dodgers <- read.csv("DodgersData.csv")
head(dodgers)
## month day attend day_of_week opponent temp skies day_night cap shirt
## 1 APR 10 56000 Tuesday Pirates 67 Clear Day NO NO
## 2 APR 11 29729 Wednesday Pirates 58 Cloudy Night NO NO
## 3 APR 12 28328 Thursday Pirates 57 Cloudy Night NO NO
## 4 APR 13 31601 Friday Padres 54 Cloudy Night NO NO
## 5 APR 14 46549 Saturday Padres 57 Cloudy Night NO NO
## 6 APR 15 38359 Sunday Padres 65 Clear Day NO NO
## fireworks bobblehead
## 1 NO NO
## 2 NO NO
## 3 NO NO
## 4 YES NO
## 5 NO NO
## 6 NO NO
names(dodgers)
## [1] "month" "day" "attend" "day_of_week" "opponent"
## [6] "temp" "skies" "day_night" "cap" "shirt"
## [11] "fireworks" "bobblehead"
#Calculate Mean and Median attendance
mean_attendance <- mean(dodgers$attend)
median_attendance <- median(dodgers$attend)
mean_attendance
## [1] 41040.07
median_attendance
## [1] 40284
#Question 1: The median attendance was 40,284, and the mean attendance was 41,040.07. I would use the median for interpretation because it represents the middle game attendance and is less affected by unusually high or low attendance values.
library(ggplot2)
ggplot(dodgers, aes(x = attend)) +
geom_histogram(bins = 15, fill = "blue", color = "white") +
labs(
title = "Dodgers Game Attendance",
x = "Attendance",
y = "Number of Games"
) +
theme_minimal()
#Question 2: The graph shows the attendance levels for Dodgers games. Most games had attendance between approximately 30,000 and 50,000 people, while fewer games had extremely low or extremely high attendance.
ggplot(dodgers, aes(x = temp, y = attend)) +
geom_point(color = "blue", size = 3) +
labs(
title = "Temperature and Dodgers Game Attendance",
x = "Temperature",
y = "Attendance"
) +
theme_minimal()
#Question 3: The scatterplot compares temperature with Dodgers game attendance. The points are spread out, so there does not appear to be a strong relationship between temperature and attendance.