Objective:

In this lab, you will practice creating APA-compliant graphs using ggplot2. Please complete each exercise by filling in the code chunks and interpreting the resulting graphs. Once you have completed the exercises, knit this document to HTML and publish it to RPubs. Make sure your YAML header includes a title, your name, and the date.

Run the below chunk to create the apa_theme()

# Load ggplot2
library(ggplot2)
apa_theme <- theme_classic() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    legend.position = "bottom",
    text = element_text(family = "serif", size = 12),
    axis.title = element_text(size = 12),
    plot.title = element_text(size = 14, hjust = 0.5)
  )

Exercise 1: Creating an APA-Compliant Bar Graph

Dataset: Use the built-in iris dataset to compare the mean sepal length across different species.

Tasks:
1. Create a bar graph that shows the average sepal length for each species in the iris dataset.
2. Add error bars representing the standard error of the mean.
3. Apply APA formatting, including a descriptive title, axis labels, and removing unnecessary grid lines.
4. Interpretation: Describe which species has the longest average sepal length and which has the shortest. Discuss the reliability of these comparisons based on the error bars.

head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
library(dplyr)

# Calculate the mean sepal length for each species
iris_summary <- iris %>%
  group_by(Species) %>%
  summarise(mean_sepal_length = mean(Sepal.Length),
            se_sepal_length = sd(Sepal.Length) / sqrt(n()))

# Create the bar graph with error bars
ggplot(iris_summary, aes(x = Species, y = mean_sepal_length)) +
  geom_bar(stat = "identity", fill = "skyblue", color = "black") +
  geom_errorbar(aes(ymin = mean_sepal_length - se_sepal_length, 
                    ymax = mean_sepal_length + se_sepal_length), 
                width = 0.2) +
  labs(title = "Mean Sepal Length by Species",
       x = "Species", y = "Mean Sepal Length (cm)") +
  apa_theme

Interpretation:
The species virginica has the longest average sepal length and setosa has the shortest. The error bars are relatively small for each group, meaning the mean estimates are reliable and meaningful.

Exercise 2: Modifying a ggplot2 Scatter Plot

Dataset: Use the built-in mtcars dataset to create a scatter plot of hp (horsepower) versus qsec (quarter-mile time).

Tasks:
1. Create a scatter plot showing the relationship between horsepower and quarter-mile time.
2. Add a line that summarizes the overall trend in the data (a line that shows the general direction the data points are heading).
3. Apply APA formatting to ensure the plot is clear and professional.
4. Interpretation: Explain whether cars with higher horsepower tend to have faster or slower quarter-mile times based on the direction of the trend line.

head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
# Create the APA-compliant scatter plot
# Scatter plot of horsepower vs. quarter-mile time
ggplot(mtcars, aes(x = hp, y = qsec)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Horsepower vs. Quarter-Mile Time",
       x = "Horsepower", y = "Quarter-Mile Time (seconds)") +
  apa_theme
## `geom_smooth()` using formula = 'y ~ x'

Interpretation:
The trend line shows a negative relationship between horsepower and quarter-mile time, showing that cars with higher horsepower tend to complete the quarter mile more quickly.

Exercise 3: Creating an APA-Compliant Line Graph

Dataset: Use the built-in airquality dataset to create a line graph showing the trend of average temperature (Temp) over the months (Month).

Tasks:
1. Calculate the average temperature for each month in the airquality dataset.
2. Create a line graph that shows the trend of average temperature over the months.
3. Apply APA formatting, ensuring the title is centered, axis labels are clear, and the graph is free of unnecessary grid lines.
4. Interpretation: Describe how temperature changes over the months. Identify which month has the highest average temperature and discuss any patterns you observe.

head(airquality)
##   Ozone Solar.R Wind Temp Month Day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6
# Create the APA-compliant line graph
# Calculate average temperature for each month
airquality_summary <- airquality %>%
  group_by(Month) %>%
  summarise(avg_temp = mean(Temp, na.rm = TRUE))

# Create the line graph of average temperature over months
ggplot(airquality_summary, aes(x = Month, y = avg_temp)) +
  geom_line(color = "blue") +
  labs(title = "Average Temperature Over the Months",
       x = "Month", y = "Average Temperature (°F)") +
  apa_theme

Interpretation: The line graph shows a clear upward trend in average temperature from May through July, peaking in July, with has the highest average temperature. After July, the temperature begins to decline through August and September.

Submission Instructions:

Ensure to knit your document to HTML format, checking that all content is correctly displayed before submission. Submit the RPubs URL to Canvas Assignments.