Data110_Project_1_MoystadAsk

Air-quality Tutorial Assignment

https://www.nytimes.com/2023/06/09/climate/air-quality-index-500.html

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ 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     
── 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

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

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 

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

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.

Reorder the legend so that it is not the default (alphabetical), but rather in chronological order.

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.

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?

From this histogram we can answer certain questions quite easily. May for example is a colder month, with temperatures rarely going above 70. While September has teh most range, ranging from low 60ies to the low 90ies.

Plot 2: Improve the histogram using ggplot

Outline the bars in white using the color = “white” command

Use alpha to add some transparency (values between 0 and 1)

Change the binwidth

Histogram of Average TEmperature by Month

Add some transparency and white borders around the histogram bars. Here July stands out for having high frequency of 85 degree temperatures. The dark purple color indicates overlaps of months due to the transparency.

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

Did this improve the readability of the plot?

This certainly changed readability. I am now concerned that values may be hidden behind onthers in the first chart, however this chart is very hard to read.

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 

Notice that the points above and below the boxplots in June and July are outliers.

Plot 4: Make the same side-by-side boxplots, but in grey-scale

Use the scale_fill_grey command for the grey-scale legend, and again, use fill=Month in the aesthetics

Side by Side Boxplots in Gray Scale

Here we just changed the color palette to gray scale using scale_fill_grey

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

Plot 5: Now make one plot on your own of any of the variables in this dataset.

It may be a scatterplot, histogram, or boxplot.

ggplot(
  data = airquality,
  mapping = aes(x = Ozone, y = Temp, na.rm = TRUE)
) +
  geom_point(aes(color = Month), na.rm = TRUE) +
  geom_smooth(method = "gam", formula = y ~ s(x, bs = "cs"), color = "#212121", alpha = 0.2, na.rm = TRUE) +
  labs(
    title = "Ozone vs Temperature",
    subtitle = "There is a corrolation between Temperature and ozone levels",
    x = "Ozone", y = "Temperature",
    color = "Months"
  ) + scale_color_tableau()

The plot shows a correlation between Temperature and Ozone levels, with the months indicated through color. This allows us to see how the ozone levels are very low on colder days, and much higher as the temperature rises. The black line of best fit was inserted to better indicate this relationship. What is also evident is that ozone levels have a clearer correlation with temperature than with a specific month. Ozone levels are associated with higher temperatures.

This was plotted out using a geom_point scatter plot function, assigning the values of ozone to the x-axis, temperature to the y-axis, and color to indicate the months. I also added a geom_smooth function and color hexcode, and an alpha of 0.2 for the line of best fit. The colors for the months was chosen from the ggthemes selection.