У меня есть два набора данных A и B по 8 столбцов в каждом. Набор данных A содержит 942 строки, а набор данных B — 5079 строк. Мне нужно сравнить набор данных A и набор данных B и выполнить нечеткое сопоставление. Если в наборе данных B есть совпадение какой-либо строки, я должен отметить «Соответствие» в наборе данных A в дополнительном столбце.
Я относительно новичок в R и не знаю, как оптимизировать код r с помощью lapply, mapply или sapply вместо forloop.
Ниже приведен мой код
##############################
# Install Necessary Packages #
##############################
#install.packages("openxlsx")
#install.packages("stringdist")
#install.packages("XLConnect")
##############################
# Load Packages #
##############################
library(openxlsx)
library(stringdist)
library(XLConnect)
cmd_newleads <- read.xlsx("Src/CMD - New Leads to Load.xlsx", sheet = "Top Leads Full Data", startRow = 1, colNames = TRUE)
cmd_newleads[c("Lead_Match","Opportunity_Match")] <- ""
c4c_leads <- read.xlsx("Src/C4C - Leads.xlsx", sheet = "Leads", startRow = 1, colNames = TRUE)
#c4c_opportunities <- read.xlsx("Src/C4C - Opportunities Data 6-24-16.xlsx", sheet = "Export 06-24-2016 04.55.46 PM", startRow = 1, colNames = TRUE)
cmd_newleads_selcols <- cmd_newleads[,c("project_name","project_address","project_city","project_state_province_region_code","project_postalcode","project_country","project_sector","project_type")]
cmd_newleads_selcols[is.na(cmd_newleads_selcols)] <- ""
#rownames(cmd_newleads_selcols)
c4cleads_selcols <- c4c_leads[,c("Lead","Address1.(Lead)","City.(Lead)","Region.(Lead)","Postal.Code.(Lead)","Country.(Lead)","Sector.(Lead)","Type.(Lead)")]
c4cleads_selcols[is.na(c4cleads_selcols)] <- ""
#cmd_c4copportunities_selcols <- c4c_opportunities[,c("project_name","project_address","project_city","project_state_province_region_code","project_postalcode","project_country","project_sector","project_type")]
rcount_cmdnewleads <- nrow(cmd_newleads)
rcount_c4cleads <- nrow(c4c_leads)
#rcount_c4copportunities <- nrow(c4c_opportunities)
for(i in 1:rcount_cmdnewleads)
{
cmd_project_name <- cmd_newleads_selcols[i,1]
cmd_project_address <- cmd_newleads_selcols[i,2]
cmd_project_city <- cmd_newleads_selcols[i,3]
cmd_project_region_code <- cmd_newleads_selcols[i,4]
cmd_project_postalcode <- cmd_newleads_selcols[i,5]
cmd_project_country <- cmd_newleads_selcols[i,6]
cmd_project_sector <- cmd_newleads_selcols[i,7]
cmd_project_type <- cmd_newleads_selcols[i,8]
for(j in 1:rcount_c4cleads)
{
c4cleads_project_name <- c4cleads_selcols[j,1]
c4cleads_project_address <- c4cleads_selcols[j,2]
c4cleads_project_city <- c4cleads_selcols[j,3]
c4cleads_project_region_code <- c4cleads_selcols[j,4]
c4cleads_project_postalcode <- c4cleads_selcols[j,5]
c4cleads_project_country <- c4cleads_selcols[j,6]
c4cleads_project_sector <- c4cleads_selcols[j,7]
c4cleads_project_type <- c4cleads_selcols[j,8]
project_percent <- stringsim(cmd_project_name,c4cleads_project_name, method="dl", p=0.1)
address_percent <- stringsim(cmd_project_address,c4cleads_project_address, method="dl", p=0.1)
city_percent <- stringsim(cmd_project_city,c4cleads_project_city, method="dl", p=0.1)
region_percent <- stringsim(cmd_project_region_code,c4cleads_project_region_code, method="dl", p=0.1)
postalcode_percent <- stringsim(cmd_project_postalcode,c4cleads_project_postalcode, method="dl", p=0.1)
country_percent <- stringsim(cmd_project_country,c4cleads_project_country, method="dl", p=0.1)
sector_percent <- stringsim(cmd_project_sector,c4cleads_project_sector, method="dl", p=0.1)
type_percent <- stringsim(cmd_project_type,c4cleads_project_type, method="dl", p=0.1)
if(project_percent > 0.833 && address_percent > 0.833 && city_percent > 0.833 && region_percent > 0.833 && postalcode_percent > 0.833 && country_percent > 0.833 && sector_percent > 0.833 && type_percent > 0.833)
{
cmd_newleads[i,51] <- c4cleads[j,c4cleads$Lead.ID]
}
else
{
cmd_newleads[i,51] <- "New Lead"
}
}
}
Примеры данных для cmd_newleads_selcols и c4cleads_selcols соответственно
project_name project_address project_city
1 Wynn Mystic Casino & Hotel 22 Chemical Ln Everett
2 Northpoint Complex Development East Street Cambridge
3 Northpoint Complex Development East Street Cambridge
4 Northpoint Complex Development East Street Cambridge
5 Northpoint Complex Development East Street Cambridge
6 Northpoint Complex Development East Street Cambridge
project_state_province_region_code project_postalcode
1 MA 02149
2 MA 02138
3 MA 02138
4 MA 02138
5 MA 02138
6 MA 02138
project_country project_sector project_type
1 United States of America Hospitality New Building
2 United States of America Apartments New Building
3 United States of America Apartments New Building
4 United States of America Apartments New Building
5 United States of America Apartments New Building
6 United States of America Apartments New Building
Lead Address1.(Lead) City.(Lead) Region.(Lead) Postal.Code.(Lead) Country.(Lead)
1 1 Hotel Brooklyn Bridge Park Old Fulton St & Furman St Brooklyn New York 11201 United States
2 10 Trinity Square Hotel 10 Trinity Square London # EC3P United Kingdom
3 100 Stewart 1900 1st Avenue Seattle Washington 98101 United States
4 1136 S Wabash # # # # Not assigned
5 115-129 37th Street 115-129 37th Street Union CIty New Jersey # United States
6 1418 W Addison 1418 w Addison Chicago # 60613 Not assigned
Sector.(Lead) Type.(Lead)
1 Hospitality New Building
2 Hospitality Brand Conversion
3 Hospitality New Building
4 High Rise Residential New Building
5 Developer New Building
6 High Rise Residential New Building
