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vlookup in r dplyr
MF
on May 26, 2020
library(dplyr)
desired_result =
df_b %>% select(-price,-sale_price) %>%
left_join(
df_a %>%
transmute(id = SKU, price = PRECIO_LISTA, sale_price = PRECIO_INDEXADO) %>%
distinct()
)Source: stackoverflow.com
vlookup in r dplyr
MF
on May 26, 2020
life_expectancy = read.csv("life_expectancy.csv", skip = 2, header = TRUE, stringsAsFactors = FALSE)
sanitation = read.csv("sanitation.csv", skip = 2, header = TRUE, stringsAsFactors = FALSE)
## rename all yearly metric columns. E.g., X1995 becomes le_1995 (for life expectancy)
names(life_expectancy)[grepl("\\bX", names(life_expectancy))] = gsub("X", "le_", names(le)[grepl("\\bX", names(life_expectancy))])
names(sanitation)[grepl("\\bX", names(sanitation))] = gsub("X", "san_", names(sanitation)[grepl("\\bX", names(sanitation))])
## lowercase column names
names(life_expectancy) = tolower(names(life_expectancy))
names(sanitation) = tolower(names(sanitation))
Source: www.rforexcelusers.com
vlookup in r dplyr
MF
on May 26, 2020
# Merge by columns with different names
merge(life_expectancy, sanitation, by.x = "cntr_nm", by.y = "country.name")
Source: www.rforexcelusers.com
vlookup in r dplyr
MF
on May 26, 2020
# Merge by multiple columns
merge(life_expectancy, income_disparity, by = c("country", "city"))
Source: www.rforexcelusers.com
vlookup in r dplyr
MF
on May 26, 2020
# Left Join: keep all rows in x even if there's no match in y
merge(life_expectancy, income_disparity, all.x = TRUE)
Source: www.rforexcelusers.com
vlookup in r dplyr
MF
on May 26, 2020
data_2012 = merge(life_expectancy[, c("country.name", "le_2012")],
sanitation[, c("country.name", "san_2012")])
head(data_2012, 10)
Source: www.rforexcelusers.com
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