Airquality Assignment

Author

Ryan Juica

Load in the library

library(tidyverse)

Warning: package 'dplyr' was built under R version 4.5.3

Warning: package 'lubridate' was built under R version 4.5.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── 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

Load the dataset into your global environment

View the data using the “head” function

head(airquality)

  Ozone Solar.R Wind Temp Month Day
1    41     190  7.4   67     5   1
2    36     118  8.0   72     5   2
3    12     149 12.6   74     5   3
4    18     313 11.5   62     5   4
5    NA      NA 14.3   56     5   5
6    28      NA 14.9   66     5   6

Calculate Summary Statistics

mean(airquality$Temp)

[1] 77.88235

mean(airquality[,4])

[1] 77.88235

Calculate Median, Standard Deviation, and Variance

median(airquality$Temp)

[1] 79

sd(airquality$Wind)

[1] 3.523001

var(airquality$Wind)

[1] 12.41154

Rename the Months from number to names

airquality1 <- airquality |>
  mutate(month_name = case_when(Month == 4 ~ "April",
                                Month == 5 ~ "May",
                                Month == 6 ~ "June",
                                Month == 7 ~ "July",
                                Month == 8 ~ "August",
                                Month == 9 ~ "September"))

Now look at the summary statistics of the dataset

summary(airquality1$month_name)

   Length     Class      Mode 
      153 character character

Month is a categorical variable with different levels, called factors.

airquality1$Month<-factor(airquality1$month_name, 
                          levels=c("May", "June","July", "August",
                                   "September"))

Plot 1: Create a histogram categorized by Month

Plot 1 Code

p1 <- airquality1 |>
  ggplot(aes(x=Temp, fill=month_name)) +
  geom_histogram(position="identity")+
  scale_fill_discrete(name = "Month", 
                      labels = c("May", "June","July", "August", "September")) +
  labs(x = "Monthly Temperatures from May - Sept", 
       y = "Frequency of Temps",
       title = "Histogram of Monthly Temperatures from May - Sept, 1973",
       caption = "New York State Department of Conservation and the National Weather Service")  #provide the data source

Plot 1 Output

p1

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Plot 2: Improve the histogram of Average Temperature by Month

Plot 2 Code

p2 <- airquality1 |>
  ggplot(aes(x=Temp, fill=month_name)) +
  geom_histogram(position="identity", alpha=0.5, binwidth = 5, color = "white")+
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September")) +
  labs(x = "Monthly Temperatures from May - Sept", 
       y = "Frequency of Temps",
       title = "Histogram of Monthly Temperatures from May - Sept, 1973",
       caption = "New York State Department of Conservation and the National Weather Service")

Plot 2 Output

p2

Plot 3: Create side-by-side boxplots categorized by Month

p3 <- airquality1 |>
  ggplot(aes(Month, Temp, fill =  month_name)) + 
  labs(x = "Months from May through September", y = "Temperatures", 
       title = "Side-by-Side Boxplot of Monthly Temperatures",
       caption = "New York State Department of Conservation and the National Weather Service") +
  geom_boxplot() +
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September"))

Plot 3 Output

p3

Plot 4: Side by Side Boxplots in Gray Scale

Plot 4 Code

p4 <- airquality1 |>
  ggplot(aes(Month, Temp, fill = month_name)) + 
  labs(x = "Monthly Temperatures", y = "Temperatures", 
       title = "Side-by-Side Boxplot of Monthly Temperatures",
       caption = "New York State Department of Conservation and the National Weather Service") +
  geom_boxplot()+
  scale_fill_grey(name = "Month", labels = c("May", "June","July", "August", "September"))

Plot 4 Output

p4

Plot 5:

p5 <- airquality1 |>
  ggplot(aes(x = Wind, y = Ozone, color= Month)) +
  geom_point(size = 2, na.rm = TRUE) +
    labs(
      x = "Wind Speed (Mph) ", 
      y = "Ozone",
      title = "Impact of Wind Speed on Ozone Levels",
      caption = "New York State Department of Conservation and the National Weather Service")

p5

Write a brief essay here

I created a scatter plot that groups each month by color. It compares two numeric variable, Wind on the x-axis and Ozone on the y-axis. This plot examines how the wind speed of the atmosphere impacts the ozone levels.

The scatter plot shows a negative relationship between wind speed and ozone levels. The higher the ozone levels the slower the wind speed.

I changed the size of the points of the scatter plot to 2 which increased the size to make it more clearer. I also groups each month by color to clearly see the difference from each month.