matchmaker R package

The goal of {matchmaker} is to provide dictionary-based cleaning for R users in a simple and intuitive manner built on the {forcats} package. Some of the features of this package include:

• preservation of factor orders
• ability to specify explicit and implicit missing values
• option to replace by fuzzy matching (regular expressions, anchored by default)
• optional variable selection by fuzzy matching

Installation

You can install {matchmaker} from CRAN:

install.packages("matchmaker")

Example

The matchmaker package has two user-facing functions that perform dictionary-based cleaning:

• match_vec() will translate the values in a single vector
• match_df() will translate values in all specified columns of a data frame

Each of these functions have four manditory options:

• x: your data. This will be a vector or data frame depending on the function.
• dictionary: This is a data frame with at least two columns specifying keys and values to modify
• from: a character or number specifying which column contains the keys
• to: a character or number specifying which column contains the values

Mostly, users will be working with match_df() to transform values across specific columns. A typical workflow would be to:

1. construct your dictionary in a spreadsheet program based on your data
2. read in your data and dictionary to data frames in R
3. match!

library("matchmaker")

# Read in data set
dat <- read.csv(matchmaker_example("coded-data.csv"),
  stringsAsFactors = FALSE
)
dat$date <- as.Date(dat$date)

# Read in dictionary
dict <- read.csv(matchmaker_example("spelling-dictionary.csv"),
  stringsAsFactors = FALSE
)

Data

This is the top of our data set, generated for example purposes

id	date	readmission	treated	facility	age_group	lab_result_01	lab_result_02	lab_result_03	has_symptoms	followup
ef267c	2019-07-08	NA	0	C	10	unk	high	inc	NA	u
e80a37	2019-07-07	y	0	3	10	inc	unk	norm	y	oui
b72883	2019-07-07	y	1	8	30	inc	norm	inc		oui
c9ee86	2019-07-09	n	1	4	40	inc	inc	unk	y	oui
40bc7a	2019-07-12	n	1	6	0	norm	unk	norm	NA	n
46566e	2019-07-14	y	NA	B	50	unk	unk	inc	NA	NA

Dictionary

The dictionary looks like this:

options	values	grp	orders
y	Yes	readmission	1
n	No	readmission	2
u	Unknown	readmission	3
.missing	Missing	readmission	4
0	Yes	treated	1
1	No	treated	2
.missing	Missing	treated	3
1	Facility 1	facility	1
2	Facility 2	facility	2
3	Facility 3	facility	3
4	Facility 4	facility	4
5	Facility 5	facility	5
6	Facility 6	facility	6
7	Facility 7	facility	7
8	Facility 8	facility	8
9	Facility 9	facility	9
10	Facility 10	facility	10
.default	Unknown	facility	11
0	0-9	age_group	1
10	10-19	age_group	2
20	20-29	age_group	3
30	30-39	age_group	4
40	40-49	age_group	5
50	50+	age_group	6
high	High	.regex ^lab_result_	1
norm	Normal	.regex ^lab_result_	2
inc	Inconclusive	.regex ^lab_result_	3
y	yes	.global	Inf
n	no	.global	Inf
u	unknown	.global	Inf
unk	unknown	.global	Inf
oui	yes	.global	Inf
.missing	missing	.global	Inf

Matching

# Clean spelling based on dictionary -----------------------------
cleaned <- match_df(dat,
  dictionary = dict,
  from = "options",
  to = "values",
  by = "grp"
)
head(cleaned)
#>       id       date readmission treated    facility age_group
#> 1 ef267c 2019-07-08     Missing     Yes     Unknown     10-19
#> 2 e80a37 2019-07-07         Yes     Yes Facility  3     10-19
#> 3 b72883 2019-07-07         Yes      No Facility  8     30-39
#> 4 c9ee86 2019-07-09          No      No Facility  4     40-49
#> 5 40bc7a 2019-07-12          No      No Facility  6       0-9
#> 6 46566e 2019-07-14         Yes Missing     Unknown       50+
#>   lab_result_01 lab_result_02 lab_result_03 has_symptoms followup
#> 1       unknown          High  Inconclusive      missing  unknown
#> 2  Inconclusive       unknown        Normal          yes      yes
#> 3  Inconclusive        Normal  Inconclusive      missing      yes
#> 4  Inconclusive  Inconclusive       unknown          yes      yes
#> 5        Normal       unknown        Normal      missing       no
#> 6       unknown       unknown  Inconclusive      missing  missing