LIBERT | Specializing Unsupervised Pretraining Models | Natural Language Processing library

So my intention here is to show a timeline of concurrent health technologies, with their duration as a rectangle or tile geom, who's width represents duration over a specified time interval on the x-axis, and by manufacturer along the Y-axis.

I have 16 cases, from 4 different manufacturers over a time from 2001-01-01 to 2016-03-31

I am using the ggplot2 and timeline packages. Following an example found online, I edited my data to have only the column headers: Device, Manufacturer, StartDate, EndDate, as well as making sure there are no NULL cases. As such I added an artificial end date to many of the technologies which are still licensed to date.

Trying again with sample data, we have:

I have a large dataframe consisting of tweets, and keyword dictionaries loaded as values that have words associated with morality (kw_Moral) and emotion (kw_Emo). In the past I have used the keyword dictionaries to subset a dataframe to get only the tweets that have one or more of the keywords present.

For example, to create a subset with only those tweets that have emotional keywords, I loaded in my keyword dictionary...

I have a large dataframe consisting of tweets, and a keyword dictionary loaded as a list that has words and word stems associated with emotion (kw_Emo). I need to find a way to count how many times any given word/word stem from kw_Emo is present each tweet. In kw_Emo, word stems are marked with an asterisk ( * ). For example, one word stem is ador*, meaning that I need to account for the presence of adorable, adore, adoring, or any pattern of letters that starts with ador….

From a previous Stack Overflow discussion (see previous question on my profile), I was greatly helped with the following solution, but it only counts exact character matches (Ex. only ador, not adorable):

1. Load relevant package.

   library(stringr)

2. Identify and remove the * from word stems in kw_Emo.

   for (x in 1:length(kw_Emo)) { if (grepl("[*]", kw_Emo[x]) == TRUE) { kw_Emo[x] <- substr(kw_Emo[x],1,nchar(kw_Emo[x])-1) } }

3. Create new columns, one for each word/word stem from kw_Emo, with default value 0.

   for (x in 1:length(keywords)) { dataframe[, keywords[x]] <- 0}

4. Split each Tweet to a vector of words, see if the keyword is equal to any, add +1 to the appropriate word/word stems' column.

   for (x in 1:nrow(dataframe)) { partials <- data.frame(str_split(dataframe[x,2], " "), stringsAsFactors=FALSE) partials <- partials[partials[] != ""] for(y in 1:length(partials)) { for (z in 1:length(keywords)) { if (keywords[z] == partials[y]) { dataframe[x, keywords[z]] <- dataframe[x, keywords[z]] + 1 } } } }

Is there a way to alter this solution to account for word stems? I'm wondering if it's possible to first use a stringr pattern to replace occurrences of a word stem with the exact characters, and then use this exact match solution. For instance, something like stringr::str_replace_all(x, "ador[a-z]+", "ador"). But I'm unsure how to do this with my large dictionary and numerous word stems. Maybe the loop removing [*], which essentially identifies all word stems, can be adapted somehow?

Here is a reproducible sample of my dataframe, called TestTweets with the text to be analysed in a column called clean_text:

dput(droplevels(head(TestTweets, 20)))

How add secondary Y axis for a frequency plot in R?

My code:

First time posting to stackoverflow, sorry if post format is wrong. wouldnt mind feedback on it if that helps with my question outlay.

Trying to receive the JSON from WU(Weather Underground). This is the JSON: