I am handling customer data that has customer first and last name. I want to clean the names of any random keystrokes. Test accounts are jumbled in the data-set and have junk names. For example in the below data I want to remove customers 2,5,9,10,12 etc. I would appreciate your help.
Customer Id FirstName LastName
1 MARY MEYER
2 GFRTYUIO UHBVYY
3 CHARLES BEAL
4 MARNI MONTANEZ
5 GDTDTTD DTTHDTHTHTHD
6 TIFFANY BAYLESS
7 CATHRYN JONES
8 TINA CUNNINGHAM
9 FGCYFCGCGFC FGCGFCHGHG
10 ADDHJSDLG DHGAHG
11 WALTER FINN
12 GFCTFCGCFGC CG GFCGFCGFCGF
13 ASDASDASD AASDASDASD
14 TYKTYKYTKTY YTKTYKTYK
15 HFHFHF HAVE
16 REBECCA CROSSWHITE
17 GHSGHG HGASGH
18 JESSICA TREMBLEY
19 GFRTYUIO UHBVYY
20 HUBHGBUHBUH YTVYVFYVYFFV
21 HEATHER WYRICK
22 JASON SPLICHAL
23 RUSTY OWENS
24 DUSTIN WILLIAMS
25 GFCGFCFGCGFC GRCGFXFGDGF
26 QWQWQW QWQWWW
27 LIWNDVLIHWDV LIAENVLIHEAV
28 DARLENE SHORTRIDGE
29 BETH HDHDHDH
30 ROBERT SHIELDS
31 GHERDHBXFH DFHFDHDFH
32 ACE TESSSSSRT
33 ALLISON AWTREY
34 UYGUGVHGVGHVG HGHGVUYYU
35 HCJHV FHJSEFHSIEHF
You can calculate variability strength of full name (combine FirstName and LastName) by calculating length of unique letters in full name divided by total number of characters in the full name. Then, just remove the names that has low variability strength. This means that you are removing the names that has a high frequency of same random keystrokes resulting in low variability strength.
I did this using charToRaw function because it very faster and using dplyr library, as below:
# Building Test Data
df <- data.frame(CustomerId = c(1, 2, 3, 4, 5, 6, 7),
FirstName = c("MARY", "FGCYFCGCGFC", "GFCTFCGCFGC", "ASDASDASD", "GDTDTTD", "WALTER", "GFCTFCGCFGC"),
LastName = c("MEYER", "FGCGFCHGHG", "GFCGFCGFCGF", "AASDASDASD", "DTTHDTHTHTHD", "FINN", "CG GFCGFCGFCGF"), stringsAsFactors = FALSE)
#test data: df
# CustomerId FirstName LastName
#1 1 MARY MEYER
#2 2 FGCYFCGCGFC FGCGFCHGHG
#3 3 GFCTFCGCFGC GFCGFCGFCGF
#4 4 ASDASDASD AASDASDASD
#5 5 GDTDTTD DTTHDTHTHTHD
#6 6 WALTER FINN
#7 7 GFCTFCGCFGC CG GFCGFCGFCGF
library(dplyr)
df %>%
## Combining FirstName and LastName
mutate(FullName = paste(FirstName, gsub(" ", "", LastName, fixed = TRUE))) %>%
group_by(FullName) %>%
## Calculating variability strength for each full name
mutate(Variability = length(unique(as.integer(charToRaw(FullName))))/nchar(FullName))%>%
## Filtering full name, I set above or equal to 0.4 (You can change this)
## Meaning we are keeping full name that has variability strength greater than or equal to 0.40
filter(Variability >= 0.40)
# A tibble: 2 x 5
# Groups: FullName [2]
# CustomerId FirstName LastName FullName Variability
# <dbl> <chr> <chr> <chr> <dbl>
#1 1 MARY MEYER MARY MEYER 0.6000000
#2 6 WALTER FINN WALTER FINN 0.9090909