How to pass data.frame for UPDATE with R DBI

user_1681681 0
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With RODBC, there were functions like sqlUpdate(channel, dat, ...) that allowed you pass dat = data.frame(...) instead of having to construct your own SQL string.

However, with R's DBI, all I see are functions like dbSendQuery(conn, statement, ...) which only take a string statement and gives no opportunity to specify a data.frame directly.

So how to UPDATE using a data.frame with DBI?

r-dbi rodbc dataframe r database
3 Answers

Building on R Yoda's answer, I made myself the helper function below. This allows using a dataframe to specify update conditions.

While I built this to run transaction updates (i.e. single rows), it can in theory update multiple rows passing a condition. However, that's not the same as updating multiple rows using an input dataframe. Maybe somebody else can build on this...


dbUpdateCustom = function(x, key_cols, con, schema_name, table_name) {
  
  if (nrow(x) != 1) stop("Input dataframe must be exactly 1 row")
  if (!all(key_cols %in% colnames(x))) stop("All columns specified in 'key_cols' must be present in 'x'")
  
  # Build the update string --------------------------------------------------

  df_key     <- dplyr::select(x,  one_of(key_cols))
  df_upt     <- dplyr::select(x, -one_of(key_cols))
  
  set_str    <- purrr::map_chr(colnames(df_upt), ~glue::glue_sql('{`.x`} = {x[[.x]]}', .con = con))
  set_str    <- paste(set_str, collapse = ", ")
  
  where_str  <- purrr::map_chr(colnames(df_key), ~glue::glue_sql("{`.x`} = {x[[.x]]}", .con = con))
  where_str  <- paste(where_str, collapse = " AND ")
  
  update_str <- glue::glue('UPDATE {schema_name}.{table_name} SET {set_str} WHERE {where_str}')
  
  # Execute ------------------------------------------------------------------
  
  query_res <- DBI::dbSendQuery(con, update_str)
  DBI::dbClearResult(query_res)

  return (invisible(TRUE))
}

Where

  • x: 1-row dataframe that contains 1+ key columns, and 1+ update columns.
  • key_cols: character vector, of 1 or more column names that are the keys (i.e. used in the WHERE clause)
user_31245330

Here is a little helper function I put together using REPLACE INTO to update a table using DBI, replacing old duplicate entries with the new values. It's basic and for my own needs, but should be easy to modify. All you need to pass to the function is the connection, table name, and dataframe. Note that the table must have a PRIMARY KEY column. I've also included a simple working example.

row_to_list <- function(Y)  suppressWarnings(split(Y, f = row(Y)))

sql_val <- function(y){
  if(!is.numeric(y)){
    return(paste0("'",y,"'"))
  }else{
    if(is.na(y)){
      return("NULL")
    }else{
      return(as.character(y))
    }
  }
}

to_sql_row <- function(x) paste0("(",paste(do.call("c", lapply(x, FUN = sql_val)), collapse = ", "),")")

bracket <- function(x) paste0("`",x,"`")

to_sql_string <- function(x) paste0("(",paste(sapply(x, FUN = bracket), collapse = ", "),")")

replace_into_table <- function(con, table_name, new_data){
  #new_data <- data.table(new_data)
  cols <- to_sql_string(names(new_data))
  vals <- paste(lapply(row_to_list(new_data), FUN = to_sql_row), collapse = ", ")
  query <- paste("REPLACE INTO", table_name, cols, "VALUES", vals)
  rs <- dbExecute(con, query)
  return(rs)
}

tb <- data.frame("id" = letters[1:20], "A" = 1:20, "B" = seq(.1,2,.1)) # sample data
dbWriteTable(con, "test_table", tb) # create table
dbExecute(con, "ALTER TABLE test_table ADD PRIMARY KEY (id)") # set primary key

new_data <- data.frame("id" = letters[19:23], "A" = 1:5, "B" = seq(101,105)) # new data
new_data[4,2] <- NA # add some NA values
new_data[5,3] <- NA
table_name <- "test_table"
replace_into_table(con, "test_table", new_data)

result <- dbReadTable(con, "test_table")
user_107034790
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