David Winsemius
2017-Aug-14 18:21 UTC
[R] tidyverse repeating error: "object 'rlang_mut_env_parent' not found"
> On Aug 14, 2017, at 11:10 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > >> On Aug 14, 2017, at 8:37 AM, Szumiloski, John <John.Szumiloski at bms.com> wrote: >> >> Thanks for the feedback Jeff. Before I pursue a bug report, let me give a full example: >> >> ###### begin console output >> >> R version 3.4.1 (2017-06-30) -- "Single Candle" >> Copyright (C) 2017 The R Foundation for Statistical Computing >> Platform: i386-w64-mingw32/i386 (32-bit) >> >> R is free software and comes with ABSOLUTELY NO WARRANTY. >> You are welcome to redistribute it under certain conditions. >> Type 'license()' or 'licence()' for distribution details. >> >> R is a collaborative project with many contributors. >> Type 'contributors()' for more information and >> 'citation()' on how to cite R or R packages in publications. >> >> Type 'demo()' for some demos, 'help()' for on-line help, or >> 'help.start()' for an HTML browser interface to help. >> Type 'q()' to quit R. >> >> Desktop> library(tidyverse) >> Loading tidyverse: ggplot2 >> Loading tidyverse: tibble >> Loading tidyverse: tidyr >> Loading tidyverse: readr >> Loading tidyverse: purrr >> Loading tidyverse: dplyr >> Conflicts with tidy packages -------------------------------------------------------------- >> filter(): dplyr, stats >> lag(): dplyr, stats >> Desktop> library(magrittr) >> >> Attaching package: ?magrittr? >> >> The following object is masked from ?package:purrr?: >> >> set_names >> >> The following object is masked from ?package:tidyr?: >> >> extract >> >> Desktop> Test <- read_csv("Test.csv") >> Parsed with column specification: >> cols( >> Tests1 = col_character(), >> Tests2 = col_character(), >> X1 = col_integer(), >> X2 = col_integer(), >> Result = col_double() >> ) >> Desktop> Test >> # A tibble: 15 x 5 >> Tests1 Tests2 X1 X2 Result >> <chr> <chr> <int> <int> <dbl> >> 1 C C 3 1 0.58 >> 2 C C 3 3 -0.78 >> 3 C C 2 2 -0.74 >> 4 C C 1 1 1.78 >> 5 C C 1 3 0.91 >> 6 A A 3 1 0.07 >> 7 A A 3 3 0.57 >> 8 A A 2 2 0.37 >> 9 A A 1 1 -1.25 >> 10 A A 1 3 0.73 >> 11 B B 3 1 2.17 >> 12 B B 3 3 -0.02 >> 13 B B 2 2 -0.17 >> 14 B B 1 1 0.37 >> 15 B B 1 3 1.28 >> Desktop> >> Desktop> ### dplyr::select >> Desktop> >> Desktop> Test %>% select(Tests, Tests2) >> Error in mut_env_parent(overscope$.top_env, lexical_env) : >> object 'rlang_mut_env_parent' not found > > I don't see a column named "Tests" > >> Desktop> >> Desktop> select(Test, Tests, Tests2) >> Error in mut_env_parent(overscope$.top_env, lexical_env) : >> object 'rlang_mut_env_parent' not found > > Ditto > >> Desktop> >> Desktop> # tibble::tibble >> Desktop> >> Desktop> Test <- Test %$% tibble(T1=Test1, Y=Result) >> Error in mut_env_parent(overscope$.top_env, lexical_env) : >> object 'rlang_mut_env_parent' not found > > This time I don't see a column names "Test1" > >> Desktop> >> Desktop> Test <- tibble(T1=Test[['Test1']], Y=Test[['Result']]) >> Error in mut_env_parent(overscope$.top_env, lexical_env) : >> object 'rlang_mut_env_parent' not found > > Ditto > >> Desktop> >> Desktop> ### tidyr::nest >> Desktop> >> Desktop> byTest <- Test %>% group_by(Tests1, Tests2) >> Desktop> nest(byTest) >> Error in mut_env_parent(overscope$.top_env, lexical_env) : >> object 'rlang_mut_env_parent' not foundAnd in this (last) case I am unable to confirm an error:> byTest <- Test %>% group_by(Tests1, Tests2) > nest(byTest)# A tibble: 3 x 3 Tests1 Tests2 data <chr> <chr> <list> 1 C C <tibble [5 x 3]> 2 A A <tibble [5 x 3]> 3 B B <tibble [5 x 3]> After spotting obvious errors in spelling x4 and not being able to reproduce the fifth example I hope I can be forgiven for not making hte effort to see if any of your (or my) versions are not up-to-date. I'm going to let you do that:> sessionInfo()R version 3.4.0 (2017-04-21) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: OS X El Capitan 10.11.6 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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] grid grDevices utils datasets graphics stats methods base other attached packages: [1] mlr_2.11 ParamHelpers_1.10 jsonlite_1.5 dplyr_0.7.0 [5] purrr_0.2.2.2 readr_1.1.1 tidyr_0.6.3 tibble_1.3.1 [9] tidyverse_1.1.1 memisc_0.99.8 cranlogs_2.1.0 changepoint_2.2.2 [13] zoo_1.8-0 ncdf4_1.16 bindrcpp_0.1 mxnet_0.10.1 [17] fields_8.15 maps_3.1.1 spam_1.4-0 akima_0.6-2 [21] data.table_1.10.4 pls_2.6-0 MASS_7.3-47 rms_5.1-1 [25] SparseM_1.77 Hmisc_4.0-3 ggplot2_2.2.1 Formula_1.2-1 [29] survival_2.41-3 sos_2.0-0 brew_1.0-6 lattice_0.20-35 loaded via a namespace (and not attached): [1] nlme_3.1-131 pbkrtest_0.4-7 lubridate_1.6.0 MBESS_4.3.0 [5] RColorBrewer_1.1-2 httr_1.2.1 tools_3.4.0 backports_1.0.5 [9] R6_2.2.2 rpart_4.1-11 lazyeval_0.2.0 mgcv_1.8-17 [13] colorspace_1.3-2 nnet_7.3-12 sp_1.2-4 gridExtra_2.2.1 [17] mnormt_1.5-5 parallelMap_1.3 compiler_3.4.0 rvest_0.3.2 [21] quantreg_5.33 htmlTable_1.9 xml2_1.1.1 influenceR_0.1.0 [25] sandwich_2.3-4 labeling_0.3 scales_0.4.1 checkmate_1.8.2 [29] polspline_1.1.12 mvtnorm_1.0-6 psych_1.7.5 stringr_1.2.0 [33] digest_0.6.12 foreign_0.8-68 minqa_1.2.4 base64enc_0.1-3 [37] pkgconfig_2.0.1 htmltools_0.3.6 lme4_1.1-13 readxl_1.0.0 [41] htmlwidgets_0.8 rlang_0.1 rstudioapi_0.6 BBmisc_1.11 [45] bindr_0.1 visNetwork_1.0.3 acepack_1.4.1 rgexf_0.15.3 [49] car_2.1-4 magrittr_1.5 Matrix_1.2-10 Rcpp_0.12.11 [53] munsell_0.4.3 viridis_0.4.0 stringi_1.1.5 multcomp_1.4-6 [57] yaml_2.1.14 plyr_1.8.4 parallel_3.4.0 forcats_0.2.0 [61] haven_1.0.0 splines_3.4.0 hms_0.3 knitr_1.15.1 [65] igraph_1.0.1 reshape2_1.4.2 codetools_0.2-15 XML_3.98-1.7 [69] glue_1.0.0 latticeExtra_0.6-28 modelr_0.1.0 nloptr_1.0.4 [73] cellranger_1.1.0 MatrixModels_0.4-1 gtable_0.2.0 assertthat_0.2.0 [77] broom_0.4.2 viridisLite_0.2.0 cluster_2.0.6 Rook_1.1-1 [81] DiagrammeR_0.9.0 TH.data_1.0-8>> Desktop> >> Desktop> ### session information >> Desktop> >> Desktop> version >> _ >> platform i386-w64-mingw32 >> arch i386 >> os mingw32 >> system i386, mingw32 >> status >> major 3 >> minor 4.1 >> year 2017 >> month 06 >> day 30 >> svn rev 72865 >> language R >> version.string R version 3.4.1 (2017-06-30) >> nickname Single Candle >> Desktop> >> Desktop> search() >> [1] ".GlobalEnv" "package:magrittr" "package:dplyr" "package:purrr" >> [5] "package:readr" "package:tidyr" "package:tibble" "package:ggplot2" >> [9] "package:tidyverse" "tools:rstudio" "package:stats" "package:graphics" >> [13] "package:grDevices" "package:utils" "package:datasets" "package:methods" >> [17] "Autoloads" "package:base" >> Desktop> >> Desktop> sessionInfo() >> R version 3.4.1 (2017-06-30) >> Platform: i386-w64-mingw32/i386 (32-bit) >> Running under: Windows 7 (build 7601) Service Pack 1 >> >> Matrix products: default >> >> locale: >> [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 >> [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C >> [5] LC_TIME=English_United States.1252 >> >> attached base packages: >> [1] stats graphics grDevices utils datasets methods base >> >> other attached packages: >> [1] magrittr_1.5 dplyr_0.7.2 purrr_0.2.3 readr_1.1.1 tidyr_0.6.3 >> [6] tibble_1.3.3 ggplot2_2.2.1 tidyverse_1.1.1 >> >> loaded via a namespace (and not attached): >> [1] rvest_0.3.2 lattice_0.20-35 foreign_0.8-69 pkgconfig_2.0.1 xml2_1.1.1 >> [6] compiler_3.4.1 stringr_1.2.0 forcats_0.2.0 parallel_3.4.1 readxl_1.0.0 >> [11] Rcpp_0.12.12 plyr_1.8.4 cellranger_1.1.0 httr_1.2.1 tools_3.4.1 >> [16] nlme_3.1-131 broom_0.4.2 R6_2.2.2 bindrcpp_0.2 bindr_0.1 >> [21] scales_0.4.1 assertthat_0.2.0 gtable_0.2.0 stringi_1.1.5 reshape2_1.4.2 >> [26] hms_0.3 munsell_0.4.3 grid_3.4.1 colorspace_1.3-2 glue_1.1.1 >> [31] lubridate_1.6.0 rlang_0.1.2 psych_1.7.5 lazyeval_0.2.0 haven_1.1.0 >> [36] modelr_0 >> >> ################# end console output >> >> Thanks again for any feedback, >> John >> John Szumiloski, Ph.D. >> Principal Scientist, Statistician >> Pharmaceutical Development / Drug Product Science & Technology >> NBR105-1-1411 >> >> Bristol-Myers Squibb >> P.O. Box 191 >> 1 Squibb Drive >> New Brunswick, NJ >> USA >> 08903-0191 >> >> (732) 227-7167 >> >> >> >> -----Original Message----- >> From: Jeff Newmiller [mailto:jdnewmil at dcn.davis.ca.us] >> Sent: Monday, 14 August, 2017 10:33 AM >> To: r-help at r-project.org; Szumiloski, John <John.Szumiloski at bms.com> >> Subject: Re: [R] tidyverse repeating error: "object 'rlang_mut_env_parent' not found" >> >> This sounds an awful lot like a bug. Read the Posting Guide to know what to do about bugs. And delaying making the reprex is _always_ a bad idea. >> -- > -- > > David Winsemius > Alameda, CA, USA > > 'Any technology distinguishable from magic is insufficiently advanced.' -Gehm's Corollary to Clarke's Third Law > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.David Winsemius Alameda, CA, USA 'Any technology distinguishable from magic is insufficiently advanced.' -Gehm's Corollary to Clarke's Third Law
Hi, and sorry for asking such an unspecific question. Does anybody know of statistical / data mining methods that are available in R that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS Enterprise Miner. I don't expect a complete list, just two or three examples or hints where and what to look for. I found some older comparisons, and the R methods mentioned there (GLMET, RF, ADABoost) are now supported by SAS (at least to some degree). And there exists a (massive) list of available models for the caret package here: https://rdrr.io/cran/caret/man/models.html, but it's hard to analyze the complete list. (I'm trying to answer a question of a colleague). Thanks, Friedrich
Zhang, Yuwei
2017-Aug-15 14:59 UTC
[R] Statistical / data mining methods in R and not in SAS?
This is very interesting. Anyone else want to weigh in on this? Thanks, Yuwei Zhang SAS Programmer Office:? 410-645-9256 E-mail:? yuwei.zhang at cvp.hcqis.org CVP 3701 Pender Drive, Suite 200 | Fairfax, VA 22030 www.cvpcorp.com Named Top Workplaces 2017 in Washington Post -----Original Message----- From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of fs Sent: Monday, August 14, 2017 3:22 PM To: r-help at r-project.org Subject: [R] Statistical / data mining methods in R and not in SAS? Hi, and sorry for asking such an unspecific question. Does anybody know of statistical / data mining methods that are available in R that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS Enterprise Miner. I don't expect a complete list, just two or three examples or hints where and what to look for. I found some older comparisons, and the R methods mentioned there (GLMET, RF, ADABoost) are now supported by SAS (at least to some degree). And there exists a (massive) list of available models for the caret package here: https://rdrr.io/cran/caret/man/models.html, but it's hard to analyze the complete list. (I'm trying to answer a question of a colleague). Thanks, Friedrich ______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
David Winsemius
2017-Aug-16 01:34 UTC
[R] Statistical / data mining methods in R and not in SAS?
> On Aug 14, 2017, at 12:22 PM, fs <mail at friedrich-schuster.de> wrote: > > Hi, and sorry for asking such an unspecific question. > > Does anybody know of statistical / data mining methods that are available in R > that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS > Enterprise Miner. I don't expect a complete list, just two or three examples > or hints where and what to look for. > > I found some older comparisons, and the R methods mentioned there (GLMET, RF, > ADABoost) are now supported by SAS (at least to some degree). > > And there exists a (massive) list of available models for the caret package > here: https://rdrr.io/cran/caret/man/models.html, but it's hard to analyze the > complete list. > > (I'm trying to answer a question of a colleague).It wasn't clear whether it was statistical procedures themselves or connections to back-end data and machine learning packages might be the metric of comparison. I also thought the question would have been better posted on a SAS website, since the CRAN Task Views provide an even more complete listing and most of us are not current users of the SAS Enterprise Miner Suite. The SAS users might have a better notion of their capacities and limitations. You might start by comparing: 1) https://www.sas.com/content/dam/SAS/en_us/doc/factsheet/sas-enterprise-miner-101369.pdf ... although that did not appear to be a comprehensive listing of available model types. With: 2a) https://cran.r-project.org/web/views/MachineLearning.html 2b) https://cran.r-project.org/web/views/Bayesian.html 2c) https://cran.r-project.org/web/views/ExtremeValue.html 2d) https://cran.r-project.org/web/views/FunctionalData.html 2e) https://cran.r-project.org/web/views/Robust.html 2f) https://cran.r-project.org/web/views/SpatioTemporal.html 2g) https://cran.r-project.org/web/views/Spatial.html Left out several Task Views since they might be probably too "ordinary", but you should look at all of them: https://cran.r-project.org/web/views/ Other websites possibly outlining areas of possible difference: https://tensorflow.rstudio.com/ https://blog.rstudio.com/2016/09/27/sparklyr-r-interface-for-apache-spark/ https://spark.rstudio.com/reference/sparklyr/latest/ml_multilayer_perceptron.html https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/TensorFlow-MNIST/td-p/318708 https://thomaswdinsmore.com/2017/04/05/sas-peddles-open-source-fud/ -- David Winsemius Alameda, CA, USA 'Any technology distinguishable from magic is insufficiently advanced.' -Gehm's Corollary to Clarke's Third Law