Air Quality HW

library(conflicted)
conflict_prefer("filter", "dplyr")

[conflicted] Will prefer dplyr::filter over any other package.

conflict_prefer("lag", "dplyr")

[conflicted] Will prefer dplyr::lag over any other package.

#Source: ChatGPT

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   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     

data("airquality")

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

mean(airquality$Temp)

[1] 77.88235

mean(airquality[,4])

[1] 77.88235

median(airquality$Temp)

[1] 79

sd(airquality$Wind)

[1] 3.523001

var(airquality$Wind)

[1] 12.41154

airquality$Month[airquality$Month == 5]<- "May"
airquality$Month[airquality$Month == 6]<- "June"
airquality$Month[airquality$Month == 7]<- "July"
airquality$Month[airquality$Month == 8]<- "August"
airquality$Month[airquality$Month == 9]<- "September"

summary(airquality$Month)

   Length     Class      Mode 
      153 character character 

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

p1 <- airquality |>
  ggplot(aes(x=Temp, fill=Month)) +
  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
p1

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

p2 <- airquality |>
  ggplot(aes(x=Temp, fill=Month)) +
  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")
p2

p3 <- airquality |>
  ggplot(aes(Month, Temp, fill = Month)) + 
  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"))
p3 

p4 <- airquality |>
  ggplot(aes(Month, Temp, fill = Month)) + 
  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"))
p4

p5 <- airquality |>
  ggplot(aes(Month, Ozone, color = Month)) + 
  labs(x = "Month", y = "Ozone Level", 
       title = "Monthly Ozone Levels in 2022",
       caption = "New York State Department of Conservation and the National Weather Service") +
  geom_point(position = position_jitter(width = 0.2, height = 0), size = 1) +
  scale_color_discrete(name = "Month", labels = c("May", "June","July", "August", "September"))
p5

Warning: Removed 37 rows containing missing values (`geom_point()`).

The plot I created presents the monthly ozone levels recorded in 2022. This data is presented within a scatter plot that has been color-coded according to the corresponding month for easier comparison. To create these modifications, I changed several parts of the code within the “p4” chart. The first major change I made was altering a factor within ggplot. Changing “Temp” to “Ozone” allowed me to display a different variable within the data set. The second major change was changing the “geom_boxplot” command to “geom_point” to display the corresponding data as a scatter plot instead of a box plot. The final major change was altering the “scale_fill_grey” command to “scale_color_discrete”, which allowed the proper display of colors within the newly created plot. The other changes of code were relatively minor and only required changing the words within quotation marks to change the labels for corresponding parts of the scatter plot.