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The Evolution of R: Growth of the R-Help Email Archives over Time Richard Kwock, Robert E. Weiss University of California, Los Angeles, Biostatistics, U.S.A. Introduction Email Activity From March 1997 to April 2012 Most Mentioned Functions in Message Body Monthly Activity of Top 20 RespondersR Software Email Counts of Archive Starter Emails Response Emails 1. Prof Brian Ripley 2. David Winsemius 3. Gabor Grothendieck 4. Peter DalgaardOpen source programming language released 1. c 2. function 3. library 4. list 5. plot 150 250 050 150 250 200 0 50 150 250 Overall Starter Response 0 200 400 600 0 100 200 300 150 0 20 40 60 Top 30 Most Mentioned Functions 4000in April 1997 for statistical computing and 0 50 100 0 50 100 Function Counts Function Counts Function Counts 0 50graphics 0 50 c 60623 paste 11977 par 6601 3000 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10Widely used in data analysis and exploration function 30607 seq 9347 return 6100 6. data.frame 7. length 8. matrix 9. rep 10. rnorm 0 50 150 250 0 50 100 150 0 50 100 150 0 50 100 150 library 23875 for 8609 factor 5991 0 50 100150 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 Email Counts list 20267 summary 8599 lapply 5635R-help Mailing List 2000 5. Uwe Ligges 6. Duncan Murdoch 7. jim holtman 8. Thomas Lumley 0 10 30 50 plot 17920 print 8514 if 5603 020 60 100 Frequency 50 100The main mailing list for discussing problems data.frame 17204 cbind 8449 sample 5576 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 0 20 60 length 15574 lm 7977 apply 5471 11. paste 12. seq 13. for 14. summary 15. print 1000 50 100 150and solutions using R, announcements, 50 100 50 100 50 100 matrix 13144 read.table 7914 nrow 5381 0 20406080benchmark codes, and more 0 rep 13065 names 7461 runif 5342All emails sent to the mailing list are archived rnorm 12976 sum 7253 str 5216 0 0 0 0 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 0 Response Emails 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 9. Marc Schwartz 10. Henrique Dallazuanna 11. Greg Snow 12. Martin Maechlereach month in a single text ﬁle 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Popular categories 16. cbind 17. lm 18. read.table 19. names 20. sum 0 20 60 100 0 20 60 100 0 20 40 60 80 50 100 0 10 20 30 0 20 40 60 50 100 Year 0 20 40 60 0 20406080The mailing list receives dozens of emails per data structure: c, list, data.frame, etcday Overall and response email counts increased from late 1997 data manipulation: rep, paste, seq, etc 0 0 to 2010 and decreased from 2010 to 2012 statistics rnorm, summary, lm, etc Time in Year 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 Data Starter emails increased until 2005 and since then appears Functions with fewer counts experience more ﬂuctuation in monthly email counts 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 approximately constant 13. Spencer Graves 14. Deepayan Sarkar 15. Ted Harding) 16. Douglas Bates 0 10 20 30 40 0 10 20 30 40 0 20 40 60 0 10 20 30R-Help Mailing List Email Counts by Year Trend of Selected Email TopicsData is composed of the mailing list archive 4000from last 15 years, April 1997 to March 2012* Computational Topics Statistical Topics Proportion Graph Topics Average Graph Topic Responses Proportion Bayesian Topics Average Bayesian Topic Responses 0.30 0.00 0.02 0.04 0.06 0.08Emails were read into R as text Graphics, Speed, Data Mining, 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 4 3000 3 0.20 3 17. Jim Lemon 18. John Fox 19. Frank E Harrell Jr 20. hadley wickhamInformation from these emails were parsed 2 Survival and Bayesian topics Email Counts 2 0 20 40 60 0.10 010 30 50 0 10203040 1 1using regular expressions 0 510 20 2000 grew in proportion as well as in 0.00 0 0 1998 2002 2006 2010 1998 2002 2006 2010 1998 2002 2006 2010 1998 2002 2006 2010 Proportion Speed Topics Average Speed Topic Responses Proportion Longitudinal Topics Average Longitudinal Topic Responses average number of responses 0.06 5 0.00 0.02 0.04 0.06 0.08 3Email 1000 and continue to have a steady 4 0.04 2 3All emails can be categorized as either a starter increase 98 02 06 10 98 02 06 10 98 02 06 10 98 02 06 10 2 0.02 1 0 1email or a response email 0.00 Longitudinal topics are Year 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1998 2002 2006 2010 1998 2002 2006 2010 1998 2002 2006 2010 1998 2002 2006 2010 Starter emails: emails not in response to any 1997 2001 2005 2009 Proportion Data Mining Topics Average Data Mining Topic Responses Proportion Survival Topics Average Survival Topic Responses decreasing in recent years in Early users such as Ripley (#1), Grothendieck (#3) and Dalgaard (#4) show early rise to a peak then a 4 5 0.00 0.02 0.04 0.06 0.08 1998 2002 2006 2010 email decrease in later years 4 0.04 3 1999 2003 2007 2011 2000 2004 2008 2012 proportion as well as in average 3 2 response emails: emails in reply to either a 0.02 2 number of responses Users such as Winsemius (#2), Murdoch (#6), and Holtman (#7) have increasing responses in recent years 1 Most active month usually occurs around March and the 1 0.00 starter email or another response email 0 0 Responders such as Ligges (#5), Schwartz (#9), Harding (#15) do not have a simple time trend and their email least active month is December 1998 2002 2006 2010 1998 2002 2006 2010 1998 2002 2006 2010 1998 2002 2006 2010Emails are composed of two sections: header Year Year response behaviors are not easily described Ratio (Response/Starter) Email Countsand message body* https://stat.ethz.ch/pipermail/r-help/ Daily/Weekly/Monthly Activity 2.0 Hour Day of Week Month Goals Ratio (Response/Starter) Overall Peak time at 8am Starter Down time at 10pm Line represents a cyclic Response Weekdays each Almost 10% of all emails 1.5Visually present trend and growth of various trend over the course of a contribute to more than sent are in March 0.095component of the mailing list day 0.07 15% of email activity 0.15 December has the lowest 0.090 Email activities 1.0 List server experiences 0.06 Saturdays and Sundays activity with less than 7% Active users max activity at 8 am 0.05 have about half the email of emails sent that month 0.085 Density Density 0.10 Popular subjects (PST), with more than 7% activity (7%) compare to The pattern of activity Density 0.5 0.04 Popular functions 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 of all emails weekdays follows behavior similar to 0.080 0.03 0.05 Year Hits a valley at 10 pm, an academic calendar 0.075 Tools From 1997 to 2003, the ratio ﬂuctuates around 1.0 response with less than 2 0.02 showing peaks when 0.070 email to starter email Consistent proportion of 0.01 0.00 classes are in sessionR Ratio increased linearly after 2003 and hits a plateau at 2.2 response to starter ratio Sun Mon Tue Wed Thu Fri Sat Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Declattice package (Deepayan Sarkar) in 2010 throughout the day. 12am 3am 6am 9am 12pm Time (PST) 3pm 6pm 9pm Day of Week MonthUniversity of California, Los Angeles, Biostatistics, U.S.A. Email: richardkwock@gmail.com WWW: http://www.biostat.ucla.edu/
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