HomeWork1

pacman:: p_load(tidyverse, zoo,lubridate)

Show the data

df <- airquality

Look at the structure of the data

str(df)

## '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

mean(df$Temp)

## [1] 77.88235

mean(df[,4])

## [1] 77.88235

Calculate Median, Standard Deviation, and Variance

median(df$Temp)

## [1] 79

sd(df$Wind)

## [1] 3.523001

var(df$Wind)

## [1] 12.41154

Change the Months from 5 - 9 to May through September

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

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

str(df)

## '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(df)

##      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

df$Month<-factor(df$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 = df,Wind,fill = Month,geom = "histogram", bins = 10)
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

p2 <- df%>%
  ggplot(aes(x=Wind, fill=Month)) +
  geom_histogram (position="identity", alpha=10, binwidth = 2, color = "white")+
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September"))
#lines(density(df$Wind),lwd = 4, col = "red"))
p2

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

fill=Month command fills each boxplot with a different color in the aesthetics

scale_fill_discrete makes the legend on the side for discrete color values

p3 <- df %>%
  ggplot(aes(Month, Wind, fill = Month)) + 
  ggtitle( "Wind Speed") +
  xlab("Months") +
  ylab("Velocity") +
  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

p4 <- df %>%
  ggplot(aes(Month, Temp, fill = Month)) + 
  ggtitle("Temperatures") +
  xlab("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 scatter plot, a histogram or boxplot

Creating a histogram with Density

this can only be archived if we set the arg to FALSE, because we are more interested in density than frequency. Density can give us the probability density of Temperature reaching a certain level

hist(df$Temp, freq = FALSE, main = "Temperature Histogram", xlab = "Temperature", ylab= "Temperature Density", las = 1, col = c("pink", "green"))

#Adding Density Curve to Histogram

hist(df$Temp, freq = FALSE, main = "Temperature Histogram", xlab = "Temperature", ylab= "Temperature Density", las = 1, col = c("pink", "green"))

#the following statement draws a density curve
lines(density(df$Temp), lwd = 4, col = "red")