How to keep the REST authentication credentials in AngularJS on page reload? - http

We built a RESTful server, quite 'pure', which uses HTTP BASIC AUTHENTICATION. This means the client needs to send username/password every request. It is simple and secure (over HTTPS). REST of course is stateless and uses no sessions, so there is no 'logon' method in the API. Every request needs to be authorized again.
On this REST server we built an AngularJS client. This is a single page application. When the user logs in to the client, the client will store the credentials and make sure the correct HTTP headers are set.
The “problem” is that when the user refreshes the browser, the app looses its state, including the authentication credentials.
What is the best way to deal with this? How can the AngularJS app keep the user logged in / remember the credentials? Whatever the solution, it MUST be secure, since it is a banking application.


Hmm, this is a tricky one. One possible solution is to use some combination of e.g. Javascrypt (http://www.fourmilab.ch/javascrypt/) and temporary localstorage/cookie.
Do not store the password at all, not even in localstorage. You can use a key derivation function to get a key from the password. With a salt and a reasonable number of iterations this could be secure enough.
Update:
See this securely store user password locally in a jquery mobile app for another good answer


This isn't an answer, I agree with Sindre but, here's the full list of browsers from StatCounter.com
IE 7 Market Share is .86%, if you're going to support that you have to support the 2% Linux users :). Just wanted to put this out there so as to have some sort of gauge on what's being used... these numbers are obviously not 100% accurate but it seems reasonable.
I think without using local/web storage APIs you can't really achieve this.
Browser Version Market Share Perc. (June 2012 to June 2013)
IE 9.0 15.49
IE 8.0 11.36
Chrome 23.0 5.63
Chrome 26.0 5.15
Chrome 21.0 4.55
Chrome 24.0 3.9
Chrome 22.0 3.59
Chrome 27.0 3.24
Safari iPad 3.19
Chrome 25.0 3.18
Chrome 20.0 2.59
Chrome 19.0 2.46
Firefox 16.0 2.42
IE 10.0 2.4
Firefox 14.0 2.19
Safari 5.1 2.11
Firefox 15.0 2.07
Firefox 19.0 1.97
Firefox 18.0 1.85
Firefox 13.0 1.84
Firefox 20.0 1.73
Safari 6.0 1.69
Firefox 17.0 1.67
Firefox 21.0 1.48
Firefox 12.0 1.43
IE 7.0 0.86
Firefox 3.6 0.73
Safari 5.0 0.68
Android 0 0.59
Opera 12.0 0.53
Opera 12.1 0.45
Firefox 10.0 0.42
Firefox 11.0 0.38
IE 6.0 0.38
Chrome 17.0 0.26
Firefox 9.0 0.26
Chrome 18.0 0.24
Firefox 4.0 0.22
Chrome 16.0 0.22
Firefox 8.0 0.21
Opera 11.6 0.18
360 Safe Browser 0 0.18
Chrome 15.0 0.17
Chrome 11.0 0.17
Chrome 14.0 0.16
Chrome 12.0 0.16
Chrome 13.0 0.15
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Firefox 3.0 0.15
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Related

Pivoting and Distributing values based on Duration

I have a small dataset weekly_data of projects were working on, and anticipated time to be spent and duration in weeks for each of the two milestones, labeled CD and CA
# A tibble: 17 x 5
dsk_proj_number hrs_per_week_cd cd_dur_weeks hrs_per_week_ca ca_dur_weeks
<fct> <dbl> <dbl> <dbl> <dbl>
1 17061 0 0 2.43 28
2 18009 0 0 1.83 12
3 18029 0 0 2.83 24
4 19029 1.5 16 2.43 28
5 19050 0 0 2.8 20
6 20012 0 0 3.4 20
7 21016 3 8 2.43 28
8 21022 0 0 4.25 16
9 21050 0 0 3.4 20
10 21061a 17.5 24 15.8 52
11 21061b 1.5 4 7.5 8
12 21061c 7.67 12 5 12
13 21061d 0 0 0 0
14 21061e 8 1 3 1
15 21094 0 0 3 8
16 22027 0 0 0.75 8
17 22068 2.92 12 2.38 8
I want to get this into a format wheree, based on the cd_dur_weeks and ca_dur_weeks durations indicated, I have the estiamted number of hours by weeks, for all the weeks, like this:
> sched %>% head(15)
# A tibble: 15 x 17
`18009` `22068` `17061` `21050` `19029` `21016` `21022` `19050` `18029` `22027` `20012` `21094` `21061a` `21061b` `21061c` `21061d` `21061e`
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 1.5 7.67 0 8
2 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 1.5 7.67 0 3
3 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 1.5 7.67 0 0
4 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 1.5 7.67 0 0
5 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 7.5 7.67 0 0
6 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 7.5 7.67 0 0
7 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 7.5 7.67 0 0
8 1.83 2.92 2.43 3.4 1.5 3 4.25 2.8 2.83 0.75 3.4 3 17.5 7.5 7.67 0 0
9 1.83 2.92 2.43 3.4 1.5 2.43 4.25 2.8 2.83 0 3.4 0 17.5 7.5 7.67 0 0
10 1.83 2.92 2.43 3.4 1.5 2.43 4.25 2.8 2.83 0 3.4 0 17.5 7.5 7.67 0 0
11 1.83 2.92 2.43 3.4 1.5 2.43 4.25 2.8 2.83 0 3.4 0 17.5 7.5 7.67 0 0
12 1.83 2.92 2.43 3.4 1.5 2.43 4.25 2.8 2.83 0 3.4 0 17.5 7.5 7.67 0 0
13 0 2.38 2.43 3.4 1.5 2.43 4.25 2.8 2.83 0 3.4 0 17.5 0 5 0 0
14 0 2.38 2.43 3.4 1.5 2.43 4.25 2.8 2.83 0 3.4 0 17.5 0 5 0 0
15 0 2.38 2.43 3.4 1.5 2.43 4.25 2.8 2.83 0 3.4 0 17.5 0 5 0 0
I was able to use pivot_wider() to make the project numbers the variable names, and each row an individual week, but was forced to use for()'s and if()'s. Seems like there should be an easier way to get this done.
Here's the code I used:
sched <- data.frame(dsk_proj_number = rezvan$dsk_proj_number)
sched$weeks <- NA
sched <- sched %>% pivot_wider(names_from = dsk_proj_number, values_from = weeks)
for(proj_num in weekly_data$dsk_proj_number){
duration_cd = weekly_data[which(weekly_data$dsk_proj_number == proj_num), "cd_dur_weeks"] %>% as.numeric
duration_ca = weekly_data[which(weekly_data$dsk_proj_number == proj_num), "ca_dur_weeks"] %>% as.numeric
if(duration_cd > 0) {
sched[1:duration_cd, proj_num] = weekly_data[which(weekly_data$dsk_proj_number == proj_num), "hrs_per_week_cd"]
}
if(duration_ca > 0) {
sched[duration_cd + 1:duration_ca, proj_num] = weekly_data[which(weekly_data$dsk_proj_number == proj_num), "hrs_per_week_ca"]
}
}
sched <- sched %>% mutate_all(coalesce, 0)

You can use rep() to repeat elements a certain number of times, and then use c() to concatenate them into a long sequence. I use rowwise from dplyr to conveniently do this row-by-row.
Then you can unnest the lists of vectors.
library(tidyverse)
sched <- weekly_data %>%
mutate(max_weeks = max(cd_dur_weeks + ca_dur_weeks)) %>%
rowwise() %>%
mutate(week = list(c(rep(hrs_per_week_cd, cd_dur_weeks), rep(hrs_per_week_ca, ca_dur_weeks), rep(0, max_weeks-cd_dur_weeks-ca_dur_weeks)))) %>%
ungroup() %>%
select(dsk_proj_number, week) %>%
pivot_wider(names_from = "dsk_proj_number", values_from = week) %>%
unnest(everything())

df %>%
select(1:3) %>%
slice(rep(1:nrow(.), cd_dur_weeks)) %>%
select(-3) %>%
mutate(milestone = 1) %>%
rename(hrs_per_week = hrs_per_week_cd) -> df1
df %>%
select(c(1,4,5)) %>%
slice(rep(1:nrow(.), ca_dur_weeks)) %>%
select(-3) %>%
mutate(milestone = 2) %>%
rename(hrs_per_week = hrs_per_week_ca) -> df2
rbind(df1, df2) %>%
arrange(dsk_proj_number, milestone) %>%
group_by(dsk_proj_number) %>%
mutate(week = seq_along(dsk_proj_number)) %>%
pivot_wider(id_cols=week, names_from=dsk_proj_number, values_from=hrs_per_week) %>%
replace(is.na(.), 0)

How do I extract this portion of a table from a text file using R using grep?

I have a file, "prf003.out",
150 lines of blah....~tables that report other things in this text file deleted.....
Aboveground Live Belowground Forest Total Total Carbon
----------------- ----------------- Stand ------------------------- Stand Removed Released
YEAR Total Merch Live Dead Dead DDW Floor Shb/Hrb Carbon Carbon from Fire
--------------------------------------------------------------------------------------------------------------
2000 15.6 15.6 6.0 0.5 0.0 4.5 2.6 0.0 29.1 0.0 0.0
2001 15.6 15.6 6.0 0.4 0.0 4.2 2.5 0.0 28.7 0.0 0.0
2002 15.6 15.6 6.0 0.4 0.0 3.9 2.5 0.0 28.4 0.0 0.0
2003 15.6 15.6 6.0 0.4 0.0 3.7 2.5 0.0 28.1 0.0 0.0
2004 15.6 15.6 6.0 0.4 0.0 3.5 2.5 0.0 27.9 0.0 0.0
2005 16.6 16.6 6.0 1.0 1.3 3.6 2.5 0.0 30.9 0.0 0.0
2006 16.6 16.6 6.0 0.9 0.8 3.8 2.4 0.0 30.6 0.0 0.0
2007 16.6 16.6 6.0 0.9 0.6 3.8 2.4 0.0 30.3 0.0 0.0
2008 16.6 16.6 6.0 0.9 0.4 3.7 2.4 0.0 30.0 0.0 0.0
2009 16.6 16.6 6.0 0.8 0.2 3.7 2.4 0.0 29.8 0.0 0.0
2010 18.1 18.1 6.3 1.2 1.0 3.8 2.4 0.0 32.8 0.0 0.0
2011 18.1 18.1 6.3 1.1 0.6 4.0 2.4 0.0 32.5 0.0 0.0
2012 18.1 18.1 6.3 1.1 0.4 3.9 2.4 0.0 32.2 0.0 0.0
2013 18.1 18.1 6.3 1.0 0.3 3.9 2.4 0.0 31.9 0.0 0.0
2014 18.1 18.1 6.3 1.0 0.2 3.8 2.4 0.0 31.7 0.0 0.0
2015 19.1 19.1 6.5 1.4 1.1 3.9 2.4 0.0 34.3 0.0 0.0
2016 19.1 19.1 6.5 1.3 0.7 4.1 2.4 0.0 34.0 0.0 0.0
2017 19.1 19.1 6.5 1.3 0.5 4.0 2.4 0.0 33.8 0.0 0.0
2018 19.1 19.1 6.5 1.2 0.3 4.0 2.4 0.0 33.5 0.0 0.0
2019 19.1 19.1 6.5 1.2 0.2 3.9 2.4 0.0 33.2 0.0 0.0
2020 19.0 19.0 6.3 1.9 1.8 4.2 2.4 0.0 35.6 0.0 0.0
2021 19.0 19.0 6.3 1.8 1.3 4.5 2.4 0.0 35.3 0.0 0.0
2022 19.0 19.0 6.3 1.7 1.0 4.6 2.4 0.0 35.0 0.0 0.0
2023 19.0 19.0 6.3 1.6 0.7 4.6 2.4 0.0 34.7 0.0 0.0
2024 19.0 19.0 6.3 1.6 0.5 4.6 2.4 0.0 34.4 0.0 0.0
2025 19.0 19.0 6.3 2.2 2.0 4.9 2.4 0.0 36.7 0.0 0.0
2026 19.0 19.0 6.3 2.1 1.3 5.3 2.4 0.0 36.4 0.0 0.0
2027 19.0 19.0 6.3 2.0 1.0 5.4 2.4 0.0 36.0 0.0 0.0
2028 19.0 19.0 6.3 1.9 0.7 5.4 2.4 0.0 35.7 0.0 0.0
2029 19.0 19.0 6.3 1.9 0.5 5.4 2.4 0.0 35.4 0.0 0.0
2030 19.4 19.4 6.5 2.2 1.4 5.6 2.4 0.0 37.5 0.0 0.0
2031 19.4 19.4 6.5 2.1 0.8 5.9 2.4 0.0 37.2 0.0 0.0
2032 19.4 19.4 6.5 2.0 0.6 5.9 2.4 0.0 36.8 0.0 0.0
2033 19.4 19.4 6.5 1.9 0.4 5.8 2.4 0.0 36.5 0.0 0.0
2034 19.4 19.4 6.5 1.9 0.3 5.7 2.4 0.0 36.1 0.0 0.0
2035 18.6 18.6 6.3 2.6 2.1 6.0 2.4 0.0 38.0 0.0 0.0
2036 18.6 18.6 6.3 2.5 1.5 6.4 2.4 0.0 37.6 0.0 0.0
2037 18.6 18.6 6.3 2.4 1.1 6.4 2.4 0.0 37.2 0.0 0.0
2038 18.6 18.6 6.3 2.3 0.8 6.5 2.4 0.0 36.9 0.0 0.0
2039 18.6 18.6 6.3 2.2 0.6 6.5 2.4 0.0 36.5 0.0 0.0
2040 19.4 19.4 6.7 2.3 1.0 6.6 2.4 0.0 38.3 0.0 0.0
2041 19.4 19.4 6.7 2.2 0.6 6.6 2.4 0.0 38.0 0.0 0.0
2042 19.4 19.4 6.7 2.1 0.5 6.5 2.4 0.0 37.6 0.0 0.0
2043 19.4 19.4 6.7 2.0 0.4 6.4 2.4 0.0 37.3 0.0 0.0
2044 19.4 19.4 6.7 2.0 0.3 6.3 2.4 0.0 36.9 0.0 0.0
2045 17.9 17.9 6.3 2.8 2.5 6.6 2.4 0.0 38.5 0.0 0.0
2046 17.9 17.9 6.3 2.7 1.8 7.0 2.4 0.0 38.1 0.0 0.0
2047 17.9 17.9 6.3 2.6 1.4 7.1 2.4 0.0 37.7 0.0 0.0
2048 17.9 17.9 6.3 2.5 1.0 7.2 2.4 0.0 37.3 0.0 0.0
2049 17.9 17.9 6.3 2.4 0.7 7.2 2.4 0.0 36.9 0.0 0.0
blah.....a few more tables
that I am trying to extract this particular table from. As you can see "blah" at the top represents a whole bunch of other tables generated in this .txt file.
Afterwards, I have a whole bunch of other tables outputted in that same file.
What I am trying to do is similar to this question, but now I am stuck: Extracting Data from Text Files
Here is what I did:
data <- readLines("prf003.out")
data
#VALUE=TRUE RETURNS EXACT MATCH OF TEXT.
cline <- grep("YEAR Total Merch Live Dead Dead DDW Floor Shb/Hrb Carbon Carbon from Fire", data, value= FALSE)
cline
#dont use str_extract, use str_extract_all
numstr <- sapply(str_extract_all(data[cline+1:51],"[0-9]"),as.numeric)
numstr
However, the output I get is wonky and doesn't format my data the way I want (i.e. just give me a copy of the original table so I can process it in R)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39]
[1,] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
[4,] 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
[5,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[6,] 5 5 5 5 5 6 6 6 6 6 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 8 8 8 8
[7,] 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 6 6 6 6
[8,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[9,] 5 5 5 5 5 6 6 6 6 6 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 8 8 8 8
[10,] 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 6 6 6 6
[11,] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[12,] 0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 3 3 3 3
[13,] 0 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 2 2 1 1 2 2 2 2
[14,] 5 4 4 4 4 0 9 9 9 8 2 1 1 0 0 4 3 3 2 2 9 8 7 6 6 2 1 0 9 9 2 1 0 9 9 6 5 4 3
[15,] 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 1 0 0 2 1 1 0 0 1 0 0 0 0 2 1 1 0
[16,] 0 0 0 0 0 3 8 6 4 2 0 6 4 3 2 1 7 5 3 2 8 3 0 7 5 0 3 0 7 5 4 8 6 4 3 1 5 1 8
[17,] 4 4 3 3 3 3 3 3 3 3 3 4 3 3 3 3 4 4 4 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 6 6 6 6
[18,] 5 2 9 7 5 6 8 8 7 7 8 0 9 9 8 9 1 0 0 9 2 5 6 6 6 9 3 4 4 4 6 9 9 8 7 0 4 4 5
[19,] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[20,] 6 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[23,] 2 2 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[24,] 9 8 8 8 7 0 0 0 0 9 2 2 2 1 1 4 4 3 3 3 5 5 5 4 4 6 6 6 5 5 7 7 6 6 6 8 7 7 6
[25,] 1 7 4 1 9 9 6 3 0 8 8 5 2 9 7 3 0 8 5 2 6 3 0 7 4 7 4 0 7 4 5 2 8 5 1 0 6 2 9
[26,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[27,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
As you can see, it takes each value of each number and puts that in a new coordinate. I just want the original table.

What about something like this
# figure out where the headers are & where the data starts
dataHeader1 <- which(grepl("Aboveground", txtFile))
dataHeader2 <- dataHeader1 + 2
dataStart <- dataHeader2 + 2
# extract the data
txtDat <- txtFile[dataStart:length(txtFile)]
txtDat <- do.call(rbind, strsplit(txtDat, split = "\\s{1,}", perl = TRUE))
class(txtDat) <- "numeric"
txtDat
# returns
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 2000 15.6 15.6 6.0 0.5 0.0 4.5 2.6 0 29.1 0 0
[2,] 2001 15.6 15.6 6.0 0.4 0.0 4.2 2.5 0 28.7 0 0
[3,] 2002 15.6 15.6 6.0 0.4 0.0 3.9 2.5 0 28.4 0 0
[4,] 2003 15.6 15.6 6.0 0.4 0.0 3.7 2.5 0 28.1 0 0
[5,] 2004 15.6 15.6 6.0 0.4 0.0 3.5 2.5 0 27.9 0 0
[6,] 2005 16.6 16.6 6.0 1.0 1.3 3.6 2.5 0 30.9 0 0
....
Note that one can sharpen the regex in order to determine where the data starts e.g.
dataHeader1 <- which(grepl("(?=.*Aboveground)(?=.*Carbon)", txtFile, perl = TRUE))
# this can be pursued arbitrarily
. I read the data via txtFile <- readLines("Path/To/test.txt") and the raw data itself looks like this
[1] "asdsalkjdaskldas+"
[2] "jsafhnjadfnhdjkasfafdajfbnjasbfjads.kbnjdasnfadsnf"
[3] "45453342542542kj ijholijfkqaef45435314"
[4] ""
[5] "dasfjasikedfnha4454 "
[6] "a"
[7] "a"
[8] "fdgfd"
[9] "\t\t6546346343"
[10] ""
[11] ""
[12] " Aboveground Live Belowground Forest Total Total Carbon"
[13] " ----------------- ----------------- Stand ------------------------- Stand Removed Released"
[14] "YEAR Total Merch Live Dead Dead DDW Floor Shb/Hrb Carbon Carbon from Fire"
[15] "--------------------------------------------------------------------------------------------------------------"
[16] "2000 15.6 15.6 6.0 0.5 0.0 4.5 2.6 0.0 29.1 0.0 0.0"
...

How to animate stroke-dashoffset with SVG animate?

To animate stroke-dashoffset I am aware of using CSS #keyframes to move the stroke-dashoffset of a SVG path. However, because I want to size the SVG with background-size: cover, I am unable to target the individual elements inside the SVG since it's being referenced as a background-image in CSS.
Is there a way to use SVG's built-in <animate /> tags to animate stroke-dashoffset?

Lion head animation example
Animation of drawing lines from zero to maximum value is implemented by changing the stroke-dashoffset from maximum to zero.
attributeName="stroke-dashoffset"
begin="0.1s;f1.end+0.4s"
values="2037;0;2037"
dur="15s"
calcMode="linear"
/>
A second animation has been added - filling with a color that starts after the animation of drawing lines is completed.
<animate id="f1"
attributeName="fill"
begin="p1.end+0.4s"
dur="10s"
values="#FCFCFC;#A9A9A9;black;#A9A9A9;#FCFCFC"
/>
Drawing and erasing lines is accomplished using the attribute:
values="2037;0;2037"
.txt {
font-size:1.2em;
color:gray;
}
h1 {
text-align: center;
}
.lion {
padding:0.25em;
margin-left:-1.5em;
float:left;
}
<body>
<h1>Lion</h1>
<div class="lion">
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"
width="200" height="200" viewBox="-30 85 600 600"
style="border:0px dotted red;">
<title>The animation is drawing lines</title>
<g transform="scale(0.85) ">
<path class="path" fill="none" stroke-width="2" stroke ="black" stroke-dasharray= "2000" stroke-dashoffset="2000" d="m272.2 113.6c-0.3 0-0.6 0-0.9 0.1-0.7 0.1 2.2 2.5 10.9 11.2 9.8 9.7 18.2 19.1 23 25.5 5.9 8.1 12.3 18.6 16.4 29.1 0.8 1.6 3.6 11.9 4.6 16.9 0.5 2.8 1 5.4 1.2 5.8 0.8 2.5 3.9-8.4 4.8-13 1.2-5.6 1.2-11.4 1-17.1-0.2-5.8-0.5-11.8-2.2-17.4-1.4-4.8-3.6-9.4-6.7-13.3-6.6-8.2-15.1-15.1-24.4-20.1-6.4-3.5-13.8-4.9-20.9-6.5-2.2-0.5-4.8-1.3-6.9-1.2zm194.7 0c-2.1 0-4.7 0.7-6.9 1.2-7.1 1.6-14.5 3.1-20.9 6.5-9.3 5-17.8 11.8-24.4 20.1-3.1 3.9-5.3 8.6-6.7 13.3-1.7 5.6-2 11.5-2.2 17.4-0.2 5.7-0.1 11.5 1 17.1 1 4.5 4 15.4 4.8 13 0.1-0.3 0.6-2.9 1.2-5.8 0.9-5 3.7-15.3 4.6-16.9 4.2-10.5 10.5-21 16.5-29.1 4.8-6.5 13.2-15.8 23-25.5 8.7-8.7 11.6-11.1 10.9-11.2-0.3 0-0.6-0.1-0.9-0.1zm-97.3 24.5c-0.8-0.1-1.4 5.7-2 8.5-0.9 4.3-1.3 8.7-2.4 12.9-1.8 6.9-4.3 13.7-7.1 20.3-2.4 5.7-6.4 13.4-8.3 16.5-5.5 8.8-9 13.7-13.9 19.6-6.1 7.3-7.4 8.8-13 14.4-10.8 10.8-19.3 15.7-30 22.1-7.3 4.4-15.3 7.6-23.1 11-17.5 7.6-36.3 14.2-55.9 20.5-12.8 4.3-26.1 7.4-38.3 13.1-18.2 8.6-36.3 18.5-51.6 31.6-11.5 9.9-21.1 22.1-29.5 34.7-5.1 7.7-9 15.2-12.6 24.7-3 6.3-4.8 12.7-6.7 19.3-1.5 5.2-3 9.8-3.6 15.7-0.3 1.9-0.8 4.5-1.1 5.7-0.9 3.4-1.3 7.6-0.7 9 0.3 1.3 1.9 1.5 2.4 0 0.5-1.4 0.7-1.8 1.5-3.1 1.8-2.9 4.4-7 6.9-10.3 9.6-12.2 19.7-24.2 31.5-34.2 14.4-12.3 30.6-22.3 47.2-31.4 18.1-10 36.9-17 56.7-25.4 7.8-3.4 14.4-5.6 23.2-10.3 2.6-1.8 3.7-1.2 2.7 1.4-0.4 0.9-0.7 2.7-0.8 4-0.2 3 0.6 3.7 2.9 2.8 1.8-0.7 2.7-1 7.8-2.6 2.2-0.7 4.1-1.5 4.3-1.9 0.3-0.7-3.8-5-7.9-8.4-2.3-1.9-4.7-4.8-4.3-5.2 0.1-0.1 2 0 3.1 0.1 2.4 0.3 2.9 0.3 10.1 0.5 11.4 0.4 11.6 0.6 10-3.5-1.3-3.3-2.6-6.3-3.9-9.7-0.7-1.3-1.1-2.5-0.9-2.7 0.2-0.2 3.6-0.5 7.9-0.5 4.2 0 8.8-0.2 10.2-0.4l2.5-0.4c1-1-0.1-4.6-0.4-6.9-0.2-1.7-0.8-3.8-1-5.2s-0.4-3.3-0.5-4.2c-0.2-1 0-1.7 0.4-1.9 0.5-0.2 1.4-0.1 2.7 0.5 1.1 0.5 3.2 1.3 4.7 1.9 1.5 0.5 3.8 1.4 5 1.9 2.7 1 4.1 1.1 4.6 0.3 0.5-0.7 1.5-3.4 1.9-5.1 1.4-5.1 11-19.2 13.7-20.1 0.8-0.2 3.4 1.4 4.6 2.4 0.4 0.4 7.6 6 9.2 7.1 1.5 0.9 2.6 0.8 3-0.5 0.1-0.4 0.6-1.6 1.1-2.6 1.8-3.9 3.7-7.2 6.6-14 0.6-2.6 1.5-1.9 5.2 4.1 5.3 8.4 12.5 15.8 21.2 21.6 3.3 2.2 2.6 1.8 4.3 2.7 1.7-0.9 1-0.5 4.3-2.7 8.7-5.8 16-13.2 21.2-21.6 3.8-6 4.7-6.7 5.2-4.1 2.9 6.7 4.8 10.1 6.6 14 0.5 1 1 2.2 1.1 2.6 0.4 1.2 1.5 1.4 3 0.5 1.6-1 8.8-6.7 9.2-7.1 1.2-1.1 3.8-2.7 4.6-2.4 2.7 0.9 12.4 15 13.7 20.1 0.5 1.8 1.5 4.4 1.9 5.1 0.5 0.8 2 0.7 4.6-0.3 1.3-0.5 3.5-1.3 5-1.9 1.5-0.5 3.6-1.4 4.7-1.9 1.3-0.6 2.2-0.8 2.7-0.5 0.5 0.2 0.6 0.8 0.4 1.9-0.1 0.9-0.4 2.8-0.5 4.2-0.2 1.4-0.7 3.4-1 5.2-0.3 2.3-1.3 5.9-0.4 6.9l2.5 0.4c1.4 0.2 6 0.4 10.2 0.4 4.3 0 7.8 0.2 7.9 0.5 0.1 0.2-0.3 1.5-0.9 2.7-1.4 3.3-2.7 6.4-3.9 9.7-1.6 4.1-1.4 3.8 10 3.5 7.2-0.2 7.8-0.2 10.1-0.5 1.1-0.1 3-0.2 3.1-0.1 0.4 0.4-1.9 3.2-4.3 5.2-4.1 3.4-8.1 7.7-7.9 8.4 0.1 0.4 2.1 1.2 4.3 1.9 5.1 1.6 6 1.9 7.8 2.6 2.3 0.9 3.1 0.2 2.9-2.8-0.1-1.3-0.4-3.1-0.8-4-1.1-2.6 0.1-3.2 2.7-1.4 8.8 4.8 15.5 7 23.2 10.3 19.9 8.4 38.6 15.4 56.7 25.4 16.5 9.1 32.8 19.1 47.2 31.4 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calcMode="linear"
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<animate id="f1"
attributeName="fill"
begin="p1.end+0.4s"
dur="10s"
values="#FCFCFC;#A9A9A9;black;#A9A9A9;#FCFCFC"
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<div class="txt">
<p> The lion (Panthera leo) is a species in the family Felidae; it is a muscular, deep-chested cat with a short, rounded head, a reduced neck and round ears, and a hairy tuft at the end of its tail. The lion is sexually dimorphic; males are larger than females with a typical weight range of 150 to 250 kg (330 to 550 lb) for males and 120 to 182 kg (265 to 400 lb) for females. Male lions have a prominent mane, which is the most recognisable feature of the species. A lion pride consists of a few adult males, related females and cubs. Groups of female lions typically hunt together, preying mostly on large ungulates. The species is an apex and keystone predator, although they scavenge when opportunities occur. Some lions have been known to hunt humans, although the species typically does not.</p>
<p>Typically, the lion inhabits grasslands and savannas but is absent in dense forests. It is usually more diurnal than other big cats, but when persecuted it adapts to being active at night and at twilight. In the Pleistocene, the lion ranged throughout Eurasia, Africa and North America but today it has been reduced to fragmented populations in Sub-Saharan Africa and one critically endangered population in western India. It has been listed as Vulnerable on the IUCN Red List since 1996 because populations in African countries have declined by about 43% since the early 1990s. Lion populations are untenable outside designated protected areas. Although the cause of the decline is not fully understood, habitat loss and conflicts with humans are the greatest causes for concern.</p>
<p>One of the most widely recognised animal symbols in human culture, the lion has been extensively depicted in sculptures and paintings, on national flags, and in contemporary films and literature. Lions have been kept in menageries since the time of the Roman Empire and have been a key species sought for exhibition in zoological gardens across the world since the late 18th century. Cultural depictions of lions were prominent in the Upper Paleolithic period; carvings and paintings from the Lascaux and Chauvet Caves in France have been dated to 17,000 years ago, and depictions have occurred in virtually all ancient and medieval cultures that coincided with the lion's former and current ranges.is not fully understood, habitat loss and conflicts with humans are the greatest causes for concern. </p>
wikipedia.org
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I found the answer. You insert the <animate /> tag within the path.
<path stroke-dashoffset="200" stroke-dasharray="200 30" stroke-width="2" stroke="#333" d="...">
<animate attributeName="stroke-dashoffset" values="0 2000" dur="5s" repeatCount="indefinite" />
</path>

How to solve this error for pollution rose?

I have an issue with R where I am trying to do a pollution rose graph and I am almost sure that the data and the code is correct but I still keep getting an error message and I couldn't figure out what does it mean. The error message is:
Error in `[[<-.data.frame`(`*tmp*`, vars[i], value = numeric(0)) :
replacement has 0 rows, data has 37.
My code is:
pollutionRose(pollution_rose, pollutant = "PM10",header= TRUE, cols = c("darkblue","green4","yellow2","red","red4"), key.position = "bottom",max.freq = 50)
and here is my data:
HH:MM:SS WD1 WS1 PM10 PM2.5 PM1
10:10:00 AM 0 0 0 0 0
10:20:00 AM 0 0 0 0 0
10:29:00 AM 254 0.4 0 0 0
10:30:00 AM 109 0.5 0 0 0
10:40:00 AM 21 1.9 0 0 0
10:50:00 AM 148 1.2 0 0 0
10:54:00 AM 222 1.1 0 0 0
10:55:00 AM 61 1 0 0 0
11:00:00 AM 109 0.6 19 4.3 1.8
11:10:00 AM 354 0.7 20.4 4.1 1.7
11:20:00 AM 5 2.6 8.3 3.8 1.6
11:29:00 AM 60 2.6 7.9 3.8 1.5
11:30:00 AM 97 1.5 18.6 3.8 1.5
11:40:00 AM 42 0.8 15.6 3.8 1.5
11:50:00 AM 52 0 10.5 4.3 1.6
12:00:00 PM 60 0.9 11.7 3.9 1.5
12:10:00 PM 74 1 9.6 4.1 1.4
12:20:00 PM 338 1.7 0 0 0
12:30:00 PM 285 4.4 0 0 0
12:40:00 PM 296 3.6 0 0 0
12:50:00 PM 241 3.3 0 0 0
1:00:00 PM 274 1.2 0 0 0
1:10:00 PM 287 1.3 15.8 4.4 1.6
1:20:00 PM 317 3 13.1 4.6 1.7
1:30:00 PM 309 2.6 10.5 3.5 1.4
1:31:00 PM 244 3.5 14.8 4.2 1.5
1:40:00 PM 251 0.9 12.8 4.1 1.5
1:50:00 PM 282 1.1 12.9 4.8 1.8
2:00:00 PM 254 2.5 9.6 4.9 1.7
2:10:00 PM 245 2.3 10.9 4.6 1.6
2:20:00 PM 207 2.1 0 0 0
2:30:00 PM 30 0 0 0 0
2:37:00 PM 62 0.7 12.9 4.3 1.6
2:40:00 PM 80 1.8 10.1 3.6 1.5
2:40:00 PM 0 0 10.1 3.6 1.5
2:50:00 PM 0 0 10 4.3 1.5
3:00:00 PM 0 0 0 0 0

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
test.bic.surv<- bic.surv(x1, surv.t, cens, factor.type=FALSE, strict=FALSE, nbest=2000)
However, whenever I tried to add in any more independent variables into x1 such as "comprisk5L" or "econriskL1", the
test.bic.surv<- bic.surv(x1, surv.t, cens, factor.type=FALSE, strict=FALSE, nbest=2000)
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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