engagement | social engagement across a variety of social platforms | Networking library
kandi X-RAY | engagement Summary
kandi X-RAY | engagement Summary
A ruby gem for measuring the engagement of URLs on various social sites. Right now this project is just useful for counting comments for URLs on a variety of social sites. It may grow into something more useful in the future.
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Top functions reviewed by kandi - BETA
- Count the number of comment comments
engagement Key Features
engagement Examples and Code Snippets
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Trending Discussions on engagement
QUESTION
[enter image description here][1]
I have a table from a questionnaire where 390 people answered the likelihood for an activity as either unlikely, likely or very likely. The table looks as follows (for a total of 390 lines)
Anniversary Babyshower Engagement Memorial Unlikely Likely Very likely Unlikely Likely Unlikely Likely Very likely Very likely Very likely Unlikely LikelyWhat I would like is a barchart for each columns separated by the occurences of unlikely, likely and very likely. I am able to pull a barchart for one column, however I would like to have a a chart where I can see each individual column in the same graph. Is there a way to do this in ggplot?
I would be grateful for your help.
Thanks.
...ANSWER
Answered 2021-May-04 at 15:49library(ggplot2)
#creating example dataset
dfx <- data.frame(
Anniversary = c(sample(c("likely", "unlikely", "very_likely"), 20, replace = TRUE)),
Annual_album = c(sample(c("likely", "unlikely", "very_likely"), 20, replace = TRUE)),
Babyshower = c(sample(c("likely", "unlikely", "very_likely"), 20, replace = TRUE)),
Childhoodmemories = c(sample(c("likely", "unlikely", "very_likely"), 20, replace = TRUE)),
stringsAsFactors = FALSE
)
#creating a function to transform any dataset to a grouped dataset based on dataset variables(here activities)
grouped_df <- function(dataset){
#creating a dataframe with each response count per activities
df_table <- as.data.frame(apply(dataset,2, table))
# creating a vector containing activities
activity <- rep(colnames(df_table), each = nrow(df_table))
# creating a vector containing responses(likely, unlikely, very_likely)
response <- rep(rownames(df_table), times = length(colnames(df_table)))
# creating a vector containing number of responses per activities(vote_num)
vote_num <- NULL
for (i in seq_along(colnames(df_table))){
name <- paste0(colnames(df_table)[i], "_num")
name <- assign(name, df_table[,i])
vote_num <- append(vote_num, name)
}
# creating final dataframe with 3 columns(activities, responses, vote_num)
df_final <- data.frame(
activity = activity,
response = response,
vote_num = vote_num,
stringsAsFactors = FALSE
)
return(df_final) #retuning dataframe
}
my_dataset <- grouped_df(dfx) #assigning the function to a variable
# creating ggplot of grouped barchart
ggplot(my_dataset, aes(fill = response, y = vote_num, x = activity)) +
geom_bar(position = "dodge", stat = "identity")
QUESTION
i used google analytics for 2 web apps one is mine and the other one i was given an access to it by someone ,but i found that both are different : 1st one has customization & behavior tabs 2nd does not have these tabs and has another tabs like retention, engagement
why is that ? is there types of Google analytics or this is related to permissions ??
...ANSWER
Answered 2021-Jun-10 at 16:05The first is Universal Analytics and the second is Google Analytics 4. They are two different tracking system, the second is the new one.
QUESTION
I don't even know what title this question should have.
So, I am working on a library to make Google-Billing
as easy as possible to integrate.
Everything started after I've upgraded the library to Google's Billing v4
which replaces Purchase#getSku
with Purchase#getSkus()
.
This is the before and after migration to billing v4
:
Before (v3 library):
...ANSWER
Answered 2021-Jun-07 at 10:27In V4.0 BillingClient method callbacks call in background thread, not in Main thread.
Workaround is to change thread to Main in BillingClient callbacks.
Call you method processPurchases() with Toast in UI thread.
QUESTION
Twitter Player Card docs suggest that in "iPhone and Android native apps, a Player Card will appear in the Tweet detail view (not in the timeline)".
There is no explicit mention of how the Player Card will appear in timelines. From experience the link appears as a bare url, without a call-to-action. This is a problem because the user has no idea the link will provide the associated rich content, as can be correctly inferred on other platforms (e.g. static image with 'play' button).
Is there a way to include a CTA for these native apps? Without one, engagement stats are poor.
Example: https://twitter.com/indieHackney/status/1399674190303801348?s=20
...ANSWER
Answered 2021-Jun-04 at 09:39I believe I may have been the author of that original documentation, but I can’t quite remember! The point of the “(not in the timeline)” statement is to say that, indeed, a player card driven from HTML metadata is not shown in the timeline view. I’m not sure how you even get a player to show on click in that Tweet, since the metadata for the page specifies a summary card, which is what is showing up.
QUESTION
I have a 73 million row dataset, and I need to filter out rows that match any of a few conditions. I have been doing this with Boolean indexing, but it's taking a really long time (~30mins) and I want to know if I can make it faster (e.g. fancy indexing, np.where, np.compress?)
My code:
...ANSWER
Answered 2021-May-18 at 11:13If you have this many rows, I imagine it will be faster to first remove the records one step at a time. Regex is typically slow so you could use that as a last step with a much smaller data frame.
For example:
QUESTION
I am currently working on our FIX client to change the StreamingQuoteDuration on our quote requests to 2 minutes in order to work around a max stream limit imposed by our counterparty. I have encountered an issue with the Quote Cancel message that is received after 2 minutes. QuickFIX/n, the FIX library that our client uses, rejects the message stating that it is missing a required field - NoQuoteEntries (tag 295).
Our counterparty claims this is not a required field in their Rules Of Engagement document but I am unable to prevent QuickFIX from rejecting the message. Does anybody know how I can achieve this? I've asked the counterparty to include that tag but they are not able or willing to do so.
We are using the FIX 4.2 protocol. Here are the FIX logs from our quote messages log:
8=FIX.4.2|9=118|35=Z|34=31|49=[Redacted]|56=[Redacted]|52=20210510-10:43:16.428|117=*|298=1|131=EUR-GBP-EUR-1-20210512|10=065
8=FIX.4.2|9=129|35=3|34=1549=[Redacted]|52=20210510-10:43:16.792|56=[Redacted]45=31|58=Required tag missing|371=295|372=Z|373=1|10=063
ANSWER
Answered 2021-May-14 at 19:02You need to customize your FIX42.xml file (the DataDictionary) to match your counterparty's published Rules of Engagement.
QUESTION
I have a custom display template, which us basically blank. I want to be able to pass a short code id to the page for it to display i.e. [h5p id="4"] I can do this easily enough on wordpress pages, but I want a single page to display different content depending on what the user selected before hand. Whereas I would have to create a custom page for each interaction which I dont want.
The format will eventually be:
...ANSWER
Answered 2021-May-13 at 12:15The do_shortcode
function will replace the shortcode with the HTML for H5P's iframe, but you will have to add a
do_action( 'wp_footer' );
to your template, e.g. at the end. It's usually called when WordPress displays a footer.
H5P interprets that action as a signal that WordPress is done building the post/page and H5P can start its work: to actually fill the H5P iframe with life.
QUESTION
I'd like to merge multiple (around ten) datasets in R. Quite a few of the datasets are different from each other, so I don't need to match them by row name or anything. I'd just like to paste them side by side, on a single dataframe so I can export them into a single sheet. For instance, I have the following two datasets:
Month Engagement Test Jan 51 1 Feb 123 2 Variable Engagement Hot 412 Cold 4124 Warm 4fd4I'd simply like to put them side by side (as in left and right) in a single data frame for exporting purposes, like this:
Month Engagement Test Variable Engagement Jan 51 1 Hot 412 Feb 123 2 Cold 4124 NA NA NA Warm 4fd4Is there any way to accomplish this? It might seem like a strange request, but do let me know if I should provide any more info! Thank you so much.
...ANSWER
Answered 2021-May-10 at 06:10Put the data in a list. Find the max number of rows from the list. For each dataframe subset the rows, dataframe with lower number of rows will be appended with NA
's.
QUESTION
Hi I am trying to switch the active business process on a record. Basically, I have two business process flows and I want to move a record (lead) to the second business process flow, it is currently on the first business process flow.
I have tried utilizing the SetProcess Action. The request is successful (204) but it is not updating the Business Process Flow on the record to the correct one.
Here is the request I am sending:
...ANSWER
Answered 2021-May-04 at 14:30I was actually able to do this the entity record has to be deleted off the PBF entity of the old BPF that you do not want and then added to the new BPF entity.
QUESTION
I would like to know if there is a way to get engagement for a month (ex. get total engagement from current date minus 30 days) for an Instagram post in the Instagram Media Insights API or total engagement from a month from Instagram User Insights API.
As of now from the IG Media Insights, it seems you can only get cumulative engagement (see snippet below taken from IG Media Insights API:
...ANSWER
Answered 2021-May-03 at 15:40For IG Media Insights you can only see lifetime metrics. If you wanted a monthly breakdown you would have to build a mechanism to query the lifetimes every month and calculate the delta to track monthly change.
For IG Account Insights you can use metrics which offer days_28
period or use the since
and until
filtering.
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