knitr::opts_chunk$set(echo = TRUE)
# Install packages if not already
if (!require(tidyverse)) install.packages("tidyverse")
## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
if (!require(lubridate)) install.packages("lubridate")
library(tidyverse)
library(lubridate)
# Read in the actual CSV file
data <- read.csv("DodgersData.csv")
# Convert variables
data$day_of_week <- factor(data$day_of_week,
levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
# Create a "Promotion" variable if any promo occurred (cap, shirt, fireworks, bobblehead)
data <- data %>%
mutate(promotion = if_else(cap == "YES" | shirt == "YES" | fireworks == "YES" | bobblehead == "YES", "Yes", "No"))
ggplot(data, aes(x = day_of_week, y = attend, fill = day_of_week)) +
geom_boxplot(show.legend = FALSE) +
labs(title = "Attendance by Day of the Week",
x = "Day",
y = "Attendance") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(data, aes(x = temp, y = attend)) +
geom_point(color = "darkred", alpha = 0.6) +
geom_smooth(method = "lm", color = "blue") +
labs(title = "Relationship Between Temperature and Attendance",
x = "Temperature (°F)",
y = "Attendance") +
theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
data %>%
mutate(month = factor(month, levels = c("APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT"))) %>%
group_by(month) %>%
summarize(total_attendance = sum(attend)) %>%
ggplot(aes(x = month, y = total_attendance, group = 1)) +
geom_line(color = "darkgreen", linewidth = 1) +
geom_point(color = "purple", size = 3) +
labs(title = "Total Attendance by Month",
x = "Month", y = "Total Attendance") +
theme_minimal()
ggplot(data, aes(x = promotion, y = attend, fill = promotion)) +
geom_boxplot(show.legend = FALSE) +
labs(title = "Attendance with vs Without Promotions",
x = "Promotion Day?",
y = "Attendance") +
theme_minimal()
top_opponents <- data %>%
group_by(opponent) %>%
summarise(avg_attendance = mean(attend)) %>%
arrange(desc(avg_attendance)) %>%
top_n(8, avg_attendance)
data %>%
filter(opponent %in% top_opponents$opponent) %>%
ggplot(aes(x = reorder(opponent, -attend), y = attend, fill = opponent)) +
geom_boxplot(show.legend = FALSE) +
labs(title = "Attendance by Opponent (Top 8)",
x = "Opponent", y = "Attendance") +
theme_minimal()
model <- lm(attend ~ temp + day_of_week + promotion, data = data)
summary(model)
##
## Call:
## lm(formula = attend ~ temp + day_of_week + promotion, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17676.2 -4040.7 -440.4 3909.7 15121.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28724.60 6999.72 4.104 0.000106 ***
## temp 73.72 92.55 0.797 0.428310
## day_of_weekTuesday 7215.01 2851.05 2.531 0.013571 *
## day_of_weekWednesday 2607.21 2682.41 0.972 0.334322
## day_of_weekThursday 2161.33 3558.55 0.607 0.545521
## day_of_weekFriday -4205.56 3259.57 -1.290 0.201102
## day_of_weekSaturday 7380.09 2634.37 2.801 0.006529 **
## day_of_weekSunday 5368.76 2691.70 1.995 0.049878 *
## promotionYes 10460.05 2067.22 5.060 3.09e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6570 on 72 degrees of freedom
## Multiple R-squared: 0.4357, Adjusted R-squared: 0.373
## F-statistic: 6.948 on 8 and 72 DF, p-value: 9.546e-07