R/vars_funs.R
vars_rename.RdBulk rename columns from one type of CCAO data to another. For
example, rename all columns pulled from SQL to their standard names used
in modeling. Or, rename all standard modeling names to "pretty" names for
publication. This function will only rename things specified in
the user-supplied dictionary argument, all other names in the data
will remain unchanged.
Options for names_from and names_to are specific to the
specified dictionary. Run this function with names_from equal
'to NULL to see a list of available options for the specified
'dictionary.
vars_rename(
data,
names_from = NULL,
names_to = NULL,
output_type = "inplace",
dictionary = ccao::vars_dict
)A data frame or tibble with columns to be renamed.
The source/name type of data. See description
The target names. See description
Output type. Either "inplace", which renames the
input data frame, or "vector", which returns a named character
vector with the construction new_col_name = old_col_name.
The dictionary used to translate names. Uses
vars_dict by default. Use vars_dict_legacy for
legacy data column names.
The input data frame with columns renamed.
Other vars_funs:
vars_recode()
# Rename column names from SQL
sample_data <- chars_sample_universe[1:5, 18:27]
sample_data
#> DT_PER_ASS CDU DT_KEY_PIN APTS EXT_WALL ROOF_CNST ROOMS BEDS BSMT BSMT_FIN
#> 1 1 <NA> <NA> 0 2 1 6 3 1 3
#> 2 1 <NA> <NA> 0 2 1 6 3 1 3
#> 3 1 AV <NA> 0 3 1 5 2 1 3
#> 4 1 AV <NA> 0 2 1 6 2 1 3
#> 5 1 <NA> <NA> 0 2 1 5 3 1 3
vars_rename(
data = sample_data,
names_from = "sql",
names_to = "standard",
dictionary = ccao::vars_dict_legacy
)
#> meta_per_ass meta_cdu meta_key_pin char_apts char_ext_wall char_roof_cnst
#> 1 1 <NA> <NA> 0 2 1
#> 2 1 <NA> <NA> 0 2 1
#> 3 1 AV <NA> 0 3 1
#> 4 1 AV <NA> 0 2 1
#> 5 1 <NA> <NA> 0 2 1
#> char_rooms char_beds char_bsmt char_bsmt_fin
#> 1 6 3 1 3
#> 2 6 3 1 3
#> 3 5 2 1 3
#> 4 6 2 1 3
#> 5 5 3 1 3
vars_rename(
data = sample_data,
names_from = "sql",
names_to = "pretty",
dictionary = ccao::vars_dict_legacy
)
#> Percent Assessed Condition, Desirability, and Utility Key PIN Apartments
#> 1 1 <NA> <NA> 0
#> 2 1 <NA> <NA> 0
#> 3 1 AV <NA> 0
#> 4 1 AV <NA> 0
#> 5 1 <NA> <NA> 0
#> Wall Material Roof Material Rooms Bedrooms Basement Basement Finish
#> 1 2 1 6 3 1 3
#> 2 2 1 6 3 1 3
#> 3 3 1 5 2 1 3
#> 4 2 1 6 2 1 3
#> 5 2 1 5 3 1 3
# No renames will occur since no column names here are from SQL
vars_rename(
data = chars_sample_athena[1:5, 1:10],
names_from = "sql",
names_to = "pretty",
dictionary = ccao::vars_dict_legacy
)
#> pin year class char_yrblt char_bldg_sf char_land_sf char_beds
#> 1 10254170360000 2015 205 1948 1775 4340 4
#> 2 09363230550000 2019 203 1923 1200 4375 3
#> 3 09363230550000 2016 203 1923 1200 4375 3
#> 4 14321260280000 2018 211 1878 2850 3125 4
#> 5 10253190450000 2018 204 1951 2469 11160 3
#> char_rooms char_fbath char_hbath
#> 1 7 2 1
#> 2 5 1 1
#> 3 5 1 1
#> 4 9 3 0
#> 5 8 2 1
# With data from Athena
sample_data_athena <- chars_sample_athena[1:5, 1:10]
sample_data_athena
#> pin year class char_yrblt char_bldg_sf char_land_sf char_beds
#> 1 10254170360000 2015 205 1948 1775 4340 4
#> 2 09363230550000 2019 203 1923 1200 4375 3
#> 3 09363230550000 2016 203 1923 1200 4375 3
#> 4 14321260280000 2018 211 1878 2850 3125 4
#> 5 10253190450000 2018 204 1951 2469 11160 3
#> char_rooms char_fbath char_hbath
#> 1 7 2 1
#> 2 5 1 1
#> 3 5 1 1
#> 4 9 3 0
#> 5 8 2 1
vars_rename(
data = sample_data_athena,
names_from = "athena",
names_to = "model",
dictionary = ccao::vars_dict
)
#> meta_pin meta_year meta_class char_yrblt char_bldg_sf char_land_sf
#> 1 10254170360000 2015 205 1948 1775 4340
#> 2 09363230550000 2019 203 1923 1200 4375
#> 3 09363230550000 2016 203 1923 1200 4375
#> 4 14321260280000 2018 211 1878 2850 3125
#> 5 10253190450000 2018 204 1951 2469 11160
#> char_beds char_rooms char_fbath char_hbath
#> 1 4 7 2 1
#> 2 3 5 1 1
#> 3 3 5 1 1
#> 4 4 9 3 0
#> 5 3 8 2 1
vars_rename(
data = sample_data_athena,
names_from = "athena",
names_to = "pretty",
dictionary = ccao::vars_dict
)
#> PIN Year Property Class Year Built Building Square Feet
#> 1 10254170360000 2015 205 1948 1775
#> 2 09363230550000 2019 203 1923 1200
#> 3 09363230550000 2016 203 1923 1200
#> 4 14321260280000 2018 211 1878 2850
#> 5 10253190450000 2018 204 1951 2469
#> Land Square Feet Bedrooms Rooms Full Baths Half Baths
#> 1 4340 4 7 2 1
#> 2 4375 3 5 1 1
#> 3 4375 3 5 1 1
#> 4 3125 4 9 3 0
#> 5 11160 3 8 2 1