4 ms·

I'm a software engineer who is already quite comfortable with Python and has more of an interest in machine learning than data science (as I understand it), is

by Eridrus 10y ago

I'm a software engineer who is already quite comfortable with Python and has more of an interest in machine learning than data science (as I understand it), is there any reason for me to learn R?

mnky9800n 10y ago
If you use M$ products you will find that SQL Server 2016 has R baked in. https://www.r-bloggers.com/demo-r-in-sql-server-2016/ https://www.r-bloggers.com/demo-r-in-sql-server-2016/
hadley 10y ago
Probably not any strong reasons. That said, if you're a software engineer, you shouldn't find it too hard to pick up enough R to be useful. You might enjoy <http://adv-r.had.co.nz> http://adv-r.had.co.nz> which describes R from more of a programming language perspective.
- deleted 10y ago
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dthal 10y ago
I use both R and python quite a bit. I prefer python as a programming language. Here's my take on 'Why learn R?': (1) R/ggplot is hands-down better for plotting than anything in python. I also think that R is better for EDA generally. (2) Many smart, knowledgable people use R and publish their code. To learn from it, you need to know enough R to read and modify it. (3) R has better package support than python in several common data analysis domains. For example, in forecasting and in graph analysis, the best R packages available are much better than the best python packages.
xaa 10y ago
I'm in bioinformatics. R is huge, Python is a distant second. So I am forced to use both. R is a terrible programming language. It's slow and syntactically inconsistent. For interactive statistical analysis it can be OK, but anything beyond a small program becomes unmanageable quickly. In particular, if you want to manipulate strings or hierarchical data structures quickly in R, good luck. For ML, scikit-learn is almost always sufficient. For statistics, statsmodels is OK but very underdeveloped compared to what is available in R. IMO plotting is equally painful in both Python and R (seaborn is a good Python library to ease the pain if you haven't seen it). Personally, I write everything in Python and call out to R as infrequently as possible using rpy2, usually only for specific statistical routines or bioinformatics-specific libraries.

by Eridrus 10y ago

I'm a software engineer who is already quite comfortable with Python and has more of an interest in machine learning than data science (as I understand it), is there any reason for me to learn R?

mnky9800n 10y ago

If you use M$ products you will find that SQL Server 2016 has R baked in. https://www.r-bloggers.com/demo-r-in-sql-server-2016/ https://www.r-bloggers.com/demo-r-in-sql-server-2016/

hadley 10y ago

Probably not any strong reasons. That said, if you're a software engineer, you shouldn't find it too hard to pick up enough R to be useful. You might enjoy <http://adv-r.had.co.nz> http://adv-r.had.co.nz> which describes R from more of a programming language perspective.

deleted 10y ago
[deleted]

dthal 10y ago

I use both R and python quite a bit. I prefer python as a programming language. Here's my take on 'Why learn R?': (1) R/ggplot is hands-down better for plotting than anything in python. I also think that R is better for EDA generally. (2) Many smart, knowledgable people use R and publish their code. To learn from it, you need to know enough R to read and modify it. (3) R has better package support than python in several common data analysis domains. For example, in forecasting and in graph analysis, the best R packages available are much better than the best python packages.

xaa 10y ago

I'm in bioinformatics. R is huge, Python is a distant second. So I am forced to use both. R is a terrible programming language. It's slow and syntactically inconsistent. For interactive statistical analysis it can be OK, but anything beyond a small program becomes unmanageable quickly. In particular, if you want to manipulate strings or hierarchical data structures quickly in R, good luck. For ML, scikit-learn is almost always sufficient. For statistics, statsmodels is OK but very underdeveloped compared to what is available in R. IMO plotting is equally painful in both Python and R (seaborn is a good Python library to ease the pain if you haven't seen it). Personally, I write everything in Python and call out to R as infrequently as possible using rpy2, usually only for specific statistical routines or bioinformatics-specific libraries.