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   1.0.0     ✔ stringr   1.5.2
## ✔ 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
ozone_stats <- airquality %>%
summarise(
mean = mean(Ozone, na.rm = TRUE),
median = median(Ozone, na.rm = TRUE),
sd = sd(Ozone, na.rm = TRUE),
min = min(Ozone, na.rm = TRUE),
max = max(Ozone, na.rm = TRUE)
)
ozone_stats
##       mean median       sd min max
## 1 42.12931   31.5 32.98788   1 168
#Your code for Temp goes here
temp_stats <- airquality %>%
summarise(
mean = mean(Temp, na.rm = TRUE),
median = median(Temp, na.rm = TRUE),
sd = sd(Temp, na.rm = TRUE),
min = min(Temp, na.rm = TRUE),
max = max(Temp, na.rm = TRUE)
)
temp_stats
##       mean median      sd min max
## 1 77.88235     79 9.46527  56  97
#Your code for Wind goes here
wind_stats <- airquality %>%
summarise(
mean = mean(Wind, na.rm = TRUE),
median = median(Wind, na.rm = TRUE),
sd = sd(Wind, na.rm = TRUE),
min = min(Wind, na.rm = TRUE),
max = max(Wind, na.rm = TRUE)
)
wind_stats
##       mean median       sd min  max
## 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?

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 = "Distribution 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?

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
#Recode Month to month names

airquality <- airquality %>%
mutate(month_name = case_when(
Month == 5 ~ "May",
Month == 6 ~ "June",
Month == 7 ~ "July",
Month == 8 ~ "August",
Month == 9 ~ "September"
))

#Boxplot

ggplot(airquality, aes(x = month_name, y = Ozone, fill = month_name)) +
geom_boxplot() +
labs(title = "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?

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(size = 3, alpha = 0.7) +
labs(title = "Temperature vs Ozone Levels 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 levels. July and August cluster towards higher ozone.

Task 5: Correlation Matrix

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

# Your code goes here
#Select numeric variables and remove missing values

corr_data <- airquality %>%
select(Ozone, Temp, Wind) %>%
na.omit()

#Correlation matrix

corr_matrix <- cor(corr_data, use = "complete.obs")

#Print correlation values

corr_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
#Visualize correlations

corrplot(corr_matrix, method = "circle", type = "upper", addCoef.col = "black")

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.

The strongest correlations is between ozone and temperature, and the weakest is the temperature and wind. Ozone and Wind have a negative correlation, meaning higher wind speeds may reduce ozone buildup. Temp and Wind are weakly correlated.

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? The highest average ozone levels occur in July or August, when temperatures are highest and wind is lower. These conditions (hot, stagnant air) allow ozone to accumulate, explaining the seasonal variation.

Submission Requirements

Publish it to Rpubs and submit your link on blackboard