Airquality-HW

Airquality Assignment-

Lydia Baick

1- Load in the library

Load library tidyverse in order to access dplyr and ggplot2

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   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── 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

The source for this dataset is the New York State Department of Conservation and the National Weather Service of 1973 for five months from May to September recorded daily.

2- Load the dataset into your global environment

Because airquality is a pre-built dataset, we can write it to our data directory to store it for later use.

data("airquality")

3- Look at the structure of the data

In the global environment, click on the row with the airquality dataset and it will take you to a “spreadsheet” view of the data.

4- View the data using the “head” function

The function, head, will only display the first 6 rows of the dataset. Notice in the global environment to the right, there are 153 observations (rows)

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

Notice that all the variables are classified as either integers or continuous values .

5- Calculate Summary Statistics

If you want to look at specific statistics, here are some variations on coding. Here are 2 different ways to calculate “mean.”

mean(airquality$Temp)

## [1] 77.88235

mean(airquality[,4])

## [1] 77.88235

For the second way to calculate the mean, the matrix [row,column] is looking for column #4, which is the Temp column and we use all rows

6- Calculate Median, Standard Deviation, and Variance

median(airquality$Temp)

## [1] 79

sd(airquality$Temp)

## [1] 9.46527

var(airquality$Temp)

## [1] 89.59133

7- Rename the Months from number to names

Sometimes we prefer the months to be numerical, but here, we need them as the month names. There are MANY ways to do this. Here is one way to convert numbers 5 - 9 to May through September

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"

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

This is one way to reorder the Months so they do not default to alphabetical (you will see another way to reorder DIRECTLY in the chunk that creates the plot below in Plot #1

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

Plot 1: Create a histogram categorized by Month

Here is a first attempt at viewing a histogram of temperature by the months May through September. We will see that temperatures increase over these months. The median temperature appears to be about 75 degrees.

fill = Month colors the histogram by months between May - Sept.
scale_fill_discrete(name = “Month”…) provides the month names on the right side as a legend in chronological order. This is a different way to order than what was shown above.
labs allows us to add a title, axes labels, and a caption for the data source

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`.

Is this plot useful in answering questions about monthly temperature values? Explain

Plot 2: Improve the histogram of Average Temperature by Month

Outline the bars in white using the color = “white” command
Use alpha to add some transparency (values between 0 and 1)
Change the binwidth
Add some transparency and white borders around the histogram bars.

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

Here July stands out for having high frequency of 85 degree temperatures. The dark purple color indicates overlaps of months due to the transparency.

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

We can see that August has the highest temperatures based on the boxplot distribution.

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

Note the points above and below the boxplots in June and July. They are outliers.

Plot 4:

Now make one new plot on your own, that is meaningfully different from the 3 I have shown you. You can select any of the variables in this dataset. Be sure to explore the dataset to see which variables are included that we have not explored yet. You may create a scatterplot, histogram, boxplot, or something else.

Be sure to include a title, axes labels, colors, and caption for the datasource in your Plot 4.

p5 <- airquality |>
ggplot(aes(x=Day, y=Month, fill=Solar.R)) +
  labs(x = "Day", y = "Month", 
       title = "Tile Plot of Solar Radiation based on Month and Day",
       caption = "New York State Department of Conservation and the National Weather Service") +
  geom_tile() + 
  coord_fixed(expand = FALSE) +
  scale_fill_viridis_c(option = "A", begin = 0.15)
p5

Then, write a brief essay here:

The plot I have chosen is a tile plot on solar radiation based on the month and day. The variables I have chosen are month ranging from May all the way to September, day representing days within each month, and Solar.R which represents the intensity of solar radiation. The tile plot show several insights. First, it looks like solar radiation generally increases from May and peaks at round July. There are also changes in solar radiation across the days within each month. In most months solar radiation is lower in the later days, typically from the 15th to the 30th. This is explained because normally at the end of each month the position the earth is to the sun shifts causing less intense radiation. My color choice employs a sequential color scale ranging from dark purple to light orange. The lighter shades of orange represent higher solar radiation which, in y opinion go with my prior perception of heat and energy. On the opposite side, the darker purple means lower radiation levels which goes with the idea, that a day with less solar radiation will typically be darker than one with radiation. I used the viridis colors but use the palette option A to get there warmer vs cool shades.