Gael's Wiki

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

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")

  

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")

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 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")

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