class: center, middle, inverse, title-slide .title[ # New York Airquality ] .author[ ### JiaJingLiew ] .date[ ### 2022/05/20 ] --- class: center, middle --- class: center, middle ##Abstract In this presentation, i will discuss about New York Air Quality, through linear regression model. The measurements was done from May to September 1973, which had measured temperature(degrees F), solar radiation(lang), etc. --- class: inverse, center, middle ###1. Introduction First, loading data and making sure the data class or other information ```r Sys.setenv(LANGUAGE="en") library(shiny) ``` ``` ## Warning: package 'shiny' was built under R version 4.1.3 ``` ```r library(psych) ``` ``` ## Warning: package 'psych' was built under R version 4.1.3 ``` ```r library(ggplot2) ``` ``` ## ## Attaching package: 'ggplot2' ``` ``` ## The following objects are masked from 'package:psych': ## ## %+%, alpha ``` --- class: inverse, center, middle ```r library(dplyr) ``` ``` ## Warning: package 'dplyr' was built under R version 4.1.3 ``` ``` ## ## 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 ``` ```r library(datasets) library(plotly) ``` ``` ## Warning: package 'plotly' was built under R version 4.1.3 ``` ``` ## ## Attaching package: 'plotly' ``` ``` ## The following object is masked from 'package:ggplot2': ## ## last_plot ``` ``` ## The following object is masked from 'package:stats': ## ## filter ``` ``` ## The following object is masked from 'package:graphics': ## ## layout ``` --- ```r data("airquality") a<-data.frame(airquality) describe(a) ``` ``` ## vars n mean sd median trimmed mad min max range skew ## Ozone 1 116 42.13 32.99 31.5 37.80 25.95 1.0 168.0 167 1.21 ## Solar.R 2 146 185.93 90.06 205.0 190.34 98.59 7.0 334.0 327 -0.42 ## Wind 3 153 9.96 3.52 9.7 9.87 3.41 1.7 20.7 19 0.34 ## Temp 4 153 77.88 9.47 79.0 78.28 8.90 56.0 97.0 41 -0.37 ## Month 5 153 6.99 1.42 7.0 6.99 1.48 5.0 9.0 4 0.00 ## Day 6 153 15.80 8.86 16.0 15.80 11.86 1.0 31.0 30 0.00 ## kurtosis se ## Ozone 1.11 3.06 ## Solar.R -1.00 7.45 ## Wind 0.03 0.28 ## Temp -0.46 0.77 ## Month -1.32 0.11 ## Day -1.22 0.72 ``` --- class: inverse, center, middle ###2. Making linear regression model From the before, we know that the data airquality has 6 variables. We can make some assumptions, likely "Ozone" and "Wind" has some relations(actually them haven't relation). --- class: center, middle ```r am1<-lm(Ozone~Wind,a) anova(am1) ``` ``` ## Analysis of Variance Table ## ## Response: Ozone ## Df Sum Sq Mean Sq F value Pr(>F) ## Wind 1 45284 45284 64.644 9.272e-13 *** ## Residuals 114 79859 701 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` ```r am2<-lm(Ozone~Wind+Temp,a) anova(am2) ``` ``` ## Analysis of Variance Table ## ## Response: Ozone ## Df Sum Sq Mean Sq F value Pr(>F) ## Wind 1 45284 45284 94.808 < 2.2e-16 *** ## Temp 1 25886 25886 54.196 3.149e-11 *** ## Residuals 113 53973 478 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- class: center, middle ###3.Ploting the graphs in bar plot Now, we know "Ozone" and "Wind" has clearly from their F values, and we are plotting the graphs ```r p1<-plot_ly(a, x=a$Day, y=a$Ozone, type="bar") %>% layout(xaxis=list(title='Day'),yaxis=list(title='Ozone')) p1 ``` ``` ## Warning: Ignoring 37 observations ``` <div id="htmlwidget-80867f884aaa7977fc31" style="width:504px;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-80867f884aaa7977fc31">{"x":{"visdat":{"31107a1e1043":["function () 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--- ```r p2<-plot_ly(a, x=a$Day, y=a$Wind, type="bar") %>% layout(xaxis=list(title='Day'), yaxis=list(title='Wind')) p2 ``` <div id="htmlwidget-9f89cfd6f12b250e8ee4" style="width:504px;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-9f89cfd6f12b250e8ee4">{"x":{"visdat":{"31102d12ce":["function () 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--- ```r p3<-plot_ly(a, x=a$Temp, y=a$Ozone, type="bar") %>% layout(xaxis=list(title='Temp'), yaxis=list(title='Ozone')) p3 ``` ``` ## Warning: Ignoring 37 observations ``` <div id="htmlwidget-847d32751869048b7a3f" style="width:504px;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-847d32751869048b7a3f">{"x":{"visdat":{"31107b865a8a":["function () 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--- ```r p4<-plot_ly(a, x=a$Temp, y=a$Wind, type="bar") %>% layout(xaxis=list(title='Temp'), yaxis=list(title='Wind')) p4 ``` <div id="htmlwidget-19981674c8ae9df47837" style="width:504px;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-19981674c8ae9df47837">{"x":{"visdat":{"3110458e1be2":["function () 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--- ```r p5<-plot_ly(a, x=a$Day, y=a$Temp, type="bar") %>% layout(xaxis=list(title='Day'), yaxis=list(title='Temp')) p5 ``` <div id="htmlwidget-ecbedf5940333332cb16" style="width:504px;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-ecbedf5940333332cb16">{"x":{"visdat":{"311019bc7be":["function () 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--- ```r p<-subplot(p1, p2, p3, p4) ``` ``` ## Warning: Ignoring 37 observations ## Warning: Ignoring 37 observations ``` ```r annotations = list( list(x=0.15,y=1.0,text="Day-Ozone",xref="paper",yref="paper",xanchor="center",yanchor="bottom",showarrow=FALSE), list(x=0.4,y=1.0,text="Day-Wind",xref="paper",yref="paper",xanchor="center",yanchor="bottom",showarrow=FALSE), list(x=0.65,y=1.0,text="Temp-Ozone",xref="paper",yref="paper",xanchor="center",yanchor="bottom",showarrow=FALSE), list(x=0.9,y=1.0,text="Temp-Wind",xref="paper",yref="paper",xanchor="center",yanchor="bottom",showarrow=FALSE)) p<-p %>%layout(annotations = annotations) p ``` <div id="htmlwidget-8582f1f52d0bb72e4943" style="width:504px;height:504px;" class="plotly html-widget"></div> <script type="application/json" 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--- ```r par(mfrow=c(2,1)) hist(a$Wind, main="Daily Wind", col="lightyellow") hist(a$Temp, main="Daily Wind", col="lightgreen") ``` <!-- -->