llaba2

1. Установить пакет CARET, выполнить команду names(getModelInfo()), ознакомиться со списком доступных методов выбора признаков. Выполните ## графический разведочный анализ данных с использование функции featurePlot() для набора данных из справочного файла пакета CARET:

x <- matrix(rnorm(50*5),ncol=5)

y <- factor(rep(c(“A”, “B”), 25))

Сохранить полученные графики в *.jpg файлы. Сделать выводы.

install.packages("caret")

## 
## The downloaded binary packages are in
##  /var/folders/fs/4v998hvs7wn66j723xbq32pxv4ld55/T//Rtmpf57Zc8/downloaded_packages

library(caret)

## Loading required package: ggplot2

## Loading required package: lattice

x <- matrix(rnorm(50*5),ncol=5)
y <- factor(rep(c("A", "B"), 25))
names(getModelInfo())

##   [1] "ada"                 "AdaBag"              "AdaBoost.M1"        
##   [4] "adaboost"            "amdai"               "ANFIS"              
##   [7] "avNNet"              "awnb"                "awtan"              
##  [10] "bag"                 "bagEarth"            "bagEarthGCV"        
##  [13] "bagFDA"              "bagFDAGCV"           "bam"                
##  [16] "bartMachine"         "bayesglm"            "binda"              
##  [19] "blackboost"          "blasso"              "blassoAveraged"     
##  [22] "bridge"              "brnn"                "BstLm"              
##  [25] "bstSm"               "bstTree"             "C5.0"               
##  [28] "C5.0Cost"            "C5.0Rules"           "C5.0Tree"           
##  [31] "cforest"             "chaid"               "CSimca"             
##  [34] "ctree"               "ctree2"              "cubist"             
##  [37] "dda"                 "deepboost"           "DENFIS"             
##  [40] "dnn"                 "dwdLinear"           "dwdPoly"            
##  [43] "dwdRadial"           "earth"               "elm"                
##  [46] "enet"                "evtree"              "extraTrees"         
##  [49] "fda"                 "FH.GBML"             "FIR.DM"             
##  [52] "foba"                "FRBCS.CHI"           "FRBCS.W"            
##  [55] "FS.HGD"              "gam"                 "gamboost"           
##  [58] "gamLoess"            "gamSpline"           "gaussprLinear"      
##  [61] "gaussprPoly"         "gaussprRadial"       "gbm_h2o"            
##  [64] "gbm"                 "gcvEarth"            "GFS.FR.MOGUL"       
##  [67] "GFS.LT.RS"           "GFS.THRIFT"          "glm.nb"             
##  [70] "glm"                 "glmboost"            "glmnet_h2o"         
##  [73] "glmnet"              "glmStepAIC"          "gpls"               
##  [76] "hda"                 "hdda"                "hdrda"              
##  [79] "HYFIS"               "icr"                 "J48"                
##  [82] "JRip"                "kernelpls"           "kknn"               
##  [85] "knn"                 "krlsPoly"            "krlsRadial"         
##  [88] "lars"                "lars2"               "lasso"              
##  [91] "lda"                 "lda2"                "leapBackward"       
##  [94] "leapForward"         "leapSeq"             "Linda"              
##  [97] "lm"                  "lmStepAIC"           "LMT"                
## [100] "loclda"              "logicBag"            "LogitBoost"         
## [103] "logreg"              "lssvmLinear"         "lssvmPoly"          
## [106] "lssvmRadial"         "lvq"                 "M5"                 
## [109] "M5Rules"             "manb"                "mda"                
## [112] "Mlda"                "mlp"                 "mlpKerasDecay"      
## [115] "mlpKerasDecayCost"   "mlpKerasDropout"     "mlpKerasDropoutCost"
## [118] "mlpML"               "mlpSGD"              "mlpWeightDecay"     
## [121] "mlpWeightDecayML"    "monmlp"              "msaenet"            
## [124] "multinom"            "mxnet"               "mxnetAdam"          
## [127] "naive_bayes"         "nb"                  "nbDiscrete"         
## [130] "nbSearch"            "neuralnet"           "nnet"               
## [133] "nnls"                "nodeHarvest"         "null"               
## [136] "OneR"                "ordinalNet"          "ordinalRF"          
## [139] "ORFlog"              "ORFpls"              "ORFridge"           
## [142] "ORFsvm"              "ownn"                "pam"                
## [145] "parRF"               "PART"                "partDSA"            
## [148] "pcaNNet"             "pcr"                 "pda"                
## [151] "pda2"                "penalized"           "PenalizedLDA"       
## [154] "plr"                 "pls"                 "plsRglm"            
## [157] "polr"                "ppr"                 "pre"                
## [160] "PRIM"                "protoclass"          "qda"                
## [163] "QdaCov"              "qrf"                 "qrnn"               
## [166] "randomGLM"           "ranger"              "rbf"                
## [169] "rbfDDA"              "Rborist"             "rda"                
## [172] "regLogistic"         "relaxo"              "rf"                 
## [175] "rFerns"              "RFlda"               "rfRules"            
## [178] "ridge"               "rlda"                "rlm"                
## [181] "rmda"                "rocc"                "rotationForest"     
## [184] "rotationForestCp"    "rpart"               "rpart1SE"           
## [187] "rpart2"              "rpartCost"           "rpartScore"         
## [190] "rqlasso"             "rqnc"                "RRF"                
## [193] "RRFglobal"           "rrlda"               "RSimca"             
## [196] "rvmLinear"           "rvmPoly"             "rvmRadial"          
## [199] "SBC"                 "sda"                 "sdwd"               
## [202] "simpls"              "SLAVE"               "slda"               
## [205] "smda"                "snn"                 "sparseLDA"          
## [208] "spikeslab"           "spls"                "stepLDA"            
## [211] "stepQDA"             "superpc"             "svmBoundrangeString"
## [214] "svmExpoString"       "svmLinear"           "svmLinear2"         
## [217] "svmLinear3"          "svmLinearWeights"    "svmLinearWeights2"  
## [220] "svmPoly"             "svmRadial"           "svmRadialCost"      
## [223] "svmRadialSigma"      "svmRadialWeights"    "svmSpectrumString"  
## [226] "tan"                 "tanSearch"           "treebag"            
## [229] "vbmpRadial"          "vglmAdjCat"          "vglmContRatio"      
## [232] "vglmCumulative"      "widekernelpls"       "WM"                 
## [235] "wsrf"                "xgbDART"             "xgbLinear"          
## [238] "xgbTree"             "xyf"

featurePlot(x,y)

2. С использование функций из пакета Fselector [2] определить важность признаков для решения задачи классификации. Использовать набор ## data(iris). Сделать выводы.

install.packages("FSelector")

## 
## The downloaded binary packages are in
##  /var/folders/fs/4v998hvs7wn66j723xbq32pxv4ld55/T//Rtmpf57Zc8/downloaded_packages

library(FSelector)
data(iris)
important <- information.gain(Species ~ ., data = iris)
print(important)

##              attr_importance
## Sepal.Length       0.4521286
## Sepal.Width        0.2672750
## Petal.Length       0.9402853
## Petal.Width        0.9554360

3. С использованием функции discretize() из пакета arules выполните преобразование непрерывной переменной в категориальную [3] различными ## методами: «interval» (равная ширина интервала), «frequency» (равная частота), «cluster» (кластеризация) и «fixed» (категории задают ## ## границы интервалов). Используйте набор данных iris. Сделайте выводы

install.packages("arules")

## 
## The downloaded binary packages are in
##  /var/folders/fs/4v998hvs7wn66j723xbq32pxv4ld55/T//Rtmpf57Zc8/downloaded_packages

library(arules)

## Loading required package: Matrix

## 
## Attaching package: 'arules'

## The following objects are masked from 'package:base':
## 
##     abbreviate, write

data(iris)
disc_interval <- discretize(iris$Sepal.Length, method = "interval", categories = 3)

## Warning in discretize(iris$Sepal.Length, method = "interval", categories = 3):
## Parameter categories is deprecated. Use breaks instead! Also, the default
## method is now frequency!

print(disc_interval)

##   [1] [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5)
##   [8] [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5)
##  [15] [5.5,6.7) [5.5,6.7) [4.3,5.5) [4.3,5.5) [5.5,6.7) [4.3,5.5) [4.3,5.5)
##  [22] [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5)
##  [29] [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [5.5,6.7) [4.3,5.5)
##  [36] [4.3,5.5) [5.5,6.7) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5)
##  [43] [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5) [4.3,5.5)
##  [50] [4.3,5.5) [6.7,7.9] [5.5,6.7) [6.7,7.9] [5.5,6.7) [5.5,6.7) [5.5,6.7)
##  [57] [5.5,6.7) [4.3,5.5) [5.5,6.7) [4.3,5.5) [4.3,5.5) [5.5,6.7) [5.5,6.7)
##  [64] [5.5,6.7) [5.5,6.7) [6.7,7.9] [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7)
##  [71] [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7) [6.7,7.9]
##  [78] [6.7,7.9] [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7)
##  [85] [4.3,5.5) [5.5,6.7) [6.7,7.9] [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7)
##  [92] [5.5,6.7) [5.5,6.7) [4.3,5.5) [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7)
##  [99] [4.3,5.5) [5.5,6.7) [5.5,6.7) [5.5,6.7) [6.7,7.9] [5.5,6.7) [5.5,6.7)
## [106] [6.7,7.9] [4.3,5.5) [6.7,7.9] [6.7,7.9] [6.7,7.9] [5.5,6.7) [5.5,6.7)
## [113] [6.7,7.9] [5.5,6.7) [5.5,6.7) [5.5,6.7) [5.5,6.7) [6.7,7.9] [6.7,7.9]
## [120] [5.5,6.7) [6.7,7.9] [5.5,6.7) [6.7,7.9] [5.5,6.7) [6.7,7.9] [6.7,7.9]
## [127] [5.5,6.7) [5.5,6.7) [5.5,6.7) [6.7,7.9] [6.7,7.9] [6.7,7.9] [5.5,6.7)
## [134] [5.5,6.7) [5.5,6.7) [6.7,7.9] [5.5,6.7) [5.5,6.7) [5.5,6.7) [6.7,7.9]
## [141] [6.7,7.9] [6.7,7.9] [5.5,6.7) [6.7,7.9] [6.7,7.9] [6.7,7.9] [5.5,6.7)
## [148] [5.5,6.7) [5.5,6.7) [5.5,6.7)
## attr(,"discretized:breaks")
## [1] 4.3 5.5 6.7 7.9
## attr(,"discretized:method")
## [1] interval
## Levels: [4.3,5.5) [5.5,6.7) [6.7,7.9]

disc_interval <- discretize(iris$Sepal.Length, method = "frequency", categories = 3)

## Warning in discretize(iris$Sepal.Length, method = "frequency", categories = 3):
## Parameter categories is deprecated. Use breaks instead! Also, the default
## method is now frequency!

print(disc_interval)

##   [1] [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [5.4,6.3) [4.3,5.4)
##   [8] [4.3,5.4) [4.3,5.4) [4.3,5.4) [5.4,6.3) [4.3,5.4) [4.3,5.4) [4.3,5.4)
##  [15] [5.4,6.3) [5.4,6.3) [5.4,6.3) [4.3,5.4) [5.4,6.3) [4.3,5.4) [5.4,6.3)
##  [22] [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4)
##  [29] [4.3,5.4) [4.3,5.4) [4.3,5.4) [5.4,6.3) [4.3,5.4) [5.4,6.3) [4.3,5.4)
##  [36] [4.3,5.4) [5.4,6.3) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4)
##  [43] [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4) [4.3,5.4)
##  [50] [4.3,5.4) [6.3,7.9] [6.3,7.9] [6.3,7.9] [5.4,6.3) [6.3,7.9] [5.4,6.3)
##  [57] [6.3,7.9] [4.3,5.4) [6.3,7.9] [4.3,5.4) [4.3,5.4) [5.4,6.3) [5.4,6.3)
##  [64] [5.4,6.3) [5.4,6.3) [6.3,7.9] [5.4,6.3) [5.4,6.3) [5.4,6.3) [5.4,6.3)
##  [71] [5.4,6.3) [5.4,6.3) [6.3,7.9] [5.4,6.3) [6.3,7.9] [6.3,7.9] [6.3,7.9]
##  [78] [6.3,7.9] [5.4,6.3) [5.4,6.3) [5.4,6.3) [5.4,6.3) [5.4,6.3) [5.4,6.3)
##  [85] [5.4,6.3) [5.4,6.3) [6.3,7.9] [6.3,7.9] [5.4,6.3) [5.4,6.3) [5.4,6.3)
##  [92] [5.4,6.3) [5.4,6.3) [4.3,5.4) [5.4,6.3) [5.4,6.3) [5.4,6.3) [5.4,6.3)
##  [99] [4.3,5.4) [5.4,6.3) [6.3,7.9] [5.4,6.3) [6.3,7.9] [6.3,7.9] [6.3,7.9]
## [106] [6.3,7.9] [4.3,5.4) [6.3,7.9] [6.3,7.9] [6.3,7.9] [6.3,7.9] [6.3,7.9]
## [113] [6.3,7.9] [5.4,6.3) [5.4,6.3) [6.3,7.9] [6.3,7.9] [6.3,7.9] [6.3,7.9]
## [120] [5.4,6.3) [6.3,7.9] [5.4,6.3) [6.3,7.9] [6.3,7.9] [6.3,7.9] [6.3,7.9]
## [127] [5.4,6.3) [5.4,6.3) [6.3,7.9] [6.3,7.9] [6.3,7.9] [6.3,7.9] [6.3,7.9]
## [134] [6.3,7.9] [5.4,6.3) [6.3,7.9] [6.3,7.9] [6.3,7.9] [5.4,6.3) [6.3,7.9]
## [141] [6.3,7.9] [6.3,7.9] [5.4,6.3) [6.3,7.9] [6.3,7.9] [6.3,7.9] [6.3,7.9]
## [148] [6.3,7.9] [5.4,6.3) [5.4,6.3)
## attr(,"discretized:breaks")
## [1] 4.3 5.4 6.3 7.9
## attr(,"discretized:method")
## [1] frequency
## Levels: [4.3,5.4) [5.4,6.3) [6.3,7.9]

disc_interval <- discretize(iris$Sepal.Length, method = "cluster", categories = 3)

## Warning in discretize(iris$Sepal.Length, method = "cluster", categories = 3):
## Parameter categories is deprecated. Use breaks instead! Also, the default
## method is now frequency!

print(disc_interval)

##   [1] [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [5.33,6.27)
##   [7] [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [5.33,6.27) [4.3,5.33) 
##  [13] [4.3,5.33)  [4.3,5.33)  [5.33,6.27) [5.33,6.27) [5.33,6.27) [4.3,5.33) 
##  [19] [5.33,6.27) [4.3,5.33)  [5.33,6.27) [4.3,5.33)  [4.3,5.33)  [4.3,5.33) 
##  [25] [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33) 
##  [31] [4.3,5.33)  [5.33,6.27) [4.3,5.33)  [5.33,6.27) [4.3,5.33)  [4.3,5.33) 
##  [37] [5.33,6.27) [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33) 
##  [43] [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33)  [4.3,5.33) 
##  [49] [4.3,5.33)  [4.3,5.33)  [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [5.33,6.27)
##  [55] [6.27,7.9]  [5.33,6.27) [6.27,7.9]  [4.3,5.33)  [6.27,7.9]  [4.3,5.33) 
##  [61] [4.3,5.33)  [5.33,6.27) [5.33,6.27) [5.33,6.27) [5.33,6.27) [6.27,7.9] 
##  [67] [5.33,6.27) [5.33,6.27) [5.33,6.27) [5.33,6.27) [5.33,6.27) [5.33,6.27)
##  [73] [6.27,7.9]  [5.33,6.27) [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9] 
##  [79] [5.33,6.27) [5.33,6.27) [5.33,6.27) [5.33,6.27) [5.33,6.27) [5.33,6.27)
##  [85] [5.33,6.27) [5.33,6.27) [6.27,7.9]  [6.27,7.9]  [5.33,6.27) [5.33,6.27)
##  [91] [5.33,6.27) [5.33,6.27) [5.33,6.27) [4.3,5.33)  [5.33,6.27) [5.33,6.27)
##  [97] [5.33,6.27) [5.33,6.27) [4.3,5.33)  [5.33,6.27) [6.27,7.9]  [5.33,6.27)
## [103] [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [4.3,5.33)  [6.27,7.9] 
## [109] [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [5.33,6.27)
## [115] [5.33,6.27) [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [5.33,6.27)
## [121] [6.27,7.9]  [5.33,6.27) [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9] 
## [127] [5.33,6.27) [5.33,6.27) [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9] 
## [133] [6.27,7.9]  [6.27,7.9]  [5.33,6.27) [6.27,7.9]  [6.27,7.9]  [6.27,7.9] 
## [139] [5.33,6.27) [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [5.33,6.27) [6.27,7.9] 
## [145] [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [6.27,7.9]  [5.33,6.27) [5.33,6.27)
## attr(,"discretized:breaks")
## [1] 4.300000 5.332732 6.272161 7.900000
## attr(,"discretized:method")
## [1] cluster
## Levels: [4.3,5.33) [5.33,6.27) [6.27,7.9]

disc_interval <- discretize(iris$Sepal.Length, method = "fixed", breaks = c(4,5,6,7))
print(disc_interval)

##   [1] [5,6) [4,5) [4,5) [4,5) [5,6) [5,6) [4,5) [5,6) [4,5) [4,5) [5,6) [4,5)
##  [13] [4,5) [4,5) [5,6) [5,6) [5,6) [5,6) [5,6) [5,6) [5,6) [5,6) [4,5) [5,6)
##  [25] [4,5) [5,6) [5,6) [5,6) [5,6) [4,5) [4,5) [5,6) [5,6) [5,6) [4,5) [5,6)
##  [37] [5,6) [4,5) [4,5) [5,6) [5,6) [4,5) [4,5) [5,6) [5,6) [4,5) [5,6) [4,5)
##  [49] [5,6) [5,6) [6,7] [6,7] [6,7] [5,6) [6,7] [5,6) [6,7] [4,5) [6,7] [5,6)
##  [61] [5,6) [5,6) [6,7] [6,7] [5,6) [6,7] [5,6) [5,6) [6,7] [5,6) [5,6) [6,7]
##  [73] [6,7] [6,7] [6,7] [6,7] [6,7] [6,7] [6,7] [5,6) [5,6) [5,6) [5,6) [6,7]
##  [85] [5,6) [6,7] [6,7] [6,7] [5,6) [5,6) [5,6) [6,7] [5,6) [5,6) [5,6) [5,6)
##  [97] [5,6) [6,7] [5,6) [5,6) [6,7] [5,6) <NA>  [6,7] [6,7] <NA>  [4,5) <NA> 
## [109] [6,7] <NA>  [6,7] [6,7] [6,7] [5,6) [5,6) [6,7] [6,7] <NA>  <NA>  [6,7]
## [121] [6,7] [5,6) <NA>  [6,7] [6,7] <NA>  [6,7] [6,7] [6,7] <NA>  <NA>  <NA> 
## [133] [6,7] [6,7] [6,7] <NA>  [6,7] [6,7] [6,7] [6,7] [6,7] [6,7] [5,6) [6,7]
## [145] [6,7] [6,7] [6,7] [6,7] [6,7] [5,6)
## attr(,"discretized:breaks")
## [1] 4 5 6 7
## attr(,"discretized:method")
## [1] fixed
## Levels: [4,5) [5,6) [6,7]

3. Установите пакет Boruta и проведите выбор признаков для набора данных data(“Ozone”) [4, 5, 6]. Построить график boxplot, сделать ## ## выводы.

install.packages("Boruta")

## 
## The downloaded binary packages are in
##  /var/folders/fs/4v998hvs7wn66j723xbq32pxv4ld55/T//Rtmpf57Zc8/downloaded_packages

library(Boruta)
data("airquality")
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

airquality <- na.omit(airquality)
airquality$OzoneLevel <- factor(ifelse(airquality$Ozone > median(airquality$Ozone, na.rm = TRUE), "High", "Low"))
set.seed(123)
result <- Boruta(OzoneLevel ~ ., data = airquality, doTrace = 2)

##  1. run of importance source...

##  2. run of importance source...

##  3. run of importance source...

##  4. run of importance source...

##  5. run of importance source...

##  6. run of importance source...

##  7. run of importance source...

##  8. run of importance source...

##  9. run of importance source...

##  10. run of importance source...

## After 10 iterations, +0.097 secs:

##  confirmed 4 attributes: Ozone, Solar.R, Temp, Wind;

##  still have 2 attributes left.

##  11. run of importance source...

##  12. run of importance source...

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##  47. run of importance source...

## After 47 iterations, +0.51 secs:

##  confirmed 1 attribute: Month;

##  still have 1 attribute left.

##  48. run of importance source...

##  49. run of importance source...

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##  51. run of importance source...

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##  61. run of importance source...

##  62. run of importance source...

## After 62 iterations, +0.64 secs:

##  confirmed 1 attribute: Day;

##  no more attributes left.

print(result)

## Boruta performed 62 iterations in 0.640862 secs.
##  6 attributes confirmed important: Day, Month, Ozone, Solar.R, Temp and
## 1 more;
##  No attributes deemed unimportant.

priznak <- getSelectedAttributes(result, withTentative = FALSE)
print(priznak)

## [1] "Ozone"   "Solar.R" "Wind"    "Temp"    "Month"   "Day"

boxplot(airquality$Solar.R ~ airquality$OzoneLevel,main = "Boxplot of Solar Radiation by Ozone Level",xlab = "Ozone Level", ylab = "Solar Radiation",col = c("lightblue", "lightgreen"))

boxplot(airquality$Wind ~ airquality$OzoneLevel,main = "Boxplot of Wind by Ozone Level",xlab = "Ozone Level", ylab = "Wind", col = c("lightblue", "lightgreen"))