R-Script Visual Runtime Error in Power BI Server

[Vmartin002]

Hi Everyone, 

I wrote a R-Script that works/runs perfectly in Power BI Desktop but doesn't work when published to the Power BI Service. I'm not able to pinpoint the error, is it the pipe operator (%<%)? Any help would be highly apperciated, I've been struggling with this issue for some time now.  Script Runtime Error: Evaulation Error: Level sets of factors are different

R-Script:

# The following code to create a dataframe and remove duplicated rows is always executed and acts as a preamble for your script:

# dataset <- data.frame(ProjectName, Commodity, WBS_code, Months, Running_Total_V2, Total_Scope_V2, RTPercentCompleteV2, Project_Description)

# dataset <- unique(dataset)

# Paste or type your script code here:

library(dplyr)

library(ggplot2)

library(RColorBrewer)

proposal_project <- unique(dataset[["Project_Description"]])

# there are duplicates due to WBS

df <- dataset %>% select(ProjectName, Commodity, Months, RTPercentCompleteV2) %>% distinct()

# for projects with multiple entries for the same percent complete, keep the highest value

df <- df %>% group_by(ProjectName, Commodity, RTPercentCompleteV2) %>% slice(which.max(Months)) %>% arrange(Months)

# group by and interpolate

pctcomplete <- seq(0.1, 1, by=0.05)

df <- df %>% group_by(ProjectName, Commodity) %>% group_modify(~ {approx(.x$RTPercentCompleteV2, .x$Months, xout=pctcomplete) %>% data.frame()}) %>% ungroup()

prop_df <- df %>% filter(ProjectName == proposal_project) # split out proposal data after transformations

df2df <- df %>% filter(ProjectName != proposal_project)

data_wo_proposal <- df2df %>% group_by(Commodity,x) %>% summarise_at(vars(y),list(mean_duration = mean))

# plot

mindiff <- min(c(0))

maxdiff <- max(c(max(data_wo_proposal$mean_duration, na.rm=TRUE), max(prop_df$y, na.rm=TRUE)))

ticks <- round(seq(mindiff, maxdiff, by=2))

tick_y <- seq(0,100, by=5)

g <- ggplot(data=data_wo_proposal, aes(x=mean_duration, y=x, colour=Commodity)) + geom_line(linetype=2) + geom_point()

final_plot <- g + geom_line(data = prop_df, aes(x=y,y=x,colour=Commodity)) + geom_point(data=prop_df,aes(x=y,y=x,colour=Commodity))

final_plot_label <-

final_plot + labs(title='Average Duration of Reference Projects vs Proposal Project', subtitle=paste('Proposal Project:',prop_df$ProjectName[1], '(solid line in figure below)'), x= 'Duration (Months)',y='Percent Complete') + theme_bw() + theme(legend.position='bottom', legend.title=element_blank()) + scale_colour_brewer(palette='Set1') + scale_x_continuous(breaks=ticks)+scale_y_continuous(labels = scales::percent_format(accuracy = 1),breaks=seq(0,1, by=.05))

final_plot_label

Thank you,

Val

[winceeS]

Hi, R Visual is currently not supported in Power BI Report Server. it's only available in PBI Service.

checkout the  documentation.

https://docs.microsoft.com/en-us/power-bi/report-server/compare-report-server-service

[Vmartin002 · Accepted solution]

Update:

I was able to figure out the issue with the R-Script Visual on the Power BI Service.

The level of factors was fixed by the following edits:

Orginal:

prop_df <- df %>% filter(ProjectName == proposal_project) 

df2df <- df %>% filter(ProjectName != proposal_project)

New:

prop_df <- df %>% filter(as.character(ProjectName)== as.character(proposal_project))

df2df <- df %>% filter(as.character(ProjectName) != as.character(proposal_project))

Also, make sure the package versions are up to date according to https://docs.microsoft.com/en-us/power-bi/connect-data/service-r-packages-support there happened to be unsupported function "group_modify" not supported by the approved dplyr package. Lastly, there seems to be some discrepancies with package versions. To ensure your R-script will run on the power BI service make sure your local machine is running Microsoft R 3.4.4 or R 3.4.4. This is the same R enviroment used on the cloud, so if it runs on your desktop with this version it will run on Power BI service. Also, use install.packages("_____") in your R-Terminal since this will allow package versions that are supported on R 3.4.4. this will help avoid ambiguity.

Best of luck!

Val