haven is an extremely easy-to-use package to import data from three software packages: SAS, STATA and SPSS. Depending on the software, you use different functions:

SAS: read_sas()

STATA: read_dta() (or read_stata(), which are identical)

SPSS: read_sav() or read_por(), depending on the file type.

All these functions take one key argument: the path to your local file. In fact, you can even pass a URL;havenwill then automatically download the file for you before importing it.

# Load the haven package

library(haven)

# Import sales.sas7bdat: sales

sales<-read_sas("sales.sas7bdat")

# Display the structure of sales

str(sales)

When inspecting the result of the read_dta() call, you will notice that one column will be imported as a labelled vector, an R equivalent for the common data structure in other statistical environments. In order to effectively continue working on the data in R, it's best to change this data into a standard R class. To convert a variable of the classlabelledto a factor, you'll need haven's as_factor() function.

# Import the data from the URL: sugar

sugar<-read_dta("http://assets.datacamp.com/production/course_1478/datasets/trade.dta")

# Structure of sugar

str(sugar)

# Convert values in Date column to dates

sugar$Date<-as.Date(as_factor(sugar$Date))

# Structure of sugar again

str(sugar)

# Import person.sav: traits

traits<-read_sav("person.sav")

# Summarize traits

summary(traits)

# Print out a subset

subset(traits,Extroversion>40&Agreeableness>40)

# Import SPSS data from the URL: work

work<-read_sav("http://s3.amazonaws.com/assets.datacamp.com/production/course_1478/datasets/employee.sav")

# Display summary of work$GENDER

summary(work$GENDER)

# Convert work$GENDER to a factor

work$GENDER<-as_factor(work$GENDER)

# Display summary of work$GENDER again

summary(work$GENDER)

Foreign

Data can be very diverse, going from character vectors to categorical variables, dates and more. It's in these cases that the additional arguments of read.dta()    will come in handy.

The arguments you will use most often are convert.dates , convert.factors ,missing.type and convert.underscore . Their meaning is pretty straightforward, as Filip explained in the video. It's all about correctly converting STATA data to standard R data structures. Type?read.dtato find out about about the default values.

# Load the foreign package

library(foreign)

# Import florida.dta and name the resulting data frame florida

florida<-read.dta("florida.dta")

# Check tail() of florida

tail(florida,n=6)

# Specify the file path using file.path(): path

path<-file.path("worldbank","edequality.dta")

# Create and print structure of edu_equal_1

edu_equal_1<-read.dta(path)

str(edu_equal_1)

# Create and print structure of edu_equal_2

edu_equal_2<-read.dta(path,convert.factors=F)

str(edu_equal_2)

# Create and print structure of edu_equal_3

edu_equal_3<-read.dta(path,convert.underscore=T)

str(edu_equal_3)

# Import international.sav as a data frame: demo

demo<-read.spss("international.sav",to.data.frame=T)

# Create boxplot of gdp variable of demo

boxplot(x=demo$gdp)

# Import international.sav as demo_1

demo_1<-read.spss("international.sav",to.data.frame=T)

# Print out the head of demo_1

head(demo_1)

# Import international.sav as demo_2

demo_2<-read.spss("international.sav",to.data.frame=T,use.value.labels=F)

# Print out the head of demo_2

head(demo_2)