NIS datasets can be challenging to work with as the .ASC files do not include
information about the variables, labels, or NA values. The read_nis()
function reads the .ASC file and returns a R object with the correct variable
names and NA values.
Usage
read_nis(
file,
year,
import_method = "readr",
col_select = NULL,
n_max = Inf,
corrected = TRUE
)Arguments
- file
Path to NIS dataset file.
- year
The year of the dataset.
- import_method
The function to import data. The default is readr.
- col_select
Columns to include in the results. Columns can be selected by name or by a numerical column index.
- n_max
The maximum number of rows/observations. The default is set all rows (
Inf).- corrected
The official corrections will be applied to the data. The default is set to TRUE.
Examples
# Read in the example NIS 2019 dataset.
if (FALSE) { # \dontrun{
NIS_2019_df <- read_nis("NIS_2019_test_data.ASC", 2019)
} # }
# Read in the first 5 observations of the example NIS 2019 dataset.
if (FALSE) { # \dontrun{
NIS_2019_df <- read_nis("NIS_2019_test_data.ASC", 2019, n_max = 5)
} # }
# Read in the first three diagnostic codes un the first 10 observations of
# the example NIS 2019 dataset.
if (FALSE) { # \dontrun{
NIS_2019_df <- read_nis("NIS_2019_test_data.ASC", 2019,
col_select = c("I10_DX1", "I10_DX2", "I10_DX3"), n_max = 10)
# This can also be done with:
NIS_2019_df <- read_nis("NIS_2019_test_data.ASC", 2019,
col_select = 19:21, n_max = 10)
} # }