arima xreg argument error caused by column name in data frame - r

I'm trying to run the code below and I'm getting the error below. The code uses auto.arima from the forecast package to determine and fit an arima model to some data. It's also using regressors in the xreg argument. I think the names of two of the columns in the xreg might be the issue, but I'm not sure why. The names of the columns are like "structure.c.NA..NA..211L.." They're the output of a function. If I run auto.arima without those columns in the xreg argument it seems to do fine. Any tips on how to solve this are greatly appreciated.
Code:
auto.arima(df2_comb[1:100,names(df2_comb)=='ECDD'], xreg = df2_comb[,names(df2_comb)!='ECDD'][1:100,names(df2_comb[,!names(df2_comb)%in%c('ECDD','order_dts')])])
Error:
Error in auto.arima(df2_comb[1:100, names(df2_comb) == "ECDD"], xreg = df2_comb[, :
xreg should be a numeric matrix or vector
Data:
dput(df2_comb[1:100,])
structure(list(ECDD = c(319.4, 319.4, 319.4, 319.4, 319.4, 319.4,
319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 319.4,
319.4, 319.4, 319.4, 319.4, 198, 142, 254, 178, 97, 113, 116,
109, 127, 102, 99, 107, 109, 89, 101, 106, 319.4, 319.4, 319.4,
319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 257, 169, 191, 115,
121, 121, 108, 110, 105, 93, 103, 93, 107, 99, 113, 319.4, 319.4,
319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 237,
106, 108, 108, 117, 99, 105, 108, 100, 93, 88, 105, 95, 109,
319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 319.4, 192,
169, 319.4, 136, 108, 163, 136, 114, 116), order_dts = c("2017-12-31 09:00:00",
"2017-12-31 10:00:00", "2017-12-31 11:00:00", "2017-12-31 12:00:00",
"2017-12-31 13:00:00", "2017-12-31 14:00:00", "2017-12-31 15:00:00",
"2017-12-31 16:00:00", "2017-12-31 17:00:00", "2017-12-31 18:00:00",
"2017-12-31 19:00:00", "2017-12-31 20:00:00", "2017-12-31 21:00:00",
"2017-12-31 22:00:00", "2017-12-31 23:00:00", "2018-01-01 00:00:00",
"2018-01-01 01:00:00", "2018-01-01 02:00:00", "2018-01-01 03:00:00",
"2018-01-01 04:00:00", "2018-01-01 05:00:00", "2018-01-01 06:00:00",
"2018-01-01 07:00:00", "2018-01-01 08:00:00", "2018-01-01 09:00:00",
"2018-01-01 10:00:00", "2018-01-01 11:00:00", "2018-01-01 12:00:00",
"2018-01-01 13:00:00", "2018-01-01 14:00:00", "2018-01-01 15:00:00",
"2018-01-01 16:00:00", "2018-01-01 17:00:00", "2018-01-01 18:00:00",
"2018-01-01 19:00:00", "2018-01-01 20:00:00", "2018-01-01 21:00:00",
"2018-01-01 22:00:00", "2018-01-01 23:00:00", "2018-01-02 00:00:00",
"2018-01-02 01:00:00", "2018-01-02 02:00:00", "2018-01-02 03:00:00",
"2018-01-02 04:00:00", "2018-01-02 05:00:00", "2018-01-02 06:00:00",
"2018-01-02 07:00:00", "2018-01-02 08:00:00", "2018-01-02 09:00:00",
"2018-01-02 10:00:00", "2018-01-02 11:00:00", "2018-01-02 12:00:00",
"2018-01-02 13:00:00", "2018-01-02 14:00:00", "2018-01-02 15:00:00",
"2018-01-02 16:00:00", "2018-01-02 17:00:00", "2018-01-02 18:00:00",
"2018-01-02 19:00:00", "2018-01-02 20:00:00", "2018-01-02 21:00:00",
"2018-01-02 22:00:00", "2018-01-02 23:00:00", "2018-01-03 00:00:00",
"2018-01-03 01:00:00", "2018-01-03 02:00:00", "2018-01-03 03:00:00",
"2018-01-03 04:00:00", "2018-01-03 05:00:00", "2018-01-03 06:00:00",
"2018-01-03 07:00:00", "2018-01-03 08:00:00", "2018-01-03 09:00:00",
"2018-01-03 10:00:00", "2018-01-03 11:00:00", "2018-01-03 12:00:00",
"2018-01-03 13:00:00", "2018-01-03 14:00:00", "2018-01-03 15:00:00",
"2018-01-03 16:00:00", "2018-01-03 17:00:00", "2018-01-03 18:00:00",
"2018-01-03 19:00:00", "2018-01-03 20:00:00", "2018-01-03 21:00:00",
"2018-01-03 22:00:00", "2018-01-03 23:00:00", "2018-01-04 00:00:00",
"2018-01-04 01:00:00", "2018-01-04 02:00:00", "2018-01-04 03:00:00",
"2018-01-04 04:00:00", "2018-01-04 05:00:00", "2018-01-04 06:00:00",
"2018-01-04 07:00:00", "2018-01-04 08:00:00", "2018-01-04 09:00:00",
"2018-01-04 10:00:00", "2018-01-04 11:00:00", "2018-01-04 12:00:00"
), structure.c.NA..NA..211L..211L..211L..211L..211L..211L..211L.. = c(0,
0, 211, 211, 211, 211, 211, 211, 211, 211, 211, 211, 211, 211,
211, 211, 211, 211, 211, 211, 211, 131, 94, 168, 117, 64, 75,
77, 72, 84, 67, 65, 71, 72, 59, 67, 70, 211, 211, 211, 211, 211,
211, 211, 211, 211, 170, 112, 126, 76, 80, 80, 71, 73, 69, 61,
68, 61, 71, 65, 75, 211, 211, 211, 211, 211, 211, 211, 211, 211,
211, 156, 70, 71, 71, 77, 65, 69, 71, 66, 61, 58, 69, 63, 72,
211, 211, 211, 211, 211, 211, 211, 211, 127, 112, 211, 90, 71,
108, 90), structure.c.NA..NA..NA..NA..48L..48L..48L..48L..48L..48L..48L.. = c(0,
0, 0, 0, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48,
48, 48, 48, 48, 48, 48, 30, 21, 38, 27, 15, 17, 17, 16, 19, 15,
15, 16, 16, 13, 15, 16, 48, 48, 48, 48, 48, 48, 48, 48, 48, 39,
25, 29, 17, 18, 18, 16, 16, 16, 14, 15, 14, 16, 15, 17, 48, 48,
48, 48, 48, 48, 48, 48, 48, 48, 36, 16, 16, 16, 18, 15, 16, 16,
15, 14, 13, 16, 14, 16, 48, 48, 48, 48, 48, 48, 48, 48, 29, 25,
48, 20, 16), S1 = c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0), S2 = c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0
), S3 = c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0),
S4 = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1), S5 = c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), S6 = c(0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), S7 = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), S8 = c(0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), S9 = c(0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), S10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), S11 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), S12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), S13 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), S14 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), S15 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), S16 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), S17 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), S18 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), S19 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0), S20 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), S21 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0), S22 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0), S23 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0)), row.names = c(NA,
100L), class = "data.frame")


library(forecast)
## it works
auto.arima(df2_comb[1:100,names(df2_comb)=='ECDD'], xreg = as.matrix(df2_comb[,names(df2_comb)!='ECDD'][1:100,names(df2_comb[,!names(df2_comb)%in%c('ECDD','order_dts')])]))
## it fails
colnames(df2_comb)[3]<- "structure_211"
colnames(df2_comb)[4]<- "structure_48"
auto.arima(df2_comb[1:100,names(df2_comb)=='ECDD'], xreg = df2_comb[,names(df2_comb)!='ECDD'][1:100,names(df2_comb[,!names(df2_comb)%in%c('ECDD','order_dts')])])
It is no related with the names, as you can see if you change them, fails in the same fashion.
But is you use xreg as.matrix it should do the trick!

Related

How to merge 2 dataframes with partial character strings?

i have a dataset that lists several possible genera of plants, and another dataset that lists all the species with their functional forms. I would like to merge these datasets in such a way that IF the genus listed in df2 is found within the SPP column of df1, the merged dataframe will include the functional form associated. ie if a sample is listed in df1 as possibly Poa OR Festuca, and df2 lists the functional form of Poa as Graminoid, the resultant merged dataframe would have all of the data from df1 AND an additional column that says "Graminoid." (also including the other columns such as LifeHistory and Origin would also be fine/helpful)
First Dataframe, containing multiple possible species (subset of first 100 rows):
structure(list(SPP = c("Abies", "Acer", "Poa OR Agrostis", "Allium schoenoprasum",
"Alnus", "Amblystegiaceae OR Anomodontaceae OR Pterobryaceae OR Meteoriaceae OR Pterigynandraceae OR Lembophyllaceae OR Hypnum OR Taxiphyllaceae OR Orthostichellaceae OR Hylocomiaceae OR Leucodontaceae OR Miyabeaceae OR Climaciaceae OR Cryphaeaceae OR Calliergonaceae OR Neckeraceae OR Moss",
"Andreaeaceae OR Moss", "Anemone", "Antennaria", "Apocynum cannabinum",
"Aralia OR Ehretiaceae OR Araliaceae", "Arctostaphylos uva-ursi",
"Artemisia", "Asteraceae", "Asteraceae OR Bidens OR Senecio",
"Astragalus", "Aulacomniaceae OR Moss", "Berberis", "Betula",
"Bidens", "Bidens OR Torricelliaceae OR Cornus OR Cardiopteridaceae",
"Boechera", "Boechera OR Arabis", "Boykinia OR Saxifraga", "Brachytheciaceae OR Plagiotheciaceae OR Moss",
"Brickellia", "Bromus", "Bryaceae OR Moss", "Bryaceae OR Mniaceae OR Splachnaceae OR Moss",
"Buxbaumiaceae", "Calamagrostis", "Campanula rotundifolia", "Carex",
"Caryophyllaceae", "Castilleja", "Celastraceae", "Celastraceae OR Paxistima",
"Cerastium", "Chamerion OR Epilobium OR Oenothera", "Chamerion",
"Chrysosplenium", "Claytonia", "Clematis", "Collinsia", "Cornus",
"Cornus OR Phacelia", "Crassulaceae", "Crepis OR Lactuca OR Centaurea OR Tragopogon OR Solidago OR Gutierrezia OR Taraxacum",
"Danthonia californica", "Delphinium geyeri", "Dichanthelium acuminatum OR Dichanthelium oligosanthes OR Panicum capillare",
"Dicranaceae", "Draba", "Dryas OR Purshia", "Echinacea angustifolia OR Eriophyllum lanatum OR Cornus canadensis",
"Elaeagnus commutata", "Elymus OR Agropyron OR Triticum", "Encalyptaceae OR Moss",
"Equisetum", "Ericaceae OR Rhododendron", "Erigeron", "Erigeron",
"Erigeron OR Taraxacum", "Eriogonum", "Erythronium", "Erythronium OR Liliaceae",
"Euphorbia glyptosperma", "Fabaceae", "Festuca", "Fragaria OR Rosa OR Rubus OR Sibbaldia OR Drymocallis OR Comarum OR Potentilla",
"Funariaceae OR Moss", "Galium", "Gaultheria", "Gentiana calycosa",
"Geranium", "Goodyera", "Grimmiaceae OR Moss", "Grimmiaceae OR Mniaceae OR Disceliaceae OR Ditrichaceae OR Drummondiaceae OR Meesiaceae OR Rhacocarpaceae OR Bryaceae OR Moss",
"Gymnomitriaceae OR Liverwort", "Hedysarum", "Hieracium triste",
"Hypericum", "Juncus", "Juniperus communis", "Koeleria macrantha OR Deschampsia cespitosa",
"Lamiaceae", "Liliaceae", "Lomatium bicolor OR Shoshonea pulvinata OR Lomatium macrocarpum OR Musineon divaricatum OR Zizia aptera",
"Lonicera", "Lotus unifoliolatus", "Luzula", "Lycopodium clavatum OR Moss",
"Melica subulata", "Menyanthes trifoliata", "Mertensia", "Micranthes",
"Micranthes OR Saxifraga", "Mniaceae OR Moss", "Mniaceae OR Splachnaceae OR Bartramiaceae OR Ditrichaceae OR Meesiaceae OR Rhizogoniaceae OR Moss",
"Moneses uniflora"), comb_S026401.R1 = c(4713, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 792, 0, 0,
0, 0, 0, 0, 0, 0, 0, 16, 31, 0, 0, 0, 133, 0, 1649, 0, 0, 0,
0, 0, 0, 29, 14, 0, 0, 0, 0, 0, 67, 0, 0, 0, 0, 0, 0, 150, 0,
19, 8, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4517,
0, 0, 0, 0, 0, 0, 0, 0, 2453, 0, 0, 0, 0, 0, 35, 0, 0, 0), comb_S026404.R1 = c(485,
0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 419, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 36, 0, 15, 0, 196, 342,
75, 0, 0, 0, 0, 0, 0, 251, 0, 0, 0, 0, 0, 9, 35, 0, 0, 0, 0,
0, 0, 0, 0, 0, 56, 57, 0, 0, 0, 787, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 104, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), comb_S026406.R1 = c(5626, 0, 0, 0, 127, 14, 0, 0, 0, 0,
0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 472, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 227, 0, 18, 0, 25, 160, 540, 0, 0, 0, 0, 0, 8, 87,
0, 0, 0, 0, 0, 0, 105, 0, 0, 0, 0, 0, 0, 34, 0, 16, 13, 11, 0,
0, 0, 2208, 0, 0, 0, 28, 0, 0, 0, 0, 0, 10, 0, 722, 0, 0, 0,
0, 0, 0, 28, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0), comb_S026409.R1 = c(2020,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 0, 0, 0, 0, 1324, 0, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0,
22, 0, 1302, 0, 0, 0, 0, 0, 4197, 0, 0, 0, 0, 0, 0, 8, 0, 0,
0, 0, 384, 0, 0, 0, 0, 69, 0, 0, 0, 442, 0, 0, 0, 0, 0, 228,
0, 0, 0), comb_S026412.R1 = c(331, 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, 0, 0, 0, 0, 0,
0, 0, 0, 31, 0, 0, 0, 0, 28, 8, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0,
0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 25, 0, 14, 0, 0, 0, 0, 0, 322,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 201, 6959, 0, 0, 0, 0, 0, 0,
0, 17, 0, 0, 0, 0, 0, 10, 0, 0, 0), comb_S026413.R1 = c(1394,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 30, 0, 0, 0, 0, 0,
0, 0, 21, 0, 0, 0, 0, 28, 0, 0, 0, 0, 26, 156, 0, 0, 0, 162,
29, 41, 0, 0, 0, 0, 0, 351, 129, 0, 0, 0, 0, 0, 0, 125, 0, 0,
0, 0, 0, 0, 44, 0, 377, 0, 0, 0, 0, 0, 1043, 0, 38, 0, 17, 0,
0, 0, 0, 0, 0, 0, 296, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0,
0, 660, 0, 0, 0), comb_S026414.R1 = c(21, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 29, 0, 0, 0, 22, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 91, 0, 0, 0, 978, 292, 52, 0, 0, 0, 0, 0, 0,
619, 0, 0, 0, 0, 0, 0, 256, 0, 22, 0, 0, 0, 0, 194, 0, 1075,
0, 0, 0, 0, 0, 5098, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1360,
0, 0, 0, 0, 0, 0, 0, 0, 66, 0, 0, 0, 0, 826, 12, 0, 0, 0), comb_S026415.R1 = c(0,
10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 0, 0, 0, 28, 0, 0,
0, 0, 0, 0, 0, 0, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 196, 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, 0, 0, 0, 0, 0), comb_S026416.R1 = c(271,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 0,
0, 0, 273, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 36, 0, 0, 0, 154, 5043,
314, 0, 0, 0, 0, 0, 11, 15, 0, 0, 0, 0, 0, 0, 49, 0, 0, 0, 0,
0, 0, 240, 0, 228, 0, 0, 0, 0, 0, 140, 31, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 714, 0, 0, 0, 0, 26, 0, 0, 0, 222, 0, 0, 0, 0, 56,
191, 0, 0, 0), comb_S026419.R1 = c(0, 0, 0, 0, 0, 0, 0, 0, 17,
0, 0, 0, 109, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 676, 0, 0, 0, 0,
0, 0, 0, 0, 0, 135, 0, 0, 0, 0, 129, 142, 126, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 6521, 0,
0, 0, 0, 0, 4088, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 412, 20, 0,
0, 0, 0, 0, 0, 0, 116, 0, 0, 0, 0, 305, 361, 0, 0, 0), comb_S026421.R1 = c(4689,
47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 0, 0, 23, 0, 0, 0, 0, 0,
0, 0, 208, 0, 0, 0, 34, 0, 0, 111, 0, 29, 0, 38, 0, 0, 0, 113,
37, 272, 0, 0, 0, 0, 0, 0, 286, 22, 0, 57, 0, 0, 13, 663, 0,
0, 0, 154, 0, 29, 376, 0, 130, 0, 0, 0, 0, 0, 442, 0, 49, 0,
191, 14, 0, 24, 0, 0, 0, 0, 2075, 187, 0, 0, 0, 102, 0, 0, 90,
3498, 0, 0, 67, 0, 0, 16, 0, 0, 0), comb_S026422.R1 = c(95, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
483, 0, 0, 0, 0, 0, 0, 0, 0, 0, 135, 0, 0, 0, 0, 0, 340, 85,
0, 0, 0, 0, 0, 0, 31, 0, 0, 0, 0, 0, 0, 178, 0, 137, 0, 0, 0,
0, 9, 0, 1174, 0, 0, 0, 0, 0, 499, 0, 0, 0, 0, 0, 0, 28, 0, 0,
0, 0, 588, 2692, 0, 0, 0, 33, 0, 0, 0, 12, 0, 0, 0, 0, 198, 26,
0, 0, 0), comb_S026423.R1 = c(360, 0, 0, 0, 0, 0, 0, 0, 14, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 614, 0, 0, 0, 0, 0, 0,
0, 0, 9, 279, 0, 0, 0, 0, 32, 251, 94, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 212, 0, 10, 0, 0, 0, 0, 0, 0, 781, 0, 0, 0, 0,
0, 1608, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1200, 0, 0, 0, 0, 76,
0, 0, 0, 2382, 0, 0, 0, 0, 149, 259, 0, 0, 0), comb_S026427.R1 = c(666,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0,
0, 356, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1545, 37, 475,
0, 0, 0, 0, 0, 0, 111, 0, 0, 0, 0, 0, 0, 136, 0, 0, 0, 0, 0,
0, 146, 0, 116, 0, 0, 0, 0, 0, 117, 0, 0, 0, 34, 0, 0, 0, 0,
0, 0, 0, 1062, 71, 0, 0, 0, 51, 0, 0, 0, 722, 0, 0, 0, 0, 0,
0, 0, 0, 0), comb_S026428.R1 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1713, 0, 0, 857, 1071,
0, 0, 1435, 0, 0, 0, 63, 0, 0, 387, 0, 0, 301, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 96, 0, 0, 0, 0, 0, 0, 0, 625, 0, 0,
0, 0, 819, 672, 0, 0, 0, 0, 0, 0, 4313, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), comb_S026429.R1 = c(21,
0, 0, 0, 0, 0, 0, 10, 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, 75, 340, 0, 0, 10, 22, 190,
0, 0, 0, 0, 0, 0, 0, 60, 0, 0, 0, 0, 0, 0, 252, 0, 165, 0, 0,
0, 0, 35, 0, 124, 0, 0, 0, 0, 0, 138, 0, 0, 0, 145, 0, 0, 0,
0, 0, 0, 0, 19, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 12,
0, 0, 0), comb_S026431.R1 = c(1545, 9, 0, 0, 0, 0, 0, 0, 10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 303, 0, 0, 0, 0, 0,
0, 0, 0, 8, 61, 18, 0, 0, 0, 67, 12, 69, 0, 0, 0, 0, 0, 0, 11,
10, 0, 0, 10, 0, 0, 21, 0, 0, 0, 0, 0, 0, 10, 0, 2395, 0, 0,
0, 0, 0, 974, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 7078, 0, 0, 0,
0, 11, 0, 0, 0, 35, 0, 0, 0, 0, 596, 269, 0, 0, 0), comb_S026430.R1 = c(322,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 507, 0, 0, 0, 0, 0, 0, 0, 0, 20, 20, 33, 0, 0, 0, 562, 6336,
336, 0, 0, 0, 0, 0, 17, 32, 0, 0, 0, 0, 0, 0, 31, 0, 0, 0, 0,
0, 0, 228, 0, 340, 0, 0, 0, 0, 0, 257, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 687, 11, 0, 0, 0, 65, 0, 0, 0, 167, 0, 0, 0, 0, 0, 141,
0, 0, 0), comb_S026432.R1 = c(2878, 0, 0, 0, 0, 0, 0, 0, 8, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 40, 19, 0, 0, 36, 0, 0, 0, 0, 0, 0,
0, 0, 0, 270, 45, 0, 0, 0, 0, 0, 30, 0, 0, 0, 0, 0, 36, 77, 0,
0, 0, 0, 0, 0, 360, 0, 0, 9, 0, 0, 0, 191, 0, 488, 8, 8, 0, 0,
0, 1428, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 852, 0, 0, 0, 0, 0,
0, 0, 0, 22, 11, 0, 0, 0, 0, 152, 0, 0, 0), comb_S026433.R1 = c(908,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 14, 0, 0, 0, 0, 15, 0,
0, 0, 293, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 67, 0, 0, 0, 2045, 25,
21, 0, 0, 0, 0, 0, 0, 237, 0, 0, 0, 0, 0, 0, 300, 0, 28, 0, 0,
0, 0, 251, 0, 564, 0, 0, 0, 0, 0, 4901, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 869, 0, 0, 0, 0, 103, 0, 0, 0, 224, 0, 0, 0, 0, 0,
0, 0, 0, 0), comb_S026434.R1 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 13,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 38, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 172, 0, 12, 0, 166, 1591, 50, 0, 0, 0, 0, 0, 0, 11,
44, 0, 0, 0, 0, 0, 33, 0, 0, 0, 0, 0, 0, 140, 0, 34, 0, 0, 0,
0, 0, 365, 0, 0, 0, 0, 0, 0, 28, 0, 0, 0, 41, 1234, 0, 0, 0,
0, 0, 0, 0, 0, 79, 0, 0, 0, 0, 548, 138, 0, 0, 0), comb_S026435.R1 = c(1961,
83, 0, 0, 0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8,
0, 0, 332, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 15, 0, 11, 0, 233, 890,
69, 0, 0, 0, 0, 0, 0, 25, 0, 0, 0, 0, 0, 0, 127, 0, 0, 0, 0,
0, 0, 31, 0, 3144, 0, 0, 0, 0, 0, 200, 0, 0, 0, 0, 0, 0, 15,
0, 0, 0, 0, 1881, 0, 0, 0, 0, 9, 0, 0, 0, 163, 0, 0, 0, 0, 224,
70, 0, 0, 0), comb_S026438.R1 = c(1944, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 22, 0, 0, 0, 0, 0, 0, 9, 30, 0, 0, 79, 0, 0, 0, 0,
0, 0, 0, 0, 57, 0, 0, 0, 0, 0, 876, 0, 0, 0, 0, 0, 0, 0, 0, 41,
0, 0, 0, 0, 0, 0, 789, 0, 0, 0, 0, 0, 197, 814, 18, 253, 0, 0,
0, 0, 0, 210, 0, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 60, 0, 0, 0,
0, 0, 0, 0, 0, 623, 0, 0, 0, 0, 474, 556, 0, 0, 0), comb_S026440.R1 = c(1955,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 108, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 148, 0, 0, 0, 438, 1653,
65, 0, 0, 0, 0, 0, 0, 101, 0, 0, 0, 0, 0, 0, 103, 0, 0, 0, 0,
0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 1954, 0, 16, 0, 0, 0, 0, 0, 0,
0, 0, 0, 224, 0, 0, 0, 0, 0, 0, 0, 0, 220, 0, 0, 0, 0, 0, 30,
0, 0, 0), comb_S026444.R1 = c(3372, 0, 0, 11, 0, 0, 0, 0, 100,
0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 18, 0, 0, 0, 14, 0, 0, 0, 0, 0,
0, 0, 0, 0, 9, 251, 0, 0, 0, 0, 19, 0, 0, 0, 0, 0, 0, 38, 19,
26, 0, 0, 0, 0, 0, 1106, 0, 0, 0, 0, 0, 22, 94, 0, 1428, 0, 0,
0, 0, 0, 2669, 0, 31, 15, 0, 0, 0, 0, 0, 0, 0, 0, 526, 0, 0,
0, 0, 86, 0, 0, 0, 58, 0, 0, 0, 0, 0, 541, 0, 0, 0), comb_S026447.R1 = c(0,
0, 0, 0, 34, 17, 0, 0, 15, 0, 0, 0, 102, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1168, 0, 0, 0, 15, 0, 0, 0, 0, 13, 41, 26, 0, 0, 0,
187, 41, 74, 0, 0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 10, 0, 1242, 0, 0, 0, 0, 0, 9354, 0, 39, 0, 0, 0,
0, 0, 0, 0, 0, 0, 464, 0, 0, 0, 0, 36, 0, 0, 0, 91, 0, 0, 0,
0, 112, 79, 91, 0, 0), comb_S026450.R1 = c(0, 564, 0, 0, 10,
0, 0, 0, 0, 8, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 36, 0, 342, 226, 0, 0, 40, 0,
0, 0, 0, 46, 0, 0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 0, 10, 0, 2260,
0, 0, 0, 0, 0, 967, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 123, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), comb_S026451.R1 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 164, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41, 0, 741, 0, 0, 0, 1227, 224,
0, 0, 0, 0, 0, 348, 0, 2118, 0, 0, 0, 0, 0, 0, 2751, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2280, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 382, 0, 0, 1468, 0, 0, 0, 0, 0, 0,
0, 0, 0), comb_S026453.R1 = c(2721, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 31, 53, 0, 0, 0, 0, 0, 0, 149, 0, 0, 0, 0, 0,
0, 0, 8, 0, 0, 739, 0, 13, 0, 193, 67, 0, 0, 0, 0, 0, 0, 0, 104,
0, 0, 0, 0, 0, 0, 153, 0, 0, 0, 0, 0, 0, 77, 0, 2338, 0, 10,
0, 0, 0, 1608, 0, 0, 0, 27, 0, 0, 0, 0, 0, 0, 0, 2144, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), comb_S026456.R1 = c(10365,
0, 0, 0, 147, 21, 0, 0, 0, 0, 0, 0, 0, 35, 0, 0, 0, 0, 0, 0,
0, 0, 0, 585, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 710, 0, 70, 0, 66,
365, 505, 0, 0, 0, 0, 0, 0, 176, 0, 0, 0, 0, 0, 0, 184, 0, 95,
0, 0, 0, 0, 32, 0, 51, 25, 37, 0, 0, 0, 732, 0, 21, 0, 30, 0,
0, 0, 9, 0, 0, 0, 1082, 9, 0, 0, 0, 0, 0, 43, 0, 62, 0, 13, 0,
0, 0, 0, 0, 0, 0), comb_S026457.R1 = c(89, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 456, 10, 0,
0, 0, 0, 0, 0, 0, 35, 253, 0, 0, 0, 0, 31, 8, 548, 0, 0, 0, 0,
0, 0, 27, 27, 0, 0, 0, 0, 0, 231, 0, 0, 0, 0, 0, 0, 0, 0, 319,
0, 0, 0, 0, 0, 6466, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1262, 0,
0, 0, 0, 50, 0, 0, 0, 630, 0, 0, 0, 0, 50, 12, 0, 0, 0), comb_S026458.R1 = c(36,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 0, 0, 0, 0, 0, 21, 13,
0, 0, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 26, 0, 0, 0, 1678, 51,
36, 0, 0, 0, 0, 0, 0, 97, 13, 0, 0, 0, 0, 0, 543, 0, 0, 0, 0,
0, 0, 66, 0, 505, 0, 0, 0, 0, 0, 29, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1193, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 811, 0,
0, 0, 0), comb_S026461.R1 = c(650, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 9, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 8, 0, 0, 28, 0, 0, 0,
0, 0, 9, 40, 829, 0, 0, 0, 834, 34, 16, 0, 0, 0, 0, 0, 0, 490,
0, 0, 0, 0, 0, 0, 100, 0, 75, 0, 0, 0, 19, 0, 0, 100, 0, 0, 0,
0, 0, 1077, 0, 54, 0, 0, 0, 0, 0, 0, 0, 0, 199, 16847, 0, 0,
0, 55, 0, 0, 0, 0, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0), comb_S026462.R1 = c(3645,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 0, 0, 0, 0, 12,
0, 9, 786, 0, 0, 0, 18, 0, 0, 0, 0, 0, 0, 3978, 0, 0, 0, 580,
0, 1341, 0, 0, 0, 0, 0, 0, 112, 0, 0, 0, 0, 0, 0, 283, 0, 0,
0, 0, 0, 0, 80, 0, 561, 36, 17, 0, 0, 0, 1111, 0, 0, 0, 77, 0,
0, 0, 0, 0, 0, 0, 1805, 14, 0, 0, 0, 0, 0, 0, 0, 213, 0, 0, 0,
0, 16, 20, 0, 0, 0), comb_S026463.R1 = c(22, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 33, 1115, 59, 0, 0, 0, 0, 0,
0, 0, 12, 0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0,
0, 0, 0, 0, 468, 153, 0, 0, 0, 0, 0, 93, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 130, 0, 0, 0, 0, 1267, 0, 0, 0, 0), comb_S026464.R1 = c(0,
764, 0, 0, 0, 0, 0, 0, 338, 0, 0, 96, 0, 0, 0, 0, 0, 307, 2313,
0, 0, 0, 0, 91, 0, 0, 44, 0, 0, 0, 127, 463, 12, 37, 0, 13, 186,
0, 35, 21, 41, 0, 0, 136, 0, 0, 0, 1019, 0, 29, 0, 0, 0, 102,
0, 0, 0, 0, 0, 22, 0, 0, 0, 373, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 45, 0, 0, 0, 0, 0, 602, 0, 0, 0, 0, 47, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), comb_S026467.R1 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 484, 0, 0, 118, 0, 0, 0, 0, 0, 0, 0, 2348, 0,
0, 0, 0, 0, 0, 0, 243, 11, 0, 9, 0, 0, 92, 82, 0, 669, 0, 0,
0, 0, 0, 0, 345, 0, 0, 0, 0, 0, 0, 195, 0, 0, 0, 0, 0, 0, 9,
0, 1479, 0, 0, 0, 0, 0, 2210, 32, 27, 0, 0, 0, 0, 0, 0, 0, 0,
0, 974, 0, 0, 23, 0, 0, 0, 0, 0, 12, 0, 0, 0, 9, 0, 0, 0, 0,
0), comb_S026466.R1 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 248, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 2171, 0, 0, 0, 14, 220, 15, 0, 0, 0, 0, 0, 0, 5733, 0, 0,
0, 0, 0, 0, 309, 0, 0, 0, 0, 0, 0, 0, 0, 524, 0, 0, 0, 18, 0,
897, 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, 0, 0, 0, 0, 0), comb_S026469.R1 = c(797, 0,
0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
29, 0, 0, 0, 0, 0, 0, 0, 0, 8, 87, 16, 0, 0, 0, 301, 0, 15, 0,
0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 35, 0, 0, 0, 0, 0, 0, 28,
0, 649, 0, 0, 0, 0, 0, 602, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
251, 0, 0, 0, 0, 132, 0, 0, 0, 225, 0, 0, 0, 0, 0, 684, 0, 0,
0), comb_S026470.R1 = c(30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1402, 0, 35, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0,
0, 0, 0, 53, 0, 0, 0, 0, 0, 0, 40, 10, 16, 0, 0, 70, 0, 0, 3301,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8085, 0, 0, 0, 0, 0, 0, 0, 0,
22, 0, 0, 0, 0, 0, 0, 0, 0, 0), comb_S026471.R1 = c(6519, 0,
0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 34, 0, 0, 0, 12, 0, 0,
0, 94, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 503, 0, 228, 0, 85, 200,
156, 0, 0, 0, 0, 0, 0, 17, 0, 0, 0, 0, 0, 0, 111, 0, 0, 0, 0,
0, 0, 0, 0, 0, 17, 10, 0, 0, 0, 522, 0, 42, 0, 51, 0, 0, 0, 0,
0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 0, 0, 311, 0, 0, 0, 0, 8, 0, 0,
0, 0), comb_S026473.R1 = c(26, 0, 0, 0, 0, 0, 0, 0, 14, 0, 0,
0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 355, 0, 0, 0, 0, 0, 0, 0,
31, 57, 633, 9, 0, 0, 0, 577, 68, 119, 0, 0, 0, 0, 0, 0, 31,
0, 0, 0, 0, 0, 0, 205, 0, 0, 0, 0, 0, 15, 15, 0, 868, 0, 0, 0,
0, 0, 3912, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 447, 0, 0, 0, 0,
0, 0, 0, 0, 140, 0, 0, 0, 0, 778, 1379, 0, 0, 0), comb_S026474.R1 = c(0,
2046, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 54, 0, 0, 0, 0, 0, 672,
0, 0, 0, 0, 338, 0, 0, 14, 0, 0, 0, 0, 159, 168, 0, 0, 0, 55,
218, 0, 0, 12, 0, 0, 0, 98, 0, 0, 262, 0, 0, 0, 0, 0, 0, 0, 0,
0, 53, 0, 0, 0, 0, 0, 319, 0, 0, 0, 0, 0, 179, 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, 0,
0, 0, 0, 71), comb_S026476.R1 = c(1181, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 10, 0, 0, 1077, 0, 0, 0,
0, 0, 0, 0, 0, 108, 0, 0, 0, 0, 0, 0, 66, 529, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 43, 0, 0, 519, 0, 0, 0, 40, 0, 0, 47, 0, 193,
0, 0, 0, 0, 0, 1435, 0, 0, 0, 0, 0, 0, 99, 0, 47, 0, 29, 167,
32, 58, 0, 0, 0, 0, 0, 0, 1029, 0, 0, 0, 0, 410, 0, 0, 0, 0),
comb_S026477.R1 = c(53, 0, 10, 0, 0, 0, 0, 0, 43, 0, 0, 0,
21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 892, 0, 0, 11, 0, 0, 0,
33, 0, 13, 0, 9, 0, 151, 0, 25, 89, 66, 15, 0, 0, 0, 0, 79,
22, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 172, 0,
0, 0, 0, 0, 177, 780, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 259,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 963, 36, 0, 0, 0),
comb_S026483.R1 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 22, 21, 49, 12, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 134, 0, 0, 0, 68, 1175, 18, 0, 0, 0, 0, 0, 0, 0, 94,
0, 0, 0, 0, 20, 0, 689, 0, 12, 0, 0, 0, 0, 97, 0, 288, 0,
0, 0, 0, 0, 280, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 34, 0, 0,
0, 0, 0, 0, 0, 0, 0, 204, 0, 0, 0, 0, 28, 76, 0, 0, 0), comb_S026484.R1 = c(170,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 302, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0,
153, 166, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52,
0, 0, 0, 0, 0, 0, 21, 0, 750, 0, 0, 0, 0, 0, 8851, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 952, 0, 0, 0, 0, 0, 0, 0, 0, 1330,
0, 0, 0, 0, 33, 1330, 0, 0, 0), comb_S026485.R1 = c(37, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8,
0, 0, 78, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 38, 0, 0, 0, 1570,
57, 69, 14, 0, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 291, 0,
0, 0, 0, 0, 0, 59, 0, 394, 0, 0, 0, 0, 0, 6387, 0, 0, 0,
0, 0, 0, 13, 0, 0, 0, 0, 2119, 31, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 962, 0, 0, 0, 0), comb_S026488.R1 = c(73, 0,
0, 0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 215,
867, 168, 0, 0, 0, 0, 0, 0, 49, 0, 0, 0, 0, 0, 0, 33, 0,
0, 0, 0, 0, 0, 0, 0, 1101, 0, 0, 0, 0, 0, 67, 0, 0, 0, 0,
10, 0, 25, 0, 0, 0, 0, 258, 0, 0, 0, 0, 0, 0, 0, 0, 282,
0, 0, 0, 0, 4219, 0, 0, 0, 0), comb_S026489.R1 = c(25, 17,
0, 0, 0, 0, 0, 0, 62, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 140, 0, 0, 0, 0, 0, 0, 0, 0, 49, 463, 83, 0, 0, 0,
331, 74, 117, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 206,
0, 0, 0, 0, 0, 0, 97, 0, 2031, 0, 0, 0, 0, 0, 227, 0, 0,
16, 0, 0, 0, 0, 0, 0, 0, 0, 396, 0, 0, 0, 0, 0, 0, 0, 0,
616, 0, 0, 0, 0, 4429, 2526, 0, 0, 0), comb_S026490.R1 = c(19,
0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 216, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 0, 0, 0,
24, 552, 333, 0, 0, 0, 0, 0, 0, 291, 0, 0, 0, 0, 0, 0, 18,
0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 3654, 60, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 433, 0, 0, 0, 0, 0, 0, 0, 0, 907,
0, 0, 0, 0, 1561, 0, 0, 38, 0), comb_S026493.R1 = c(3353,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 103, 0, 0, 0,
0, 0, 0, 144, 0, 0, 0, 0, 0, 0, 13, 0, 0, 22, 129, 0, 0,
0, 87, 216, 145, 0, 0, 0, 0, 0, 0, 82, 0, 0, 0, 0, 0, 0,
221, 0, 0, 0, 0, 0, 8, 133, 0, 344, 0, 0, 0, 0, 0, 59, 0,
0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 4523, 0, 8, 0, 0, 49, 0, 0,
0, 150, 0, 0, 0, 0, 0, 38, 0, 0, 0)), row.names = c(NA, 100L
), class = "data.frame")
second dataframe, containing functional classes(subset of first 200 rows):
structure(list(CODE = c("AGOSSP", "ALYALY", "ARABSP", "ARESER",
"BUPAME", "CALLSP", "CENSTO", "CERNUT", "CIRSCA", "CLAPER", "CLAPUL",
"CLARUB", "COLGRA", "COLLIN", "COLPAR", "CYNOFF", "DESCSP", "DESSOP",
"DRABSP", "DRAREP", "EPIBRA", "GALAPA", "GALBIF", "GAYHUM", "GENAMA",
"GERBIC", "GERVIS", "HOLUMB", "LACSER", "LAMAMB", "LAPRED", "LAPSQU",
"LOGARV", "MADEXI", "MADISP", "MEDLUP", "MELLIN", "MELOFF", "MICGRA",
"MINRUB", "MONPAR", "MYOSTR", "OROUNI", "ORTTEN", "PHAFRA", "PHALIN",
"POLDOU", "POLMIN", "PULDOU", "THLARV", "TRADUB", "TRIAUR", "VERARV",
"VERTHA", "VERVER", "AVEFAT", "BROARV", "BROBRI", "BROJAP", "BROTEC"
), NameScientific = c("Agoseris sp", "Alyssum alyssoides", "Arabis sp",
"Arenaria serpyllifolia", "Bupleurum americanum", "Callitriche sp",
"Centaurea stoebe", "Cerastium nutans", "Cirsium scariosum",
"Claytonia perfoliata", "Clarkia pulchella", "Claytonia rubra",
"Collomia grandiflora", "Collomia linearis", "Collinsia parviflora",
"Cynoglossum officinale", "Descurainia sp", "Descurainia sophia",
"Draba sp", "Draba reptans", "Epilobium brachycarpum", "Galium aparine",
"Galium bifolium", "Gayophytum humile", "Gentianella amarella",
"Geranium bicknellii", "Geranium viscosissimum", "Holosteum umbellatum",
"Lactuca serriola", "Lamium amplexicaule", "Lappula redowskii",
"Lappula squarrosa", "Logfia arvensis", "Madia exigua", "Madia sp",
"Medicago lupulina", "Melampyrum lineare", "Melilotus officinalis",
"Microsteris gracilis", "Minuartia rubella", "Montia parvifolia",
"Myosotis stricta", "Orobanche uniflora", "Orthocarpus tenuifolius",
"Phacelia franklinii", "Phacelia linearis", "Polygonum douglasii",
"Polygonum minimum", "Polygonum douglasii", "Thlaspi arvense",
"Tragopogon dubius", "Trifolium aureum", "Veronica arvensis",
"Verbascum thapsus", "Veronica verna", "Avena fatua", "Bromus arvensis",
"Bromus briziformis", "Bromus japonicus", "Bromus tectorum"),
Genus = c("Agoseris", "Alyssum", "Arabis", "ARENARIA", "Bupleurum",
"Callitiriche", "Centaurea", "Cerastium", "Cirsium", "Claytonia",
"Clarkia", "Claytonia", "Collomia", "Collomia", "Collinsia",
"Cynoglossum", "Descurainia", "Descurainia", "Draba", "DRABA",
"Epilobium", "Galium", "Galium", "Gayophytum", "GENTIANELLA",
"GERANIUM", "Geranium", "Holosteum", "Lactuca", "Lamium",
"Lappula", "Lappula", "Logfia", "Madia", "MADIA", "Medicago",
"Melampyrum", "Melilotus", "Microsteris", "MINUARTIA", NA,
"Myosotis", "Orobanche", "ORTHOCARPUS", "Phacelia", "Phacelia",
"Polygonum", "POLYGONUM", "Polygonum", "Thlaspi", "Tragopogon",
"Trifolium", "Veronica", "Verbascum", "Veronica", "Avena",
"BROMUS", "Bromus", "Bromus", "Bromus"), Species = c("sp",
"alyssoides", "sp", "SERPYLLIFOLIA", "americanum", "sp",
"stoebe", "nutans", "scariosum", "perfoliata", "pulchella",
"rubra", "grandiflora", "linearis", "parviflora", "officinale",
"sp", "sophia", "sp", "REPTANS", "brachycarpum", "aparine",
"bifolium", "humile", "AMARELLA", "BICKNELLII", "viscosissimum",
"umbellatum", "serriola", "amplexicaule", "redowskii", "squarrosa",
"arvensis", "exigua", "SP", "lupulina", "lineare", "officinalis",
"gracilis", "RUBELLA", NA, "stricta", "uniflora", "TENUIFOLIUS",
"franklinii", "linearis", "douglasii", "MINIMUM", "douglasii",
"arvense", "dubius", "aureum", "arvensis", "thapsus", "verna",
"fatua", "ARVENSIS", "briziformis", "japonicus", "tectorum"
), Family = c("Asteraceae", "Brassicaceae", "Brassicaceae",
"Caryophyllaceae", "Apiaceae", "Callitrichaceae", "Asteraceae",
"Caryophyllaceae", "Asteraceae", "Montiaceae", "Onagraceae",
"Montiaceae", "Polemoniaceae", "Polemoniaceae", "Plantaginaceae",
"Boraginaceae", "Brassicaceae", "Brassicaceae", "Brassicaceae",
"Brassicaceae", "Onagraceae", "Rubiaceae", "Rubiaceae", "Onagraceae",
"Gentianaceae", "Gerianaceae", "Geraniaceae", "Caryophyllaceae",
"Asteraceae", "Lamiaceae", "Boraginaceae", "Boraginaceae",
"Asteraceae", "Asteraceae", "Asteraceae", "Fabaceae", "Orobanchaceae",
"Fabaceae", "Polemoniaceae", "Caryophyllaceae", NA, "Boraginaceae",
"Orobanchaceae", "Scrophulariaceae", "Hydrophyllaceae", "Hydrophyllaceae",
"Polygonaceae", "Polygonaceae", "Polygonaceae", "Brassicaceae",
"Asteraceae", "Fabaceae", "Plantaginaceae", "Scrophulariaceae",
"Plantaginaceae", "Poaceae", "Poaceae", "Poaceae", "Poaceae",
"Poaceae"), Form = c("Forb", "Forb", "Forb", "Forb", "Forb",
"Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb",
"Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb",
"Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb",
"Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb",
"Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb",
"Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb", "Forb",
"Forb", "Forb", "Graminoid", "Graminoid", "Graminoid", "Graminoid",
"Graminoid"), LifeHistory = c("Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual", "Annual", "Annual", "Annual",
"Annual", "Annual", "Annual"), Origin = c("Native", "Exotic",
"Native", "Exotic", "Native", "UNK", "Exotic", "Native",
"Native", "Native", "Native", "Native", "Native", "Native",
"Native", "Exotic", "UNK", "Exotic", "Native", "Native",
"Native", "Native", "Native", "Native", "Native", "Native",
"Native", "Exotic", "Exotic", "Exotic", "Native", "Exotic",
"Exotic", "Native", "Native", "Exotic", "Native", "Exotic",
"Native", "Native", "Native", "Exotic", "Native", "Native",
"Native", "Native", "Native", "Native", "Native", "Exotic",
"Exotic", "Exotic", "Exotic", "Exotic", "Exotic", "Exotic",
"Exotic", "Exotic", "Exotic", "Exotic"), C_Value = c(NA,
"0", NA, "0", "5", NA, "0", "4", "5", "3", "4", "4", NA,
"4", "3", "0", NA, "0", NA, NA, "4", "3", NA, NA, "4", "3",
"4", "0", "0", "1", NA, "1", "0", NA, NA, "0", "6", "0",
"3", "5", "5", "0", "4", "4", NA, "3", "4", "5", "4", "0",
"0", "0", "0", "0", "1", "1", "1", "1", "0", "0"), X = c("",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "")), row.names = c(NA,
60L), class = "data.frame")
I'm very new to r- i've been trying to figure out if some part of fuzzyjoin could work, also using ore simple %in% to try and merge... but generally pretty lost

The following code merges the two data.frames by SPP and Genus after splitting SPP by the separator "OR" and creating a temporary data set with one row per unique value of SPP.
It uses packages dplyr and tidyr.
library(dplyr)
spp <- strsplit(df1$SPP, "OR")
spp <- lapply(spp, trimws)
spp_len <- sapply(spp, length)
new_row <- rep(NA_character_, max(spp_len))
names(new_row) <- sprintf("SPP_%d", seq_len(max(spp_len)))
result <- t(mapply(\(x, n) {
if(length(x)) new_row[1:n] <- x
new_row
}, spp, spp_len)) %>%
as.data.frame()
rm(new_row)
result <- result %>%
bind_cols(df1[-1]) %>%
tidyr::pivot_longer(starts_with("SPP"), values_to = "SPP") |>
select(-name) %>%
relocate(SPP) %>%
tidyr::drop_na() %>%
left_join(df2, by = c("SPP" = "Genus"))
str(result)

cross product calcuation by store level in r

I have the following data (Which is a slight adaptation of an earlier question):
The dimensions are: [1] 131 46
I am trying to create a list of chain by chain product sales.
# A tibble: 6 x 46
L5 ` Chain100` ` Chain103` ` Chain104` ` Chain106` ` Chain109` ` Chain15 ` ` Chain17 ` ` Chain19 ` ` Chain20 `
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ABBE~ 0 0 0 0 0 0 0 0 0
2 ANHE~ 0 0 0 0 0 0 0 0 0
3 ANHE~ 0 0 0 0 0 0 0 0 0
4 B E 0 0 0 0 0 0 0 0 0
5 BACA~ 124 339 16 32 0 0 0 0 0
6 BACA~ 0 0 0 0 0 0 0 0 0
# ... with 36 more variables: ` Chain23 ` <dbl>, ` Chain25 ` <dbl>, ` Chain42 ` <dbl>, ` Chain43 ` <dbl>, ` Chain44
# ` <dbl>, ` Chain47 ` <dbl>, ` Chain48 ` <dbl>, ` Chain49 ` <dbl>, ` Chain50 ` <dbl>, ` Chain52 ` <dbl>, `
# Chain54 ` <dbl>, ` Chain57 ` <dbl>, ` Chain60 ` <dbl>, ` Chain61 ` <dbl>, ` Chain62 ` <dbl>, ` Chain65 ` <dbl>,
# ` Chain66 ` <dbl>, ` Chain67 ` <dbl>, ` Chain68 ` <dbl>, ` Chain70 ` <dbl>, ` Chain71 ` <dbl>, ` Chain72
# ` <dbl>, ` Chain75 ` <dbl>, ` Chain8 ` <dbl>, ` Chain80 ` <dbl>, ` Chain82 ` <dbl>, ` Chain83 ` <dbl>, ` Chain84
# ` <dbl>, ` Chain87 ` <dbl>, ` Chain88 ` <dbl>, ` Chain89 ` <dbl>, ` Chain90 ` <dbl>, ` Chain91 ` <dbl>, `
# Chain93 ` <dbl>, ` Chain96 ` <dbl>, ` Chain99 ` <dbl>
The data looks like the above. So I am interested in trying to create multiple lists for each product. That is chain100 and chain103 both sold BACARDI SILVER (along with a few other chains).
What I am trying to do is to make multiple lists which look like the following:
Chain1 Chain2 Chain3 Chain4
Chain1 -
BACARDI SILVER Chain2 234 -
Chain3 72 541 -
Chain4 0 231 0 -
So for each row or product there will be multiple lists (comparing the cross product purchases in each "chain").
Data:
structure(list(L5 = structure(1:131, .Label = c("ABBEY DE LEFFE BLONDE PALE AL",
"ANHEUSER WORLD LAGER", "ANHEUSER WORLD SELECT", "B E", "BACARDI SILVER",
"BACARDI SILVER LIMON", "BACARDI SILVER MOJITO", "BACARDI SILVER MOJITO PARTY P",
"BACARDI SILVER O3", "BACARDI SILVER RAZ", "BACARDI SILVER SAMPLER",
"BACARDI SILVER SIGNATURE", "BASS PALE ALE", "BEACH BUM BLONDE ALE",
"BECKS", "BECKS DARK", "BECKS OKTOBERFEST", "BECKS PREMIER LIGHT",
"BEST OF BELGIUM", "BODDINGTONS PUB CREAM ALE", "BREWMASTERS PRIVATE RESERVE",
"BUD ICE", "BUD ICE LIGHT", "BUD LIGHT", "BUD LIGHT CHELADA",
"BUD LIGHT GOLDEN WHEAT", "BUD LIGHT LIME", "BUD LIGHT LIME LIME A RITA",
"BUD LIGHT PARTY PACK", "BUD LIGHT PLATINUM LAGER", "BUDWEISER",
"BUDWEISER AMERICAN ALE", "BUDWEISER BREWMASTERS PROJECT", "BUDWEISER CHELADA",
"BUDWEISER DRY", "BUDWEISER HAPPY HOLIDAYS", "BUDWEISER ICE DRAFT",
"BUDWEISER MILLENNIUM", "BUDWEISER SELECT", "BUDWEISER SELECT 55",
"BUSCH", "BUSCH ICE", "BUSCH LIGHT", "BUSCH NA", "CZECHVAR",
"DOC OTIS HARD LEMON", "GOOSE ISLAND 312 URBAN WHEAT", "GOOSE ISLAND CHRISTMAS ALE",
"GOOSE ISLAND HONKERS ALE", "GOOSE ISLAND IPA", "GOOSE ISLAND SEASONAL",
"GROLSCH AMBER ALE", "GROLSCH LAGER", "GROLSCH LIGHT LAGER",
"GROLSCH SUMMER BLONDE", "HAAKE BECK NA", "HARBIN LAGER", "HOEGAARDEN WHITE ALE",
"HURRICANE HIGH GRAVITY", "HURRICANE HIGH GRAVITY LAGER", "HURRICANE MALT LIQUOR",
"JACKS PUMPKIN SPICE ALE", "KILLARNEYS RED LAGER", "KING COBRA",
"KIRIN ICHIBAN", "KIRIN ICHIBAN LAGER", "KIRIN LAGER", "KIRIN LIGHT",
"LANDSHARK LAGER", "LOWENBRAU", "MARGARITAVILLE", "MARGARITAVILLE 5 O CLOCK",
"MICHELOB", "MICHELOB AMBER BOCK", "MICHELOB BLACK AND TAN",
"MICHELOB DRY", "MICHELOB DUNKEL WEISSE", "MICHELOB GOLDEN DRAFT",
"MICHELOB GOLDEN DRAFT LIGHT", "MICHELOB HEFEWEIZEN", "MICHELOB HONEY LAGER",
"MICHELOB IRISH RED ALE", "MICHELOB LIGHT", "MICHELOB MARZEN",
"MICHELOB PALE ALE", "MICHELOB SEASONAL", "MICHELOB SPCLTY ALES & LGRS W",
"MICHELOB SPECIALTY ALES & LAG", "MICHELOB ULTR POMEGRANAT RSPB",
"MICHELOB ULTR TUSCN ORNG GRAP", "MICHELOB ULTRA", "MICHELOB ULTRA AMBER",
"MICHELOB ULTRA DRAGON FRUIT P", "MICHELOB ULTRA FRUIT SAMPLER",
"MICHELOB ULTRA LIGHT", "MICHELOB ULTRA LIME CACTUS", "NATTY DADDY LAGER",
"NATURAL ICE", "NATURAL LIGHT", "ODOULS", "ODOULS AMBER", "PEELS",
"RED WOLF", "REDBRIDGE", "ROCK GREEN LIGHT", "ROCK LIGHT", "ROLLING ROCK EXTRA PALE",
"ROLLING ROCK LIGHT", "SAINT PAULI GIRL", "SAINT PAULI GIRL DARK",
"SAINT PAULI N A", "SHADOWS WILD BLACK LAGER", "SHOCK TOP BELGIAN WHITE ALE",
"SHOCK TOP PUMPKIN WHEAT", "SHOCK TOP RASPBERRY WHEAT ALE", "SHOCK TOP SEASONAL",
"SHOCK TOP VARIETY PACK", "SHOCK TOP WHEAT IPA", "SPRING HEAT SPICED WHEAT",
"STELLA ARTOIS LAGER", "STONE MILL", "TAKE 6 HOME", "TEQUIZA",
"TIGER LAGER", "TILT", "TILT 8 PERCENT", "ULTRA 19TH HOLE", "WHITBREAD TRADITIONAL PALE AL",
"WILD BLUE", "WILD HOP", "WILD HOP ORGANIC LAGER"), class = "factor"),
` Chain100` = c(0, 0, 0, 0, 124, 0, 0, 0, 45, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 208, 154, 1053, 0, 0, 0, 0, 0, 0,
1046, 0, 0, 0, 0, 0, 0, 0, 0, 0, 661, 1, 585, 64, 0, 41,
0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0,
0, 0, 0, 0, 0, 0, 0, 180, 127, 27, 0, 0, 0, 0, 31, 63, 0,
361, 0, 0, 0, 9, 0, 0, 0, 241, 0, 0, 0, 0, 0, 0, 233, 508,
146, 45, 0, 0, 0, 0, 0, 256, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 52, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain103` = c(0,
0, 0, 0, 339, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 530, 284, 2309, 0, 0, 0, 0, 0, 0, 2252, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1464, 3, 1391, 171, 0, 157, 0, 0, 0, 0, 0, 3,
47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 85, 0, 0, 0, 0, 0, 0,
0, 0, 422, 249, 91, 0, 0, 1, 6, 43, 131, 0, 853, 0, 0, 0,
18, 0, 0, 0, 138, 0, 0, 0, 0, 0, 0, 401, 1188, 375, 113,
0, 0, 0, 0, 0, 587, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 158, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain104` = c(0, 0,
0, 0, 16, 0, 0, 0, 33, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 91, 17, 336, 0, 0, 0, 0, 0, 0, 312, 0, 0, 0, 0, 0, 0,
0, 0, 0, 238, 5, 295, 38, 0, 3, 0, 0, 0, 0, 0, 0, 4, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 53, 25,
14, 0, 0, 0, 0, 0, 3, 0, 104, 0, 0, 0, 0, 0, 0, 0, 99, 0,
0, 0, 0, 0, 0, 16, 154, 16, 0, 0, 0, 0, 0, 0, 49, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), ` Chain106` = c(0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 633, 266, 2230, 0, 0, 0, 0,
0, 0, 2115, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1540, 103, 1561, 190,
0, 194, 0, 0, 0, 0, 0, 7, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0,
77, 83, 0, 0, 0, 0, 0, 0, 0, 0, 528, 225, 112, 0, 0, 74,
168, 43, 113, 0, 865, 0, 0, 0, 5, 0, 0, 0, 20, 0, 0, 0, 0,
0, 0, 299, 1236, 373, 116, 0, 0, 0, 0, 0, 501, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 208, 0, 0, 0, 0, 0, 0, 0,
0), ` Chain109` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 104, 36, 346, 0, 0, 0, 0, 0, 0, 297,
0, 0, 0, 0, 0, 0, 0, 0, 0, 237, 8, 303, 31, 0, 35, 0, 0,
0, 0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0,
0, 0, 0, 0, 0, 55, 20, 17, 0, 0, 12, 17, 0, 4, 0, 109, 0,
0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 29, 179, 32, 6, 0,
0, 0, 0, 0, 46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 16, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain15 ` = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21,
0, 663, 0, 1, 17, 0, 0, 14, 466, 2, 0, 0, 0, 0, 0, 0, 0,
30, 263, 0, 336, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 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, 0, 0, 0, 0, 0, 0, 135, 0, 0, 0, 0,
0, 0, 55, 129, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain17 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 180, 0, 0, 2, 0, 0, 0, 149, 2, 0, 0, 0, 0, 0, 0,
0, 0, 92, 0, 106, 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, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 0, 0, 0,
0, 0, 0, 38, 50, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain19 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 124, 0, 0, 0, 0, 0, 0, 94, 0, 0, 0, 0, 0, 0, 0,
3, 0, 62, 0, 74, 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, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 34, 0, 0, 0, 0,
0, 0, 0, 27, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain20 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 100, 0, 0, 0, 0, 0, 0, 82, 0, 0, 0, 0, 0, 0, 0,
16, 0, 54, 0, 66, 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, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 11, 0, 0, 0,
0, 0, 0, 6, 22, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain23 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,
0, 5, 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, 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, 0, 0, 0, 0, 0,
0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain25 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 0, 86, 0, 0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 0,
0, 0, 37, 0, 14, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,
0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 8, 6, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain42 ` = c(63,
0, 0, 0, 173, 0, 376, 0, 7, 265, 0, 0, 346, 0, 518, 326,
25, 160, 6, 169, 0, 663, 249, 3570, 243, 0, 521, 0, 0, 0,
3382, 87, 0, 147, 0, 2, 0, 0, 1620, 0, 1513, 98, 1655, 319,
12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 48, 5, 157, 0, 37, 0, 6,
0, 52, 65, 0, 0, 12, 489, 66, 0, 0, 393, 650, 0, 0, 19, 0,
0, 0, 52, 0, 990, 96, 23, 150, 51, 20, 286, 105, 1430, 537,
0, 25, 0, 326, 0, 821, 1697, 471, 181, 0, 0, 77, 322, 70,
744, 0, 0, 0, 0, 0, 156, 0, 0, 0, 0, 0, 0, 482, 2, 0, 68,
0, 32, 45, 0, 0, 0, 0, 5), ` Chain43 ` = c(37, 0, 0, 0, 4,
0, 0, 0, 0, 24, 0, 101, 252, 0, 602, 225, 35, 107, 0, 210,
0, 1343, 0, 6191, 279, 244, 2003, 242, 0, 642, 5266, 64,
16, 20, 0, 0, 0, 0, 2755, 2284, 2598, 59, 2992, 566, 30,
0, 205, 6, 36, 96, 39, 0, 0, 0, 0, 31, 0, 327, 18, 43, 0,
0, 0, 188, 15, 6, 0, 14, 1061, 0, 30, 0, 121, 1175, 0, 0,
0, 0, 0, 0, 35, 0, 1069, 3, 0, 54, 21, 0, 424, 0, 2972, 535,
144, 9, 78, 457, 0, 2064, 3224, 845, 431, 0, 0, 455, 0, 29,
1180, 0, 261, 111, 36, 0, 539, 37, 390, 193, 15, 34, 0, 1400,
0, 103, 0, 0, 327, 14, 6, 0, 219, 0, 0), ` Chain44 ` = c(45,
0, 0, 0, 27, 0, 62, 6, 0, 104, 0, 113, 167, 0, 359, 209,
15, 62, 0, 139, 0, 694, 59, 3604, 207, 495, 1092, 0, 24,
0, 3085, 273, 0, 46, 0, 0, 0, 0, 1609, 969, 1377, 81, 1580,
337, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 0, 166, 0, 52,
0, 0, 0, 98, 35, 0, 0, 11, 618, 1, 0, 0, 174, 566, 0, 0,
29, 0, 0, 0, 55, 0, 820, 29, 3, 120, 55, 0, 285, 65, 1430,
371, 138, 0, 0, 284, 0, 909, 1683, 455, 177, 0, 0, 177, 2,
120, 722, 0, 0, 0, 0, 0, 209, 0, 0, 0, 0, 0, 0, 503, 0, 0,
0, 0, 9, 62, 0, 0, 89, 0, 0), ` Chain47 ` = c(48, 0, 0, 0,
117, 0, 314, 20, 0, 247, 0, 29, 261, 0, 477, 276, 8, 108,
0, 145, 0, 698, 219, 3641, 231, 167, 1108, 0, 0, 0, 3272,
368, 0, 89, 0, 0, 0, 0, 1647, 163, 1453, 79, 1662, 343, 23,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 1, 154, 0, 51, 0, 0, 0,
62, 36, 0, 0, 4, 613, 33, 0, 0, 239, 632, 0, 0, 100, 0, 0,
0, 126, 32, 921, 87, 69, 135, 185, 0, 298, 173, 1440, 496,
0, 65, 0, 320, 0, 843, 1715, 457, 185, 0, 0, 73, 2, 271,
737, 0, 0, 0, 0, 0, 240, 0, 0, 0, 0, 0, 0, 455, 0, 0, 1,
0, 24, 77, 0, 0, 0, 0, 0), ` Chain48 ` = c(46, 38, 0, 71,
631, 0, 137, 0, 287, 476, 0, 0, 315, 44, 473, 280, 29, 180,
12, 137, 0, 1241, 838, 5938, 0, 0, 0, 0, 0, 0, 5550, 0, 0,
0, 47, 28, 0, 0, 3154, 0, 2664, 69, 2973, 602, 8, 0, 0, 0,
0, 0, 0, 0, 484, 15, 38, 45, 27, 133, 0, 0, 0, 54, 0, 105,
70, 0, 0, 19, 0, 141, 0, 0, 917, 1129, 0, 0, 0, 0, 0, 0,
102, 0, 1883, 22, 0, 0, 0, 63, 33, 25, 2355, 958, 0, 0, 0,
58, 0, 1243, 2922, 877, 349, 374, 0, 61, 401, 0, 1251, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 333, 102, 0, 163, 39, 158,
11, 0, 0, 0, 96, 0), ` Chain49 ` = c(4, 30, 37, 67, 721,
0, 29, 0, 376, 452, 2, 0, 44, 8, 25, 39, 0, 0, 0, 13, 0,
853, 490, 3820, 0, 0, 0, 0, 0, 0, 3716, 0, 0, 0, 67, 11,
0, 0, 1632, 0, 1974, 0, 2066, 390, 0, 0, 0, 0, 0, 0, 0, 0,
252, 0, 8, 0, 0, 18, 0, 0, 0, 1, 0, 52, 5, 0, 0, 0, 0, 0,
0, 0, 710, 792, 0, 0, 0, 0, 0, 0, 95, 0, 1363, 0, 0, 0, 0,
22, 0, 0, 1717, 95, 0, 0, 0, 0, 0, 874, 1979, 606, 180, 143,
0, 6, 41, 0, 833, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 46,
13, 0, 90, 0, 17, 0, 0, 0, 0, 19, 0), ` Chain50 ` = c(0,
0, 126, 0, 357, 133, 0, 0, 453, 415, 25, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 732, 523, 2985, 0, 0, 0, 0, 0, 0, 2927, 0, 0,
0, 83, 0, 1, 0, 0, 0, 1722, 0, 1767, 413, 0, 14, 0, 0, 0,
0, 0, 0, 188, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 28, 0, 3,
0, 0, 0, 0, 0, 687, 629, 6, 0, 0, 0, 0, 8, 139, 0, 1106,
0, 0, 0, 0, 15, 0, 0, 1387, 0, 0, 0, 0, 0, 0, 706, 1660,
475, 221, 0, 0, 0, 0, 0, 667, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 182, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain52 ` = c(0,
0, 0, 0, 450, 0, 0, 0, 440, 122, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 699, 518, 2710, 0, 0, 0, 0, 0, 0, 2670, 0, 0, 0,
67, 0, 0, 0, 0, 0, 1650, 8, 1686, 417, 0, 186, 0, 0, 0, 0,
0, 0, 155, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 14, 0, 21, 0,
0, 0, 0, 0, 726, 572, 194, 0, 0, 0, 0, 99, 199, 0, 1032,
0, 0, 0, 17, 24, 0, 0, 932, 0, 0, 0, 0, 0, 0, 729, 1550,
459, 215, 0, 6, 0, 0, 0, 685, 4, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 244, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain54 ` = c(0,
0, 0, 0, 418, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 672, 484, 2349, 0, 0, 0, 0, 0, 0, 2232, 0, 0, 0, 72, 0,
0, 0, 0, 0, 1411, 38, 1381, 399, 0, 241, 0, 0, 0, 0, 0, 15,
151, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 52, 3, 0, 27, 0, 0, 0,
0, 0, 648, 405, 172, 1, 0, 7, 27, 139, 194, 0, 966, 0, 0,
0, 42, 0, 0, 0, 177, 0, 0, 0, 0, 0, 0, 639, 1293, 432, 264,
0, 52, 0, 0, 0, 633, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 307, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain57 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 552, 346, 1586, 0, 0, 0, 0, 0, 0, 1627, 0, 0, 0, 74, 0,
0, 1, 0, 0, 1037, 37, 1060, 311, 0, 191, 0, 0, 0, 0, 0, 29,
168, 0, 0, 0, 0, 0, 0, 0, 13, 0, 116, 49, 0, 0, 37, 0, 0,
0, 0, 0, 482, 310, 111, 0, 0, 75, 304, 96, 151, 0, 685, 0,
0, 0, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 424, 899, 296, 199,
0, 50, 0, 0, 0, 457, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 258, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain60 ` = c(21,
0, 0, 0, 9, 0, 359, 7, 0, 169, 13, 0, 236, 0, 327, 205, 8,
75, 0, 86, 0, 296, 70, 1707, 71, 0, 333, 0, 0, 0, 1649, 61,
0, 55, 0, 0, 0, 0, 954, 0, 700, 0, 873, 239, 12, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 89, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 389, 0, 0, 0, 184, 468, 0, 0, 26, 0, 0, 0, 0, 0, 549,
26, 14, 64, 28, 1, 120, 0, 1066, 420, 0, 0, 0, 121, 0, 319,
662, 419, 41, 1, 0, 41, 147, 51, 483, 0, 0, 0, 0, 0, 94,
0, 0, 0, 0, 0, 0, 247, 42, 0, 0, 0, 49, 0, 0, 0, 116, 0,
11), ` Chain61 ` = c(0, 0, 0, 0, 0, 0, 88, 0, 0, 0, 0, 22,
135, 0, 262, 98, 0, 58, 0, 113, 0, 426, 3, 1695, 0, 79, 641,
50, 0, 152, 1564, 47, 0, 0, 0, 0, 0, 0, 858, 794, 796, 0,
911, 203, 0, 0, 66, 0, 31, 48, 23, 0, 0, 0, 0, 0, 0, 106,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 354, 0, 49, 11, 125, 347, 0,
0, 0, 0, 0, 0, 0, 0, 456, 0, 0, 5, 6, 0, 192, 0, 909, 286,
0, 0, 51, 185, 0, 460, 672, 378, 178, 0, 0, 139, 0, 23, 264,
7, 92, 31, 0, 23, 198, 0, 138, 48, 13, 0, 0, 395, 0, 0, 0,
0, 0, 0, 0, 0, 89, 0, 0), ` Chain62 ` = c(41, 0, 0, 0, 35,
0, 176, 7, 0, 23, 0, 35, 167, 0, 232, 143, 0, 92, 0, 85,
0, 254, 21, 1464, 25, 245, 596, 0, 25, 0, 1348, 196, 0, 23,
0, 0, 0, 0, 788, 537, 608, 0, 703, 212, 11, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 101, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 358,
0, 0, 0, 141, 362, 0, 0, 7, 0, 0, 0, 0, 0, 392, 23, 6, 45,
40, 0, 183, 0, 822, 279, 0, 0, 0, 179, 0, 282, 562, 353,
101, 0, 0, 46, 0, 72, 367, 0, 0, 0, 0, 0, 181, 0, 0, 0, 0,
0, 0, 264, 8, 0, 0, 0, 5, 0, 0, 0, 170, 0, 0), ` Chain65 ` = c(58,
0, 0, 3, 7, 0, 382, 20, 18, 258, 0, 0, 530, 0, 691, 473,
0, 138, 0, 184, 0, 984, 280, 5247, 60, 58, 682, 0, 0, 0,
5073, 307, 0, 63, 0, 6, 0, 0, 2635, 95, 2176, 1, 2711, 734,
23, 0, 0, 0, 0, 0, 0, 0, 381, 0, 0, 0, 0, 164, 0, 0, 0, 5,
0, 0, 0, 0, 0, 0, 453, 0, 0, 0, 588, 1419, 0, 0, 40, 0, 0,
0, 27, 0, 1623, 70, 35, 75, 109, 58, 230, 0, 2989, 1200,
0, 0, 0, 229, 0, 1028, 2053, 1278, 130, 546, 0, 96, 180,
124, 1404, 0, 0, 0, 0, 0, 207, 0, 0, 0, 0, 0, 0, 520, 64,
0, 0, 0, 90, 0, 0, 0, 213, 28, 0), ` Chain66 ` = c(0, 0,
0, 48, 193, 0, 43, 1, 121, 290, 9, 13, 5, 0, 4, 9, 0, 0,
0, 0, 5, 647, 258, 2806, 54, 8, 188, 0, 0, 0, 2638, 20, 0,
0, 1, 1, 0, 0, 691, 61, 1478, 42, 1653, 322, 0, 0, 0, 0,
0, 0, 0, 0, 242, 0, 0, 0, 0, 0, 0, 83, 0, 0, 0, 10, 0, 0,
0, 2, 113, 0, 26, 0, 427, 573, 0, 0, 0, 0, 0, 0, 87, 0, 767,
0, 0, 4, 0, 27, 49, 0, 1236, 31, 18, 4, 0, 63, 0, 560, 1041,
470, 48, 0, 0, 0, 13, 0, 564, 0, 0, 0, 0, 0, 12, 12, 14,
0, 0, 0, 0, 29, 0, 0, 0, 0, 2, 45, 0, 13, 13, 0, 0), ` Chain67 ` = c(0,
0, 0, 0, 180, 0, 0, 0, 336, 224, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 617, 308, 2310, 0, 0, 100, 44, 0, 80, 2135, 0, 0,
0, 1, 0, 0, 0, 64, 48, 1227, 23, 1354, 261, 0, 8, 2, 3, 1,
1, 0, 0, 221, 0, 0, 1, 0, 0, 49, 0, 0, 0, 0, 0, 0, 0, 0,
0, 33, 0, 8, 0, 514, 512, 6, 0, 0, 5, 0, 0, 62, 0, 753, 0,
0, 0, 0, 4, 21, 0, 1091, 0, 4, 0, 14, 25, 0, 433, 921, 458,
46, 0, 0, 0, 0, 0, 509, 0, 0, 0, 0, 0, 9, 0, 1, 31, 7, 0,
0, 18, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain68 ` = c(0,
0, 0, 0, 25, 0, 18, 7, 3, 27, 0, 22, 0, 0, 0, 0, 0, 0, 0,
0, 0, 68, 10, 470, 33, 23, 132, 0, 2, 0, 387, 19, 0, 0, 0,
0, 0, 0, 65, 64, 282, 18, 298, 75, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 35, 0, 0,
0, 0, 33, 0, 0, 2, 0, 0, 0, 0, 0, 58, 5, 0, 18, 0, 0, 21,
1, 173, 0, 16, 0, 0, 35, 0, 58, 126, 24, 0, 0, 0, 0, 0, 3,
52, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 2,
34, 0, 0, 0, 0, 0), ` Chain70 ` = c(0, 0, 0, 0, 356, 0, 0,
0, 303, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 422, 319, 1508,
0, 0, 0, 0, 0, 0, 1429, 0, 0, 0, 1, 0, 0, 0, 0, 0, 884, 0,
943, 192, 0, 123, 0, 0, 0, 0, 0, 0, 126, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 548, 387, 122, 0,
0, 0, 0, 0, 103, 0, 673, 0, 0, 0, 3, 0, 0, 0, 551, 0, 0,
0, 0, 0, 0, 251, 716, 393, 123, 0, 0, 0, 0, 0, 390, 35, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 191, 0, 0, 0, 0, 0,
0, 0, 0), ` Chain71 ` = c(0, 0, 0, 0, 54, 0, 20, 2, 14, 26,
0, 6, 0, 0, 0, 0, 3, 5, 0, 0, 0, 80, 26, 474, 30, 9, 111,
0, 0, 0, 377, 39, 0, 0, 0, 0, 0, 0, 64, 10, 277, 27, 285,
56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41, 0, 0,
0, 0, 0, 0, 0, 0, 57, 0, 0, 0, 2, 5, 0, 0, 15, 0, 0, 0, 0,
11, 54, 13, 3, 20, 0, 4, 17, 9, 155, 2, 0, 10, 0, 32, 0,
80, 127, 26, 0, 0, 0, 0, 0, 12, 49, 0, 0, 0, 0, 0, 3, 0,
0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 12, 35, 0, 0, 1, 0, 0), ` Chain72 ` = c(0,
0, 0, 0, 244, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 315, 243, 1039, 0, 0, 0, 0, 0, 0, 1020, 0, 0, 0, 0, 0,
0, 0, 0, 0, 688, 0, 781, 155, 0, 135, 0, 0, 0, 0, 0, 0, 81,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0,
429, 257, 93, 0, 0, 14, 13, 0, 84, 0, 491, 0, 0, 0, 16, 0,
0, 0, 72, 0, 0, 0, 0, 0, 0, 169, 659, 297, 136, 0, 0, 0,
0, 0, 247, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 196,
0, 0, 0, 0, 0, 0, 0, 0), ` Chain75 ` = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 332, 230, 818,
0, 0, 0, 0, 0, 0, 805, 0, 0, 0, 0, 0, 0, 0, 0, 0, 731, 0,
708, 152, 0, 173, 0, 0, 0, 0, 0, 0, 114, 0, 0, 0, 0, 0, 0,
0, 0, 0, 71, 0, 0, 0, 0, 0, 0, 0, 0, 0, 423, 241, 123, 0,
0, 92, 139, 0, 121, 0, 467, 0, 0, 0, 19, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 150, 643, 262, 127, 0, 0, 0, 0, 0, 264, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 220, 0, 0, 0, 0, 0,
0, 0, 0), ` Chain8 ` = c(0, 0, 0, 11, 72, 15, 0, 0, 97,
43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 202, 131, 959, 0, 0,
0, 0, 0, 0, 919, 0, 0, 0, 0, 0, 0, 0, 55, 0, 519, 0, 495,
78, 0, 44, 0, 0, 0, 0, 0, 0, 44, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 219, 124, 41, 0, 0, 0, 0,
0, 46, 0, 414, 0, 0, 0, 3, 4, 0, 0, 428, 0, 0, 0, 0, 0, 0,
247, 482, 167, 75, 0, 0, 0, 0, 0, 243, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 54, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain80 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0,
0, 53, 0, 573, 0, 2, 58, 0, 0, 0, 312, 0, 0, 0, 0, 0, 0,
0, 3, 1, 201, 0, 247, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 88, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 26, 0, 0,
0, 0, 0, 0, 12, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), ` Chain82 ` = c(0, 0, 0, 0, 28, 0, 0, 0, 41, 11, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 42, 218, 0, 0, 0, 0, 0, 0,
207, 0, 0, 0, 0, 0, 0, 0, 0, 0, 108, 0, 208, 0, 0, 34, 0,
0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 59, 47, 0, 0, 0, 0, 0, 0, 0, 0, 105, 0,
0, 0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 0, 104, 65, 0, 0,
0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 14, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain83 ` = c(0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 200,
0, 1151, 4, 0, 92, 0, 0, 43, 696, 0, 0, 5, 0, 0, 0, 0, 101,
0, 450, 0, 442, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 181, 0, 0, 0, 0, 21, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 89, 0, 0, 0,
0, 0, 72, 70, 232, 0, 0, 0, 0, 0, 0, 0, 33, 0, 0, 0, 0, 0,
3, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
` Chain84 ` = c(0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 0, 104, 45, 744, 1, 9, 61, 0, 0, 0, 557,
0, 0, 1, 0, 0, 0, 0, 15, 3, 372, 0, 479, 0, 0, 34, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 97, 0, 0, 0,
0, 0, 0, 0, 0, 76, 55, 0, 0, 0, 0, 0, 0, 0, 0, 114, 0, 0,
0, 0, 0, 0, 0, 62, 0, 0, 0, 0, 0, 0, 29, 221, 71, 0, 0, 0,
0, 0, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
23, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain87 ` = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1,
8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0,
8, 0, 0, 1, 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, 0, 3, 3, 0, 0, 0, 0, 0, 0,
0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4,
3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), ` Chain88 ` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0,
0, 57, 0, 558, 1, 7, 41, 0, 0, 0, 395, 0, 0, 2, 0, 0, 0,
0, 45, 0, 219, 0, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 85, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 47, 0, 0,
0, 0, 0, 0, 24, 115, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), ` Chain89 ` = c(0, 0, 0, 0, 1, 0, 0, 0, 1, 3, 0, 0, 5,
0, 6, 0, 0, 0, 0, 0, 0, 147, 0, 843, 0, 0, 0, 0, 0, 0, 717,
0, 0, 0, 0, 0, 0, 0, 88, 0, 450, 0, 317, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 165, 0, 0, 0, 0,
0, 0, 0, 0, 9, 4, 0, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 0, 0,
0, 0, 0, 102, 11, 0, 0, 0, 0, 0, 48, 199, 0, 0, 5, 0, 0,
0, 0, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), ` Chain90 ` = c(0, 0, 0, 0, 8, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 58, 0, 332,
0, 0, 0, 0, 0, 0, 292, 0, 0, 0, 0, 0, 0, 0, 29, 0, 149, 0,
64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 84, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 0, 0, 0,
0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 48, 0, 0, 0, 0, 0, 0, 16,
51, 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, 0, 0, 0, 0, 0, 0, 0), ` Chain91 ` = c(0,
0, 0, 0, 10, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 44, 0, 359, 0, 0, 0, 0, 0, 0, 372, 0, 0, 0, 0, 0, 0, 0,
0, 0, 149, 0, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 107, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0,
0, 0, 0, 0, 0, 0, 0, 0, 27, 0, 0, 0, 0, 0, 0, 0, 52, 0, 0,
0, 0, 0, 0, 22, 87, 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, 0, 0, 0, 0, 0, 0, 0),
` Chain93 ` = c(0, 0, 0, 0, 14, 0, 0, 0, 6, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 50, 0, 354, 0, 0, 0, 0, 0, 0, 411, 0,
0, 0, 0, 0, 0, 0, 0, 0, 202, 0, 60, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 108, 0, 0, 0, 0, 0,
0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 0, 0, 0, 0, 0,
0, 0, 41, 0, 0, 0, 0, 0, 0, 30, 97, 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, 0,
0, 0, 0, 0, 0, 0), ` Chain96 ` = c(0, 0, 0, 0, 20, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 294, 0,
0, 0, 0, 0, 0, 347, 0, 0, 0, 0, 0, 0, 0, 0, 0, 185, 0, 42,
7, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 0,
0, 112, 0, 0, 0, 0, 0, 0, 0, 0, 43, 7, 0, 0, 0, 0, 0, 0,
0, 0, 42, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 28,
55, 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, 0, 0, 0, 0, 0, 0, 0), ` Chain99 ` = c(0,
0, 0, 4, 73, 0, 25, 0, 20, 42, 17, 0, 3, 0, 7, 15, 0, 0,
0, 0, 3, 118, 27, 695, 0, 0, 0, 0, 0, 0, 732, 0, 0, 0, 0,
4, 0, 0, 85, 0, 385, 28, 333, 43, 0, 4, 0, 0, 0, 0, 0, 0,
15, 0, 0, 0, 0, 0, 0, 41, 5, 0, 3, 84, 2, 0, 0, 0, 0, 0,
0, 0, 68, 59, 0, 0, 0, 0, 0, 0, 0, 0, 171, 0, 0, 0, 0, 3,
0, 13, 132, 40, 0, 0, 0, 7, 0, 108, 214, 17, 0, 1, 0, 3,
29, 0, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 1, 0, 0,
0, 0, 34, 0, 0, 0, 1, 0)), row.names = c(NA, -131L), class = c("tbl_df",
"tbl", "data.frame"))

ggplot with two manual color scales

I am trying to reuse some old code which I have used to make two separate plots in past, and would like to pout it together into one now.
However I have few problems
color_var <- vector(mode = "double",length = length(OP_2016$risk))
color_var[color_var== '0']<- NA
color_var[OP_2016$risk>=1 & OP_2016$risk<12] <- "yellow"
color_var[OP_2016$risk>=12] <- "red"
ggplot(OP_2016)+
geom_col(aes(x = short_date, y = risk, color = color_var , group = 1), size= 0.9) +
scale_y_continuous(limits = c(0, 100),name = "Accumulated EBHours")+
scale_color_identity("Risk Level", breaks= levels(as.factor(color_var))[c(1,2)],
labels = c("High >12 EBH","Medium 0-12EBH"),
guide = "legend"
)+
geom_line(aes(x = short_date, y= 12), linetype= "dotted", size = 0.8, colour = "red")+
# scale_color_manual("Varieties", values =c( "British Queen"= "orchid1"))+
geom_line(data = dis_fun_df, aes(x= date, y = rating, colour = "green"))
Problems:
Bars and and boxes in the legend are not filled,
I can not add manual color for geom_line and add it to the legend, that I have added from other plot.
Apologies, data set to reproduce the plot is a bit big.
dis_fun_df <- structure(list(date = structure(c(15534, 15540, 15548, 15555,
15562, 15573, 15580), class = "Date"), rating = c(10.2, 30, 61.6666666666667,
81.6666666666667, 95.8333333333333, 99.1666666666667, 100)), row.names = c(NA,
-7L), class = c("tbl_df", "tbl", "data.frame"))
OP_2016 <- structure(list(date = structure(c(1342224000, 1342227600, 1342231200,
1342234800, 1342238400, 1342242000, 1342245600, 1342249200, 1342252800,
1342256400, 1342260000, 1342263600, 1342267200, 1342270800, 1342274400,
1342278000, 1342281600, 1342285200, 1342288800, 1342292400, 1342296000,
1342299600, 1342303200, 1342306800, 1342310400, 1342314000, 1342317600,
1342321200, 1342324800, 1342328400, 1342332000, 1342335600, 1342339200,
1342342800, 1342346400, 1342350000, 1342353600, 1342357200, 1342360800,
1342364400, 1342368000, 1342371600, 1342375200, 1342378800, 1342382400,
1342386000, 1342389600, 1342393200, 1342396800, 1342400400, 1342404000,
1342407600, 1342411200, 1342414800, 1342418400, 1342422000, 1342425600,
1342429200, 1342432800, 1342436400, 1342440000, 1342443600, 1342447200,
1342450800, 1342454400, 1342458000, 1342461600, 1342465200, 1342468800,
1342472400, 1342476000, 1342479600, 1342483200, 1342486800, 1342490400,
1342494000, 1342497600, 1342501200, 1342504800, 1342508400, 1342512000,
1342515600, 1342519200, 1342522800, 1342526400, 1342530000, 1342533600,
1342537200, 1342540800, 1342544400, 1342548000, 1342551600, 1342555200,
1342558800, 1342562400, 1342566000, 1342569600, 1342573200, 1342576800,
1342580400, 1342584000, 1342587600, 1342591200, 1342594800, 1342598400,
1342602000, 1342605600, 1342609200, 1342612800, 1342616400, 1342620000,
1342623600, 1342627200, 1342630800, 1342634400, 1342638000, 1342641600,
1342645200, 1342648800, 1342652400, 1342656000, 1342659600, 1342663200,
1342666800, 1342670400, 1342674000, 1342677600, 1342681200, 1342684800,
1342688400, 1342692000, 1342695600, 1342699200, 1342702800, 1342706400,
1342710000, 1342713600, 1342717200, 1342720800, 1342724400, 1342728000,
1342731600, 1342735200, 1342738800, 1342742400, 1342746000, 1342749600,
1342753200, 1342756800, 1342760400, 1342764000, 1342767600, 1342771200,
1342774800, 1342778400, 1342782000, 1342785600, 1342789200, 1342792800,
1342796400, 1342800000, 1342803600, 1342807200, 1342810800, 1342814400,
1342818000, 1342821600, 1342825200, 1342828800, 1342832400, 1342836000,
1342839600, 1342843200, 1342846800, 1342850400, 1342854000, 1342857600,
1342861200, 1342864800, 1342868400, 1342872000, 1342875600, 1342879200,
1342882800, 1342886400, 1342890000, 1342893600, 1342897200, 1342900800,
1342904400, 1342908000, 1342911600, 1342915200, 1342918800, 1342922400,
1342926000, 1342929600, 1342933200, 1342936800, 1342940400, 1342944000,
1342947600, 1342951200, 1342954800, 1342958400, 1342962000, 1342965600,
1342969200, 1342972800, 1342976400, 1342980000, 1342983600, 1342987200,
1342990800, 1342994400, 1342998000, 1343001600, 1343005200, 1343008800,
1343012400, 1343016000, 1343019600, 1343023200, 1343026800, 1343030400,
1343034000, 1343037600, 1343041200, 1343044800, 1343048400, 1343052000,
1343055600, 1343059200, 1343062800, 1343066400, 1343070000, 1343073600,
1343077200, 1343080800, 1343084400, 1343088000, 1343091600, 1343095200,
1343098800, 1343102400, 1343106000, 1343109600, 1343113200, 1343116800,
1343120400, 1343124000, 1343127600, 1343131200, 1343134800, 1343138400,
1343142000, 1343145600, 1343149200, 1343152800, 1343156400, 1343160000,
1343163600, 1343167200, 1343170800, 1343174400, 1343178000, 1343181600,
1343185200, 1343188800, 1343192400, 1343196000, 1343199600, 1343203200,
1343206800, 1343210400, 1343214000, 1343217600, 1343221200, 1343224800,
1343228400, 1343232000, 1343235600, 1343239200, 1343242800, 1343246400,
1343250000, 1343253600, 1343257200, 1343260800, 1343264400, 1343268000,
1343271600, 1343275200, 1343278800, 1343282400, 1343286000, 1343289600,
1343293200, 1343296800, 1343300400, 1343304000, 1343307600, 1343311200,
1343314800, 1343318400, 1343322000, 1343325600, 1343329200, 1343332800,
1343336400, 1343340000, 1343343600, 1343347200, 1343350800, 1343354400,
1343358000, 1343361600, 1343365200, 1343368800, 1343372400, 1343376000,
1343379600, 1343383200, 1343386800, 1343390400, 1343394000, 1343397600,
1343401200, 1343404800, 1343408400, 1343412000, 1343415600, 1343419200,
1343422800, 1343426400, 1343430000, 1343433600, 1343437200, 1343440800,
1343444400, 1343448000, 1343451600, 1343455200, 1343458800, 1343462400,
1343466000, 1343469600, 1343473200, 1343476800, 1343480400, 1343484000,
1343487600, 1343491200, 1343494800, 1343498400, 1343502000, 1343505600,
1343509200, 1343512800, 1343516400, 1343520000, 1343523600, 1343527200,
1343530800, 1343534400, 1343538000, 1343541600, 1343545200, 1343548800,
1343552400, 1343556000, 1343559600, 1343563200, 1343566800, 1343570400,
1343574000, 1343577600, 1343581200, 1343584800, 1343588400, 1343592000,
1343595600, 1343599200, 1343602800, 1343606400, 1343610000, 1343613600,
1343617200, 1343620800, 1343624400, 1343628000, 1343631600, 1343635200,
1343638800, 1343642400, 1343646000, 1343649600, 1343653200, 1343656800,
1343660400, 1343664000, 1343667600, 1343671200, 1343674800, 1343678400,
1343682000, 1343685600, 1343689200, 1343692800, 1343696400, 1343700000,
1343703600, 1343707200, 1343710800, 1343714400, 1343718000, 1343721600,
1343725200, 1343728800, 1343732400, 1343736000, 1343739600, 1343743200,
1343746800, 1343750400, 1343754000, 1343757600, 1343761200, 1343764800,
1343768400, 1343772000, 1343775600, 1343779200, 1343782800, 1343786400,
1343790000, 1343793600, 1343797200, 1343800800, 1343804400, 1343808000,
1343811600, 1343815200, 1343818800, 1343822400, 1343826000, 1343829600,
1343833200, 1343836800, 1343840400, 1343844000, 1343847600, 1343851200,
1343854800, 1343858400, 1343862000, 1343865600, 1343869200, 1343872800,
1343876400, 1343880000, 1343883600, 1343887200, 1343890800, 1343894400,
1343898000, 1343901600, 1343905200, 1343908800, 1343912400, 1343916000,
1343919600, 1343923200, 1343926800, 1343930400, 1343934000, 1343937600,
1343941200, 1343944800, 1343948400, 1343952000, 1343955600, 1343959200,
1343962800, 1343966400, 1343970000, 1343973600, 1343977200, 1343980800,
1343984400, 1343988000, 1343991600, 1343995200, 1343998800, 1344002400,
1344006000, 1344009600, 1344013200, 1344016800, 1344020400, 1344024000,
1344027600, 1344031200, 1344034800, 1344038400, 1344042000, 1344045600,
1344049200, 1344052800, 1344056400, 1344060000, 1344063600, 1344067200,
1344070800, 1344074400, 1344078000, 1344081600, 1344085200, 1344088800,
1344092400, 1344096000, 1344099600, 1344103200, 1344106800, 1344110400,
1344114000, 1344117600, 1344121200, 1344124800, 1344128400, 1344132000,
1344135600, 1344139200, 1344142800, 1344146400, 1344150000, 1344153600,
1344157200, 1344160800, 1344164400, 1344168000, 1344171600, 1344175200,
1344178800, 1344182400, 1344186000, 1344189600, 1344193200, 1344196800,
1344200400, 1344204000, 1344207600, 1344211200, 1344214800, 1344218400,
1344222000, 1344225600, 1344229200, 1344232800, 1344236400, 1344240000,
1344243600, 1344247200, 1344250800, 1344254400, 1344258000, 1344261600,
1344265200, 1344268800, 1344272400, 1344276000, 1344279600, 1344283200,
1344286800, 1344290400, 1344294000, 1344297600, 1344301200, 1344304800,
1344308400, 1344312000, 1344315600, 1344319200, 1344322800, 1344326400,
1344330000, 1344333600, 1344337200, 1344340800, 1344344400, 1344348000,
1344351600, 1344355200, 1344358800, 1344362400, 1344366000, 1344369600,
1344373200, 1344376800, 1344380400, 1344384000, 1344387600, 1344391200,
1344394800, 1344398400, 1344402000, 1344405600, 1344409200, 1344412800,
1344416400, 1344420000, 1344423600, 1344427200, 1344430800, 1344434400,
1344438000, 1344441600, 1344445200, 1344448800, 1344452400, 1344456000,
1344459600, 1344463200, 1344466800, 1344470400, 1344474000, 1344477600,
1344481200, 1344484800, 1344488400, 1344492000, 1344495600, 1344499200,
1344502800, 1344506400, 1344510000, 1344513600, 1344517200, 1344520800,
1344524400, 1344528000, 1344531600, 1344535200, 1344538800, 1344542400,
1344546000, 1344549600, 1344553200, 1344556800, 1344560400, 1344564000,
1344567600, 1344571200, 1344574800, 1344578400, 1344582000, 1344585600,
1344589200, 1344592800, 1344596400, 1344600000, 1344603600, 1344607200,
1344610800, 1344614400, 1344618000, 1344621600, 1344625200, 1344628800,
1344632400, 1344636000, 1344639600, 1344643200, 1344646800, 1344650400,
1344654000, 1344657600, 1344661200, 1344664800, 1344668400, 1344672000,
1344675600, 1344679200, 1344682800, 1344686400, 1344690000, 1344693600,
1344697200, 1344700800, 1344704400, 1344708000, 1344711600, 1344715200,
1344718800, 1344722400, 1344726000, 1344729600, 1344733200, 1344736800,
1344740400, 1344744000, 1344747600, 1344751200, 1344754800, 1344758400,
1344762000, 1344765600, 1344769200, 1344772800, 1344776400, 1344780000,
1344783600, 1344787200, 1344790800, 1344794400, 1344798000, 1344801600,
1344805200, 1344808800, 1344812400, 1344816000, 1344819600, 1344823200,
1344826800, 1344830400, 1344834000, 1344837600, 1344841200, 1344844800,
1344848400, 1344852000, 1344855600, 1344859200, 1344862800, 1344866400,
1344870000, 1344873600, 1344877200, 1344880800, 1344884400, 1344888000,
1344891600, 1344895200, 1344898800, 1344902400, 1344906000, 1344909600,
1344913200, 1344916800, 1344920400, 1344924000, 1344927600, 1344931200,
1344934800, 1344938400, 1344942000, 1344945600, 1344949200, 1344952800,
1344956400, 1344960000, 1344963600, 1344967200, 1344970800, 1344974400,
1344978000, 1344981600, 1344985200, 1344988800, 1344992400, 1344996000,
1344999600, 1345003200, 1345006800, 1345010400, 1345014000, 1345017600,
1345021200, 1345024800, 1345028400, 1345032000, 1345035600, 1345039200,
1345042800, 1345046400, 1345050000, 1345053600, 1345057200, 1345060800,
1345064400, 1345068000, 1345071600, 1345075200, 1345078800, 1345082400,
1345086000, 1345089600, 1345093200, 1345096800, 1345100400, 1345104000,
1345107600, 1345111200, 1345114800, 1345118400, 1345122000, 1345125600,
1345129200, 1345132800, 1345136400, 1345140000, 1345143600, 1345147200,
1345150800, 1345154400, 1345158000, 1345161600, 1345165200, 1345168800,
1345172400, 1345176000, 1345179600, 1345183200, 1345186800, 1345190400,
1345194000, 1345197600, 1345201200, 1345204800, 1345208400, 1345212000,
1345215600, 1345219200, 1345222800, 1345226400, 1345230000, 1345233600,
1345237200, 1345240800, 1345244400, 1345248000, 1345251600, 1345255200,
1345258800, 1345262400, 1345266000, 1345269600, 1345273200, 1345276800,
1345280400, 1345284000, 1345287600, 1345291200, 1345294800, 1345298400,
1345302000, 1345305600, 1345309200, 1345312800, 1345316400, 1345320000,
1345323600, 1345327200, 1345330800, 1345334400, 1345338000, 1345341600,
1345345200, 1345348800, 1345352400, 1345356000, 1345359600, 1345363200,
1345366800, 1345370400, 1345374000, 1345377600, 1345381200, 1345384800,
1345388400, 1345392000, 1345395600, 1345399200, 1345402800, 1345406400,
1345410000, 1345413600, 1345417200, 1345420800, 1345424400, 1345428000,
1345431600, 1345435200, 1345438800, 1345442400, 1345446000, 1345449600,
1345453200, 1345456800, 1345460400, 1345464000, 1345467600, 1345471200,
1345474800, 1345478400, 1345482000, 1345485600, 1345489200, 1345492800,
1345496400, 1345500000, 1345503600, 1345507200, 1345510800, 1345514400,
1345518000, 1345521600, 1345525200, 1345528800, 1345532400, 1345536000,
1345539600, 1345543200, 1345546800, 1345550400, 1345554000, 1345557600,
1345561200, 1345564800, 1345568400, 1345572000, 1345575600, 1345579200,
1345582800, 1345586400, 1345590000, 1345593600, 1345597200, 1345600800,
1345604400, 1345608000, 1345611600, 1345615200, 1345618800, 1345622400,
1345626000, 1345629600, 1345633200, 1345636800, 1345640400, 1345644000,
1345647600, 1345651200, 1345654800, 1345658400, 1345662000, 1345665600,
1345669200, 1345672800, 1345676400, 1345680000, 1345683600, 1345687200,
1345690800, 1345694400, 1345698000, 1345701600, 1345705200, 1345708800,
1345712400, 1345716000, 1345719600, 1345723200, 1345726800, 1345730400,
1345734000, 1345737600, 1345741200, 1345744800, 1345748400, 1345752000,
1345755600, 1345759200, 1345762800, 1345766400, 1345770000, 1345773600,
1345777200, 1345780800, 1345784400, 1345788000, 1345791600, 1345795200,
1345798800, 1345802400, 1345806000, 1345809600, 1345813200, 1345816800,
1345820400, 1345824000, 1345827600, 1345831200, 1345834800, 1345838400,
1345842000, 1345845600, 1345849200, 1345852800, 1345856400, 1345860000,
1345863600, 1345867200, 1345870800, 1345874400, 1345878000, 1345881600,
1345885200, 1345888800, 1345892400, 1345896000, 1345899600, 1345903200,
1345906800, 1345910400, 1345914000, 1345917600, 1345921200, 1345924800,
1345928400, 1345932000, 1345935600, 1345939200, 1345942800, 1345946400,
1345950000, 1345953600, 1345957200, 1345960800, 1345964400, 1345968000,
1345971600, 1345975200, 1345978800, 1345982400, 1345986000, 1345989600,
1345993200, 1345996800, 1346000400, 1346004000, 1346007600, 1346011200,
1346014800, 1346018400, 1346022000, 1346025600, 1346029200, 1346032800,
1346036400, 1346040000, 1346043600, 1346047200, 1346050800, 1346054400,
1346058000, 1346061600, 1346065200, 1346068800, 1346072400, 1346076000,
1346079600, 1346083200, 1346086800, 1346090400, 1346094000, 1346097600,
1346101200, 1346104800, 1346108400), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), risk = c(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, 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, 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, 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, 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, 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, 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, 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,
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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 0, 0, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 7,
8, 9, 10, 11, 0, 0, 0, 12, 13, 14, 15, 16, 17, 18, 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,
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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 2, 3, 4, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 1, 2, 3,
4, 5, 6, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 2, 3, 4, 5, 6, 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, 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, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0)), row.names = c(NA, -1080L), class = c("tbl_df",
"tbl", "data.frame"))

I think this might do the trick, using fill instead of colour
OP_2016$date <- as.Date(OP_2016$date)
color_var <- vector(mode = "double",length = length(OP_2016$risk))
color_var[color_var== '0']<- NA
color_var[OP_2016$risk>=1 & OP_2016$risk<12] <- "yellow"
color_var[OP_2016$risk>=12] <- "red"
ggplot(OP_2016)+
geom_col(aes(x = date, y = risk, group = 1,fill=color_var), size= 0.9) +
scale_y_continuous(limits = c(0, 100),name = "Accumulated EBHours")+
scale_fill_identity("Risk Level", breaks= levels(as.factor(color_var))[c(1,2)],
labels = c("High >12 EBH","Medium 0-12EBH"),
guide = "legend"
)+
geom_line(aes(x = date, y= 12), linetype= "dotted", size = 0.8, colour = "red")+
geom_line(data = dis_fun_df, aes(x= date, y = rating),colour = "green")
To my knowledge ggplot does not support multiple scales of the same type, but others would know better than I.
UPDATE:
For anyone looking to actually use multiple scales for the same type of geom the {ggnewscale} package should provide the functionality that you are looking for:
https://github.com/eliocamp/ggnewscale

R cut based on prespecified levels

I am trying to cut my numeric values such that I also get a count for the number of zeros. Not sure how to accomplish that. These are my goals.
1) I specifically get a count of number of zeros.
2) Option to cut the remaining non-zero values into many different
bins.
Right now I tried this below and I cannot get any count of number of zeros.
c1 <- cut(df$Col1, breaks = seq(0, 1442, by = 53.25))
Expected Output
(0] (0,53.2] (53.2,106] (106,160] (160,213] (213,266] (266,320] (320,373] (373,426] (426,479]
1652 1 6 1 34 6 1 1 8 2
(479,532] (532,586] (586,639] (639,692] (692,746] (746,799] (799,852] (852,905] (905,958]
0 0 4 1 0 0 1 0 0
(958,1.01e+03] (1.01e+03,1.06e+03] (1.06e+03,1.12e+03] (1.12e+03,1.17e+03] (1.17e+03,1.22e+03] (1.22e+03,1.28e+03] (1.28e+03,1.33e+03] (1.33e+03,1.38e+03] (1.38e+03,1.44e+03]
0 0 0 0 0 0 0 0 0
dput(df$Col1)
structure(c(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, 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, 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, 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, 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, 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, 0, 198, 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, 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, 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, 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 182.71, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
199, 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, 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, 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, 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, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 445, 0, 0, 176.02, 0, 192,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 198, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 204, 0, 0, 0, 207, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 209, 0, 0, 161.19, 0, 0, 106, 0, 0, 0, 0, 0, 0, 0,
0, 100, 0, 0, 0, 0, 0, 0, 0, 200, 0, 0, 0, 195, 0, 0, 0, 0, 398,
0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 204, 0, 89.37, 0, 0, 0,
0, 0, 0, 194, 0, 0, 0, 0, 212, 0, 0, 0, 0, 212, 211, 0, 402,
219, 0, 0, 244, 194, 0, 183.75, 0, 0, 0, 0, 0, 0, 0, 104, 197,
0, 0, 53.25, 0, 0, 0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 383,
314, 202, 0, 0, 0, 0, 204, 227, 0, 205, 211, 670, 230.39, 0,
0, 110, 801, 595, 0, 0, 0, 438, 0, 397, 203, 209, 0, 209, 0,
258, 0, 0, 213, 0, 201, 174.84, 213, 0, 407, 208, 218, 365.7,
205, 595, 0, 608, 601, 183, 381.56, 421, 1442, 408), label = "Col1", class = c("labelled",
"numeric"))

The ( of (0,53.2] on the left of each bin means an "open-end", meaning values above that boundary. (x is your df$Col1.)
And it looks like you want the table of the cut, so this is the starting point:
head(table(cut(x, breaks = seq(0, 1442, by = 53.25))))
# (0,53.2] (53.2,106] (106,160] (160,213] (213,266] (266,320]
# 1 6 1 34 6 1
Two options. Either use right-closed:
head(table(cut(x, breaks = seq(0, 1442, by = 53.25), right = FALSE)))
# [0,53.2) [53.2,106) [106,160) [160,213) [213,266) [266,320)
# 1652 7 1 32 8 1
(Realize that this will change some of your bin counts, as you can see above.) Or explicitly provide something "to the left" of your first bin:
head(table(cut(x, breaks = c(-Inf, seq(0, 1442, by = 53.25)))))
# (-Inf,0] (0,53.2] (53.2,106] (106,160] (160,213] (213,266]
# 1652 1 6 1 34 6
This retains the original bin counts and ensures you have all of your zeroes (and, if present, any negative values).

Each ggplot on separate pdf page - plot row by row [closed]

Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 6 years ago.
Improve this question
I would like to put one line plot on each page of pdf file. All the data for plotting is stored in single data frame. Each row should be plotted.
That's how the data looks like:
structure(list(`10` = c(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, 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, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `34` = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 370500, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1091361.9, 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, 1512409.6,
0, 0, 0, 0, 0, 0), `59` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 4231358.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 5995680.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2266775, 0, 0, 0, 0, 0, 0, 6864490.1, 0, 0,
0, 0, 0, 0), `84` = c(0, 0, 0, 0, 1783350, 0, 0, 0, 1177650,
0, 0, 0, 0, 0, 0, 0, 0, 4316664.7, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 9262556.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2831286.1, 0, 0, 0, 0, 0, 0, 10643218.2,
0, 0, 0, 0, 0, 0), `110` = c(0, 0, 0, 0, 1778743.3, 0, 0, 0,
1465966.7, 0, 0, 0, 0, 0, 0, 0, 0, 3111700, 0, 0, 1955337.5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5584784.4, 5584784.4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3092525, 0,
0, 0, 0, 0, 0, 7847143.8, 0, 0, 0, 0, 0, 0), `134` = c(0, 0,
0, 0, 1121869.4, 0, 0, 0, 1439430.6, 0, 0, 0, 0, 0, 0, 0, 0,
2854250, 0, 0, 0, 0, 0, 0, 914890, 0, 0, 847880, 0, 0, 0, 0,
0, 0, 0, 8191800, 0, 0, 0, 0, 0, 0, 1830904.5, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1650150, 0, 0, 837130, 0, 0, 0, 4925095.1, 0,
0, 0, 0, 0, 0), `165` = c(0, 0, 0, 0, 1432775, 0, 0, 0, 1394186.1,
0, 1120183.3, 0, 0, 0, 0, 0, 0, 2262421.7, 0, 0, 0, 615660, 0,
0, 1292795.8, 0, 0, 712622.5, 0, 0, 0, 0, 0, 0, 0, 2683469.4,
0, 0, 0, 0, 0, 0, 2318485.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1561800,
0, 0, 0, 0, 0, 0, 4382993.7, 0, 0, 763460, 0, 0, 0), `199` = c(0,
0, 0, 0, 1314220, 0, 0, 0, 1439718.8, 0, 1929266.7, 0, 0, 0,
1101800, 0, 0, 2759366.7, 0, 0, 0, 1291728.6, 0, 0, 2489775.6,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2858345.8, 0, 0, 0, 1819542.1,
0, 0, 1497640.3, 0, 0, 0, 1300250, 0, 0, 0, 0, 0, 0, 1566875,
0, 0, 0, 0, 0, 0, 4625895.6, 0, 0, 1308158.3, 0, 0, 0), `234` = c(1257250,
0, 0, 0, 0, 0, 0, 0, 1276080, 0, 1848500, 0, 0, 0, 1529350, 0,
0, 2155275, 0, 0, 0, 2023041.9, 0, 0, 1966447.7, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1184200, 1184200, 0, 0, 1652350, 0, 0, 2018581.7,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1835225, 0, 0, 0, 0, 0, 0, 4639414.7,
0, 0, 720715, 0, 0, 0), `257` = c(0, 0, 0, 0, 0, 669442.5, 0,
0, 1253026.7, 0, 960410, 960410, 0, 0, 1258267.5, 0, 0, 1707392.5,
0, 0, 0, 563280, 0, 0, 2403237.9, 0, 0, 0, 1044100, 0, 2075700,
0, 0, 0, 0, 0, 5718450, 0, 0, 1704550, 0, 0, 1350286.9, 0, 0,
0, 0, 2011700, 0, 0, 0, 0, 0, 1739500, 0, 0, 0, 0, 0, 0, 4612520.8,
4612520.8, 0, 0, 0, 0, 0), `362` = c(0, 1593500, 0, 0, 0, 1610625.3,
0, 0, 1234902.5, 0, 0, 1481036.8, 0, 0, 1583647.5, 0, 0, 1752089.2,
0, 0, 0, 0, 0, 0, 2410809.2, 0, 0, 0, 654940, 0, 0, 0, 0, 0,
0, 0, 7014905.6, 0, 0, 0, 0, 0, 1165672.1, 0, 0, 0, 0, 0, 0,
0, 1029910, 0, 0, 2153087.5, 0, 0, 0, 422920, 0, 0, 0, 7495855.9,
0, 0, 0, 0, 0), `433` = c(0, 0, 0, 0, 0, 1340283.9, 0, 0, 1268996.9,
0, 0, 1416683.3, 0, 0, 1047862.5, 0, 0, 1819653.8, 0, 0, 0, 0,
0, 0, 2227565.7, 0, 0, 0, 763765, 0, 0, 1595430, 0, 0, 0, 0,
4894549, 0, 0, 0, 0, 0, 1061375.4, 0, 0, 0, 0, 0, 2251950, 0,
1042130, 0, 0, 2055300, 0, 0, 0, 696278.3, 0, 0, 0, 5353797.8,
0, 0, 0, 0, 0), `506` = c(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, 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, 0, 0, 0, 2020300,
2020300, 0, 0, 0, 0, 0, 0, 7681526, 0, 0, 0, 0, 0), `581` = c(0,
0, 1749237.5, 0, 0, 0, 2421665.8, 0, 0, 1773262.5, 0, 0, 2251004.3,
0, 0, 2570175, 0, 0, 3379756.9, 0, 0, 0, 2054455.6, 0, 0, 2518270.8,
0, 0, 0, 0, 0, 0, 2917968.2, 0, 0, 0, 0, 7004350, 0, 0, 1451600,
0, 0, 1394411, 0, 0, 0, 0, 0, 2507858.3, 0, 2377012.5, 0, 0,
3719165.4, 0, 0, 0, 1472870.3, 0, 0, 9666916.1, 0, 0, 1730300,
0, 0), `652` = c(0, 0, 476910, 476910, 0, 0, 1149078.8, 1149078.8,
0, 1082468.7, 0, 0, 882769.7, 0, 0, 1370449.4, 1370449.4, 0,
1529049, 1529049, 0, 0, 943632.2, 0, 0, 916587.8, 0, 0, 0, 988261.1,
0, 0, 1778007.1, 1778007.1, 0, 0, 0, 3087304.8, 3087304.8, 0,
782860, 782860, 0, 510158.5, 510158.5, 0, 0, 0, 0, 1503750, 0,
1100677.5, 1100677.5, 0, 1669260, 1669260, 0, 0, 770733.2, 0,
0, 4939242.8, 4939242.8, 0, 643564.4, 643564.4, 0), `733` = c(0,
0, 0, 1095060, 0, 0, 0, 1674089.3, 0, 1252101.3, 0, 0, 1259111,
0, 0, 0, 2429293.3, 0, 0, 2326928.3, 0, 0, 1259216.5, 0, 0, 1238837.5,
0, 0, 0, 1224858.3, 0, 0, 0, 2952529.9, 0, 0, 0, 0, 4626414.7,
0, 0, 1121440, 0, 0, 1025386.2, 0, 0, 0, 0, 1917900, 0, 0, 2197533.3,
0, 0, 2840155.5, 0, 0, 1054285.7, 0, 0, 0, 7516814.2, 0, 0, 1329434.4,
0), `818` = c(0, 0, 0, 720551.1, 0, 0, 0, 714662.7, 0, 617012.9,
0, 0, 549850.8, 0, 0, 0, 1197460, 0, 0, 771979.2, 0, 0, 585847.5,
585847.5, 0, 875475.4, 0, 0, 0, 576774, 0, 0, 0, 1147389.8, 0,
0, 0, 0, 2292421.7, 0, 0, 755258.3, 0, 0, 0, 0, 0, 0, 0, 858930,
0, 0, 1242668.3, 0, 0, 1580088.3, 0, 0, 641938.6, 641938.6, 0,
0, 3838660.4, 0, 0, 733140.8, 733140.8), `896` = c(0, 0, 0, 590480,
0, 0, 0, 817087.6, 0, 569869.5, 0, 0, 650822.5, 650822.5, 0,
0, 1624052.5, 0, 0, 682570.8, 0, 0, 0, 1538800, 0, 690488.6,
690488.6, 0, 0, 797923.9, 0, 0, 0, 1204889.3, 0, 0, 0, 0, 2184432.2,
0, 0, 676654.7, 0, 0, 0, 210680, 0, 0, 0, 791152.5, 0, 0, 1599855.8,
0, 0, 1358543.8, 0, 0, 0, 931288, 0, 0, 4683895.2, 0, 0, 0, 1202806
), `972` = c(0, 0, 0, 799116.4, 0, 0, 0, 759169.9, 0, 408845,
0, 0, 0, 948980, 0, 0, 968766.7, 0, 0, 675349.7, 0, 0, 0, 0,
0, 0, 1811117.6, 0, 0, 609098.5, 0, 0, 0, 1073749.1, 0, 0, 0,
0, 2392258.9, 0, 0, 743580, 0, 0, 0, 1020485, 0, 0, 0, 446596.7,
0, 0, 1178583, 0, 0, 1438261.7, 0, 0, 0, 1133057.9, 0, 0, 4445814.7,
0, 0, 0, 1057776.9), `1039` = c(0, 0, 0, 447255.3, 0, 0, 0, 609409.1,
0, 304340, 0, 0, 0, 0, 0, 0, 694232.8, 0, 0, 473015.3, 0, 0,
0, 0, 0, 0, 419524.9, 0, 0, 447760.6, 0, 0, 0, 932513.5, 0, 0,
0, 0, 1251960.5, 0, 0, 276560, 0, 0, 0, 259640, 0, 0, 0, 354995,
0, 0, 1570222.5, 0, 0, 1021822, 0, 0, 0, 811614, 0, 0, 2941698.2,
0, 0, 0, 1199942.5), Gene = 1:67), .Names = c("10", "34", "59",
"84", "110", "134", "165", "199", "234", "257", "362", "433",
"506", "581", "652", "733", "818", "896", "972", "1039", "Gene"
), row.names = c(NA, 67L), class = "data.frame")
I have tried something like that so far...:
for(i in 1:nrow(Tra_decon)){
Tra_decon_melt <- melt(Tra_decon[i,], id = "Gene")
pdf("Test_plot.pdf", onefile = TRUE)
ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
dev.off()
}

A solution without a for loop (and so faster) is this one:
plots <- lapply(1:nrow(Tra_decon), function(i){
Tra_decon_melt <- melt(Tra_decon[i,], id = "Gene")
ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
})
pdf("Test_plot.pdf", onefile = TRUE)
plots
dev.off()

This works for me:
pdf("Test_plot.pdf", onefile = TRUE)
for(i in 1:nrow(Tra_decon)){
Tra_decon_melt <- melt(Tra_decon[i,], id.vars = "Gene")
plot<-list()
plot[[i]]<-ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(plot[[i]])
}
dev.off()

Resources