Contributed Examples

The following contains examples contributed by the community

This section contains examples using DataFramesMeta.jl package.

Transform

1. This example is available in this link. We want to reproduce the following R codes:

library(dplyr)

women_new <- rbind(women, c(NA, 1), c(NA, NA))
women_new %>%
  filter(height %>% complete.cases) %>%
  mutate(sector = character(n()),
         sector = replace(sector, height >= 0 & height <= 60, "1"),
         sector = replace(sector, height >= 61 & height <= 67, "2"),
         sector = replace(sector, height >= 68 & height <= 72, "3"))

Solution using DataFramesMeta.jl:

using DataFrames
using DataFramesMeta
using Lazy: @>
using RDatasets

women = dataset("datasets", "women");
women_new = vcat(
              women,
              DataFrame(Height = [NA; NA], Weight = @data [1; NA])
            )

@> begin
    women_new
    @where !isna(:Height)
    @transform(
        Class = @> begin
            function (x)
                 0 <= x <= 60 ?  1 :
                61 <= x <= 67 ?  2 :
                68 <= x <= 72 ?  3 :
                NA
            end
            map(:Height)
        end
    )
end

Without removing the NA:

@> begin
    women_new
    @transform(
        Class = @> begin
            function (x)
                isna(x)       ? NA :
                 0 <= x <= 60 ?  1 :
                61 <= x <= 67 ?  2 :
                68 <= x <= 72 ?  3 :
                NA
            end
            map(:Height)
        end
    )
end

Solution using Query.jl:

using DataFrames
using Query
using RDatasets

women = dataset("datasets", "women");
women_new = vcat(
              women,
              DataFrame(Height = [NA; NA], Weight = @data [1; NA])
            )

@from i in women_new begin
    @where !isnull(i.Height)
    @select {
        i.Height, i.Weight,
        class = 0 <= i.Height <= 60 ?  1 :
               61 <= i.Height <= 67 ?  2 :
               68 <= i.Height <= 72 ?  3 :
                0
    }
    @collect DataFrame
end

Without removing the NA:

@from i in women_new begin
    @select {
        i.Height, i.Weight,
        class = 0 <= i.Height <= 60 ?  1 :
               61 <= i.Height <= 67 ?  2 :
               68 <= i.Height <= 72 ?  3 :
                0
    }
    @collect DataFrame
end

Filter

Summarize

Join