[1] "T" [1] 2002 Call: survfit(formula = Surv(KESTO, POIS) ~ strata(OSITE), data = D[D$VUOSI0 == BY & D$TLAJI1 == TLAJI[Tind], ]) records n.max n.start events median 0.95LCL 0.95UCL strata(OSITE)=18-29 1237 1237 1237 1237 71 65 76 strata(OSITE)=30-39 3951 3951 3951 3951 74 69 81 strata(OSITE)=40-49 5513 5513 5513 5513 79 74 84 strata(OSITE)=50-59 10779 10779 10779 10779 168 159 173 strata(OSITE)=60-64 1087 1087 1087 1087 168 139 196 [1] "L" [1] 2002 Call: survfit(formula = Surv(KESTO, POIS) ~ strata(OSITE), data = D[D$VUOSI0 == BY & D$TLAJI1 == TLAJI[Tind], ]) records n.max n.start events median 0.95LCL 0.95UCL strata(OSITE)=18-29 245 245 245 245 24 18 30 strata(OSITE)=30-39 1172 1172 1172 1172 15 13 18 strata(OSITE)=40-49 1627 1627 1627 1627 19 16 20 strata(OSITE)=50-59 1475 1475 1475 1475 24 19 26 strata(OSITE)=60-64 84 84 84 84 24 13 33 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2003_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2004_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2005_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2006_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2007_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2008_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2009_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2010_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2011_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2012_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2013_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2014_strata_IKA10T0" 18-29 30-39 40-49 50-59 60-64 22265 60987 96193 138801 25299 [1] "xyplot_tyottlom_kesto_km_2015_strata_IKA10T0" > writeLines(optsel, file.path(png1, + paste("selectlist_tyott_lom_km_", BY , "_", G, ".html", sep=""))); > ###dev.off(); > #### > ## http://stat.ethz.ch/R-manual/R-patched/library/survival/html/coxph.html > ##?writeLines() > > ############################################## > > options(scipen=10); > png1 <- "\\\\prov14\\pro\\Tutkimus\\TUTK_TILASTOT\\WWW\\PICS" > tbl1 <- "\\\\prov14\\pro\\Tutkimus\\TUTK_TILASTOT\\WWW\\TBL" > #### > D <- read.svo("TJ2.SVO"); Survo data file TJ2.SVO: record=146 bytes, M1=32 L=64 M=23 N=343612 > str(D) 'data.frame': 343612 obs. of 23 variables: $ HETU : chr "010142-0308" "010142-0308" "010142-0308" "010142-032A" ... $ SP : chr "N" "N" "N" "N" ... $ mIKAT0 : num 60.2 60.5 60.7 60 60 ... $ IKAL1 : chr "60+__" "60+__" "60+__" "60+__" ... $ IKATO : num 60.2 60.4 60.6 60 60 ... $ IKA5T0 : chr "60-64" "60-64" "60-64" "60-64" ... $ IKA10T0 : chr "60-64" "60-64" "60-64" "60-64" ... $ JID : num 2 2 2 3 4 5 5 5 5 5 ... $ PERID : num 3 4 5 6 7 8 9 10 11 12 ... $ ALKUPVM : chr "2002-03-01" "2002-05-27" "2002-08-19" "2002-01-01" ... $ LOPPUPVM: chr "2002-04-28" "2002-07-21" "2002-09-30" "2002-01-31" ... $ SYSDATE : chr "2015-06-02" "2015-06-02" "2015-06-02" "2015-06-02" ... $ EVENT : int NA NA NA NA NA NA NA NA NA NA ... $ KESTO : int 56 54 40 29 29 36 2 8 29 4 ... $ DURT : int 56 54 40 29 0 36 0 0 29 0 ... $ DURL : int 0 0 0 0 29 0 2 8 0 4 ... $ TLAJI1 : chr "T" "T" "T" "T" ... $ TLAJI2 : chr "T" "T" "T" "T" ... $ DPVM1 : int 4783 4699 4628 4870 4870 4592 4589 4386 4025 4020 ... $ VUOSI0 : chr "2002" "2002" "2002" "2002" ... $ IDPER : int 1 2 3 1 1 1 2 3 4 5 ... $ POIS : int 1 1 1 1 1 1 1 1 1 1 ... $ ONE : int 1 1 1 1 1 1 1 1 1 1 ... - attr(*, "status.info")= chr "Survo data file TJ2.SVO: record=146 bytes, M1=32 L=64 M=23 N=343612" - attr(*, "status.description")= chr "Aggregated from data TMP11 by variable PERIM1=32 L=64 M=23 N=343612" - attr(*, "status.varname")= chr "HETU 43612" "SP 43612" "mIKAT0 43612" "IKAL1 43612" ... - attr(*, "status.vartype")= chr "SA ed from data TMP11 by variable PERIM1=32 L=64 M=23 N=343612" "SA " "4A " "SA " ... - attr(*, "status.varlen")= int 11 1 4 5 4 5 5 8 8 10 ... > T0 <- 2002; > #### > D <- D[D$IKATO < 65, ]; > D$VUOSI0 <- as.numeric(D$VUOSI0) > D$V0 <- as.numeric(D$VUOSI0); > tbl <- table(D$VUOSI0) > Vuodet <- names(tbl) > Vuodet [1] "2002" "2003" "2004" "2005" "2006" "2007" "2008" "2009" "2010" "2011" [11] "2012" "2013" "2014" "2015" > table(D$SP) M N 134087 209458 > f_abshist(D$IDPER) > #table(D$IDPER, D$VUOSI) > #plot(D$VUOSI, D$IDPER) > #### > library(survival) > library(KMsurv) > ### Pois = 0 oikealta sensuroitu > ## ¦ R CUR+1,A> > ### > ThisY <- as.numeric(format(Sys.Date(), "%Y")) > ###[1] 2011 > Levs <- sort(unique(D$VUOSI0)) > Levs; [1] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 > optsel <- c(); > #### > ###### > Pname <- "Työttömyyden ja lomautusten kesto" > ## > > G <- c("IDPER", "IKA10T0")[2] ; > sink(file.path(tbl1, paste0("KM_statistics_tyottlom_surv_", G, ".txt"))); > ### > writeLines(optsel, file.path(png1, + paste("selectlist_tyott_lom_km_", BY , "_", G, ".html", sep=""))); > ###dev.off(); > #### > ## http://stat.ethz.ch/R-manual/R-patched/library/survival/html/coxph.html > ##?writeLines() > > ####### SYSTEM ############ > > ipath <- "//Prov14/pro/Tutkimus/D/DW/LYYTI/BATCH/TIL"; > options(scipen=10); > png1 <- "\\\\prov14\\pro\\Tutkimus\\TUTK_TILASTOT\\WWW\\TSTO" > Files <- list.files(path=ipath, + pattern="til[_]C_JASENET[_]SOP[_]ALA_JLAJI[0-9]+[-].*") > Files[1:3] [1] "til_C_JASENET_SOP_ALA_JLAJI2015-03-09.txt" [2] "til_C_JASENET_SOP_ALA_JLAJI2015-03-10.txt" [3] "til_C_JASENET_SOP_ALA_JLAJI2015-03-11.txt" > out1 <- data.frame(); > for(f in seq(Files)) { + + if(f < 3) { + tbl <- read.txt(file=file.path(ipath,Files[f]), + sep=" " , quote="\"")} else { + + tbl <- read.txt(file= ..." ... [TRUNCATED] > head(out1); Group.1 Group.2 x 1 2015-03-09 MDU-V 50 2 2015-03-09 MDUSY 4 3 2015-03-09 MEITK 5 4 2015-03-09 NELMA 91 5 2015-03-09 NELTY 18 6 2015-03-09 NORM 82753 > ##http://www.sr.bham.ac.uk/~ajrs/R/r-plot_data.html > > > Tran <- range(out1$Group.1) > tbl2 <- tapply(out1$x, out1$Group.2, mean) > tbl2 <- sort(tbl2) > ##par( > # par(mfrow=c(2,1)); > > f_png(file.path(png1, "barplot_n_membership_code_average.png")); > # > barplot(tbl2, log="y", ylab="N (log-scale)", + + main="Average amount of members by membership code", cex.main=.8, + col="maroon", las=2, cex.n .... [TRUNCATED] > mtext(side=3, paste0("Time period: ", paste(Tran , collapse=" - ")), + cex=.7) > grid(ny=NULL, nx=NA , col="grey", lty=2); > ##?grid > dev.off(); windows 2 > #d$Group.1 <- as.Date(d$Group.1, format="YYYY-MM-DD") > ## > d <- out1[out1$Group.2=="NORM",] > d$Group.1 <- as.Date(d$Group.1, format="YYYY-MM-DD") > GROUP <- d$Group.2[1]; > LastT <- d$Group.1[nrow(d)]; > apu <- range(d$x); > apu [1] 81186 82782 > mx <- mean(diff(d$x)); mx [1] -14.37736 > sdx <- sd(diff(d$x)); sdx [1] 96.05935 > d$diffx <- c(NA, diff(d$x)); > d$Poikk <- as.numeric( abs(d$diff) > sdx); > d$Poikk [1] NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 [26] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [51] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 [76] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [101] 0 0 0 0 0 0 0 > YLIM <- c(apu[1]-diff(apu), apu[2]+diff(apu)); > YLIM [1] 79590 84378 > d2 <- d[d$Poikk==1, ] > f_png(file.path(png1, "tsplot_n_member_date_type_NORM.png")); > plot(d$Group.1, d$x, ylim =YLIM, + main=paste0("Membership code = ", GROUP), + xlab="Time (date)", cex.axis=.8, + type="l", cex.axis=.6, col="blue ..." ... [TRUNCATED] > grid( , col="grey", lty=2) > abline(v=d2$Group.1, col="red", lty=2 ) > text(d2$Group.1, d2$x+diff(apu)*.15, + paste(substr(d2$Group.1,1,5),"\n", substr(d2$Group.1,6,10)), + , cex=.4, pos=3, col="red") > mtext(side=3, "Greater changes indicated by the dotted red lines", + col="red", cex=.7); > ## > text(LastT, YLIM[1], LastT, srt=90, cex=.4, pos=3 ) > text(LastT, d$x[nrow(d) ], d$x[nrow(d)], col="blue", srt=0, cex=.4, pos=3 ) > text(d$Group.1[1], d$x[1], d$x[1], col="blue", srt=0, cex=.4, pos=3 ) > text(d$Group.1[1], YLIM[1], d$Group.1[1], srt=90, cex=.4, pos=3 ) > ### > dev.off(); windows 2 > #str(tbl) > ######### END SYSTEM ######## Survo data file T2: record=113 bytes, M1=30 L=64 M=4 N=6732268 > ##### SYSTEM ##### > #### Tehdään tapahtumaindeksointi ### > > > str(D) 'data.frame': 6732268 obs. of 4 variables: $ ID : chr "010145-076H" "010145-076H" "010145-076H" "010145-076H" ... $ VKK: chr "2009-01" "2009-02" "2009-03" "2009-04" ... $ PVT: num 0 0 0 0 0 0 0 0 0 0 ... $ PVL: num 0 0 0 0 0 0 0 0 0 0 ... - attr(*, "status.info")= chr "Survo data file T2: record=113 bytes, M1=30 L=64 M=4 N=6732268" - attr(*, "status.description")= chr "Aggregated from data TMP11 by variable HET0 L=64 M=4 N=6732268" - attr(*, "status.varname")= chr "ID ~str(HET) " "VKK " "PVT " "PVL " - attr(*, "status.vartype")= chr "SA- ed from data TMP11 by variable HET0 L=64 M=4 N=6732268" "SA " "4A " "4A " - attr(*, "status.varlen")= int 11 7 4 4 > ## Parameters > ### function > "f_eventcounter1" <- function(data, id, x_input, y_order, y_length) { + + ## lasketaan aika + t0 <- Sys.time(); + ## .... [TRUNCATED] > ########################### > ############################### > > > A <- f_eventcounter1(data=D, id="ID", x_input="PVT", y_order="ORD_T", y_length .... 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6730000 Time difference of 2.377399 hours > #### > D <- data.frame(D,A$ORDER, A$LEN); > names(D)[(ncol(D)-1):ncol(D)] <- c("ORD_T","LEN_T"); > ## Lomautukset > A <- f_eventcounter1(data=D, id="ID", x_input="PVL", y_order="ORD_L", y_length="LEN_L"); [1] 10000 [1] 20000 [1] 30000 [1] 40000 [1] 50000 [1] 60000 [1] 70000 [1] 80000 [1] 90000 [1] 100000 [1] 110000 [1] 120000 [1] 130000 [1] 140000 [1] 150000 [1] 160000 [1] 170000 [1] 180000 [1] 190000 [1] 200000 [1] 210000 [1] 220000 [1] 230000 [1] 240000 [1] 250000 [1] 260000 [1] 270000 [1] 280000 [1] 290000 [1] 300000 [1] 310000 [1] 320000 [1] 330000 [1] 340000 [1] 350000 [1] 360000 [1] 370000 [1] 380000 [1] 390000 [1] 400000 [1] 410000 [1] 420000 [1] 430000 [1] 440000 [1] 450000 [1] 460000 [1] 470000 [1] 480000 [1] 490000 [1] 500000 [1] 510000 [1] 520000 [1] 530000 [1] 540000 [1] 550000 [1] 560000 [1] 570000 [1] 580000 [1] 590000 [1] 600000 [1] 610000 [1] 620000 [1] 630000 [1] 640000 [1] 650000 [1] 660000 [1] 670000 [1] 680000 [1] 690000 [1] 700000 [1] 710000 [1] 720000 [1] 730000 [1] 740000 [1] 750000 [1] 760000 [1] 770000 [1] 780000 [1] 790000 [1] 800000 [1] 810000 [1] 820000 [1] 830000 [1] 840000 [1] 850000 [1] 860000 [1] 870000 [1] 880000 [1] 890000 [1] 900000 [1] 910000 [1] 920000 [1] 930000 [1] 940000 [1] 950000 [1] 960000 [1] 970000 [1] 980000 [1] 990000 [1] 1000000 [1] 1010000 [1] 1020000 [1] 1030000 [1] 1040000 [1] 1050000 [1] 1060000 [1] 1070000 [1] 1080000 [1] 1090000 [1] 1100000 [1] 1110000 [1] 1120000 [1] 1130000 [1] 1140000 [1] 1150000 [1] 1160000 [1] 1170000 [1] 1180000 [1] 1190000 [1] 1200000 [1] 1210000 [1] 1220000 [1] 1230000 [1] 1240000 [1] 1250000 [1] 1260000 [1] 1270000 [1] 1280000 [1] 1290000 [1] 1300000 [1] 1310000 [1] 1320000 [1] 1330000 [1] 1340000 [1] 1350000 [1] 1360000 [1] 1370000 [1] 1380000 [1] 1390000 [1] 1400000 [1] 1410000 [1] 1420000 [1] 1430000 [1] 1440000 [1] 1450000 [1] 1460000 [1] 1470000 [1] 1480000 [1] 1490000 [1] 1500000 [1] 1510000 [1] 1520000 [1] 1530000 [1] 1540000 [1] 1550000 [1] 1560000 [1] 1570000 [1] 1580000 [1] 1590000 [1] 1600000 [1] 1610000 [1] 1620000 [1] 1630000 [1] 1640000 [1] 1650000 [1] 1660000 [1] 1670000 [1] 1680000 [1] 1690000 [1] 1700000 [1] 1710000 [1] 1720000 [1] 1730000 [1] 1740000 [1] 1750000 [1] 1760000 [1] 1770000 [1] 1780000 [1] 1790000 [1] 1800000 [1] 1810000 [1] 1820000 [1] 1830000 [1] 1840000 [1] 1850000 [1] 1860000 [1] 1870000 [1] 1880000 [1] 1890000 [1] 1900000 [1] 1910000 [1] 1920000 [1] 1930000 [1] 1940000 [1] 1950000 [1] 1960000 [1] 1970000 [1] 1980000 [1] 1990000 [1] 2000000 [1] 2010000 [1] 2020000 [1] 2030000 [1] 2040000 [1] 2050000 [1] 2060000 [1] 2070000 [1] 2080000 [1] 2090000 [1] 2100000 [1] 2110000 [1] 2120000 [1] 2130000 [1] 2140000 [1] 2150000 [1] 2160000 [1] 2170000 [1] 2180000 [1] 2190000 [1] 2200000 [1] 2210000 [1] 2220000 [1] 2230000 [1] 2240000 [1] 2250000 [1] 2260000 [1] 2270000 [1] 2280000 [1] 2290000 [1] 2300000 [1] 2310000 [1] 2320000 [1] 2330000 [1] 2340000 [1] 2350000 [1] 2360000 [1] 2370000 [1] 2380000 [1] 2390000 [1] 2400000 [1] 2410000 [1] 2420000 [1] 2430000 [1] 2440000 [1] 2450000 [1] 2460000 [1] 2470000 [1] 2480000 [1] 2490000 [1] 2500000 [1] 2510000 [1] 2520000 [1] 2530000 [1] 2540000 [1] 2550000 [1] 2560000 [1] 2570000 [1] 2580000 [1] 2590000 [1] 2600000 [1] 2610000 [1] 2620000 [1] 2630000 [1] 2640000 [1] 2650000 [1] 2660000 [1] 2670000 [1] 2680000 [1] 2690000 [1] 2700000 [1] 2710000 [1] 2720000 [1] 2730000 [1] 2740000 [1] 2750000 [1] 2760000 [1] 2770000 [1] 2780000 [1] 2790000 [1] 2800000 [1] 2810000 [1] 2820000 [1] 2830000 [1] 2840000 [1] 2850000 [1] 2860000 [1] 2870000 [1] 2880000 [1] 2890000 [1] 2900000 [1] 2910000 [1] 2920000 [1] 2930000 [1] 2940000 [1] 2950000 [1] 2960000 [1] 2970000 [1] 2980000 [1] 2990000 [1] 3000000 [1] 3010000 [1] 3020000 [1] 3030000 [1] 3040000 [1] 3050000 [1] 3060000 [1] 3070000 [1] 3080000 [1] 3090000 [1] 3100000 [1] 3110000 [1] 3120000 [1] 3130000 [1] 3140000 [1] 3150000 [1] 3160000 [1] 3170000 [1] 3180000 [1] 3190000 [1] 3200000 [1] 3210000 [1] 3220000 [1] 3230000 [1] 3240000 [1] 3250000 [1] 3260000 [1] 3270000 [1] 3280000 [1] 3290000 [1] 3300000 [1] 3310000 [1] 3320000 [1] 3330000 [1] 3340000 [1] 3350000 [1] 3360000 [1] 3370000 [1] 3380000 [1] 3390000 [1] 3400000 [1] 3410000 [1] 3420000 [1] 3430000 [1] 3440000 [1] 3450000 [1] 3460000 [1] 3470000 [1] 3480000 [1] 3490000 [1] 3500000 [1] 3510000 [1] 3520000 [1] 3530000 [1] 3540000 [1] 3550000 [1] 3560000 [1] 3570000 [1] 3580000 [1] 3590000 [1] 3600000 [1] 3610000 [1] 3620000 [1] 3630000 [1] 3640000 [1] 3650000 [1] 3660000 [1] 3670000 [1] 3680000 [1] 3690000 [1] 3700000 [1] 3710000 [1] 3720000 [1] 3730000 [1] 3740000 [1] 3750000 [1] 3760000 [1] 3770000 [1] 3780000 [1] 3790000 [1] 3800000 [1] 3810000 [1] 3820000 [1] 3830000 [1] 3840000 [1] 3850000 [1] 3860000 [1] 3870000 [1] 3880000 [1] 3890000 [1] 3900000 [1] 3910000 [1] 3920000 [1] 3930000 [1] 3940000 [1] 3950000 [1] 3960000 [1] 3970000 [1] 3980000 [1] 3990000 [1] 4000000 [1] 4010000 [1] 4020000 [1] 4030000 [1] 4040000 [1] 4050000 [1] 4060000 [1] 4070000 [1] 4080000 [1] 4090000 [1] 4100000 [1] 4110000 [1] 4120000 [1] 4130000 [1] 4140000 [1] 4150000 [1] 4160000 [1] 4170000 [1] 4180000 [1] 4190000 [1] 4200000 [1] 4210000 [1] 4220000 [1] 4230000 [1] 4240000 [1] 4250000 [1] 4260000 [1] 4270000 [1] 4280000 [1] 4290000 [1] 4300000 [1] 4310000 [1] 4320000 [1] 4330000 [1] 4340000 [1] 4350000 [1] 4360000 [1] 4370000 [1] 4380000 [1] 4390000 [1] 4400000 [1] 4410000 [1] 4420000 [1] 4430000 [1] 4440000 [1] 4450000 [1] 4460000 [1] 4470000 [1] 4480000 [1] 4490000 [1] 4500000 [1] 4510000 [1] 4520000 [1] 4530000 [1] 4540000 [1] 4550000 [1] 4560000 [1] 4570000 [1] 4580000 [1] 4590000 [1] 4600000 [1] 4610000 [1] 4620000 [1] 4630000 [1] 4640000 [1] 4650000 [1] 4660000 [1] 4670000 [1] 4680000 [1] 4690000 [1] 4700000 [1] 4710000 [1] 4720000 [1] 4730000 [1] 4740000 [1] 4750000 [1] 4760000 [1] 4770000 [1] 4780000 [1] 4790000 [1] 4800000 [1] 4810000 [1] 4820000 [1] 4830000 [1] 4840000 [1] 4850000 [1] 4860000 [1] 4870000 [1] 4880000 [1] 4890000 [1] 4900000 [1] 4910000 [1] 4920000 [1] 4930000 [1] 4940000 [1] 4950000 [1] 4960000 [1] 4970000 [1] 4980000 [1] 4990000 [1] 5000000 [1] 5010000 [1] 5020000 [1] 5030000 [1] 5040000 [1] 5050000 [1] 5060000 [1] 5070000 [1] 5080000 [1] 5090000 [1] 5100000 [1] 5110000 [1] 5120000 [1] 5130000 [1] 5140000 [1] 5150000 [1] 5160000 [1] 5170000 [1] 5180000 [1] 5190000 [1] 5200000 [1] 5210000 [1] 5220000 [1] 5230000 [1] 5240000 [1] 5250000 [1] 5260000 [1] 5270000 [1] 5280000 [1] 5290000 [1] 5300000 [1] 5310000 [1] 5320000 [1] 5330000 [1] 5340000 [1] 5350000 [1] 5360000 [1] 5370000 [1] 5380000 [1] 5390000 [1] 5400000 [1] 5410000 [1] 5420000 [1] 5430000 [1] 5440000 [1] 5450000 [1] 5460000 [1] 5470000 [1] 5480000 [1] 5490000 [1] 5500000 [1] 5510000 [1] 5520000 [1] 5530000 [1] 5540000 [1] 5550000 [1] 5560000 [1] 5570000 [1] 5580000 [1] 5590000 [1] 5600000 [1] 5610000 [1] 5620000 [1] 5630000 [1] 5640000 [1] 5650000 [1] 5660000 [1] 5670000 [1] 5680000 [1] 5690000 [1] 5700000 [1] 5710000 [1] 5720000 [1] 5730000 [1] 5740000 [1] 5750000 [1] 5760000 [1] 5770000 [1] 5780000 [1] 5790000 [1] 5800000 [1] 5810000 [1] 5820000 [1] 5830000 [1] 5840000 [1] 5850000 [1] 5860000 [1] 5870000 [1] 5880000 [1] 5890000 [1] 5900000 [1] 5910000 [1] 5920000 [1] 5930000 [1] 5940000 [1] 5950000 [1] 5960000 [1] 5970000 [1] 5980000 [1] 5990000 [1] 6000000 [1] 6010000 [1] 6020000 [1] 6030000 [1] 6040000 [1] 6050000 [1] 6060000 [1] 6070000 [1] 6080000 [1] 6090000 [1] 6100000 [1] 6110000 [1] 6120000 [1] 6130000 [1] 6140000 [1] 6150000 [1] 6160000 [1] 6170000 [1] 6180000 [1] 6190000 [1] 6200000 [1] 6210000 [1] 6220000 [1] 6230000 [1] 6240000 [1] 6250000 [1] 6260000 [1] 6270000 [1] 6280000 [1] 6290000 [1] 6300000 [1] 6310000 [1] 6320000 [1] 6330000 [1] 6340000 [1] 6350000 [1] 6360000 [1] 6370000 [1] 6380000 [1] 6390000 [1] 6400000 [1] 6410000 [1] 6420000 [1] 6430000 [1] 6440000 [1] 6450000 [1] 6460000 [1] 6470000 [1] 6480000 [1] 6490000 [1] 6500000 [1] 6510000 [1] 6520000 [1] 6530000 [1] 6540000 [1] 6550000 [1] 6560000 [1] 6570000 [1] 6580000 [1] 6590000 [1] 6600000 [1] 6610000 [1] 6620000 [1] 6630000 [1] 6640000 [1] 6650000 [1] 6660000 [1] 6670000 [1] 6680000 [1] 6690000 [1] 6700000 [1] 6710000 [1] 6720000 [1] 6730000 Time difference of 2.366935 hours > #### > D <- data.frame(D,A$ORDER, A$LEN); > names(D)[(ncol(D)-1):ncol(D)] <- c("ORD_L","LEN_L"); > ### > write.svo(D, "K1.SVO"); > ############################ --- Please select a CRAN mirror for use in this session --- package ‘sqldf’ successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\petri.palmu\AppData\Local\Temp\RtmpoVpBOm\downloaded_packages package ‘sqldf’ successfully unpacked and MD5 sums checked