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
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?

For the Ozone, the mean is higher than the median, suggesting that it may be a right-skewed distribution as well its large standard deviation is indicating a higher variability. For the Temperature, the mean and median are close, suggesting that it may be symmetrical without skewness, also its standard deviation show a moderate variability. Lastly, the Wind are also identical in terms of mean and median, suggesting a symmetrical distribution with little skewness and its standard deviation indicates a low variability. Overall, the Ozone indicates the greatest in terms of skewness & spread.

Task 2: Histogram

Generate the histogram for Ozone.

#Your code goes here
ggplot(airquality, aes(x = Ozone)) +
  geom_histogram(binwidth = 10, fill = "blue", color = "black") +
  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?

The distribution of the Ozone levels in the dataset shows a right skew. And yes there is a presence of outliers 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.

# Your code here
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 = "Monthly Ozone levels", 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?

Across these months, Ozone levels are a bit similar, but the month with the highest median ozone is July and its outliers are May, June, August, and September, showing that the ozone levels during those months can be high.

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_name)) +
  geom_point(size = 3, alpha = 1, na.rm = TRUE) +
  labs(title = "Temperature vs Ozone", 
       x = "Temperature (Farenheit)", 
       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.

The relationship is definitely positive, therefore the temperature increases, so will the levels of Ozone. July & August indicates a higher temperature as well its ozone levels. Lastly September is slightly lower, therefore cooler and less ozone.

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 = "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.

The strong correlation is with Ozone & Temperature (0.69) and the one with a weaker correlation is Wind & Temperature which is at (-0.51). Based off of this it may indicate that there is a good relationship between Ozone & Temperature levels.

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
##   <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?

Based on the summary table, August is the highest in terms of average ozone level. The Wind & Temperature will vary across the months as the high average temperature will have a tendency of having low wind speeds. In terms of Environmental factors, a Hot temperature in the summer will likely increase Ozone levels, while times with higher wind speeds will have less Ozone.

Submission Requirements

Publish it to Rpubs and submit your link on blackboard