triming records everything after 7 characters - trim
I am trying to remove/trim everything after 7 characters.
issue #1 removing everything after 6 or 7 chracters
example: 1Q7 4B7 MY NAME IS MARY
Results I want: 1Q7 4B7
issue #2 removing one space
example: EQ9 2IQ
Results I want: EQ9 2IQ
Please assist.
Thanks
SUBSTRING
SELECT SUBSTR(column, 1, 6) AS MYCOLUMN FROM TABLE
Something like this should do the trick; you may have to fiddle with the numbers.
More SUBSTR Examples
Related
How do I find the sum of a category under a subset?
So... I'm very illiterate when it comes to RStudio and I'm using this program for a class... I'm trying to figure out how to sum a subset of a category. I apologize in advance if this doesn't make sense but I'll do my best to explain because I have no clue what I'm doing and would also appreciate an explanation of why and not just what the answer would be. Note: The two lines I included are part of the directions I have to follow, not something I just typed in because I knew how to - I don't... It's the last part, the sum, that I am not explained how to do and thus I don't know what to do and would appreciate help figuring out. For example, I have this: category_name category2_name 1 ABC 2 ABC 3 ABC 4 ABC 5 ABC 6 BDE 5 EFG 7 EFG I wanted to find the sum of these numbers, so I was told to put in this: sum(dataname$category_name) After doing this, I'm asked to type this in, apparently creating a subset. allabc <- subset(dataname, dataname$category_name2 == "abc") I created this subset and now I have a new table popped up with this subset. I'm asked to sum only the numbers of this ABC subset... I have absolutely no clue on how to do this. If someone could help me out, I'd really appreciate it!
R is the software you are using. It is case-sensitive. So "abc" is not equal to "ABC". The arguments are the "things" you put inside functions. Some arguments have the same name as the functions (which is a little confusing at first, but you get used to this eventually). So when I say the subset argument, I am talking about your second argument to the subset function, which you didn't name. That's ok, but when starting to learn R, try to always name your arguments. So, allabc <- subset(dataname, dataname$category_name2 == "abc") Needs to be changed to: allabc <- subset(dataname, subset=category2_name == "ABC") And you also don't need to specify the name of the data again in the subset argument, since you've done that already in the first argument (which you didn't name, but almost everyone never bothers to do that).
This is the most easily done using tidyverse.
# Your data
data <- data.frame(category_name = 1:8, category_name2 = c(rep("ABC", 5), "BDE", "EFG", "EFG"))
# Installing tidyverse
install.packages("tidyverse")
# Loading tidyverse
library(tidyverse)
# For each category_name2 the category_name is summed
data %>%
group_by(category_name2) %>%
summarise(sum_by_group = sum(category_name))
# Output
category_name2 sum_by_group
ABC 15
BDE 6
EFG 15
R bad row data not shown when read to data.table, but written to file
Sample input tab-delimited text file, note there is bad data from this source file, the enclosing " at end of line 3 is two lines down. So there is 1 complete blank line, followed by a line with just the double-quote character, then continued good data on the next line. id ca cb cc cd 1 hi bye hey nope 2 ab cd ef "quoted text here" 3 gh ij kl "quoted text but end quote is 2 lines down " 4 mn op qr lalalala when I read this into R, tried using read.csv and fread, with/without 'blank.lines.skip = T' for fread, I get the following data table: id ca cb cc cd 1 1 hi bye hey nope 2 2 ab cd ef quoted text here 3 3 gh ij kl quoted text but end quote is 2 lines down 4 4 mn op qr lalalala The data table does not show the 'bad' lines. OK, good! However, when I go to write out this data table, tried both write.table and fwrite, those 2 bad lines of /nothing/, and the double-quote, are written out just like they show in the input file! I've tried doing: dt[complete.cases(dt),], dt[!apply(dt == "", 1, all),] to clear out empty data before writing out, but it does nothing. The data table still only shows those 4 entries. Where is R keeping this 'missing' data? How can I clear out that bad data? I hope this is a 'one-off' bad output from the source (good ol' US Govt!), but I think they saved this from an xls file, which had bad formatting in a column, causing the text file to contain this mistake, but they obviously did not check the output.
After sitting back and thinking through the reading functions, because that column (cd) data is quoted, there's actually two newline characters at the end of the string, which is not shown in the data table element! So writing out that element will result in writing those two line breaks.
All I needed to do was:
dt$cd <- gsub("[\r\n","",dt$cd)
and that fixed it, the output written to file now has correct rows of data.
I wish I could remove my question...but maybe someday someone will come across the same "issue". I should have stepped back and thought about it before posting the question.
Skip comment line in csv file using R
I have a csv file which looks like this- #this is a dataset #this contains rows and columns ID value1 value2 value3 AA 5 6 5 BB 8 2 9 CC 3 5 2 I want read the csv file excluding those comment lines. It is possible to read mentioning that when it is '#' skip those line.But here the problem is there is an empty line after comments and also for my different csv file it can be various numbers of comment lines.But the main header will always start with "ID" from where i want to read the csv. It is possible to specify somehow that when it is ID read from there? if yes then please give an example. Thanks in advance!!
Use the comment.char option:
read.delim('filename', comment.char = '#')
Empty lines will be skipped automatically by default (blank.lines.skip = TRUE). You can also specify a fixed number of lines to skip via skip = number. However, it’s not possible to specify that it should start reading at a given line starting with 'ID' (but like I’ve said it’s not necessary here).
For those looking for a tidyverse approach, this will make the job, similarly as in #Konrad Rudolph's answer:
readr::read_delim('filename', comment = '#')
If you know in advance the number of line beofre headers, you can use skip option (here 3 lines):
read.table("myfile.csv",skip=3, header=T)
Technique for finding bad data in read.csv in R
I am reading in a file of data that looks like this:
userId, fullName,email,password,activated,registrationDate,locale,notifyOnUpdates,lastSyncTime,plan_id,plan_period_months,plan_price,plan_exp_date,plan_is_trial,plan_is_trial_used,q_hear,q_occupation,pp_subid,pp_payments,pp_since,pp_cancelled,apikey
"2","John Smith,"john.smith#gmail.com","a","1","2004-07-23 14:19:32","en_US","1","2011-04-07 07:29:17","3",\N,\N,\N,"0","1",\N,\N,\N,\N,\N,\N,"d7734dce-4ae2-102a-8951-0040ca38ff83"
but the actual file as around 20000 records. I use the following R code to read it in:
user = read.csv("~/Desktop/dbdump/users.txt", na.strings = "\\N", quote="")
And the reason I have quote="" is because without it the import stops prematurely. I end up with a total of 9569 observations. Why I don't understand why exactly the quote="" overcomes this problem, it seems to do so.
Except that it introduces other problems that I have to 'fix'. The first one I saw is that the dates end up being strings which include the quotes, which don't want to convert to actual dates when I use to.Date() on them.
Now I could fix the strings and hack my way through. But better to know more about what I am doing. Can someone explain:
Why does the quote="" fix the 'bad data'
What is a best-practice technique to figure out what is causing the read.csv to stop early? (If I just look at the input data at +/- the indicated row, I don't see anything amiss).
Here are the lines 'near' the 'problem'. I don't see the damage do you?
"16888","user1","user1#gmail.com","TeilS12","1","2008-01-19 08:47:45","en_US","0","2008-02-23 16:51:53","1",\N,\N,\N,"0","0","article","student",\N,\N,\N,\N,"ad949a8e-17ed-102b-9237-0040ca390025"
"16889","user2","user2#gmail.com","Gaspar","1","2008-01-19 10:34:11","en_US","1",\N,"1",\N,\N,\N,"0","0","email","journalist",\N,\N,\N,\N,"8b90f63a-17fc-102b-9237-0040ca390025"
"16890","user3","user3#gmail.com","boomblaadje","1","2008-01-19 14:36:54","en_US","0",\N,"1",\N,\N,\N,"0","0","article","student",\N,\N,\N,\N,"73f31f4a-181e-102b-9237-0040ca390025"
"16891","user4","user4#gmail.com","mytyty","1","2008-01-19 15:10:45","en_US","1","2008-01-19 15:16:45","1",\N,\N,\N,"0","0","google-ad","student",\N,\N,\N,\N,"2e48e308-1823-102b-9237-0040ca390025"
"16892","user5","user5#gmail.com","08091969","1","2008-01-19 15:12:50","en_US","1",\N,"1",\N,\N,\N,"0","0","dont","dont",\N,\N,\N,\N,"79051bc8-1823-102b-9237-0040ca390025"
* Update *
It's more tricky. Even though the total number of rows imported is 9569, if I look at the last few rows they correspond to the last few rows of data. Therefore I surmise that something happened during the import to cause a lot of rows to be skipped. In fact 15914 - 9569 = 6345 records. When I have the quote="" in there I get 15914.
So my question can be modified: Is there a way to get read.csv to report about rows it decides not to import?
* UPDATE 2 *
#Dwin, I had to remove na.strings="\N" because the count.fields function doesn't permit it. With that, I get this output which looks interesting but I don't understand it.
3 4 22 23 24
1 83 15466 178 4
Your second command produces a lots of data (and stops when max.print is reached.) But the first row is this:
[1] 2 4 2 3 5 3 3 3 5 3 3 3 2 3 4 2 3 2 2 3 2 2 4 2 4 3 5 4 3 4 3 3 3 3 3 2 4
Which I don't understand if the output is supposed to show how many fields there are in each record of input. Clearly the first lines all have more than 2,4,2 etc fields... Feel like I am getting closer, but still confused!
The count.fields function can be very useful in identifying where to look for malformed data.
This gives a tabulation of fields per line ignores quoting, possibly a problem if there are embedded commas:
table( count.fields("~/Desktop/dbdump/users.txt", quote="", sep=",") )
This give a tabulation ignoring both quotes and "#"(octothorpe) as a comment character:
table( count.fields("~/Desktop/dbdump/users.txt", quote="", comment.char="") )
Atfer seeing what you report for the first tabulation..... most of which were as desired ... You can get a list of the line positions with non-22 values (using the comma and non-quote settings):
which( count.fields("~/Desktop/dbdump/users.txt", quote="", sep=",") != 22)
Sometimes the problem can be solved with fill=TRUE if the only difficulty is missing commas at the ends of lines.
One problem I have spotted (thanks to data.table) is the missing quote (") after John Smith. Could this be a problem also for other lines you have?
If I add the "missing" quote after John Smith, it reads fine.
I saved this data to data.txt:
userId, fullName,email,password,activated,registrationDate,locale,notifyOnUpdates,lastSyncTime,plan_id,plan_period_months,plan_price,plan_exp_date,plan_is_trial,plan_is_trial_used,q_hear,q_occupation,pp_subid,pp_payments,pp_since,pp_cancelled,apikey
"2","John Smith","john.smith#gmail.com","a","1","2004-07-23 14:19:32","en_US","1","2011-04-07 07:29:17","3",\N,\N,\N,"0","1",\N,\N,\N,\N,\N,\N,"d7734dce-4ae2-102a-8951-0040ca38ff83"
"16888","user1","user1#gmail.com","TeilS12","1","2008-01-19 08:47:45","en_US","0","2008-02-23 16:51:53","1",\N,\N,\N,"0","0","article","student",\N,\N,\N,\N,"ad949a8e-17ed-102b-9237-0040ca390025"
"16889","user2","user2#gmail.com","Gaspar","1","2008-01-19 10:34:11","en_US","1",\N,"1",\N,\N,\N,"0","0","email","journalist",\N,\N,\N,\N,"8b90f63a-17fc-102b-9237-0040ca390025"
"16890","user3","user3#gmail.com","boomblaadje","1","2008-01-19 14:36:54","en_US","0",\N,"1",\N,\N,\N,"0","0","article","student",\N,\N,\N,\N,"73f31f4a-181e-102b-9237-0040ca390025"
"16891","user4","user4#gmail.com","mytyty","1","2008-01-19 15:10:45","en_US","1","2008-01-19 15:16:45","1",\N,\N,\N,"0","0","google-ad","student",\N,\N,\N,\N,"2e48e308-1823-102b-9237-0040ca390025"
"16892","user5","user5#gmail.com","08091969","1","2008-01-19 15:12:50","en_US","1",\N,"1",\N,\N,\N,"0","0","dont","dont",\N,\N,\N,\N,"79051bc8-1823-102b-9237-0040ca390025"
And this is a code. Both fread and read.csv works fine.
require(data.table)
dat1 <- fread("data.txt", header = T, na.strings = "\\N")
dat1
dat2 <- read.csv("data.txt", header = T, na.strings = "\\N")
dat2
simple rank formula
I'm looking for a mathmatical ranking formula. Sample is 2008 2009 2010 A 5 6 4 B 6 7 5 C 7 8 2 I want to add a rank column for each period code field rank 2008 2009 2010 2008 2009 2010 B 6 7 5 2 1 1 A 5 6 4 3 2 2 C 7 2 2 1 3 3 please do not reply with methods that loop thru the rows and columns, incrementing the rank value as it goes, that's easy. I'm looking for a formula much like finding the percent total (item / total). I know i've seen this before but an havning a tough time locating it. Thanks in advance!
sort ((letters_col, number_col) descending by number_col) As efficient as your sort alg. Then number the rows, of course Edit I really got upset by your comment "please don't up vote this answer, sorting and loop is not what I'm asking for. i specifically stated this in my original question. " , and the negative votes, because, as you may have noted by the various answers received, it's basically correct. However, I remained pondering where and how you may "have seen this before". Well, I think I got the answer: You saw this in Excel. Look at this: This is the result after entering the formulas and sorting by column H. It's exactly what you want ...
What are you using? If you're using Excel, you're looking for RANK(num, ref). =RANK(B2,B$2:B$9) I don't know of any programming language that has that built in, it would always require a loop of some form.
If you want the rank of a single element, you can do it in O(n) by looping through the elements, counting how many have value above the given element, and adding 1. If you want the rank of all the elements, the best (and really only) way is to sort the elements. Anything else you do will be equivalent to sorting (there is no "formula")
Are you using T-SQL? T-SQL RANK() may pull what you want.