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)