=====Start a script =====
  
  library(stargazer)
  rm(list = ls())

set working directory

  setwd("/path/to/my/directory")

data from excel
  dataset <- readXL("data.xlsx", rownames=FALSE, header=TRUE, na="", sheet="Sheet1", stringsAsFactors=TRUE)

data from Rdata
  
=====Summary Stats=====

  stargazer(dataset, type = "text", title= "Summary Statistics", digits = 2, out="output.csv")
  
type can be html, text or, by default, latex.

  stargazer(dataset, type = "text", title= "Summary Statistics", summary.stat=c("n","mean", "median", "p90","sd","min", "max"), digits = 2, out="output.csv")
  
    
=====Regressions=====

linear
  reg1 <- lm(depvar ~ var1 + var2 + var3, data=dataset)
use rlm for robust regressions  
  
  
probit
  reg2 <- glm(dummyvar ~ var1 + var2 + var3, family=binomial(link=probit), data = dataset)
  
table
  stargazer(reg1, reg2, header=FALSE, title="Regression Table", type='html', digits=2 , out="output.html")
  
  
=====Exemples=====
  install.packages("MASS")
  install.packages("readODS")
  install.packages("tidyverse")
  install.packages("devtools")
  devtools::install_github("gvelasq/tidytab")



#load libraries
  #import ods
  library(readODS)
  #import xls/xlsx  
  library(readxl)
  #robust lm
  library(MASS)
  #for tables
  library(stargazer)
  library(tidytab)
  # for data manipulation
  library(tidyverse)
  
# set work directory
  setwd("/home/glebelg/nextcloud/Work/time to code/")

# import ods
  db1<-read_ods(path = "dbtest.ods", sheet = 1)
  db2<-read_excel("dbtest.xlsx")

#generate variables in dataset
  db1$niveauscore <- db1$niveau * db1$score
    
#option2 - si beaucoup d'opérations!
    attach(db2)
    db2$niveauscore <- niveau * score
    detach(db2)

    attach(db1)
    db1$niveauminscore <- niveau - score
    detach(db1)
    
    
    
# OLS regressions
    
    model1 <- rlm(score ~ age, data=db1)
    model2 <- rlm(score ~ niveau, data=db1)
    model3 <- rlm(score ~ age + niveau, data=db1)
    
    stargazer(model1, model2, model3, type="text",
              title="Regression Results",p.auto=TRUE, single.row=FALSE,
              ci=FALSE, ci.level=0.9, omit.stat=c("ser"))

#summary statistics table      
  stargazer(as.data.frame(db1), type="text", summary=TRUE)

# tabulate
  table ("Age"=db1$age,"Sexe"= db1$sexe)
  db1 %>% tab(age, sexe)
  db1 %>% tab1(age, sexe)
  db1 %>% ftab( sexe, age)

=====Import/Export=====
import excel
  library(readxl)
  db <- read_excel("db.xlsx")
  View(db)
import stata
  library(haven)
  db <- read_dta("db.dta")
  View(db)
  
export excel
  install.packages("xlsx")
  library(xlsx)
  write.xlsx(db, file = "db.xlsx")

=====Data Manipulation=====

====drop publicates====
  newdata <- distinct(dataset,varname, .keep_all = TRUE)