Not that pandas/scipy/numpy don't make an admirable job. You can do something like this, but it's nowhere near as ergonomic as it is R. At the end of the day, R is fundamentally a language for data exploration, whereas with python those facilities are bolted on top of a general purpose environment.

    flights.iloc[0:10, flights.columns.get_indexer(['year', 'month', 'day'])])

    flights %>% select("year", "month", "day") %>% head(10)

    flights[['year', 'month', 'day']].head(10)

    head(flights[c("year", "month", "day")], 10)

    flights[1:10, c("year", "month", "day")]

    flights[["year", "month", "day"]].head(10)

     SELECT year, month, day
       FROM flights
      LIMIT 10

for the data.table fans

Which is arguably the superior way to handle tabular data in 2020.

Big mistake, btw. It took me years to unlearn all of the terrible habits I picked up from the R world. Do yourself a favor and start with python, if only to learn proper programming practices and techniques before diving into R.

One of course is allowed to learn more than one thing. Maybe play with a bondage and discipline language to expose yourself to the concepts the parent comment is advocating for.

As a student I constantly complained that we were being taught these useless languages. As a grownup I realize that while some of the Comp Sci faculty may’ve been out of touch, their goal was not teaching us commercially viable skills. They were endeavoring to teach us how to think. Once you know how to think you can express those thoughts in nearly any language, no matter how hostile to those thoughts it may be.

But maybe you just want to get things done, and if that’s so, the answer for data problems is basically one or more of R, Python, Julia, etc.

Biologists like it for single cell analysis. They use Seurat and save the data as an object and load it up/ pass around around for analysis. Its actually kinda neat.

R's ggplot2 library is top tier in making graphs.

RStudio makes it very accessible.