Exploratory Analysis Part1 Coursera DataScience Specialisation
Download as PPTX, PDF4 likes1,075 views
The document discusses exploratory data analysis techniques in R, including various plotting systems and graph types. It provides code examples for creating boxplots, histograms, bar plots, and scatter plots in Base, Lattice, and ggplot2. It also covers downloading data, transforming data, adding scales and themes, and creating faceted plots. The final challenge involves creating a boxplot with rectangles to represent regions and jittered points to show trends over years.
![Final Challenge
R code
library(ggplot2)
#Reads in data
data = read.csv("final.csv")
#Preparing for the rectangle background
areas=unique(subset(data, select=c(Planning_Area,Planning_Region)))
areas=areas[order(areas$Planning_Region),]
areas$rectid=1:nrow(areas)
rectdata = areas %>% group_by(Planning_Region) %>% summarise(xstart=min(rectid)-
0.5,xend= max(rectid)+0.5)
#Order the levels
data$Planning_Area=factor(data$Planning_Area,
levels=as.character(areas[order(areas$Planning_Region),]$Planning_Area))](https://image.slidesharecdn.com/presentation1-141107085534-conversion-gate02/85/Exploratory-Analysis-Part1-Coursera-DataScience-Specialisation-37-320.jpg)
Ad
More Related Content
Viewers also liked (16)
Similar to Exploratory Analysis Part1 Coursera DataScience Specialisation (20)
Ad
Recently uploaded (20)
Ad
Exploratory Analysis Part1 Coursera DataScience Specialisation
- 1. Exploratory Data Analysis Wesley GOI
- 2. In today’s session • Principles behind exploratory analyses • Plotting data out on to popular exploratory graphs • Plotting Systems in R • Base (Week1) • Lattice (Week2) • GGPLOT2 (Week2) • Choosing and using Graphic Devices aka the output formats Scripts can be downloaded at: https://www.dropbox.com/s/ii1yj8f650d4l1q/lesson1.r?dl=0 https://www.dropbox.com/s/eme44h6lrhn775l/final.r?dl=0
- 3. Principles behind exploratory analyses • Show comparisons • Show causality, mechanism, explanation • Show multivariate data • Integrate multiple modes of evidence • Describe and document the evidence • Content is king • SPEED
- 4. Dimensionality • Five-number summary • Boxplots • Histograms • Density plot • Barplot Multiple-overlayed 1D plots Scatter plots
- 5. Downloading our dataset R code dir.create("exploring_data") setwd(“exploring_data”) download.file(“http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/therbook.zip",dest="data.zip") unzip(“data.zip”)
- 6. R code Boxplots weather = read.table("SilwoodWeather.txt",h=T) onemonth = subset(weather, month==1 & yr == 2004) boxplot(onemonth$rain) Header = T
- 7. Histograms R code hist(weather$upper) rug(weather$upper) ticks for each value
- 8. Barplot R code Barplot( table(weather$month), col = "wheat", main = "Number of Observations in Months”)
- 9. Raster Vector PNG PDF SVG grDevices Filesize small medium medium Scalable No Yes Yes Web friendly Yes No Yes
- 10. Plotting Systems Plotting Systems Base Lattice Grid Libraries lattice grid, gridExtras ggplot2 Example functions hist✔ barplot✔ boxplot✔ Plot xyplot (scatterplots) bwplot (boxplots) levelplot qplot ggplot geom Facetted plots Yes Yes Yes Grammar of NO No Yes graphics Interface with statistical functions Yes Partial Partial + Workarounds Cannot be mixed
- 11. Base plots: Scatterplot R code data1 = read.table("scatter1.txt", h=T) data2 = read.table("scatter2.txt", h=T)
- 12. Base plots: Scatterplot R code data1 = read.table("scatter1.txt", h=T) data2 = read.table("scatter2.txt", h=T) #Color with(data1, plot(xv, ys, col="red")) #Regression Line with(data1, abline(lm(ys~xv))) Color
- 13. Base plots: Scatterplot Set symbol to represent data point
- 14. Base plots: Scatterplot R code data1 = read.table("scatter1.txt", h=T) data2 = read.table("scatter2.txt", h=T) #Color with(data1, plot(xv, ys, col="red")) with(data1, abline(lm(ys~xv))) #shape with(data2, points(xv2, ys2, col="blue", pch =11)) Symbol shape
- 15. Base plots: Scatterplot R code data1 = read.table("scatter1.txt", h=T) data2 = read.table("scatter2.txt", h=T) #Color with(data1, plot(xv, ys, col="red")) with(data1, abline(lm(ys~xv))) #shape with(data2, points(xv2, ys2, col="blue", pch =11)) Symbol shape
- 16. Base plots: Using par for multiple plots R code par(mfrow=c(1,2)) with(data1, plot(xv, ys, col="red")) with(data1, abline(lm(ys~xv))) #Plot2 with(data2, plot(xv2, ys2, col="blue", pch =11)) title(“My Title", outer=TRUE)
- 17. Par: To set global settings R code mfrow( mar=c(5.1,4.1,4.1,2.1), oma=c(2,2,2,2) )
- 18. Lattice productivity = read.table("productivity.txt",h=T) # of species in forest against differing productivity library(lattice) #plotting xyplot( x~y, productivity, xlab=list(label="Productivity"), ylab=list(label="Mammal Species")) R code Formular Data frame
- 20. Lattice productivity = read.table("productivity.txt",h=T) # of species in forest against differing productivity library(lattice) #plotting xyplot( x~y, productivity, xlab=list(label="Productivity"), ylab=list(label="Mammal Species")) xyplot( x~y | f, productivity, xlab=list(label="Productivity"), ylab=list(label="Mammal Species")) R code Formular Data frame given
- 22. ggplot2 • Grammar of graphics (gg) • Based on GRID plotting system, cannot be mixed with base ggplot2.org
- 23. ggplot Components • Data & relationship • GEOMetric Object • Statistical transformation • Scales • Coordinate system • Facetting
- 24. ggplot Data
- 25. ggplot Mapping
- 26. ggplot Geometric objects aka Geoms Coordinate system wrt scales Log scale / sqrt / log ratio Title Plot Theme etc
- 27. ggplot Geometric objects aka Geoms
- 28. ggplot Components • Data & relationship ✔ • GEOMetric Object • Statistical transformation • Scales • Coordinate system • Facetting R code Rmbr to change month into a factor data.frame Aesthetics function which maps the relationships ggplot(weather, aes(x=month, y=upper))+ geom_boxplot()
- 29. ggplot Components • Data & relationship ✔ • GEOMetric Object ✔ • Statistical transformation✔ • Scales • Coordinate system • Facetting R code weather2 = weather %>% group_by(month) %>% summarise(average.upper = mean(upper)) ggplot(weather2, aes(month, average.upper))+ geom_bar(stat="identity")
- 30. ggplot Components • Data & relationship ✔ • GEOMetric Object ✔ • Statistical transformation✔ • Scales • Coordinate system • Facetting R code weather2 = weather %>% group_by(month) %>% summarise(average.upper = mean(upper)) ggplot(weather2, aes(month, average.upper))+ geom_bar(stat="identity")
- 31. ggplot Components • Data & relationship ✔ • GEOMetric Object ✔ • Statistical transformation✔ • Scales✔ • Coordinate system • Facetting R code plot2 = ggplot(weather2, aes(month, average.upper))+ geom_bar(aes(fill=month),stat="identity")+ scale_fill_brewer(palette="Set3")+ xlab("Months")+ ylab("Upper Quantile")+theme_bw()
- 32. ggplot Components • Data & relationship ✔ • GEOMetric Object ✔ • Statistical transformation✔ • Scales✔ • Coordinate system • Facetting R code plot2 = ggplot(weather2, aes(month, average.upper))+ geom_bar(aes(fill=month),stat="identity")+ scale_fill_brewer(palette="Set3")+ xlab("Months")+ ylab("Upper Quantile")+theme_bw()
- 33. ggplot
- 34. qplot A separate function which wraps ggplot, for simpler syntax R code qplot(month, upper, fill=month, data=weather, facets = ~yr, geom="bar", stat="identity")
- 35. Ethos behind visualization http://keylines.com/network-visualization
- 36. Final Challenge
- 37. Final Challenge R code library(ggplot2) #Reads in data data = read.csv("final.csv") #Preparing for the rectangle background areas=unique(subset(data, select=c(Planning_Area,Planning_Region))) areas=areas[order(areas$Planning_Region),] areas$rectid=1:nrow(areas) rectdata = areas %>% group_by(Planning_Region) %>% summarise(xstart=min(rectid)- 0.5,xend= max(rectid)+0.5) #Order the levels data$Planning_Area=factor(data$Planning_Area, levels=as.character(areas[order(areas$Planning_Region),]$Planning_Area))
- 38. Final challenge #Plot p0 = ggplot(data, aes(Planning_Area, Unit_Price____psm_))+ geom_boxplot(outlier.colour=NA)+ geom_rect(data=rectdata,aes(xmin=xstart,xmax=xend,ymin = -Inf, ymax = Inf, fill = Planning_Region,group=Planning_Region), alpha = 0.4,inherit.aes=F)+ geom_jitter(alpha=0.40, aes(color=as.factor(Year)))+ scale_color_brewer("Year", palette='RdBu')+ scale_fill_brewer(palette="Set1",name='Region')+ theme_minimal()+ theme(axis.text.x = element_text(angle=45, hjust=1, vjust=1))+ xlab("Planning Area")+ylab("Unit Price (PSM)") R code #Save plot ggsave(p0, file="areaboxplots.pdf",w=20,h=10,units="in",dpi=300)
- 39. “Above all else show the data.” ― Edward R. Tufte, The Visual Display of Quantitative Information Thank you for your time
- 40. gridExtras
Editor's Notes
- #3: In this course we will be learning how to
- #4: In this course we will be learning how to
- #5: In this course we will be learning how to
- #6: In this course we will be learning how to
- #9: barplot(table(weather$month), col = "wheat", main = "Number of Observations in Months")
- #12: In this course we will be learning how to
- #13: In this course we will be learning how to
- #14: In this course we will be learning how to
- #15: In this course we will be learning how to
- #16: In this course we will be learning how to
- #17: In this course we will be learning how to title("My Title", outer=TRUE)
- #18: In this course we will be learning how to
- #29: ggplot(weather, aes(month, upper))+ geom_boxplot()
- #30: ggplot(weather, aes(month, upper))+ geom_boxplot()
- #31: ggplot(weather, aes(month, upper))+ geom_boxplot()
- #32: ggplot(weather, aes(month, upper))+ geom_boxplot()
- #33: ggplot(weather, aes(month, upper))+ geom_boxplot()
- #34: ggplot(weather, aes(month, upper))+ geom_boxplot()
- #38: In this course we will be learning how to
- #39: In this course we will be learning how to