I have a 6 gb csv file that I am trying to read into R using read.csv.sql from the sqldf package. For some reason, the result returns 0 results. What is wrong with my code? I get a warning message of "closing unused connection" which may not be related to the fact that no results are returned. My code is below.
TestData <- read.csv.sql("2025_nonroad_ff10_NCD20130831_23feb2015_v3_part1.csv", sql = "select * from file where poll == 'EXH__100414';", header=TRUE, skip=27, eol="\n", sep=",")
If I run a simpler SQL statement: select * from file limit 2, the result is:
Perhaps this revision might help:
TestData <- read.csv.sql("2025_nonroad_ff10_NCD20130831_23feb2015_v3_part1.csv", sql = "select * from file where poll = 'EXH__100414'", header=TRUE, skip=27, eol="\n", sep=",")
They were only minor changes:
removed double equals in the sql statement
removed closing semi-colon, in other programs a closing semi-colon is needed but in read.csv.sql it is not
If that doesn't work we need to try to isolate the problem
Try a simpler SQL such as showing just first two records. select * from file limit 2, does that even work?
If it works, then it means everything else is working but your original sql condition is bad, wrong etc.
If not, it means there is something else wrong with the rest of the read.csv.sql arguments, or perhaps with the file, or read.csv.sql itself.
Related
I'm trying to analyze data stored in an SQL database (MS SQL server) in R, and on a mac. Typical queries might return a few GB of data, and the entire database is a few TB. So far, I've been using the R package odbc, and it seems to work pretty well.
However, dbFetch() seems really slow. For example, a somewhat complex query returns all results in ~6 minutes in SQL server, but if I run it with odbc and then try dbFetch, it takes close to an hour to get the full 4 GB into a data.frame. I've tried fetching in chunks, which helps modestly: https://stackoverflow.com/a/59220710/8400969. I'm wondering if there is another way to more quickly pipe the data to my mac, and I like the line of thinking here: Quickly reading very large tables as dataframes
What are some strategies for speeding up dbFetch when the results of queries are a few GB of data? If the issue is generating a data.frame object from larger tables, are there savings available by "fetching" in a different manner? Are there other packages that might help?
Thanks for your ideas and suggestions!
My answer includes use of a different package. I use RODBC which is found in cran at https://cran.r-project.org/web/packages/RODBC/index.html.
This has saved me SO MUCH frustration and wasted time that came from my previous method of exporting each query result to .csv to load it into my R environment. I found regular ODBC to be much slower than RODBC.
I use the following functions:
sqlQuery() wraps the function that opens the connection to the SQL db with the first argument (in parentheses) and the query itself as the second argument. Put the query itself in quote marks.
odbcConnect() is itself the first argument in sqlquery(). The argument in odbcConnect() is the name of your connection to the SQL db. Put the connection name in quote marks.
odbcCloseAll() is the final function for this task set. Use this after each sqlQuery() to close the connection and save yourself from annoying warning messages.
Here is a simple example.
library(RODBC)
result <- sqlQuery(odbcConnect("ODBCConnectionName"),
"SELECT *
FROM dbo.table
WHERE Collection_ID = 2498")
odbcCloseAll()
Here is the same example PLUS data manipulation directly from the query result.
library(dplyr)
library(RODBC)
result <- sqlQuery(odbcConnect("ODBCConnectionName"),
"SELECT *
FROM dbo.table
WHERE Collection_ID = 2498") %>%
mutate(matchid = paste0(schoolID, "-", studentID)) %>%
distinct(matchid, .keep_all - TRUE)
odbcCloseAll()
I would suggest using the dbcooper found on github. https://github.com/chriscardillo/dbcooper
I have found huge improvements in speed when querying large datasets.
Firstly, Add your connection to your environment.
conn <- DBI::dbConnect(odbc::odbc(),
Driver = "",
Server = "",
Database = "",
UID="",
PWD="")
devtools::install_github("chriscardillo/dbcooper")
library(dbcooper)
dbcooper::dbc_init(con = conn,
con_id = "test",
tables = c("schema.table"))
This adds the function test_schema_table() to your environment which is used to call the data. To collect into your environment use scheme_table %>% collect()
Here is a microbenchmark I did to compare the results of both DBI and dbcooper.
mbm <- microbenchmark::microbenchmark(
DBI = DBI::dbFetch(DBI::dbSendQuery(conn,qry)),
dbcooper = ava_qry() %>% collect() , times=5
)
Here are the results of a microbenchmark I did to compare DBI with dbcooper.
So I am currently working with a connecting to an Access database. I am able to get connected to the Access DB which is located on my local system. This is actually connected to a SharePoint list. I would love to automate the process handling this SharePoint list with an R and Access combo! What I want to be able to do actually pretty basic, I want to introduce new data via a .csv which is processed for the relevant content and then compared to the current Access DB and finally the new information uploaded from r to Access.
I've learned that you need to pair the bit version of your Windows OS, Office version, and R version. So I am x64 on all of the above. This allowed me to connect to the Access DB. You also need the 'Microsoft Access Database Engine 2016 Redistributable' which is essentially the driver for the connection.
So what I have so far is:
library(odbc)
library(DBI)
file_path <- "C:/user/Documents/R Projects/...pathtofile.../filename.accdb"
accdb_con <- dbConnect(drv = odbc(), .connection_string = paste0("Driver={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=",file_path,";"))
access.db <- dbReadTable(accdb_con, "sNPS Deep Dives")
That now connects!
I then read in a .csv of new information
new.df <- read.csv("C:/user/Documents/R projects/...pathtofile.csv", header=T, stringsAsFactors=FALSE, na.strings=c("","NA"))
an example of the data set might just look something like this:
date <- c("15/10/2018","15/10/2018", "16/10/2018", "12/11/2018", "07/09/2018")
score <- c("6", "10", "7", "10", "9")
group <- c("a","b", "b", "a", "b")
CaseID <- c("301", "302", "303", "304", "305")
new.df <- data.frame(date,score,group,CaseID)
new.df$date <- as.character(new.df$date)
new.df$score <- as.numeric(new.df$score)
new.df$group <- as.character(new.df$group)
new.df$CaseID <- as.numeric(new.df$CaseID)
Notably there are more columns in the Access DB that people will fill in by hand with further information.
and I process it to be ready go into the Access DB.
probably not that interesting...
Then I compare the the new data against the Access DB as such:
library(dplyr)
new <- anti_join(new.df, access.db, by= "Case.ID")
Now I've tried:
dbWriteTable(access.db.copy, new, append = TRUE)
dbAppendTable(access.db.copy, new)
I don't seem to be able to get this to go anywhere
I am getting an error:
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘dbWriteTable’ for signature ‘"ACCESS", "data.frame", "missing"’
I've seen plenty of posts in which people are having trouble connecting to an Access DB but I haven't seen anything about writing new data into that database.
I know this isn't quite a reproducible example but it seems like a difficult problem to recreate since it's a connection problem between different tools. I would be happy to provide example sets that might make this easier
I would appreciate any direction you all can provide.
Thanks!
Edit:
It appears that Bing Sun was right, I was missing an argument. So it appears that we need something more like:
dbWriteTable(access.db.copy, "Name of table",new, append = TRUE)
Which produces the error:
Error in result_insert_dataframe(rs#ptr, values) :
nanodbc/nanodbc.cpp:1944: HY104: [Microsoft][ODBC Microsoft Access Driver]Invalid precision value
I wonder if this may something that is an error from Access about a file type?
now if I use the append I don't get an error I get a 0 for output
dbAppendTable(access.db.copy, "Name of table", new, append= TRUE)
With output:
[1] 0
But I don't see any of the new values when I check the Access file.
I know it's years later, but hopefully this will help someone else with this issue since you're right CrayCrayTown, there aren't very many posts covering this issue.
I've run into this problem repeatedly when dealing with R and MS Access. The solution that I've come up with is pretty "hacky" but it accomplishes what's trying to be done...just not very eloquently.
The way I do this is with a combo of RODBC and DBI packages.
First, I open a connection to the DB with RODBC, and use that connection to write my data to the DB as an intermediary table:
chan <- RODBC::odbcDriverConnection(connection = "/path/to/database.accdb")
RODBC::sqlSave(channel = chan,
dat = df,
tablename = "tbl_intermediary",
rownames = FALSE,
append = FALSE)
RODBC::odbcClose(chan)
rm(chan)
Make sure to close the RODBC connection, I also destroy it for good measure, because why not? I use RODBC for the intermediary table because it supports batch insert statements. I know that the same thing can, in theory, be done with DBI with DBI::dbAppendTable()(but we wouldn't be on this post if that worked how we had hoped). I tried this in a previous SO question here, but it didn't solve my problem. I also don't know how big my intermediary tables could get in the future. Hopefully by the time they get too big we'll be in a different DBMS.
Next, I reopen the connection, this time with DBI, and send a statement to the DB to write those data from the intermediary table to the final resting place for those data, and then drop the intermediary table.
con <- DBI::dbConnect(odbc::odbc(), .connection_string = "/path/to/database.accdb")
DBI::dbSendStatement(
conn = con,
statement = 'UPDATE
tbl_intermediary INNER JOIN final_tbl ON tbl_intermediary.SampleID = final_tbl.sampleNumber
SET
final_tbl.field1 = [tbl_intermediary].[field1],
final_tbl.notes = IIf(Nz([tbl_intermediary].[Notes],"")="",[final_tbl].[notes],[final_tbl].[notes] & "; Newest Notes: " & [tbl_intermediary].[Notes]);'
)
DBI::dbSendStatement(
conn = con,
statement = 'DROP TABLE tbl_intermediary;'
DBI::dbDisconnect(con)
rm(con)
)
The main reason why I chose this method is because some of the SQL I use with Access also has some VBA in it. When I send the SQL-VBA hybrid string with RODBC, I get assorted errors in the IIF() and Nz() functions (see example above). From the RODBC CRAN docs the query argument for the sqlQuery() function is strictly assumed to be a valid SQL statement. So, RODBC has no clue how to interpret the IIf() and Nz() MS Access functions. I think this also has to do with how the ODBC driver handles communication as well (please, someone correct me if I'm wrong about this).
As I understand it, DBI::dbSendStatment() however lets the database engine you're working with interpret how to use the statement argument you provide. In the situation above, the VBA is executed exactly how I would expect if it were run in Access directly. As per the DBI docs, for interactive use you'll generally want to use dbGetQuery or dbExecute.
I have a number of large data files (.csv) on my local drive that I need to read in R, filter rows/columns, and then combine. Each file has about 33,000 rows and 575 columns.
I read this post: Quickly reading very large tables as dataframes and decided to use "sqldf".
This is the short version of my code:
Housing <- file("file location on my disk")
Housing_filtered <- sqldf('SELECT Var1 FROM Housing', file.format = list(eol="/n")) *I am using Windows
I see "Housing_filtered" data.frame is created with Var1, but zero observations. This is my very first experience with sqldf. I am not sure why zero observations are returned.
I also used "read.csv.sql" and still I see zero observations.
Housing_filtered <- read.csv.sql(file = "file location on my disk",
sql = "select Var01 from file",
eol = "/n",
header = TRUE, sep = ",")
You never really imported the file as a data.frame like you think.
You've opened a connection to a file. You mentioned that it is a CSV. Your code should look something like this if it is a normal CSV file:
Housing <- read.csv("my_file.csv")
Housing_filtered <- sqldf('SELECT Var1 FROM Housing')
If there's something non-standard about this CSV file please mention what it is and how it was created.
Also, to another point that was made in the comments, if you do for some reason need to manually input the line breaks use \n where you were using /n. Any error is not being caused by that change, but rather you're getting passed 1 problem and on to another, probably due to improperly handling missing data, space, commas in text fields that aren't being handled, etc.
If there are still data errors can you please use R code to create a small file that is reflective of the relevant characteristics of your data and which produces the same error when you import it? This may help.
At present to insert data in a PostgreSQL table I have to create an empty table and then do an insert into table values ... along with a dataframe collapsed insto a single string with all the values. It doesn't work for large sized dataframes.
The dbWtriteTable() doesn't work for PostgreSQL and gives the following error...
Error in postgresqlpqExec(new.con, sql4) : RS-DBI driver: (could not Retrieve the result : ERROR: syntax error at or near "STDIN" LINE 1: COPY "table_1" FROM STDIN
I have tried the following hack as suggested in answer to a similar question asked before. Here's the link... How do I write data from R to PostgreSQL tables with an autoincrementing primary key?
body_lines <- deparse(body(RPostgreSQL::postgresqlWriteTable))
new_body_lines <- sub(
'postgresqlTableRef(name), "FROM STDIN")',
'postgresqlTableRef(name), "(", paste(shQuote(names(value)), collapse = ","), ") FROM STDIN")',
body_lines,
fixed = TRUE
)
fn <- RPostgreSQL::postgresqlWriteTable
body(fn) <- parse(text = new_body_lines)
while("RPostgreSQL" %in% search()) detach("package:RPostgreSQL")
assignInNamespace("postgresqlWriteTable", fn, "RPostgreSQL")
This hack still doesn't work for me. The postgresqlWriteTable() throws exactly the same error...
What exactly is the problem here?
As an alternative I have tried using dbWriteTable2() from caroline package. And it throws a different error...
Error in postgresqlExecStatement(conn, statement, ...) :
RS-DBI driver: (could not Retrieve the result : ERROR: column "id" does not exist in table_1
)
creating NAs/NULLs for for fields of table that are missing in your df
Error in postgresqlExecStatement(conn, statement, ...) :
RS-DBI driver: (could not Retrieve the result : ERROR: column "id" does not exist in table_1
)
Is there any other method to write a large dataframe into a table in PostgreSQL directly?
Ok, I'm not sure why dbWriteTable() would be failing; there may be some kind of version/protocol mismatch. Perhaps you could try installing the latest versions of R, the RPostgreSQL package, and upgrading the PostgreSQL server on your system, if possible.
Regarding the insert into workaround failing for large data, what is often done in the IT world when large amounts of data must be moved and a one-shot transfer is infeasible/impractical/flaky is what is sometimes referred to as batching or batch processing. Basically, you divide the data into smaller chunks and send each chunk one at a time.
As a random example, a few years ago I wrote some Java code to query for employee information from an HR LDAP server which was constrained to only provide 1000 records at a time. So basically I had to write a loop to keep sending the same request (with the query state tracked using some kind of weird cookie-based mechanism) and accumulating the records into a local database until the server reported the query complete.
Here's some code that manually constructs the SQL to create an empty table based on a given data.frame, and then insert the content of the data.frame into the table using a parameterized batch size. It's mostly built around calls to paste() to build the SQL strings, and dbSendQuery() to send the actual queries. I also use postgresqlDataType() for the table creation.
## connect to the DB
library('RPostgreSQL'); ## loads DBI automatically
drv <- dbDriver('PostgreSQL');
con <- dbConnect(drv,host=...,port=...,dbname=...,user=...,password=...);
## define helper functions
createEmptyTable <- function(con,tn,df) {
sql <- paste0("create table \"",tn,"\" (",paste0(collapse=',','"',names(df),'" ',sapply(df[0,],postgresqlDataType)),");");
dbSendQuery(con,sql);
invisible();
};
insertBatch <- function(con,tn,df,size=100L) {
if (nrow(df)==0L) return(invisible());
cnt <- (nrow(df)-1L)%/%size+1L;
for (i in seq(0L,len=cnt)) {
sql <- paste0("insert into \"",tn,"\" values (",do.call(paste,c(sep=',',collapse='),(',lapply(df[seq(i*size+1L,min(nrow(df),(i+1L)*size)),],shQuote))),");");
dbSendQuery(con,sql);
};
invisible();
};
## generate test data
NC <- 1e2L; NR <- 1e3L; df <- as.data.frame(replicate(NC,runif(NR)));
## run it
tn <- 't1';
dbRemoveTable(con,tn);
createEmptyTable(con,tn,df);
insertBatch(con,tn,df);
res <- dbReadTable(con,tn);
all.equal(df,res);
## [1] TRUE
Note that I didn't bother prepending a row.names column to the database table, unlike dbWriteTable(), which always seems to include such a column (and doesn't seem to provide any means of preventing it).
I had the same error while working through this example.
For me worked:
dbWriteTable(con, "cartable", value = df, overwrite = T, append = F, row.names = FALSE)
While I have configured a table "cartable" in pgAdmin. So an empty table existed and I had to overwrite that table with values.
So the answer showing batch processing given earlier is 99.99% correct. However, it doesn't work on windows because of a tiny argument required at the 'insertBatch' function.
(was not able to add a comment for the same answer)
The 'shQuote' function requires an argument type = 'cmd2' for it to work.
However, to add an argument there, you need this answer:
[https://stackoverflow.com/questions/6827299/r-apply-function-with-multiple-parameters][1]
So, the new 'insertBatch' function becomes:
insertBatch <- function(con,tn,df,size=100L) {
if (nrow(df)==0L) return(invisible());
cnt <- (nrow(df)-1L)%/%size+1L;
for (i in seq(0L,len=cnt)) {
sql <- paste0("insert into \"",tn,"\" values (",do.call(paste,c(sep=',',collapse='),(',lapply(df[seq(i*size+1L,min(nrow(df),(i+1L)*size)),],shQuote,type = 'cmd2'))),");");
dbSendQuery(con,sql);
};
invisible();
};
I've recently begun using RODBC to connect to PostgreSQL as I couldn't get RPostgreSQL to compile and run in Windows x64. I've found that read performance is similar between the two packages, but write performance is not. For example, using RODBC (where z is a ~6.1M row dataframe):
library(RODBC)
con <- odbcConnect("PostgreSQL84")
#autoCommit=FALSE seems to speed things up
odbcSetAutoCommit(con, autoCommit = FALSE)
system.time(sqlSave(con, z, "ERASE111", fast = TRUE))
user system elapsed
275.34 369.86 1979.59
odbcEndTran(con, commit = TRUE)
odbcCloseAll()
Whereas for the same ~6.1M row dataframe using RPostgreSQL (under 32-bit):
library(RPostgreSQL)
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname="gisdb", user="postgres", password="...")
system.time(dbWriteTable(con, "ERASE222", z))
user system elapsed
467.57 56.62 668.29
dbDisconnect(con)
So, in this test, RPostgreSQL is about 3X as fast as RODBC in writing tables. This performance ratio seems to stay more-or-less constant regardless of the number of rows in the dataframe (but the number of columns has far less effect). I do notice that RPostgreSQL uses something like COPY <table> FROM STDIN while RODBC issues a bunch of INSERT INTO <table> (columns...) VALUES (...) queries. I also notice that RODBC seems to choose int8 for integers, while RPostgreSQL chooses int4 where appropriate.
I need to do this kind of dataframe copy often, so I would very sincerely appreciate any advice on speeding up RODBC. For example, is this just inherent in ODBC, or am I not calling it properly?
It seems there is no immediate answer to this, so I'll post a kludgy workaround in case it is helpful for anyone.
Sharpie is correct--COPY FROM is by far the fastest way to get data into Postgres. Based on his suggestion, I've hacked together a function that gives a significant performance boost over RODBC::sqlSave(). For example, writing a 1.1 million row (by 24 column) dataframe took 960 seconds (elapsed) via sqlSave vs 69 seconds using the function below. I wouldn't have expected this since the data are written once to disk then again to the db.
library(RODBC)
con <- odbcConnect("PostgreSQL90")
#create the table
createTab <- function(dat, datname) {
#make an empty table, saving the trouble of making it by hand
res <- sqlSave(con, dat[1, ], datname)
res <- sqlQuery(con, paste("TRUNCATE TABLE",datname))
#write the dataframe
outfile = paste(datname, ".csv", sep = "")
write.csv(dat, outfile)
gc() # don't know why, but memory is
# not released after writing large csv?
# now copy the data into the table. If this doesn't work,
# be sure that postgres has read permissions for the path
sqlQuery(con,
paste("COPY ", datname, " FROM '",
getwd(), "/", datname,
".csv' WITH NULL AS 'NA' DELIMITER ',' CSV HEADER;",
sep=""))
unlink(outfile)
}
odbcClose(con)