search for: dbgetpreparedqueri

Dear R helpers Suppose following is an output due to some R process. I wish to save it as a table in 'temp.db' df <- data.frame(x = c(5, 4, 3, 11), y = c(25, 16, 9, 121))     library(RSQLite)     write('** Initializing','')     drv <- dbDriver("SQLite", shared.cache = TRUE)     con <- dbConnect(drv, dbname = "temp.db",

All: I am using RSqlite and want to be able to update individual values in a record, such as with this simple example: library(RSQLite) drv<-dbDriver("SQLite") con<-dbConnect(drv,"test.db") my.data<-data.frame(countries=c("US","UK","Canada","Australia","NewZealand"),vals=c(52,36,74,10,98))

Hi there, When I extract data from SQLite to R, the data types (or modes) of the extracted data seems to be determined by the value of the first row. Please see the following example. When I put the missing values first, the column extracted is of the mode character. > str(dbGetQuery(sql.industry, + "select pya_var from annual_data3 + order by

Hello, I would like to compress a long string (character vector), store the compressed string in the text field of a SQLite database (using RSQLite), and then load the text back into memory and decompress it back into the the original string. My character vector can be compressed considerably using standard gzip/bzip2 compression. In theory it should be much faster for me to compress/decompress

Hi, I'm working in R 2.11.1 x64 on Windows x86_64-pc-mingw32. I'm trying to insert a very large CSV file into a SQLite database. I'm pretty new to working with databases in R, so I apologize if I'm overlooking something obvious here. I'm trying to work with the American Community Survey data, which is two 1.3GB csv files. I have enough RAM to read one of them into memory,