How to create a column that sums duplicated rows and then delete only one of the duplicates in R?-CodePudding

I have a really huge dataset and I'm looking for the simplest (and fastest) way to create a column that sums the value from one specific column and then proceed to leave only one of the duplicates.

My dataset looks like this:

data <- data.frame(DATE_INTER = c("2015-05-29", "2013-12-13", "2009-09-08"),
                   DATE_SAIDA = c("2015-06-10", "2013-12-15", "2009-10-20"),
                   GRUPO_AIH = c("09081997", "13122006", "13122006"),
                   DIAS_PERMANENCIA = c(12, 2, 42))

I need to use the column "GRUPO_AIH" to check for duplicates. My final output would be something like this:

I've already tried this, but it takes too long and after it's done, I can't even filter anything with dplyr that R stops working.

data <- data %>%
  group_by(GRUPO_AIH) %>%
  mutate(DIAS_PERMANENCIA2 = sum(DIAS_PERMANENCIA))

Any suggestions?

CodePudding user response：

here is the answer. Just one observation, in your provided dataset example you dont really have any duplicated values in GRUPO_AIH variable so i changed to GRUPO_AIH = c("09081997", "13122006", "13122006"),

data %>%
  group_by(GRUPO_AIH) %>%
  mutate(DIAS_PERMANENCIA = sum(DIAS_PERMANENCIA)) %>%
  filter(duplicated(GRUPO_AIH) == FALSE)




DATE_INTER DATE_SAIDA GRUPO_AIH DIAS_PERMANENCIA
  <chr>      <chr>      <chr>                <dbl>
1 2015-05-29 2015-06-10 09081997                12
2 2013-12-13 2013-12-15 13122006                44

OBS: também sou sanitarista atuando com dados na vigilância kk

CodePudding user response：

If you have a "very large dataset" (millions of rows) perhaps this will be fastest:

library(data.table)
data <- data.frame(DATE_INTER = c("2015-05-29", "2013-12-13", "2009-09-08"),
                   DATE_SAIDA = c("2015-06-10", "2013-12-15", "2009-10-20"),
                   GRUPO_AIH = c("09081997", "13122206", "13122206"),
                   DIAS_PERMANENCIA = c(12, 2, 42))

data_dt <- setDT(data)
setkey(data_dt, GRUPO_AIH)

data_dt[, DIAS_PERMANENCIA := sum(DIAS_PERMANENCIA), by = "GRUPO_AIH"]
data_final <- as.data.frame(data_dt[!duplicated(data_dt, by = "GRUPO_AIH")])
data_final
#>   DATE_INTER DATE_SAIDA GRUPO_AIH DIAS_PERMANENCIA
#> 1 2015-05-29 2015-06-10  09081997               12
#> 2 2013-12-13 2013-12-15  13122206               44

^{Created on 2022-05-31 by the}

# The difference in speed will likely become more pronounced as the size of the data increases
data <- data.frame(DATE_INTER = rep(c("2015-05-29", "2013-12-13", "2009-09-08"), times = 10e6),
                   DATE_SAIDA = rep(c("2015-06-10", "2013-12-15", "2009-10-20"), times = 10e6),
                   GRUPO_AIH = rep(c("09081997", "13122206", "13122206"), times = 10e6),
                   DIAS_PERMANENCIA = rep(c(12, 2, 42), times = 10e6))

dplyr_func <- function(data){
  data_final <- data %>%
    dplyr::group_by(GRUPO_AIH) %>%
    dplyr::mutate(DIAS_PERMANENCIA = sum(DIAS_PERMANENCIA)) %>%
    dplyr::filter(duplicated(GRUPO_AIH) == FALSE)
  return(data_final)
}

dt_func <- function(data){
  data_dt <- data.table::setDT(data)
  data.table::setkey(data_dt, GRUPO_AIH)
  data_dt[, DIAS_PERMANENCIA := sum(DIAS_PERMANENCIA), by = "GRUPO_AIH"]
  data_final <- as.data.frame(data_dt[!duplicated(data_dt, by = "GRUPO_AIH")])
  return(data_final)
}

res <- microbenchmark(dplyr_func(data), dt_func(data), times = 2)
autoplot(res)
#> Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Created on 2022-05-31 by the reprex package (v2.0.1)