This is section

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

EXplore the data

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

mean(airquality$Temp)
## [1] 77.88235
mean(airquality[,4])
## [1] 77.88235

Calculate Median,Standard Deviation, and Variance

median(airquality$Ozone)
## [1] NA
median(airquality$Solar.R)
## [1] NA
median(airquality$Wind)
## [1] 9.7
sd(airquality$Ozone)
## [1] NA
sd(airquality$Wind)
## [1] 3.523001
var(airquality$Temp)
## [1] 89.59133

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 ...
head(airquality,10)
##    Ozone Solar.R Wind Temp Month Day
## 1     41     190  7.4   67   May   1
## 2     36     118  8.0   72   May   2
## 3     12     149 12.6   74   May   3
## 4     18     313 11.5   62   May   4
## 5     NA      NA 14.3   56   May   5
## 6     28      NA 14.9   66   May   6
## 7     23     299  8.6   65   May   7
## 8     19      99 13.8   59   May   8
## 9      8      19 20.1   61   May   9
## 10    NA     194  8.6   69   May  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  
##

When I apply the funtion median to ozone, Solar.R, the results were NA. But in the summary, they have the values. Why?

mean(airquality$ozone)
## Warning in mean.default(airquality$ozone): argument is not numeric or logical:
## returning NA
## [1] NA

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 ggplot 2 package) R is case sensitive. Be careful. The difference of the max. and min. temperature is 41 so ‘20 bins’ are good choice for the histogram.

p1 <- qplot(data = airquality,Temp,fill = Month,geom = "histogram", alpha = 0.5, bins = 20,color =I("gray"))
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 gray using the color = “gray” command

I changed the binwidth = 2 to compare the qplot histogram and the ggplot histogram.

The results seemed different in some parts. The frequency of temperature 80 to 85 is different from the qplot histogram and the ggplot histogram although they have the same data. The count of qplot = 46 and the count of ggpot = 19. Because the qplot’s count in y-axis is the sum of each month’s frequency and the qqplot’s count in y-axis shows the frequency of each month as its color.

p2 <- airquality %>%
  ggplot(aes(x=Temp, fill=Month)) +
  geom_histogram(position="identity", alpha=0.5, binwidth = 2, color = "gray")+
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September"))
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 <- airquality %>%
  ggplot(aes(Month, Temp, fill = Month)) + 
  ggtitle("Temperatures") +
  xlab("Monthly 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

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.

First, make a easy histogram. It seems that the boxplot is more useful than the histogram regarding temperature and month.

p2 <- airquality %>%
 ggplot(aes(x= Month,y= Temp, fill=Month)) +
  stat_summary(fun = mean, geom = "col", alpha = 0.5)

p2

Correlations

I want to see each variable’s correlations so that what makes a bad influence to air quality(that is ozone level) I used these code but see only the correlation of Ozone and Solar.R and I got an error about render.

{r}
columns <-c('Ozone','Solar.R','Wind','Temp')
rows <-rowSums(is.na(airquality)) == 0
round(cor(airquality[row,col]),3)
{r}
Col <-c('Wind','Temp')
row <-rowSums(is.na(airquality)) == 0
round(cor(airquality[row,col]),1)

I failed to make the correlation table. However, in general, it is known that the temperature increases, the ground level ozone increases as well. As result of the scatter plot, we can see that temperature and ozone are strongly related. First, make scatter plot using qplot.

qplot(Temp,Ozone,data=airquality,color=Month,geom='point')
## Warning: Removed 37 rows containing missing values (geom_point).

Using ggplot

To add a regression line, use the function geom_smooth() The default confidence level is 0.95. I chaged it to 0.90 for pratice.

ggplot(airquality,aes(x=Temp, y=Ozone)) + geom_point(aes(shape = Month, color = Month)) + geom_smooth(method="lm" ,level=0.90)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).