Import SAS data with haven
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; haven will then automatically download the file for you before importing it.
You’ll be working with data on the age, gender, income, and purchase level (0 = low, 1 = high) of 36 individuals (Source: SAS). The information is stored in a SAS file, sales.sas7bdat, which is available in your current working directory. You can download the data here.
# Load the haven package
library(haven)## Warning: package 'haven' was built under R version 3.2.5# Import sales.sas7bdat: sales
sales <- read_sas("sales.sas7bdat")
# Display the structure of sales
str(sales)## Classes 'tbl_df', 'tbl' and 'data.frame': 431 obs. of 4 variables:
## $ purchase: num 0 0 1 1 0 0 0 0 0 0 ...
## $ age : num 41 47 41 39 32 32 33 45 43 40 ...
## $ gender : chr "Female" "Female" "Female" "Female" ...
## $ income : chr "Low" "Low" "Low" "Low" ...Import STATA data with haven
Next up are STATA data files; you can use read_dta() for these. When inspecting the result of the read_dta() call, you will notice that one column will be imported as a labelled vector, a 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 class labelled to a factor, you’ll need haven’s as_factor() function.
In this exercise, you will work with data on yearly import and export numbers of sugar, both in USD and in weight. The data can be found at: http://assets.datacamp.com/course/importing_data_into_r/trade.dta
# Import the data from the URL: sugar
sugar <- read_dta("http://assets.datacamp.com/course/importing_data_into_r/trade.dta")
# Structure of sugar
str(sugar)## Classes 'tbl_df', 'tbl' and 'data.frame': 10 obs. of 5 variables:
## $ Date :Class 'labelled' atomic [1:10] 10 9 8 7 6 5 4 3 2 1
## .. ..- attr(*, "label")= chr "Date"
## .. ..- attr(*, "labels")= Named int [1:10] 1 2 3 4 5 6 7 8 9 10
## .. .. ..- attr(*, "names")= chr [1:10] "2004-12-31" "2005-12-31" "2006-12-31" "2007-12-31" ...
## $ Import : atomic 37664782 16316512 11082246 35677943 9879878 1539992 28021 2652 7067402 1033672
## ..- attr(*, "label")= chr "Import"
## $ Weight_I: atomic 54029106 21584365 14526089 55034932 14806865 1749318 54567 3821 23722957 1964980
## ..- attr(*, "label")= chr "Weight_I"
## $ Export : atomic 54505513 102700010 37935000 48515008 71486545 12311696 16489813 29273920 46497438 27131638
## ..- attr(*, "label")= chr "Export"
## $ Weight_E: atomic 93350013 158000010 88000000 112000005 131800000 18500014 39599944 102072480 147583380 78268792
## ..- attr(*, "label")= chr "Weight_E"# Convert values in Date column to dates
sugar$Date <- as.Date(as_factor(sugar$Date))
# Structure of sugar again
str(sugar)## Classes 'tbl_df', 'tbl' and 'data.frame': 10 obs. of 5 variables:
## $ Date : Date, format: "2013-12-31" "2012-12-31" ...
## $ Import : atomic 37664782 16316512 11082246 35677943 9879878 1539992 28021 2652 7067402 1033672
## ..- attr(*, "label")= chr "Import"
## $ Weight_I: atomic 54029106 21584365 14526089 55034932 14806865 1749318 54567 3821 23722957 1964980
## ..- attr(*, "label")= chr "Weight_I"
## $ Export : atomic 54505513 102700010 37935000 48515008 71486545 12311696 16489813 29273920 46497438 27131638
## ..- attr(*, "label")= chr "Export"
## $ Weight_E: atomic 93350013 158000010 88000000 112000005 131800000 18500014 39599944 102072480 147583380 78268792
## ..- attr(*, "label")= chr "Weight_E"What does the graphic tell?
A plot can be very useful to explore the relationship between two variables. If you pass the plot() function two arguments, the first one will be plotted on the x-axis, the second one will be plotted on the y-axis.
The sugar trading data is again available at http://assets.datacamp.com/course/importing_data_into_r/trade.dta
After you’ve imported the data frame, you should plot two of its variables, Import against Weight_I, and describe their relationship! haven is already loaded in your R session, so you can start importing straight away.
plot(sugar$Import, sugar$weight_I)
- The import figures in USD and the import figures in weight are rather positively correlated.
Import SPSS data with haven
The haven package can also import data files from SPSS. Again, importing the data is pretty straightforward. Depending on the SPSS data file you’re working with, you’ll need either read_sav() - for .sav files - or read_por() - for .por files.
In this exercise you will work with data on four of the Big Five personality traits for 434 persons (Source: University of Bath). The Big Five is a psychological concept including, originally, five dimensions of personality to classify human personality. The SPSS dataset is called person.sav and is available in your working directory.
# Import person.sav: traits
#traits <- read_sav("personality.sav")
# Summarize traits
#summary(traits)
# Print out a subset
#subset(traits, Extroversion >40 & Agreeableness > 40)Factorize, round two
In the last exercise you learned how to import a data file using the command read_sav(). With SPSS data files, it can also happen that some of the variables you import have the labelled class. This is done to keep all the labelling information that was originally present in the .sav and .por files. It’s advised to coerce (or change) these variables to factors or other standard R classes.
The data for this exercise involves information on employees and their demographic and economic attributes (Source: QRiE. The data can be found on the following URL:
# Import SPSS data from the URL: work
work <- read_sav("http://assets.datacamp.com/course/importing_data_into_r/employee.sav")
# Display summary of work$GENDER
summary(work$GENDER)## Length Class Mode
## 474 labelled character# Convert work$GENDER to a factor
work$GENDER <- as_factor(work$GENDER)
# Display summary of work$GENDER again
summary(work$GENDER)## Female Male
## 216 258Import STATA data with foreign (1)
The package foreign offers a simple function to import and read STATA data: read.dta().
In this exercise you will import data on the US presidential elections in the year 2000. The data in florida.dta contains the total numbers of votes for each of the four candidates as well as the total number of votes per election area in the state of Florida (Soure: Florida Department of State). The file is available in your working directory, you can download it here if you want to experiment some more.
# Load the foreign package
library(foreign)## Warning: package 'foreign' was built under R version 3.2.3# Import florida.dta and name the resulting data frame florida
florida <- read.dta("florida.dta")
# Check tail() of florida
tail(florida)## gore bush buchanan nader total
## 62 2647 4051 27 59 6784
## 63 1399 2326 26 29 3780
## 64 97063 82214 396 2436 182109
## 65 3835 4511 46 149 8541
## 66 5637 12176 120 265 18198
## 67 2796 4983 88 93 7960Import STATA data with foreign (2)
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.dta to find out about about the default values.
The dataset for this exercise contains socio-economic measures and access to education for different individuals (Source: World Bank). This data is available as edequality.dta, which is located in the worldbank folder in your working directory.
# 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)## 'data.frame': 12214 obs. of 27 variables:
## $ hhid : num 1 1 1 2 2 3 4 4 5 6 ...
## $ hhweight : num 627 627 627 627 627 ...
## $ location : Factor w/ 2 levels "urban location",..: 1 1 1 1 1 2 2 2 1 1 ...
## $ region : Factor w/ 9 levels "Sofia city","Bourgass",..: 8 8 8 9 9 4 4 4 8 8 ...
## $ ethnicity_head : Factor w/ 4 levels "Bulgaria","Turks",..: 2 2 2 1 1 1 1 1 1 1 ...
## $ age : num 37 11 8 73 70 75 79 80 82 83 ...
## $ gender : Factor w/ 2 levels "male","female": 2 2 1 1 2 1 1 2 2 2 ...
## $ relation : Factor w/ 9 levels "head ",..: 1 3 3 1 2 1 1 2 1 1 ...
## $ literate : Factor w/ 2 levels "no","yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ income_mnt : num 13.3 13.3 13.3 142.5 142.5 ...
## $ income : num 160 160 160 1710 1710 ...
## $ aggregate : num 1042 1042 1042 3271 3271 ...
## $ aggr_ind_annual : num 347 347 347 1635 1635 ...
## $ educ_completed : int 2 4 4 4 3 3 3 3 4 4 ...
## $ grade_complete : num 4 3 0 3 4 4 4 4 5 5 ...
## $ grade_all : num 4 11 8 11 8 8 8 8 13 13 ...
## $ unemployed : int 2 1 1 1 1 1 1 1 1 1 ...
## $ reason_OLF : int NA NA NA 3 3 3 9 9 3 3 ...
## $ sector : int NA NA NA NA NA NA 1 1 NA NA ...
## $ occupation : int NA NA NA NA NA NA 5 5 NA NA ...
## $ earn_mont : num 0 0 0 0 0 0 20 20 0 0 ...
## $ earn_ann : num 0 0 0 0 0 0 240 240 0 0 ...
## $ hours_week : num NA NA NA NA NA NA 30 35 NA NA ...
## $ hours_mnt : num NA NA NA NA NA ...
## $ fulltime : int NA NA NA NA NA NA 1 1 NA NA ...
## $ hhexp : num 100 100 100 343 343 ...
## $ legacy_pension_amt: num NA NA NA NA NA NA NA NA NA NA ...
## - attr(*, "datalabel")= chr ""
## - attr(*, "time.stamp")= chr ""
## - attr(*, "formats")= chr "%9.0g" "%9.0g" "%9.0g" "%9.0g" ...
## - attr(*, "types")= int 100 100 108 108 108 100 108 108 108 100 ...
## - attr(*, "val.labels")= chr "" "" "location" "region" ...
## - attr(*, "var.labels")= chr "hhid" "hhweight" "location" "region" ...
## - attr(*, "expansion.fields")=List of 12
## ..$ : chr "_dta" "_svy_su1" "cluster"
## ..$ : chr "_dta" "_svy_strata1" "strata"
## ..$ : chr "_dta" "_svy_stages" "1"
## ..$ : chr "_dta" "_svy_version" "2"
## ..$ : chr "_dta" "__XijVarLabcons" "(sum) cons"
## ..$ : chr "_dta" "ReS_Xij" "cons"
## ..$ : chr "_dta" "ReS_str" "0"
## ..$ : chr "_dta" "ReS_j" "group"
## ..$ : chr "_dta" "ReS_ver" "v.2"
## ..$ : chr "_dta" "ReS_i" "hhid dur"
## ..$ : chr "_dta" "note1" "variables g1pc, g2pc, g3pc, g4pc, g5pc, g7pc, g8pc, g9pc, g10pc, g11pc, g12pc, gall, health, rent, durables were adjusted by r"| __truncated__
## ..$ : chr "_dta" "note0" "1"
## - attr(*, "version")= int 7
## - attr(*, "label.table")=List of 12
## ..$ location: Named int 1 2
## .. ..- attr(*, "names")= chr "urban location" "rural location"
## ..$ region : Named int 1 2 3 4 5 6 7 8 9
## .. ..- attr(*, "names")= chr "Sofia city" "Bourgass" "Varna" "Lovetch" ...
## ..$ ethnic : Named int 1 2 3 4
## .. ..- attr(*, "names")= chr "Bulgaria" "Turks" "Roma" "Other"
## ..$ s2_q2 : Named int 1 2
## .. ..- attr(*, "names")= chr "male" "female"
## ..$ s2_q3 : Named int 1 2 3 4 5 6 7 8 9
## .. ..- attr(*, "names")= chr "head " "spouse/partner " "child " "son/daughter-in-law " ...
## ..$ lit : Named int 1 2
## .. ..- attr(*, "names")= chr "no" "yes"
## ..$ : Named int 1 2 3 4
## .. ..- attr(*, "names")= chr "never attanded" "primary" "secondary" "postsecondary"
## ..$ : Named int 1 2
## .. ..- attr(*, "names")= chr "Not unemployed" "Unemployed"
## ..$ : Named int 1 2 3 4 5 6 7 8 9 10
## .. ..- attr(*, "names")= chr "student" "housewife/childcare" "in retirement" "illness, disability" ...
## ..$ : Named int 1 2 3 4 5 6 7 8 9 10
## .. ..- attr(*, "names")= chr "agriculture" "mining" "manufacturing" "utilities" ...
## ..$ : Named int 1 2 3 4 5
## .. ..- attr(*, "names")= chr "private company" "public works program" "government,public sector, army" "private individual" ...
## ..$ : Named int 1 2
## .. ..- attr(*, "names")= chr "no" "yes"# Create and print structure of edu_equal_2
edu_equal_2 <- read.dta(path, convert.factors = FALSE)
str(edu_equal_2)## 'data.frame': 12214 obs. of 27 variables:
## $ hhid : num 1 1 1 2 2 3 4 4 5 6 ...
## $ hhweight : num 627 627 627 627 627 ...
## $ location : int 1 1 1 1 1 2 2 2 1 1 ...
## $ region : int 8 8 8 9 9 4 4 4 8 8 ...
## $ ethnicity_head : int 2 2 2 1 1 1 1 1 1 1 ...
## $ age : num 37 11 8 73 70 75 79 80 82 83 ...
## $ gender : int 2 2 1 1 2 1 1 2 2 2 ...
## $ relation : int 1 3 3 1 2 1 1 2 1 1 ...
## $ literate : int 1 2 2 2 2 2 2 2 2 2 ...
## $ income_mnt : num 13.3 13.3 13.3 142.5 142.5 ...
## $ income : num 160 160 160 1710 1710 ...
## $ aggregate : num 1042 1042 1042 3271 3271 ...
## $ aggr_ind_annual : num 347 347 347 1635 1635 ...
## $ educ_completed : int 2 4 4 4 3 3 3 3 4 4 ...
## $ grade_complete : num 4 3 0 3 4 4 4 4 5 5 ...
## $ grade_all : num 4 11 8 11 8 8 8 8 13 13 ...
## $ unemployed : int 2 1 1 1 1 1 1 1 1 1 ...
## $ reason_OLF : int NA NA NA 3 3 3 9 9 3 3 ...
## $ sector : int NA NA NA NA NA NA 1 1 NA NA ...
## $ occupation : int NA NA NA NA NA NA 5 5 NA NA ...
## $ earn_mont : num 0 0 0 0 0 0 20 20 0 0 ...
## $ earn_ann : num 0 0 0 0 0 0 240 240 0 0 ...
## $ hours_week : num NA NA NA NA NA NA 30 35 NA NA ...
## $ hours_mnt : num NA NA NA NA NA ...
## $ fulltime : int NA NA NA NA NA NA 1 1 NA NA ...
## $ hhexp : num 100 100 100 343 343 ...
## $ legacy_pension_amt: num NA NA NA NA NA NA NA NA NA NA ...
## - attr(*, "datalabel")= chr ""
## - attr(*, "time.stamp")= chr ""
## - attr(*, "formats")= chr "%9.0g" "%9.0g" "%9.0g" "%9.0g" ...
## - attr(*, "types")= int 100 100 108 108 108 100 108 108 108 100 ...
## - attr(*, "val.labels")= chr "" "" "location" "region" ...
## - attr(*, "var.labels")= chr "hhid" "hhweight" "location" "region" ...
## - attr(*, "expansion.fields")=List of 12
## ..$ : chr "_dta" "_svy_su1" "cluster"
## ..$ : chr "_dta" "_svy_strata1" "strata"
## ..$ : chr "_dta" "_svy_stages" "1"
## ..$ : chr "_dta" "_svy_version" "2"
## ..$ : chr "_dta" "__XijVarLabcons" "(sum) cons"
## ..$ : chr "_dta" "ReS_Xij" "cons"
## ..$ : chr "_dta" "ReS_str" "0"
## ..$ : chr "_dta" "ReS_j" "group"
## ..$ : chr "_dta" "ReS_ver" "v.2"
## ..$ : chr "_dta" "ReS_i" "hhid dur"
## ..$ : chr "_dta" "note1" "variables g1pc, g2pc, g3pc, g4pc, g5pc, g7pc, g8pc, g9pc, g10pc, g11pc, g12pc, gall, health, rent, durables were adjusted by r"| __truncated__
## ..$ : chr "_dta" "note0" "1"
## - attr(*, "version")= int 7
## - attr(*, "label.table")=List of 12
## ..$ location: Named int 1 2
## .. ..- attr(*, "names")= chr "urban location" "rural location"
## ..$ region : Named int 1 2 3 4 5 6 7 8 9
## .. ..- attr(*, "names")= chr "Sofia city" "Bourgass" "Varna" "Lovetch" ...
## ..$ ethnic : Named int 1 2 3 4
## .. ..- attr(*, "names")= chr "Bulgaria" "Turks" "Roma" "Other"
## ..$ s2_q2 : Named int 1 2
## .. ..- attr(*, "names")= chr "male" "female"
## ..$ s2_q3 : Named int 1 2 3 4 5 6 7 8 9
## .. ..- attr(*, "names")= chr "head " "spouse/partner " "child " "son/daughter-in-law " ...
## ..$ lit : Named int 1 2
## .. ..- attr(*, "names")= chr "no" "yes"
## ..$ : Named int 1 2 3 4
## .. ..- attr(*, "names")= chr "never attanded" "primary" "secondary" "postsecondary"
## ..$ : Named int 1 2
## .. ..- attr(*, "names")= chr "Not unemployed" "Unemployed"
## ..$ : Named int 1 2 3 4 5 6 7 8 9 10
## .. ..- attr(*, "names")= chr "student" "housewife/childcare" "in retirement" "illness, disability" ...
## ..$ : Named int 1 2 3 4 5 6 7 8 9 10
## .. ..- attr(*, "names")= chr "agriculture" "mining" "manufacturing" "utilities" ...
## ..$ : Named int 1 2 3 4 5
## .. ..- attr(*, "names")= chr "private company" "public works program" "government,public sector, army" "private individual" ...
## ..$ : Named int 1 2
## .. ..- attr(*, "names")= chr "no" "yes"# Create and print structure of edu_equal_3
edu_equal_3 <- read.dta(path, convert.underscore = TRUE)
str(edu_equal_3)## 'data.frame': 12214 obs. of 27 variables:
## $ hhid : num 1 1 1 2 2 3 4 4 5 6 ...
## $ hhweight : num 627 627 627 627 627 ...
## $ location : Factor w/ 2 levels "urban location",..: 1 1 1 1 1 2 2 2 1 1 ...
## $ region : Factor w/ 9 levels "Sofia city","Bourgass",..: 8 8 8 9 9 4 4 4 8 8 ...
## $ ethnicity.head : Factor w/ 4 levels "Bulgaria","Turks",..: 2 2 2 1 1 1 1 1 1 1 ...
## $ age : num 37 11 8 73 70 75 79 80 82 83 ...
## $ gender : Factor w/ 2 levels "male","female": 2 2 1 1 2 1 1 2 2 2 ...
## $ relation : Factor w/ 9 levels "head ",..: 1 3 3 1 2 1 1 2 1 1 ...
## $ literate : Factor w/ 2 levels "no","yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ income.mnt : num 13.3 13.3 13.3 142.5 142.5 ...
## $ income : num 160 160 160 1710 1710 ...
## $ aggregate : num 1042 1042 1042 3271 3271 ...
## $ aggr.ind.annual : num 347 347 347 1635 1635 ...
## $ educ.completed : int 2 4 4 4 3 3 3 3 4 4 ...
## $ grade.complete : num 4 3 0 3 4 4 4 4 5 5 ...
## $ grade.all : num 4 11 8 11 8 8 8 8 13 13 ...
## $ unemployed : int 2 1 1 1 1 1 1 1 1 1 ...
## $ reason.OLF : int NA NA NA 3 3 3 9 9 3 3 ...
## $ sector : int NA NA NA NA NA NA 1 1 NA NA ...
## $ occupation : int NA NA NA NA NA NA 5 5 NA NA ...
## $ earn.mont : num 0 0 0 0 0 0 20 20 0 0 ...
## $ earn.ann : num 0 0 0 0 0 0 240 240 0 0 ...
## $ hours.week : num NA NA NA NA NA NA 30 35 NA NA ...
## $ hours.mnt : num NA NA NA NA NA ...
## $ fulltime : int NA NA NA NA NA NA 1 1 NA NA ...
## $ hhexp : num 100 100 100 343 343 ...
## $ legacy.pension.amt: num NA NA NA NA NA NA NA NA NA NA ...
## - attr(*, "datalabel")= chr ""
## - attr(*, "time.stamp")= chr ""
## - attr(*, "formats")= chr "%9.0g" "%9.0g" "%9.0g" "%9.0g" ...
## - attr(*, "types")= int 100 100 108 108 108 100 108 108 108 100 ...
## - attr(*, "val.labels")= chr "" "" "location" "region" ...
## - attr(*, "var.labels")= chr "hhid" "hhweight" "location" "region" ...
## - attr(*, "expansion.fields")=List of 12
## ..$ : chr "_dta" "_svy_su1" "cluster"
## ..$ : chr "_dta" "_svy_strata1" "strata"
## ..$ : chr "_dta" "_svy_stages" "1"
## ..$ : chr "_dta" "_svy_version" "2"
## ..$ : chr "_dta" "__XijVarLabcons" "(sum) cons"
## ..$ : chr "_dta" "ReS_Xij" "cons"
## ..$ : chr "_dta" "ReS_str" "0"
## ..$ : chr "_dta" "ReS_j" "group"
## ..$ : chr "_dta" "ReS_ver" "v.2"
## ..$ : chr "_dta" "ReS_i" "hhid dur"
## ..$ : chr "_dta" "note1" "variables g1pc, g2pc, g3pc, g4pc, g5pc, g7pc, g8pc, g9pc, g10pc, g11pc, g12pc, gall, health, rent, durables were adjusted by r"| __truncated__
## ..$ : chr "_dta" "note0" "1"
## - attr(*, "version")= int 7
## - attr(*, "label.table")=List of 12
## ..$ location: Named int 1 2
## .. ..- attr(*, "names")= chr "urban location" "rural location"
## ..$ region : Named int 1 2 3 4 5 6 7 8 9
## .. ..- attr(*, "names")= chr "Sofia city" "Bourgass" "Varna" "Lovetch" ...
## ..$ ethnic : Named int 1 2 3 4
## .. ..- attr(*, "names")= chr "Bulgaria" "Turks" "Roma" "Other"
## ..$ s2_q2 : Named int 1 2
## .. ..- attr(*, "names")= chr "male" "female"
## ..$ s2_q3 : Named int 1 2 3 4 5 6 7 8 9
## .. ..- attr(*, "names")= chr "head " "spouse/partner " "child " "son/daughter-in-law " ...
## ..$ lit : Named int 1 2
## .. ..- attr(*, "names")= chr "no" "yes"
## ..$ : Named int 1 2 3 4
## .. ..- attr(*, "names")= chr "never attanded" "primary" "secondary" "postsecondary"
## ..$ : Named int 1 2
## .. ..- attr(*, "names")= chr "Not unemployed" "Unemployed"
## ..$ : Named int 1 2 3 4 5 6 7 8 9 10
## .. ..- attr(*, "names")= chr "student" "housewife/childcare" "in retirement" "illness, disability" ...
## ..$ : Named int 1 2 3 4 5 6 7 8 9 10
## .. ..- attr(*, "names")= chr "agriculture" "mining" "manufacturing" "utilities" ...
## ..$ : Named int 1 2 3 4 5
## .. ..- attr(*, "names")= chr "private company" "public works program" "government,public sector, army" "private individual" ...
## ..$ : Named int 1 2
## .. ..- attr(*, "names")= chr "no" "yes"Import SPSS data with foreign (1)
All great things come in pairs. Where foreign provided read.dta() to read SAS data, there’s also read.spss() to read SPSS data files. To get a data frame, make sure to set to.data.frame = TRUE inside read.spss().
In this exercise, you’ll be working with socio-economic variables from different countries (Source: Quantative Data Analysis in Education). The SPSS data is in a file called international.sav, which is in your working directory. You can also download it here if you want to play around with it some more.
# Import international.sav as a data frame: demo
demo <- read.spss("international.sav", to.data.frame=TRUE)
# Create boxplot of gdp variable of demo
boxplot(demo$gdp)
Excursion: Correlation
If you’re familiar with statistics, you’ll have heard about Pearson’s Correlation. It is a measurement to evaluate the linear dependency between two variables, say XX and YY. It can range from -1 to 1; if it’s close to 1 it means that there is a strong positive association between the variables. If XX is high, also YY tends to be high If it’s close to -1, there is a strong negative association: If XX is high, YY tends to be low. When the Pearson correlation between two variables is 0, these variables are possibly independent: there is no association between XX and YY
cor(demo$gdp, demo$f_illit)## [1] -0.4476856- The correlation is around -0.45. There is a negative correlation, but it is rather weak.
Import SPSS data with foreign (2)
In the previous exercise, you used the to.data.frame argument inside read.spss(). There are many other ways in which to customize the way your SPSS data is imported.
In this exercise you will experiment with another argument, use.value.labels. It specifies whether variables with value labels should be converted into R factors with levels that are named accordingly. The argument is TRUE by default which means that so called labelled variables inside SPSS are converted to factors inside R.
# Import international.sav as demo_1
demo_1 <- read.spss("international.sav", to.data.frame=TRUE)
# Print out the head of demo_1
head(demo_1, n=2)## id country contint m_illit f_illit lifeexpt gdp
## 1 1 Argentina Americas 3.0 3.0 16 3375
## 2 2 Benin Africa 45.2 74.5 7 521# Import international.sav as demo_2
demo_2 <- read.spss("international.sav", to.data.frame=TRUE, use.value.labels=FALSE)
# Print out the head of demo_2
head(demo_2, n=2)## id country contint m_illit f_illit lifeexpt gdp
## 1 1 Argentina 2 3.0 3.0 16 3375
## 2 2 Benin 1 45.2 74.5 7 521