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):

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     2.1.5
## ✔ forcats   1.0.1     ✔ stringr   1.5.1
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── 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 errors
library(corrplot)
## corrplot 0.95 loaded
data("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
summary_ozone <- 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)
)




summary_ozone
##   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
summary_temp <- 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)
)




summary_temp
##   mean_temp median_temp sd_temp min_temp max_temp
## 1  77.88235          79 9.46527       56       97
#Your code for Wind goes here
summary_wind <- 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)
)




summary_wind
##   mean_wind median_wind  sd_wind min_wind max_wind
## 1  9.957516         9.7 3.523001      1.7     20.7

Question: 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?

The mean and median Ozone are not equal, suggesting right skew—a few days have very high ozone levels.

Temperature has similar mean and median values, indicating a roughly symmetric distribution.

Wind shows low variability (small SD) compared to Ozone, meaning wind speeds are more consistent.

Overall, Ozone values vary widely across days, while temperature and wind are relatively stable.

Task 2: Histogram

Generate the histogram for Ozone.

#Your code goes here
ggplot(airquality, aes(x = Ozone)) +
geom_histogram(binwidth = 10, fill = "#1f77b4", color = "black") +
labs(title = "Histogram of Ozone Concentration",
x = "Ozone (ppb)",
y = "Count") +
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?

The ozone distribution is right-skewed, with most observations between 0–100 ppb but some extreme high values above 150. This indicates that high ozone days are less frequent but notable. A few outliers appear at the high end, possibly representing particularly hot, stagnant air conditions.

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, fill = month_name)) +
geom_boxplot() +
labs(title = "Boxplot of Ozone Levels by Month",
x = "Month",
y = "Ozone (ppb)") +
theme_minimal() +
theme(legend.position = "none")
## 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?

July and August show the highest median ozone levels.

May and September have lower ozone, reflecting cooler temperatures and cleaner air.

Outliers in each month suggest days with unusually high ozone—likely due to weather conditions like heat or low wind.

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(alpha = 0.7) +
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.

There is a positive relationship between temperature and ozone—higher temperatures correspond with higher ozone levels. Warmer months (July and August) cluster in the upper-right, supporting the idea that hot, calm days promote ozone formation.

Task 5: Correlation Matrix

Compute and visualize the correlation matrix for Ozone, Temp, and Wind.

# Your code goes here
cor_matrix <- cor(
airquality |> select(Ozone, Temp, Wind),
use = "complete.obs"
)




cor_matrix
##            Ozone       Temp       Wind
## Ozone  1.0000000  0.6983603 -0.6015465
## Temp   0.6983603  1.0000000 -0.5110750
## Wind  -0.6015465 -0.5110750  1.0000000
cor_matrix <- cor(
  airquality |> select(Ozone, Temp, Wind),
  use = "complete.obs"
)

corrplot(cor_matrix, method = "color", type = "upper", order = "hclust",
         tl.col = "black", addCoef.col = "black",
         title = "Correlation Matrix for Air Quality Variables")

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.

Ozone vs. Temperature (r ≈ 0.70) → strong positive correlation: ozone tends to rise on warmer days.

Ozone vs. Wind (r ≈ -0.60) → strong negative correlation: higher wind disperses ozone.

Temperature vs. Wind (r ≈ -0.40) → moderate negative correlation: hotter days are usually calmer. These relationships align with environmental expectations—high ozone forms on hot, stagnant days with little wind.

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
summary_table <- airquality |>
group_by(month_name) |>
summarise(
Count = n(),
Avg_Ozone = mean(Ozone, na.rm = TRUE),
Avg_Temp = mean(Temp, na.rm = TRUE),
Avg_Wind = mean(Wind, na.rm = TRUE)
)




summary_table
## # A tibble: 5 × 5
##   month_name Count Avg_Ozone Avg_Temp Avg_Wind
##   <chr>      <int>     <dbl>    <dbl>    <dbl>
## 1 August        31      60.0     84.0     8.79
## 2 July          31      59.1     83.9     8.94
## 3 June          30      29.4     79.1    10.3 
## 4 May           31      23.6     65.5    11.6 
## 5 September     30      31.4     76.9    10.2

Question: Which month has the highest average ozone level? How do temperature and wind speed vary across months? What environmental factors might explain these differences?

July shows the highest average ozone and temperature, consistent with hot summer air and stronger ozone formation.

May and September show lower ozone but higher wind speeds, suggesting cleaner air due to stronger ventilation.

Overall, ozone levels increase as temperatures rise and wind decreases, typical for urban smog dynamics in the summer months.

Submission Requirements

Publish it to Rpubs and submit your link on blackboard