Help with filtering with specific condition

[ksingh19]

Hello!

Trying to find Product Ids that have different Product Types in the following sample data. In this case, looking for solution with all rows where Product ID is M12

df <- data.frame(
  stringsAsFactors = FALSE,
       check.names = FALSE,
              Date = c("2022-01-01", "2022-01-01", "2022-01-01"),
           Country = c("Canada", "Canada", "USA"),
    `Product Type` = c("AFC", "FBF", "CBF"),
      `Product ID` = c("M10", "M12", "M12")
)%>%
  mutate(Date = ymd(Date))

Thanks for the help!

[FJCC]

This is conveniently done with the filter() function from the dplyr package.

library(dplyr)

library(lubridate)
df <- data.frame(
  stringsAsFactors = FALSE,
  check.names = FALSE,
  Date = c("2022-01-01", "2022-01-01", "2022-01-01"),
  Country = c("Canada", "Canada", "USA"),
  `Product Type` = c("AFC", "FBF", "CBF"),
  `Product ID` = c("M10", "M12", "M12")
)%>%
  mutate(Date = ymd(Date))
dfFiltered <- df |> filter(`Product ID` == "M12")
dfFiltered
#>         Date Country Product Type Product ID
#> 1 2022-01-01  Canada          FBF        M12
#> 2 2022-01-01     USA          CBF        M12

^{Created on 2022-08-25 by the reprex package (v2.0.1)}

[ksingh19]

Well! I am looking for solution without knowing Product ID to look at al those Ids that have different Types. I am trying to check those and find data discrepancy.

Thanks!

[FactOREO]

You could something like this:

library(tidyverse)

df |>
  group_by(`Product ID`) |>
  summarise(
    different_prodTypes = n_distinct(`Product Type`)
  ) |>
  ungroup() |>
  filter(different_prodTypes > 1)
#> # A tibble: 1 × 2
#>   `Product ID` different_prodTypes
#>   <chr>                      <int>
#> 1 M12                            2

^{Created on 2022-08-25 by the reprex package (v2.0.1)}

This will show you all Product IDs with more than one Product Type.

Kind regards

[ksingh19]

Thanks @FactOREO!
Is there a way to see Product Types along with it. It will make it easier for us to clear discrepancies in data.

[FactOREO]

You could do something like this:

library(tidyverse)

df |>
  group_by(`Product ID`) |>
  summarise(
    different_prodTypes = n_distinct(`Product Type`),
    the_prodTypes = list(unique(`Product Type`))
  ) |>
  ungroup() |>
  filter(different_prodTypes > 1) |>
  unnest(the_prodTypes)
#> # A tibble: 2 × 3
#>   `Product ID` different_prodTypes the_prodTypes
#>   <chr>                      <int> <chr>        
#> 1 M12                            2 FBF          
#> 2 M12                            2 CBF

^{Created on 2022-08-25 by the reprex package (v2.0.1)}

It's in long format, for wide format use a counter along the different Product Types and reshape it with e.g. pivot_wider or another function that does this and you like to use.

Kind regards

[ksingh19]

Thank you @FactOREO!