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.

ozone_stats <- 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))

ozone_stats
##   mean_ozone median_ozone sd_ozone min_ozone max_ozone
## 1   42.12931         31.5 32.98788         1       168
temp_stats <- 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))

temp_stats
##   mean_temp median_temp sd_temp min_temp max_temp
## 1  77.88235          79 9.46527       56       97
wind_stats <- 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))

wind_stats
##   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? For Ozone, the mean is higher than the median, this means that there is a right skewed distribution. And that there are higher ozone values that raise the average. The standard deviation is very big, showing that ozone levels can change a lot.

For Temperature, the mean and median are close, so there is a even distribution.

For Wind, the mean and median are also similar, which shows it’s evenly distributed. The standard deviation is smaller compared to ozone and temperature, showing less variability.

Task 2: Histogram

Generate the histogram for Ozone.

library(ggplot2)
ggplot(airquality, aes(x = Ozone)) +
  geom_histogram(binwidth = 20, fill = "#1f77b4", color = "black") +
  labs(title = "Histogram of Ozone Levels", x = "Ozone", 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 and unimodal. There are some outliers, days with high ozone levels above 150. But overall, most days have moderate ozone concentrations, but a few extreme values that pull the average upward.

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.

ggplot(airquality, aes(x = factor(Month), y = Ozone)) +
  geom_boxplot(fill = "#1f77b4", color = "black") +
  labs(title = "Boxplot of Ozone Levels by Month", x = "Month (5 = May, 9 = September)", y = "Ozone") +
  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?

Ozone levels are different accross the months, they start lower in May and June, then increase in July and August, and then drop again in September. July has the highest median ozone level, showing that during the summer there is the strongest ozone concentrations. September has the most outliers, these outliers mos likely show a few warm days where conditions caused temporary ozone spikes.

Task 4: Scatterplot

Produce the scatterplot of Temp vs. Ozone, colored by Month.

ggplot(airquality, aes(x = Temp, y = Ozone, color = factor(Month))) +
  geom_point(alpha = 0.7) +
  labs(title = "Scatterplot of Temperature vs. Ozone", x = "Temperature", y = "Ozone", 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 clear positive relationship between temperature and ozone levels. July and August show the most clustering in the upper right corner of the graph, meaning these warmer months must have higher ozone levels Meanwhile, May and June cluster near the lower temperatures and ozone values. The pattern shows that ozone increases during hotter months.

Task 5: Correlation Matrix

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

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
corrplot(
  cor_matrix, method = "color", type = "upper",
  tl.col = "black", tl.srt = 45, addCoef.col = "black",
  title = "Correlation Matrix of Ozone, Temperature, and Wind")

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 correlation is between Ozone and Temperature, which is positive as temperature rises, ozone levels also increase. The weakest correlation is between Ozone and Wind, which is negative meaning higher wind speeds are have a corrolation to to lower ozone levels. Temperature and Wind also have a small negative correlation, showing that hotter days usually have calmer winds.

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.

summary_table <- 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)
  )

summary_table
## # 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    10.2

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?

August has the highest average ozone level. Temperature increases from June to Auguust and then decreases at September, following the normal summer temperature pattern. Wind speeds are higher in the cooler months, May and September and lower in the hotter months. These patterns show that heat, sunlight, and low wind during the summer create the right enviorement for higher ozone.

Submission Requirements

Publish it to Rpubs and submit your link on blackboard