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# Load required libraries for data manipulation and visualization
library(dplyr)
library(ggplot2)
# Create a sample dataset with random values
data <- data.frame(
group = rep(c("A", "B", "C"), each = 10),
value = rnorm(30, mean = 50, sd = 15),
count = sample(1:100, 30)
)
# Process data using tidyverse pipes and functions
result <- data %>%
filter(count > 20) %>%
group_by(group) %>%
summarise(
mean_value = mean(value),
median_count = median(count),
n = n(),
.groups = "drop"
)
# Create visualization with ggplot2
plot_output = ggplot(result, aes(x = group, y = mean_value, fill = group)) +
geom_col(alpha = 0.8, show.legend = FALSE) +
labs(title = "Summary Statistics by Group", x = "Group", y = "Mean Value") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
print(plot_output)
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