R data analysis

AIR POLLUTION ANALYSIS USING R SOFTWARE It is very important to use correct comma separated values (csv) files. Remember to save as csv in LibreOffice Calc or Microsoft Office Excel and to replace all commas to points, then all semicolons to commas and finally all Sense dades to NA. After all that you can use your data in R Studio using the instructions found in the table below.

excel sublimetext rstudio

Read dataframe martorell<-read.csv(“E://PollutionData/martorell2015.csv”)
Calculate quartiles, median, min, max, outliers summary (martorell)
Create boxplot boxplot(martorell$NO)
Create histogram hist(martorell$NO, breaks=20, xlab=”NO(micrograms/m3)”, main=”Martorell air pollution”, col= “pink”)
Install and load normality test install.packages(” nortest “) library(nortest)  
Shapiro- Wilk normality test Anderson-Darling normality test Cramer-von-Mises normality test Kolmogorov-Smirnov normality test Pearson normality test Shapiro-Francia normality test shapiro.test(martorell$NO) ad.test(martorell$NO) cvm.test(martorell$NO) lillie.test(martorell$NO) pearson.test(martorell$NO) sf.test(martorell$NO)
library (e1071) skewness(x), kurtosis(x)
skewness (negative:left tail, positive: right tail, normal:0) skewness(martorell$NO)
kurtosis (negative : platycurtic, positive : leptocurtic, normal :0) kurtosis(martorell$NO)
Student t test (compare 2 normal data) t.test(martorell$NO, santandreu$NO)
U Mann Whitney (compare 2 non normal data) wilcox.test(martorell$NO, santandreu$NO)
Compare > 2 normal groups ANOVA test
Compare > 2 non-normal grous Kruskal-Wallis test
Homocedascity (equal variability) leveneTest(x) in car library
View possible correlation pairs (martorell)
Object type : class (x) numeric, date, time series, dataframe, etc
Convert numeric to date martorell$date<-as.Date(martorell$date)
Create time series martorellNO.ts <- ts(martorell$NO, start=c(2015, 1, 1), end=c(2015, 12,31), frequency=365)
Bind column data NOcompare<-cbind(martorellNO.ts,santandreuNO.ts)
Plotting multiple time series plot(NOcompare, plot.type=”m”,col=c(“blue”, “red”))
Subset time series mytssummer2015 <- window(myts, start=c(2015, 6,21), end=c(2015, 9,22))
Correlation plot library (corrplot) corrplot.mixed (martorell)
Linear model fit<-lm(martorell$NO~martorell$NO2) summary(fit)
martorellhour Regarding NO2 levels found in previous image: Is Martorell following the NO2 air pollution standards of the EU? What is the EU decission 3 about NO2 pollution? In Spanish (authentic and valid) Analyse all EU air standards and compare to WHO  air standards
> plot(martorell$date, martorell$NO, type = "l", xlab = "year 2015",ylab = "Nitric oxide (microg/m3)")
nohour
Normality tests install.packages(“nortest”) normalitytest normality2 > plot(martorell$date, martorell$NO, type = “l”, xlab = “year 2015”,ylab = “Nitric oxide (microg/m3)”, main=”Air pollution in Martorell (8760 observations)”) 8760 > plot(martorell$date[1:168], martorell$NO[1:168], type = “l”, xlab = “year 2015”,ylab = “Nitric oxide (microg/m3)”, main=”Air pollution in Martorell”) oneweek > plot(martorell$date[144:168], martorell$NO[144:168], type = “l”, xlab = “year 2015”,ylab = “Nitric oxide (microg/m3)”, main=”Air pollution in Martorell (7th January)”) 7january Time series air pollution comparison Martorell vs Sant Andreu de la Barca (2015) sabmartorell T- test (normal data) or U Mann- Withney (non-normal data) ttest Air pollutant in Martorell (2015) martorell2015ts1 One week forecast of air pollution in Martorell oneweekforecastets

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