Airquality Homework Assignment

Author

Renato Chavez

Published

February 5, 2023

Airquality Tutorial and Homework Assignment

Load in the Dataset

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

# install.packages("tidyverse")
library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0     ✔ purrr   1.0.1
✔ tibble  3.1.8     ✔ dplyr   1.1.0
✔ tidyr   1.3.0     ✔ stringr 1.5.0
✔ readr   2.1.3     ✔ forcats 1.0.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Load the Dataset into your global environment

airquality <- airquality

Look at the structure of the data

str(airquality)

'data.frame':   153 obs. of  6 variables:
 $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
 $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
 $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
 $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
 $ Month  : int  5 5 5 5 5 5 5 5 5 5 ...
 $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

Calculating 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

Calculate Median, Standard Deviation, and Variance

median(airquality$Temp)

[1] 79

sd(airquality$Wind)

[1] 3.523001

var(airquality$Wind)

[1] 12.41154

Change the Months from 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"

Look at the summary statistics of the dataset, and see how Month has changed to have characters instead of numbers

str(airquality)

'data.frame':   153 obs. of  6 variables:
 $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
 $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
 $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
 $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
 $ Month  : chr  "May" "May" "May" "May" ...
 $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

summary(airquality)

     Ozone           Solar.R           Wind             Temp      
 Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00  
 1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00  
 Median : 31.50   Median :205.0   Median : 9.700   Median :79.00  
 Mean   : 42.13   Mean   :185.9   Mean   : 9.958   Mean   :77.88  
 3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500   3rd Qu.:85.00  
 Max.   :168.00   Max.   :334.0   Max.   :20.700   Max.   :97.00  
 NA's   :37       NA's   :7                                       
    Month                Day      
 Length:153         Min.   : 1.0  
 Class :character   1st Qu.: 8.0  
 Mode  :character   Median :16.0  
                    Mean   :15.8  
                    3rd Qu.:23.0  
                    Max.   :31.0

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

Reorder the Months so they do not default to alphabetical

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

Plot 1: Create a histogram categorized by Month with qplot

Qplot stands for “Quick-Plot” (in the ggplot2 package)

p1 <- qplot(data = airquality,Temp,fill = Month,geom = "histogram", bins = 20)

Warning: `qplot()` was deprecated in ggplot2 3.4.0.

p1

Plot 2: Make a histogram using ggplot

ggplot is more sophisticated than qplot, but still uses ggplot2 package Reorder the legend so that it is not the default (alphabetical), but rather in order that months come Outline the bars in white using the color = “white” command

Histogram of Average Temperature by Month

p2 <- airquality %>%
  ggplot(aes(x=Temp, fill=Month)) +
  geom_histogram(position="identity", alpha=0.5, bandwidth = 5, color = "white")+
  scale_fill_discrete(name = "Month", labels = c("May", "June", "July", "August", "September"))

Warning in geom_histogram(position = "identity", alpha = 0.5, bandwidth = 5, :
Ignoring unknown parameters: `bandwidth`

p2

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

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

fill=Month command fills each boxplot with a different color in aesthetics scale_fill_discrete makes the legend on the side for discrete color values

Side by Side Boxplots of Average Temperature by Month

p3 <- airquality %>%
  ggplot(aes(Month, Temp, fill = Month)) +
  ggtitle("Temperatures") +
  xlab("Montly Temperatures") +
  ylab("Frequency") +
  geom_boxplot() +
  scale_fill_discrete(name = "Month", labels = c("May", "June", "July", "August", "September"))
p3

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

p4 <- airquality %>%
  ggplot(aes(Month, Temp, fill = Month)) +
  ggtitle("Monthly Temperature Variations") +
  xlab("Monthly Temperatures") + 
  ylab("Frequency") +
  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.

p5 <- airquality %>%
  ggplot(aes(Solar.R, fill = Month)) +
  ggtitle("Monthly Solar Levels") +
  xlab("Solar Radiation Levels") + 
  geom_histogram() +
  theme(axis.text.y = element_blank()) +
  scale_fill_discrete(name = "Month", labels = c("May", "June", "July", "August", "September"))
p5

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

Warning: Removed 7 rows containing non-finite values (`stat_bin()`).

This is a histogram that represents the solar radiation levels from May to September. I notice that the months of May and June show the highest solar radiation levels. This is expected as that is the time when summer starts. I believe that it is important to notice that solar radiation can still afect us throughout the year. Therefore, we all need to take care of our skin throughout the entire year and not just during the summer months. Even when we think that it is a cloudy day, solar radiation can affect our health.