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install.packages('plyr', repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/admin/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)
## package 'plyr' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\admin\AppData\Local\Temp\RtmpEnE8qB\downloaded_packages
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.2
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data("airquality")
airquality <- airquality %>%
  mutate(Date = as.Date(paste(1973, Month, Day, sep = "-")))
head(airquality)
##   Ozone Solar.R Wind Temp Month Day       Date
## 1    41     190  7.4   67     5   1 1973-05-01
## 2    36     118  8.0   72     5   2 1973-05-02
## 3    12     149 12.6   74     5   3 1973-05-03
## 4    18     313 11.5   62     5   4 1973-05-04
## 5    NA      NA 14.3   56     5   5 1973-05-05
## 6    28      NA 14.9   66     5   6 1973-05-06
timeplot <- ggplot(airquality, aes(x = Date, y = Ozone)) +
  geom_line(color = "blue") +
  labs(title = "Time Series Plot of Ozone Levels in NY 1973",
       x = "Date",
       y = "Ozone")
timeplot

data("iris")
summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 
options(repos = c(CRAN = "http://cran.us.r-project.org"))
install.packages("psych")
## Installing package into 'C:/Users/admin/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)
## package 'psych' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\admin\AppData\Local\Temp\RtmpEnE8qB\downloaded_packages
library(psych)
## Warning: package 'psych' was built under R version 4.3.3
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
describe(iris)
##              vars   n mean   sd median trimmed  mad min max range  skew
## Sepal.Length    1 150 5.84 0.83   5.80    5.81 1.04 4.3 7.9   3.6  0.31
## Sepal.Width     2 150 3.06 0.44   3.00    3.04 0.44 2.0 4.4   2.4  0.31
## Petal.Length    3 150 3.76 1.77   4.35    3.76 1.85 1.0 6.9   5.9 -0.27
## Petal.Width     4 150 1.20 0.76   1.30    1.18 1.04 0.1 2.5   2.4 -0.10
## Species*        5 150 2.00 0.82   2.00    2.00 1.48 1.0 3.0   2.0  0.00
##              kurtosis   se
## Sepal.Length    -0.61 0.07
## Sepal.Width      0.14 0.04
## Petal.Length    -1.42 0.14
## Petal.Width     -1.36 0.06
## Species*        -1.52 0.07
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  labs(title = "Scatter Plot of Sepal Length vs Sepal Width",
       x = "Sepal Length",
       y = "Sepal Width")

regmodel <- lm(Petal.Width ~ Petal.Length, data = iris)
summary(regmodel)
## 
## Call:
## lm(formula = Petal.Width ~ Petal.Length, data = iris)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.56515 -0.12358 -0.01898  0.13288  0.64272 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.363076   0.039762  -9.131  4.7e-16 ***
## Petal.Length  0.415755   0.009582  43.387  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2065 on 148 degrees of freedom
## Multiple R-squared:  0.9271, Adjusted R-squared:  0.9266 
## F-statistic:  1882 on 1 and 148 DF,  p-value: < 2.2e-16
coefficients <-coef(regmodel)
coefficients
##  (Intercept) Petal.Length 
##   -0.3630755    0.4157554
covariance <- cov(iris$Petal.Length, iris$Petal.Width)
variance <- var(iris$Petal.Length)
covariance/variance
## [1] 0.4157554