Airquality Assignment

Airquality Assignment

Load the library

library(tidyverse)

Load the dataset into your global environment

data("airquality")

Look at the structure of the data

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$Wind)

[1] 9.957516

var(airquality$Wind)

[1] 12.41154

Rename the Months from number to names

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"

Now look at the summary statistics of the dataset

summary(airquality$Month)

   Length     Class      Mode 
      153 character character 

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

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

Plot 1: Create a histogram categorized by Month

Plot 1 Code

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

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 <- airquality |>
  ggplot(aes(x=Temp, fill=Month)) +
  geom_histogram(position="identity", alpha=0.4, binwidth = 3, 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 <- 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"))

Plot 3 Output

p3

Plot 4: Side by Side Boxplots in Gray Scale

Plot 4 Code

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"))

Plot 4 Output

p4

Plot 5:

My own plot

p5 <- airquality |>
  ggplot(aes(x = Wind, fill = Month)) +
  geom_histogram(alpha = 0.4, binwidth = 2) +
  labs(
    title = "Histogram of Wind Speed by Month",
    x = "Wind Speed",
    y = "Frecuencia"
  )

Plot 5 Output

p5

Essay

The plot I created is a histogram. I learned how to use the program and played a little with the values for now. For this plot, I mostly copied and pasted the code because editing it by myself gave me many errors. Little by little, I will experiment more and learn better how it works. I am very excited to keep learning and improving with this program.

For Plot 5, I created a histogram of wind speed by month. A histogram shows how often different values appear in a dataset. This helps us see the distribution of wind speeds from May to September in 1973. I chose this variable because we had not used wind in previous plots, so it is interesting to explore something different. In this plot we can see that some months have more frequent moderate wind speeds, and other months have slightly different patterns.

I used ggplot() and geom_histogram() to make the plot. I added fill = Month to color the bars by month, alpha = 0.4 to make them slightly transparent, and binwidth = 2 to control the size of the bins.