This function takes an npi_results S3 object returned by
npi_search and flattens its list columns. It unnests the
lists columns and left joins them by npi. You can optionally specify
which columns from df to include.
Usage
# S3 method for npi_results
npi_flatten(df, cols = NULL, key = "npi")Arguments
- df
A data frame containing the results of a call to
npi_search.- cols
If non-NULL, only the named columns specified here will be be flattened and returned along with
npi.- key
A quoted column name from
dfto use as a matching key. The default value is"npi".
Details
The names of unnested columns are prefixed by the name of their originating list column to avoid name clashes and show their lineage. List columns containing all NULL data will be absent from the result because there are no columns to unnest.
Examples
# Flatten all list columns
data(npis)
npi_flatten(npis)
#> # A tibble: 48 × 42
#> npi basic_fi…¹ basic…² basic…³ basic…⁴ basic…⁵ basic…⁶ basic…⁷ basic…⁸
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA COWNAN PA NO F 2016-1… 2018-0… A
#> 2 1194276360 ALYSSA COWNAN PA NO F 2016-1… 2018-0… A
#> 3 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 4 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 5 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 6 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 7 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 8 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 9 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 10 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> # … with 38 more rows, 33 more variables: basic_name <chr>,
#> # basic_name_prefix <chr>, basic_middle_name <chr>,
#> # basic_organization_name <chr>, basic_organizational_subpart <chr>,
#> # basic_authorized_official_credential <chr>,
#> # basic_authorized_official_first_name <chr>,
#> # basic_authorized_official_last_name <chr>,
#> # basic_authorized_official_middle_name <chr>, …
# Only flatten specified columns
npi_flatten(npis, cols = c("basic", "identifiers"))
#> # A tibble: 12 × 25
#> npi basic_fi…¹ basic…² basic…³ basic…⁴ basic…⁵ basic…⁶ basic…⁷ basic…⁸
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA COWNAN PA NO F 2016-1… 2018-0… A
#> 2 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 3 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 4 1346604592 SARAH LOWRY OTR/L YES F 2016-0… 2018-0… A
#> 5 1427454529 YONGHONG TAN NA NO F 2014-1… 2018-1… A
#> 6 1558362566 AMY TIERST… M.D. YES F 2005-0… 2019-0… A
#> 7 1558713628 ROBYN NOHLING FNP-BC… YES F 2016-0… 2018-0… A
#> 8 1639173065 SAKSHI DUA M.D. YES F 2005-0… 2019-0… A
#> 9 1639173065 SAKSHI DUA M.D. YES F 2005-0… 2019-0… A
#> 10 1639173065 SAKSHI DUA M.D. YES F 2005-0… 2019-0… A
#> 11 1790786416 NOAH GOLDMAN M.D. NO M 2005-0… 2018-0… A
#> 12 1962983775 NA NA NA NA NA 2018-0… 2018-0… A
#> # … with 16 more variables: basic_name <chr>, basic_name_prefix <chr>,
#> # basic_middle_name <chr>, basic_organization_name <chr>,
#> # basic_organizational_subpart <chr>,
#> # basic_authorized_official_credential <chr>,
#> # basic_authorized_official_first_name <chr>,
#> # basic_authorized_official_last_name <chr>,
#> # basic_authorized_official_middle_name <chr>, …