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   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── 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)
## Warning: package 'corrplot' was built under R version 4.4.3
## 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 for ozone is significantly greater than the median, which indicates a strong right skew. The very high standard deviation of 32 indicates high variability.

The mean and median for temperature are very close, indicating a fairly symmetric distribution and no significant skew. The standard distribution of ~9.5 suggests some variation.

For wind, the mean and median are also very close. The standard deviation indicates a moderate spread.

Task 2: Histogram

Generate the histogram for Ozone.

#Your code goes here
library(ggplot2)

ggplot(airquality, aes(x=Ozone)) +
  geom_histogram(binwidth = 10, fill="orange", color="gray") +
  labs(title="Ozone", x="Ozone", y="Count")
## 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?

This graph is unimodal with a single peak around 20. The location of the peak makes the graph highly assymetrical, not normal. It is also skewed right. There looks like 1 or 2 extreme outliers on the far right side of the graph.

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=c("red","skyblue","green","orange","purple")) +
  labs(title="Tracking Ozone by Month",
       x="Month", y="Ozone levels")
## 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 highest in August and July, then they immediately drop. All months have at least one exception except July. September has many outliers, indicating some extraordinary event that probably happened over consecutive days. July has the highest median ozone level.

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=Month)) +
  geom_point() +
  labs(
    title="Temperature vs. Ozone",
    x="Temperature", y="Ozone Level",
    color="Month"
  )
## 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 looks to be some positive correlation between temperature and ozone levels. Month 5 (May) seems to own the lowest values. June and July look to be clustered together just over 80 degrees.

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
corrplot(cor_matrix, method="color", type="upper", order="hclust", tl.col="black", tl.srt=45, addCoef.col="black", title="Correlating 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.

These three variables are each moderately correlated with each other. The strongest correlation is between temperature and ozone levels (0.7), which is positive. Wind ~ Ozone and Wind ~ Temperature have moderate negative correlations, which indicate that the value of one of those values can be used with moderate confidence to predict the other value.

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) |>
  summarise(
    Count=n(),
    Avg_ozone=mean(Ozone, na.rm=TRUE),
    Avg_temp=mean(Temp),
    Avg_wind=mean(Wind)
  )
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?

Month 8 (August) has the highest average ozone level. Average temperature increases through August, then drops. Average wind decreases through August, then increases. Aside from the negative correlation indicated, these can be mapped to environmental factors. In our hemisphere, temperatures tend to peak in July and August. For atmospherical reasons I don’t fully understand, average wind speeds do drop in this time period, which I wish wasn’t true.

Submission Requirements

Publish it to Rpubs and submit your link on blackboard