Creating a custom legend with a subset of data ggplot [duplicate] - r

This question already has an answer here:
Remove legend entries for some factors levels
(1 answer)
Closed 3 years ago.
I am creating some stacked barplots in which there are about 50 different factors. I would like a legend that just maps the colors for the 8 most abundant factors.
I first calculated the mean abundance. Then I have ordered the factor by this. And can also get the corresponding colors, but I don't know if I should 'independently' create a legend, or if I am missing another 'easy' way with ggplot.
Any help appreciated.
thanks
## calculating the OTUs mean abundance
## instead of labelling all 50 in the legend just label top 5 or 10 for label
meanAbund<-as.data.frame(colSums(dat.grouped[,grep("Otu",colnames(dat.grouped))]))
meanAbund$Name<-as.character(tax_tab1[match(rownames(meanAbund),rownames(tax_tab1)),ncol(tax_tab1)])
colnames(meanAbund)<-c("mean_abund","Name")
top8_meanAbund<-meanAbund[rev(order(meanAbund$mean_abund)),][1:8]
# use melted dataframe for plotting
## now ordered by mean abundance
dat.groupedM$Level<-factor(dat.groupedM$Level,meanAbund[rev(order(meanAbund$mean_abund)),"Name"])
#top8 (mean is greater than 0.1 %)
# top 8 colors
library(RColorBrewer)
n <- length(unique(dat.groupedM$Level))
# generate divergent color palete, cause so many colors
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
set.seed(200)
mycols<-sample(col_vector, n)
top8cols<-mycols[1:8]
#pie(rep(1,n), mycols)
ggplot(dat.groupedM, aes(x = Time, y = value, fill = Level))+
geom_bar(stat = "identity") +
facet_wrap(~Diet)+
scale_fill_manual(values = mycols) +
theme_bw()+
theme(legend.position = "none")+
ggtitle(paste(level,"composition - Cohort 1"))
thanks getting closer now I have this, see below. But it isn't using my divergent palette "mycols" which I had specified with scale_fill_manual before now I changed to this as per suggestion. Okay now this is correct !!
scale_fill_manual(values = mycols,breaks = top10_meanAbund$Name)+
> dput(dat.groupedM)
structure(list(Diet = structure(c(2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L), .Label = c("Control", "RC", "WD"), class = "factor"), Time = structure(c(2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L), .Label = c("Control", "ZT2", "ZT14"
), class = "factor"), variable = structure(c(1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 16L, 16L,
16L, 16L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 19L, 19L, 19L,
19L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L,
23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 26L,
26L, 26L, 26L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 29L, 29L,
29L, 29L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 32L, 32L, 32L,
32L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L,
36L, 36L, 36L, 36L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 39L,
39L, 39L, 39L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L, 42L, 42L,
42L, 42L, 43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 45L, 45L, 45L,
45L, 46L, 46L, 46L, 46L, 47L, 47L, 47L, 47L, 48L, 48L, 48L, 48L,
49L, 49L, 49L, 49L, 50L, 50L, 50L, 50L), .Label = c("Otu1", "Otu108",
"Otu11", "Otu112", "Otu136", "Otu14", "Otu155", "Otu157", "Otu158",
"Otu161", "Otu182", "Otu186", "Otu197", "Otu21", "Otu23", "Otu242",
"Otu244", "Otu247", "Otu27", "Otu29", "Otu30", "Otu314", "Otu35",
"Otu36", "Otu378", "Otu4", "Otu40", "Otu42", "Otu427", "Otu44",
"Otu464", "Otu49", "Otu53", "Otu535", "Otu56", "Otu585", "Otu607",
"Otu623", "Otu629", "Otu645", "Otu648", "Otu664", "Otu69", "Otu715",
"Otu730", "Otu741", "Otu76", "Otu77", "Otu86", "Otu90"), class = "factor"),
value = c(0.125797907894198, 0.236708006778536, 0.577822547980787,
0.590703405992419, 0.010616635104027, 0.0101758392492056,
0.000833768625886666, 0.00075132991625995, 3.6675879491077e-05,
4.75000300636826e-05, 1.80354502712558e-05, 7.12272349413578e-06,
0.0923297961192989, 0.0516121598003722, 0.00943059557285678,
0.0071906839719107, 0.00539229454875785, 0.00506484304043506,
0.00338347504700533, 0.0025680210124108, 0.00175591025013762,
0.00395104856215105, 0.0561412864297836, 0.0389581641347799,
0.000170273382201598, 0.00010844375756416, 2.43702775724318e-05,
2.69013349522259e-05, 0.00832912099453767, 0.010385453199374,
0.000178806881780292, 0.000192961017676232, 0.000133411286157921,
0.000160277350764036, 3.61247330219488e-05, 2.18172717187001e-05,
0.0052992653709469, 0.0157284378186556, 1.02095850472487e-05,
6.26578426928999e-06, 1.60700750910741e-05, 1.5642472562587e-05,
6.62816031320301e-06, 0.00184183762620945, 0.000976314979621793,
0.000837378482800852, 0.00119405390180218, 0.000749043101546972,
2.53563183604468e-05, 6.40671433657518e-05, 2.15845735655314e-05,
2.23193546150843e-05, 0.021152108912735, 0.0154293683430873,
0.0179088127298003, 0.0243504748685249, 0.112274814726721,
0.117619628857172, 3.02064928526444e-05, 4.05741755377207e-05,
0.00181223384695308, 0.00207625195815772, 0.000943789542460111,
0.000206467245724716, 0.00154532777776536, 0.00226885210972427,
0.000119002475328241, 0.00022113732768583, 0.000235485770787934,
0.000458323057092208, 0.000652960109256795, 0.000724862646259261,
0.0727879614169974, 0.0343552622468531, 0.00966883214135748,
0.0121723948692721, 0.0502079899784375, 0.0642059286182179,
0.0744606748095006, 0.0279356500162451, 0.0165712179668264,
0.0155282557608783, 0.0161360297824822, 0.0176198016904319,
0.0025636247608102, 0.0011990003339988, 8.03074966867612e-05,
2.33672477744522e-05, 0.082133947866647, 0.000268818474649765,
9.9394282675768e-05, 5.50800067064597e-05, 0.0740561864383357,
0.0623723308152442, 0.0122016944534841, 0.00661733731518039,
0.000206774473028281, 0.000181997559545235, 0.000504582984323445,
0.0003864059666649, 0.260050134018791, 0.262172946886686,
0.18452199172589, 0.173786000484964, 0.0269969369566207,
0.0314904830827211, 0.00193643447967831, 0.00224489247051206,
3.93537896032742e-05, 1.28428312370815e-05, 0.0088546261393493,
0.0167354308377304, 1.21169704358824e-05, 2.12185497965253e-05,
6.62816031320301e-06, 1.43770979648094e-05, 1.55017664132774e-05,
4.91659313365037e-05, 0.000125001966670021, 6.22422609777641e-05,
2.67226640566305e-05, 3.7715397236691e-05, 0.000156595002030591,
0.000177342276671081, 9.9289511018095e-05, 3.26074531511788e-05,
0.0133283089983016, 0.016339336685079, 0.000374046362264717,
0.000314832095026693, 0.000101840989635774, 0.0499536313744285,
3.07770376000906e-05, 2.61927046816827e-05, 6.62816031320301e-06,
6.26578426928999e-06, 0.0190857963424119, 0.033561743200736,
0.00670074972877479, 0.00537788085712994, 1.18971385403729e-05,
1.11445994651961e-05, 6.62816031320301e-06, 6.26578426928999e-06,
1.14712208113288e-05, 2.05354865936513e-05, 7.60426873897666e-06,
6.26578426928999e-06, 1.20062319150955e-05, 1.36539983715138e-05,
6.62816031320301e-06, 7.2222843532018e-06, 9.9140609239925e-06,
9.94926827158573e-06, 7.53483093323745e-06, 8.69321376638133e-06,
1.21169704358824e-05, 1.19091870744721e-05, 6.62816031320301e-06,
7.23507396088365e-06, 2.46118496456816e-05, 2.17587837491135e-05,
6.62816031320301e-06, 6.26578426928999e-06, 1.82559375344064e-05,
1.89730194735652e-05, 6.62816031320301e-06, 6.26578426928999e-06,
0.00660850850197291, 0.0212222385456563, 0.00211118341459913,
0.00141420689575609, 9.26797200368037e-06, 8.90032999685065e-06,
7.54855398500617e-06, 6.26578426928999e-06, 2.23089983289096e-05,
2.17587837491135e-05, 6.62816031320301e-06, 6.26578426928999e-06,
5.45457596965578e-06, 5.45457596965578e-06, 6.62816031320301e-06,
5.98284905868666e-06, 3.2907497931271e-05, 2.32160410324433e-05,
6.62816031320301e-06, 1.51092819784857e-05, 1.21169704358824e-05,
1.19091870744721e-05, 7.91517227728171e-06, 7.23507396088365e-06,
4.18664545392701e-05, 4.14341191547126e-05, 0.000151073775168509,
0.000399628089283355, 9.9140609239925e-06, 1.4300121286468e-05,
7.53483093323745e-06, 6.26578426928999e-06), Level = structure(c(1L,
1L, 1L, 1L, 25L, 25L, 25L, 25L, 3L, 3L, 3L, 3L, 7L, 7L, 7L,
7L, 28L, 28L, 28L, 28L, 9L, 9L, 9L, 9L, 31L, 31L, 31L, 31L,
27L, 27L, 27L, 27L, 30L, 30L, 30L, 30L, 26L, 26L, 26L, 26L,
42L, 42L, 42L, 42L, 39L, 39L, 39L, 39L, 11L, 11L, 11L, 11L,
13L, 13L, 13L, 13L, 4L, 4L, 4L, 4L, 33L, 33L, 33L, 33L, 36L,
36L, 36L, 36L, 43L, 43L, 43L, 43L, 6L, 6L, 6L, 6L, 5L, 5L,
5L, 5L, 15L, 15L, 15L, 15L, 38L, 38L, 38L, 38L, 10L, 10L,
10L, 10L, 8L, 8L, 8L, 8L, 45L, 45L, 45L, 45L, 2L, 2L, 2L,
2L, 16L, 16L, 16L, 16L, 24L, 24L, 24L, 24L, 44L, 44L, 44L,
44L, 18L, 18L, 18L, 18L, 46L, 46L, 46L, 46L, 20L, 20L, 20L,
20L, 12L, 12L, 12L, 12L, 47L, 47L, 47L, 47L, 17L, 17L, 17L,
17L, 32L, 32L, 32L, 32L, 48L, 48L, 48L, 48L, 49L, 49L, 49L,
49L, 50L, 50L, 50L, 50L, 34L, 34L, 34L, 34L, 29L, 29L, 29L,
29L, 40L, 40L, 40L, 40L, 19L, 19L, 19L, 19L, 35L, 35L, 35L,
35L, 37L, 37L, 37L, 37L, 41L, 41L, 41L, 41L, 14L, 14L, 14L,
14L, 21L, 21L, 21L, 21L, 23L, 23L, 23L, 23L, 22L, 22L, 22L,
22L), .Label = c("Akkermansia", "Bacteroides", "Pseudomonas",
"Anaeroplasma", "Acetatifactor", "Lactobacillus", "Clostridium_XlVa",
"Oscillibacter", "Eubacterium", "Alistipes", "Acinetobacter",
"Parabacteroides", "Parasutterella", "Bacillus", "Clostridium_sensu_stricto",
"Turicibacter", "Clostridium_IV", "Staphylococcus", "Bifidobacterium",
"Romboutsia", "Escherichia/Shigella", "Listeria", "Enterococcus",
"Lactococcus", "Flavonifractor", "Ruminococcus", "Lachnospiracea_incertae_sedis",
"Clostridium_XlVb", "Stenotrophomonas", "Eisenbergiella",
"Hungatella", "Adhaeribacter", "Butyricicoccus", "Rhizobium",
"Prevotella", "Intestinimonas", "Sphingobium", "Dorea", "Enterorhabdus",
"Shewanella", "Bradyrhizobium", "Desulfovibrio", "Clostridium_XVIII",
"Anaerostipes", "Asaccharobacter", "Anaerotruncus", "Eggerthella",
"Butyricimonas", "Bilophila", "Anaerococcus"), class = "factor")), row.names = c(2L,
3L, 4L, 5L, 7L, 8L, 9L, 10L, 12L, 13L, 14L, 15L, 17L, 18L, 19L,
20L, 22L, 23L, 24L, 25L, 27L, 28L, 29L, 30L, 32L, 33L, 34L, 35L,
37L, 38L, 39L, 40L, 42L, 43L, 44L, 45L, 47L, 48L, 49L, 50L, 52L,
53L, 54L, 55L, 57L, 58L, 59L, 60L, 62L, 63L, 64L, 65L, 67L, 68L,
69L, 70L, 72L, 73L, 74L, 75L, 77L, 78L, 79L, 80L, 82L, 83L, 84L,
85L, 87L, 88L, 89L, 90L, 92L, 93L, 94L, 95L, 97L, 98L, 99L, 100L,
102L, 103L, 104L, 105L, 107L, 108L, 109L, 110L, 112L, 113L, 114L,
115L, 117L, 118L, 119L, 120L, 122L, 123L, 124L, 125L, 127L, 128L,
129L, 130L, 132L, 133L, 134L, 135L, 137L, 138L, 139L, 140L, 142L,
143L, 144L, 145L, 147L, 148L, 149L, 150L, 152L, 153L, 154L, 155L,
157L, 158L, 159L, 160L, 162L, 163L, 164L, 165L, 167L, 168L, 169L,
170L, 172L, 173L, 174L, 175L, 177L, 178L, 179L, 180L, 182L, 183L,
184L, 185L, 187L, 188L, 189L, 190L, 192L, 193L, 194L, 195L, 197L,
198L, 199L, 200L, 202L, 203L, 204L, 205L, 207L, 208L, 209L, 210L,
212L, 213L, 214L, 215L, 217L, 218L, 219L, 220L, 222L, 223L, 224L,
225L, 227L, 228L, 229L, 230L, 232L, 233L, 234L, 235L, 237L, 238L,
239L, 240L, 242L, 243L, 244L, 245L, 247L, 248L, 249L, 250L), class = "data.frame")


I couldn't get your code to run, so I'll answer your question with dummy data instead.
Let's generate some unequally distributed data, letters in this case.
set.seed(0)
df <- data.frame(x = LETTERS[c(rpois(100, 1) + 1, rpois(100, 8) + 1)])
We can determine the 8 most frequently occuring letters:
most_freq <- names(tail(sort(table(df$x)), 8))
Which we can then put into any of the discrete scales functions (including the manual one) as the breaks argument:
ggplot(df, aes(x, fill = x)) +
geom_bar() +
scale_fill_discrete(breaks = most_freq)
And as you can see, the categories not in the breaks still get a colour but don't get mentioned in the legend.

Related

How to create a barplot based on a condition in ggplot2 (R)?

I have the following code:
set.seed(123)
CIS_data_5 <- data.frame(
CIS$P20,
CIS$P3
)
CIS$P3 <- factor(CIS$P3, labels = c("Mejor", "(NO LEER) Igual", "Peor", "N.S.", "N.C."))
n <- as.numeric(c(CIS$P20))
P20 <- sample(n, 2787, replace = TRUE, prob = NULL)
P20labs <- c("16-29", "30-44", "45-64", ">65", "N.C.")
cut_points <- c(16, 30, 45, 65, Inf)
i <- findInterval(P20, cut_points)
P20_fac <- P20labs[i]
P20_fac[is.na(P20)] <- P20labs[length(P20labs)]
P20_fac <- factor(P20_fac, levels = P20labs)
where P3 has 5 categories, indicating the socioeconomic perception of a sample of Spanish population, and P20_fac has 5 other categories that indicate the age of the respondents.
My desired outcome would be to have 5 joint graphs, in which the socioeconomic situation (P3) is reflected according to the age of the respondents (P20_fac).
I have been stuck on thinking how I can possibly represent this graphically, and after many an hour spent, I have completely run out of ideas (though I think that it can somehow be done by means of facet_wrap() or facet_grid()).
Any help would be much appreciated!
Many thanks in advance!
EDIT
Regarding what is my question, I have already stated that "My desired outcome would be to have 5 (grouped together) graphs, in which the socioeconomic situation (P3) is represented based on the age of the respondents (P20_fac)."
If I knew how to answer it, I most surely would not be making this question on this forum.
UPDATE
The closest that I've got to the desired result is by running the following code:
plot(CIS$P3 ~ P20_fac, data = CIS)
However, it only gets me the graphical results for the first age interval ("16-29") and in vertical instead of horizontal form.
dput(CIS_data_5)
> dput(CIS_data_5)
structure(list(CIS.P20 = structure(c(48L, 33L, 28L, 36L, 32L,
24L, 27L, 50L, 55L, 34L, 48L, 22L, 37L, 24L, 35L, 35L, 19L, 57L,
32L, 45L, 13L, 23L, 24L, 5L, 8L, 34L, 2L, 52L, 66L, 43L, 54L,
24L, 27L, 40L, 40L, 64L, 29L, 12L, 52L, 7L, 26L, 40L, 59L, 19L,
24L, 44L, 19L, 62L, 26L, 16L, 40L, 31L, 14L, 15L, 29L, 24L, 41L,
33L, 41L, 3L, 39L, 4L, 44L, 39L, 1L, 28L, 40L, 8L, 49L, 59L,
50L, 8L, 8L, 9L, 41L, 35L, 35L, 65L, 40L, 38L, 29L, 24L, 54L,
40L, 21L, 29L, 3L, 27L, 45L, 50L, 27L, 50L, 60L, 50L, 41L, 56L,
57L, 33L, 45L, 31L, 58L, 3L, 16L, 43L, 25L, 40L, 32L, 17L, 58L,
46L, 15L, 40L, 22L, 28L, 11L, 38L, 50L, 22L, 25L, 47L, 17L, 32L,
36L, 49L, 40L, 23L, 17L, 34L, 8L, 37L, 67L, 37L, 52L, 60L, 5L,
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29L, 16L, 47L, 52L, 44L, 29L, 61L, 25L, 55L, 8L, 5L, 56L, 45L,
51L, 25L, 11L, 26L, 35L, 33L, 41L, 71L, 35L, 63L, 38L, 32L, 53L,
31L, 52L, 44L, 34L, 50L, 40L, 21L, 36L, 76L, 40L, 23L, 42L, 25L,
14L, 58L, 38L, 37L, 69L, 46L, 46L, 49L, 42L, 28L, 55L, 13L, 45L,
12L, 42L, 32L, 11L, 32L, 36L, 61L, 19L, 28L, 31L, 35L, 22L, 15L,
50L, 64L, 32L, 59L, 33L, 24L, 19L, 49L, 36L, 26L, 57L, 57L, 34L,
29L, 7L, 30L, 42L, 47L, 53L, 55L, 55L, 35L, 26L, 20L, 55L, 61L,
43L, 31L, 30L, 52L, 27L, 63L, 25L, 47L, 63L, 23L, 2L, 47L, 12L,
37L, 34L, 46L, 30L, 62L, 52L, 27L, 3L, 59L, 50L, 5L, 34L, 23L
), levels = c("16", "17", "18", "19", "20", "21", "22", "23",
"24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34",
"35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45",
"46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56",
"57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67",
"68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78",
"79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89",
"90", "91", "93", "N.C. "), class = "factor"), CIS.P3 = structure(c(3L,
3L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 2L,
2L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
1L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 1L,
2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 3L, 1L,
3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L,
1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L, 3L,
2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L, 2L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 2L,
1L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L,
3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L,
2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L,
2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 3L,
2L, 2L, 4L, 1L, 3L, 3L, 3L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L,
2L, 2L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 1L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 1L, 2L, 3L, 3L, 2L, 1L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 2L, 3L,
2L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L,
4L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 1L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 2L,
1L, 3L, 1L, 3L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 2L,
3L, 3L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 2L, 2L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 3L, 3L,
3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L,
1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L,
1L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 3L,
3L, 2L, 3L, 1L, 1L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 2L, 3L, 1L,
3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L,
3L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 2L,
2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L,
1L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 3L, 1L, 3L, 3L,
1L, 1L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 5L, 1L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 3L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 1L, 3L,
2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 3L,
3L, 2L, 1L, 2L, 5L, 1L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L,
2L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 3L, 2L, 2L, 2L,
2L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 2L,
2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 3L,
3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
3L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 1L,
1L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 3L, 2L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L,
2L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 1L,
3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 2L,
2L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L,
2L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 1L,
3L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 2L, 3L, 1L,
3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 3L, 1L, 3L, 2L, 5L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 2L,
2L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L,
1L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 3L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L,
1L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 3L, 2L, 3L, 4L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L,
3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L,
3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 2L, 1L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 2L, 3L,
3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 4L,
2L, 1L, 2L, 2L, 3L, 1L, 3L, 4L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 1L, 1L,
2L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 2L,
1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 4L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L,
3L, 1L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 1L,
2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 3L,
1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 1L,
4L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 3L, 3L,
2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L,
1L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 1L,
3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
3L, 2L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L,
2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 1L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L,
3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L,
3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L,
4L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 3L,
2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 1L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L,
1L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 1L, 2L,
2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 1L,
2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 4L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 3L,
3L, 3L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 3L, 2L, 2L, 3L, 1L, 3L, 2L,
2L, 3L, 3L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 1L), levels = c("Mejor", "(NO LEER) Igual", "Peor", "N.S.",
"N.C."), class = "factor")), class = "data.frame", row.names = c(NA,
-2787L))

Maybe you want something like this using facet_wrap:
set.seed(123)
CIS_data_5 <- data.frame(
CIS$CIS.P20,
CIS$CIS.P3
)
CIS$P3 <- factor(CIS$CIS.P3, labels = c("Mejor", "(NO LEER) Igual", "Peor", "N.S.", "N.C."))
n <- as.numeric(c(CIS$CIS.P20))
P20 <- sample(n, 2787, replace = TRUE, prob = NULL)
P20labs <- c("16-29", "30-44", "45-64", ">65", "N.C.")
cut_points <- c(16, 30, 45, 65, Inf)
i <- findInterval(P20, cut_points)
P20_fac <- P20labs[i]
P20_fac[is.na(P20)] <- P20labs[length(P20labs)]
P20_fac <- factor(P20_fac, levels = P20labs)
library(dplyr)
library(ggplot2)
data.frame(P3 = CIS$P3,
P20_fac = P20_fac) %>%
ggplot(aes(x = P20_fac)) +
geom_bar() +
coord_flip() +
facet_wrap(~P3)
Created on 2022-09-25 with reprex v2.0.2

Nonlinear model convergence

I have a time series data set and each time series has datapoint of 30-year from different/same species. I am developing a forecasting model using the first 23 years of data from each time series data point and I am using the rest 7 years as test set to know the predictive ability of model but the nonlinear model (Model 6 and Model 7) don't give succinct result?
Data:
DD <- structure(list(Plot = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A",
"B", "C", "D"), class = "factor"), Species = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("BD", "BG"), class = "factor"), Year = c(37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L,
64L, 65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L,
47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L,
43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L,
56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L,
52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L,
65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L,
61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L, 43L,
44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L, 39L,
40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L,
53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L), Count = c(81L, 45L, 96L, 44L, 24L, 8L, 28L, 32L, 39L, 29L,
40L, 17L, 4L, 12L, 18L, 11L, 63L, 98L, 78L, 76L, 67L, 36L, 56L,
43L, 81L, 8L, 14L, 20L, 25L, 19L, 135L, 91L, 171L, 88L, 59L,
1L, 1L, 1L, 2L, 1L, 11L, 9L, 34L, 15L, 32L, 21L, 33L, 43L, 39L,
20L, 6L, 3L, 9L, 9L, 28L, 16L, 15L, 2L, 1L, 1L, 34L, 16L, 19L,
35L, 32L, 7L, 2L, 30L, 29L, 25L, 28L, 11L, 31L, 31L, 28L, 27L,
34L, 110L, 87L, 103L, 72L, 19L, 46L, 43L, 107L, 32L, 26L, 31L,
12L, 29L, 23L, 40L, 50L, 23L, 34L, 11L, 9L, 4L, 24L, 55L, 14L,
16L, 51L, 43L, 2L, 13L, 8L, 96L, 52L, 118L, 32L, 1L, 8L, 17L,
34L, 29L, 38L, 15L, 4L, 38L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L,
4L, 6L, 4L, 4L, 10L, 6L, 7L, 9L, 15L, 30L, 25L, 36L, 13L, 17L,
43L, 36L, 60L, 50L, 26L, 13L, 13L, 27L, 18L, 56L, 96L, 16L, 54L,
2L, 2L, 9L, 5L, 5L, 6L, 2L, 6L, 2L, 3L, 4L, 3L, 136L, 71L, 116L,
28L, 23L, 76L, 64L, 98L, 58L, 26L, 13L, 13L, 13L, 18L, 19L, 24L,
18L, 17L, 3L, 23L, 19L, 9L, 11L, 13L, 20L, 29L, 29L, 17L, 20L,
26L, 71L, 63L, 53L, 54L, 20L, 22L, 18L, 93L, 50L, 18L, 12L, 12L,
31L), LogCount = c(1.908385019, 1.653212514, 1.982271233, 1.643462676,
1.380211242, 0.903089987, 1.447158031, 1.505109978, 1.591064607,
1.462397998, 1.602059991, 1.230448921, 0.602059991, 1.079181206,
1.255272505, 1.041392685, 1.799340549, 1.991226076, 1.892094603,
1.880813592, 1.826074803, 1.556302501, 1.748188027, 1.633468456,
1.908485019, 0.903089987, 1.146128035, 1.301029996, 1.397940009,
1.278753601, 2.130333768, 1.95904139, 2.2329961, 1.94448267,
1.770852012, 0, 0, 0, 0.30102999, 0, 1.0411392685, 0.954242509,
1.531478917, 1.176031259, 1.505149978, 1.322219295, 1.51851394,
1.6334684456, 1.591064607, 1.301029996, 0.77815125, 0.477121255,
0.954242509, 0.954242509, 1.447158031, 1.204119983, 1.176091259,
0.301029996, 0, 0, 1.531478917, 1.204119983, 1.278753501, 1.544068044,
1.505149978, 0.084509804, 0.301029996, 1.477121255, 1.462397998,
1.397940009, 1.447158031, 1.041392685, 1.491361694, 1.491361694,
1.447158031, 1.431363754, 1.531478917, 2.041392685, 1.939519253,
2.012837225, 1.857332495, 1.278753601, 1.662757382, 1.633468456,
2.029383778, 1.505149978, 1.414973348, 1.491361594, 1.079181245,
1.462397998, 1.361727835, 1.602059991, 1.698970004, 1.361727836,
1.531478917, 1.041392685, 0.954242509, 0.602059991, 1.380211242,
1.740362689, 1.146128036, 1.204119983, 1.707570176, 1.633468456,
0.301029996, 1.113943352, 0.903089987, 1.982271233, 1.716003344,
2.071882007, 1.50514997, 0, 0.903089987, 1.230448921, 1.53147891,
1.2397998, 1.57978359, 1.176091259, 0.602059991, 1.57978359,
0.301029996, 0, 0, 0, 0, 0, 0.477121255, 0.477121255, 0.602059991,
0.77815125, 0.602059991, 0.602059991, 1, 0.77815125, 0.84509804,
0.95424509, 1.176091259, 1.477121255, 1.39790009, 1.555302501,
1.113943352, 1.230448921, 1.633468456, 1.555302501, 1.77815125,
1.698970004, 1.414973348, 1.113943352, 1.113943352, 1.431353754,
1.255272505, 1.748188027, 1.982271233, 1.204119983, 1.73239376,
1.431363754, 1.361727835, 0.954242509, 0.698970004, 0.698970004,
0.77815125, 0.301029996, 0.77815125, 0.301029996, 0.477121255,
0.602059991, 0.477121255, 2.133538908, 1.851258349, 2.064457989,
1.447158031, 1.361727836, 1.880813592, 1.806179974, 1.991226076,
1.763427994, 1.414973348, 1.113943352, 1.113943352, 1.113943352,
1.255272505, 1.278753601, 1.380211242, 1.255272505, 1.230446921,
0.477121255, 1.361727835, 1.278753601, 0.954242509, 1.0411392685,
1.113943352, 1.301029996, 1.462397998, 1.462397998, 1.230448921,
1.301029995, 1.414973348, 1.851258349, 1.799340549, 1.72427587,
1.73239376, 1.301029996, 1.342422681, 1.255272505, 1.968482949,
1.698970004, 1.255272505, 1.079181246, 1.079181246, 1.491361694
), Diff = c(-0.255272505, 0.329058719, -0.338818557, -0.263241434,
-0.077121255, 0.544068044, 0.057991947, 0.085910629, -0.128666609,
0.139661993, -0.37161107, -0.62838893, 0.477121255, 0.176091259,
-0.21387982, 0.757947864, 0.191885527, -0.099131473, -0.011281011,
-0.054738789, -0.269772302, 0.191885526, -0.114719571, 0.275016563,
-1.005395032, 0.243038049, 0.15490196, 0.096910013, -0.119186408,
NA, -0.171292376, 0.273954718, -0.288513438, -0.17363066, -1.770852012,
0, 0, 0.301029996, -0.301029996, 1.041392685, -0.087150176, 0.577235408,
-0.355387658, 0.329058719, -0.182930683, 0.196294545, 0.110954516,
-0.042403849, -0.290034611, -0.522878746, -0.301029995, 0.477121254,
0, 0.492915522, -0.243038048, -0.028028724, -0.875061263, -0.301029996,
0, 1.531078917, -0.32735893, 0.070633618, 0.265310043, -0.038918066,
-0.660051938, -0.544068044, 1.176091259, -0.014723257, -0.064457989,
0.049218022, -0.405765346, 0.449969009, 0, -0.044203663, -0.015794267,
0.100115153, 0.509913768, -0.101873432, 0.073317972, -0.155504729,
-0.578578895, 0.384054231, -0.029289376, 0.395915322, -0.5202338,
-0.09017663, 0.076388346, -0.412180448, 0.383216752, -0.100670162,
0.240332155, 0.096910013, -0.337242168, 0.169751081, -0.490086232,
-0.087150176, -0.352182518, 0.778151251, 0.360151447, -0.594234653,
0.057991947, 0.503450193, -0.07410172, -1.33243846, 0.812913356,
-0.210853365, 1.079181246, -0.266267889, 0.355878663, -0.566732029,
-1.505149978, 0.903089987, 0.327358934, 0.301029996, -0.069080919,
0.117385599, -0.403692338, -0.574031268, 0.977723606, -1.278753601,
-0.301029996, 0, 0, 0, 0, 0.477121255, 0, 0.124938736, 0.176091259,
-0.176091259, 0, 0.397490009, -0.2218485, 0.06690679, 0.10914469,
0.22184875, 0.301029996, -0.079181206, 0.158362092, -0.442359149,
0.116505569, 0.403019535, -0.077165955, 0.221848749, -0.079181206,
-0.283996656, -0.301029996, 0, 0.317420412, -0.176091259, 0.492915522,
0.23483206, -0.77815125, 0.528273777, -0.301029996, -0.069635928,
-0.407485327, -0.255272505, 0, 0.079181246, -0.477121254, 0.477121254,
-0.477121254, 0.176091259, 0.124938736, -0.124938736, 1.656417653,
-0.282280559, 0.21319964, -0.617299958, -0.085430195, 0.5191085756,
-0.074533518, 0.185045102, -0.227798082, -0.348454546, -0.301029996,
0, 0, 0.141329153, 0.023481096, 0.101457641, -0.124938737, -0.024823584,
-0.753327666, 0.884606581, -0.082974235, -0.324511092, 0.087150176,
0.072550667, 0.187086644, 0.161368002, 0, -0.231949077, 0.070581075,
0.113903352, 0.436285001, -0.00519178, -0.075054679, 0.00811789,
-0.431363764, 0.041392685, -0.087150176, 0.713210444, -0.269512945,
-0.443697499, -0.176091259, 0, 0.412180448, -0.148939013)), class = "data.frame", row.names = c(NA,
-210L))
Code:
for(f in 1:11){
for(b in 1:5){
for (c in 1:5){
#To select test sets 1,2,3,4, and 5 years beyond the training set:
#Calculate the mean of abundance for the training set years.
Model1<-lm(mean~1, data=DD1)
#
Output2:
2 3 0.676209994477288 1.9365051784348e-09 4.44089209850063e-16
3 53 11.9236453578109 2.06371097988267e-09 1.13686837721616e-13
4 31 1.94583877614293 1.11022302462516e-15 1.99840144432528e-15
5 4 8.06660449042397 1.48071350736245e-08 3.19744231092045e-14
6 5 10.5321102149558 9.31706267692789e-10 1.4210854715202e-14
..

First, please see the time series graph of counts for different species and plots below.
library(ggplot2)
ggplot(DD, aes(Year, Count)) +
geom_point() +
geom_line() +
facet_grid(Plot ~ Species) +
scale_y_log10()
It is seen that there is no obvious trend which can be fitted by power or log-power function using nls.
Second, as I understand you are trying to use nls to predict outside the training data set. Usually it is not quite an effective to use least square models because of auto-correlated nature of time-series.
Third, the simplest prediction algorithm is Holt-Winters (see "dirty" implementation below). You can use as well a ton of other algorithms like ARIMA, exponential smoothing state space etc.
x <- ts(DD[DD$Species == "BG" & DD$Plot == "elq1a3", ]$LogCount)
m <- HoltWinters(x, gamma = FALSE)
library(forecast)
f <- forecast(m, 2)
plot(f, main = "elq1a3 at BG")
Fourth, about your algorithm in question, it throws
Error in qr.solve(QR.B, cc) : singular matrix 'a' in solve.
The reason is that in the first step of for-loop (at f = b = c = 1 DD2 data frame contains just one row. And executing
Model6<-nls(Diff~1+Count^T,start=list(T=1),trace=TRUE,algorithm ="plinear",data=DD2)
means that you are trying to fit a curve using only one data point, which is impossible.
However if you change f value in for-loop from 1:11 to 2:11 another error thrown:
Error in nls(Diff ~ 1 + Count^T, start = list(T = 1), trace = TRUE,
algorithm = "plinear", : step factor 0.000488281 reduced below
minFactor 0.000976562.
In this case you cannot use "naive" approach used by plinear algorithm with self-starting inital value and, e.g. nls.control(min.factor = 1e-5). You must feed all initial coefficients explicitely with default Gauss-Newton algorithm. Quite exausting, please try yourself :)

Broom::tidy error with dataframe of nnet::multinom models

I am generating multinom models using nnet, with a model fitted for each city in the dataset. When I attempt to use tidy with these models, I get the following error:
Error in probs[i, -1, drop = FALSE] : subscript out of bounds
However, if I produce a model for each City separately, and then use tidy I do not receive an error for any of the models. I am also able to use glace without an error.
What might be causing this error?
library(broom)
library(dplyr)
library(nnet)
dfstack <- structure(list(Var1 = c(73L, 71L, 66L, 75L, 96L, 98L, 98L, 65L,
75L, 74L, 71L, 98L, 100L, 87L, 78L, 50L, 73L, 82L, 70L, 70L,
31L, 34L, 32L, 100L, 100L, 100L, 54L, 51L, 36L, 48L, 66L, 60L,
59L, 72L, 76L, 90L, 85L, 76L, 55L, 53L, 42L, 54L, 54L, 10L, 34L,
18L, 6L, 16L, 63L, 41L, 68L, 55L, 52L, 57L, 64L, 61L, 68L, 44L,
33L, 19L, 38L, 54L, 44L, 87L, 100L, 100L, 63L, 75L, 76L, 100L,
100L, 64L, 95L, 90L, 99L, 98L, 87L, 62L, 62L, 88L, 79L, 85L),
Status = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("A",
"B", "C"), class = "factor"), City = structure(c(3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Denver", "Miami", "NYC"), class = "factor"),
ID = structure(c(52L, 63L, 74L, 77L, 78L, 79L, 80L, 81L,
82L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 64L,
31L, 42L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 32L, 1L, 12L,
23L, 25L, 26L, 27L, 28L, 29L, 30L, 2L, 3L, 4L, 5L, 65L, 66L,
67L, 68L, 69L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 6L,
7L, 8L, 9L, 10L, 11L, 13L, 70L, 71L, 72L, 73L, 75L, 76L,
41L, 43L, 44L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
24L), .Label = c("Denver1", "Denver10", "Denver11", "Denver12",
"Denver13", "Denver14", "Denver15", "Denver16", "Denver17",
"Denver18", "Denver19", "Denver2", "Denver20", "Denver21",
"Denver22", "Denver23", "Denver24", "Denver25", "Denver26",
"Denver27", "Denver28", "Denver29", "Denver3", "Denver30",
"Denver4", "Denver5", "Denver6", "Denver7", "Denver8", "Denver9",
"Miami1", "Miami10", "Miami11", "Miami12", "Miami13", "Miami14",
"Miami15", "Miami16", "Miami17", "Miami18", "Miami19", "Miami2",
"Miami20", "Miami21", "Miami3", "Miami4", "Miami5", "Miami6",
"Miami7", "Miami8", "Miami9", "NYC1", "NYC10", "NYC11", "NYC12",
"NYC13", "NYC14", "NYC15", "NYC16", "NYC17", "NYC18", "NYC19",
"NYC2", "NYC20", "NYC21", "NYC22", "NYC23", "NYC24", "NYC25",
"NYC26", "NYC27", "NYC28", "NYC29", "NYC3", "NYC30", "NYC31",
"NYC4", "NYC5", "NYC6", "NYC7", "NYC8", "NYC9"), class = "factor")), class = "data.frame", row.names = c(NA, -82L), .Names = c("Var1", "Status", "City", "ID"))
Model.List <- dfstack %>% group_by(City) %>% do(modfits = multinom(Status~Var1, data=.))
tidy(Model.List, modfits) # produces error
glance(Model.List, modfits) # no error
# no error when each city on its own
df1 <- dfstack %>% filter(City == "NYC") %>% do(modfit1 = multinom(Status~Var1, data=.))
tidy(df1, modfit1)
df2 <- dfstack %>% filter(City == "Miami") %>% do(modfit1 = multinom(Status~Var1, data=.))
tidy(df2, modfit1)
df3 <- dfstack %>% filter(City == "Denver") %>% do(modfit1 = multinom(Status~Var1, data=.))
tidy(df3, modfit1)

Don't ask me to explain why, but I figured it out.
tidy.multinom calls summary.multinom which calls vcov.multinom which calls multinomHess. The error was being generated down in multinomHess, which is only run when the Hessian matrix is not generated in the original call to multinom. That is to say, you don't necessarily need to spend the time calculating the Hessian matrix if you don't intend to use the summary object.
For some reason, when the multinom objects are formed within the do call, summary.multinom is unable to calculate the Hessian matrix. This can be circumvented by calling multinom with Hess = TRUE. See below:
Model.List <-
dfstack %>%
group_by(City) %>%
do(modfits = multinom(Status~Var1,
data=.,
Hess = TRUE))
tidy(Model.List, modfits)
glance(Model.List, modfits)
In your original code, glance did not cast an error because glance.multinom does not rely on summary.multinom.

removing rows from dataframe in two different columns R

I have a dataframe of results. There are multiple comparisons for Cruise_Strata. I have two columns of cruise_strata (Cruise1_Strata1 and Cruise2_Strata2). The problem I found is that there are "duplicate" records in the dataframe. For example one row will have
Cruise_Strata1 Cruise_Strata2
201501.35 201502.35
and another row will have
Cruise_Strata1 Cruise_Strata2
201502.35 201501.35
The rows have the same results for the remaining columns. I would like to be able to identify rows where this happens and remove one row from the dataset, but do not know how to go about it. I cant use duplicate because they are not duplicates.
Any help would be appreciated.
Here is the dataframe.
dput(result5)
structure(list(Cruise_Strata1 = structure(c(1L, 1L, 2L, 2L, 3L,
3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L,
11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L,
17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L, 23L,
24L, 24L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L, 29L, 29L, 30L,
30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L, 34L, 35L, 35L, 36L, 36L,
37L, 37L, 38L, 38L, 39L, 39L, 40L, 40L, 41L, 41L, 42L, 42L, 43L,
43L, 44L, 44L, 45L, 45L, 46L, 46L, 47L, 47L, 48L, 48L, 49L, 49L,
50L, 50L, 51L, 51L, 52L, 52L, 53L, 53L, 54L, 54L, 55L, 55L, 56L,
56L, 57L, 57L, 58L, 58L, 59L, 59L, 60L, 60L, 61L, 61L, 62L, 62L,
63L, 63L, 64L, 64L, 65L, 65L, 66L, 66L), .Label = c("201501.10",
"201501.11", "201501.13", "201501.14", "201501.15", "201501.17",
"201501.18", "201501.19", "201501.21", "201501.22", "201501.23",
"201501.24", "201501.25", "201501.26", "201501.27", "201501.29",
"201501.30", "201501.31", "201501.33", "201501.34", "201501.35",
"201501.9", "201502.10", "201502.11", "201502.13", "201502.14",
"201502.15", "201502.17", "201502.18", "201502.19", "201502.21",
"201502.22", "201502.23", "201502.24", "201502.25", "201502.26",
"201502.27", "201502.29", "201502.30", "201502.31", "201502.33",
"201502.34", "201502.35", "201502.9", "201503.10", "201503.11",
"201503.13", "201503.14", "201503.15", "201503.17", "201503.18",
"201503.19", "201503.21", "201503.22", "201503.23", "201503.24",
"201503.25", "201503.26", "201503.27", "201503.29", "201503.30",
"201503.31", "201503.33", "201503.34", "201503.35", "201503.9"
), class = "factor"), Cruise_Strata2 = structure(c(23L, 45L,
24L, 46L, 25L, 47L, 26L, 48L, 27L, 49L, 28L, 50L, 29L, 51L, 30L,
52L, 31L, 53L, 32L, 54L, 33L, 55L, 34L, 56L, 35L, 57L, 36L, 58L,
37L, 59L, 38L, 60L, 39L, 61L, 40L, 62L, 41L, 63L, 42L, 64L, 43L,
65L, 44L, 66L, 1L, 45L, 2L, 46L, 3L, 47L, 4L, 48L, 5L, 49L, 6L,
50L, 7L, 51L, 8L, 52L, 9L, 53L, 10L, 54L, 11L, 55L, 12L, 56L,
13L, 57L, 14L, 58L, 15L, 59L, 16L, 60L, 17L, 61L, 18L, 62L, 19L,
63L, 20L, 64L, 21L, 65L, 22L, 66L, 1L, 23L, 2L, 24L, 3L, 25L,
4L, 26L, 5L, 27L, 6L, 28L, 7L, 29L, 8L, 30L, 9L, 31L, 10L, 32L,
11L, 33L, 12L, 34L, 13L, 35L, 14L, 36L, 15L, 37L, 16L, 38L, 17L,
39L, 18L, 40L, 19L, 41L, 20L, 42L, 21L, 43L, 22L, 44L), .Label = c("201501.10",
"201501.11", "201501.13", "201501.14", "201501.15", "201501.17",
"201501.18", "201501.19", "201501.21", "201501.22", "201501.23",
"201501.24", "201501.25", "201501.26", "201501.27", "201501.29",
"201501.30", "201501.31", "201501.33", "201501.34", "201501.35",
"201501.9", "201502.10", "201502.11", "201502.13", "201502.14",
"201502.15", "201502.17", "201502.18", "201502.19", "201502.21",
"201502.22", "201502.23", "201502.24", "201502.25", "201502.26",
"201502.27", "201502.29", "201502.30", "201502.31", "201502.33",
"201502.34", "201502.35", "201502.9", "201503.10", "201503.11",
"201503.13", "201503.14", "201503.15", "201503.17", "201503.18",
"201503.19", "201503.21", "201503.22", "201503.23", "201503.24",
"201503.25", "201503.26", "201503.27", "201503.29", "201503.30",
"201503.31", "201503.33", "201503.34", "201503.35", "201503.9"
), class = "factor"), P_value = c(0.63, 0.6793, 0.0319, 0.0289,
0.9516, 0.8128, 0.9967, 0.3071, 0.9641, 0.0246, 0.7967, 0.2551,
0.2329, 0.3725, 0.0269, 0.3796, 0.0245, 0.5562, 0.9952, 0.5176,
0.5596, 0.9966, 0.32, 0.6402, 0.7691, 0.9671, 0.9396, 0.9, 0.9024,
0.3624, 0.0433, 0.3402, 0.5302, 0.787, 0.0295, 0.3638, 0.006,
0.701, 0.6323, 0.0366, 2e-04, 0.0011, 0.8849, 0.3, 0.63, 0.9738,
0.0319, 0.5197, 0.9516, 0.7369, 0.9967, 0.2276, 0.9641, 0.0158,
0.7967, 0.6332, 0.2329, 0.0322, 0.0269, 0.3013, 0.0245, 0.0129,
0.9952, 0.795, 0.5596, 0.7277, 0.32, 0.747, 0.7691, 0.3817, 0.9396,
0.7961, 0.9024, 0.4164, 0.0433, 0.0028, 0.5302, 0.2864, 0.0295,
0.7036, 0.006, 0, 0.6323, 0.002, 2e-04, 0.9548, 0.8849, 0.0546,
0.6793, 0.9738, 0.0289, 0.5197, 0.8128, 0.7369, 0.3071, 0.2276,
0.0246, 0.0158, 0.2551, 0.6332, 0.3725, 0.0322, 0.3796, 0.3013,
0.5562, 0.0129, 0.5176, 0.795, 0.9966, 0.7277, 0.6402, 0.747,
0.9671, 0.3817, 0.9, 0.7961, 0.3624, 0.4164, 0.3402, 0.0028,
0.787, 0.2864, 0.3638, 0.7036, 0.701, 0, 0.0366, 0.002, 0.0011,
0.9548, 0.3, 0.0546), Cruise1 = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("201501",
"201502", "201503"), class = "factor"), Cruise1_Strata1 = structure(c(1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L,
9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L,
16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L,
22L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L,
8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L,
14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L,
21L, 21L, 22L, 22L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L,
6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L,
13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L,
20L, 20L, 21L, 21L, 22L, 22L), .Label = c("10", "11", "13", "14",
"15", "17", "18", "19", "21", "22", "23", "24", "25", "26", "27",
"29", "30", "31", "33", "34", "35", "9"), class = "factor"),
Cruise2 = structure(c(2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L,
2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L,
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L,
3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L,
1L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L), .Label = c("201501", "201502", "201503"), class = "factor"),
Cruise2_Strata2 = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L,
4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L,
11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L,
17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L,
9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L,
15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L,
21L, 21L, 22L, 22L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L,
6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L,
12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L,
18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L), .Label = c("10",
"11", "13", "14", "15", "17", "18", "19", "21", "22", "23",
"24", "25", "26", "27", "29", "30", "31", "33", "34", "35",
"9"), class = "factor"), adjuste_p = c(1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.792, 1, 1, 1, 0.0264,
0.1452, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.3696, 1,
1, 1, 1, 0.792, 0, 1, 0.264, 0.0264, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0.3696, 1, 1, 1, 1, 1, 0, 1, 0.264,
0.1452, 1, 1, 1)), .Names = c("Cruise_Strata1", "Cruise_Strata2",
"P_value", "Cruise1", "Cruise1_Strata1", "Cruise2", "Cruise2_Strata2",
"adjuste_p"), row.names = c(1453L, 2905L, 1520L, 2972L, 1587L,
3039L, 1654L, 3106L, 1721L, 3173L, 1788L, 3240L, 1855L, 3307L,
1922L, 3374L, 1989L, 3441L, 2056L, 3508L, 2123L, 3575L, 2190L,
3642L, 2257L, 3709L, 2324L, 3776L, 2391L, 3843L, 2458L, 3910L,
2525L, 3977L, 2592L, 4044L, 2659L, 4111L, 2726L, 4178L, 2793L,
4245L, 2860L, 4312L, 23L, 2927L, 90L, 2994L, 157L, 3061L, 224L,
3128L, 291L, 3195L, 358L, 3262L, 425L, 3329L, 492L, 3396L, 559L,
3463L, 626L, 3530L, 693L, 3597L, 760L, 3664L, 827L, 3731L, 894L,
3798L, 961L, 3865L, 1028L, 3932L, 1095L, 3999L, 1162L, 4066L,
1229L, 4133L, 1296L, 4200L, 1363L, 4267L, 1430L, 4334L, 45L,
1497L, 112L, 1564L, 179L, 1631L, 246L, 1698L, 313L, 1765L, 380L,
1832L, 447L, 1899L, 514L, 1966L, 581L, 2033L, 648L, 2100L, 715L,
2167L, 782L, 2234L, 849L, 2301L, 916L, 2368L, 983L, 2435L, 1050L,
2502L, 1117L, 2569L, 1184L, 2636L, 1251L, 2703L, 1318L, 2770L,
1385L, 2837L, 1452L, 2904L), class = "data.frame")
R Info
R version 3.2.1 (2015-06-18)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

Does this give you your desired result?
duplicated(apply(cbind(result5$Cruise_Strata1, df$Cruise_Strata2), 1,
function(x) paste(min(x), max(x))))
You can use the resulting logical vector to subset your data.
First you create a vector pasting the values in Cruise_Strata1 and Cruise_Strata2. Doing this you move the smaller of the two to the front and the larger one to the end (or you could do it vice versa). This is just a trick so that you can apply the duplicated function and recognize the duplicates.
Note: this approach will remove duplicates of the form:
Cruise_Strata1 Cruise_Strata2
x y
y x
As well as (if this is not desired let me know):
Cruise_Strata1 Cruise_Strata2
x y
x y

For a generic data frame df with duplicated values in Cruise_Strata1 and Cruise_Strata2:
df$dupe <- 0
for(i in 1:(length(df$Cruise_Strata1)-1))
{
for(j in (i+1):length(df$Cruise_Strata1))
if(df$Cruise_Strata1[i]==df$Cruise_Strata2[j])
{print(df[c(i,j),]); df$dupe[i] = 1;break}
}
df[df$dupe != 1,]

Ranking according to value across two variables - r

I have this dataframe:
df<-data.frame(
var1 = c(rep(c(rep(1,2), rep(2,3), rep(3,2), rep(4,1)),2), 1),
var2 = c(rep(1,8), rep(2,8),3)
)
df
var1 var2
#1 1 1
#2 1 1
#3 2 1
#4 2 1
#5 2 1
#6 3 1
#7 3 1
#8 4 1
#9 1 2
#10 1 2
#11 2 2
#12 2 2
#13 2 2
#14 3 2
#15 3 2
#16 4 2
#17 1 3
I would like to make a third variable that is a rank. Rows get the highest rank if 1) they have the lowest numbers in var2 - and then according to how low the numbers are in var1. e.g. Rows 1 and 2 with var2=1 and var1=1 should be ranked 1. Whereas, rows 9 and 10 with var2=2 and var1=1 would be ranked 5.
If my data are arranged in ascending order of var2 and then var1, I did the following using my favorite R function rle to achieve the ranking I'm after:
rle(df$var1)
N <- length(rle(df$var1)$lengths)
df$ranks <- rep(1:N, rle(df$var1)$lengths)
df
var1 var2 ranks
#1 1 1 1
#2 1 1 1
#3 2 1 2
#4 2 1 2
#5 2 1 2
#6 3 1 3
#7 3 1 3
#8 4 1 4
#9 1 2 5
#10 1 2 5
#11 2 2 6
#12 2 2 6
#13 2 2 6
#14 3 2 7
#15 3 2 7
#16 4 2 8
#17 1 3 9
This works, but it requires my df to be pre-sorted. I'd like a solution that does not need this. I feel like this should be a simple one-liner using rank and that I'm having a blind-spot. Any help appreciated - thanks.
EDIT 1:
- adding a larger example for testing of suggested answer
dput(df1)
df1 <- structure(list(var1 = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 7L,
7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 6L, 9L, 10L, 10L, 10L, 11L,
12L, 12L, 12L, 13L, 14L, 14L, 14L, 14L, 15L, 16L, 16L, 16L, 16L,
16L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 19L, 19L,
20L, 20L, 21L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 1L, 2L, 2L,
2L, 2L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L,
8L, 9L, 10L, 10L, 10L, 10L, 3L, 11L, 11L, 11L, 11L, 12L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L,
12L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L,
18L, 18L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 21L, 22L,
22L, 22L, 23L, 25L, 24L, 24L, 24L, 24L, 24L, 26L, 26L, 26L, 26L,
26L, 27L, 27L, 27L, 27L, 27L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L,
11L, 12L, 12L, 13L, 14L, 15L, 16L, 17L, 17L, 18L, 18L, 19L, 19L,
19L, 19L, 20L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L,
23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L,
25L, 25L, 26L, 26L, 26L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 5L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L), var2 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), ranks = c(1L, 1L,
1L, 1L, 1L, 12L, 12L, 12L, 12L, 12L, 19L, 19L, 19L, 19L, 19L,
20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 23L, 23L, 23L,
23L, 23L, 24L, 24L, 24L, 24L, 24L, 22L, 25L, 2L, 2L, 2L, 3L,
4L, 4L, 4L, 5L, 6L, 6L, 6L, 6L, 7L, 8L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 13L, 13L, 14L,
15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L,
17L, 17L, 18L, 18L, 18L, 18L, 18L, 26L, 37L, 37L, 37L, 37L, 47L,
48L, 48L, 48L, 48L, 49L, 49L, 49L, 49L, 49L, 50L, 50L, 50L, 50L,
51L, 52L, 27L, 27L, 27L, 27L, 46L, 28L, 28L, 28L, 28L, 29L, 30L,
30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L,
29L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L,
35L, 35L, 36L, 36L, 36L, 36L, 36L, 38L, 38L, 38L, 38L, 39L, 40L,
40L, 40L, 41L, 43L, 42L, 42L, 42L, 42L, 42L, 44L, 44L, 44L, 44L,
44L, 45L, 45L, 45L, 45L, 45L, 53L, 64L, 64L, 64L, 64L, 74L, 74L,
74L, 74L, 74L, 75L, 75L, 75L, 75L, 75L, 76L, 76L, 76L, 76L, 76L,
77L, 77L, 77L, 77L, 77L, 78L, 78L, 78L, 78L, 78L, 79L, 80L, 80L,
80L, 80L, 54L, 54L, 54L, 54L, 55L, 56L, 56L, 57L, 58L, 59L, 60L,
61L, 61L, 62L, 62L, 63L, 63L, 63L, 63L, 65L, 66L, 66L, 66L, 66L,
66L, 67L, 67L, 67L, 67L, 67L, 68L, 68L, 68L, 68L, 68L, 69L, 69L,
69L, 69L, 69L, 70L, 70L, 70L, 70L, 70L, 71L, 71L, 71L, 72L, 72L,
73L, 73L, 73L, 73L, 73L, 81L, 81L, 81L, 81L, 81L, 82L, 82L, 82L,
82L, 82L, 83L, 83L, 83L, 83L, 83L, 84L, 84L, 84L, 84L, 85L, 86L,
87L, 87L, 87L, 87L, 88L, 88L, 88L, 88L, 88L)), .Names = c("var1",
"var2", "ranks"), row.names = c(NA, -300L), class = "data.frame")
The ranks variable was got from this suggested answer:
df1$ranks1 <- dense_rank(paste(df1$var2, df1$var1))

Solutions involving paste[0] will only work if values within each vector are integer with a fixed number of digits. This is because paste converts to character and:
character (lexicographic) ordering differs from numeric: rank(c(1 , 2, 11)); rank(as.character(c(1 , 2, 11)))
concatenation introduces ambiguities: paste0(2,12); paste0(21,2)
Peter Dalgaard made a relevant post in 2011 http://r.789695.n4.nabble.com/Function-rank-for-data-frames-or-multiple-vectors-td3765685.html
For now ignore identical rows. Note that rank(x) == order(order(x)) and order accepts multiple ordering columns so if you don't mind how identical rows are split order(order(df$var2, df$var1)) does the job.
This splits identical rows according to their original ordering. There are a number of ways of ranking identical rows http://en.wikipedia.org/wiki/Ranking#Strategies_for_assigning_rankings.
In 2011 Peter Dalgaard suggested ave(order(order(df$var2, df$var1)), df$var2, df$var1) which gives what Wikipedia calls 'Fractional ranking' and in base::rank is the default ties.method="average".
Your example is what Wikipedia calls 'Dense ranking' which isn't available in base::rank but - as commented by David Arenburg - is provided by dplyr::dense_rank, so you can library(dyplr) and use:
dense_rank(ave(order(order(df$var2, df$var1)), df$var2, df$var1))
Looking at the code for dense_rank it is just
function (x)
{
r <- rank(x)
match(r, sort(unique(r)))
}
suggesting that if you don't want to load dplyr and are happy with a 2-statement solution creating another variable - e.g. r - you could use
r <- ave(order(order(df$var2, df$var1)), df$var2, df$var1); match(r, sort(unique(r)))
Edited to add ...
You can make things a little neater by realising that a data frame is really a list so to rank by columns in order of occurrence:
dense_rank(ave(order(do.call(order, df)), df))
You are ranking by columns in reverse order of occurrence so
dense_rank(ave(order(do.call(order, rev(df))), df))
or explicitly specifying columns and their order
dense_rank(ave(order(do.call(order, df[,2:1])), df[,2:1]))

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