A newcomer’s (angry) guide to R
arrgh.tim-smith.us
A newcomer’s (angry) guide to R
1–10 of 232 posts
Re: A newcomer’s (angry) guide to R
#2I think many of the gotchas and annoying parts of base R are solved by using tools from the tidyverse: http://github.com/tidyverse. For example, the pain of needing to specify `stringsAsFactors=FALSE` is solved in the tibble package by setting a sensible default.
At any rate, at least it's not Pandas and matplotlib...
Re: A newcomer’s (angry) guide to R
#3> There are subtle differences and some authorities prefer ...I am confused here. If you're testing for equality, R requires you to use == and not =. If you try to test for equality with =, it throws an error instead of treating it as an assignment. That's good. But who is trying to test for equality with <-?
Re: A newcomer’s (angry) guide to R
#4> There are subtle differences and some authorities prefer ...I am confused here. If you're testing for equality, R requires you to use == and not =. If you try to test for equality with =, it throws an error instead of treating it as an assignment. That's good. But who is trying to test for equality with <-?
He's not testing for equality. He's assigning to a variable.
Re: A newcomer’s (angry) guide to R
#5> There are subtle differences and some authorities prefer ...I am confused here. If you're testing for equality, R requires you to use == and not =. If you try to test for equality with =, it throws an error instead of treating it as an assignment. That's good. But who is trying to test for equality with <-?
He's not testing for equality. He's assigning to a variable.
He's saying to use = instead of <- because it won't let you assign to a variable there. But that's because it assumes you mistyped the equality operator. The only reason you need safety there is because it's easy to forget that == is the equality operator, not =. It's not easy to confuse == and <-.
Re: A newcomer’s (angry) guide to R
#6>A R factor is a sequence type much like a character atomic vector except that the values of the factor are constrained to a set of string values, called “levels”. For example, if you have a table of measurements of some widgets and each row corresponds to a single measurement of a single widget, you could have a factor-typed column called measurement.type containing the values “length”, “width”, “height”, “weight”, and “hue”, with the corresponding numeric measurements stored in a “value” column.
This is a very bad example of what factors are for in R, because it makes it seem like factors are for defining variables or keys in key value pairs. You can use them for that, but it isn't the intended use. A better example would be:
suppose you were comparing the amount of sugar in fruits based on several growing locations, and you had three columns:
| Fruit | Location | Density (g/L) |
Fruit would be a factor variable (let's say it takes the possibilities of apple, banana, orange), and location could be too, if it were a discrete set of possibilities (as opposed to lat/lon coords)
This author seems to forget that R was built for working with data in an analytical setting, unlike all of the languages he's comparing it to. It has creeped into other areas, but that seems to be because in the hands of a skilled user it is far easier to implement a data analysis solution. I'm sure someone will come in and say how much better pandas is, but on the small datasets, I'll stick with R, especially with how brittle and buggy matplotlib is.
Re: A newcomer’s (angry) guide to R
#7I think many of the gotchas and annoying parts of base R are solved by using tools from the tidyverse: http://github.com/tidyverse . For example, the pain of needing to specify `stringsAsFactors=FALSE` is solved in the tibble package by setting a sensible default. At any rate, at least it's not Pandas and matplotlib...
> At any rate, at least it's not Pandas and matplotlib...
There's a certain yin and yang to the space, isn't there? You get to choose between a hacky language that has pretty good tooling built on top of it, or a pretty good language with hacky tooling built on top of it.
I think that Python is probably winning because being a decent language gives you a decent escape hatch, whereas no amount of great libraries can save you from having to go through the bizarro language.
That said, R may be bizarro, but at least, once you learn it, it's predictable. Whereas I'm not sure even Pandas really knows whether a given call to .loc will copy or refer to the original data.
Re: A newcomer’s (angry) guide to R
#8>A R factor is a sequence type much like a character atomic vector except that the values of the factor are constrained to a set of string values, called “levels”. For example, if you have a table of measurements of some widgets and each row corresponds to a single measurement of a single widget, you could have a factor-typed column called measurement.type containing the values “length”, “width”, “height”, “weight”,…
> This is a very bad example of what factors are for in R, because it makes it seem like factors are for defining variables or keys in key value pairs
That is the approach for tidy data, which is used a lot in the R tidyverse (http://tidyr.tidyverse.org/articles/tidy-data.html)
Re: A newcomer’s (angry) guide to R
#9Having used Python, JSL, Julia, R and Matlab; I agree with most of the things in R. R is an extremely ugly language. It seems to be created by people who wear capris and uggs (both at the same time). But, R has incredible packages, especially the work done by Hadley Wickam. ggplot2 is beautiful. It is utterly gorgeous. It is what Ted Baker is to the capri guys that designed the language itself.
Re: A newcomer’s (angry) guide to R
#10I think many of the gotchas and annoying parts of base R are solved by using tools from the tidyverse: http://github.com/tidyverse . For example, the pain of needing to specify `stringsAsFactors=FALSE` is solved in the tibble package by setting a sensible default. At any rate, at least it's not Pandas and matplotlib...
> At any rate, at least it's not Pandas and matplotlib... There's a certain yin and yang to the space, isn't there? You get to choose between a hacky language that has pretty good tooling built on top of it, or a pretty good language with hacky tooling built on top of it. I think that Python is probably winning because being a decent language gives you a decent escape hatch, whereas no amount of great libraries can s…
Pandas has a lot of defensive copying. I love Pandas, but I think it demands a lot from the user. When I started using it I was new to traditional coding (i.e. knowing anything about datastructures), and have come from R. Over time as I've learned a real amount about legitimate data structures, I've become far far better with Pandas.