README.md

In jangorecki/logR: Log R expressions

logR

Extended logging solution:

• [x] transactional logging
• [x] insert log getting sequence id
• [x] evaluate expression with timing and exception handling
• [x] update log with status and metadata
• [x] log to postgres database
• [x] records errors, warnings, messages, interrupts
• [x] log process metadata: in/out nrow, flexible list of custom metadata
• [x] hierarchical logging: parent log id collected in case of nested logging
• [x] high precision timing with optional microbenchmarkCore
• [x] support parallel processing
• [ ] email notification on alerts

Installation

install.packages("logR", repos = c("https://jangorecki.gitlab.io/logR", "https://cran.rstudio.com"))

For high precision timing install suggested package microbenchmarkCore.

install.packages("microbenchmarkCore", repos = "https://olafmersmann.github.io/drat")

Usage

1. Connect to postgres db with ?logR_connect, it will use environment variables if available. Connection is assigned to getOption("logR.conn") logical value returned, FALSE on failure otherwise TRUE.
2. Create db table with ?logR_schema.
3. Use ?logR call as wrapper over your processes.
4. Use ?logR_dump to dump logs.

An example from SO answer.

library(logR)

# setup connection, default to env vars: `POSTGRES_DB`, etc.
# if you have docker then: docker run --rm -p 127.0.0.1:5432:5432 -e POSTGRES_PASSWORD=postgres --name pg-logr postgres:9.5
logR_connect()
# [1] TRUE

# create logr table
logR_schema()

# make some logging and calls

logR(1+2) # OK
#[1] 3
logR(log(-1)) # warning
#[1] NaN
f = function() stop("an error")
logR(r <- f()) # stop
#NULL
g = function(n) data.frame(a=sample(letters, n, TRUE))
logR(df <- g(4)) # out rows
#  a
#1 u
#2 c
#3 w
#4 p

# try CTRL+C / 'stop' button to interrupt
logR(Sys.sleep(15))

# wrapper to: dbReadTable(conn = getOption("logR.conn"), name = "logr")
logR_dump()
#   logr_id              logr_start          expr    status alert                logr_end      timing in_rows out_rows  mail message cond_call  cond_message
#1:       1 2016-02-08 16:35:00.148         1 + 2   success FALSE 2016-02-08 16:35:00.157 0.000049163      NA       NA FALSE      NA        NA            NA
#2:       2 2016-02-08 16:35:00.164       log(-1)   warning  TRUE 2016-02-08 16:35:00.171 0.000170801      NA       NA FALSE      NA   log(-1) NaNs produced
#3:       3 2016-02-08 16:35:00.180      r <- f()     error  TRUE 2016-02-08 16:35:00.187 0.000136896      NA       NA FALSE      NA       f()      an error
#4:       4 2016-02-08 16:35:00.197    df <- g(4)   success FALSE 2016-02-08 16:35:00.213 0.000696145      NA        4 FALSE      NA        NA            NA
#5:       5 2016-02-08 16:35:00.223 Sys.sleep(15) interrupt  TRUE 2016-02-08 16:35:05.434 5.202319000      NA       NA FALSE      NA        NA            NA

For more details see R scripts in tests directory and read manual.

Working examples of logR usage can also be found in big.data.table and pg packages. logR can be easily located as suggested dependency and works conditionally.

License

GPL-3

jangorecki/logR documentation built on May 18, 2019, 12:24 p.m.