add README example

.github/workflows/R-CMD-check.yaml

@@ -18,7 +18,7 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: macos-latest,   r: 'release'}
+          # - {os: macos-latest,   r: 'release'}
           - {os: windows-latest, r: 'release'}
           # - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: 'release'}

R/open_mariadb.R

@@ -0,0 +1,25 @@
+#' @rdname open_mysql
+#' @export 
+open_mariadb <- function(dbname=1, dbinfo=NULL) {
+  dbinfo = dbinfo %||% get_dbInfo()
+
+  if (is.numeric(dbname)) dbname = dbinfo$dbname[dbname]  
+  bold(ok(sprintf("[info] opening db: %s", dbname)))
+
+  # dev = odbc::odbc()
+  # odbc::odbcListDrivers()
+  dev = RMariaDB::MariaDB()
+  port = 3306
+  if (!is.null(dbinfo$port)) port = dbinfo$port
+
+  con <- dbConnect(dev, 
+    load_data_local_infile = TRUE,
+    host=dbinfo$host, port = port,
+    user=dbinfo$user, 
+    password=as.character(dbinfo$pwd), 
+    database = dbname, 
+    dbname = dbname)
+
+  str(dbGetInfo(con))
+  return(con)
+}

R/open_mysql.R

@@ -0,0 +1,47 @@
+#' Con database connection
+#'
+#' @examples
+#' \dontrun{
+#' dbinfo <- get_dbInfo() # see which db to read
+#' open_mysql()
+#' open_mariadb()
+#' }
+#'
+#' @export
+#' @import DBI
+open_mysql <- function(dbname = 1, dbinfo = NULL) {
+  dbinfo <- dbinfo %||% get_dbInfo()
+
+  if (is.numeric(dbname)) dbname <- dbinfo$dbname[dbname]
+  bold(Ipaper::ok(sprintf("[info] opening db: %s", dbname)))
+
+  dev <- RMySQL::MySQL()
+  # dev = odbc::odbc()
+  # odbc::odbcListDrivers()
+  port <- 3306
+  if (!is.null(dbinfo$port)) port <- dbinfo$port
+
+  con <- dbConnect(dev,
+    # driver = "MySQL ODBC 8.1 ANSI Driver",
+    host = dbinfo$host, port = port,
+    user = dbinfo$user,
+    password = as.character(dbinfo$pwd),
+    dbname = dbname
+  )
+  return(con)
+}
+
+
+#' @import DBI dplyr crayon
+#' @importMethodsFrom DBI dbSendQuery
+#' @importMethodsFrom RMySQL dbSendQuery
+setMethod(
+  "dbSendQuery", c("MySQLConnection", "character"),
+  # import S4 method from RMySQL
+  function(conn, statement, ...) {
+    RMySQL:::checkValid(conn)
+
+    rsId <- .Call(RMySQL:::RS_MySQL_exec, conn@Id, as.character(statement))
+    new("MySQLResult", Id = rsId)
+  }
+)

R/tool_dbase.R

@@ -1,17 +1,3 @@
-
-#' @import DBI dplyr crayon
-#' @importMethodsFrom DBI dbSendQuery
-#' @importMethodsFrom RMySQL dbSendQuery
-setMethod("dbSendQuery", c("MySQLConnection", "character"),
-  # import S4 method from RMySQL
-  function(conn, statement, ...) {
-    RMySQL:::checkValid(conn)
-
-    rsId <- .Call( RMySQL:::RS_MySQL_exec, conn@Id, as.character(statement))
-    new("MySQLResult", Id = rsId)
-  }
-)
-
 #' @export
 db_info <- function(con) {
   str(dbGetInfo(con))

README.Rmd

@@ -0,0 +1,72 @@
+---
+output: github_document
+---
+
+```{r, echo = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#",
+  fig.width = 10, fig.height = 5,
+  fig.align = "center",
+  fig.path  = "man/Figure/",
+  dev = 'svg'
+)
+```
+
+# tidydb2
+
+<!-- badges: start -->
+[![R-CMD-check](https://github.com/rpkgs/tidydb2/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rpkgs/tidydb2/actions/workflows/R-CMD-check.yaml)
+[![codecov](https://codecov.io/gh/rpkgs/tidydb2/branch/master/graph/badge.svg)](https://app.codecov.io/gh/rpkgs/tidydb2)
+<!-- [![CRAN](http://www.r-pkg.org/badges/version/tidydb2)](https://cran.r-project.org/package=tidydb2) -->
+<!-- [![total](http://cranlogs.r-pkg.org/badges/grand-total/tidydb2)](https://www.rpackages.io/package/tidydb2) -->
+<!-- [![monthly](http://cranlogs.r-pkg.org/badges/tidydb2)](https://www.rpackages.io/package/tidydb2) -->
+<!-- badges: end -->
+
+## Goals
+
+> 更便捷的访问数据库
+
+建立气象数据库，一行命令查询数据。
+
+## TODO
+
+- [ ] 读取数据的性能测试
+
+- [ ] 添加写入数据的函数
+
+## Installation
+
+```r
+remotes::install_github("rpkgs/tidydb2")
+```
+
+## Example
+
+This is a basic example which shows you how to solve a common problem:
+
+```{r}
+library(tidydb2)
+library(dplyr)
+library(Ipaper)
+
+## hourly气象数据
+dbinfo <- get_dbInfo("duckdb")
+f <- dbinfo$hourly_2020_2022$db
+db <- dbase$new(f)
+db
+
+site0 = 50246L
+date0 <- make_datetime(2022, 1, 1, 0)
+
+# 0.1s可以读回数据, 这里应该是UTC时间
+system.time(
+  d <- db$tbl |>
+    filter(site == site0, date > date0) |>
+    select(date, PRS, TEM, RHU, tigan) |>
+    collect()
+)
+
+print(d)
+db$close()
+```

README.md

@@ -1,6 +1,8 @@
+
 # tidydb2
 
 <!-- badges: start -->
+
 [![R-CMD-check](https://github.com/rpkgs/tidydb2/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rpkgs/tidydb2/actions/workflows/R-CMD-check.yaml)
 [![codecov](https://codecov.io/gh/rpkgs/tidydb2/branch/master/graph/badge.svg)](https://app.codecov.io/gh/rpkgs/tidydb2)
 <!-- [![CRAN](http://www.r-pkg.org/badges/version/tidydb2)](https://cran.r-project.org/package=tidydb2) -->
@@ -16,9 +18,9 @@
 
 ## TODO
 
-- [ ] 读取数据的性能测试
+-   [ ] 读取数据的性能测试
 
-- [ ] 添加写入数据的函数
+-   [ ] 添加写入数据的函数
 
 ## Installation
 
@@ -32,4 +34,76 @@ This is a basic example which shows you how to solve a common problem:
 
 ``` r
 library(tidydb2)
+library(dplyr)
+# 
+# Attaching package: 'dplyr'
+# The following objects are masked from 'package:stats':
+# 
+#     filter, lag
+# The following objects are masked from 'package:base':
+# 
+#     intersect, setdiff, setequal, union
+library(Ipaper)
+# Loading required package: magrittr
+# Registered S3 method overwritten by 'Ipaper':
+#   method           from      
+#   print.data.table data.table
+
+## hourly气象数据
+dbinfo <- get_dbInfo("duckdb")
+f <- dbinfo$hourly_2020_2022$db
+db <- dbase$new(f)
+db
+# [db   ]: z:/DATA/China/ChinaMet_hourly_mete2000/data/China_Mete2000_hourly_full_2020-2022_tidy.duckdb
+# [size ]: 4133.5 Mb
+# [Opened Table]: China_Mete2000_hourly_full_2020-2022
+# [ALL   Tables]: China_Mete2000_hourly_full_2020-2022
+# # Source:   table<China_Mete2000_hourly_full_2020-2022> [?? x 20]
+# # Database: DuckDB v0.9.2 [hydro@Windows 10 x64:R 4.3.2/z:/DATA/China/ChinaMet_hourly_mete2000/data/China_Mete2000_hourly_full_2020-2022_tidy.duckdb]
+#    date                 site PRE_1h   PRS PRS_Max PRS_Min PRS_Sea   RHU RHU_Min
+#    <dttm>              <int>  <dbl> <dbl>   <dbl>   <dbl>   <dbl> <dbl>   <dbl>
+#  1 2020-01-01 00:00:00 45011     NA   NA      NA      NA    1028.    NA      NA
+#  2 2020-01-01 00:00:00 50136      0  971.    971.    970.   1032.    68      67
+#  3 2020-01-01 00:00:00 50137      0  989.    989.    988.   1030.    70      67
+#  4 2020-01-01 00:00:00 50246      0  977.    977.    977.   1027.    53      47
+#  5 2020-01-01 00:00:00 50247      0  960.    960.    960.   1032.    68      68
+#  6 2020-01-01 00:00:00 50349      0  960.    960.    959.   1027     44      43
+#  7 2020-01-01 00:00:00 50353      0  999.    999.    998.   1024.    68      68
+#  8 2020-01-01 00:00:00 50425      0  959.    959.    958.   1041.    69      68
+#  9 2020-01-01 00:00:00 50431      0  940.    940.    939.   1040.    68      67
+# 10 2020-01-01 00:00:00 50434      0  937     937     937.   1039.    67      67
+# # ℹ more rows
+# # ℹ 11 more variables: TEM <dbl>, TEM_Max <dbl>, TEM_Min <dbl>, tigan <dbl>,
+# #   WIN_D_Avg_2mi <dbl>, WIN_D_INST_Max <dbl>, WIN_D_S_Max <dbl>,
+# #   WIN_S_Avg_2mi <dbl>, WIN_S_Inst_Max <dbl>, WIN_S_Max <dbl>, windpower <dbl>
+
+site0 = 50246L
+date0 <- make_datetime(2022, 1, 1, 0)
+
+# 0.1s可以读回数据, 这里应该是UTC时间
+system.time(
+  d <- db$tbl |>
+    filter(site == site0, date > date0) |>
+    select(date, PRS, TEM, RHU, tigan) |>
+    collect()
+)
+#    user  system elapsed 
+#    0.64    0.30    3.03
+
+print(d)
+# # A tibble: 8,759 × 5
+#    date                  PRS   TEM   RHU tigan
+#    <dttm>              <dbl> <dbl> <dbl> <dbl>
+#  1 2022-01-01 01:00:00  978  -31.1    71 -36.3
+#  2 2022-01-01 02:00:00  977. -29.9    70 -35.0
+#  3 2022-01-01 03:00:00  976. -28.6    69 -33.6
+#  4 2022-01-01 04:00:00  975. -26.6    66 -31.5
+#  5 2022-01-01 05:00:00  974. -25.7    63 -30.6
+#  6 2022-01-01 06:00:00  973. -24.7    61 -29.5
+#  7 2022-01-01 07:00:00  973. -24.4    61 -29.2
+#  8 2022-01-01 08:00:00  973. -24.8    63 -29.6
+#  9 2022-01-01 09:00:00  972. -25.1    65 -29.9
+# 10 2022-01-01 10:00:00  972. -25.2    72 -29.8
+# # ℹ 8,749 more rows
+db$close()
 ```

scripts/test_dbase.R

@@ -1,18 +1,35 @@
 library(dplyr)
 library(duckdb)
+library(Ipaper)
 
 ## hourly气象数据
 dbinfo <- get_dbInfo("duckdb")
 f = dbinfo$hourly_2020_2022$db
 db = dbase$new(f)
 db
 
+site0 = 50246L
+date0 <- make_datetime(2022, 1, 1, 0)
+
+# 0.1s可以读回数据, 这里应该是UTC时间
+system.time(
+  d <- db$tbl |> 
+    filter(site == site0, date > date0) |> 
+    select(date, PRS, TEM, RHU, tigan) |> 
+    collect()
+)
+
+print(d)
+db$close()
+
+
 d <- db$read_data(site == 50246L) # first time about 20s, 16s可以读进来所有数据
-d <- db$read_data(site == 50246L) # 0.6s
+d <- db$read_data(site == 50246L & date > date0) # 0.6s
 # db$close()
 
 ## 径流数据
 f = "z:/DATA/China/ChinaRunoff_latest/ChinaWater_latest/back_up/ChinaWater_river (20200603H22-20210116H14).db"
 db <- dbase$new(f)
 db
 db$read_data(stcd == 10701210L)
+