stat545 | data wrangling , exploration , & analysis with R | Data Visualization library

I would like to use the named entity recognition functions in the monkeylearn package in R.

As part of the setting up process, we need to do the following:

"To get an API key for MonkeyLearn, register at http://monkeylearn.com/. Note that MonkeyLearn supports registration through GitHub, which makes the registration process really easy. For ease of use, save your API key as an environment variable as described at http://stat545.com/bit003_api-key-env-var.html. You might also want to use the usethis::edit_r_environ() function to modify .Renviron.

All functions of the package will conveniently look for your API key using Sys.getenv("MONKEYLEARN_KEY") so if your API key is an environment variable called “MONKEYLEARN_KEY” you don’t need to input it manually.

Please also create a “MONKEYLEARN_PLAN” environment variable indicating whether your Monkeylearn plan is “free”, “team”, “business” or “custom”. If you do not indicate it by default it will be “free” with a message. If your plan is “custom” you’ll need a third environment variable “MONKEYLEARN_RATE” indicating the maximum amount of requests per minute that you can make to the API. If you do not indicate it, by default it will be 120 with a message."

I've gotten the API key, but as a layman, I couldn't understand the guide on saving the api key as environment variable. Could anyone provide a step by step guide for Windows please?

Thank you.

I have a data frame consisting of data from financial statements (e.g. Income Statement, Balance Sheet, Cash Flow Statement), with each row referring to a financial statement entry (e.g. revenue, profits) and with each column referring to a particular year.

An example of the data would be the following:

I imagine this question is duplicated, but I could not find any working answer to use dplyr in an easy and elegant way to add subgroup counts after a group_by. If this question is duplicate, please delete. If you want a code to reproduce, I´ll do that. Please, don´t click on "negative".

I´ve tried to use spread, but it was not useful, after, I´ve tried to follow the instructions here, once it helps to Count unique by group in a data frame, but it does not work. The same solution is here, but the output is strange.

What I have:

What I really want (using simple code... I imagine dplyr can handle that without having to use gather()), is to insert three new columns, for each factor level.

My code:

I am balancing several versions of R and want to change my R libraries loaded depending on which R and which operating system I'm using. As such, I want to stick with base R functions.

I was reading this page to see what the base R equivalent to stringr::str_extract was:

http://stat545.com/block022_regular-expression.html

It suggested I could replicate this functionality with grep. However, I haven't been able to get grep to do more than return the whole string if there is a match. Is this possible with grep alone, or do I need to combine it with another function? In my case I'm trying to distinguish between CentOS versions 6 and 7.

I am trying to compile a package vignette such that an .md file remains in the vignette folder so that it is still visible on github. I am using devtools for all of this. I have looked at this approach and will outline it below:

I have auto-generated a vignette template use devtools::use_vignette(). Then I have modified the .Rmd file to look like this (truncated template version):