R visual in Power BI: with pbiviz and ggplot2

Transcription

1 R visual in Power BI: with pbiviz and ggplot2 Leila Etaati AI MVP, Consultant, Data Science from RADACAD

2 Agenda Data Behavior Analysis Charts (Centre and Distribution Data) More Charts with ggplot2 Package in Power BI R and Power BI Predefined R Custom Visual in Office Store Create your Own Power BI Custom Visual using R

3 Leila Etaati Microsoft AI MVP, PhD, Consultant, Trainer and Data Scientist. International speaker in Microsoft Ignite USA 2017, Microsoft Insight Summit 2017, PASS Summit 2017, Microsoft NZ Ignite 2016, PASS BA, PASS24H, SQL Rally, SQL Saturday in Oregon, Vienna, Auckland, Melbourne, Sydney, Brisbane.

4 Graphic and Visualization Static Graph Dynamic Graph Device Format

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7 Data Behavior Analysis Charts (Centre and Distribution Data)

8 Exploring and Understanding data install.packages("readr") library(readr) usedcars <- read_csv("c:/users/leila/dropbox/leila Speak/Difnity/usedcars.csv") View(usedcars) str(usedcars) summary(usedcars$year) summary(usedcars[c("price", "mileage")])

9 Measuring the central tendency mean and median The average is also known as the mean mean(c(36000, 44000, 56000)) Median is the value that occurs halfway through an ordered list of values median(c(36000, 44000, 56000)) 12,23,34,22,28,36,27,30,21,20,65 Average or Mean is 28 12,20,21,22,23,27,28,30,34,36,65 Median is 27 5,2,12,23,34,22,28,36,27,30,21,20,65,90,80 Average or Mean is 33 2,5,12, 20,21,22,23,27,28,30,34,36,65,90,80 Median 27

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11 Measuring spread quartiles and the fivenumber boxplot(usedcars$price, main="boxplot of Used Car Prices", ylab="price ($)") boxplot(usedcars$mileage, main="boxplot of Used Car Mileage", ylab="odometer (mi.)")

12 Visualizing numeric variables histograms hist(usedcars$price, main = "Histogram of Used Car Prices", xlab = "Price ($)") hist(usedcars$mileage, main = "Histogram of Used Car Mileage", xlab = "Odometer (mi.)")

13 Normalization in Power BI for Calories Burn

14 More Charts with ggplot2 Package in Power BI

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16 Grouping data diagram-facet library(ggplot2) t<-ggplot(mpg, aes(x=cty, y=hwy,size=cyl)) + geom_point(pch=21)+scale_size_continuous(range=c(1,5)) t library(reshape2) t<-ggplot(mpg, aes(x=cty, y=hwy,color=cyl)) + geom_point(size=5) t<-t + facet_wrap(year~ drv) t

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19 library(ggplot2) library(scales) theme_set(theme_classic()) # prep data df <- read.csv(" colnames(df) <- c("continent", "1952", "1957") left_label <- paste(df$continent, round(df$`1952`),sep=", ") right_label <- paste(df$continent, round(df$`1957`),sep=", ") df$class <- ifelse((df$`1957` - df$`1952`) < 0, "red", "green") # Plot p <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=f) + geom_vline(xintercept=1, linetype="dashed", size=.1) + geom_vline(xintercept=2, linetype="dashed", size=.1) + scale_color_manual(labels = c("up", "Down"), values = c("green"="#00ba38", "red"="#f8766d")) + # color of lines labs(x="", y="mean GdpPerCap") + # Axis labels xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`)))) # X and Y axis limits # Add texts p <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5) p <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5) p <- p + geom_text(label="time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5) # title p <- p + geom_text(label="time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5) # title # Minify theme p + theme(panel.background = element_blank(), panel.grid = element_blank(), axis.ticks = element_blank(), axis.text.x = element_blank(), panel.border = element_blank(), plot.margin = unit(c(1,2,1,2), "cm"))

20 Custom Column Width Chart

21 Custom Column Width Chart

22 Region Gas Pop End point Start point North America Oceania Europe Africa Middle East Central America Asia

23 Calculate the End Point Require data : df<-data.frame(region=dataset$region,population= dataset$pop,width=dataset$gas) Calculate the End Point: df$w <- cumsum(df$width) #cumulative sums.

24 Calculate the Start Point df$wm <- df$w df$width End point width

25 Chart Label df$greengas<- with(df, wm + (w wm)/2)

26 Create your own R visualization inside Power BI

27 Create your Own Power BI Custom Visual using R

28 Create your Own Power BI Custom Visual using R

29 R Custom R visual-install nogejs 1- Download NodeJS 4.0+: 2- Command prompt : npm install -g powerbi-visualstools 3- in command prompt pbiviz (confirm it already installed) 4-

30 R Custom R visual-install Power BI visual tools npm install -g powerbi-visuals-tools

31 Create Custom Visual In document pbiviz package

32 R Custom R visual-create a rhtml Template pbiviz new samplerhtmlvisual -t rhtml

33 Change R codes source('./r_files/flatten_html.r') ############### Library Declarations ############### libraryrequireinstall("ggplot2"); libraryrequireinstall("plotly") #################################################### ################### Actual code #################### g = qplot(`petal.length`, data = iris, fill = `Species`, main = Sys.time()); #################################################### ############# Create and save widget ############### p = ggplotly(g); internalsavewidget(p, 'out.html'); ####################################################

34 Finally!

35 1- Download NodeJS 4.0+: 2- Command prompt : npm install -g powerbi-visualstools 3- in command prompt pbiviz 4-

36 R Custom R visual npm install -g powerbi-visuals-tools

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38 User Predefined R Custom Visual in Power BI

39 Create your own R visualization inside Power BI using R codes Write your own R code inside Power Query R and Power BI Predefined R Custom Visual in Office Store Create your Own Power BI Custom Visual using R

40 Resources