# Reshape data for ggplot
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Define grouping variables
grouping_vars <- c("Gender", "Age", "Years_Work_exp", "Hospital_Unit", "Years_Education")
# Initialize an empty plot object
combined_plot <- NULL
# Loop through each grouping variable and create plots
for (var in grouping_vars) {
p <- ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(
aes_string(group = var),
method = "t.test",
label.y = min(long_data$Score) - 10 # Position p-values below the boxplot
) +
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste("Comparison by", var)
) +
theme_minimal() +
theme(plot.margin = margin(20, 20, 40, 20)) # Add space below the plot
# Add each plot to combined_plot
combined_plot <- if (is.null(combined_plot)) p else combined_plot + p
}
# Combine all plots into a grid
combined_plot <- combined_plot + plot_layout(ncol = 2) # Arrange in 2 columns
print(combined_plot)
# Reshape data for ggplot
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Define grouping variables
grouping_vars <- c("Gender", "Age", "Years_Work_exp", "Hospital_Unit", "Years_Education")
# Initialize an empty plot object
combined_plot <- NULL
# Loop through each grouping variable and create plots
for (var in grouping_vars) {
p <- ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(
aes_string(group = var),
method = "wilcox.test", # Non-parametric Wilcoxon test
label.y.npc = "bottom",
vjust = -1.5 # Position p-values below the graph
) +
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste("Non-Parametric Comparison by", var)
) +
theme_minimal() +
theme(plot.margin = unit(c(1, 1, 2, 1), "cm")) # Add extra bottom margin for space
combined_plot <- if (is.null(combined_plot)) p else combined_plot + p
}
# Combine all plots into a grid
combined_plot <- combined_plot + plot_layout(ncol = 2) # Arrange in 2 columns
print(combined_plot)
# Reshape data for ggplot
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Define grouping variables
grouping_vars <- c("Gender", "Age", "Years_Work_exp", "Hospital_Unit", "Years_Education")
# Loop through each grouping variable and create separate plots
for (var in grouping_vars) {
# Generate individual plot
p <- ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(
aes_string(group = var),
method = "wilcox.test", # Non-parametric test
label.y.npc = "bottom",
vjust = -1.5 # Position p-values below the graph
) +
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste("Non-Parametric Comparison by", var)
) +
theme_minimal() +
theme(plot.margin = unit(c(1, 1, 2, 1), "cm")) # Extra space for p-values
# Print the plot for the current variable
print(p)
}
# Reshape data for ggplot
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Define grouping variables
grouping_vars <- c("Gender", "Age", "Years_Work_exp", "Hospital_Unit", "Years_Education")
# Loop through each grouping variable and create separate plots
for (var in grouping_vars) {
# Generate individual plot
p <- ggplot(long_data, aes_string(x = "Score_Type", y = "Score", fill = var)) +
geom_boxplot() +
stat_compare_means(
aes_string(group = var),
method = "wilcox.test", # Non-parametric test
label.y.npc = "bottom",
vjust = -1.5 # Position p-values below the graph
) +
labs(
fill = var,
x = "Score Type",
y = "Score",
title = paste(" Comparison of Knowledge score  by", var)
) +
theme_minimal() +
theme(plot.margin = unit(c(1, 1, 2, 1), "cm")) # Extra space for p-values
# Print the plot for the current variable
print(p)
}
# Perform Wilcoxon Signed-Rank Test
wilcox_result <- wilcox.test(data$Pre_Score, data$Post_score, paired = TRUE)
# Print Test Result
print(wilcox_result)
# Reshape data for visualization
library(tidyr)
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Boxplot for Pre and Post Scores
library(ggplot2)
ggplot(long_data, aes(x = Score_Type, y = Score, fill = Score_Type)) +
geom_boxplot() +
labs(
title = "Comparison of Knowledge: Pre-Score vs. Post-Score",
x = "Score Type",
y = "Score"
) +
theme_minimal()
# Perform Wilcoxon Signed-Rank Test
wilcox_result <- wilcox.test(data$Pre_Score, data$Post_score, paired = TRUE)
# Reshape data for visualization
library(tidyr)
library(ggplot2)
library(ggpubr)
long_data <- data %>%
pivot_longer(cols = c("Pre_Score", "Post_score"),
names_to = "Score_Type", values_to = "Score")
# Boxplot with P-value
ggplot(long_data, aes(x = Score_Type, y = Score, fill = Score_Type)) +
geom_boxplot() +
stat_compare_means(
method = "wilcox.test",
paired = TRUE,
label = "p.format" # Display p-value
) +
labs(
title = "Comparison of Knowledge: Pre-Score vs. Post-Score",
x = "Score Type",
y = "Score"
) +
theme_minimal()