R Notebook
——-Loading Data into R
#setwd("F:\\1_FreeMLVideo_R")
#change to your own working directory
setwd("C:\\Users\\joe\\Desktop\\RStudio")
## read in csv and tex files
resp1=read.csv("Resp1.csv",header=T)
head(resp1)str(resp1)## 'data.frame': 32 obs. of 2 variables:
## $ fitness : int 0 0 0 0 0 0 0 0 1 1 ...
## $ respiration: num 3.94 4.26 4.16 3.76 4.07 3.57 4.11 4.18 4 4.16 ...resp2=read.table("Resp2.txt",header=T)
head(resp2)#read in the CSV data UCL website:
#https://archive.ics.uci.edu/ml/datasets/Wine+Quality
winer1=read.csv("winequality-red.csv",header=T)
#header= T will read in column names as well
head(winer1)summary(winer1)## fixed.acidity.volatile.acidity.citric.acid.residual.sugar.chlorides.free.sulfur.dioxide.total.sulfur.dioxide.density.pH.sulphates.alcohol.quality
## 6.7;0.46;0.24;1.7;0.077;18;34;0.9948;3.39;0.6;10.6;6 : 4
## 7.2;0.36;0.46;2.1;0.074;24;44;0.99534;3.4;0.85;11;7 : 4
## 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 : 4
## 7.5;0.51;0.02;1.7;0.084;13;31;0.99538;3.36;0.54;10.5;6: 4
## 11.5;0.18;0.51;4;0.104;4;23;0.9996;3.28;0.97;10.1;6 : 3
## 6.4;0.64;0.21;1.8;0.081;14;31;0.99689;3.59;0.66;9.8;5 : 3
## (Other) :1577winer1=read.csv("winequality-red.csv",header=T,sep=",")
#header= T will read in column names as well
head(winer1)summary(winer1)## fixed.acidity.volatile.acidity.citric.acid.residual.sugar.chlorides.free.sulfur.dioxide.total.sulfur.dioxide.density.pH.sulphates.alcohol.quality
## 6.7;0.46;0.24;1.7;0.077;18;34;0.9948;3.39;0.6;10.6;6 : 4
## 7.2;0.36;0.46;2.1;0.074;24;44;0.99534;3.4;0.85;11;7 : 4
## 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 : 4
## 7.5;0.51;0.02;1.7;0.084;13;31;0.99538;3.36;0.54;10.5;6: 4
## 11.5;0.18;0.51;4;0.104;4;23;0.9996;3.28;0.97;10.1;6 : 3
## 6.4;0.64;0.21;1.8;0.081;14;31;0.99689;3.59;0.66;9.8;5 : 3
## (Other) :1577#specify the correct seperator
winer=read.table("winequality-red.csv",header=T,sep=";")
#header= T will read in column names as well
head(winer)summary(winer)## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 4.60 Min. :0.1200 Min. :0.000 Min. : 0.900
## 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090 1st Qu.: 1.900
## Median : 7.90 Median :0.5200 Median :0.260 Median : 2.200
## Mean : 8.32 Mean :0.5278 Mean :0.271 Mean : 2.539
## 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420 3rd Qu.: 2.600
## Max. :15.90 Max. :1.5800 Max. :1.000 Max. :15.500
## chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. :0.01200 Min. : 1.00 Min. : 6.00
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00
## Median :0.07900 Median :14.00 Median : 38.00
## Mean :0.08747 Mean :15.87 Mean : 46.47
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00
## Max. :0.61100 Max. :72.00 Max. :289.00
## density pH sulphates alcohol
## Min. :0.9901 Min. :2.740 Min. :0.3300 Min. : 8.40
## 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50
## Median :0.9968 Median :3.310 Median :0.6200 Median :10.20
## Mean :0.9967 Mean :3.311 Mean :0.6581 Mean :10.42
## 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10
## Max. :1.0037 Max. :4.010 Max. :2.0000 Max. :14.90
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.636
## 3rd Qu.:6.000
## Max. :8.000##Read in excel data
#excel
#summary(boston1)
library(readxl)
dfb <- read_excel("boston1.xls")## New names:
## * `` -> ...8
## * `` -> ...9
## * `` -> ...10head(dfb)summary(dfb)## MV INDUS NOX RM
## Min. : 5.00 Min. : 0.46 Min. :38.50 Min. :3.561
## 1st Qu.:17.02 1st Qu.: 5.19 1st Qu.:44.90 1st Qu.:5.886
## Median :21.20 Median : 9.69 Median :53.80 Median :6.208
## Mean :22.53 Mean :11.14 Mean :55.47 Mean :6.285
## 3rd Qu.:25.00 3rd Qu.:18.10 3rd Qu.:62.40 3rd Qu.:6.623
## Max. :50.00 Max. :27.74 Max. :87.10 Max. :8.780
## TAX PT LSTAT ...8
## Min. :187.0 Min. :12.60 Min. : 1.73 Mode:logical
## 1st Qu.:279.0 1st Qu.:17.40 1st Qu.: 6.95 NA's:506
## Median :330.0 Median :19.05 Median :11.36
## Mean :408.2 Mean :18.46 Mean :12.65
## 3rd Qu.:666.0 3rd Qu.:20.20 3rd Qu.:16.95
## Max. :711.0 Max. :22.00 Max. :37.97
## ...9 ...10
## Mode:logical Length:506
## NA's:506 Class :character
## Mode :character
##
##
## #Using RCurl to read in csv data hosted online on github and other #sites
library(RCurl)## Loading required package: bitopsdata1= read.csv(text=getURL("https://raw.githubusercontent.com/sciruela/Happiness-Salaries/master/data.csv"))
head(data1)summary(data1)## Country Salary Happiness SP
## Australia: 1 Min. : 453 Min. :180.0 Min. : 1.000
## Austria : 1 1st Qu.:1268 1st Qu.:218.3 1st Qu.: 1.000
## Belgium : 1 Median :1756 Median :241.7 Median : 2.500
## Brazil : 1 Mean :1643 Mean :234.6 Mean : 4.643
## Canada : 1 3rd Qu.:2043 3rd Qu.:253.3 3rd Qu.: 7.250
## Cyprus : 1 Max. :2749 Max. :273.5 Max. :20.000
## (Other) :22
## Fitch Moody
## Min. : 1.000 Min. : 1.000
## 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 2.000 Median : 1.000
## Mean : 4.357 Mean : 4.429
## 3rd Qu.: 7.000 3rd Qu.: 6.000
## Max. :17.000 Max. :20.000
## data2=read.csv(text=getURL("https://raw.githubusercontent.com/opetchey/RREEBES/master/Beninca_etal_2008_Nature/data/nutrients_original.csv"), skip=7, header=T)
head(data2)summary(data2)## Date Day.number NO2 NO3
## :7815 Min. : 69.0 Min. : 0.04 Min. : 0.000
## 01/02/93: 1 1st Qu.: 710.2 1st Qu.: 0.29 1st Qu.: 0.850
## 01/03/93: 1 Median :1373.5 Median : 0.50 Median : 1.640
## 01/04/91: 1 Mean :1364.4 Mean : 2.23 Mean : 3.941
## 01/04/96: 1 3rd Qu.:2021.8 3rd Qu.: 1.12 3rd Qu.: 3.030
## 01/04/97: 1 Max. :2658.0 Max. :45.90 Max. :89.630
## (Other) : 343 NA's :7815 NA's :7816 NA's :7816
## NH4 Total.dissolved.inorganic.nitrogen
## Min. : 0.070 Min. : 1.26
## 1st Qu.: 2.737 1st Qu.: 5.44
## Median : 4.700 Median : 8.59
## Mean : 7.288 Mean : 13.50
## 3rd Qu.: 7.138 3rd Qu.: 13.48
## Max. :165.600 Max. :194.66
## NA's :7819 NA's :7820
## Soluble.reactive.phosphorus X
## Min. : 0.000 Mode:logical
## 1st Qu.: 0.520 NA's:8163
## Median : 1.320
## Mean : 4.629
## 3rd Qu.: 4.285
## Max. :46.500
## NA's :7815
## X.1
## :8158
## All nutrient concentrations are in micromol/L : 1
## Empty spaces are missing data : 1
## N.B. : 1
## The analysis in Beninc<U+0088> et al. (Nature 2008) included all nutrient data from 16/06/1991 until 20/10/1997: 1
## Zero values are nutrient concentrations under the detection limit. : 1
## ———— Index and Subset of Data
data(iris)
??iris## starting httpd help server ... donestr(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...summary(iris$Species)## setosa versicolor virginica
## 50 50 50head(iris, 10)df6 = iris[1:6,]
df2 = iris[,1:2]
vars = c("Sepal.Length", "Petal.Width", "Species")
nd=iris[vars]
str(nd)## 'data.frame': 150 obs. of 3 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...head(nd)vars = names (iris) %in% c("Species")
nd = iris[!vars]
str(nd)## 'data.frame': 150 obs. of 4 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...head(nd)#exclude column three and four
nd = iris[c(-3,-4)]
head(nd)df_setosa=subset(iris,iris$Species=="setosa")
str(df_setosa)## 'data.frame': 50 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...summary(df_setosa)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.300 Min. :1.000 Min. :0.100
## 1st Qu.:4.800 1st Qu.:3.200 1st Qu.:1.400 1st Qu.:0.200
## Median :5.000 Median :3.400 Median :1.500 Median :0.200
## Mean :5.006 Mean :3.428 Mean :1.462 Mean :0.246
## 3rd Qu.:5.200 3rd Qu.:3.675 3rd Qu.:1.575 3rd Qu.:0.300
## Max. :5.800 Max. :4.400 Max. :1.900 Max. :0.600
## Species
## setosa :50
## versicolor: 0
## virginica : 0
##
##
## ———— Cleaning Up Data
library(MASS)
data()
##Randomly distributed NAs
data(airquality)
??airquality ##more info about these 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 ...head(airquality)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
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
## aq=na.omit(airquality) #remove rows containing NAs
head(aq)summary(aq)## Ozone Solar.R Wind Temp
## Min. : 1.0 Min. : 7.0 Min. : 2.30 Min. :57.00
## 1st Qu.: 18.0 1st Qu.:113.5 1st Qu.: 7.40 1st Qu.:71.00
## Median : 31.0 Median :207.0 Median : 9.70 Median :79.00
## Mean : 42.1 Mean :184.8 Mean : 9.94 Mean :77.79
## 3rd Qu.: 62.0 3rd Qu.:255.5 3rd Qu.:11.50 3rd Qu.:84.50
## Max. :168.0 Max. :334.0 Max. :20.70 Max. :97.00
## Month Day
## Min. :5.000 Min. : 1.00
## 1st Qu.:6.000 1st Qu.: 9.00
## Median :7.000 Median :16.00
## Mean :7.216 Mean :15.95
## 3rd Qu.:9.000 3rd Qu.:22.50
## Max. :9.000 Max. :31.00str(aq)## 'data.frame': 111 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 23 19 8 16 11 14 ...
## $ Solar.R: int 190 118 149 313 299 99 19 256 290 274 ...
## $ Wind : num 7.4 8 12.6 11.5 8.6 13.8 20.1 9.7 9.2 10.9 ...
## $ Temp : int 67 72 74 62 65 59 61 69 66 68 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 7 8 9 12 13 14 ...
## - attr(*, "na.action")= 'omit' Named int 5 6 10 11 25 26 27 32 33 34 ...
## ..- attr(*, "names")= chr "5" "6" "10" "11" ...aq2=airquality[complete.cases(airquality), ] #only retain non-NA rows
head(aq2)summary(aq2)## Ozone Solar.R Wind Temp
## Min. : 1.0 Min. : 7.0 Min. : 2.30 Min. :57.00
## 1st Qu.: 18.0 1st Qu.:113.5 1st Qu.: 7.40 1st Qu.:71.00
## Median : 31.0 Median :207.0 Median : 9.70 Median :79.00
## Mean : 42.1 Mean :184.8 Mean : 9.94 Mean :77.79
## 3rd Qu.: 62.0 3rd Qu.:255.5 3rd Qu.:11.50 3rd Qu.:84.50
## Max. :168.0 Max. :334.0 Max. :20.70 Max. :97.00
## Month Day
## Min. :5.000 Min. : 1.00
## 1st Qu.:6.000 1st Qu.: 9.00
## Median :7.000 Median :16.00
## Mean :7.216 Mean :15.95
## 3rd Qu.:9.000 3rd Qu.:22.50
## Max. :9.000 Max. :31.00## replace NAs with 0
aqty=airquality
aqty[is.na(aqty)]<-0
head(aqty)summary(aqty)## Ozone Solar.R Wind Temp
## Min. : 0.00 Min. : 0.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 4.00 1st Qu.: 95.0 1st Qu.: 7.400 1st Qu.:72.00
## Median : 21.00 Median :194.0 Median : 9.700 Median :79.00
## Mean : 31.94 Mean :177.4 Mean : 9.958 Mean :77.88
## 3rd Qu.: 46.00 3rd Qu.:256.0 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0## replcae missing values with average values
meanOzone=mean(airquality$Ozone,na.rm=T)
# remove NAs while computing mean of Ozone
#with na mean value will be na
aqty.fix=ifelse(is.na(airquality$Ozone),meanOzone,airquality$Ozone)
summary(aqty.fix)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 21.00 42.13 42.13 46.00 168.001+1## [1] 2##visualize the patterns of NAs
library(mice)## Loading required package: lattice##
## Attaching package: 'mice'## The following object is masked from 'package:RCurl':
##
## complete## The following objects are masked from 'package:base':
##
## cbind, rbindaqty2=airquality
md.pattern(aqty2)
## Wind Temp Month Day Solar.R Ozone
## 111 1 1 1 1 1 1 0
## 35 1 1 1 1 1 0 1
## 5 1 1 1 1 0 1 1
## 2 1 1 1 1 0 0 2
## 0 0 0 0 7 37 44#111 observations with no values
library(VIM) #visualize the pattern of NAs## Loading required package: colorspace## Loading required package: grid## Loading required package: data.table## VIM is ready to use.
## Since version 4.0.0 the GUI is in its own package VIMGUI.
##
## Please use the package to use the new (and old) GUI.## Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleepmp <- aggr(aqty2, col=c('navyblue','yellow'),
numbers=TRUE, sortVars=TRUE,
labels=names(aqty2), cex.axis=.7,
gap=3, ylab=c("Missing data","Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## Ozone 0.24183007
## Solar.R 0.04575163
## Wind 0.00000000
## Temp 0.00000000
## Month 0.00000000
## Day 0.00000000#72.5% observations in the entire data have no missing values
#22.9% missing values in Ozone
#impute
#500 iterataions of predictive mean mapping for imputing
#5 datasets
im_aqty<- mice(aqty2, m=5, maxit = 50, method = 'pmm', seed = 500)##
## iter imp variable
## 1 1 Ozone Solar.R
## 1 2 Ozone Solar.R
## 1 3 Ozone Solar.R
## 1 4 Ozone Solar.R
## 1 5 Ozone Solar.R
## 2 1 Ozone Solar.R
## 2 2 Ozone Solar.R
## 2 3 Ozone Solar.R
## 2 4 Ozone Solar.R
## 2 5 Ozone Solar.R
## 3 1 Ozone Solar.R
## 3 2 Ozone Solar.R
## 3 3 Ozone Solar.R
## 3 4 Ozone Solar.R
## 3 5 Ozone Solar.R
## 4 1 Ozone Solar.R
## 4 2 Ozone Solar.R
## 4 3 Ozone Solar.R
## 4 4 Ozone Solar.R
## 4 5 Ozone Solar.R
## 5 1 Ozone Solar.R
## 5 2 Ozone Solar.R
## 5 3 Ozone Solar.R
## 5 4 Ozone Solar.R
## 5 5 Ozone Solar.R
## 6 1 Ozone Solar.R
## 6 2 Ozone Solar.R
## 6 3 Ozone Solar.R
## 6 4 Ozone Solar.R
## 6 5 Ozone Solar.R
## 7 1 Ozone Solar.R
## 7 2 Ozone Solar.R
## 7 3 Ozone Solar.R
## 7 4 Ozone Solar.R
## 7 5 Ozone Solar.R
## 8 1 Ozone Solar.R
## 8 2 Ozone Solar.R
## 8 3 Ozone Solar.R
## 8 4 Ozone Solar.R
## 8 5 Ozone Solar.R
## 9 1 Ozone Solar.R
## 9 2 Ozone Solar.R
## 9 3 Ozone Solar.R
## 9 4 Ozone Solar.R
## 9 5 Ozone Solar.R
## 10 1 Ozone Solar.R
## 10 2 Ozone Solar.R
## 10 3 Ozone Solar.R
## 10 4 Ozone Solar.R
## 10 5 Ozone Solar.R
## 11 1 Ozone Solar.R
## 11 2 Ozone Solar.R
## 11 3 Ozone Solar.R
## 11 4 Ozone Solar.R
## 11 5 Ozone Solar.R
## 12 1 Ozone Solar.R
## 12 2 Ozone Solar.R
## 12 3 Ozone Solar.R
## 12 4 Ozone Solar.R
## 12 5 Ozone Solar.R
## 13 1 Ozone Solar.R
## 13 2 Ozone Solar.R
## 13 3 Ozone Solar.R
## 13 4 Ozone Solar.R
## 13 5 Ozone Solar.R
## 14 1 Ozone Solar.R
## 14 2 Ozone Solar.R
## 14 3 Ozone Solar.R
## 14 4 Ozone Solar.R
## 14 5 Ozone Solar.R
## 15 1 Ozone Solar.R
## 15 2 Ozone Solar.R
## 15 3 Ozone Solar.R
## 15 4 Ozone Solar.R
## 15 5 Ozone Solar.R
## 16 1 Ozone Solar.R
## 16 2 Ozone Solar.R
## 16 3 Ozone Solar.R
## 16 4 Ozone Solar.R
## 16 5 Ozone Solar.R
## 17 1 Ozone Solar.R
## 17 2 Ozone Solar.R
## 17 3 Ozone Solar.R
## 17 4 Ozone Solar.R
## 17 5 Ozone Solar.R
## 18 1 Ozone Solar.R
## 18 2 Ozone Solar.R
## 18 3 Ozone Solar.R
## 18 4 Ozone Solar.R
## 18 5 Ozone Solar.R
## 19 1 Ozone Solar.R
## 19 2 Ozone Solar.R
## 19 3 Ozone Solar.R
## 19 4 Ozone Solar.R
## 19 5 Ozone Solar.R
## 20 1 Ozone Solar.R
## 20 2 Ozone Solar.R
## 20 3 Ozone Solar.R
## 20 4 Ozone Solar.R
## 20 5 Ozone Solar.R
## 21 1 Ozone Solar.R
## 21 2 Ozone Solar.R
## 21 3 Ozone Solar.R
## 21 4 Ozone Solar.R
## 21 5 Ozone Solar.R
## 22 1 Ozone Solar.R
## 22 2 Ozone Solar.R
## 22 3 Ozone Solar.R
## 22 4 Ozone Solar.R
## 22 5 Ozone Solar.R
## 23 1 Ozone Solar.R
## 23 2 Ozone Solar.R
## 23 3 Ozone Solar.R
## 23 4 Ozone Solar.R
## 23 5 Ozone Solar.R
## 24 1 Ozone Solar.R
## 24 2 Ozone Solar.R
## 24 3 Ozone Solar.R
## 24 4 Ozone Solar.R
## 24 5 Ozone Solar.R
## 25 1 Ozone Solar.R
## 25 2 Ozone Solar.R
## 25 3 Ozone Solar.R
## 25 4 Ozone Solar.R
## 25 5 Ozone Solar.R
## 26 1 Ozone Solar.R
## 26 2 Ozone Solar.R
## 26 3 Ozone Solar.R
## 26 4 Ozone Solar.R
## 26 5 Ozone Solar.R
## 27 1 Ozone Solar.R
## 27 2 Ozone Solar.R
## 27 3 Ozone Solar.R
## 27 4 Ozone Solar.R
## 27 5 Ozone Solar.R
## 28 1 Ozone Solar.R
## 28 2 Ozone Solar.R
## 28 3 Ozone Solar.R
## 28 4 Ozone Solar.R
## 28 5 Ozone Solar.R
## 29 1 Ozone Solar.R
## 29 2 Ozone Solar.R
## 29 3 Ozone Solar.R
## 29 4 Ozone Solar.R
## 29 5 Ozone Solar.R
## 30 1 Ozone Solar.R
## 30 2 Ozone Solar.R
## 30 3 Ozone Solar.R
## 30 4 Ozone Solar.R
## 30 5 Ozone Solar.R
## 31 1 Ozone Solar.R
## 31 2 Ozone Solar.R
## 31 3 Ozone Solar.R
## 31 4 Ozone Solar.R
## 31 5 Ozone Solar.R
## 32 1 Ozone Solar.R
## 32 2 Ozone Solar.R
## 32 3 Ozone Solar.R
## 32 4 Ozone Solar.R
## 32 5 Ozone Solar.R
## 33 1 Ozone Solar.R
## 33 2 Ozone Solar.R
## 33 3 Ozone Solar.R
## 33 4 Ozone Solar.R
## 33 5 Ozone Solar.R
## 34 1 Ozone Solar.R
## 34 2 Ozone Solar.R
## 34 3 Ozone Solar.R
## 34 4 Ozone Solar.R
## 34 5 Ozone Solar.R
## 35 1 Ozone Solar.R
## 35 2 Ozone Solar.R
## 35 3 Ozone Solar.R
## 35 4 Ozone Solar.R
## 35 5 Ozone Solar.R
## 36 1 Ozone Solar.R
## 36 2 Ozone Solar.R
## 36 3 Ozone Solar.R
## 36 4 Ozone Solar.R
## 36 5 Ozone Solar.R
## 37 1 Ozone Solar.R
## 37 2 Ozone Solar.R
## 37 3 Ozone Solar.R
## 37 4 Ozone Solar.R
## 37 5 Ozone Solar.R
## 38 1 Ozone Solar.R
## 38 2 Ozone Solar.R
## 38 3 Ozone Solar.R
## 38 4 Ozone Solar.R
## 38 5 Ozone Solar.R
## 39 1 Ozone Solar.R
## 39 2 Ozone Solar.R
## 39 3 Ozone Solar.R
## 39 4 Ozone Solar.R
## 39 5 Ozone Solar.R
## 40 1 Ozone Solar.R
## 40 2 Ozone Solar.R
## 40 3 Ozone Solar.R
## 40 4 Ozone Solar.R
## 40 5 Ozone Solar.R
## 41 1 Ozone Solar.R
## 41 2 Ozone Solar.R
## 41 3 Ozone Solar.R
## 41 4 Ozone Solar.R
## 41 5 Ozone Solar.R
## 42 1 Ozone Solar.R
## 42 2 Ozone Solar.R
## 42 3 Ozone Solar.R
## 42 4 Ozone Solar.R
## 42 5 Ozone Solar.R
## 43 1 Ozone Solar.R
## 43 2 Ozone Solar.R
## 43 3 Ozone Solar.R
## 43 4 Ozone Solar.R
## 43 5 Ozone Solar.R
## 44 1 Ozone Solar.R
## 44 2 Ozone Solar.R
## 44 3 Ozone Solar.R
## 44 4 Ozone Solar.R
## 44 5 Ozone Solar.R
## 45 1 Ozone Solar.R
## 45 2 Ozone Solar.R
## 45 3 Ozone Solar.R
## 45 4 Ozone Solar.R
## 45 5 Ozone Solar.R
## 46 1 Ozone Solar.R
## 46 2 Ozone Solar.R
## 46 3 Ozone Solar.R
## 46 4 Ozone Solar.R
## 46 5 Ozone Solar.R
## 47 1 Ozone Solar.R
## 47 2 Ozone Solar.R
## 47 3 Ozone Solar.R
## 47 4 Ozone Solar.R
## 47 5 Ozone Solar.R
## 48 1 Ozone Solar.R
## 48 2 Ozone Solar.R
## 48 3 Ozone Solar.R
## 48 4 Ozone Solar.R
## 48 5 Ozone Solar.R
## 49 1 Ozone Solar.R
## 49 2 Ozone Solar.R
## 49 3 Ozone Solar.R
## 49 4 Ozone Solar.R
## 49 5 Ozone Solar.R
## 50 1 Ozone Solar.R
## 50 2 Ozone Solar.R
## 50 3 Ozone Solar.R
## 50 4 Ozone Solar.R
## 50 5 Ozone Solar.R#50 iterataions of predictive mean mapping for imputing
summary(im_aqty)## Class: mids
## Number of multiple imputations: 5
## Imputation methods:
## Ozone Solar.R Wind Temp Month Day
## "pmm" "pmm" "" "" "" ""
## PredictorMatrix:
## Ozone Solar.R Wind Temp Month Day
## Ozone 0 1 1 1 1 1
## Solar.R 1 0 1 1 1 1
## Wind 1 1 0 1 1 1
## Temp 1 1 1 0 1 1
## Month 1 1 1 1 0 1
## Day 1 1 1 1 1 0im_aqty$imp$Ozone #values imputed in ozone#get back the completed dataset u
completedData <- complete(im_aqty,1)
head(completedData)