Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if barcode='16187DD5016' then delete; if barcode='16187DD8007' then delete; if barcode='16187DD7010' then delete; if barcode='16187DD7007' then delete; if barcode='16187DD8005' then delete; if barcode='16187DD6004' then delete; if barcode='16187DD5008' then delete; if barcode='16187DD7012' then delete; if barcode='16187DD6010' then delete; run; quit; Title'2016 Asilomar Stress Relief champagin yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_ pearsonresid; var resid; proc print data=resids (obs=3);run; Can someone please help me convert my code to R? Any help would be much appreciated. Thanks, Andrew Harmon
Converting SAS Code
14 messages · Andrew Harmon, Tobias Fellinger, Michael Dewey +10 more
Hello, in my experience the most direct path of converting SAS code to R is by using dplyr. dplyr provides the filter function, the first part of your code could look like this, assuming your datasets are stored as data.frames: library(dplyr) yield <- filter(stress, field != "YV", field != "HV", barcode != "16187DD4015", barcode != "16187DD6002") (and so on for the other barcodes.) For mixed effects look into the lme4 package, lmer should use the reml criterion per default, the model specifications work very different in R. Look into the vingette [1] of the lme4 package chapter 2.1. gives an explanation of the used model formulas. You should get the coeficients of the fitted glmer model with the coef function. The Plots and univariate statistics work very different in R, have a look at the functions group_by and summarise provided by the dplyr package for calculating univariate statistics by groups, and the ggplot 2 package for plotting. Tobi [1] https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf
On Fri, 2017-09-29 at 07:47 -0500, Andrew Harmon wrote:
Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if barcode='16187DD5016' then delete; if barcode='16187DD8007' then delete; if barcode='16187DD7010' then delete; if barcode='16187DD7007' then delete; if barcode='16187DD8005' then delete; if barcode='16187DD6004' then delete; if barcode='16187DD5008' then delete; if barcode='16187DD7012' then delete; if barcode='16187DD6010' then delete; run; quit; Title'2016 Asilomar Stress Relief champagin yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_ pearsonresid; var resid; proc print data=resids (obs=3);run; Can someone please help me convert my code to R? Any help would be much appreciated. Thanks, Andrew Harmon [[alternative HTML version deleted]]
______________________________________________ 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.
You might get better answers if you 1 - break this down into separate issues 2 - tell us what you want to achieve in words rather than SAS, we all read English but few of us speak SAS 3 - post in plain text not HTML as HTML mangles your post
On 29/09/2017 13:47, Andrew Harmon wrote:
Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if barcode='16187DD5016' then delete; if barcode='16187DD8007' then delete; if barcode='16187DD7010' then delete; if barcode='16187DD7007' then delete; if barcode='16187DD8005' then delete; if barcode='16187DD6004' then delete; if barcode='16187DD5008' then delete; if barcode='16187DD7012' then delete; if barcode='16187DD6010' then delete; run; quit; Title'2016 Asilomar Stress Relief champagin yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_ pearsonresid; var resid; proc print data=resids (obs=3);run; Can someone please help me convert my code to R? Any help would be much appreciated. Thanks, Andrew Harmon [[alternative HTML version deleted]]
______________________________________________ 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. --- This email has been checked for viruses by AVG. http://www.avg.com
Regarding point 3, as a moderator I have been helping Andrew get this post out to the list over the past week. His previous attempts were encoded in some way that the listserv rejected. He sent me the post via his gmail account and viewing the source I saw it had at least both plain test and HTML an I said it was worth a try to post it. Certainly on my mail client his post displays acceptably with the notice that the HTML alternative was removed. Kevin
On 09/29/2017 09:51 AM, Michael Dewey wrote:
You might get better answers if you 1 - break this down into separate issues 2 - tell us what you want to achieve in words rather than SAS, we all read English but few of us speak SAS 3 - post in plain text not HTML as HTML mangles your post On 29/09/2017 13:47, Andrew Harmon wrote:
Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if barcode='16187DD5016' then delete; if barcode='16187DD8007' then delete; if barcode='16187DD7010' then delete; if barcode='16187DD7007' then delete; if barcode='16187DD8005' then delete; if barcode='16187DD6004' then delete; if barcode='16187DD5008' then delete; if barcode='16187DD7012' then delete; if barcode='16187DD6010' then delete; run; quit; Title'2016 Asilomar Stress Relief champagin yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_ pearsonresid; var resid; proc print data=resids (obs=3);run; Can someone please help me convert my code to R? Any help would be much appreciated. Thanks, Andrew Harmon ????[[alternative HTML version deleted]]
______________________________________________ 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. --- This email has been checked for viruses by AVG. http://www.avg.com
Kevin E. Thorpe Head of Biostatistics, Applied Health Research Centre (AHRC) Li Ka Shing Knowledge Institute of St. Michael's Hospital Assistant Professor, Dalla Lana School of Public Health University of Toronto email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016
I will offer an opinion, with which others may fairly take issue. If you are coming from SAS and wish to learn R, you should forget about SAS entirely; it is ancient and convoluted. But more to the point, as others have already suggested, you will only confuse and hamstring yourself trying to convert the programming paradigms of one language into another. Better to consider the **tasks** you wish to accomplish and learn how to approach them in the new language. I would add that this especially includes learning about R's varied data structures for which there is no cognate in SAS I think (correction requested if I'm wrong about this). If this is a one-off, just finding a local resource to do the job for you might be the best approach. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Sep 29, 2017 at 10:21 AM, Kevin E. Thorpe <kevin.thorpe at utoronto.ca> wrote:
Regarding point 3, as a moderator I have been helping Andrew get this post out to the list over the past week. His previous attempts were encoded in some way that the listserv rejected. He sent me the post via his gmail account and viewing the source I saw it had at least both plain test and HTML an I said it was worth a try to post it. Certainly on my mail client his post displays acceptably with the notice that the HTML alternative was removed. Kevin On 09/29/2017 09:51 AM, Michael Dewey wrote:
You might get better answers if you 1 - break this down into separate issues 2 - tell us what you want to achieve in words rather than SAS, we all read English but few of us speak SAS 3 - post in plain text not HTML as HTML mangles your post On 29/09/2017 13:47, Andrew Harmon wrote:
Hello all,
My statistical analysis training up until this point has been entirely
done
in SAS. The code I frequently used was:
*Yield Champagin;
data yield;
set stress;
if field='YV' then delete;
if field='HB' then delete;
if barcode='16187DD4015' then delete;
if barcode='16187DD6002' then delete;
if barcode='16187DD2007' then delete;
if barcode='16187DD5016' then delete;
if barcode='16187DD8007' then delete;
if barcode='16187DD7010' then delete;
if barcode='16187DD7007' then delete;
if barcode='16187DD8005' then delete;
if barcode='16187DD6004' then delete;
if barcode='16187DD5008' then delete;
if barcode='16187DD7012' then delete;
if barcode='16187DD6010' then delete;
run; quit;
Title'2016 Asilomar Stress Relief champagin yield';
proc mixed method=reml data=yield;
class rep Management Foliar_Fungicide Chemical_Treatment;
model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment
Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr;
random rep rep*Management rep*Management*Foliar_Fungicide;
lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff;
ods output diffs=ppp lsmeans=means;
ods listing exclude diffs lsmeans;
run; quit;
%include'C:\Users\harmon12\Desktop\pdmix800.sas';
%pdmix800(ppp,means,alpha=0.10,sort=yes);
ods graphics off;
run; quit;
proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_
pearsonresid; var resid;
proc print data=resids (obs=3);run;
Can someone please help me convert my code to R? Any help would be much
appreciated.
Thanks,
Andrew Harmon
[[alternative HTML version deleted]]
______________________________________________ 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/posti ng-guide.html and provide commented, minimal, self-contained, reproducible code. --- This email has been checked for viruses by AVG. http://www.avg.com
-- Kevin E. Thorpe Head of Biostatistics, Applied Health Research Centre (AHRC) Li Ka Shing Knowledge Institute of St. Michael's Hospital Assistant Professor, Dalla Lana School of Public Health University of Toronto email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016
______________________________________________ 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/posti ng-guide.html and provide commented, minimal, self-contained, reproducible code.
I wish to second this approach to learning R. I tried for several years to translate other stat programs or provide parallel analyses with R. This dabbling-in-R approach did not work . When a transferred to a research unit that could ill afford commercial software, I devoted my entire time to doing everything in R. This was a difficult learning process, but I eventually became proficient in R. The conceptual paradigm for R is only marginally commensurate with that of standard statistical software. You must immerse yourself in R to become proficient. Good luck, Joe Bert Gunter <bgunter.4567 at gmail.com> Sent by: "R-help" <r-help-bounces at r-project.org> 09/29/2017 02:09 PM To "Kevin E. Thorpe" <kevin.thorpe at utoronto.ca>, cc R-help <r-help at r-project.org>, Andrew Harmon <andrewharmon42 at gmail.com> Subject Re: [R] Converting SAS Code I will offer an opinion, with which others may fairly take issue. If you are coming from SAS and wish to learn R, you should forget about SAS entirely; it is ancient and convoluted. But more to the point, as others have already suggested, you will only confuse and hamstring yourself trying to convert the programming paradigms of one language into another. Better to consider the **tasks** you wish to accomplish and learn how to approach them in the new language. I would add that this especially includes learning about R's varied data structures for which there is no cognate in SAS I think (correction requested if I'm wrong about this). If this is a one-off, just finding a local resource to do the job for you might be the best approach. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Sep 29, 2017 at 10:21 AM, Kevin E. Thorpe <kevin.thorpe at utoronto.ca> wrote:
Regarding point 3, as a moderator I have been helping Andrew get this
post
out to the list over the past week. His previous attempts were encoded
in
some way that the listserv rejected. He sent me the post via his gmail account and viewing the source I saw it had at least both plain test and HTML an I said it was worth a try to post it. Certainly on my mail
client
his post displays acceptably with the notice that the HTML alternative
was
removed. Kevin On 09/29/2017 09:51 AM, Michael Dewey wrote:
You might get better answers if you 1 - break this down into separate issues 2 - tell us what you want to achieve in words rather than SAS, we all read English but few of us speak SAS 3 - post in plain text not HTML as HTML mangles your post On 29/09/2017 13:47, Andrew Harmon wrote:
Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if barcode='16187DD5016' then delete; if barcode='16187DD8007' then delete; if barcode='16187DD7010' then delete; if barcode='16187DD7007' then delete; if barcode='16187DD8005' then delete; if barcode='16187DD6004' then delete; if barcode='16187DD5008' then delete; if barcode='16187DD7012' then delete; if barcode='16187DD6010' then delete; run; quit; Title'2016 Asilomar Stress Relief champagin yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_
=Management|Foliar_Fungicide|Chemical_Treatment
Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_ pearsonresid; var resid; proc print data=resids (obs=3);run; Can someone please help me convert my code to R? Any help would be
much
appreciated.
Thanks,
Andrew Harmon
[[alternative HTML version deleted]]
______________________________________________ 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/posti ng-guide.html and provide commented, minimal, self-contained, reproducible code. --- This email has been checked for viruses by AVG. http://www.avg.com
-- Kevin E. Thorpe Head of Biostatistics, Applied Health Research Centre (AHRC) Li Ka Shing Knowledge Institute of St. Michael's Hospital Assistant Professor, Dalla Lana School of Public Health University of Toronto email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016
______________________________________________ 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/posti ng-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ 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.
All HTML emails have a plain text part along with the HTML part... but it is usually invisible to the author and is automatically generated by the email composing software and some software is better than others at that job (by a lot). However, without a doubt, sending the email in text form at least means the sender saw the plain text part before it was sent, which removes a potentially huge barrier to communication. And GMail definitely can send plain text.
Sent from my phone. Please excuse my brevity. On September 29, 2017 10:21:50 AM PDT, "Kevin E. Thorpe" <kevin.thorpe at utoronto.ca> wrote: >Regarding point 3, as a moderator I have been helping Andrew get this >post out to the list over the past week. His previous attempts were >encoded in some way that the listserv rejected. He sent me the post via > >his gmail account and viewing the source I saw it had at least both >plain test and HTML an I said it was worth a try to post it. Certainly >on my mail client his post displays acceptably with the notice that the > >HTML alternative was removed. > >Kevin > >On 09/29/2017 09:51 AM, Michael Dewey wrote: >> You might get better answers if you >> >> 1 - break this down into separate issues >> 2 - tell us what you want to achieve in words rather than SAS, we all > >> read English but few of us speak SAS >> 3 - post in plain text not HTML as HTML mangles your post >> >> On 29/09/2017 13:47, Andrew Harmon wrote: >>> Hello all, >>> >>> My statistical analysis training up until this point has been >>> entirely done >>> in SAS. The code I frequently used was: >>> >>> *Yield Champagin; >>> >>> data yield; >>> >>> set stress; >>> >>> if field='YV' then delete; >>> >>> if field='HB' then delete; >>> >>> if barcode='16187DD4015' then delete; >>> >>> if barcode='16187DD6002' then delete; >>> >>> if barcode='16187DD2007' then delete; >>> >>> if barcode='16187DD5016' then delete; >>> >>> if barcode='16187DD8007' then delete; >>> >>> if barcode='16187DD7010' then delete; >>> >>> if barcode='16187DD7007' then delete; >>> >>> if barcode='16187DD8005' then delete; >>> >>> if barcode='16187DD6004' then delete; >>> >>> if barcode='16187DD5008' then delete; >>> >>> if barcode='16187DD7012' then delete; >>> >>> if barcode='16187DD6010' then delete; >>> >>> run; quit; >>> >>> >>> >>> Title'2016 Asilomar Stress Relief champagin yield'; >>> >>> proc mixed method=reml data=yield; >>> >>> class rep Management Foliar_Fungicide Chemical_Treatment; >>> >>> model Grain_Yield__Mg_h_ >=Management|Foliar_Fungicide|Chemical_Treatment >>> Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; >>> >>> random rep rep*Management rep*Management*Foliar_Fungicide; >>> >>> lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; >>> >>> ods output diffs=ppp lsmeans=means; >>> >>> ods listing exclude diffs lsmeans; >>> >>> run; quit; >>> >>> %include'C:\Users\harmon12\Desktop\pdmix800.sas'; >>> >>> %pdmix800(ppp,means,alpha=0.10,sort=yes); >>> >>> ods graphics off; >>> >>> run; quit; >>> >>> proc univariate data=resids normal plot; id Barcode >Grain_Yield__Mg_h_ >>> pearsonresid; var resid; >>> proc print data=resids (obs=3);run; >>> >>> Can someone please help me convert my code to R? Any help would be >much >>> appreciated. >>> >>> >>> Thanks, >>> >>> >>> Andrew Harmon >>> >>> ????[[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> 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. >>> >>> --- >>> This email has been checked for viruses by AVG. >>> http://www.avg.com >>> >>> >> > >-- >Kevin E. Thorpe >Head of Biostatistics, Applied Health Research Centre (AHRC) >Li Ka Shing Knowledge Institute of St. Michael's Hospital >Assistant Professor, Dalla Lana School of Public Health >University of Toronto >email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016 > >______________________________________________ >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.
Hi, I would echo Bert's comments below. The last thing that you want to try to do is to convert SAS code to R code on a "line for line" basis. The programming paradigm of R, which is built upon vectorized operations, takes a "whole object" approach for efficiency. SAS does not, since it is generally based upon 1970's era, main frame style, programming techniques using macros, etc. If this is not a one off and something that you might find yourself doing with some frequency, you might consider investing in Bob Muenchen's book, R for SAS and SPSS Users: https://www.amazon.com/gp/product/1461406846/ <https://www.amazon.com/gp/product/1461406846/> While it is now a few years old, it is still relevant in terms of pointing you in the direction of basic and conceptual linkages between the languages. Regards, Marc Schwartz
On Sep 29, 2017, at 2:09 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: I will offer an opinion, with which others may fairly take issue. If you are coming from SAS and wish to learn R, you should forget about SAS entirely; it is ancient and convoluted. But more to the point, as others have already suggested, you will only confuse and hamstring yourself trying to convert the programming paradigms of one language into another. Better to consider the **tasks** you wish to accomplish and learn how to approach them in the new language. I would add that this especially includes learning about R's varied data structures for which there is no cognate in SAS I think (correction requested if I'm wrong about this). If this is a one-off, just finding a local resource to do the job for you might be the best approach. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Sep 29, 2017 at 10:21 AM, Kevin E. Thorpe <kevin.thorpe at utoronto.ca> wrote:
Regarding point 3, as a moderator I have been helping Andrew get this post out to the list over the past week. His previous attempts were encoded in some way that the listserv rejected. He sent me the post via his gmail account and viewing the source I saw it had at least both plain test and HTML an I said it was worth a try to post it. Certainly on my mail client his post displays acceptably with the notice that the HTML alternative was removed. Kevin On 09/29/2017 09:51 AM, Michael Dewey wrote:
You might get better answers if you 1 - break this down into separate issues 2 - tell us what you want to achieve in words rather than SAS, we all read English but few of us speak SAS 3 - post in plain text not HTML as HTML mangles your post On 29/09/2017 13:47, Andrew Harmon wrote:
Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if barcode='16187DD5016' then delete; if barcode='16187DD8007' then delete; if barcode='16187DD7010' then delete; if barcode='16187DD7007' then delete; if barcode='16187DD8005' then delete; if barcode='16187DD6004' then delete; if barcode='16187DD5008' then delete; if barcode='16187DD7012' then delete; if barcode='16187DD6010' then delete; run; quit; Title'2016 Asilomar Stress Relief champagin yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_ pearsonresid; var resid; proc print data=resids (obs=3);run; Can someone please help me convert my code to R? Any help would be much appreciated. Thanks, Andrew Harmon [[alternative HTML version deleted]]
On 30/09/17 07:45, JLucke at ria.buffalo.edu wrote:
<SNIP>
The conceptual paradigm for R is only marginally commensurate with that of standard statistical software. You must immerse yourself in R to become proficient.
Fortune nomination. cheers, Rolf
Technical Editor ANZJS Department of Statistics University of Auckland Phone: +64-9-373-7599 ext. 88276
For the initial data step, assuming a data frame named stress already exists, and using base R, you can start with something like this:
barcodes.to.delete <- c('16187DD4015', '16187DD6002', {complete the comma-delimited vector of barcodes you don't want} )
yield <- subset(stress, !(barcode %in% barcodes.to.delete) )
yield <- subset(yield , !(field %in% c('YY','HB') )
## the above three lines could be done in a single line, but it would be long, ugly, hard to read, and hard to validate.
## easier to split it into a few steps
## another way, still using base R, and with a different syntax for the subsetting
records.to.drop <- stress$barcode %in% barcodes.to.delete | stress$yield %in% c('YY', 'HB')
yield <- stress[ !records.to.drop , ]
I think these are examples of doing it "the R way", not thinking in terms of directly translating SAS code to R code.
I used to use SAS a lot, but I don't know what the line
*Yield Champagin;
does.
--
Don MacQueen
Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062
Lab cell 925-724-7509
On 9/29/17, 5:47 AM, "R-help on behalf of Andrew Harmon" <r-help-bounces at r-project.org on behalf of andrewharmon42 at gmail.com> wrote:
Hello all,
My statistical analysis training up until this point has been entirely done
in SAS. The code I frequently used was:
*Yield Champagin;
data yield;
set stress;
if field='YV' then delete;
if field='HB' then delete;
if barcode='16187DD4015' then delete;
if barcode='16187DD6002' then delete;
if barcode='16187DD2007' then delete;
if barcode='16187DD5016' then delete;
if barcode='16187DD8007' then delete;
if barcode='16187DD7010' then delete;
if barcode='16187DD7007' then delete;
if barcode='16187DD8005' then delete;
if barcode='16187DD6004' then delete;
if barcode='16187DD5008' then delete;
if barcode='16187DD7012' then delete;
if barcode='16187DD6010' then delete;
run; quit;
Title'2016 Asilomar Stress Relief champagin yield';
proc mixed method=reml data=yield;
class rep Management Foliar_Fungicide Chemical_Treatment;
model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment
Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr;
random rep rep*Management rep*Management*Foliar_Fungicide;
lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff;
ods output diffs=ppp lsmeans=means;
ods listing exclude diffs lsmeans;
run; quit;
%include'C:\Users\harmon12\Desktop\pdmix800.sas';
%pdmix800(ppp,means,alpha=0.10,sort=yes);
ods graphics off;
run; quit;
proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_
pearsonresid; var resid;
proc print data=resids (obs=3);run;
Can someone please help me convert my code to R? Any help would be much
appreciated.
Thanks,
Andrew Harmon
______________________________________________
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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.
On 29 Sep 2017, at 22:43 , MacQueen, Don <macqueen1 at llnl.gov> wrote: I used to use SAS a lot, but I don't know what the line *Yield Champagin; does.
Nothing. It's a comment...
Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com
On Fri, Sep 29, 2017 at 2:32 PM, peter dalgaard <pdalgd at gmail.com> wrote:
On 29 Sep 2017, at 22:43 , MacQueen, Don <macqueen1 at llnl.gov> wrote: I used to use SAS a lot, but I don't know what the line *Yield Champagin; does.
Nothing. It's a comment...
Fortune nomination! Peter
On 9/29/2017 3:37 PM, Rolf Turner wrote:
On 30/09/17 07:45, JLucke at ria.buffalo.edu wrote: <SNIP>
The conceptual paradigm for R is only marginally commensurate with that of standard statistical software. You must immerse yourself in R to become proficient.
Fortune nomination.
For newer list members wondering what Rolf is talking about try: library(fortunes) fortune() to get a flavor! There are many pearls of wisdom.
cheers, Rolf
On 30 Sep 2017, at 14:22 , Robert Baer <rbaer at atsu.edu> wrote: On 9/29/2017 3:37 PM, Rolf Turner wrote:
On 30/09/17 07:45, JLucke at ria.buffalo.edu wrote: <SNIP>
The conceptual paradigm for R is only marginally commensurate with that of standard statistical software. You must immerse yourself in R to become proficient.
Fortune nomination.
For newer list members wondering what Rolf is talking about try: library(fortunes) fortune() to get a flavor! There are many pearls of wisdom.
Also, to get roughly back on topic, try
fortune("SAS")
several times, or more quickly
fortune("reverse the procedure")
fortune("Poalis")
-pd
cheers, Rolf
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______________________________________________ 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.
Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com