An opinionated view of the Tidyverse “dialect” of the R language

Post by hadley » Sat, Jul 06, 2019, 3:01 PM UTC

RosanaAnaDana wrote: post #108

barcadad wrote: post #89

Earlier quoted context omitted.

His critique is more about the impact of the full ecosystem effect of the Tidyverse, not what you are referring to, which is just the dplyr semantics. The Tidyverse demands that it's many related packages use tidy data principles and lock users into that approach, which differs from base-R. Much of this discussion is really just a debate about dplyr and magrittr rather than the fragmentation that the broader tidyvers…

I think any one making the point of speed of development have seriously missed the boat. Lets be real: tibbles suck. Once you get the hang of data.table syntax for matrix operations, its superiority becomes impeccably clear. I run a data science group a large geospatial company and we develop day in and day out in R and python. We've purged tidyverse as much as possible from all of our code base. We've moved complete…

Post by kgwgk » Sat, Jul 06, 2019, 4:26 PM UTC

hadley wrote: post #138

kgwgk wrote: post #137

Earlier quoted context omitted.

My point is that sometimes you don't have a single "thread" of calculation. Putting rbind into a pipe like you did is somewhat artificial (broken symmetry) and doesn't work so well if there is some pre-processing before the merge and some post-processing after the merge (or if we have two or more essentially different arguments in a function that need some preprocessing). You may say that having multiple pipes, one m…

I don’t think anyone is arguing that the pipe should be the -only- form of composition. Just that it’s a useful form when you have a linear sequence of transformations. Sometimes it’s useful to force something close to being linear into a linear form for consistency, but typically you would switch to an alternate form of composition, typically assigning to intermediate variables. It’s easy to find examples of using t…

Post by wodenokoto » Sat, Jul 06, 2019, 5:53 PM UTC

notafraudster wrote: post #83

robust wrote: post #80

Earlier quoted context omitted.

Can you provide an example of a problem that requires multiple tidyverse operations, but could be solved equally well using only lapply?

I run a large scale national survey. We download the data from our survey platform. Survey respondents are asked 100+ questions. The questions change week to week and so the column names are not consistent. We exclude respondents who appear to be cheating the system (rushing through questions, straight-lining, skipping almost every question, etc.) As part of our completion check, we want to do a row-wise map of the d…

    # One line to make the data tidy.
    # The form of data will be 3 columns: id, question, answer, and no, we don't care what the columns are called, except for id.
    tidydf % gather("question", "answer", -id) 

    # one line to do your check
    tidydf %>% group_by(id) %>% summarise(n_NA = sum(is.na(answer)))

Post by kgwgk » Sat, Jul 06, 2019, 6:36 PM UTC

roenxi wrote: post #134

kgwgk wrote: post #131

Earlier quoted context omitted.

If you find that the example above is not "human readable" then the following code will make your head explode because it cannot be transformed into a pipe representation (or at least it's not so straightforward): the_data x), variable_c = variable_a/variable_b)

Maybe consider: the_data % rbind(read.csv('/path/to/data/file2.csv')) %>% filter(variable_a > x) %>% mutate(variable_c = variable_a/variable_b) You've thrown out a few variations on a theme here, but I'm not sure where you are trying to angle towards. There are a lot of ways to format code, and I can't tell you what works best for you. But this isn't shaking the paradigm of having an initial block of data (here separ…

  df  x]
  df$variable_c 

  df  x)
  df[,”variable_c”]