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Schuch666 opened this issue Aug 10, 2018 · 13 comments
Closed

orca error #1322

Schuch666 opened this issue Aug 10, 2018 · 13 comments

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@Schuch666
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orca function works with the Rpackage Examples code:

p <- plot_ly(z = ~volcano) %>% add_surface()
orca(p, "surface-plot.png")

but with a slightly larger dataset (dataframe with date and 1 variable with 8784 observations) test.zip

test <- readRDS("test.Rds")
p <- plot_ly(x = test$date, y = test$no, type = "scatter", mode = "lines")
orca(p,"test.png")

returns the following error:

Error in process_initialize(self, private, command, args, stdout, stderr, :
processx error, create process: #206 Filename or extension is too long.
at 'win/processx.c:761

but p plots fine on RStudio... how to avoid this BUG / limitation?
@cpsievert
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Thanks, fixed via c4f1001

@Schuch666
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Thank you @cpsievert

@0navarro
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0navarro commented Oct 9, 2018

I am having this same issue using Orca's Windows release 1.1.1. Does this release include the bugfix mentioned above?

@cpsievert
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It shoud be fixed on the master branch: devtools::install_github("ropensci/plotly")

@0navarro
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0navarro commented Oct 9, 2018

Thanks @cpsievert

@wasimaftab
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I just installed plotly using "devtools::install_github("ropensci/plotly")"
packageVersion('plotly')
[1] ‘4.8.0.9000’
I still get this error
Any idea?

@ndrubins
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Same here,

My sessionInfo() is:

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] plotly_4.8.0.9000 webshot_0.5.1     dplyr_0.7.8       bindrcpp_0.2.2    ggplot2_3.1.0    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         RColorBrewer_1.1-2 git2r_0.23.0       pillar_1.3.0       compiler_3.5.1     later_0.7.5        plyr_1.8.4        
 [8] bindr_0.1.1        tools_3.5.1        digest_0.6.18      memoise_1.1.0      jsonlite_1.6       tibble_1.4.2       gtable_0.2.0      
[15] viridisLite_0.3.0  pkgconfig_2.0.2    rlang_0.3.0.1      shiny_1.2.0        rstudioapi_0.8     crosstalk_1.0.1    curl_3.2          
[22] yaml_2.2.0         withr_2.1.2        httr_1.4.0         devtools_1.13.6    htmlwidgets_1.3    grid_3.5.1         tidyselect_0.2.5  
[29] glue_1.3.0         data.table_1.11.8  R6_2.3.0           processx_3.2.0     purrr_0.2.5        tidyr_0.8.2        magrittr_1.5      
[36] ps_1.1.0           promises_1.0.1     scales_1.0.0       htmltools_0.3.6    assertthat_0.2.0   xtable_1.8-3       mime_0.6          
[43] colorspace_1.3-2   httpuv_1.4.5       lazyeval_0.2.1     munsell_0.5.0      crayon_1.3.4

Right before installing plotly (using devtools::install_github("ropensci/plotly"), I installed the orca utility which required the mapbox token to be set as and env).

@cpsievert
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Please report devtools::session_info() instead of sessionInfo()

@ndrubins
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Sure:

> devtools::session_info()
Session info -------------------------------------------------------------------------------------------------------------
 setting  value                       
 version  R version 3.5.1 (2018-07-02)
 system   x86_64, darwin15.6.0        
 ui       RStudio (1.1.456)           
 language (EN)                        
 collate  en_US.UTF-8                 
 tz       America/Los_Angeles         
 date     2018-12-17                  

Packages -----------------------------------------------------------------------------------------------------------------
 package     * version    date       source                          
 assertthat    0.2.0      2017-04-11 CRAN (R 3.5.0)                  
 base        * 3.5.1      2018-07-05 local                           
 bindr         0.1.1      2018-03-13 cran (@0.1.1)                   
 bindrcpp      0.2.2      2018-03-29 cran (@0.2.2)                   
 colorspace    1.3-2      2016-12-14 cran (@1.3-2)                   
 compiler      3.5.1      2018-07-05 local                           
 crayon        1.3.4      2017-09-16 cran (@1.3.4)                   
 data.table    1.11.8     2018-09-30 cran (@1.11.8)                  
 datasets    * 3.5.1      2018-07-05 local                           
 devtools      1.13.6     2018-06-27 CRAN (R 3.5.0)                  
 digest        0.6.18     2018-10-10 CRAN (R 3.5.0)                  
 dplyr       * 0.7.8      2018-11-10 cran (@0.7.8)                   
 ggplot2     * 3.1.0      2018-10-25 cran (@3.1.0)                   
 glue          1.3.0      2018-07-17 cran (@1.3.0)                   
 graphics    * 3.5.1      2018-07-05 local                           
 grDevices   * 3.5.1      2018-07-05 local                           
 grid          3.5.1      2018-07-05 local                           
 gtable        0.2.0      2016-02-26 cran (@0.2.0)                   
 htmltools     0.3.6      2017-04-28 cran (@0.3.6)                   
 htmlwidgets   1.3        2018-09-30 cran (@1.3)                     
 httr          1.4.0      2018-12-11 cran (@1.4.0)                   
 jsonlite      1.6        2018-12-07 cran (@1.6)                     
 lazyeval      0.2.1      2017-10-29 cran (@0.2.1)                   
 magrittr      1.5        2014-11-22 CRAN (R 3.5.0)                  
 memoise       1.1.0      2017-04-21 CRAN (R 3.5.0)                  
 methods     * 3.5.1      2018-07-05 local                           
 munsell       0.5.0      2018-06-12 cran (@0.5.0)                   
 pillar        1.3.0      2018-07-14 cran (@1.3.0)                   
 pkgconfig     2.0.2      2018-08-16 CRAN (R 3.5.0)                  
 plotly      * 4.8.0.9000 2018-12-17 Github (ropensci/plotly@c6b8a5d)
 plyr          1.8.4      2016-06-08 cran (@1.8.4)                   
 processx      3.2.0      2018-08-16 CRAN (R 3.5.0)                  
 ps            1.1.0      2018-08-10 CRAN (R 3.5.0)                  
 purrr         0.2.5      2018-05-29 cran (@0.2.5)                   
 R6            2.3.0      2018-10-04 CRAN (R 3.5.0)                  
 Rcpp          1.0.0      2018-11-07 cran (@1.0.0)                   
 rlang         0.3.0.1    2018-10-25 cran (@0.3.0.1)                 
 rstudioapi    0.8        2018-10-02 CRAN (R 3.5.0)                  
 scales        1.0.0      2018-08-09 cran (@1.0.0)                   
 stats       * 3.5.1      2018-07-05 local                           
 tibble        1.4.2      2018-01-22 cran (@1.4.2)                   
 tidyr         0.8.2      2018-10-28 cran (@0.8.2)                   
 tidyselect    0.2.5      2018-10-11 cran (@0.2.5)                   
 tools         3.5.1      2018-07-05 local                           
 utils       * 3.5.1      2018-07-05 local                           
 viridisLite   0.3.0      2018-02-01 cran (@0.3.0)                   
 webshot     * 0.5.1      2018-09-28 CRAN (R 3.5.0)                  
 withr         2.1.2      2018-03-15 CRAN (R 3.5.0)                  
 yaml          2.2.0      2018-07-25 cran (@2.2.0)

Thanks a lot.

@cpsievert
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Also, a reproducible example, if possible. Thanks.

@ndrubins
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Thanks a lot for the quick response.

Here's an example where I build a (sparse) heatmap and add to it a column dendrogram:

library(ggplot2)
library(dplyr)
library(plotly)

#build the heatmap data.frame
row.text <- strsplit("ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP",split=",")[[1]]
col.text <- strsplit("APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm",split=",")[[1]]
vals <- as.numeric(strsplit("1.82390874094432,NA,NA,1.72124639904717,NA,1.69897000433602,1.34678748622466,NA,2.74472749489669,3.76955107862173,3.76955107862173,NA,1.72124639904717,1.3767507096021,1.3767507096021,NA,NA,3.76955107862173,1.72124639904717,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.60205999132796,3.60205999132796,NA,NA,NA,NA,NA,NA,NA,NA,2.20760831050175,NA,2.20760831050175,NA,NA,NA,NA,NA,NA,3.30102999566398,1.69897000433602,1.48148606012211,NA,NA,NA,NA,NA,NA,NA,2.72124639904717,NA,3.60205999132796,2.36653154442041,1.55284196865778,3.13667713987954,2.46852108295775,2.74472749489669,3.13667713987954,2.72124639904717,2.46852108295775,2.29242982390206,NA,NA,NA,2.72124639904717,1.3767507096021,3.60205999132796,NA,NA,1.30980391997149,NA,NA,NA,1.30980391997149,NA,NA,NA,1.79588001734408,NA,NA,NA,NA,NA,NA,NA,NA,1.30980391997149",split=",")[[1]])
plot.df <- data.frame(row = row.text, column = col.text, value = vals)

#build the column dendrogram, via clustering
plot.mat <- reshape2::acast(plot.df,column ~ row)
col.hc <- cluster::agnes(plot.mat,diss=FALSE,method="complete")
sorted.col.hc <- dendsort::dendsort(as.hclust(col.hc))
col.dend <- as.dendrogram(sorted.col.hc)
col.dendex <- dendextend::as.ggdend(col.dend)
leaf.heights <- dplyr::filter(col.dendex$nodes,!is.na(leaf))$height
leaf.xs <- dplyr::filter(col.dendex$nodes,!is.na(leaf))$x
leaf.seqments.idx <- which(col.dendex$segments$yend %in% leaf.heights & col.dendex$segments$x %in% leaf.xs)
col.dendex$segments$yend[leaf.seqments.idx] <- max(col.dendex$segments$yend[leaf.seqments.idx])
col.dendex$segments$col[leaf.seqments.idx] <- "black"
col.dendex$labels$label <- ""
col.dendex$labels$y <- max(col.dendex$segments$yend[leaf.seqments.idx])
col.dendex$labels$x <- col.dendex$segments$x[leaf.seqments.idx]
col.dendex$labels$col <- "black"
col.dendex$segments$lwd <- 0.5
col.ggdend <- ggplot(col.dendex,labels=F)+guides(fill=F)+theme_minimal()+
  theme(axis.title=element_blank(),axis.text=element_blank(),axis.ticks=element_blank(),panel.grid=element_blank(),legend.position="none",legend.text=element_blank(),legend.background=element_blank(),legend.key=element_blank())

legend.title <- "-log10(Q-value)"
color.vec <- c("black","yellow")

#build the heatmap
p <- plot_ly(z=c(plot.df$value),x=plot.df$column,y=plot.df$row,colors=grDevices::colorRamp(color.vec),type="heatmap",colorbar=list(title=legend.title,len=0.4)) %>%
  layout(yaxis=list(title=NULL),xaxis=list(title=NULL))

#add the dendrogram
p <- plotly::subplot(col.ggdend,plotly::plotly_empty(),p %>% plotly::layout(showlegend = F),nrows=2,margin=c(0,0,0,0),heights=c(0.2,0.8),widths=c(0.8,0.2))

I'm able to view the image of the heatmap + dendrogram on the RStudio viewer. However, when I try to save it to a pdf using:
plotly::orca(p, file="p.pdf")

I get the error:

done with code 1 in 41.38 sec - failed or incomplete task(s)
Error in processx::run("orca", args, echo = TRUE, spinner = TRUE, ...) : 
  System command error

Thanks a lot.

@cpsievert
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Thanks. I thikn this may be due to another issue, could you file a new one?

@ndrubins
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Just did.

Thanks.

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5 participants
@ndrubins @cpsievert @0navarro @wasimaftab @Schuch666