We have an R program that filters some data in a table and creates a new table with the results. On Windows and OSX, the program runs and our table is created properly. However, on our Linux (Ubuntu 12.04) server, the same R program produces a table with garbage data.
When we compare the garbage data produced on Linux to the proper data, we find that:
Seemingly arbitrary numbers in columns that should have text values
Extra rows
We think the issue is something with encoding, but all of our efforts to change the encoding of the database have failed so far.
Our R script uses RMySQL to connect with a MySQL Database, filter the contents, and write it to a new table (using the dbReadTable and dbWriteTable commands).
We know that the commands themselves are not the problem, as we are able to examine the data.frame before and after filtering them - the problem is with dbWriteTable.
These two links seem to be closest to the solution to our problem, but we have to wait for the pull request to go through:
https://github.com/jeffreyhorner/RMySQL/issues/6
https://github.com/gagern/RMySQL/commit/b0fbef105ca61d69992a2ec5a5eafde30530b8d5
And these are also relevant:
http://zee.balogh.sk/?p=928
What does character set and collation mean exactly?
From past experience I will suggest that this is not a problem in dbWriteTable;
and is not even an encoding issue!
It is likely that you have stringsAsFactors = T when writing the data.frame,
and those numbers are the factor numbers.
Related
I have a database in SQL Server that I am trying to convert into a MySQL database, so I can host it on AWS and move everything off-premises. From this link, it seems like normally this is no big deal, although that link doesn't seem to migrate from a .bak file so much as from your local instance of SQL Server that is running and contains the database in question. No big deal, I can work with that.
However when I actually use MySQL Workbench to migrate using these steps, it gets to the Bulk Data Transfer step, and then comes up with odd errors.
I get errors like the following:
ERROR: OptionalyticsCoreDB-Prod.UserTokens:Inserting Data: Data too long for column 'token' at row 1
ERROR: OptionalyticsCoreDB-Prod.UserTokens:Failed copying 6 rows
ERROR: OptionalyticsCoreDB-Prod.UserLogs:Inserting Data: Data too long for column 'ActionTaken' at row 1
ERROR: OptionalyticsCoreDB-Prod.UserLogs:Failed copying 244 rows
However the data should not be "too long." These columns are nvarchar(MAX) in SQL Server, and the data for them is often very short in the specified rows, nothing that approaches the maximum value for an nvarchar.
Links like this and this show that there used to be, almost a decade ago, bugs with nvarchar formats, but they've been fixed for years now. I have checked and even updated and restarted my software and then computer - I have up-to-date versions of MySQL and MySQL Workbench. So what's going on?
What is the problem here, and how do I get my database successfully migrated? Surely it's possible to migrate from SQL Server to MySQL, right?
I have answered my own question... Apparently there IS some sort of bug with Workbench when translating SQL Server nvarchar(MAX) columns. I output the schema migration to a script and examined it, it was translating those columns as varchar(0). After replacing all of them with TEXT columns, the completed migration worked.
Frustrating lesson.
I am attempting to analyze some data in RStudio which originates from a MySQL database, so I used dbConnect to connect to said database, and copied the single table I needed for this project. I then used R to clean the data a bit, getting rid of some un-needed columns. So far, so good.
My problems arose when I realized my data had some outliers, and I needed to delete rows which contained obvious outlier data. This is something I have no problem doing in SQL, but lack the R experience to do effectively. So I looked into it, and found out about sqldf, a package which bills itself as a way to use SQL commands to manipulate data.frames. Perfect! But I'm having some trouble with this, as sqldf seems to require a database connection of some kind. Is there a way to simply connect to a data.frame I have in my global environment in RStudio?
Q: Couldn't you just manipulate the data in MySQL before importing it to R?
A: Yes, and that's what I'll do if I have to, but I'd like to understand sqldf better.
Try:
options(sqldf.driver = "SQLite")
sqldf("select * from book;", drv = 'SQLite')
I have a large .sql file, created as a backup from a MySQL database (containing several tables), and I would like to search elements within it from R.
Ideally, there would be a read.sql function that would turn the tables into some R list with data.frames in it. Is there something that comes close? If not, can RSQLite or RMySQL help? (going through the reference manuals, I don't see a simple function for what I described)
No can do, boss. For R to interpret your MySQL database file, it would have to do a large part of what the DBMS itself does. That's a tall order, infeasible in the general case.
Would this return what you seek (which I think upon review you will admit is not yet particularly well described):
require(RMySQL)
drv <- dbDriver("MySQL")
con <- dbConnect(drv)
dbListTables(con)
# Or
names(dbGetInfo(drv))
If these are just source code than all you would need is readLines. If you are looking for an R-engine that can take SQL code and produce useful results then the sqldf package may provide some help. It parses SQL code embedded in quoted strings and applies it either to dataframe objects in memory or to disk-resident tables (or both). Its default driver for disk files is SQLite but other drivers can be used.
My workaround so far (I am also a newbie with db) is to export the database as .csv file in the phpMyAdmin (need to tick "Export tables as separate files" in the "custom" method). And then use read_csv() on tables I want to work with.
It is not ideal because I would like to export the database and work on it on my computer with R (creating functions that will work when accessing the database that is online) and access the real database later, when I have done all my testing. But from the answers here, it seems the .sql export would not help for that anyway (?) and that I would need to recreate the db locally...
I have reached the limit of RAM in analyzing large datasets in R. I think my next step is to import these data into a MySQL database and use the RMySQL package. Largely because I don't know database lingo, I haven't been able to figure out how to get beyond installing MySQL with hours of Googling and RSeeking (I am running MySQL and MySQL Workbench on Mac OSX 10.6, but can also run Ubuntu 10.04).
Is there a good reference on how to get started with this usage? At this point I don't want to do any sort of relational databasing. I just want to import .csv files into a local MySQL database and do the subsetting in with RMySQL.
I appreciate any pointers (including "You're way off base!" as I'm new to R and newer to large datasets... this one's around 80 mb)
The documentation for RMySQL is pretty good - but it does assume that you know the basics of SQL. These are:
creating a database
creating a table
getting data into the table
getting data out of the table
Step 1 is easy: in the MySQL console, simply "create database DBNAME". Or from the command line, use mysqladmin, or there are often MySQL admin GUIs.
Step 2 is a little more difficult, since you have to specify the table fields and their type. This will depend on the contents of your CSV (or other delimited) file. A simple example would look something like:
use DBNAME;
create table mydata(
id INT(11) NOT NULL AUTO_INCREMENT PRIMARY KEY,
height FLOAT(3,2)
);
Which says create a table with 2 fields: id, which will be the primary key (so has to be unique) and will autoincrement as new records are added; and height, which here is specified as a float (a numeric type), with 3 digits total and 2 after the decimal point (e.g. 100.27). It's important that you understand data types.
Step 3 - there are various ways to import data to a table. One of the easiest is to use the mysqlimport utility. In the example above, assuming that your data are in a file with the same name as the table (mydata), the first column a tab character and the second the height variable (with no header row), this would work:
mysqlimport -u DBUSERNAME -pDBPASSWORD DBNAME mydata
Step 4 - requires that you know how to run MySQL queries. Again, a simple example:
select * from mydata where height > 50;
Means "fetch all rows (id + height) from the table mydata where height is more than 50".
Once you have mastered those basics, you can move to more complex examples such as creating 2 or more tables and running queries that join data from each.
Then - you can turn to the RMySQL manual. In RMySQL, you set up the database connection, then use SQL query syntax to return rows from the table as a data frame. So it really is important that you get the SQL part - the RMySQL part is easy.
There are heaps of MySQL and SQL tutorials on the web, including the "official" tutorial at the MySQL website. Just Google search "mysql tutorial".
Personally, I don't consider 80 Mb to be a large dataset at all; I'm surprised that this is causing a RAM issue and I'm sure that native R functions can handle it quite easily. But it's good to learn new skill such as SQL, even if you don't need them for this problem.
I have a pretty good suggestion. For 80MB use SQLite. SQLite is a super public domain, lightweight, super fast file-based database that works (almost) just like a SQL database.
http://www.sqlite.org/index.html
You don't have to worry about running any kind of server or permissions, your database handle is just a file.
Also, it stores all data as a string, so you don't even have to worry about storing the data as types (since all you need to do is emulate a single text table anyway).
Someone else mentioned sqldf:
http://code.google.com/p/sqldf/
which does interact with SQLite:
http://code.google.com/p/sqldf/#9._How_do_I_examine_the_layout_that_SQLite_uses_for_a_table?_whi
So your SQL create statement would be like this
create table tablename (
id INT(11) INTEGER PRIMARY KEY,
first_column_name TEXT,
second_column_name TEXT,
third_column_name TEXT
);
Otherwise, neilfws' explanation is a pretty good one.
P.S. I'm also a little surprised that your script is choking on 80mb. It's not possible in R to just seek through the file in chunks without opening it all up in memory?
The sqldf package might give you an easier way to do what you need: http://code.google.com/p/sqldf/. Especially if you are the only person using the database.
Edit: Here is why I think it would be useful in this case (from the website):
With sqldf the user is freed from having to do the following, all of which are automatically done:
database setup
writing the create table statement which defines each table
importing and exporting to and from the database
coercing of the returned columns to the appropriate class in common cases
See also here: Quickly reading very large tables as dataframes in R
I agree with what's been said so far. Though I guess getting started with MySQL (databases) in general is not a bad idea for the long if you are going to deal with data. I mean I checked your profile which says finance PhD student. I don't know if that means quant. finance, but it is likely that you will come across really large datasets in your career. I you can afford some time, I would recommend to learn something about databases. It just helps.
The documentation of MySQL itself is pretty solid and you can a lot of additional (specific) help here at SO.
I run MySQL with MySQL workbench on Mac OS X Snow Leopard too. So here´s what helped me to get it done comparatively easy.
I installed MAMP , which gives my an local Apache webserver with PHP, MySQL and the MySQL tool PHPmyadmin, which can be used as a nice webbased alternative for MySQL workbench (which is not always super stable on a Mac :) . You will have a little widget to start and stop servers and can access some basic configuration settings (such as ports through your browser) . It´s really one-click install here.
Install the Rpackage RMySQL . I will put my connection string here, maybe that helps:
Create your databases with MySQL workbench. INT and VARCHAR (for categorical variables that contain characters) should be the field types you basically need at the beginning.
Try to find the import routine that works best for you. I don't know if you are a shell / terminal guy – if so you'll like what was suggested by neilfws. You could also use LOAD DATA INFILE which is I prefer since it's only one query as opposed to INSERT INTO (line by line)
If you specify the problems that you have more accurately, you'll get some more specific help – so feel free to ask ;)
I assume you have to work a lot with time series data – there is a project (TSMySQL) around that use R and relational databases (such as MySQL, but also available for other DBMS) to store time series data. Besides you can even connect R to FAME (which is popular among financers, but expensive). The last paragraph is certainly nothing basic, but I thought it might help you to consider if it´s worth the hustle to dive into it a little deeper.
Practical Computing for Biologists as a nice (though subject-specific) introduction to SQLite
Chapter 15. Data Organization and Databases
I have been charged with maintaining a legacy classic ASP application. The application uses an ODBC system DSN to connect to a MySQL database.
We had to recently update the servers to satisfy some licencing requirements. We were on Windows, with MySQL 4.x and the 3.51 ODBC driver. We moved to a Linux machine running MySQL 5.1.43 and are running the 5.1.6 ODBC driver on the new IIS server.
Users almost instantly started reporting errors as such:
Row cannot be located for updating.
Some values may have been changed
since it was last read.
This is a ghost error, and the same data changes, on the same record, at a different time won't always produce the error. It is also intermittent between different records, as in, sometimes, no matter what values I plug in, I haven't been able to repro the defect on all records.
It is happening across 70 of about 120 scripts, many over 1,000 lines long.
The only consistency I can find is that on all of the scripts that fail, they are all reading/writing floats to the DB. Fields that have a null value don't seem to crash, but if there is a value like '19' in the database (note the no decimal places) that seems to fail, whereas, '19.00' does not. Most floats are defined as 11,2.
The scripts are using ADODB and recordsets. Updates are done with the following pattern:
select * from table where ID =
udpdated recordID
update properties of the record from the form
call RecordSet.Update and RecordSet.Close
The error is generated from the RecordSet.Update command.
I have created a workaround, where rather than select/copy/update I generate an SQL statement that I execute. This works flawlessly (obviously, an UPDATE statement with a where clause is more focused and doesn't consider fields not updated), so I have a pretty good feeling that it is a rounding issue with the floats that is causing a mis-match with the re-retrieval of the record on the update call.
I really would prefer NOT re-writing 100's of these instances (a grep across the source directly finds 280+ update calls).
Can anyone confirm that the issue here is related to floats/rounding?
And if so, is there a global fix I can apply?
Thanks in advance,
-jc
Have a look at MySQL Forums :: ODBC :: Row cannot be located for updating.
They seem to have found some workaround and some explanations as well..
I ran into a similar issue with a VBA macro utilizing 4.1. When upgraded to 5 errors started popping up.
For me the issue was that values being returned to VBA from MySQL was in a unhandled (by VBA) decimal format.
A CAST on the numbers when querying helped to fix the issue.
So for your issue perhaps the ODBC/ASP combination is recording/reading values differently then what you might expect them to be.