Introduction to crosswalkr

Researchers often must compile master data sets from a number of smaller data sets that are not consistent in terms of variable names or value encodings. This can be especially true for large administrative data sets that span multiple years and/or departments. Other times, teams of researchers must work together to maintain a master data set and it is important for replicability and future collaboration that the team rely on consistent naming and encoding conventions.

For example, let's say there are three flat files of student information that need to be merged into a single large data set for analysis.

File 1

|sid|lname|state|t_score| |:--|:--|:--|:--| |1|Jackson|VA|74| |2|Harrison|KY|86| |3|Nixon|IL|78|

File 2

|stu_id|last_name|st|test_score| |:--|:--|:--|:--| |4|Washington|35|92| |5|Roosevelt|11|67| |6|Taylor|47|68|

File 3

|s_id|name|sta|score| |:--|:--|:--|:--| |7|Tyler|North Dakota|91| |8|Grant|South Dakota|82| |9|Adams|Illinois|89|

It is clear that these files contain the same basic information, but neither the names nor encodings for state | st | sta are consistent.

One solution is to just fix these one at a time before joining them. For example:

file_1 <- data.frame(sid = c(1:3),
                     lname = c('Jackson','Harrison','Nixon'),
                     stat = c('VA','KY','IL'),
                     t_score = c(74,86,78),
                     stringsAsFactors = FALSE)

file_2 <- data.frame(stu_id = c(4:6),
                     last_name = c('Washington','Roosevelt','Taylor'),
                     st = c(35,11,47),
                     test_score = c(92,82,89),
                     stringsAsFactors = FALSE)

file_3 <- data.frame(s_id = c(7:9),
                     name = c('Tyler','Grant','Adams'),
                     sta = c('North Dakota','South Dakota','Illinois'),
                     score = c(91,82,89),
                     stringsAsFactors = FALSE)
df1 <- file_1 %>%
    rename(id = sid,
           last_name = lname,
           stabbr = stat,
           score = t_score)

df2 <- file_2 %>%
    rename(id = stu_id,
           stabbr = st,
           score = test_score) %>%
    mutate(stabbr = as.character(stabbr))

df3 <- file_3 %>%
    rename(id = s_id,
           stabbr = sta,
           last_name = name)

df <- rbind(df1, df2, df3)

The problem, of course, is there is a lot of room for error since the renaming process has to be repeated for each data frame.

Using a crosswalk file

Instead, it makes more sense to create a crosswalk data set that aligns old (or raw) column names with new (or clean) column names and, if desired, labels. The crosswalk to join these files could be:

|clean|label|file_1_raw|file_2_raw|file_3_raw| |:--|:--|:--|:--|:--| |id|Student ID|sid|stu_id|s_id| |last_name|Student last name|lname|last_name|name| |stabbr|State abbreviation|stat|st|sta| |score|Test score|t_score|test_score|score|

crosswalk <- data.frame(clean = c('id','last_name','stabbr','score'),
                        label = c('Student ID','Student last name',
                                  'State abbreviation','Test score'),
                        file_1_raw = c('sid','lname','stat','t_score'),
                        file_2_raw = c('stu_id','last_name','st','test_score'),
                        file_3_raw = c('s_id','name','sta','score'),
                        stringsAsFactors = FALSE)

The crosswalk file (cw_file) could be:

  1. Data frame object already in memory
  2. A string with path and name (e.g., './path/to/crosswalk.csv') of a flat file of one of the following types:
    1. Comma separated (*.csv)
    2. Tab separated (*.tsv)
    3. Other delimited (*.txt) with delimiter option set to delimiter string (e.g., delimiter = '|')
    4. Excel (*.xls or *.xlsx) with sheet option set to sheet number or string name (defaulting to the first sheet)
    5. R data (*.rdata, *.rda, *.rds)
    6. Stata data (*.dta)

If given a string to the cw_file argument, renamefrom() and encodefrom() determine the type of file by its ending.


To rename using the renamefrom() command:

df1 <- renamefrom(file_1, cw_file = crosswalk, raw = file_1_raw, clean = clean, label = label)
df2 <- renamefrom(file_2, cw_file = crosswalk, raw = file_2_raw, clean = clean, label = label)
df3 <- renamefrom(file_3, cw_file = crosswalk, raw = file_3_raw, clean = clean, label = label)

df <- rbind(df1, df2, df3)

And check out the labels:


As new raw data files are added to the project, they could simply be given a new column in the crosswalk file that mapped their raw column names to the clean versions.


These same example files have inconsistent encodings for state: one uses two-letter abbreviations, another the FIPS code, and another the full name. Again, instead of fixing each one at a time, a separate crosswalk for encoding these values could be used. The crosswalkr package includes a state-level crosswalk, stcrosswalk:


The encodefrom() function works much like renamefrom(). The only difference is that a vector of encoded values is returned that can be added to an existing dataframe.

encodefrom() returns either base R factors or labels depending on whether the input data frame is a tibble.


df1$state <- encodefrom(file_1, var = stat, stcrosswalk, raw = stabbr, clean = stfips, label = stname)
sapply(df1, class)

labelled vector

file_1_ <- file_1 %>% tbl_df()
df1$state <- encodefrom(file_1_, var = stat, stcrosswalk, raw = stabbr,
                        clean = stfips, label = stname)

Combined example: dplyr chain

The renamefrom() and encodefrom() functions can be combined in a dplyr chain.

df <- rbind(file_1 %>%
            tbl_df() %>%
            renamefrom(., crosswalk, file_1_raw, clean, label) %>%
            mutate(stabbr = encodefrom(., stabbr, stcrosswalk, stabbr, stfips, stname)),

            ## append file 2
            file_2 %>%
            tbl_df() %>%
            renamefrom(., crosswalk, file_2_raw, clean, label) %>%
            mutate(stabbr = encodefrom(., stabbr, stcrosswalk, stfips, stfips, stname)),

            ## append file 3
            file_3 %>%
            tbl_df() %>%
            renamefrom(., crosswalk, file_3_raw, clean, label) %>%
            mutate(stabbr = encodefrom(., stabbr, stcrosswalk, stname, stfips, stname)))


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crosswalkr documentation built on Jan. 8, 2020, 5:07 p.m.