I am trying to use a R package called GOSemSim, it requires to import a lot of data into variables with a specific format like this:
data1 = c("one", "two", "three")
data2 = c("A", "B", "C")
When the list of data that I try to import into a variable is longer than 293 then I get the following error message:
argument 293 is empty
THere are no error with the "" or comma, I computed it with linux, it does not matter what data it is.
This is really weird basically, I tried on two computers with no luck. I tried to import it as a CSV file but the R package won't allow it.
Anyone knows why you cannot import more than 293 data?
Update:
Here is the code and my data at the same time, it is a one liner in R which has never been a problem for me!
OQ = c("GO:0000003", "GO:0000070", "GO:0000077", "GO:0000079", "GO:0000082", "GO:0000086", "GO:0000122", "GO:0000212", "GO:0000226", "GO:0000278", "GO:0000279", "GO:0000280", "GO:0000724", "GO:0000725", "GO:0000819", "GO:0000910", "GO:0001932", "GO:0002118", "GO:0002121", "GO:0002165", "GO:0003002", "GO:0003006", "GO:0006022", "GO:0006030", "GO:0006040", "GO:0006139", "GO:0006259", "GO:0006260", "GO:0006261", "GO:0006267", "GO:0006270", "GO:0006275", "GO:0006277", "GO:0006281", "GO:0006302", "GO:0006304", "GO:0006305", "GO:0006306", "GO:0006310", "GO:0006323", "GO:0006325", "GO:0006342", "GO:0006351", "GO:0006355", "GO:0006357", "GO:0006366", "GO:0006464", "GO:0006468", "GO:0006479", "GO:0006725", "GO:0006807", "GO:0006928", "GO:0006950", "GO:0006974", "GO:0006996", "GO:0007010", "GO:0007017", "GO:0007018", "GO:0007049", "GO:0007051", "GO:0007059", "GO:0007062", "GO:0007067", "GO:0007076", "GO:0007088", "GO:0007093", "GO:0007095", "GO:0007098", "GO:0007126", "GO:0007127", "GO:0007131", "GO:0007140", "GO:0007141", "GO:0007143", "GO:0007154", "GO:0007155", "GO:0007156", "GO:0007259", "GO:0007266", "GO:0007275", "GO:0007276", "GO:0007281", "GO:0007282", "GO:0007292", "GO:0007304", "GO:0007307", "GO:0007346", "GO:0007350", "GO:0007365", "GO:0007367", "GO:0007379", "GO:0007389", "GO:0007399", "GO:0007400", "GO:0007417", "GO:0007420", "GO:0007423", "GO:0007444", "GO:0007472", "GO:0007476", "GO:0007552", "GO:0007560", "GO:0008104", "GO:0008213", "GO:0008283", "GO:0008284", "GO:0008315", "GO:0008356", "GO:0009059", "GO:0009611", "GO:0009653", "GO:0009790", "GO:0009791", "GO:0009880", "GO:0009886", "GO:0009887", "GO:0009888", "GO:0009889", "GO:0009890", "GO:0009892", "GO:0009893", "GO:0009896", "GO:0009968", "GO:0009987", "GO:0010032", "GO:0010033", "GO:0010092", "GO:0010389", "GO:0010468", "GO:0010498", "GO:0010556", "GO:0010558", "GO:0010564", "GO:0010604", "GO:0010605", "GO:0010608", "GO:0010629", "GO:0010648", "GO:0010948", "GO:0014016", "GO:0014017", "GO:0014070", "GO:0016043", "GO:0016055", "GO:0016070", "GO:0016310", "GO:0016319", "GO:0016321", "GO:0016441", "GO:0016458", "GO:0016568", "GO:0016569", "GO:0016570", "GO:0016571", "GO:0016572", "GO:0017145", "GO:0018130", "GO:0019219", "GO:0019222", "GO:0019438", "GO:0019827", "GO:0019953", "GO:0022402", "GO:0022403", "GO:0022404", "GO:0022412", "GO:0022414", "GO:0022610", "GO:0023052", "GO:0023057", "GO:0030111", "GO:0030154", "GO:0030178", "GO:0030182", "GO:0030261", "GO:0030422", "GO:0030703", "GO:0030727", "GO:0031023", "GO:0031047", "GO:0031050", "GO:0031056", "GO:0031060", "GO:0031123", "GO:0031145", "GO:0031175", "GO:0031323", "GO:0031324", "GO:0031325", "GO:0031326", "GO:0031327", "GO:0031331", "GO:0031398", "GO:0031399", "GO:0031401", "GO:0031570", "GO:0031572", "GO:0031935", "GO:0032268", "GO:0032270", "GO:0032501", "GO:0032502", "GO:0032504", "GO:0032507", "GO:0032774", "GO:0032776", "GO:0032886", "GO:0033043", "GO:0033044", "GO:0033260", "GO:0033301", "GO:0033554", "GO:0034622", "GO:0034641", "GO:0034645", "GO:0034654", "GO:0034754", "GO:0034968", "GO:0035023", "GO:0035107", "GO:0035114", "GO:0035120", "GO:0035186", "GO:0035194", "GO:0035195", "GO:0035220", "GO:0035282", "GO:0035295", "GO:0035825", "GO:0036211", "GO:0036388", "GO:0040029", "GO:0042060", "GO:0042221", "GO:0042445", "GO:0043009", "GO:0043066", "GO:0043069", "GO:0043161", "GO:0043170", "GO:0043331", "GO:0043412", "GO:0043414", "GO:0043549", "GO:0043631", "GO:0043933", "GO:0044237", "GO:0044249", "GO:0044260", "GO:0044271", "GO:0044419", "GO:0044700", "GO:0044702", "GO:0044703", "GO:0044707", "GO:0044728", "GO:0044763", "GO:0044767", "GO:0044770", "GO:0044771", "GO:0044772", "GO:0044773", "GO:0044774", "GO:0044786", "GO:0044818", "GO:0044839", "GO:0044843", "GO:0044848", "GO:0045132", "GO:0045165", "GO:0045168", "GO:0045185", "GO:0045448", "GO:0045455", "GO:0045787", "GO:0045814", "GO:0045859", "GO:0045892", "GO:0045931", "GO:0045934", "GO:0046331", "GO:0046425", "GO:0046483", "GO:0046580", "GO:0046605", "GO:0046777", "GO:0048070", "GO:0048134", "GO:0048135", "GO:0048285", "GO:0048311", "GO:0048468", "GO:0048477", "GO:0048513", "GO:0048518", "GO:0048519", "GO:0048522", "GO:0048523", "GO:0048563", "GO:0048569", "GO:0048583", "GO:0048585", "GO:0048609", "GO:0048646", "GO:0048666", "GO:0048699", "GO:0048704", "GO:0048705", "GO:0048706", "GO:0048707", "GO:0048731", "GO:0048736", "GO:0048737", "GO:0048754", "GO:0048856", "GO:0048863", "GO:0048865", "GO:0048867", "GO:0048869", "GO:0050789", "GO:0050793", "GO:0050794", "GO:0050896", "GO:0051052", "GO:0051058", "GO:0051128", "GO:0051171", "GO:0051172", "GO:0051225", "GO:0051235", "GO:0051246", "GO:0051247", "GO:0051252", "GO:0051253", "GO:0051276", "GO:0051297", "GO:0051299", "GO:0051301", "GO:0051302", "GO:0051321", "GO:0051325", "GO:0051329", "GO:0051338", "GO:0051351", "GO:0051443", "GO:0051445", "GO:0051641", "GO:0051646", "GO:0051651", "GO:0051704", "GO:0051716", "GO:0051726", "GO:0051783", "GO:0051785", "GO:0060255", "GO:0060429", "GO:0060548", "GO:0060688", "GO:0060966", "GO:0060968", "GO:0060993", "GO:0061138", "GO:0065003", "GO:0065004", "GO:0065007", "GO:0070192", "GO:0070507", "GO:0070887", "GO:0070918", "GO:0071103", "GO:0071359", "GO:0071822", "GO:0071824", "GO:0071840", "GO:0071897", "GO:0071900", "GO:0072028", "GO:0072078", "GO:0072079", "GO:0072088", "GO:0080090", "GO:0090068", "GO:0090304", "GO:0090306", "GO:0098609", "GO:1901071", "GO:1901360", "GO:1901362", "GO:1901576", "GO:1901987", "GO:1901988", "GO:1901990", "GO:1901991", "GO:1902275", "GO:1902299", "GO:1902589", "GO:1902679", "GO:1902749", "GO:1903046", "GO:1903047", "GO:1903308", "GO:1903322", "GO:2000026", "GO:2000112", "GO:2000113", "GO:2001141")
The error message in itself is informative. If one tries to make it reproducible, it's best to work with small subsets. It usually helps to have a dead stare at your data before trying to reproduce the behavior. For example,
OQ = c("GO:0000003", "GO:2001141", )
Notice that there are two elements of this character vector. Or are they?
Error in c("GO:0000003", "GO:2001141", ) : argument 3 is empty
Number 3 is the key. R is expecting three elements. Notice the comma after the second element. Once you remove it, you'll be able to create the QQ variable. Scan your real example. I'm sure there's a , , somewhere.
EDIT
I tried copy/pasting your code into a script in Rstudio and it produced the error you describe. If you scroll right, you'll notice that syntax coloring is not working at around position 5000. I have folded the code so that it fits on screen and it runs fine.
This is how I folded the vector and it worked.
OQ = c("GO:0000003", "GO:0000070", "GO:0000077", "GO:0000079", "GO:0000082", "GO:0000086", "GO:0000122",
"GO:0000212", "GO:0000226", "GO:0000278", "GO:0000279", "GO:0000280", "GO:0000724", "GO:0000725",
"GO:0000819", "GO:0000910", "GO:0001932", "GO:0002118", "GO:0002121", "GO:0002165", "GO:0003002",
"GO:0003006", "GO:0006022", "GO:0006030", "GO:0006040", "GO:0006139", "GO:0006259", "GO:0006260",
"GO:0006261", "GO:0006267", "GO:0006270", "GO:0006275", "GO:0006277", "GO:0006281", "GO:0006302",
"GO:0006304", "GO:0006305", "GO:0006306", "GO:0006310", "GO:0006323", "GO:0006325", "GO:0006342",
"GO:0006351", "GO:0006355", "GO:0006357", "GO:0006366", "GO:0006464", "GO:0006468", "GO:0006479",
"GO:0006725", "GO:0006807", "GO:0006928", "GO:0006950", "GO:0006974", "GO:0006996", "GO:0007010",
"GO:0007017", "GO:0007018", "GO:0007049", "GO:0007051", "GO:0007059", "GO:0007062", "GO:0007067",
"GO:0007076", "GO:0007088", "GO:0007093", "GO:0007095", "GO:0007098", "GO:0007126", "GO:0007127",
"GO:0007131", "GO:0007140", "GO:0007141", "GO:0007143", "GO:0007154", "GO:0007155", "GO:0007156",
"GO:0007259", "GO:0007266", "GO:0007275", "GO:0007276", "GO:0007281", "GO:0007282", "GO:0007292",
"GO:0007304", "GO:0007307", "GO:0007346", "GO:0007350", "GO:0007365", "GO:0007367", "GO:0007379",
"GO:0007389", "GO:0007399", "GO:0007400", "GO:0007417", "GO:0007420", "GO:0007423", "GO:0007444",
"GO:0007472", "GO:0007476", "GO:0007552", "GO:0007560", "GO:0008104", "GO:0008213", "GO:0008283",
"GO:0008284", "GO:0008315", "GO:0008356", "GO:0009059", "GO:0009611", "GO:0009653", "GO:0009790",
"GO:0009791", "GO:0009880", "GO:0009886", "GO:0009887", "GO:0009888", "GO:0009889", "GO:0009890",
"GO:0009892", "GO:0009893", "GO:0009896", "GO:0009968", "GO:0009987", "GO:0010032", "GO:0010033",
"GO:0010092", "GO:0010389", "GO:0010468", "GO:0010498", "GO:0010556", "GO:0010558", "GO:0010564",
"GO:0010604", "GO:0010605", "GO:0010608", "GO:0010629", "GO:0010648", "GO:0010948", "GO:0014016",
"GO:0014017", "GO:0014070", "GO:0016043", "GO:0016055", "GO:0016070", "GO:0016310", "GO:0016319",
"GO:0016321", "GO:0016441", "GO:0016458", "GO:0016568", "GO:0016569", "GO:0016570", "GO:0016571",
"GO:0016572", "GO:0017145", "GO:0018130", "GO:0019219", "GO:0019222", "GO:0019438", "GO:0019827",
"GO:0019953", "GO:0022402", "GO:0022403", "GO:0022404", "GO:0022412", "GO:0022414", "GO:0022610",
"GO:0023052", "GO:0023057", "GO:0030111", "GO:0030154", "GO:0030178", "GO:0030182", "GO:0030261",
"GO:0030422", "GO:0030703", "GO:0030727", "GO:0031023", "GO:0031047", "GO:0031050", "GO:0031056",
"GO:0031060", "GO:0031123", "GO:0031145", "GO:0031175", "GO:0031323", "GO:0031324", "GO:0031325",
"GO:0031326", "GO:0031327", "GO:0031331", "GO:0031398", "GO:0031399", "GO:0031401", "GO:0031570",
"GO:0031572", "GO:0031935", "GO:0032268", "GO:0032270", "GO:0032501", "GO:0032502", "GO:0032504",
"GO:0032507", "GO:0032774", "GO:0032776", "GO:0032886", "GO:0033043", "GO:0033044", "GO:0033260",
"GO:0033301", "GO:0033554", "GO:0034622", "GO:0034641", "GO:0034645", "GO:0034654", "GO:0034754",
"GO:0034968", "GO:0035023", "GO:0035107", "GO:0035114", "GO:0035120", "GO:0035186", "GO:0035194",
"GO:0035195", "GO:0035220", "GO:0035282", "GO:0035295", "GO:0035825", "GO:0036211", "GO:0036388",
"GO:0040029", "GO:0042060", "GO:0042221", "GO:0042445", "GO:0043009", "GO:0043066", "GO:0043069",
"GO:0043161", "GO:0043170", "GO:0043331", "GO:0043412", "GO:0043414", "GO:0043549", "GO:0043631",
"GO:0043933", "GO:0044237", "GO:0044249", "GO:0044260", "GO:0044271", "GO:0044419", "GO:0044700",
"GO:0044702", "GO:0044703", "GO:0044707", "GO:0044728", "GO:0044763", "GO:0044767", "GO:0044770",
"GO:0044771", "GO:0044772", "GO:0044773", "GO:0044774", "GO:0044786", "GO:0044818", "GO:0044839",
"GO:0044843", "GO:0044848", "GO:0045132", "GO:0045165", "GO:0045168", "GO:0045185", "GO:0045448",
"GO:0045455", "GO:0045787", "GO:0045814", "GO:0045859", "GO:0045892", "GO:0045931", "GO:0045934",
"GO:0046331", "GO:0046425", "GO:0046483", "GO:0046580", "GO:0046605", "GO:0046777", "GO:0048070",
"GO:0048134", "GO:0048135", "GO:0048285", "GO:0048311", "GO:0048468", "GO:0048477", "GO:0048513",
"GO:0048518", "GO:0048519", "GO:0048522", "GO:0048523", "GO:0048563", "GO:0048569", "GO:0048583",
"GO:0048585", "GO:0048609", "GO:0048646", "GO:0048666", "GO:0048699", "GO:0048704", "GO:0048705",
"GO:0048706", "GO:0048707", "GO:0048731", "GO:0048736", "GO:0048737", "GO:0048754", "GO:0048856",
"GO:0048863", "GO:0048865", "GO:0048867", "GO:0048869", "GO:0050789", "GO:0050793", "GO:0050794",
"GO:0050896", "GO:0051052", "GO:0051058", "GO:0051128", "GO:0051171", "GO:0051172", "GO:0051225",
"GO:0051235", "GO:0051246", "GO:0051247", "GO:0051252", "GO:0051253", "GO:0051276", "GO:0051297",
"GO:0051299", "GO:0051301", "GO:0051302", "GO:0051321", "GO:0051325", "GO:0051329", "GO:0051338",
"GO:0051351", "GO:0051443", "GO:0051445", "GO:0051641", "GO:0051646", "GO:0051651", "GO:0051704",
"GO:0051716", "GO:0051726", "GO:0051783", "GO:0051785", "GO:0060255", "GO:0060429", "GO:0060548",
"GO:0060688", "GO:0060966", "GO:0060968", "GO:0060993", "GO:0061138", "GO:0065003", "GO:0065004",
"GO:0065007", "GO:0070192", "GO:0070507", "GO:0070887", "GO:0070918", "GO:0071103", "GO:0071359",
"GO:0071822", "GO:0071824", "GO:0071840", "GO:0071897", "GO:0071900", "GO:0072028", "GO:0072078",
"GO:0072079", "GO:0072088", "GO:0080090", "GO:0090068", "GO:0090304", "GO:0090306", "GO:0098609",
"GO:1901071", "GO:1901360", "GO:1901362", "GO:1901576", "GO:1901987", "GO:1901988", "GO:1901990",
"GO:1901991", "GO:1902275", "GO:1902299", "GO:1902589", "GO:1902679", "GO:1902749", "GO:1903046",
"GO:1903047", "GO:1903308", "GO:1903322", "GO:2000026", "GO:2000112", "GO:2000113", "GO:2001141")
Related
I'm currently running a time series script in R-Markdown where I pass the values of Percent Use and another vector of the time values. I am processing the two separate vectors with the regular c() function within r. The percentage vector is able to be passed through when compilining like normal, however I am running into trouble with the date/time vector. The length of both vectors are 749, the percentage vector just has values 0-100 passed within them. THe date/time vector has strings passed into them as such:
dt=c('2022-06-19 14:05:00.0','2022-06-19 14:06:00.0', ....
If I only pass a few arguments into the dt vector, it will compile regularly, however, once I increase the size to around half of what it needs to be I start getting the following error:
Error: Incomplete expression: dt=c('2022-06-19 12:40:00.0','2022-06-19 12:41:00.0','2022-06-19 12:42:00.0','2022-06-19 12:43:00.0','2022-06-19 12:44:00.0','2022-06-19 12:45:00.0','2022-06-19 12:46:00.0','2022-06-19 12:47:00.0','2022-06-19 12:48:00.0','2022-06-19 12:49:00.0','2022-06-19 12:50:00.0','2022-06-19 12:51:00.0','2022-06-19 12:52:00.0','2022-06-19 12:53:00.0','2022-06-19 12:54:00.0','2022-06-19 12:55:00.0','2022-06-19 12:56:00.0','2022-06-19 12:57:00.0','2022-06-19 12:58:00.0','2022-06-19 12:59:00.0','2022-06-19 13:00:00.0','2022-06-19 13:01:00.0','2022-06-19 13:02:00.0','2022-06-19 13:03:00.0','2022-06-19 13:04:00.0','2022-06-19 13:05:00.0','2022-06-19 13:06:00.0','2022-06-19 13:07:00.0','2022-06-19 13:08:00.0','2022-06-19 13:09:00.0','2022-06-19 13:10:00.0','2022-06-19 13:11:00.0','2022-06-19 13:12:00.0','2022-06-19 13:13:00.0','2022-06-19 13:14:00.0','2022-06-19 13:15:00.0','2022-06-19 13:16:00.0','2022-06-19 13:17:00.0','2022-06-19 13:18:00.0','2022-06-19 13:19:00.0','2022
At first I believed it could be a parenthesis in the wrong place, however, there is no mistakes with that. I've looked at other articles with somewhat similar issues and have seen a concept of a maximum size vector allowed, however the percentage vector was able to pass all 700. Is there a way to bypass this error, I feel that it is a memory/storage issue with R.
The full code is a lot but it is:
dt=c('2022-06-19 12:40:00.0','2022-06-19 12:41:00.0','2022-06-19 12:42:00.0','2022-06-19 12:43:00.0','2022-06-19 12:44:00.0','2022-06-19 12:45:00.0','2022-06-19 12:46:00.0','2022-06-19 12:47:00.0','2022-06-19 12:48:00.0','2022-06-19 12:49:00.0','2022-06-19 12:50:00.0','2022-06-19 12:51:00.0','2022-06-19 12:52:00.0','2022-06-19 12:53:00.0','2022-06-19 12:54:00.0','2022-06-19 12:55:00.0','2022-06-19 12:56:00.0','2022-06-19 12:57:00.0','2022-06-19 12:58:00.0','2022-06-19 12:59:00.0','2022-06-19 13:00:00.0','2022-06-19 13:01:00.0','2022-06-19 13:02:00.0','2022-06-19 13:03:00.0','2022-06-19 13:04:00.0','2022-06-19 13:05:00.0','2022-06-19 13:06:00.0','2022-06-19 13:07:00.0','2022-06-19 13:08:00.0','2022-06-19 13:09:00.0','2022-06-19 13:10:00.0','2022-06-19 13:11:00.0','2022-06-19 13:12:00.0','2022-06-19 13:13:00.0','2022-06-19 13:14:00.0','2022-06-19 13:15:00.0','2022-06-19 13:16:00.0','2022-06-19 13:17:00.0','2022-06-19 13:18:00.0','2022-06-19 13:19:00.0','2022-06-19 13:20:00.0','2022-06-19 13:21:00.0','2022-06-19 13:22:00.0','2022-06-19 13:23:00.0','2022-06-19 13:24:00.0','2022-06-19 13:25:00.0','2022-06-19 13:26:00.0','2022-06-19 13:27:00.0','2022-06-19 13:28:00.0','2022-06-19 13:29:00.0','2022-06-19 13:30:00.0','2022-06-19 13:31:00.0','2022-06-19 13:32:00.0','2022-06-19 13:33:00.0','2022-06-19 13:34:00.0','2022-06-19 13:35:00.0','2022-06-19 13:36:00.0','2022-06-19 13:37:00.0','2022-06-19 13:38:00.0','2022-06-19 13:39:00.0','2022-06-19 13:40:00.0','2022-06-19 13:41:00.0','2022-06-19 13:42:00.0','2022-06-19 13:43:00.0','2022-06-19 13:44:00.0','2022-06-19 13:45:00.0','2022-06-19 13:46:00.0','2022-06-19 13:47:00.0','2022-06-19 13:48:00.0','2022-06-19 13:49:00.0','2022-06-19 13:50:00.0','2022-06-19 13:51:00.0','2022-06-19 13:52:00.0','2022-06-19 13:53:00.0','2022-06-19 13:54:00.0','2022-06-19 13:55:00.0','2022-06-19 13:56:00.0','2022-06-19 13:57:00.0','2022-06-19 13:58:00.0','2022-06-19 13:59:00.0','2022-06-19 14:00:00.0','2022-06-19 14:01:00.0','2022-06-19 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22:14:00.0','2022-06-19 22:15:00.0','2022-06-19 22:16:00.0','2022-06-19 22:17:00.0','2022-06-19 22:18:00.0','2022-06-19 22:19:00.0','2022-06-19 22:20:00.0','2022-06-19 22:21:00.0','2022-06-19 22:22:00.0','2022-06-19 22:23:00.0','2022-06-19 22:24:00.0','2022-06-19 22:25:00.0','2022-06-19 22:26:00.0','2022-06-19 22:27:00.0','2022-06-19 22:28:00.0','2022-06-19 22:29:00.0','2022-06-19 22:30:00.0','2022-06-19 22:31:00.0','2022-06-19 22:32:00.0','2022-06-19 22:33:00.0','2022-06-19 22:34:00.0','2022-06-19 22:35:00.0','2022-06-19 22:36:00.0','2022-06-19 22:37:00.0','2022-06-19 22:38:00.0','2022-06-19 22:39:00.0','2022-06-19 22:40:00.0','2022-06-19 22:41:00.0','2022-06-19 22:42:00.0','2022-06-19 22:43:00.0','2022-06-19 22:44:00.0','2022-06-19 22:45:00.0','2022-06-19 22:46:00.0','2022-06-19 22:47:00.0','2022-06-19 22:48:00.0','2022-06-19 22:49:00.0','2022-06-19 22:50:00.0','2022-06-19 22:51:00.0','2022-06-19 22:52:00.0','2022-06-19 22:53:00.0','2022-06-19 22:54:00.0','2022-06-19 22:55:00.0','2022-06-19 22:56:00.0','2022-06-19 22:57:00.0','2022-06-19 22:58:00.0','2022-06-19 22:59:00.0','2022-06-19 23:00:00.0','2022-06-19 23:01:00.0','2022-06-19 23:02:00.0','2022-06-19 23:03:00.0','2022-06-19 23:04:00.0','2022-06-19 23:05:00.0','2022-06-19 23:06:00.0','2022-06-19 23:07:00.0','2022-06-19 23:08:00.0','2022-06-19 23:09:00.0','2022-06-19 23:10:00.0','2022-06-19 23:11:00.0','2022-06-19 23:12:00.0','2022-06-19 23:13:00.0','2022-06-19 23:14:00.0','2022-06-19 23:15:00.0','2022-06-19 23:16:00.0','2022-06-19 23:17:00.0','2022-06-19 23:18:00.0','2022-06-19 23:19:00.0','2022-06-19 23:20:00.0','2022-06-19 23:21:00.0','2022-06-19 23:22:00.0','2022-06-19 23:23:00.')
I can understand saying there is a issue with the quotations, but I cant find it anywhere. It seems as there is a maximum allowed in a vector, if I compile only the first quarter of the entries it works, else wise on R studio it gives an error, and in the terminal it just expects more code so it gives you a + to add more instead of >.
I'm using R 3.6.1 and RStudio 1.3.1056.
When pasting a long character string into RStudio using either paste() or paste0() or even c() or just simply assigning the string to an object, I get some strange results, and it looks to be unique to RStudio (if you place this code directly into R it works just fine; the correct value is given for nchar()):
thing <- paste0("(blah blah
'100869017', '100895297', '100937037', '100952542', '100953872', '100958290', '100977291', '100978521', '100982570', '100983764', '100986439', '100987969', '100988635', '100988637', '100989748', '100992594', '100998300', '100998306', '101000068', '101000556', '101002036', '101002550', '101002813', '101002871', '101002872', '101003492', '101003787', '101003789', '101003830', '101004348', '101004349', '101004400', '101004401', '101005323', '101005738', '101006388', '101006411', '101006413', '101006414', '101006416', '101006417', '101006419', '101006440', '101006441', '101006442', '101006443', '101006444', '101006445', '101006446', '101006447', '101006448', '101006449', '101006450', '101006451', '101006452', '101006453', '101006454', '101006455', '101006456', '101006457', '101006458', '101006554', '101006588', '101006608', '101008736', '101009658', '101011518', '101011680', '101011681', '101012457', '101012495', '101014157', '101014197', '101014240', '101014244', '101014248', '101014301', '101014302', '101014303', '101014304', '101014358', '101014480', '101014481', '101015219', '101017560', '101019383', '101019396', '101019454', '101019480', '101019481', '101019567', '101020977', '101022585', '101024007', '101024436', '101028376', '101028377', '101028405', '101029814', '101030739', '101030940', '101031364', '101031368', '101032356', '101032399', '101032440', '101032441', '101032442', '101032443', '101032444', '101032462', '101032468', '101032482', '101032483', '101032484', '101032485', '101032486', '101032487', '101032488', '101032489', '101032590', '101032591', '101032735', '101032987', '101033456', '101036227', '101037275', '101038196', '101038279', '101038930', '101038932', '101038938', '101039576', '101041116', '101041233', '101041288', '101042815', '101043166', '101043280', '101043281', '101043282', '101043285', '101043288', '101043307', '101043302', '101043329', '101043405', '101043837', '101045392', '101045635', '101046419', '101046440', '101046441', '101047082', '101047224', '101047227', '101047275', '101047281', '101047286', '101047287', '101047288', '101047290', '101047293', '101047295', '101047297', '101047304', '101048355', '101048439', '101048480', '101048905', '101048921', '101050905', '101052305', '101052442', '101052448', '101052449', '101052480', '101052481', '101052485', '101052487', '101052489', '101052522', '101052550', '101052551', '101053187', '101055017', '101055036', '101055039', '101055220', '101055258', '101055313', '101055316', '101055317', '101059567', '101060256', '101060554', '101060810', '101060817', '101061738', '101061739', '101061762', '101062369', '101062469', '101063528', '101063909', '101065440', '101065471', '101065473', '101065536', '101065760', '101065784', '101065805', '101065813', '101068343', '101068346', '101069329', '101069472', '101069478', '101069771', '101069871', '101069902', '101070895', '101071301', '101071303', '101072911', '101072914', '101072915', '101072944', '101072946', '101072949', '101072972', '101072981', '101072984', '101072985', '101073389', '101073806', '101074467', '101074469', '101074650', '101074709', '101074721', '101074869', '101075639', '101075881', '101075887', '101075888', '101076841', '101076843', '101076884', '101076885', '101076889', '101076930', '101077036', '101077872', '101077877', '101078006', '101078141', '101078834', '101079626', '101079624', '101079658', '101080128', '101080146', '101080341', '101080389', '101080732', '101080738', '101080931', '101081744', '101081745', '101082123', '101082443', '101082445', '101082447', '101085919', '101086763', '101086774', '101086801', '101086915', '101086964', '101086965', '101087006', '101088057', '101088465', '101089884', '101089915', '101089945', '101090159', '101090197', '101090225', '101090226', '101090227', '101090229', '101090293', '101091218', '101091232', '101091238', '101091239', '101091635', '101091655', '101092773', '101092997', '101093029', '101093064', '101093067', '101093255', '101093344', '101097283', '101097668', '101098444', '101098514', '101099068', '101099073', '101099076', '101099141', '101099170', '101099172', '101099173', '101099175', '101099177', '101099178', '101099194', '101099204', '101099206', '101099581', '101099666', '101100002', '101100179', '101100492', '101100617', '101101080', '101101088', '101101091', '101101092', '101101115', '101101117', '101101150', '101101158', '101102050', '101102086', '101102101', '101102108', '101102169', '101102650', '101102712', '101103376', '101106299', '101106618', '101107257', '101107277', '101108114', '101108119', '101108670', '101108702', '101108707', '101109772', '101109774', '101109779', '101111022', '101111029', '101113873', '101114376', '101114390', '101115163', '101115246', '101115247', '101115357', '101115358', '101116813', '101116819', '101116870', '101116877', '101118108', '101118175', '101118178', '101118277', '101118441', '101118449', '101118471', '101118505', '101118631', '101119051', '101119448', '101119914', '101120073', '101120076', '101120127', '101120292', '101120334', '101120387', '101120389', '101122367', '101122822', '101122881', '101122886', '101124670', '101124838', '101125490', '101125610', '101126329', '101127340', '101127341', '101127342', '101127343', '101127346', '101127347', '101127360', '101127853', '101127855', '101127856', '101127857', '101128128', '101128126', '101128132', '101128135', '101130135', '101131523', '101132622', '101132648', '101132850', '101132870', '101132931', '101132938', '101132990', '101132994', '101133104', '101133206', '101133248', '101134597', '101134599', '101134611', '101134649', '101134661', '101134704', '101134771', '101135221', '101135276', '101135278', '101135409', '101135444', '101135518', '101135630', '101135633', '101135632', '101137571', '101137750', '101137812', '101137875', '101138237', '101139907', '101139931', '101139968', '101140076', '101140148', '101140181', '101140250', '101140253', '101140460', '101140462', '101140466', '101140469', '101140518', '101150986', '101150987', '101150990', '101150994', '101150995', '101151373', '101151376', '101151416', '101151418', '101151419', '101151434', '101151437', '101151891', '101151974', '101151978', '101151979', '101151996', '101152030', '101152031', '101152032', '101152037', '101152062', '101152063', '101152066', '101152068', '101152069', '101152070', '101152072', '101152073', '101152074', '101152077', '101152078', '101152080', '101152081', '101152083', '101152085', '101152087', '101152088', '101152089', '101152100', '101152103', '101152105', '101153684', '101153944', '101153966', '101153996', '101153999', '101155013', '101155141', '101155149', '101155311', '101155560', '101155880', '101155882', '101155883', '101155884', '101155905', '101156458', '101156459', '101156511', '101156524', '101156546', '101156547', '101156596', '101156611', '101156641', '101156664', '101156752', '101156786', '101156801', '101156842', '101156885', '101156888', '101156892', '101157753', '101157844', '101157881', '101157905', '101157927', '101158001', '101158011', '101158025', '101158028', '101158034', '101158061', '101158081', '101158084', '101158103', '101158107', '101159736', '101160183', '101160203', '101160234', '101160373', '101160377', '101160381', '101160378', '101160451', '101160551', '101162202', '101162245', '101162247', '101162249', '101162492', '101162538', '101162585', '101162595', '101162627', '101162630', '101162634', '101162792', '101162848', '101162876', '101162904', '101164138', '101164337', '101165132', '101165133', '101165134'
blah blah)")
nchar(thing)
The output of nchar() is 4130. In reality, nchar() should be showing 7603.
Why would I do something like this in a script? In this case, it was a SQL query written into a script and being run via RStudio.
Even stranger is removing the "blah blah" at the top and bottom of the string:
thing <- paste0("'100869017', '100895297', '100937037', '100952542', '100953872', '100958290', '100977291', '100978521', '100982570', '100983764', '100986439', '100987969', '100988635', '100988637', '100989748', '100992594', '100998300', '100998306', '101000068', '101000556', '101002036', '101002550', '101002813', '101002871', '101002872', '101003492', '101003787', '101003789', '101003830', '101004348', '101004349', '101004400', '101004401', '101005323', '101005738', '101006388', '101006411', '101006413', '101006414', '101006416', '101006417', '101006419', '101006440', '101006441', '101006442', '101006443', '101006444', '101006445', '101006446', '101006447', '101006448', '101006449', '101006450', '101006451', '101006452', '101006453', '101006454', '101006455', '101006456', '101006457', '101006458', '101006554', '101006588', '101006608', '101008736', '101009658', '101011518', '101011680', '101011681', '101012457', '101012495', '101014157', '101014197', '101014240', '101014244', '101014248', '101014301', '101014302', '101014303', '101014304', '101014358', '101014480', '101014481', '101015219', '101017560', '101019383', '101019396', '101019454', '101019480', '101019481', '101019567', '101020977', '101022585', '101024007', '101024436', '101028376', '101028377', '101028405', '101029814', '101030739', '101030940', '101031364', '101031368', '101032356', '101032399', '101032440', '101032441', '101032442', '101032443', '101032444', '101032462', '101032468', '101032482', '101032483', '101032484', '101032485', '101032486', '101032487', '101032488', '101032489', '101032590', '101032591', '101032735', '101032987', '101033456', '101036227', '101037275', '101038196', '101038279', '101038930', '101038932', '101038938', '101039576', '101041116', '101041233', '101041288', '101042815', '101043166', '101043280', '101043281', '101043282', '101043285', '101043288', '101043307', '101043302', '101043329', '101043405', '101043837', '101045392', '101045635', '101046419', '101046440', '101046441', '101047082', '101047224', '101047227', '101047275', '101047281', '101047286', '101047287', '101047288', '101047290', '101047293', '101047295', '101047297', '101047304', '101048355', '101048439', '101048480', '101048905', '101048921', '101050905', '101052305', '101052442', '101052448', '101052449', '101052480', '101052481', '101052485', '101052487', '101052489', '101052522', '101052550', '101052551', '101053187', '101055017', '101055036', '101055039', '101055220', '101055258', '101055313', '101055316', '101055317', '101059567', '101060256', '101060554', '101060810', '101060817', '101061738', '101061739', '101061762', '101062369', '101062469', '101063528', '101063909', '101065440', '101065471', '101065473', '101065536', '101065760', '101065784', '101065805', '101065813', '101068343', '101068346', '101069329', '101069472', '101069478', '101069771', '101069871', '101069902', '101070895', '101071301', '101071303', '101072911', '101072914', '101072915', '101072944', '101072946', '101072949', '101072972', '101072981', '101072984', '101072985', '101073389', '101073806', '101074467', '101074469', '101074650', '101074709', '101074721', '101074869', '101075639', '101075881', '101075887', '101075888', '101076841', '101076843', '101076884', '101076885', '101076889', '101076930', '101077036', '101077872', '101077877', '101078006', '101078141', '101078834', '101079626', '101079624', '101079658', '101080128', '101080146', '101080341', '101080389', '101080732', '101080738', '101080931', '101081744', '101081745', '101082123', '101082443', '101082445', '101082447', '101085919', '101086763', '101086774', '101086801', '101086915', '101086964', '101086965', '101087006', '101088057', '101088465', '101089884', '101089915', '101089945', '101090159', '101090197', '101090225', '101090226', '101090227', '101090229', '101090293', '101091218', '101091232', '101091238', '101091239', '101091635', '101091655', '101092773', '101092997', '101093029', '101093064', '101093067', '101093255', '101093344', '101097283', '101097668', '101098444', '101098514', '101099068', '101099073', '101099076', '101099141', '101099170', '101099172', '101099173', '101099175', '101099177', '101099178', '101099194', '101099204', '101099206', '101099581', '101099666', '101100002', '101100179', '101100492', '101100617', '101101080', '101101088', '101101091', '101101092', '101101115', '101101117', '101101150', '101101158', '101102050', '101102086', '101102101', '101102108', '101102169', '101102650', '101102712', '101103376', '101106299', '101106618', '101107257', '101107277', '101108114', '101108119', '101108670', '101108702', '101108707', '101109772', '101109774', '101109779', '101111022', '101111029', '101113873', '101114376', '101114390', '101115163', '101115246', '101115247', '101115357', '101115358', '101116813', '101116819', '101116870', '101116877', '101118108', '101118175', '101118178', '101118277', '101118441', '101118449', '101118471', '101118505', '101118631', '101119051', '101119448', '101119914', '101120073', '101120076', '101120127', '101120292', '101120334', '101120387', '101120389', '101122367', '101122822', '101122881', '101122886', '101124670', '101124838', '101125490', '101125610', '101126329', '101127340', '101127341', '101127342', '101127343', '101127346', '101127347', '101127360', '101127853', '101127855', '101127856', '101127857', '101128128', '101128126', '101128132', '101128135', '101130135', '101131523', '101132622', '101132648', '101132850', '101132870', '101132931', '101132938', '101132990', '101132994', '101133104', '101133206', '101133248', '101134597', '101134599', '101134611', '101134649', '101134661', '101134704', '101134771', '101135221', '101135276', '101135278', '101135409', '101135444', '101135518', '101135630', '101135633', '101135632', '101137571', '101137750', '101137812', '101137875', '101138237', '101139907', '101139931', '101139968', '101140076', '101140148', '101140181', '101140250', '101140253', '101140460', '101140462', '101140466', '101140469', '101140518', '101150986', '101150987', '101150990', '101150994', '101150995', '101151373', '101151376', '101151416', '101151418', '101151419', '101151434', '101151437', '101151891', '101151974', '101151978', '101151979', '101151996', '101152030', '101152031', '101152032', '101152037', '101152062', '101152063', '101152066', '101152068', '101152069', '101152070', '101152072', '101152073', '101152074', '101152077', '101152078', '101152080', '101152081', '101152083', '101152085', '101152087', '101152088', '101152089', '101152100', '101152103', '101152105', '101153684', '101153944', '101153966', '101153996', '101153999', '101155013', '101155141', '101155149', '101155311', '101155560', '101155880', '101155882', '101155883', '101155884', '101155905', '101156458', '101156459', '101156511', '101156524', '101156546', '101156547', '101156596', '101156611', '101156641', '101156664', '101156752', '101156786', '101156801', '101156842', '101156885', '101156888', '101156892', '101157753', '101157844', '101157881', '101157905', '101157927', '101158001', '101158011', '101158025', '101158028', '101158034', '101158061', '101158081', '101158084', '101158103', '101158107', '101159736', '101160183', '101160203', '101160234', '101160373', '101160377', '101160381', '101160378', '101160451', '101160551', '101162202', '101162245', '101162247', '101162249', '101162492', '101162538', '101162585', '101162595', '101162627', '101162630', '101162634', '101162792', '101162848', '101162876', '101162904', '101164138', '101164337', '101165132', '101165133', '101165134'")
In this case the console hangs with the + as if awaiting further input.
Again, take those examples and put them directly into R and nchar() provides the correct count.
The worst part of this is that in the first example, the object is created but is truncated, and the final part of the string is retained, i.e. the ending "blah blah". This has resulted in SQL queries excluding some criteria - over 3400 characters worth of criteria!
If anyone was to say that this is an ugly way to write SQL queries, I'd agree. And I certainly wouldn't want a single string so long anywhere in my code, but there's a solid chance they could show up in a team environment where a user is less familiar with R and RStudio.
RStudio gives absolutely no indicator or warning that this is done as far as I can tell.
Is there any way to avoid this behavior besides splitting strings or sourcing SQL scripts or text files?
I have a data frame which contains three columns.
A|B|c
10|0|0
10|5|0
10|10|0
15|0|0
15|5|0
15|10|0
When I interpolate the above data frame:
df<-approxm(df,206,method="linear")
Here is the output:
A|B|c
10|0|0
10|1|0
10|2|0
10|3|0
10|4|0
10|5|0
10|6|0
10|7|0
10|8|0
10|9|0
10|10|0
11|8|0
12|6|0
13|4|0
14|2|0
15|0|0
15|1|0
15|2|0
15|3|0
15|4|0
15|5|0
15|6|0
15|7|0
15|8|0
15|9|0
15|10|0
Here in this output Column A with values 11,12,13 and 14 are not interpolated properly.
My Expected output is:
A|B|c
10|0|0
10|1|0
10|2|0
10|3|0
10|4|0
10|5|0
10|6|0
10|7|0
10|8|0
10|9|0
10|10|0
11|0|0
11|1|0
11|2|0
11|3|0
11|4|0
11|5|0
11|6|0
11|7|0
11|8|0
11|9|0
11|10|0
12|0|0
12|1|0
12|2|0
12|3|0
12|4|0
12|5|0
12|6|0
12|7|0
12|8|0
12|9|0
12|10|0
13|0|0
13|1|0
13|2|0
13|3|0
13|4|0
13|5|0
13|6|0
13|7|0
13|8|0
13|9|0
13|10|0
14|0|0
14|1|0
14|2|0
14|3|0
14|4|0
14|5|0
14|6|0
14|7|0
14|8|0
14|9|0
14|10|0
15|0|0
15|1|0
15|2|0
15|3|0
15|4|0
15|5|0
15|6|0
15|7|0
15|8|0
15|9|0
15|10|0
This is my expected output.
But I'm not getting this expected output.
I don't know where my code gets wrong.
Can someone help me out?
Complete function worked out.
tidyr::complete(df,A=full_seq(A,1),nesting(B=full_seq(B,1)),fill=list(c=0))
I have my data in a csv file in the given format.
csv file data
Latitude,Longitude,Temperature
20,84.01,15.93913043
28.48,77.13,16.62857143
28.68,77.2,17.81333333
31.32,78.16,2.472222222
31.531,77.112,5.228
28.11,77,21.85
31.5,77.09,7.910526316
31.43,76.57,11.444
28.7,77.15,17.708
30.55,77.35,15.30526316
26.95,78.96,16.46818182
27.44,79.39,15.74090909
26.58,81.59,15.90952381
25.33,80.43,18.465
29.45,77.34,14.15238095
20.42,86.47,19.83181818
29.52,75.5,14.135
24.17,72.43,20.065
25.1,76.19,18.59444444
30.975,76.517,14.88421053
28.8,76.13,16.05
29.54,75.04,15.295
24.32,72.3,18.84782609
23.86,72.13,20.49444444
30.19,74.95,13.996
22.36,73.16,22.365
30.78,75.84,13.75652174
21.86,73.52,24.725
21.5,70.44,22.812
21.36,69.75,22.33125
30.32,78.05,15.35
20.92,72.89,21.3
17.69,74.02,23.45
28.3278,77.2467,17.87857143
20.17,79.98,21.11428571
24,76.73,23.67857143
22.76,74.59,19.97619048
22.03,74.97,20.85
29.57,80.23,12.70869565
29.95,79.9,13.425
25.92,83.56,16.67
27.6,81.58,15.128
24.68,78.4,19.1
18.11,76.02,22.84285714
20.39,78.11,24.98571429
24.57,80.82,23.35714286
21.283,76.198,22.98235294
21.81,80.18,20.16666667
24.5,81.3,16.22857143
22.09,82.17,18.93636364
30.35,76.87,14.77
19.7,81.7,19.98823529
18.9,81.35,19.16956522
28.9917,77.701,15.43636364
28.39,77.83,15.745
27.58,77.98,16.52631579
27.03,79.92,17.40526316
26.57,80.48,12.67
25.17,80.91,24.75
26.55,79.55,17.12727273
22.443,77.03,18.825
30.19,78.18,15.72857143
29.87,77.88,14.75454545
26.54,81.83,15.75416667
29.93,77.97,12.96666667
26.127,81.94,17.54666667
26.43,82.57,16.63684211
29.34,80.09,11.82631579
27.14,83.53,15.82
21.1,86.5,19.82
20.25,85.82,21.31
21.13,86.57,19.352
20.23,86.18,19.52173913
20.46,85.9,20.74
27.17,78.03,17.292
20.83,84.33,19.224
21.89,84.03,19.47142857
20.01,83.01,20.295
19,83.01,21.24285714
18.53,73.83,22.47142857
18.81,82.71,19.04545455
18.01,82.01,20.73076923
25,84.01,16.952
25.03,85.6,18.48125
19.92,83.16,19.975
26.21,84.35,16.15454545
26.58,84.38,16.1952381
25.73,85.23,15.9375
26.38,85.73,16.39
25.98,85.66,16.176
30.48,78.05,8.985
18.35,81.88,21.736
26.54,85.72,16.53043478
26,85.01,16.104
26.9,75.8,18.97272727
25.92,86.8,16.01904762
26.42,74.62,21.04545455
24.87,85.53,14.9
24.8,85.04,16.236
25.91,86.55,16.17
25.3,83,20.13333333
25.52,87.57,17.13181818
25.37,86.48,17.56190476
25.01,86,16.42727273
26.9,76.35,18.836
25.23,87.03,15.3875
26.07,76.37,17.324
25.41,75.65,18.03684211
25.12,75.93,17.93333333
19.65,78.52,21.51052632
24.58,76.13,19.8
26.16,75.78,19.24285714
24.9,74.61,18.74583333
18.56,77.88,23.68571429
18.83,78.93,22.75238095
17.01,78.97,21.63684211
18.43,79.08,22.78181818
17.21,77.58,22.36363636
26.51,85.28,15.988
18.01,79.6,24.295
16.5,78.23,22.368
28.62,77.27,17.135
15.45,78.46,24.09444444
16.85,79.47,23.58571429
16.35,80.43,23.988
17.62,74.07,22.97777778
16.36,80.84,22.976
14.68,77.67,23.62
17.87,82.34,19.12
17.7,83,21.62083333
14.11,78.15,23.52352941
14.43,78.8,23.92857143
17.24,81.1,22.6
23.55,74.44,26.72
23.51,74.38,19.105
28.28,75.12,18.33333333
12.38,78.21,23.22857143
28.35,75.58,16.25714286
11.33,76.8,15.97391304
13.15,80.23,23.65263158
13.63,79.73,22.688
12.71,77.81,21.03809524
11.62,79.53,24.01818182
13.61,79.34,22.35454545
11.01,76.94,24.765
11.37,76.63,14.824
13.25,80.31,26.384
11.13,79.07,25.87
11.22,78.87,24.86
26.25,82,16.25217391
10.77,76.7,26.32
11.15,78.15,26.03888889
10.53,76.28,27.46666667
24.57,73.7,9.8
9.62,76.42,28.135
12.95,78.25,22.26111111
23.86,73.86,20.24
12.97,77.18,23.35652174
9.55,77.934,26.18636364
10.117,77.583,24.93157895
13.663,76.916,22.21363636
9.2,77.88,26.98
8.43,76.99,26.33809524
8.73,77.7,27.02380952
11.26,75.77,28.36470588
8.43,77.3,26.47619048
11.0617,76.21,21.59
12.15,76.8,22.73809524
13.34,75.8,20.49047619
11.91,76.93,23.37619048
25.79,73.3,18.55652174
13.72,75.72,22.75263158
12.42,75.73,19.85
26.8,75.8,18.80416667
25.75,71.4,24.05
25.38,72.5,22.38
27.9,78.07,17.15789474
26.12,91.82,18.00555556
14.611,74.846,21.89
12.65,74.96,25.74736842
16.18,77.32,24.73888889
15.48,74.98,22.80909091
27.58,75.13,17.195
27.15,73.78,21.04166667
17.35,76.8,23.78888889
27.93,74.98,17.32105263
26.351,92.633,17.57142857
25.87,93.43,15.88
14.78,75.33,23.655
15.9,75.55,23.05454545
16.98,75.75,23.73333333
27.53,76.6,17.01052632
26.44,89.94,15.684
26.35,90.65,18.98
26.48,90.9,15.688
29.49,73.5,13.752
14.03,77.27,23.605
29.93,73.88,14.30588235
26.65,91.33,16.59565217
16.15,75.6,19.57692308
13.96,75.56,22.95714286
26.71,93.13,15.95714286
26.72,77.89,19.03846154
25.11,76.47,19.04444444
26.16,90.63,16.86818182
26.51,93.96,16.56956522
26.467,90.283,16.615
27.2,77.46,15.84545455
26.72,94.18,16.61
26.55,77,18.58695652
27.467,95.02,16.452
10.08,77.05,16.04
25.36,74.63,19.18571429
14.283,74.45,25.65238095
24.6,92.55,18.965
25.56,91.86,11.03478261
24.85,92.3,23.05
25.454,92.2069,12.044
9.834,76.95,22.456
28.333,79.417,15.99090909
27.71,81.91,14.88571429
25.87,84.13,12.81666667
26.78,78.98,20.1
26.33,79.97,17.7125
25.68,91.92,14.98888889
25.2,90.63,19.11578947
25.52,91.27,10.316
26.25,81.37,17.04210526
24.58,72.7,14.35
26.95,94.62,17.43333333
27.48,95.31,16.108
23.53,84.81,16.07619048
26.97,93.87,15.712
25.5,90.58,16.09473684
23.78,85.88,17.42631579
24.83,87.2,17.484
25.23,86.65,22.075
27.47,94.55,17.31052632
24.5,87.81,17.99130435
26.52,90.48,16.925
24.49,86.66,21.56875
17.43,78.47,23.636
24.2,86.3,16.83
24.16,83.8,16.635
22.47,88.09,18.27777778
24.8,92.78,18.168
22.69,86.43,17.15238095
22.27,87.92,19.71666667
29.46,79.65,9.25
22.3,87.3,18.97
22.36,88.43,18.58571429
22.662,88.873,19.12
22.97,88.48,21.24545455
23.4,88.49,17.99166667
23.53,91.46,17.92
32.37,75.55,14.44583333
23.21,87.88,19.11666667
32.65,74.8,13.45454545
23.24,87.04,18.892
23.91,87.52,19.42272727
31.18,77.13,4.424
24.092,88.27,21.4
25.62,88.14,17.50555556
31.33,75.33,13.91428571
23.8,91.27,19.10555556
26.52,88.72,13.65625
24.4,89.39,15.97826087
34.05,74.8,3.419047619
34.14,74.87,2.961904762
25.31,88.76,17.65263158
34.06,74.71,3.036363636
27.07,88.46,13.75652174
27.15,88.4,11.696
24.07,91.61,18.56842105
34.35,74.4,1.118181818
25.18,93.01,18.8
33.888,74.9808,5.125
33.64,74.94,1.635
23.43,85.3,17.4
24.18,91.82,22.1
23.48,93.3,14.836
24.208,92.675,18.988
23.95,92.49,18.305
33.22,75.26,7.57
23.33,92.83,17.485
22.5055,92.895,18.21578947
32.9,75.167,11.42
22.95,92.93,16.78
22.48,92.98,19.40454545
26.72,92.08,16.804
18.22,74.48,23.43333333
17.26,74.42,17.07647059
30.15,75.41,17.36666667
31.63,74.83,14.29444444
31.46,77.22,5.661904762
30.73,76.8,15.395
31.32,75.3,13.80769231
30.91,74.66,13.28947368
20.76,73.69,23.83333333
31.37,74.97,12.724
31.38,76.024,14.865
20.4,72.833,20.672
23.303,70.36,20.628
31.39,75.36,14.52142857
21.144,72.75,24.92222222
23.37,68.54,19.37222222
20.8,70.7,22.295
20.143,74.794,26.94444444
18.97,73.04,24.73157895
18.33,73.79,22.23684211
21.02,75.53,23.15833333
28.47,77.03,21.1
17.67,75.92,24.94545455
19.03,73.17,24.74615385
22.94,79.22,17.4
25.688,78.4419,18.9
22.94,81.08,15.76818182
22.6,82.13,18.308
21.1,81.034,21.95
21.97,81.24,20.3375
22.37,82.733,17.77083333
21.23,81.7,14.96428571
25.35,81.3,16.92
26.79,82.72,18.1125
26.48,83.77,16.86666667
29.36,79.46,9.733333333
25.92,84.13,16.616
28.98,79.4,16.068
25.72,82.68,16.572
25.17,82.6,16.175
29.62,79.62,13.245
30.7,76.9,16.096
30.36,75.53,13.78888889
22.8,73.22,20.07083333
22.58,72.28,20.46
22.73,72.88,23.5875
23.27,72.65,21.43888889
30.36,76.45,13.97
22.88,74.23,21.42
17.69,74.02,20.988
18.53,73.8,21.98095238
28.1,77,14.965
28.08,76.59,17.8
31.1,77.17,6.628571429
28.28,76.15,17.94
30.1,77.28,14.07777778
29.68,77,13.628
29.7,77.03,14.21764706
29.38,77,12.85384615
28.611,76.651,15.08947368
28.8836,76.619,15.695
29.78,76.4,13.67
31.35,76.7525,13.14347826
31.45,76.26,13.85238095
28.66,77.31,17.71578947
29.58,76.51,14.625
29.3,75.45,15.28421053
29.068,75.476,16.05263158
28.63,77.15,16.575
28.8,76.97,15.12
30.90444,76.96,8.48
30.89916,76.96,12.392
31.5858,77.0748,6.884
32.3521,76.0608,7.844
32.1994,76.3249,11.67777778
28.58,76.9,15.6
29.13,75.7,15.608
30.2,75.88,14.72857143
31.09,76.38,15.6
31.13,76.12,14.7
30.6,76.1,13.49090909
30.67,74.74,13.825
21.7,73.01,14.45
21.73,72.55,23.42352941
32.05,75.42,13.04736842
30.62,76.41,13.49090909
31.6,75.98,16.1
20.54,73.16,22.70833333
22.73,71.62,24.21538462
28.63,77,17.245
19.18,72.98,27.00666667
20.9,74.8,23.6
23.83,73,21.71333333
21.16,73.79,23.51578947
28.63,77.36,16.836
28.63,77.37,17.252
28.23,77.18,15.85454545
22.34,70.91,21.075
21.37,74.23,24.24210526
19.09,74.75,21.21818182
19.36,75.95,23.3047619
16,73.7,25.14166667
16.67,74.16,22.89545455
18.4,76.56,23.275
15.48,73.91,26.81764706
19.85,75.87,21
20.94,77.78,23.285
18.72,76.36,23.89375
19.73,77.15,21.85263158
20.13,77.13,25.08125
20.7,77.65,20.05
21.16,79.65,21.32
21.49,80.15,18.30416667
22.3,79.67,23.19090909
26.4772,77.9866,17.328
26.5727,78.81,16.856
22.0683,79.5488,16.036
24.5972,77.75,18.8625
24.42,74.8708,18.93809524
24.06,75.1,18.93333333
22.88,84.56,16.75
23.444,75.044,18.97916667
22.7,75.89,20.97
20.53,76.19,23.49
21.82,75.62,22.50952381
23.83,78.69,26.7875
23.18,77.06,20.295
25.03,79.5,18.79
23.82,79.4,17.095
25.67,76.69,21.26
22.314,74.353,19.064
22.056,78.94,16.83333333
21.86,77.937,19.58333333
23.21,79.96,17.56190476
22.6,80.3,16.14761905
23.28,81.33,16.37142857
24.73,80.21,19.6625
23.525,80.84,17.63888889
24.41,81.86,18.38461538
23.09,83.14,15.60666667
24.07,82.61,16.95555556
23.12,81.7,17.71111111
21.88,83.4,19.548
23.515,77.81,20.57142857
22.58,81.13,19.17368421
21.103,82.083,20.92380952
20.66,81.53,20.27647059
20.25,81.5,20.64444444
30.756,75.163,12.795
18.8,80.8,21.0952381
28.01,79.11,16.25
27.55,78.65,15.1
27.22,79.03,17.38947368
16.98,73.32,24.70526316
30.05,79,8.456
30.74,76.73,16.32727273
25.21,82.27,22.54
19.8,85.83,21.5952381
20.84,86.32,20.155
22.03,84.05,19.05217391
21.33,83.62,19.915
20.12,85.08,19.904
21.61,85.56,18.728
20.83,85.1,19.656
21.36,83.88,18.896
20.52,86.43,19.76190476
19.3,84.8,22.65333333
19.25,82.54,19.03333333
24.95,84,18.05
23.55,83.96,16.288
25.0286,73.89,18.44117647
11.28,76.23,25.91666667
26.26,72.99,18.52
15.42,75.63,23.7
22.9,88.37,16.58095238
27.28,88.23,10.93333333
26.02,94.53,10.45
I tried drawing a contour using contour(), contourplot() etc, I am not sure how to make my data regular to get a contour . I tried akima interp() but getting error given below. not sure what that means and how to correct my data to get a contour.
*Error in is.finite(x) : default method not implemented for type 'list'*
someone please help me out. Thanks.
This works for me (by the way, dumping this size data file, even though it's only moderate-sized, is kind of a pain for responders: if you can post it somewhere that's easier. I had to save the HTML page source and extract the info that way, as it was too big for me to cut & paste from the clipboard):
X <- read.csv("tempcontour.txt")
library(akima)
## needed to specify the 'duplicate' argument (see ?interp)
interp1 <- with(X,interp(Latitude,Longitude,Temperature,
duplicate="mean"))
names(interp1) <- colnames(X) ## assumes colnames are in Lat/Long/Temp order
png("interp1.png")
with(interp1,contour(Longitude,Latitude,Temperature,
ylab="Latitude",xlab="Longitude"))
dev.off()
I am trying to use the x12 function in the x12 package for R.
My problem is, when using time series object (tso) with monthly data and each observation is a large number (11 or more digits), the function is making a spec file which x12a.exe (binaries) can not read.
x12 binaries does not allow the spec file to be wider then 132 column.
In my example, the spec file have 144 columns, which I believe give me this error message in R:"ERROR: Input record longer than limit : 133".
When I am using smaller numbers (fewer columns) in the spec file, there are no problem so far. When creating the spec file on my own, when using x12-arima for windows, I have never seen the problem before, because I always use the "free" format (one observation per line) for the series in x12-arima.
My question is: How do I make the format for the time series object = "free", or some how just one observation per line, in the "Rout.spc" file, while using x12 function in the x12 package for R?
I am using R version 2.15.2 and R-studio version 0.97.318
Attached is my example code in R-studio, output in R-console, and the spec file
"Rstudio"
library(x12)
alt <- read.csv2("alt.csv",header=T)
tal <- ts(data=alt,start=c(1995,4),freq=12)
x12path <- shortPathName("C:\\Dokumenter\\X_12_Arima_Program\\x12a\\x12a.exe")
x12tal <- x12(tso=tal,automdl=T,x12path=x12path,period=12,trendma=23)
"Console"
C:\Dokumenter\Eksperimentering\x12>md gra
C:\Dokumenter\Eksperimentering\x12>C:\DOKUME~1\X_12_A~2\x12a\x12a.exe Rout -g gra
X-12-ARIMA Seasonal Adjustment Program
Version Number 0.3 Build 192
Execution began Mar 12, 2013 23.46.25
Reading input spec file from Rout.spc
Storing any program output into Rout.out
Storing any program error messages into Rout.err
ERROR: Input record longer than limit : 133
Line 6: start=1995.4
^
ERROR: Expected an real number not "111"
Program error(s) halt execution for Rout.spc
Check error file Rout.err
Error messages generated from processing the X-12-ARIMA spec file
Rout.spc:
Error in readx12Out(file, freq_series = frequency(tso), start_series = start(tso), :
Error! No proper run of x12! Check your parameter settings.
"The spec file: Rout.spc"
series{
title="R Output for X12a"
decimals=2
start=1995.4
period=12
data=(
14056669449 12785389868 12772341230 12342935128 12081332395 12110109950 12367542268 12911930417 12836340370 12214486074 12057940408 11555540809
10002847699 9199284760 8704422249 8492914782 8507816348 8470254675 8665139772 8653204621 9177471163 9676069791 9483990311 9825510541
7613345714 7168896536 7527318694 7721174940 7584049271 7586159794 7411383039 7565724342 7555103032 7148551906 7792379395 7493885451
6636374143 6390731897 6160711917 6003196233 5955867663 5868369296 5858314348 6098506333 6297774946 6074680955 6132163345 5875098456
5198306672 4891946405 4875765641 4834436461 4835096514 4804664875 4684550404 4733459404 5056773308 4912329843 5080643820 4568733581
4286693348 3898776528 3872776341 3842469172 3756957390 3782676505 3924066331 3810475969 3943259720 3665136687 3962811976 3449264257
3120637669 2813261665 2692920289 2652153941 2557247524 2658115616 2777287302 2688976703 2712004412 2596430893 2520548046 2455531008
2429263753 2187017586 2181610529 2139024441 2008850781 2049874584 2110715482 2218937956 2565352715 2635375627 2598584163 2435211675
2433625715 2350144562 2298764466 2242464445 2288528533 2532374821 2696862060 2877128057 3086285374 3309497319 3684989376 3709283880
3483967873 3294407926 3465439983 3546006197 3526166213 3625899404 3774201496 3941610691 4325836434 4466576126 4115121591 4036118609
3824882119 3552896925 3649624960 3570454122 3622089655 3662984491 3601306018 3604389348 3620162022 3401732239 3158217491 2896252892
2800864675 2630474256 2668229303 2631120097 2343131082 2163910930 2108285015 2067601541 2099699134 1803097392 1742652674 1626660618
1560369744 1448264771 1419659828 1547101381 1310783818 1358686467 1300281852 1315247637 1380387680 1286158497 1329769957 1272124521
1185603967 1125238745 1217223861 1265616553 1222054134 1279497332 1499392605 1810208712 2314301847 2908395453 3388479445 3441615991
3432688695 3691000321 3891303059 4111250935 4258776704 4586315450 5050122946 5156728599 5550332779 5769588984 5943764465 6032516246
5765718572 5521116586 5498458566 5374456514 5130561755 5219814632 5542173962 6883624616 7744043244 7913799960 7416210299 7127265644
6790509897 6562709494 6390985216 6126897801 5855125688 6259675447 6439114484 6634617502 6771498442 6674343925 6295709586 5890916431
5545655270 5315444742 5205711894 5115065476 4648229650 4724377012 4816989052 5049928441 5041395923
)
}
transform{
function=auto
}
automdl {
maxorder=(3,2)
maxdiff=(1,1)
balanced=yes
savelog=(adf amd b5m mu)
}
forecast {
}
x11{
sigmalim=(1.5,2.5)
trendma=23
excludefcst=yes
final=(user)
appendfcst=yes
savelog=all
}