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This package offers a pair of functions, renamefrom() and
encodefrom(), for renaming and encoding data frames using external
crosswalk files. It is especially useful when constructing master data
sets from multiple smaller data sets that do not name or encode
variables consistently across files. Based on renamefrom and
encodefrom Stata commands written by Sally Hudson and
team.
Install the latest release version from CRAN with
install.packages('crosswalkr')
Install the latest development version from Github with
devtools::install_github('btskinner/crosswalkr')
library(crosswalkr)
library(dplyr)
library(haven)
## starting data frame
df <- data.frame(state = c('Kentucky','Tennessee','Virginia'),
fips = c(21,47,51),
region = c('South','South','South'))
df
## state fips region
## 1 Kentucky 21 South
## 2 Tennessee 47 South
## 3 Virginia 51 South
## crosswalk with which to convert old names to new names with labels
cw <- data.frame(old_name = c('state','fips'),
new_name = c('stname','stfips'),
label = c('Full state name', 'FIPS code'))
cw
## old_name new_name label
## 1 state stname Full state name
## 2 fips stfips FIPS code
Convert old variable names to new names and add labels from crosswalk.
df1 <- renamefrom(df, cw_file = cw, raw = old_name, clean = new_name, label = label)
df1
## stname stfips
## 1 Kentucky 21
## 2 Tennessee 47
## 3 Virginia 51
Convert old variable names to new names using old names as labels (ignoring labels in crosswalk).
df2 <- renamefrom(df, cw_file = cw, raw = old_name, clean = new_name, name_label = TRUE)
df2
## stname stfips
## 1 Kentucky 21
## 2 Tennessee 47
## 3 Virginia 51
Convert old variable names to new names, but keep unmatched old names in the data frame.
df3 <- renamefrom(df, cw_file = cw, raw = old_name, clean = new_name, drop_extra = FALSE)
df3
## stname stfips region
## 1 Kentucky 21 South
## 2 Tennessee 47 South
## 3 Virginia 51 South
## starting data frame
df <- data.frame(state = c('Kentucky','Tennessee','Virginia'),
stfips = c(21,47,51),
cenregnm = c('South','South','South'))
df
## state stfips cenregnm
## 1 Kentucky 21 South
## 2 Tennessee 47 South
## 3 Virginia 51 South
## use state crosswalk data file from package
cw <- get(data(stcrosswalk))
cw
## # A tibble: 51 x 7
## stfips stabbr stname cenreg cenregnm cendiv cendivnm
## <int> <chr> <chr> <int> <chr> <int> <chr>
## 1 1 AL Alabama 3 South 6 East South Central
## 2 2 AK Alaska 4 West 9 Pacific
## 3 4 AZ Arizona 4 West 8 Mountain
## 4 5 AR Arkansas 3 South 7 West South Central
## 5 6 CA California 4 West 9 Pacific
## 6 8 CO Colorado 4 West 8 Mountain
## 7 9 CT Connecticut 1 Northeast 1 New England
## 8 10 DE Delaware 3 South 5 South Atlantic
## 9 11 DC District of Columbia 3 South 5 South Atlantic
## 10 12 FL Florida 3 South 5 South Atlantic
## # ... with 41 more rows
Create a new column with factor-encoded values
df$state2 <- encodefrom(df, var = state, cw_file = cw, raw = stname, clean = stfips, label = stabbr)
df
## state stfips cenregnm state2
## 1 Kentucky 21 South KY
## 2 Tennessee 47 South TN
## 3 Virginia 51 South VA
Create a new column with labelled values.
## convert to tbl_df
df <- tibble::as_tibble(df)
df$state3 <- encodefrom(df, var = state, cw_file = cw, raw = stname, clean = stfips, label = stabbr)
Create new column with factor-encoded values (ignores the fact that df
is a tibble)
df$state4 <- encodefrom(df, var = state, cw_file = cw, raw = stname, clean = stfips, label = stabbr, ignore_tibble = TRUE)
Show factors with labels:
as_factor(df)
## # A tibble: 3 x 6
## state stfips cenregnm state2 state3 state4
## <chr> <dbl> <chr> <fct> <fct> <fct>
## 1 Kentucky 21 South KY KY KY
## 2 Tennessee 47 South TN TN TN
## 3 Virginia 51 South VA VA VA
Show factors without labels:
zap_labels(df)
## # A tibble: 3 x 6
## state stfips cenregnm state2 state3 state4
## <chr> <dbl> <chr> <fct> <int> <fct>
## 1 Kentucky 21 South KY 21 KY
## 2 Tennessee 47 South TN 47 TN
## 3 Virginia 51 South VA 51 VA