In 2020 I wrote a package called libr to deal with the problem of managing multiple related datasets. This package allows you to manage data from several popular data sources: SAS, Excel, CSV, and R. While the package received generally positive feedback, users requested some features that the package did not support:
- Users wanted to examine the data before loading it into memory.
- Users wanted to pick only some datasets to load into memory.
- Users wanted to subset all datasets in the library based on a condition.
After consideration of these requests, I determined that these features would be most easily satisfied by creating a new package. That package is fetch.
Key Features
The fetch package allows you to retrieve data from any of several different data sources while minimizing memory usage. There are only two steps to retrieve data:
- Create a data catalog
- Fetch data from the catalog
The above two steps are accomplished by the following functions:
-
catalog(): Defines a data catalog for a particular data source -
fetch(): Pulls a data item from the catalog and loads it into memory
The package has the following key features:
- The
catalog()function allows you to explore the data before reading from the source. - The
fetch()function loads only the data you need for your analysis. - The “where” parameter on the
catalog()function allows you to subset across all datasets in the catalog. - The “select” and “top” parameters on the
fetch()function let you further limit the data returned. - The “import_spec” parameters on the
catalog()andfetch()functions give you control over the column data types.
How to use fetch
Let’s start with a simple example. In this example we will create a data catalog, examine its contents, and fetch a dataset from the catalog. First, create and view the data catalog:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create catalog
ct <- catalog(pkg, engines$csv)
# View catalog
ct
# data catalog: 6 items
# - Source: C:/packages/fetch/inst/extdata
# - Engine: csv
# - Items:
# data item 'ADAE': 56 cols 150 rows
# data item 'ADEX': 17 cols 348 rows
# data item 'ADPR': 37 cols 552 rows
# data item 'ADPSGA': 42 cols 695 rows
# data item 'ADSL': 56 cols 87 rows
# data item 'ADVS': 37 cols 3617 rowsAs can be seen above, this catalog has 6 csv files in it. The number of columns and rows are displayed for each file. Information about each file is contained in the catalog. This information can be accessed using list notion, like so:
# View info for catalog item
ct$ADEX
# data item 'ADEX': 17 cols 348 rows
# - Engine: csv
# - Size: 70.7 Kb
# - Last Modified: 2020-09-18 14:30:22
# Name Column Class Label Format NAs MaxChar
# 1 ADEX STUDYID character <NA> NA 0 3
# 2 ADEX USUBJID character <NA> NA 0 10
# 3 ADEX SUBJID character <NA> NA 0 3
# 4 ADEX SITEID character <NA> NA 0 2
# 5 ADEX TRTP character <NA> NA 8 5
# 6 ADEX TRTPN numeric <NA> NA 8 1
# 7 ADEX TRTA character <NA> NA 8 5
# 8 ADEX TRTAN numeric <NA> NA 8 1
# 9 ADEX RANDFL character <NA> NA 0 1
# 10 ADEX SAFFL character <NA> NA 0 1
# 11 ADEX MITTFL character <NA> NA 0 1
# 12 ADEX PPROTFL character <NA> NA 0 1
# 13 ADEX PARAM character <NA> NA 0 45
# 14 ADEX PARAMCD character <NA> NA 0 8
# 15 ADEX PARAMN numeric <NA> NA 0 1
# 16 ADEX AVAL numeric <NA> NA 16 4
# 17 ADEX AVALCAT1 character <NA> NA 87 10When the catalog item is printed, it shows a data dictionary for the specified dataset. Column names and data types are displayed, along with some other useful attributes of your data.
Once the catalog is created, you are ready to fetch data from the catalog:
# Fetch data from a catalog
dt <- fetch(ct$ADEX)
# View data
dt
# A tibble: 348 × 17
# STUDYID USUBJID SUBJID SITEID TRTP TRTPN TRTA TRTAN RANDFL SAFFL MITTFL PPROTFL
# <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr>
# 1 ABC ABC-01-049 049 01 ARM D 4 ARM D 4 Y Y Y Y
# 2 ABC ABC-01-049 049 01 ARM D 4 ARM D 4 Y Y Y Y
# 3 ABC ABC-01-049 049 01 ARM D 4 ARM D 4 Y Y Y Y
# 4 ABC ABC-01-049 049 01 ARM D 4 ARM D 4 Y Y Y Y
# 5 ABC ABC-01-050 050 01 ARM B 2 ARM B 2 Y Y Y Y
# 6 ABC ABC-01-050 050 01 ARM B 2 ARM B 2 Y Y Y Y
# 7 ABC ABC-01-050 050 01 ARM B 2 ARM B 2 Y Y Y Y
# 8 ABC ABC-01-050 050 01 ARM B 2 ARM B 2 Y Y Y Y
# 9 ABC ABC-01-051 051 01 ARM A 1 ARM A 1 Y Y Y Y
# 10 ABC ABC-01-051 051 01 ARM A 1 ARM A 1 Y Y Y Y
# 338 more rows
# 5 more variables: PARAM <chr>, PARAMCD <chr>, PARAMN <dbl>, AVAL <dbl>,
# AVALCAT1 <chr>
# Use `print(n = ...)` to see more rowsAt this point, you can proceed with your analysis, or fetch additional datasets from the catalog as needed. So easy!
Additional Features
The above example covers the very basics. Now let’s look at some more useful features of the fetch package.
Apply a Where Expression to a Fetch Operation
What if you are performing an analysis on a subset of data? Is there
a way to apply a subset when the data is fetched? You can apply subset
criteria using the “where” parameter on the fetch()
function. Like this:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create catalog
ct <- catalog(pkg, engines$csv)
# Subset data for a specific subject
dt <- fetch(ct$ADVS, where = expression(SUBJID == '049'))
# View data subset
dt
# A tibble: 44 × 37
# STUDYID USUBJID SUBJID SITEID SRCDOM SRCVAR SRCSEQ TRTP TRTPN TRTA TRTAN RANDFL
# <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr>
# 1 ABC ABC-01-049 049 01 VS VSSTRESN 9 ARM D 4 ARM D 4 Y
# 2 ABC ABC-01-049 049 01 VS VSSTRESN 41 ARM D 4 ARM D 4 Y
# 3 ABC ABC-01-049 049 01 NA NA NA ARM D 4 ARM D 4 Y
# 4 ABC ABC-01-049 049 01 VS VSSTRESN 34 ARM D 4 ARM D 4 Y
# 5 ABC ABC-01-049 049 01 VS VSSTRESN 35 ARM D 4 ARM D 4 Y
# 6 ABC ABC-01-049 049 01 VS VSSTRESN 36 ARM D 4 ARM D 4 Y
# 7 ABC ABC-01-049 049 01 VS VSSTRESN 37 ARM D 4 ARM D 4 Y
# 8 ABC ABC-01-049 049 01 VS VSSTRESN 38 ARM D 4 ARM D 4 Y
# 9 ABC ABC-01-049 049 01 VS VSSTRESN 39 ARM D 4 ARM D 4 Y
# 10 ABC ABC-01-049 049 01 VS VSSTRESN 40 ARM D 4 ARM D 4 Y
# 34 more rows
# 25 more variables: SAFFL <chr>, MITTFL <chr>, PPROTFL <chr>, TRTSDT <chr>,
# TRTEDT <chr>, ADT <chr>, ADY <dbl>, ADTF <lgl>, AVISIT <chr>, AVISITN <dbl>,
# PARAM <chr>, PARAMCD <chr>, PARAMN <dbl>, PARAMTYP <chr>, AVAL <dbl>, BASE <dbl>,
# CHG <dbl>, AWRANGE <chr>, AWTARGET <dbl>, AWTDIFF <dbl>, AWLO <dbl>, AWHI <dbl>,
# AWU <chr>, ABLFL <chr>, ANL01FL <chr>
# Use `print(n = ...)` to see more rowsThe expression function can be used to define any R
expression. This function accepts unquoted variable names, logical
operators, and R functions like is.null() and
is.na(). You can use the the “where” parameter with an
expression to narrow down the data you are working with and reduce the
memory footprint of your program.
Get Top N Rows
Another way to limit the data returned by your program is to use the “top” parameter. This parameter allows you to return only the “top N” number of rows in the target data item. The “top” parameter is useful for exploratory purposes. Here is an example:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create catalog
ct <- catalog(pkg, engines$csv)
# Subset data for a specific subject
dt <- fetch(ct$ADVS, top = 5, where = expression(SUBJID == '049'))
# View results
dt
# A tibble: 5 × 37
# STUDYID USUBJID SUBJID SITEID SRCDOM SRCVAR SRCSEQ TRTP TRTPN TRTA TRTAN RANDFL SAFFL
# <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
# 1 ABC ABC-01-… 049 01 VS VSSTR… 9 ARM D 4 ARM D 4 Y Y
# 2 ABC ABC-01-… 049 01 VS VSSTR… 41 ARM D 4 ARM D 4 Y Y
# 3 ABC ABC-01-… 049 01 NA NA NA ARM D 4 ARM D 4 Y Y
# 4 ABC ABC-01-… 049 01 VS VSSTR… 34 ARM D 4 ARM D 4 Y Y
# 5 ABC ABC-01-… 049 01 VS VSSTR… 35 ARM D 4 ARM D 4 Y Y
# 24 more variables: MITTFL <chr>, PPROTFL <chr>, TRTSDT <chr>, TRTEDT <chr>,
# ADT <chr>, ADY <dbl>, ADTF <lgl>, AVISIT <chr>, AVISITN <dbl>, PARAM <chr>,
# PARAMCD <chr>, PARAMN <dbl>, PARAMTYP <chr>, AVAL <dbl>, BASE <dbl>, CHG <dbl>,
# AWRANGE <chr>, AWTARGET <dbl>, AWTDIFF <dbl>, AWLO <dbl>, AWHI <dbl>, AWU <chr>,
# ABLFL <chr>, ANL01FL <chr>The above fetch returned the top 5 rows of the dataset for subject ‘049’. Note that the “top” parameter can be used with or without the “where” parameter.
Apply a Pattern to a Catalog
The previous examples loaded all datasets from the data source into
the data catalog. There may be instances, however, when you have a very
large number of datasets, and want to limit the data items loaded into
the catalog. For this situation, you can use the “pattern” parameter on
the catalog() function, like so:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create catalog, applying pattern to dataset names
ct <- catalog(pkg, engines$csv, pattern = "*S*")
# View catalog
ct
# data catalog: 3 items
# - Source: C:/packages/fetch/inst/extdata
# - Engine: csv
# - Pattern: *S*
# - Items:
# data item 'ADPSGA': 42 cols 695 rows
# data item 'ADSL': 56 cols 87 rows
# data item 'ADVS': 37 cols 3617 rowsThe above example limits the items in the catalog to those with an “S” in the name. While this is not a realistic example, it nevertheless demonstrates how the “pattern” parameter can be used to reduce the datasets loaded into the catalog.
Apply a Where Expression to an Entire Catalog
Sometimes you want to subset all the datasets in your catalog. This
subset can be accomplished with the “where” parameter on the
catalog() function. Observe:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create catalog without where expression
ct1 <- catalog(pkg, engines$csv)
# View catalog
ct1
# data catalog: 6 items
# - Source: C:/packages/fetch/inst/extdata
# - Engine: csv
# - Items:
# data item 'ADAE': 56 cols 150 rows
# data item 'ADEX': 17 cols 348 rows
# data item 'ADPR': 37 cols 552 rows
# data item 'ADPSGA': 42 cols 695 rows
# data item 'ADSL': 56 cols 87 rows
# data item 'ADVS': 37 cols 3617 rows
# Create catalog with where expression
ct2 <- catalog(pkg, engines$csv, where = expression(SUBJID == '049'))
# View catalog
ct2
# data catalog: 6 items
# - Source: C:/packages/fetch/inst/extdata
# - Engine: csv
# - Where: SUBJID == "049"
# - Items:
# # data item 'ADAE': 56 cols 5 rows
# - Where: SUBJID == "049"
# # data item 'ADEX': 17 cols 4 rows
# - Where: SUBJID == "049"
# # data item 'ADPR': 37 cols 7 rows
# - Where: SUBJID == "049"
# # data item 'ADPSGA': 42 cols 10 rows
# - Where: SUBJID == "049"
# # data item 'ADSL': 56 cols 1 rows
# - Where: SUBJID == "049"
# # data item 'ADVS': 37 cols 44 rows
# - Where: SUBJID == "049"Notice that a where expression has been added to each data item in the catalog, and the row counts for each item have been reduced. Now let’s fetch some data from this catalog:
# Subset data for a specific subject
dt1 <- fetch(ct2$ADVS)
# View results of ADVS fetch
dt1
# A tibble: 44 × 37
# STUDYID USUBJID SUBJID SITEID SRCDOM SRCVAR SRCSEQ TRTP TRTPN TRTA TRTAN RANDFL SAFFL
# <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
# 1 ABC ABC-01-049 049 01 VS VSSTRESN 9 ARM D 4 ARM D 4 Y Y
# 2 ABC ABC-01-049 049 01 VS VSSTRESN 41 ARM D 4 ARM D 4 Y Y
# 3 ABC ABC-01-049 049 01 NA NA NA ARM D 4 ARM D 4 Y Y
# 4 ABC ABC-01-049 049 01 VS VSSTRESN 34 ARM D 4 ARM D 4 Y Y
# 5 ABC ABC-01-049 049 01 VS VSSTRESN 35 ARM D 4 ARM D 4 Y Y
# 6 ABC ABC-01-049 049 01 VS VSSTRESN 36 ARM D 4 ARM D 4 Y Y
# 7 ABC ABC-01-049 049 01 VS VSSTRESN 37 ARM D 4 ARM D 4 Y Y
# 8 ABC ABC-01-049 049 01 VS VSSTRESN 38 ARM D 4 ARM D 4 Y Y
# 9 ABC ABC-01-049 049 01 VS VSSTRESN 39 ARM D 4 ARM D 4 Y Y
# 10 ABC ABC-01-049 049 01 VS VSSTRESN 40 ARM D 4 ARM D 4 Y Y
# 34 more rows
# 24 more variables: MITTFL <chr>, PPROTFL <chr>, TRTSDT <chr>, TRTEDT <chr>, ADT <chr>,
# ADY <dbl>, ADTF <lgl>, AVISIT <chr>, AVISITN <dbl>, PARAM <chr>, PARAMCD <chr>,
# PARAMN <dbl>, PARAMTYP <chr>, AVAL <dbl>, BASE <dbl>, CHG <dbl>, AWRANGE <chr>,
# AWTARGET <dbl>, AWTDIFF <dbl>, AWLO <dbl>, AWHI <dbl>, AWU <chr>, ABLFL <chr>,
# ANL01FL <chr>
# Use `print(n = ...)` to see more rows
# View results of ADSL fetch
dt2 <- fetch(ct2$ADSL)
# A tibble: 1 × 56
# STUDYID USUBJID SUBJID SITEID AGE AGEU AGEGR1 SEX RACE RACEN ETHNIC ETHNICN COUNTRY
# <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <lgl>
# 1 ABC ABC-01-049 049 01 39 YEARS 30-39 y… M WHITE 5 NOT H… 2 NA
# 43 more variables: ARM <chr>, ACTARM <lgl>, TRT01P <chr>, TRT01PN <dbl>, TRT01A <chr>,
# TRT01AN <dbl>, TRTSDT <chr>, TRTEDT <chr>, TRTDURN <dbl>, TRTDURU <chr>, TR01SDT <chr>,
# TR01EDT <chr>, INCNFL <chr>, RANDFL <chr>, RANDEXC1 <lgl>, RANDEXC2 <chr>, RANDEXC3 <chr>,
# RANDEXC4 <chr>, SAFFL <chr>, SAFEXC1 <chr>, SAFEXC2 <chr>, MITTFL <chr>, MITTEXC1 <chr>,
# MITTEXC2 <chr>, PPROTFL <chr>, PPROTEX1 <chr>, PPROTEX2 <chr>, PPROTEX3 <chr>,
# PPROTEX4 <chr>, COMPLFL <chr>, STDYDISP <chr>, STDYREAS <chr>, INCNDT <chr>, RANDDT <chr>,
# DTHDT <lgl>, DTHFL <chr>, MISSDOSE <dbl>, TP1TRTR <chr>, TP2TRTR <chr>, TP3TRTR <chr>,...As you can see, both the ADVS and ADSL datasets have been subset for subject ‘049’. This feature of the fetch package makes it easy to target just the rows you are looking for.
Add Import Specifications to a Catalog
Some data files contain accurate data type information within them, and others do not. For example, CSV files do not contain data type information, but RDATA files do. If the data type information is not available, the data engine will try to guess the data types. However, it does not always guess accurately. Sometimes you need to help the data engine determine what the data type of a column is supposed to be. Import specs allow you to to specify the correct data type for a column.
Examine the dictionary for the ADVS dataset:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create catalog without import spec
ct <- catalog(pkg, engines$csv)
# View dictionary for ADVS
ct$ADVS
# data item 'ADVS': 37 cols 3617 rows
# - Engine: csv
# - Size: 1.1 Mb
# - Last Modified: 2020-09-18 14:30:22
# Name Column Class Label Format NAs MaxChar
# 1 ADVS STUDYID character <NA> NA 0 3
# 2 ADVS USUBJID character <NA> NA 0 10
# 3 ADVS SUBJID character <NA> NA 0 3
# 4 ADVS SITEID character <NA> NA 0 2
# 5 ADVS SRCDOM character <NA> NA 85 2
# 6 ADVS SRCVAR character <NA> NA 85 8
# 7 ADVS SRCSEQ numeric <NA> NA 85 2
# 8 ADVS TRTP character <NA> NA 85 5
# 9 ADVS TRTPN numeric <NA> NA 85 1
# 10 ADVS TRTA character <NA> NA 85 5
# 11 ADVS TRTAN numeric <NA> NA 85 1
# 12 ADVS RANDFL character <NA> NA 0 1
# 13 ADVS SAFFL character <NA> NA 0 1
# 14 ADVS MITTFL character <NA> NA 0 1
# 15 ADVS PPROTFL character <NA> NA 0 1
# 16 ADVS TRTSDT character <NA> NA 54 9 # Character by default
# 17 ADVS TRTEDT character <NA> NA 119 9 # Character by default
# 18 ADVS ADT character <NA> NA 0 9
# 19 ADVS ADY numeric <NA> NA 54 4
# 20 ADVS ADTF logical <NA> NA 3617 0
# 21 ADVS AVISIT character <NA> NA 50 14
# 22 ADVS AVISITN numeric <NA> NA 50 2
# 23 ADVS PARAM character <NA> NA 0 35
# 24 ADVS PARAMCD character <NA> NA 0 6
# 25 ADVS PARAMN numeric <NA> NA 0 1
# 26 ADVS PARAMTYP character <NA> NA 3532 7
# 27 ADVS AVAL numeric <NA> NA 0 5
# 28 ADVS BASE numeric <NA> NA 70 5
# 29 ADVS CHG numeric <NA> NA 1312 4
# 30 ADVS AWRANGE character <NA> NA 1331 25
# 31 ADVS AWTARGET numeric <NA> NA 1331 3
# 32 ADVS AWTDIFF numeric <NA> NA 1331 2
# 33 ADVS AWLO numeric <NA> NA 1331 3
# 34 ADVS AWHI numeric <NA> NA 1331 3
# 35 ADVS AWU character <NA> NA 1331 4
# 36 ADVS ABLFL character <NA> NA 2869 1
# 37 ADVS ANL01FL character <NA> NA 448 1Note that the data types of the “TRTSDT” and “TRTEDT” are listed as character. In fact, these columns are supposed to be a date. You can force the date data type assignment with an import spec, as follows:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create import spec
spc <- import_spec(TRTSDT = "date=%d%b%Y",
TRTEDT = "date=%d%b%Y")
# Create catalog with import spec
ct <- catalog(pkg, engines$csv, import_specs = spc)
# View dictionary for ADVS with Import Spec
ct$ADVS
# data item 'ADVS': 37 cols 3617 rows
# - Engine: csv
# - Size: 1.1 Mb
# - Last Modified: 2020-09-18 14:30:22
# Name Column Class Label Format NAs MaxChar
# 1 ADVS STUDYID character <NA> NA 0 3
# 2 ADVS USUBJID character <NA> NA 0 10
# 3 ADVS SUBJID character <NA> NA 0 3
# 4 ADVS SITEID character <NA> NA 0 2
# 5 ADVS SRCDOM character <NA> NA 85 2
# 6 ADVS SRCVAR character <NA> NA 85 8
# 7 ADVS SRCSEQ numeric <NA> NA 85 2
# 8 ADVS TRTP character <NA> NA 85 5
# 9 ADVS TRTPN numeric <NA> NA 85 1
# 10 ADVS TRTA character <NA> NA 85 5
# 11 ADVS TRTAN numeric <NA> NA 85 1
# 12 ADVS RANDFL character <NA> NA 0 1
# 13 ADVS SAFFL character <NA> NA 0 1
# 14 ADVS MITTFL character <NA> NA 0 1
# 15 ADVS PPROTFL character <NA> NA 0 1
# 16 ADVS TRTSDT Date <NA> NA 54 10 # Converted to Date
# 17 ADVS TRTEDT Date <NA> NA 119 10 # Converted to Date
# 18 ADVS ADT character <NA> NA 0 9
# 19 ADVS ADY numeric <NA> NA 54 4
# 20 ADVS ADTF logical <NA> NA 3617 0
# 21 ADVS AVISIT character <NA> NA 50 14
# 22 ADVS AVISITN numeric <NA> NA 50 2
# 23 ADVS PARAM character <NA> NA 0 35
# 24 ADVS PARAMCD character <NA> NA 0 6
# 25 ADVS PARAMN numeric <NA> NA 0 1
# 26 ADVS PARAMTYP character <NA> NA 3532 7
# 27 ADVS AVAL numeric <NA> NA 0 5
# 28 ADVS BASE numeric <NA> NA 70 5
# 29 ADVS CHG numeric <NA> NA 1312 4
# 30 ADVS AWRANGE character <NA> NA 1331 25
# 31 ADVS AWTARGET numeric <NA> NA 1331 3
# 32 ADVS AWTDIFF numeric <NA> NA 1331 2
# 33 ADVS AWLO numeric <NA> NA 1331 3
# 34 ADVS AWHI numeric <NA> NA 1331 3
# 35 ADVS AWU character <NA> NA 1331 4
# 36 ADVS ABLFL character <NA> NA 2869 1
# 37 ADVS ANL01FL character <NA> NA 448 1Observe that the columns “TRTSDT” and “TRTEDT” are now converted to
Date columns. This import spec will apply to all “TRTSDT” and “TRTEDT”
columns in all datasets. The format “%d%b%Y” was written specifically to
read in the data value this files uses. See the
import_spec() documentation for more information on these
data formats, and for other possible data type specifications. Also see
the specs() documentation if you want to assign a unique
import specifications to each data item in the catalog.
Add Import Specifications to a Fetch
You may also add an import specification to the fetch operation. The
import specification is defined with the import_spec()
function in the same way as the catalog operation:
# Get sample data directory
pkg <- system.file("extdata", package = "fetch")
# Create import spec
spc <- import_spec(TRTSDT = "date=%d%b%Y",
TRTEDT = "date=%d%b%Y")
# Create catalog with import spec
ct <- catalog(pkg, engines$csv, import_specs = spc)
# View dictionary for ADVS with Import Spec
ct$ADVS
# data item 'ADVS': 37 cols 3617 rows
# - Engine: csv
# - Size: 1.1 Mb
# - Last Modified: 2020-09-18 14:30:22
# Name Column Class Label Format NAs MaxChar
# 1 ADVS STUDYID character <NA> NA 0 3
# 2 ADVS USUBJID character <NA> NA 0 10
# 3 ADVS SUBJID character <NA> NA 0 3
# 4 ADVS SITEID character <NA> NA 0 2
# 5 ADVS SRCDOM character <NA> NA 85 2
# 6 ADVS SRCVAR character <NA> NA 85 8
# 7 ADVS SRCSEQ numeric <NA> NA 85 2
# 8 ADVS TRTP character <NA> NA 85 5
# 9 ADVS TRTPN numeric <NA> NA 85 1
# 10 ADVS TRTA character <NA> NA 85 5
# 11 ADVS TRTAN numeric <NA> NA 85 1
# 12 ADVS RANDFL character <NA> NA 0 1
# 13 ADVS SAFFL character <NA> NA 0 1
# 14 ADVS MITTFL character <NA> NA 0 1
# 15 ADVS PPROTFL character <NA> NA 0 1
# 16 ADVS TRTSDT Date <NA> NA 54 10
# 17 ADVS TRTEDT Date <NA> NA 119 10
# 18 ADVS ADT character <NA> NA 0 9
# 19 ADVS ADY numeric <NA> NA 54 4
# 20 ADVS ADTF logical <NA> NA 3617 0
# 21 ADVS AVISIT character <NA> NA 50 14
# 22 ADVS AVISITN numeric <NA> NA 50 2
# 23 ADVS PARAM character <NA> NA 0 35
# 24 ADVS PARAMCD character <NA> NA 0 6
# 25 ADVS PARAMN numeric <NA> NA 0 1
# 26 ADVS PARAMTYP character <NA> NA 3532 7
# 27 ADVS AVAL numeric <NA> NA 0 5
# 28 ADVS BASE numeric <NA> NA 70 5
# 29 ADVS CHG numeric <NA> NA 1312 4
# 30 ADVS AWRANGE character <NA> NA 1331 25
# 31 ADVS AWTARGET numeric <NA> NA 1331 3
# 32 ADVS AWTDIFF numeric <NA> NA 1331 2
# 33 ADVS AWLO numeric <NA> NA 1331 3
# 34 ADVS AWHI numeric <NA> NA 1331 3
# 35 ADVS AWU character <NA> NA 1331 4
# 36 ADVS ABLFL character <NA> NA 2869 1
# 37 ADVS ANL01FL character <NA> NA 448 1The results of this operation are similar to the previous example, when we applied the import spec to the catalog. The difference is that now the import spec applies only to this specific fetch operation, not the entire catalog.
Next Steps
For next steps, it is suggested to play around with the
fetch functions, and note how easy these functions are
to use. You may also wish to review the documentation for the
catalog(), fetch(), import_spec()
functions. The documentation contains some additional examples and
descriptions of all parameters.