Martins6

# get shiny serves plus tidyverse packages image
FROM rocker/shiny-verse:latest

# system libraries of general use
RUN apt-get update && apt-get install -y \
    sudo \
    pandoc \
    pandoc-citeproc \
    libcurl4-gnutls-dev \
    libcairo2-dev \

Martins6

# Capture 
output <- capture.output(q1 %>% show_query())
# Concatenate character vector to a one length character vector
final_output <- paste(tail(output, -1), collapse=" ")
final_output

Martins6

library(tidyverse)

knitr::knit2pdf('path\to\file.Rnw')

file_log <- read_file('path\to\file.log')

error_text <- 
  file_log %>%
  stringr::str_split('\n') %>%
  unlist %>%

Martins6

# Non Parametric Bootstrap Bias Correction and Confidence Interval

# Creating the Bootstrap Functions
bootstrap <- function(df, sample_prop = 1, B, stat_fun){
  "Perfom a bootstrap with B samples applying the stat_fun for
  calculating the Theta statistic. The prop. of sample you take
  each Bootstrap step from the data is sample_prop.
  
  stat_fun must only take one argument: df.
  

Martins6

import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile

samplerate, data = wavfile.read('Data/microphone-results.wav')
n = np.arange(len(data))

N = len(n)
fhat = np.fft.fft(data, N)
PSD = fhat * np.conj(fhat) / N

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{
    "python.pythonPath": "path/to/python",
    "python.formatting.provider": "autopep8",
    "python.formatting.blackPath": "venv/bin/black",
    "python.linting.enabled": true,
    "python.linting.pylintEnabled": false,
    "python.linting.flake8Enabled": true,
    "python.formatting.autopep8Args": [
      "--max-line-length",
      "80",

Martins6

#!/bin/bash

file_path=/home/adriel_martins/.update_upgrade.log
echo "** TURN ** : $(date +%d-%m-%Y)" >> $file_path
apt update >> $file_path
apt upgrade -y >> $file_path

Martins6

SELECT *,
CASE
  WHEN e.email like '%_@_%_.__%'
      AND NOT regexp_like(coalesce(trim(regexp_extract(e.email, '.+?(?=@)')), '$'),
                          '[^a-zA-Z0-9\._-\ã\õ\ẽ\é\ó\ú]')
    THEN lower(e.email)
  ELSE 'N/A'
END
) AS email,
CASE

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def multinomial_goodness_of_fit(
    f_obs,
    p_exp=None,
    b=1000,
    delta=0
) -> list:
    """
    Calculate the multinomial goodness of fit through Log Likelihood Ratio test statistic with the
    null distribution calculated via Monte Carlo simulation.

Martins6

# Three main sources for this script:
# https://github.com/rocker-org/rocker-versioned2/issues/144
# https://jsta.github.io/r-docker-tutorial/02-Launching-Docker.html
# https://stackoverflow.com/questions/65456814/docker-apple-silicon-m1-preview-mysql-no-matching-manifest-for-linux-arm64-v8

# After running the script, access the RStudio on the address http://localhost:8787/

docker run --rm \
  --platform linux/x86_64 \
  -p 8787:8787 \