Skip to content

Latest commit

 

History

History
105 lines (75 loc) · 2.77 KB

04-01-creating_log_files_manually.md

File metadata and controls

105 lines (75 loc) · 2.77 KB
Raw

(creating-log-files-explicitly)=

Creating log files explicitly

We start by describing how to explicitly generate log files as part of the statistical processing code.

::::{tab-set}

:::{tab-item} Stata

global logdir "${rootdir}/logs"
cap mkdir "$logdir"
local c_date = c(current_date)
local cdate = subinstr("`c_date'", " ", "_", .)
local c_time = c(current_time)
local ctime = subinstr("`c_time'", ":", "_", .)
local globallog = "$logdir/logfile_`cdate'-`ctime'-`c(username)'.log"
log using "`globallog'", name(global) replace text

How to potentially do this automatically at each start, see Stata manual.

:::

:::{tab-item} R

# This will only log output ("stdout") and warnings/messages ("stderr"), but not the commands themselves!

logfile.name <- paste0("logfile_", Sys.Date(),"-",format(as.POSIXct(Sys.time()), format = "%H_%M"),"-",Sys.info()["user"], ".log")
globallog    <- file(file.path(rootdir,logfile.name), open = "wt")
# Send output to logfile
sink(globallog, split=TRUE)
sink(globallog, type = "message")

## revert output back to the console 
sink(type = "message")
sink()
close(globallog)

Using tidylog for logging data manipulations in R

You can use tidylog to monitor tidy and dplyr data manipulations. It needs to be loaded after the relevant packages, as it redefines some of their commands.

Sample code:

#install.packages("tidylog")
library(dplyr)
library(tidylog)


# Example data manipulation
data <- data %>%
  filter(!is.na(variable)) %>%
  mutate(new_variable = variable * 2) %>%
  group_by(group_variable) %>%
  summarize(mean_value = mean(new_variable, na.rm = TRUE))

:::

:::{tab-item} MATLAB

% The "diary" function should achieve this. Not a MATLAB expert!

:::

:::{tab-item} Python

from datetime import datetime
def track_calls(func):
    def wrapper(*args, **kwargs):
        with open('function_log.txt', 'a') as f:
            timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            f.write(f"[{timestamp}] Calling {func.__name__} with args: {args}, kwargs: {kwargs}\n")
        result = func(*args, **kwargs)
        return result
    return wrapper

# Usage
@track_calls
def my_function(x, y,default="TRUE"):
    return x + y

my_function(1, 2,default="false")

will output

[2024-12-15 12:05:37] Calling my_function with args: (1, 2), kwargs: {'default': 'false'}

See also the Python logging documentation for controlled output. :::

::::

While some software (Stata, MATLAB) will create log files that contain commands and output, others (R, Python) will (by default) create log files that contain only output.