I am trying to analyze data from flow cytometry, where there is a package that was developed like 10 years ago. It requires a few dependencies packages that I was able to install all.
Now when I tried to run it with the first function to create a gate frame for a winlist processed fcs file.
create_gate_frame(frame = archframe1x36x16, inputfile =c("facsdata/TrungTran/Gelfree-8-lane7-5_1_1_A5.fcs"), popdesc = "frames/popdescriptions/array1xpopdesc.txt").
I just got the following errors that I don't know how to solve. So, any help would be very much appreciated.
uneven number of tokens: 1013
The last keyword is dropped.
uneven number of tokens: 1013
The last keyword is dropped.
Error in mat[, c(scatters, dims1, dims2, PE)] : subscript out of bounds
I am new to unix and can someone help me with following query?
I have a deployment log file and there I wanted to search for two pattern (eg: started , completed ) and after getting the out put I just want to calculate and print the time taken between the started step and completed step
I tried a lot I’m able to print the the out put but unable to print the time taken between those steps
Thanks in advance,
Mahi
Below is the sample deployment log and at the starting line i am searching for "DeploymentStart" and, at the end of the line "DeploymentCompleted".
After printing this out put i want to calculate the time taken between deploymentStart time and DeployemntCompleted time.
us>InterimDeploymentStart1523623301227ETA0submittedCount3suppressedCount0listRejectedCount0<
/Pair>batchSplitRejectedCount0emailRendererRejectedCount0
Apr13 12:41:49,492 INFO com.epsilon.deploymentStatusNotifier.util.DeploymentStatusNotificationConverter taskScheduler-2 - JobMessage createdcom.epsilon.common.jobs.model.JobMessage#530c458e
Apr13 12:41:49,488 INFO com.epsilon.deploymentStatusNotifier.util.DeploymentStatusNotificationConverter taskScheduler-2 - JobMessage createdcom.epsilon.comm
JobMessage>de841c92-6682-4696-883c-3087a9194531HarmonyPipelinecompletedDeploymentCompleted1523623301227ETA0
I apologize in advance because I'm extremely new to coding and was thrust into it just a few days ago by my boss for a project.
My data set is called s1. S1 has 123 variables and 4 of them have some form of "QISSUE" in their name. I want to take these four variables and duplicate them all, adding "Rec" to the end of each one (That way I can freely play with the new variables, while still maintaining the actual ones).
Running this line of code keeps giving me an error:
b<- llply(s1[,
str_c(names(s1)
[str_detect(names(s1), fixed("QISSUE"))],
"Rec")],table)
The error is as such:
Error in `[.data.frame`(s1, , str_c(names(s1)[str_detect(names(s1), fixed("QISSUE")) & :
undefined columns selected
Thank you!
Use this to get the subset. Of course there is other ways to do that with simpler code
b<- llply(s1[,
names(s1)[str_detect(names(s1), fixed("QISSUE"))]
],c)
nwnam=str_c(names(s1)[str_detect(names(s1), fixed("QISSUE"))],"Rec")
ndf=data.frame(do.call(cbind,b));colnames(ndf)=nwnam
ndf
# of course you can do
cbind(s1,ndf)
I was making a package in R and would like it to make it as a trial version for a period of 30 days .
Well my question is how to make a code self destructive depends on number of days ??
I had played with time and date package for a while where i came to know ,
Sys.Date() could give todays date , so i get forard with something below
today=Sys.Date()
a=today
b=a+1
if(a==today)
{
print(paste("today is sunday"))
if(b==today){
print(paste("today is monday"))
}
I know it is stupid work whatever i had done , my sole idea was to fix the 1st use of package as starting day ,and every day it will increment till 30 days ,when it will reach the limit it will automatically destroy using
file.remove () <- through which I can remove some file ........
May be I am clear with my ideas .
Sorry for the novice question .
Add this condition to the license. ("30 days for free, after that you'll have to pay".) and expect users to comply with this.
There is really nothing else you can do.
Well, actually you can. For example, on the first occasion your code is run, save the current date to a file in a certain location (say, "~/.datetocheck"). Then every time your code is run, check for the existence of this file, and if it exists, compare the dates. If more than 30 days have passed, give an error message:
stop("Time is over! You have to pay!")
The problem is that nothing prevents the user from simply deleting this file.
I know there are other tools around like awstats or splunk, but I wonder whether there is some serious (web)server logfile analysis going on in R. I might not be the first thought to do it in R, but still R has nice visualization capabilities and also nice spatial packages. Do you know of any? Or is there a R package / code that handles the most common log file formats that one could build on? Or is it simply a very bad idea?
In connection with a project to build an analytics toolbox for our Network Ops guys,
i built one of these about two months ago. My employer has no problem if i open source it, so if anyone is interested i can put it up on my github repo. I assume it's most useful to this group if i build an R Package. I won't be able to do that straight away though
because i need to research the docs on package building with non-R code (it might be as simple as tossing the python bytecode files in /exec along with a suitable python runtime, but i have no idea).
I was actually suprised that i needed to undertake a project of this sort. There are at least several excellent open source and free log file parsers/viewers (including the excellent Webalyzer and AWStats) but neither parse server error logs (parsing server access logs is the primary use case for both).
If you are not familiar with error logs or with the difference between them and access
logs, in sum, Apache servers (likewsie, nginx and IIS) record two distinct logs and store them to disk by default next to each other in the same directory. On Mac OS X,
that directory in /var, just below root:
$> pwd
/var/log/apache2
$> ls
access_log error_log
For network diagnostics, error logs are often far more useful than the access logs.
They also happen to be significantly more difficult to process because of the unstructured nature of the data in many of the fields and more significantly, because the data file
you are left with after parsing is an irregular time series--you might have multiple entries keyed to a single timestamp, then the next entry is three seconds later, and so forth.
i wanted an app that i could toss in raw error logs (of any size, but usually several hundred MB at a time) have something useful come out the other end--which in this case, had to be some pre-packaged analytics and also a data cube available inside R for command-line analytics. Given this, i coded the raw-log parser in python, while the processor (e.g., gridding the parser output to create a regular time series) and all analytics and data visualization, i coded in R.
I have been building analytics tools for a long time, but only in the past
four years have i been using R. So my first impression--immediately upon parsing a raw log file and loading the data frame in R is what a pleasure R is to work with and how it is so well suited for tasks of this sort. A few welcome suprises:
Serialization. To persist working data in R is a single command
(save). I knew this, but i didn't know how efficient is this binary
format. Thee actual data: for every 50 MB of raw logfiles parsed, the
.RData representation was about 500 KB--100 : 1 compression. (Note: i
pushed this down further to about 300 : 1 by using the data.table
library and manually setting compression level argument to the save
function);
IO. My Data Warehouse relies heavily on a lightweight datastructure
server that resides entirely in RAM and writes to disk
asynchronously, called redis. The proect itself is only about two
years old, yet there's already a redis client for R in CRAN (by B.W.
Lewis, version 1.6.1 as of this post);
Primary Data Analysis. The purpose of this Project was to build a
Library for our Network Ops guys to use. My goal was a "one command =
one data view" type interface. So for instance, i used the excellent
googleVis Package to create a professional-looking
scrollable/paginated HTML tables with sortable columns, in which i
loaded a data frame of aggregated data (>5,000 lines). Just those few
interactive elments--e.g., sorting a column--delivered useful
descriptive analytics. Another example, i wrote a lot of thin
wrappers over some basic data juggling and table-like functions; each
of these functions i would for instance, bind to a clickable button
on a tabbed web page. Again, this was a pleasure to do in R, in part
becasue quite often the function required no wrapper, the single
command with the arguments supplied was enough to generate a useful
view of the data.
A couple of examples of the last bullet:
# what are the most common issues that cause an error to be logged?
err_order = function(df){
t0 = xtabs(~Issue_Descr, df)
m = cbind( names(t0), t0)
rownames(m) = NULL
colnames(m) = c("Cause", "Count")
x = m[,2]
x = as.numeric(x)
ndx = order(x, decreasing=T)
m = m[ndx,]
m1 = data.frame(Cause=m[,1], Count=as.numeric(m[,2]),
CountAsProp=100*as.numeric(m[,2])/dim(df)[1])
subset(m1, CountAsProp >= 1.)
}
# calling this function, passing in a data frame, returns something like:
Cause Count CountAsProp
1 'connect to unix://var/ failed' 200 40.0
2 'object buffered to temp file' 185 37.0
3 'connection refused' 94 18.8
The Primary Data Cube Displayed for Interactive Analysis Using googleVis:
A contingency table (from an xtab function call) displayed using googleVis)
It is in fact an excellent idea. R also has very good date/time capabilities, can do cluster analysis or use any variety of machine learning alogorithms, has three different regexp engines to parse etc pp.
And it may not be a novel idea. A few years ago I was in brief email contact with someone using R for proactive (rather than reactive) logfile analysis: Read the logs, (in their case) build time-series models, predict hot spots. That is so obviously a good idea. It was one of the Department of Energy labs but I no longer have a URL. Even outside of temporal patterns there is a lot one could do here.
I have used R to load and parse IIS Log files with some success here is my code.
Load IIS Log files
require(data.table)
setwd("Log File Directory")
# get a list of all the log files
log_files <- Sys.glob("*.log")
# This line
# 1) reads each log file
# 2) concatenates them
IIS <- do.call( "rbind", lapply( log_files, read.csv, sep = " ", header = FALSE, comment.char = "#", na.strings = "-" ) )
# Add field names - Copy the "Fields" line from one of the log files :header line
colnames(IIS) <- c("date", "time", "s_ip", "cs_method", "cs_uri_stem", "cs_uri_query", "s_port", "cs_username", "c_ip", "cs_User_Agent", "sc_status", "sc_substatus", "sc_win32_status", "sc_bytes", "cs_bytes", "time-taken")
#Change it to a data.table
IIS <- data.table( IIS )
#Query at will
IIS[, .N, by = list(sc_status,cs_username, cs_uri_stem,sc_win32_status) ]
I did a logfile-analysis recently using R. It was no real komplex thing, mostly descriptive tables. R's build-in functions were sufficient for this job.
The problem was the data storage as my logfiles were about 10 GB. Revolutions R does offer new methods to handle such big data, but I at last decided to use a MySQL-database as a backend (which in fact reduced the size to 2 GB though normalization).
That could also solve your problem in reading logfiles in R.
#!python
import argparse
import csv
import cStringIO as StringIO
class OurDialect:
escapechar = ','
delimiter = ' '
quoting = csv.QUOTE_NONE
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--source', type=str, dest='line', default=[['''54.67.81.141 - - [01/Apr/2015:13:39:22 +0000] "GET / HTTP/1.1" 502 173 "-" "curl/7.41.0" "-"'''], ['''54.67.81.141 - - [01/Apr/2015:13:39:22 +0000] "GET / HTTP/1.1" 502 173 "-" "curl/7.41.0" "-"''']])
arguments = parser.parse_args()
try:
with open(arguments.line, 'wb') as fin:
line = fin.readlines()
except:
pass
finally:
line = arguments.line
header = ['IP', 'Ident', 'User', 'Timestamp', 'Offset', 'HTTP Verb', 'HTTP Endpoint', 'HTTP Version', 'HTTP Return code', 'Size in bytes', 'User-Agent']
lines = [[l[:-1].replace('[', '"').replace(']', '"').replace('"', '') for l in l1] for l1 in line]
out = StringIO.StringIO()
writer = csv.writer(out)
writer.writerow(header)
writer = csv.writer(out,dialect=OurDialect)
writer.writerows([[l1 for l1 in l] for l in lines])
print(out.getvalue())
Demo output:
IP,Ident,User,Timestamp,Offset,HTTP Verb,HTTP Endpoint,HTTP Version,HTTP Return code,Size in bytes,User-Agent
54.67.81.141, -, -, 01/Apr/2015:13:39:22, +0000, GET, /, HTTP/1.1, 502, 173, -, curl/7.41.0, -
54.67.81.141, -, -, 01/Apr/2015:13:39:22, +0000, GET, /, HTTP/1.1, 502, 173, -, curl/7.41.0, -
This format can easily be read into R using read.csv. And, it doesn't require any 3rd party libraries.