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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)))

I did a scatter plot using seaborn from three columns ['Category','Installs' and 'Gross Income'] and a hue map using the category column from my dataset. However in the legend, other than the category column which I want to appear, there is a big smug at the end showing one of the columns used in the scatter plot, Installs. I'll like to remove this element, but from searching through other questions hear and the documentation of seaborn and matplotlib I'm at a loss on how to proceed.

Here is a snippet of the code I'm working with:

Good afternoon,

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Query

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Sample dataset:

I am trying to use a recursive CTE + window functions to find the last outcome of a series of buy/sell orders.

First, here's some nomenclature:

• field_id is the store's ID.
• Field_number is an order number, but can be reused by the same person
• Field_date is the date of the initial order.
• Field_inserted is when this specific transaction occcurred.
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Unfortunately, because of the way the systems work, I do NOT get the cost when an item is returned, so figuring out the last outcome for an order is complicated (did we wind up selling any). I need to match the purchase with the sale, Which normally works pretty well. However, there are cases such as below when it fails, and I'm trying to find a way to do this in one pass, possibly using a recursive CTE.

Here's some code.