Lancet | fast AOP library for Android App | Aspect Oriented library

I would like to create a polar heatmap like the heatmap from the Lancet paper "Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants":

I appreciate the idea of annotating the age each layer of ring represents (age 5 to 19 years) by creating a fan-shaped opening of the polar heatmap (manually circled in red). I refer to 5-19 as the Y-AXIS LABELS hereafter.

Below is the code from @Cyrus Mohammadian describing how to arrange the positions of Y-AXIS LABELS of polar heatmaps. I replicate Cyrus Mohammadian's code below:

I wish to create choropleth world map with insets showing small regions hardly discernible in world map. Below is an example of the kind of world map I wish to plot:

The above figure is from an article in the Lance, "Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017". In this figure, below the world map, many small regions are magnified in insets.

I am able to create a world map in R, but only the large map without insets. But I would like to ask if there is R code readily available to create insets for these small locations?

I am new to scikit. I have a pandas DataFrame with 1 column containing text that I want to cluster. As an end result I would like to have my dataframe showing an extra column with the cluster gourp it belongs to, for each row. My DF looks like this:

I am working on a Shiny app that generates various plots and allows users to change graphing parameters. For this, I'm using a combination of selectInput, numericInput, and checkboxInput functions after the plot is generated (conditionalPanel). I packaged the code so that the data.frame used for the graph is calculated reactively (to allow flexible subsetting before plotting). Everything works great, but when I want to update some graphing parameters (such as colors to use via selectInput), the code breaks down because it evaluates prematurely before I select all the necessary colors (i.e. when there are 4 colors needed, code breaks right after selecting the first color).

I know about debounce function to delay evaluation, but I don't want to use it because:

• I like the instant changes in the graph when I update other parameters
• Selection of colors can take some time, so it is difficult to set a predetermined delay time/time interval

One solution can be to add an actionButton shown conditionally (along with other graphing parameters) to regulate the firing of the reactive input values (see below). This is not ideal because after changing parameters, I need to click on update to renew the graph. Also, I'm not sure how this would even work because the km_graph is already a reactive plot object. Alternatively, is there a solution for regulating selectInput specifically so that until all the colors selected, it is not evaluated?

I read several posts on this issue but I couldn't find a solution that works with the design of my code. I will try to write sections of my ui and server to give an idea of what I'm trying to do:

I want to create a data frame based in two data frames distincts.

The first one has the name of journals ans its respective impact factor. The second data frame has the names of the journals that I want to search.

df1:

I am trying to produce a faceted lollipop graph that shows how often a particular journal (x) occurs for four particular groups (grp).

I can produce the faceted graph, but I want to order each facet (i.e. grp) by the number of times the journal occurs (val).

I have tried adjusting the factor levels and labels, I am unable to reorder the graph so that each grp is in descending order of val.

I read a paper about negative binomial regression:"We modelled the number of Ecoli bloodstream infections and E coli UTIs per month using negative-binomial regression (incorporating overdispersion), assuming the same underlying population(no offset)." The figure as the followings

I also have a set of data, want to figure the infection like the figure with month/year, how can I do that? thank you very much

Actual code: I am trying to scrape from several pages from a medical products website for each of their products based on different product detail features(name, brand, packaging, availability, and price). I am able to go through each of the pages and collect the data as demonstrated in the internal python output. However, only the last 25 products are transcribed in the CSV file instead of all requested in the scraping process.

I am trying to set up a sparse matrix (dok_matrix) of journal co-occurences. Unfortunately, my solution is (too) inefficient to be of any use and I couldn't find any solution online.

EDIT: I would also like to create the sparse matrix directly, not by first creating a dense matrix and then turning it into a sparse matrix.

I start with a dataframe of how often certain journal are cited together. In this example, Nature and Science are cited together 3 times. I would like to end up with a sparse, symmetric matrix where the rows and columns are journals and the non-empty entries are how often these journals are cited together. I.e., here the full matrix would have four rows (Lancet, Nature, NEJM, Science) and four columns (Lancet, Nature, NEJM, Science) and three non-zero entries. Since my real data is much larger, I would like to use a sparse matrix representation.

What I currently do in my code is to update the non-zero entries with the values from my Dataframe. Unfortunately, the comparison of journal names is quite time-consuming and my question is, whether there is a quicker way of setting up a sparse matrix here.

My understanding is that my dataframe is close to a dok_matrix anyways, with the journal combination being equivalent to the tuple used as a key in the dok_matrix. However, I do not know how to make this transformation.

Any help is appreciated!