HW6
James Chowdhury
Objective:
You will use R to analyze the built-in airquality dataset, applying descriptive statistics techniques to explore environmental data. The assignment covers measures of central tendency, spread, histograms, boxplots, scatterplots, correlations, and summary tables, aligning with the Week 6 agenda on Descriptive Statistics.
Dataset
Source: Built-in R dataset airquality.
Description: Contains 153 observations of daily air quality measurements in New York from May to September 1973.
Variables (selected for this assignment):
- Ozone: Mean ozone concentration in parts per billion (ppb, numeric).
- Temp: Maximum daily temperature in Fahrenheit (numeric).
- Wind: Average wind speed in miles per hour (numeric).
- Month: Month of the year (5 = May, 6 = June, …, 9 = September, categorical).
Notes
-The airquality dataset has missing values in Ozone and Solar.R. The code uses na.rm = TRUE or use = “complete.obs” to handle them.
-If you encounter errors, check that tidyverse and corrplot are installed and loaded.
-Feel free to modify plot aesthetics (e.g., colors, binwidth) to enhance clarity.
Instructions:
Complete the following tasks using R to analyze the airquality dataset. Submit your Rpubs link that includes code, outputs (tables and plots), and written interpretations for each task. Ensure you load the dataset using data(airquality) and install/load the tidyverse and corrplot packages.
#Load your dataset
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.2.0
## ── 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 errorslibrary(corrplot)## corrplot 0.95 loadeddata("airquality")Tasks and Questions
Task 1: Measures of Central Tendency and Spread
Using functions you learned this week, compute mean, median, standard deviation, min, and max separately for Ozone, Temp, and Wind.
#Your code for Ozone goes here
airquality |>
summarise(
mean_ozone = mean(Ozone, na.rm = TRUE),
median_ozone = median(Ozone, na.rm = TRUE),
sd_ozone = sd(Ozone, na.rm = TRUE),
min_ozone = min(Ozone, na.rm = TRUE),
max_ozone = max(Ozone, na.rm = TRUE)
)## mean_ozone median_ozone sd_ozone min_ozone max_ozone
## 1 42.12931 31.5 32.98788 1 168#Your code for Temp goes here
airquality |>
summarise(
mean_temp = mean(Temp, na.rm = TRUE),
median_temp = median(Temp, na.rm = TRUE),
sd_temp = sd(Temp, na.rm = TRUE),
min_temp = min(Temp, na.rm = TRUE),
max_temp = max(Temp, na.rm = TRUE)
)## mean_temp median_temp sd_temp min_temp max_temp
## 1 77.88235 79 9.46527 56 97#Your code for Wind goes here
airquality |>
summarise(
mean_wind = mean(Wind, na.rm = TRUE),
median_wind = median(Wind, na.rm = TRUE),
sd_wind = sd(Wind, na.rm = TRUE),
min_wind = min(Wind, na.rm = TRUE),
max_wind = max(Wind, na.rm = TRUE)
)## mean_wind median_wind sd_wind min_wind max_wind
## 1 9.957516 9.7 3.523001 1.7 20.7Question: Compare the mean and median for each variable. Are they similar or different, and what does this suggest about the distribution (e.g., skewness)? What does the standard deviation indicate about variability?
Answer: Ozone has a mean greater than the median, suggesting a right-skewed distribution.
Temperature’s mean and median are close, indicating a fairly symmetric distribution.
Wind shows moderate variability with mean and median being similar.
Standard deviation shows that ozone has higher variability compared to temperature and wind.
Task 2: Histogram
Generate the histogram for Ozone.
#Your code goes here
ggplot(airquality, aes(x = Ozone)) +
geom_histogram(binwidth = 10, fill = "skyblue", color = "black") +
labs(
title = "Histogram of Ozone Levels",
x = "Ozone (ppb)",
y = "Frequency"
) +
theme_minimal()## Warning: Removed 37 rows containing non-finite outside the scale range
## (`stat_bin()`).
Question: Describe the shape of the ozone distribution (e.g., normal, skewed, unimodal). Are there any outliers or unusual features? Answer: The ozone distribution is right-skewed.
Most values are concentrated at lower ozone levels.
A few high values suggest the presence of outliers.
Task 3: Boxplot
Create a boxplot of ozone levels (Ozone) by month, with months
displayed as names (May, June, July, August, September) instead of
numbers (5–9).Recode the Month variable into a new column called
month_name with month names using case_when
from week 4.Generate a boxplot of Ozone by month_name.
# Your code here
airquality <- airquality |>
mutate(
month_name = case_when(
Month == 5 ~ "May",
Month == 6 ~ "June",
Month == 7 ~ "July",
Month == 8 ~ "August",
Month == 9 ~ "September"
)
)
ggplot(airquality, aes(x = month_name, y = Ozone)) +
geom_boxplot(fill = "lightgreen") +
labs(
title = "Ozone Levels by Month",
x = "Month",
y = "Ozone (ppb)"
) +
theme_minimal()## Warning: Removed 37 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
Question: How do ozone levels vary across months? Which month has the highest median ozone? Are there outliers in any month, and what might they indicate?
Answer: Ozone levels increase from May and peak in July and August.
August has the highest median ozone.
Several outliers appear, likely due to extreme pollution days.
Task 4: Scatterplot
Produce the scatterplot of Temp vs. Ozone, colored by Month.
# Your code goes here
ggplot(airquality, aes(x = Temp, y = Ozone, color = factor(Month))) +
geom_point() +
labs(
title = "Temperature vs Ozone by Month",
x = "Temperature (F)",
y = "Ozone (ppb)",
color = "Month"
) +
theme_minimal()## Warning: Removed 37 rows containing missing values or values outside the scale range
## (`geom_point()`).
Question: Is there a visible relationship between temperature and ozone levels? Do certain months cluster together (e.g., higher ozone in warmer months)? Describe any patterns.
Answer: There is a positive relationship between temperature and ozone.
Higher ozone levels occur on hotter days.
Summer months (July, August) cluster at higher temperature and ozone values.
Task 5: Correlation Matrix
Compute and visualize the correlation matrix for Ozone, Temp, and Wind.
# Your code goes here
air_corr <- airquality |>
select(Ozone, Temp, Wind) |>
na.omit()
corr_matrix <- cor(air_corr)
corrplot(corr_matrix, method = "circle")
Question: Identify the strongest and weakest correlations. For example, is ozone more strongly correlated with temperature or wind speed? Explain what the correlation values suggest about relationships between variables.
Answer: Ozone has a strong positive correlation with temperature.
Ozone is negatively correlated with wind speed.
This suggests higher ozone levels occur on hot, low-wind days.
Task 6: Summary Table
Generate the summary table grouped by Month.Generate the summary table grouped by Month. It should include count, average mean of ozone, average mean of temperature, and average mean of wind per month.
# your code goes here
airquality |>
group_by(Month) |>
summarise(
count = n(),
avg_ozone = mean(Ozone, na.rm = TRUE),
avg_temp = mean(Temp, na.rm = TRUE),
avg_wind = mean(Wind, na.rm = TRUE)
)## # A tibble: 5 × 5
## Month count avg_ozone avg_temp avg_wind
## <int> <int> <dbl> <dbl> <dbl>
## 1 5 31 23.6 65.5 11.6
## 2 6 30 29.4 79.1 10.3
## 3 7 31 59.1 83.9 8.94
## 4 8 31 60.0 84.0 8.79
## 5 9 30 31.4 76.9 10.2Question: Which month has the highest average ozone level? How do temperature and wind speed vary across months? What environmental factors might explain these differences?
Answer: August has the highest average ozone.
Temperature increases from May to July and declines afterward.
Wind speed tends to decrease in summer, which may contribute to higher ozone accumulation.
Submission Requirements
Publish it to Rpubs and submit your link on blackboard