How to Separate Data Column in R - r

I have data structured 4464 in rows, and 1 column.
Data should be in 4464 in rows and 8 columns.
Data contains:
Each file has a two
line header, followed by data.The columns are: julian day, ten minute
interval marker, temperature, pressure, wind speed, and wind direction. The
ten minute interval marker is a number between 1 and 144 representing time.
data comes from here
There are total of 12 data files like this, and my goal is put them in one 3D array.
But I am stuck to fix this data form.
Example of Data
Jan 13 Station : 8900 Harry
Lat : 83.00S Long : 121.40W Elev : 957 M
1 1 -7.8 879.0 5.6 360.0 444.0 9.1
1 2 -7.9 879.1 4.6 360.0 444.0 9.1
1 3 -7.6 879.2 4.1 345.0 444.0 9.1
1 4 -7.6 879.3 4.1 339.0 444.0 9.1
1 5 -7.6 879.4 4.8 340.0 444.0 9.1
1 6 -7.9 879.4 3.6 340.0 444.0 9.1
1 7 -8.0 879.3 4.6 340.0 444.0 9.1
1 8 -8.0 879.4 4.1 340.0 444.0 9.1
1 9 -8.2 879.4 5.8 338.0 444.0 9.1
1 10 -8.4 879.5 4.6 339.0 444.0 9.1
I tried and researched few things, but I don't know what the best way is.
My code was (Could not code with data.frame...):
setwd("/Users/Gizmo/Documents/Henry")
dir()
h13<-dir()
henry<-read.csv(h13[1],skip=2,header=FALSE)
colnames(c("J-Day","MinInter","Temp","Pressure","WindSpeed","WindDir","Ext1","Ext2"))
I looked at other questions, and guide and data.frame seems like the best way, but I could not code. (Ended up with data dimension NULL.)
Please give me advice on this. Thank you.


Your problem seems to be using read.csv instead of read.table:
henry <- read.table(text='Jan 13 Station : 8900 Harry
Lat : 83.00S Long : 121.40W Elev : 957 M
1 1 -7.8 879.0 5.6 360.0 444.0 9.1
1 2 -7.9 879.1 4.6 360.0 444.0 9.1
1 3 -7.6 879.2 4.1 345.0 444.0 9.1
1 4 -7.6 879.3 4.1 339.0 444.0 9.1
1 5 -7.6 879.4 4.8 340.0 444.0 9.1
1 6 -7.9 879.4 3.6 340.0 444.0 9.1
1 7 -8.0 879.3 4.6 340.0 444.0 9.1
1 8 -8.0 879.4 4.1 340.0 444.0 9.1
1 9 -8.2 879.4 5.8 338.0 444.0 9.1
1 10 -8.4 879.5 4.6 339.0 444.0 9.1', header=FALSE, skip=2)
names(henry) <- c("J-Day","MinInter","Temp","Pressure","WindSpeed","WindDir","Ext1","Ext2")
Result:
> henry
J-Day MinInter Temp Pressure WindSpeed WindDir Ext1 Ext2
1 1 1 -7.8 879.0 5.6 360 444 9.1
2 1 2 -7.9 879.1 4.6 360 444 9.1
3 1 3 -7.6 879.2 4.1 345 444 9.1
4 1 4 -7.6 879.3 4.1 339 444 9.1
5 1 5 -7.6 879.4 4.8 340 444 9.1
6 1 6 -7.9 879.4 3.6 340 444 9.1
7 1 7 -8.0 879.3 4.6 340 444 9.1
8 1 8 -8.0 879.4 4.1 340 444 9.1
9 1 9 -8.2 879.4 5.8 338 444 9.1
10 1 10 -8.4 879.5 4.6 339 444 9.1
> str(henry)
'data.frame': 10 obs. of 8 variables:
$ J-Day : int 1 1 1 1 1 1 1 1 1 1
$ MinInter : int 1 2 3 4 5 6 7 8 9 10
$ Temp : num -7.8 -7.9 -7.6 -7.6 -7.6 -7.9 -8 -8 -8.2 -8.4
$ Pressure : num 879 879 879 879 879 ...
$ WindSpeed: num 5.6 4.6 4.1 4.1 4.8 3.6 4.6 4.1 5.8 4.6
$ WindDir : num 360 360 345 339 340 340 340 340 338 339
$ Ext1 : num 444 444 444 444 444 444 444 444 444 444
$ Ext2 : num 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1

Related

qtgrace/xmgrace non-overlaping data sets

I'm using qtgrace for MacOS and when I plotted two data in qtgrace I got something like this:
Overlapping data sets
However, I would like to plot something like this:
Non-overlapping data sets
My data 1:
0 14
0.1 6
0.2 14
0.3 14
0.4 14
0.5 14
0.6 14
0.7 14
0.8 6
0.9 6
1 6
1.1 6
1.2 6
1.3 6
1.4 6
1.5 6
1.6 6
1.7 6
1.8 6
1.9 6
2 6
2.1 6
2.2 6
2.3 6
2.4 6
2.5 6
2.6 6
2.7 6
2.8 6
2.9 6
3 6
3.1 6
3.2 6
3.3 6
3.4 6
3.5 6
3.6 6
3.7 6
3.8 6
3.9 6
4 6
4.1 6
4.2 6
4.3 6
4.4 6
4.5 6
4.6 6
4.7 6
4.8 6
4.9 6
5 6
5.1 6
5.2 6
5.3 6
5.4 6
5.5 6
5.6 6
5.7 6
5.8 6
5.9 6
6 6
6.1 6
6.2 6
6.3 6
6.4 6
6.5 6
6.6 6
6.7 6
6.8 6
6.9 6
7 6
7.1 6
7.2 6
7.3 2
7.4 6
7.5 2
7.6 2
7.7 2
7.8 2
7.9 6
8 2
8.1 6
8.2 2
8.3 2
8.4 6
8.5 6
8.6 6
8.7 2
8.8 6
8.9 19
9 19
9.1 6
9.2 6
9.3 6
9.4 2
9.5 2
9.6 2
9.7 2
9.8 2
9.9 2
10 2
10.1 2
10.2 2
10.3 2
10.4 2
10.5 2
10.6 2
10.7 2
10.8 2
10.9 2
11 2
11.1 2
11.2 2
11.3 2
11.4 2
11.5 2
11.6 2
11.7 2
11.8 2
11.9 2
12 2
12.1 2
12.2 2
12.3 2
12.4 2
12.5 2
12.6 2
12.7 2
12.8 2
12.9 2
13 2
13.1 2
13.2 2
13.3 2
13.4 2
13.5 2
13.6 2
13.7 2
13.8 2
13.9 2
14 2
14.1 2
14.2 2
14.3 2
14.4 2
14.5 2
14.6 2
14.7 2
14.8 2
14.9 2
15 2
15.1 2
15.2 2
15.3 2
15.4 2
15.5 2
15.6 2
15.7 2
15.8 2
15.9 2
16 2
16.1 2
16.2 2
16.3 2
16.4 2
16.5 2
16.6 2
16.7 2
16.8 2
16.9 2
17 2
17.1 2
17.2 2
17.3 2
17.4 2
17.5 2
17.6 2
17.7 2
17.8 2
17.9 2
18 2
18.1 2
18.2 2
18.3 2
18.4 2
18.5 2
18.6 2
18.7 2
18.8 2
18.9 2
19 2
19.1 2
19.2 2
19.3 2
19.4 2
19.5 2
19.6 2
19.7 2
19.8 2
19.9 2
20 2
20.1 2
20.2 2
20.3 2
20.4 2
20.5 2
20.6 2
20.7 2
20.8 2
20.9 2
21 2
21.1 2
21.2 2
21.3 2
21.4 2
21.5 2
21.6 2
21.7 2
21.8 7
21.9 2
22 2
22.1 2
22.2 2
22.3 7
22.4 7
22.5 7
22.6 7
22.7 7
22.8 2
22.9 2
23 7
23.1 7
23.2 7
23.3 7
23.4 7
23.5 2
23.6 2
23.7 2
23.8 2
23.9 2
24 2
24.1 2
24.2 2
24.3 2
24.4 2
24.5 2
24.6 2
24.7 2
24.8 2
24.9 2
25 2
. .
. .
. .
Data 2:
0 4
0.1 4
0.2 4
0.3 4
0.4 4
0.5 4
0.6 4
0.7 4
0.8 4
0.9 4
1 2
1.1 4
1.2 4
1.3 4
1.4 4
1.5 4
1.6 4
1.7 4
1.8 4
1.9 4
2 4
2.1 4
2.2 4
2.3 4
2.4 4
2.5 4
2.6 4
2.7 4
2.8 4
2.9 4
3 4
3.1 4
3.2 4
3.3 4
3.4 4
3.5 4
3.6 4
3.7 4
3.8 4
3.9 4
4 4
4.1 4
4.2 4
4.3 4
4.4 4
4.5 4
4.6 4
4.7 4
4.8 4
4.9 4
5 4
5.1 4
5.2 4
5.3 4
5.4 4
5.5 4
5.6 4
5.7 4
5.8 4
5.9 4
6 4
6.1 4
6.2 4
6.3 4
6.4 4
6.5 4
6.6 4
6.7 4
6.8 4
6.9 4
7 4
7.1 4
7.2 4
7.3 4
7.4 4
7.5 4
7.6 4
7.7 4
7.8 4
7.9 4
8 4
8.1 4
8.2 4
8.3 4
8.4 2
8.5 4
8.6 4
8.7 4
8.8 4
8.9 4
9 4
9.1 4
9.2 4
9.3 4
9.4 4
9.5 4
9.6 4
9.7 4
9.8 4
9.9 4
10 4
10.1 4
10.2 4
10.3 4
10.4 4
10.5 2
10.6 2
10.7 4
10.8 2
10.9 2
11 2
11.1 2
11.2 4
11.3 4
11.4 2
11.5 2
11.6 2
11.7 2
11.8 2
11.9 2
12 2
12.1 2
12.2 2
12.3 2
12.4 4
12.5 4
12.6 2
12.7 2
12.8 4
12.9 2
13 2
13.1 4
13.2 4
13.3 4
13.4 4
13.5 10
13.6 2
13.7 2
13.8 2
13.9 2
14 2
14.1 2
14.2 2
14.3 10
14.4 2
14.5 2
14.6 4
14.7 2
14.8 2
14.9 4
15 2
15.1 10
15.2 2
15.3 2
15.4 2
15.5 2
15.6 2
15.7 2
15.8 2
15.9 2
16 2
16.1 2
16.2 2
16.3 2
16.4 2
16.5 2
16.6 2
16.7 2
16.8 2
16.9 2
17 2
17.1 2
17.2 2
17.3 2
17.4 2
17.5 2
17.6 2
17.7 2
17.8 2
17.9 2
18 2
18.1 2
18.2 2
18.3 2
18.4 2
18.5 2
18.6 2
18.7 2
18.8 2
18.9 2
19 2
19.1 2
19.2 2
19.3 2
19.4 2
19.5 2
19.6 2
19.7 2
19.8 2
19.9 2
20 2
20.1 2
20.2 2
20.3 2
20.4 2
20.5 2
20.6 2
20.7 2
20.8 2
20.9 2
21 2
21.1 2
21.2 2
21.3 2
21.4 2
21.5 2
21.6 2
21.7 2
21.8 2
21.9 2
22 2
22.1 2
22.2 2
22.3 2
22.4 2
22.5 2
22.6 2
22.7 2
22.8 2
22.9 2
23 2
23.1 2
23.2 2
23.3 2
23.4 2
23.5 2
23.6 2
23.7 2
23.8 2
23.9 2
24 2
24.1 2
24.2 2
24.3 2
24.4 2
24.5 2
24.6 2
24.7 2
24.8 2
24.9 2
25 2
. .
. .
. .
The data are in two separate xvg file from GROMACS cluster analysis. I wanna plot five different sets in a manner which I can see all data without superposing.
Thank you!

I think the best approach would be to write a script that takes the original files and spits out new files with shifted y values. However, since you have asked for a qt/xmgrace solution, here is how you do it:
Load up all the datasets into qtgrace
Open the "Data -> Transformations -> Evaluate expression..." dialog
Select in the left and right columns a dataset and in the textbox below enter the formula y = y + 0.1. Click "apply". This will shift the dataset up by 0.1
Select the next dataset in the same way and use the formula y = y + 0.2. Click apply
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Reformat data frame into a single row

I want to format my data frame into a single row.
So I have this
RT 200 201 202 203 204 205
2 2.5 3.5 4.5 5.5 6.5 7.5
3 2.6 3.6 4.6 5.6 6.6 7.6
4 2.7 3.7 4.7 5.7 6.7 7.7
And I want this:
m/z 200 201 202 203 204 205 200 201 202 203 204 205 200 201 202 203 204 205
RT 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
Sa 2.5 3.5 4.5 5.5 6.5 7.5 2.6 3.6 4.6 5.6 6.6 7.6 2.7 3.7 4.7 5.7 6.7 7.7
Can anyone provide me code for this?
Note: I want to add row names "m/z" and "Sa" to the rows instead of leaving it blank.

The type is integer, yet it has a decimal point. Why?

When I check the type, it said "integer", but has decimal point. If I change it to numeric, it become integer(no decimal point).
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> typeof(data$fare_amount)
[1] "integer"
> data$fare_amount
[1] 5.5 6.5 8.0 13.5 5.5 9.5 7.5 8.0 16.0 8.0 5.5 7.0 8.0 5.0 9.5 23.0 5.0 6.0 17.5 12.0 8.5 13.0
[23] 6.5 4.5 52.0 14.5 7.5 4.5 9.0 10.0 15.0 11.5 6.0 12.5 7.5 8.0 6.5 7.5 31.5 10.0 10.0 10.0 4.0 8.5
[45] 24.0 8.5 5.5 14.0 11.0 4.5 9.0 7.5 22.0 8.5 24.0 36.5 15.0 10.5 9.5 17.0 4.5 6.0 6.5 11.5 16.0 6.5
[67] 7.0 20.0 13.5 30.0 8.0 11.0 6.5 11.5 6.5 37.0 5.5 12.5 8.5 58.5 13.5 8.5 9.0 6.0 6.5 9.0 38.0 4.5
[89] 10.0 9.0 44.5 11.0 12.0 4.5 14.5 8.5 32.0 9.5 4.5 6.0 6.5 6.0 31.5 52.0 10.5 12.0 5.5 24.5 7.0 5.5
[111] 16.5 5.0 5.5 6.5 3.5 11.5 13.0 6.0 14.0 3.5
42 Levels: 13.5 16.0 5.5 6.5 7.5 8.0 9.5 12.0 17.5 23.0 5.0 6.0 7.0 10.0 13.0 14.5 4.5 52.0 8.5 9.0 11.5 12.5 ... 3.5
> temp <- as.numeric(data$fare_amount)
> temp
[1] 3 4 6 1 3 7 5 6 2 6 3 13 6 11 7 10 11 12 9 8 19 15 4 17 18 16 5 17 20 14 23 21 12 22 5 6 4 5
[39] 24 14 14 14 28 19 27 19 3 26 25 17 20 5 31 19 27 32 23 29 7 30 17 12 4 21 2 4 13 33 1 34 6 25 4 21 4 35
[77] 3 22 19 36 1 19 20 12 4 20 37 17 14 20 39 25 8 17 16 19 38 7 17 12 4 12 24 18 29 8 3 40 13 3 41 11 3 4
[115] 42 21 15 12 26 42

plot() in R doesn't give scatter plot

I was expecting points but got this when I did
plot(data$v3,data$v2)
my data
V2 V3
2 -2.0 2.7
3 0.5 3.9
4 1.3 4.5
5 5.7 6.0
6 10.4 8.7
7 3.4 2.7
8 7.6 3.2
9 4.1 5.6
10 5.0 9.2
11 8.5 11.7
12 12.3 6.8
13 16.1 13.0
14 13.2 11.9
15 8.8 8.6
16 7.9 6.1
17 1.1 4.9
18 3.0 1.0
19 4.5 7.2
20 2.7 2.7
21 7.6 7.6
I tried searching but from my understanding the function is supposed to give points, not bars. How do I fix this?

when applying the BMA packages in R : Error in terms.formula(formula, special, data = data) : '.' in formula and no 'data' argument

I am using the BMA packages in R (test.bic.surv) to estimating the Cox proportional model from a large set of variables (100 base variables and about 60 lags for each of them). When I try the first set of testing with the following codes, it works.
x1<- x[,c( "comprisk", "compriskL1", "compriskL2", "compriskL3", "compriskL4", "econrisk", "econrisk_1", "econrisk_2", "econrisk_3", "econrisk_4", "econrisk_5", "finrisk", "finrisk_1", "finrisk_2", "finrisk_3", "finrisk_4", "finrisk_5", "polrisk", "polrisk_1","polrisk_2","polrisk_3","polrisk_4","polrisk_5","polrisk_6","polrisk_7","polrisk_8","polrisk_9","polrisk_10","polrisk_11","polrisk_12")]
surv.t<- x$crisis1
cens<- x$cen1
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However, whenever I tried to add in any more independent variables into x1 such as "comprisk5L" or "econriskL1", the
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showed me the error like this :
"Error in terms.formula(formula, special, data = data) : '.' in formula and no 'data' argument".
I have searched through the web for several days but couldn't figure out where was the problem . Can anyone please tell me what to do?? Thank you so much in advance!!!:)
Here is what the sample data looks like:
crisis1 cen1 comprisk econrisk econrisk_1 econrisk_2 econrisk_3 econrisk_4
1 0 1 57.0 25.5 3.3 6.7 4.0 6.7
2 0 1 57.0 25.5 3.3 6.7 4.0 6.7
3 0 1 57.0 25.5 3.3 6.7 4.0 6.7
4 0 1 58.5 26.5 3.8 7.5 4.0 7.5
5 0 1 58.5 27.0 3.8 7.5 4.0 7.5
6 0 1 58.5 26.0 3.8 7.5 4.0 7.5
7 0 1 59.0 26.5 3.8 7.5 4.0 7.5
8 0 1 59.0 26.5 3.8 7.5 4.0 7.5
9 0 1 59.0 27.0 3.8 7.5 4.0 7.5
10 0 1 59.0 26.5 3.8 7.5 4.0 7.5
11 0 1 59.0 26.5 3.8 7.5 4.0 7.5
12 0 1 59.0 27.0 3.8 7.5 4.0 7.5
13 0 1 59.0 27.0 3.8 7.5 4.0 7.5
14 0 1 57.5 27.0 3.8 7.5 4.0 7.5
15 0 1 57.5 27.5 3.8 7.5 4.0 7.5
16 0 1 57.0 27.5 3.3 6.7 4.0 6.7
17 0 1 57.0 27.5 3.3 6.7 4.0 6.7
18 0 1 57.0 27.5 3.3 6.7 4.0 6.7
19 0 1 56.0 27.0 3.3 6.7 4.0 6.7
20 0 1 56.5 28.5 2.9 5.8 4.0 5.8
21 0 1 55.5 26.5 2.9 5.8 4.0 5.8
22 0 1 55.0 26.0 2.9 5.8 4.0 5.8
23 0 1 55.0 26.0 2.9 5.8 4.0 5.8
24 0 1 55.0 26.0 2.9 5.8 4.0 5.8
25 0 1 55.0 26.0 2.9 5.8 4.0 5.8
26 0 1 54.5 25.5 2.9 5.8 6.5 5.8
27 0 1 54.0 25.5 2.9 5.8 6.5 5.8
28 0 1 53.5 25.5 2.5 5.0 6.5 5.0
29 0 1 53.5 25.5 2.5 5.0 6.5 5.0
30 0 1 54.0 26.5 2.5 5.0 6.5 5.0
31 0 1 54.0 26.5 2.5 5.0 6.5 5.0
32 0 1 54.0 26.5 2.5 5.0 6.5 5.0
33 0 1 56.0 26.5 2.5 5.0 6.5 5.0
34 0 1 56.0 27.0 2.5 5.0 6.5 5.0
35 0 1 57.0 27.0 2.5 5.0 6.5 5.0
36 0 1 58.0 27.0 2.9 5.8 6.5 5.8
37 1 1 59.0 28.5 2.9 5.8 6.5 5.8
38 1 1 60.0 29.5 2.9 5.8 6.5 5.8
39 1 1 59.5 29.5 2.9 5.8 6.5 5.8
40 1 1 60.0 29.5 2.9 5.8 6.5 5.8
41 1 1 59.5 29.5 2.9 5.8 6.5 5.8
42 1 1 59.0 28.0 2.9 5.8 6.5 5.8
43 1 1 59.5 28.0 2.9 5.8 6.5 5.8
44 1 1 59.5 28.0 2.9 5.8 6.5 5.8
45 1 1 59.5 28.5 2.9 5.8 6.5 5.8
46 1 1 56.0 28.0 2.9 5.8 6.5 5.8
47 1 1 54.0 28.0 2.5 5.0 6.5 5.0
48 1 1 53.0 24.5 2.1 4.2 6.5 4.2
49 1 1 53.0 25.0 2.1 4.2 6.5 4.2
50 1 1 54.0 26.0 2.1 4.2 6.5 4.2
51 1 1 54.5 26.0 2.1 4.2 6.5 4.2
52 1 1 54.5 25.5 2.1 4.2 6.5 4.2
53 1 1 54.0 24.0 2.1 4.2 6.0 4.2
54 1 1 54.0 24.0 2.1 4.2 6.0 4.2
55 1 1 55.0 24.0 2.1 4.2 6.0 4.2
56 1 1 55.0 24.0 2.1 4.2 6.0 4.2
57 1 1 55.0 24.0 2.1 4.2 6.0 4.2
58 1 1 55.0 24.5 2.1 4.2 6.0 4.2
59 1 1 55.0 24.5 2.1 4.2 6.0 4.2
60 1 1 55.0 25.0 2.1 4.2 6.0 4.2
61 1 1 55.0 23.5 2.1 4.2 6.0 4.2
62 1 1 55.0 24.0 2.1 4.2 6.0 4.2
63 1 1 55.0 23.5 2.1 4.2 6.5 4.2
64 1 1 55.0 23.5 1.7 3.3 6.5 3.3
65 1 1 55.0 22.5 1.7 3.3 6.5 3.3
66 1 1 56.0 25.5 1.3 2.5 6.5 2.5
67 1 1 56.0 25.5 1.3 2.5 6.5 2.5
68 1 1 56.5 25.0 1.3 2.5 6.5 2.5
69 1 1 58.5 29.5 1.3 2.5 6.5 2.5
70 1 1 58.5 28.5 1.3 2.5 6.5 2.5
71 1 1 58.5 28.5 1.3 2.5 6.5 2.5
72 1 1 59.5 29.5 1.3 2.5 6.5 2.5
73 1 1 61.5 33.0 1.3 2.5 6.0 2.5
74 1 1 61.0 33.0 1.3 2.5 6.0 2.5
75 1 1 61.5 32.0 1.7 3.3 6.0 3.3
76 1 1 59.5 32.0 1.7 3.3 6.0 3.3
77 1 1 60.0 32.5 1.7 3.3 6.0 3.3
78 1 1 57.5 32.5 2.1 4.2 6.0 4.2
79 1 1 58.0 33.0 2.1 4.2 6.0 4.2
80 1 1 58.5 32.5 2.1 4.2 6.0 4.2
81 1 1 57.5 31.5 2.1 4.2 5.0 4.2
82 1 1 57.5 31.5 2.1 4.2 5.0 4.2
83 1 1 59.0 31.5 2.5 5.0 5.0 5.0
84 1 1 58.5 30.5 2.5 5.0 4.0 5.0
85 0 1 55.5 27.5 2.5 5.0 3.5 5.0
86 0 1 54.0 27.5 2.5 5.0 3.5 5.0
87 0 1 53.5 27.0 2.5 5.0 3.5 5.0
88 0 1 53.0 27.0 2.5 5.0 3.5 5.0
89 0 1 53.0 27.5 2.1 4.2 3.5 4.2
90 0 1 52.5 27.0 2.1 4.2 3.5 4.2
91 0 1 50.5 27.5 2.1 4.2 3.5 4.2
92 0 1 51.5 27.5 2.1 4.2 3.5 4.2
93 0 1 51.5 27.0 2.5 5.0 3.5 5.0
94 0 1 52.0 27.0 2.5 5.0 3.5 5.0
95 0 1 52.0 27.0 2.5 5.0 3.5 5.0
96 0 1 52.0 28.0 2.5 5.0 3.5 5.0
97 0 1 52.5 28.5 2.5 5.0 3.5 5.0
98 0 1 54.0 28.5 2.5 5.0 3.5 5.0
99 0 1 54.0 29.0 2.5 5.0 4.0 5.0
100 0 1 53.0 28.0 2.5 5.0 4.0 5.0
101 0 1 52.5 28.0 2.1 4.2 3.5 4.2
102 0 1 52.5 28.0 2.1 4.2 3.5 4.2
103 0 1 53.0 28.0 2.1 4.2 3.5 4.2
104 0 1 53.0 28.0 2.1 4.2 3.5 4.2
105 0 1 52.5 26.0 2.1 4.2 4.0 4.2
106 0 1 54.0 26.5 2.1 4.2 4.0 4.2
107 0 1 53.5 26.5 2.1 4.2 4.0 4.2
108 0 1 53.5 26.5 2.1 4.2 4.0 4.2
109 1 1 56.0 29.5 2.1 4.2 5.0 4.2
110 1 1 53.5 27.0 2.1 4.2 4.0 4.2
111 1 1 53.5 27.0 2.1 4.2 4.0 4.2
112 1 1 53.5 26.5 2.1 4.2 5.0 4.2
113 1 1 54.0 26.5 2.1 4.2 5.0 4.2
114 1 1 52.5 24.0 2.1 4.2 4.0 4.2
115 1 1 53.0 24.5 2.1 4.2 5.0 4.2
116 1 1 54.0 26.0 2.1 4.2 4.0 4.2
117 1 1 54.0 26.0 2.1 4.2 4.0 4.2
118 1 1 54.5 26.0 2.1 4.2 4.0 4.2
119 1 1 52.5 24.5 2.1 4.2 3.5 4.2
120 1 1 52.5 24.5 2.1 4.2 3.5 4.2
121 1 1 54.0 27.5 2.1 4.2 4.0 4.2
122 1 1 54.0 27.5 2.1 4.2 4.0 4.2
123 1 1 53.0 28.5 2.1 4.2 4.0 4.2
124 1 1 53.0 28.5 2.1 4.2 4.0 4.2
125 1 1 52.5 28.0 2.1 4.2 4.0 4.2
126 1 1 52.5 27.5 2.1 4.2 4.0 4.2
127 1 1 53.0 28.0 2.1 4.2 4.5 4.2
128 1 1 53.5 28.0 2.5 5.0 4.5 5.0
129 1 1 54.5 28.0 2.5 5.0 4.5 5.0
130 1 1 54.0 26.5 2.5 5.0 3.5 5.0
131 1 1 53.5 26.0 2.5 5.0 3.5 5.0
132 1 1 54.5 26.5 2.5 5.0 3.5 5.0
133 0 1 55.5 28.0 2.5 5.0 3.5 5.0
134 0 1 56.0 28.0 2.5 5.0 3.5 5.0
135 0 1 56.0 28.0 2.5 5.0 3.5 5.0
136 0 1 54.5 27.5 2.5 5.8 3.5 5.8
137 0 1 56.0 24.5 2.9 5.8 5.0 5.8
138 0 1 58.5 29.0 2.9 5.8 5.0 5.8
139 0 1 57.5 28.5 2.9 5.8 5.0 5.8
140 0 1 57.0 28.5 2.9 5.8 5.0 5.8
141 0 1 57.0 28.5 2.9 5.8 5.0 5.8
142 0 1 58.0 28.5 2.9 5.8 5.0 5.8
143 0 1 58.0 29.5 2.9 5.8 5.0 5.8
144 0 1 59.0 29.5 2.9 5.8 5.0 5.8
145 0 1 59.0 31.0 2.9 5.8 5.5 5.8
146 0 1 59.0 31.0 2.9 5.8 5.5 5.8
147 0 1 58.5 31.0 2.9 5.8 5.5 5.8
148 0 1 58.5 31.0 2.9 5.8 5.5 5.8
149 0 1 58.5 32.0 2.5 5.0 5.5 5.0
150 0 1 58.0 32.0 2.5 5.0 5.5 5.0
151 0 1 56.8 32.5 2.5 5.0 5.5 5.0
152 0 1 58.3 31.5 3.8 7.5 5.5 7.5
153 0 1 59.0 37.0 0.5 8.5 5.5 9.5
154 0 1 59.2 37.5 1.0 8.5 5.5 9.5
155 0 1 61.0 39.5 0.5 9.0 8.0 9.0
156 0 1 60.5 39.5 0.5 9.0 8.0 9.0
157 0 1 60.0 39.5 0.5 9.0 8.0 9.0
158 0 1 59.2 39.0 0.5 8.5 8.0 9.0
159 0 1 59.5 39.5 0.5 8.5 8.5 9.0
160 0 1 59.5 39.5 0.5 8.5 8.5 9.0
161 0 1 59.5 39.5 0.5 8.5 8.5 9.0
162 0 1 59.2 39.0 0.5 8.0 8.5 9.0
163 0 1 58.7 39.0 0.5 8.0 8.5 9.0
164 0 1 58.5 38.5 0.5 7.5 8.5 9.0
165 0 1 58.0 35.0 1.0 4.0 8.5 8.0
166 0 1 57.0 35.0 1.0 4.0 8.5 8.0
167 0 1 56.2 33.5 0.5 4.0 7.5 8.0
168 0 1 56.5 34.0 1.0 4.0 7.5 8.0
169 0 1 54.7 33.5 1.0 8.5 7.5 6.0
170 0 1 52.7 30.5 1.0 6.0 7.5 6.0
171 0 1 52.7 30.5 1.0 6.0 7.5 6.0
172 0 1 54.0 33.0 1.0 8.5 7.5 6.0
173 0 1 52.1 32.7 0.2 8.5 8.0 6.0
174 0 1 50.8 32.2 0.2 8.0 8.0 6.0
175 0 1 52.1 32.2 0.2 8.0 8.0 6.0
176 0 1 51.9 32.2 0.2 8.0 8.0 6.0
177 0 1 51.7 31.5 1.0 7.0 7.5 6.0
178 0 1 51.5 31.5 1.0 7.0 7.5 6.0
179 0 1 52.7 31.5 1.0 7.0 7.5 6.0
180 0 1 52.5 31.5 1.0 7.0 7.5 6.0
181 0 1 54.5 33.5 1.0 8.5 8.5 3.5
182 0 1 55.5 33.5 1.0 8.5 8.5 3.5
183 0 1 56.7 35.0 1.0 9.0 8.5 3.5
184 0 1 56.2 35.0 1.0 9.0 8.5 3.5
185 0 1 55.5 35.0 1.0 9.0 8.5 3.5
186 0 1 56.2 35.0 1.0 9.0 8.5 3.5
187 0 1 56.7 35.0 1.0 9.0 8.5 3.5
188 0 1 56.0 34.0 1.0 9.0 7.5 3.5
189 0 1 55.0 34.0 1.0 9.0 7.5 3.5
190 0 1 55.5 34.0 1.0 9.0 7.5 3.5
191 0 1 55.2 34.0 1.0 9.0 7.5 3.5
192 0 1 59.0 37.0 1.0 9.0 8.5 3.5
193 0 1 62.2 42.0 1.0 9.5 8.0 8.5
194 0 1 61.8 42.0 1.0 9.5 8.0 8.5
195 0 1 60.2 41.0 1.0 9.5 8.0 8.5
196 0 1 63.7 41.0 1.0 9.5 8.0 8.5
197 0 1 60.2 37.0 1.0 8.5 8.0 8.5
198 0 1 64.2 42.0 1.0 9.5 9.0 8.5
199 0 1 63.0 40.0 1.0 8.5 8.0 8.5
200 0 1 61.5 38.5 1.0 8.5 8.0 8.5
201 0 1 61.7 38.5 1.0 8.5 8.0 8.5
202 0 1 62.0 38.5 1.0 8.5 8.0 8.5
203 0 1 62.0 38.5 1.0 8.5 8.0 8.5
204 0 1 62.2 38.5 1.0 8.5 8.0 8.5
205 0 1 61.5 38.5 1.0 8.5 8.0 8.5
206 0 1 61.2 38.0 1.0 8.5 8.0 8.5
207 0 1 60.5 38.0 1.0 8.5 8.0 8.5
208 0 1 61.0 38.0 1.0 8.5 8.0 8.5
209 0 1 61.5 38.0 1.0 8.5 8.0 8.5
210 0 1 61.7 38.0 1.0 8.5 8.0 8.5
211 0 1 62.0 38.0 1.0 8.5 8.0 8.5
212 0 1 61.7 38.0 1.0 8.5 8.0 8.5
213 0 1 61.5 38.0 1.0 8.5 8.0 8.5
214 0 1 61.2 38.0 1.0 8.5 8.0 8.5
215 0 1 63.7 40.5 1.0 8.0 9.0 8.5
216 0 1 63.7 40.5 1.0 8.0 9.0 8.5
217 0 1 63.7 40.5 1.0 8.0 9.0 8.5
218 0 1 65.7 43.5 1.0 9.5 8.5 9.5
219 0 1 65.5 43.5 1.0 9.5 8.5 9.5
220 0 1 65.5 43.5 1.0 9.5 8.5 9.5
221 0 1 65.0 43.5 1.0 9.5 8.5 9.5
222 0 1 65.0 43.5 1.0 9.5 8.5 9.5
223 0 1 65.0 43.5 1.0 9.5 8.5 9.5
224 0 1 66.2 43.5 1.0 10.0 9.5 8.0
225 0 1 66.2 43.5 1.0 10.0 9.5 8.0
226 0 1 66.2 43.5 1.0 10.0 9.5 8.0
227 0 1 66.0 44.0 1.0 10.0 9.5 8.5
228 0 1 65.7 44.0 1.0 10.0 9.5 8.5
229 0 1 65.5 43.5 1.0 9.5 9.5 8.5
230 0 1 65.5 43.0 1.0 10.0 9.0 8.5
231 0 1 65.5 43.0 1.0 10.0 9.0 8.5
232 0 1 68.2 43.0 1.0 10.0 9.0 8.5
233 0 1 71.5 44.5 1.0 10.0 9.0 9.5
234 0 1 71.7 44.5 1.0 10.0 9.0 9.5
235 0 1 73.2 44.5 1.0 10.0 9.0 9.5
236 0 1 74.7 44.5 1.0 10.0 9.0 9.5
237 0 1 74.7 44.5 1.0 10.0 9.0 9.5
238 0 1 74.7 44.5 1.0 10.0 9.0 9.5
239 0 1 75.5 45.0 1.0 10.0 9.0 10.0
240 0 1 75.5 45.0 1.0 10.0 9.0 10.0
241 0 1 76.0 45.0 1.0 10.0 9.0 10.0
242 0 1 76.7 44.5 1.0 10.0 8.5 10.0
243 0 1 76.7 44.5 1.0 10.0 8.5 10.0
244 0 1 76.7 44.5 1.0 10.0 8.5 10.0
245 0 1 78.0 44.5 1.0 10.0 8.5 10.0
246 0 1 78.0 44.5 1.0 10.0 8.5 10.0
247 0 1 77.0 44.5 1.0 10.0 8.5 10.0
248 0 1 77.2 44.5 1.0 10.0 8.5 10.0
249 0 1 77.2 44.5 1.0 10.0 8.5 10.0
250 0 1 77.7 44.5 1.0 10.0 8.5 10.0

Here is your answer:
test.bic.surv <- bic.surv(
x[, 3:ncol(x)],
x$crisis1, x$cen1, factor.type=FALSE, strict=FALSE, nbest=2000, maxCol=50
)
You have to provide maxCol parameter. Default is 30 so it is probably not enough for your needs.

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