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   1.5.2
## ✔ 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)
## Warning: package 'corrplot' was built under R version 4.5.2
## 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.

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
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
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 average value exceeds the median, implying a right-skewed pattern. The relatively large standard deviation further reflects substantial variability within the data.In contrast, Temperature shows mean and median values that are nearly identical, hinting at a roughly symmetrical distribution with minimal skewness. Its standard deviation points to a moderate level of variation.

Task 2: Histogram

Generate the histogram for Ozone.

ggplot(airquality, aes(x = Ozone)) +
  geom_histogram(binwidth = 10, fill = "red", color = "green") +
  labs(title = "Histogram for Ozone Levels", x = "Ozone Levels") +
  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? it’s right skew and the outliers is around 150

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.

library(ggplot2)
airquality <- airquality |>
  mutate(month_name = factor(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 = "Ozone levels by month", x = "Month", y = "Ozone (PPB)") +
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 mostly similar, but July has the highest median. Outliers in May, June, August, and September show that ozone levels can be higher during those months.

Task 4: Scatterplot

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

ggplot(airquality, aes(x = Temp, y = Ozone, color = month_name)) +
  geom_point(size = 4, alpha = 2, na.rm = TRUE) +
  labs(title = "Temperature vs Ozone", 
       x = "Temperature F", 
       y = "Ozone",
       Color = "Month") +
  theme_minimal()
## Ignoring unknown labels:
## • Color : "Month"

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.

It’s a positive relationship and the relationship is that higher temperatures lead to higher ozone levels. July and August show this most clearly, while September has cooler temperatures and lower ozone.

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", order = "hclust",
         tl.col = "white", tl.srt = 50, addCoef.col = "white",
         title = "Correlation Matrix")

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 and Temperature have the strongest positive correlation 0.69, while Wind and Temperature show a negative one -0.51, suggesting that ozone levels tend to rise with increasing temperature.

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_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
##   <fct>      <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? it’s shows that August records the highest average ozone level. when temperatures rise, wind speeds usually drop. Overall, hotter weather tends to increase ozone, while stronger winds help reduce it.

Submission Requirements

Publish it to Rpubs and submit your link on blackboard