Introduction

This report compares catch-per-unit-effort (CPUE) between two fishing areas (Area A and Area B).
We use summary statistics, visualization, and a two-sample t-test to evaluate whether mean CPUE differs between the two areas.

Load Required Packages

library(tidyverse)

Create Sample Fisheries Data

cpue_data <- data.frame(
  Area = rep(c("Area A", "Area B"), each = 12),
  CPUE = c(1.2, 1.4, 1.1, 1.3, 1.5, 1.6, 1.2, 1.3, 1.4, 1.5, 1.3, 1.2,
           1.8, 1.7, 1.9, 2.0, 1.6, 1.8, 1.9, 2.1, 1.7, 1.8, 2.0, 1.9)
)

head(cpue_data)

##     Area CPUE
## 1 Area A  1.2
## 2 Area A  1.4
## 3 Area A  1.1
## 4 Area A  1.3
## 5 Area A  1.5
## 6 Area A  1.6

Summary Statistics

summary_stats <- cpue_data %>%
  group_by(Area) %>%
  summarise(
    Mean_CPUE = mean(CPUE),
    SD_CPUE = sd(CPUE),
    N = n()
  )

summary_stats

## # A tibble: 2 × 4
##   Area   Mean_CPUE SD_CPUE     N
##   <chr>      <dbl>   <dbl> <int>
## 1 Area A      1.33   0.150    12
## 2 Area B      1.85   0.145    12

Visualization

ggplot(cpue_data, aes(x = Area, y = CPUE, fill = Area)) +
  geom_boxplot(alpha = 0.7) +
  geom_jitter(width = 0.1, color = "black") +
  theme_minimal() +
  labs(
    title = "Comparison of CPUE Between Fishing Areas",
    x = "Fishing Area",
    y = "CPUE"
  )

Hypothesis Testing (Two-sample t-test)

t_test_result <- t.test(CPUE ~ Area, data = cpue_data)

t_test_result

## 
##  Welch Two Sample t-test
## 
## data:  CPUE by Area
## t = -8.5979, df = 21.973, p-value = 1.766e-08
## alternative hypothesis: true difference in means between group Area A and group Area B is not equal to 0
## 95 percent confidence interval:
##  -0.6412998 -0.3920336
## sample estimates:
## mean in group Area A mean in group Area B 
##             1.333333             1.850000

Interpretation

The two-sample t-test evaluates whether the mean CPUE differs between Area A and Area B.

Null hypothesis (H0): Mean CPUE is equal between areas.
Alternative hypothesis (H1): Mean CPUE differs between areas.

If the p-value is less than 0.05, we conclude that CPUE is significantly different.

Conclusion

This example demonstrates a reproducible fisheries data analysis workflow using R Markdown, including data handling, visualization, statistical testing, and interpretation.

CPUE Comparison Between Fishing Areas

Jhen Hsu

01/30/2026