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     1.2.1     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.3     ✔ tibble    3.3.1
## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.2     
## ── 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)
## 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.

mean(airquality$Ozone, na.rm = TRUE)
## [1] 42.12931
median(airquality$Ozone, na.rm = TRUE)
## [1] 31.5
sd(airquality$Ozone, na.rm = TRUE)
## [1] 32.98788
min(airquality$Ozone, na.rm = TRUE)
## [1] 1
max(airquality$Ozone, na.rm = TRUE)
## [1] 168
mean(airquality$Temp, na.rm = TRUE)
## [1] 77.88235
median(airquality$Temp, na.rm = TRUE)
## [1] 79
sd(airquality$Temp, na.rm = TRUE)
## [1] 9.46527
min(airquality$Temp, na.rm = TRUE)
## [1] 56
max(airquality$Temp, na.rm = TRUE)
## [1] 97
#Your code for Wind goes here
mean(airquality$Wind, na.rm = TRUE)
## [1] 9.957516
median(airquality$Wind, na.rm = TRUE)
## [1] 9.7
sd(airquality$Wind, na.rm = TRUE)
## [1] 3.523001
min(airquality$Wind, na.rm = TRUE)
## [1] 1.7
max(airquality$Wind, na.rm = TRUE)
## [1] 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?

Temp and wind have similar mean and median, meaning the data is symmetric, but ozone is right-skewed since its mean is greater than the median. Ozone has a higher variability, temp is low, and wind is moderate.

Task 2: Histogram

Generate the histogram for Ozone.

#Your code goes here
hist(airquality$Ozone)

Question: Describe the shape of the ozone distribution (e.g., normal, skewed, unimodal). Are there any outliers or unusual features?

The distribution is skewed to the right, and there are outliers past the 150 mark.

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"
  
  )
  
)

boxplot(Ozone ~ month_name,
        data = airquality,
        main = "Ozone levels by month",
        xlab = "Month",
        ylab = "level")

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 has the highest median ozone layer, with august being slightly lower, and the other months being much lower. June, May, and September have outliers and they show measurements much higher than the rest.

Task 4: Scatterplot

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

# Your code goes here

plot(Ozone ~ Temp,
     data = airquality,
     col = factor(month_name),
     main = "Ozone vs Temp bu Month",
     xlab = "Temp (°F)",
     ylab = "Ozone"
     )

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 visible relationship, with certain months being clustered around the right and having higher ozone levels.

Task 5: Correlation Matrix

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

# Your code goes here

cor_matx <- cor(airquality[, c("Ozone", "Temp", "Wind")], use = "complete.obs")

cor_matx
##            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_matx, 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.

The strongest correlation is between ozone level and temperature, and the weakest comes from wind and ozone. These suggest that ozone and temperature have a strong relationship with each other, and wind and ozone have a weak relation.

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

sum_month <- airquality |> group_by(Month) |> 
  summarise(
  count = n(),
  mean_ozone = mean(Ozone, na.rm = TRUE),
  mean_temp = mean(Temp, na.rm = TRUE),
  mean_wind = mean(Wind, na.rm = TRUE)
)

sum_month
## # A tibble: 5 × 5
##   Month count mean_ozone mean_temp mean_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 mean ozone level. The temperature increases around June, July, and August, dipping down again in September. Wind speed gets lower around July and August, rising up again in September. The increase in temperature can come from the months of June, July, and August being considered summer months, so temperature would be higher and wind speed lower.

Submission Requirements

Publish it to Rpubs and submit your link on blackboard