apoorvalal

#%%
from google.cloud import translate_v2
from googletrans import Translator

import os
from pathlib import Path
import pandas as pd
# %%
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] ="/home/alal/keys/sandbox-7f8884d01b79.json"
translate_client = translate_v2.Client()

apoorvalal

# %% ####################################################
library(alpaca); library(fixest)
# %% ####################################################
data(psid, package = "bife")
head(psid)

# %% https://cran.r-project.org/web/packages/alpaca/vignettes/howto.html
stat <- alpaca::feglm(LFP ~ KID1 + KID2 + KID3 + log(INCH) | ID + TIME, psid, binomial("probit"))
# Fernandez-Val + Weidner bias correction - https://www-sciencedirect-com.stanford.idm.oclc.org/science/article/pii/S0304407615002997?via%3Dihub
stat.bc <- biasCorr(stat)

apoorvalal

# %% ####################################################
rm(list = ls())
library(LalRUtils)
LalRUtils::libreq(tidyverse, data.table, fst, collapse, fixest, rio, foreach, magrittr,
                  janitor, tictoc, modelsummary, hablar, patchwork, RPushbullet, IRdisplay,
                  did, panelView)
theme_set(lal_plot_theme()) # add _d() for dark
options(repr.plot.width=12, repr.plot.height=9)
options(ggplot2.discrete.fill = RColorBrewer::brewer.pal(7, "Set2"))
options(ggplot2.discrete.colour = RColorBrewer::brewer.pal(7, "Set2"))

apoorvalal

import pandas as pd
import sqlite3
# %%
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000]
        }

df = pd.DataFrame(Cars, columns= ['Brand', 'Price'])
print (df)

apoorvalal

# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: linux-64
@EXPLICIT
https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2020.6.24-0.conda
https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.33.1-h53a641e_7.conda
https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.conda
https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-9.1.0-hdf63c60_0.conda

apoorvalal

apoorvalal

"","Package","LibPath","Version","Priority","Depends","Imports","LinkingTo","Suggests","Enhances","License","License_is_FOSS","License_restricts_use","OS_type","MD5sum","NeedsCompilation","Built"
"abind","abind","/home/alal/R/x86_64-pc-linux-gnu-library/3.6","1.4-5",NA,"R (>= 1.5.0)","methods, utils",NA,NA,NA,"LGPL (>= 2)",NA,NA,NA,NA,"no","3.6.2"
"acepack","acepack","/home/alal/R/x86_64-pc-linux-gnu-library/3.6","1.4.1",NA,NA,NA,NA,"testthat",NA,"MIT + file LICENSE",NA,NA,NA,NA,"yes","3.6.2"
"AER","AER","/home/alal/R/x86_64-pc-linux-gnu-library/3.6","1.2-9",NA,"R (>= 3.0.0), car (>= 2.0-19), lmtest, sandwich (>= 2.4-0),
survival (>= 2.37-5), zoo","stats, Formula (>= 0.2-0)",NA,"boot, dynlm, effects, fGarch, forecast, foreign, ineq,
KernSmooth, lattice, longmemo, MASS, mlogit, nlme, nnet, np,
plm, pscl, quantreg, rgl, ROCR, rugarch, sampleSelection,
scatterplot3d, strucchange, systemfit (>= 1.1-20), truncreg,
tseries, urca, vars",NA,"GPL-2 | GPL-3",NA,NA,NA,NA,"no","3.6.2"
"alpaca","alpaca","/home/alal/R/x86_

apoorvalal

apoorvalal

*! v0.1 Apoorva Lal
// returns dataset with dataset's <describe> output
// useful for very wide datasets / datasets with useless/uninformative
// variable names
// e.g. USAID's Demographic and Health Surveys

pr define varlabelbook
    syntax [anything]
    qui compress
    qui ds `anything'

apoorvalal

#!/bin/bash
#SBATCH --job-name=cron
#SBATCH --begin=now
#SBATCH --dependency=singleton
#SBATCH --time=00:02:00
#SBATCH -p hns
#SBATCH --mem-per-cpu=4GB
#SBATCH --mail-type=FAIL

# module loads