The stdlib collection typically uses only libraries which are critical or are dependencies for Base (e.g., LinearAlgebra, Random, Dates, Test). For example, Dates used not to be a stdlib, but got added later on (mostly because many packages use it as dependency and the language added the structs). A good candidate for a new stdlib is Missings which provides a very general use struct. However, DataFrames is part of the JuliaData organization which is doing a good job at maintaining it. The JuliaStats ecosystem has a few which also provide a nice toolkit (StatsBase, Distributions, StatsModels, GLM).
In the case with R, DataFrames are provided by the R language, but it really sucks (plotting too)! That is why one uses tidyverse and data.table. The good aspects of the Data/Stats/Plotting are provided on top of base. Julia needs to improve its plotting critically and keep improving Data/Stats, but by no means basing those ecosystems will guarantee better development.