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Correct misspelled data using a dictionary

Source: R/dta_replace.R
dta_replace.Rd

dta_replace() corrects misspelled entries in a data frame or tibble based on a provided dictionary. The dictionary specifies the correct values for misspelled entries in a specified column.

Usage

dta_replace(dat, dict, .name, .wrong, .correct)

Arguments

dat

A data frame or tibble containing the data to be corrected.

dict

A data frame or tibble serving as the dictionary, with columns specifying the correct and incorrect spellings.

.name

The column in both dat and dict to match entries by (e.g., a unique identifier).

.wrong

The column in dict containing the misspelled values to be corrected.

.correct

The column in dict containing the correct values for the misspelled entries.

Value

A data frame or tibble with corrected entries.

Details

The function first validates that dat and dict are data frames or tibbles. It then fills missing values in the dict for the columns specified in .name and .correct, using a downward fill strategy. Finally, it replaces misspelled values in dat using a dictionary lookup facilitated by matchmaker::match_df().

Examples

# Example data with misspelled characters / strings

data("data_misspelled")
dta_gtable(head(data_misspelled))



  
id
      region
      age
      height
      weight
      blood_group
      marital_status
      education
      ses
      r
      python
      sas
      stata
      spss
      excel
    
STM/7539
southern
46
1.85
77
ba
Other
BSC
medium
n
n
yes
tru
n
n
STM/7993
South
45
1.64
53
AB
maried
Bsc
Midle
y
N
n
no
tru
N
STM/7387
southern
37
1.61
75
B
single
BSC
Middle
N
tru
no
Yes
No
n
STM/5598
West
45
1.80
69
B
Single
MSc
low
y
N
y
fasle
no
Yes
STM/5901
South
51
1.81
53
A+
Others
Bachelors
Medium
no
yes
Yes
n
tru
Yes
STM/7529
North-East
56
1.35
56
O
Single
Bsc
Mediam
yes
fasle
no
yes
tru
no

data("dict_misspelled")
dta_gtable(dict_misspelled)



  
variable
      old
      new
    
region
southern
South


wset
West


North-East
North East
blood_group
ba
AB


ab



o
O


A+
A


a

marital_status
maried
Married


single
Single


Singel



Others
Other
education
BSC
Bachelors


Bsc



Bachelers



MSc
Masters


PhD
Doctorate


Doctoral

ses
medium
Middle


Midle



Mediam



low
Low


Hihg
High
.global
fasle
No


N



n



no



yes
Yes


y



tru


# Correct the misspelled entries in `dat` using the
# `dict` dictionary

result <- dta_replace(
  dat = data_misspelled, 
  dict = dict_misspelled, 
  .name = variable, 
  .wrong = old, 
  .correct = new
)
dta_gtable(head(result))



  
id
      region
      age
      height
      weight
      blood_group
      marital_status
      education
      ses
      r
      python
      sas
      stata
      spss
      excel
    
STM/7539
South
46
1.85
77
AB
Other
Bachelors
Middle
No
No
Yes
Yes
No
No
STM/7993
South
45
1.64
53
AB
Married
Bachelors
Middle
Yes
No
No
No
Yes
No
STM/7387
South
37
1.61
75
B
Single
Bachelors
Middle
No
Yes
No
Yes
No
No
STM/5598
West
45
1.80
69
B
Single
Masters
Low
Yes
No
Yes
No
No
Yes
STM/5901
South
51
1.81
53
A
Other
Bachelors
Medium
No
Yes
Yes
No
Yes
Yes
STM/7529
North East
56
1.35
56
O
Single
Bachelors
Middle
Yes
No
No
Yes
Yes
No