Check what can be loaded
Using ILSAfile.info() we can identify different
populations within an ILSA.
Moreover, we can also use this information for loading an specific population into the memory. Using the ‘REDS’ 2021 example data provided by this package:
ILSAfile.info(inputdir = system.file("extdata/reds", package = "ILSAmerge"))
## Population Files MB
## 1 BCGV1 4 0.3
## 2 BSGV1 4 0.4
## 3 BTGV1 4 0.3Load all information
We can decide to load a population, like BCGV1, using
justload():
loaded <- justload(inputdir = system.file("extdata/reds", package = "ILSAmerge"),
population = "BCGV1")This will result in a list of elements loaded by haven,
in our example, a list of 11 elements:
class(loaded)
## [1] "list"
length(loaded)
## [1] 4
loaded[[1]]
## # A tibble: 10 × 314
## IDCNTRY IDSCHOOL ITLANGC IP1G00A IP1G00B IP1G00C
## <dbl+lbl> <dbl+lb> <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+lbl>
## 1 784 [United Arab E… 1001 53 [Arabic] 2 [Mid] 3 [March] 9 (NA) [Omi…
## 2 784 [United Arab E… 1002 53 [Arabic] 1 [Early] 2 [February] 3 [Late]
## 3 784 [United Arab E… 1003 53 [Arabic] 1 [Early] 2 [February] 3 [Late]
## 4 784 [United Arab E… 1004 1 [English] 3 [Late] 2 [February] 3 [Late]
## 5 784 [United Arab E… 1005 53 [Arabic] 1 [Early] 3 [March] 3 [Late]
## 6 784 [United Arab E… 1006 53 [Arabic] 2 [Mid] 2 [February] 2 [Mid]
## 7 784 [United Arab E… 1007 53 [Arabic] 1 [Early] 3 [March] 3 [Late]
## 8 784 [United Arab E… 1008 53 [Arabic] 1 [Early] 3 [March] 2 [Mid]
## 9 784 [United Arab E… 1009 53 [Arabic] 2 [Mid] 3 [March] 1 [Early]
## 10 784 [United Arab E… 1010 53 [Arabic] 2 [Mid] 6 [June] 3 [Late]
## # ℹ 308 more variables: IP1G00D <dbl+lbl>, IP1GIAA <dbl+lbl>,
## # IP1GIAB <dbl+lbl>, IP1GIAC <dbl+lbl>, IP1GIAD <dbl+lbl>, IP1GIAE <dbl+lbl>,
## # IP1GIAF <dbl+lbl>, IP1GIAG <dbl+lbl>, IP1GIBA <dbl+lbl>, IP1GIBB <dbl+lbl>,
## # IP1GIBC <dbl+lbl>, IP1GIBD <dbl+lbl>, IP1G01A <dbl+lbl>, IP1G01B <dbl+lbl>,
## # IP1G01C1 <dbl+lbl>, IP1G01C2 <dbl+lbl>, IP2G01A1 <dbl+lbl>,
## # IP1G01AA <dbl+lbl>, IP1G02A <dbl+lbl>, IP1G02B <dbl+lbl>,
## # IP1G02C <dbl+lbl>, IP1G02D <dbl+lbl>, IP1G02E <dbl+lbl>, …Load only column information
For some purposes, we might need to load only the column information,
most probably to check if the attributes are correct. We can do this by
setting justattributes = TRUE:
loaded <- justload(inputdir = system.file("extdata/reds", package = "ILSAmerge"),
population = "BCGV1", justattributes = TRUE)This will load all 11 datasets with 0 rows each:
length(loaded)
## [1] 4
loaded[[1]]
## # A tibble: 0 × 314
## # ℹ 314 variables: IDCNTRY <dbl+lbl>, IDSCHOOL <dbl+lbl>, ITLANGC <dbl+lbl>,
## # IP1G00A <dbl+lbl>, IP1G00B <dbl+lbl>, IP1G00C <dbl+lbl>, IP1G00D <dbl+lbl>,
## # IP1GIAA <dbl+lbl>, IP1GIAB <dbl+lbl>, IP1GIAC <dbl+lbl>, IP1GIAD <dbl+lbl>,
## # IP1GIAE <dbl+lbl>, IP1GIAF <dbl+lbl>, IP1GIAG <dbl+lbl>, IP1GIBA <dbl+lbl>,
## # IP1GIBB <dbl+lbl>, IP1GIBC <dbl+lbl>, IP1GIBD <dbl+lbl>, IP1G01A <dbl+lbl>,
## # IP1G01B <dbl+lbl>, IP1G01C1 <dbl+lbl>, IP1G01C2 <dbl+lbl>,
## # IP2G01A1 <dbl+lbl>, IP1G01AA <dbl+lbl>, IP1G02A <dbl+lbl>, …Read a single file
We can also read any SPSS file or any file produced by
ILSAmerge() by using readILSA(). For example,
to read the student questionnaire of Burkina Faso in REDS 2021:
dirdat <- system.file("extdata/reds", package = "ILSAmerge")
bfa <- readILSA(file.path(dirdat,"bsgbfav1.sav"))
bfa
## # A tibble: 10 × 334
## IDCNTRY IDSCHOOL IDSTUD SEX ASDAGE ITLANGS IS1G01
## <dbl+lbl> <dbl+lb> <dbl+> <dbl+lbl> <dbl+l> <dbl+lbl> <dbl+lbl>
## 1 854 [Burkina Faso] 1001 1.00e7 1 [Female] 13.8 2 [French] 0 [I did no…
## 2 854 [Burkina Faso] 1001 1.00e7 1 [Female] 18.3 2 [French] 0 [I did no…
## 3 854 [Burkina Faso] 1001 1.00e7 1 [Female] 14.2 2 [French] 0 [I did no…
## 4 854 [Burkina Faso] 1001 1.00e7 1 [Female] 13.8 2 [French] 0 [I did no…
## 5 854 [Burkina Faso] 1001 1.00e7 1 [Female] 15.3 2 [French] 0 [I did no…
## 6 854 [Burkina Faso] 1001 1.00e7 1 [Female] 15 2 [French] 0 [I did no…
## 7 854 [Burkina Faso] 1001 1.00e7 1 [Female] 13.9 2 [French] 0 [I did no…
## 8 854 [Burkina Faso] 1001 1.00e7 2 [Male] 12.4 2 [French] 0 [I did no…
## 9 854 [Burkina Faso] 1001 1.00e7 2 [Male] 16.3 2 [French] 0 [I did no…
## 10 854 [Burkina Faso] 1001 1.00e7 2 [Male] 14.1 2 [French] 0 [I did no…
## # ℹ 327 more variables: IS1G02A <dbl+lbl>, IS1G02B <dbl+lbl>,
## # IS1G02C <dbl+lbl>, IS1G03 <dbl+lbl>, IS1G04A <dbl+lbl>, IS1G04B <dbl+lbl>,
## # IS1G04C <dbl+lbl>, IS1G04D <dbl+lbl>, IS1G04E <dbl+lbl>, IS2G04F <dbl+lbl>,
## # IS2G04G <dbl+lbl>, IS1G05A <dbl+lbl>, IS1G05B <dbl+lbl>, IS1G05C <dbl+lbl>,
## # IS1G06 <dbl+lbl>, IS1G07A <dbl+lbl>, IS1G07B <dbl+lbl>, IS1G07C <dbl+lbl>,
## # IS1G07D <dbl+lbl>, IS1G07E <dbl+lbl>, IS1G07F <dbl+lbl>, IS1G07G <dbl+lbl>,
## # IS1G07H <dbl+lbl>, IS1G07I <dbl+lbl>, IS2G07J <dbl+lbl>, …Meanwhile, for reading a merged ILSA file, we run also
readILSA() but on the merged file:
# merge data
ILSAmerge(inputdir = dirdat,outputdir = tempdir(),filetype = "sav")
## 12 files detected. Merging into 3 files.
## Merging BCGV1. Type 1 of 3.
## Merging dataset 1 of 4.
## Merging dataset 2 of 4.
## Merging dataset 3 of 4.
## Merging dataset 4 of 4.
## Merging BCGV1 took 0 seconds or 0 minutes.
## Merging BSGV1. Type 2 of 3.
## Merging dataset 1 of 4.
## Merging dataset 2 of 4.
## Merging dataset 3 of 4.
## Merging dataset 4 of 4.
## Merging BSGV1 took 0 seconds or 0 minutes.
## Merging BTGV1. Type 3 of 3.
## Merging dataset 1 of 4.
## Merging dataset 2 of 4.
## Merging dataset 3 of 4.
## Merging dataset 4 of 4.
## Merging BTGV1 took 0 seconds or 0 minutes.
## Merging took 0 seconds or 0 minutes.
# rename merged files
ILSArename(inputdir = tempdir())
## 3 ILSAmerge() file(s) found.
## 3 ILSAmerge() file(s) renamed.
stu <- readILSA(file.path(tempdir(),"REDS_2021_student.sav"))
stu
## # A tibble: 40 × 336
## CNTRY IDCNTRY_STR IDCNTRY IDSCHOOL IDSTUD SEX ASDAGE ITLANGS
## <chr> <chr> <dbl+lbl> <dbl+lb> <dbl+> <dbl+lbl> <dbl+lb> <dbl+lbl>
## 1 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13 53 [Arabic]
## 2 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 12.7 53 [Arabic]
## 3 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13.4 53 [Arabic]
## 4 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13.8 53 [Arabic]
## 5 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13.4 53 [Arabic]
## 6 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13.4 53 [Arabic]
## 7 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13.2 53 [Arabic]
## 8 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13.8 53 [Arabic]
## 9 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 13.8 53 [Arabic]
## 10 ARE United Arab … 784 [Uni… 1001 1.00e7 2 [Male] 12.2 53 [Arabic]
## # ℹ 30 more rows
## # ℹ 328 more variables: IS1G01 <dbl+lbl>, IS1G02A <dbl+lbl>, IS1G02B <dbl+lbl>,
## # IS1G02C <dbl+lbl>, IS1G03 <dbl+lbl>, IS1G04A <dbl+lbl>, IS1G04B <dbl+lbl>,
## # IS1G04C <dbl+lbl>, IS1G04D <dbl+lbl>, IS1G04E <dbl+lbl>, IS2G04F <dbl+lbl>,
## # IS2G04G <dbl+lbl>, IS1G05A <dbl+lbl>, IS1G05B <dbl+lbl>, IS1G05C <dbl+lbl>,
## # IS1G06 <dbl+lbl>, IS1G07A <dbl+lbl>, IS1G07B <dbl+lbl>, IS1G07C <dbl+lbl>,
## # IS1G07D <dbl+lbl>, IS1G07E <dbl+lbl>, IS1G07F <dbl+lbl>, …Remove tibble attributes
As we can see, bfa, and stu are both
tibbles produced by package haven. Nonetheless, this type
of objects can produce some errors while using other packages for
analysis. Therefore, we should always create another object that is a
simple data frame so we can treat directly with the numeric data, always
trying also to remove all labeled missing values. For this, we use
untibble():