fuzzy_rbind() binds dataframes based on columns with slightly different names.
fuzzy_rbind( df1, df2, threshold, method = "jw", q = 1, p = 0, bt = 0, useBytes = FALSE, weight = c(d = 1, i = 1, t = 1) )
df1 | The first dataframe to be bound. |
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df2 | The second dataframe to be bound. |
threshold | The maximum string distance between column names, if the distance between columns is greater than this threshold the columns will not be bound. |
method | The type of string distance calculation to use. Possible methods are : osa, lv, dl, hamming, lcs, qgram, cosine, jaccard, jw, and soundex. See package stringdist for more information. Default: 'jw', Default: 'jw' |
q | Size of the q-gram used in string distance calculation. Default: 1 |
p | Only used with method "jw", the Jaro-Winkler penatly size. Default: 0 |
bt | Only used with method "jw" with p > 0, Winkler's boost threshold. Default: 0 |
useBytes | Whether or not to perform byte-wise comparison. Default: FALSE |
weight | Only used with methods "osa" or "dl", a vector representing the penalty for deletion, insertion, substitution, and transposition, in that order. Default: c(d = 1, i = 1, t = 1) |
fuzzy_rbind() returns a dataframe that has bound the two inputted dataframes based on the closest matching columns, column names from dataframe 1 are preserved.
When using datasets often times column names are slightly different, and fuzzy_rbind()
helps
to bind dataframes using fuzzy matching of the column names.
if (FALSE) { if(interactive()){ mtcars_colnames_messy = mtcars colnames(mtcars_colnames_messy)[1:5] = paste0(colnames(mtcars)[1:5], "_17") colnames(mtcars_colnames_messy)[6:11] = paste0(colnames(mtcars)[6:11], "_2017") x = fuzzy_rbind(mtcars, mtcars_colnames_messy, .5) x = fuzzy_rbind(mtcars, mtcars_colnames_messy, .2) } }