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I have the following dataset to calculate standardized effect size for Soil_N and Soil_P for which I used the code below for each replicate.

I've been working on some survey data using the survey package. I read the documentation available on post-stratification and calibration, however I got stuck trying to calibrate the sampling weights on a total known for the population that is not the population total.

To make my self clear I prepared an example: Let's say I have income information for a sample stratified by sex, which lets me create the svydesign object:

I'm newbie with R. There is a code like the following, and for that code, variable name wt_itvex_divided_by_4 should be dynamically replaced with wt_itvex_divided_by_3 or wt_itvex_divided_by_2

I have made a regression that looks like this:

I want to perform a two-sample (welch's) t-test on the equality of two means, one of which is obtained using simple random sampling (srsmean), and the other which is calculated using survey weighting with the survey package (mean_weighted). I also conduct a t-test between mean_weighted and the mean obtained when weighting and stratification are both implemented in the survey design (mean_strat).

I know there is a svyttest() function, however, as far as I can tell, this function only tests the means of two samples within one survey design, not means obtained with different survey designs.

I also tried using rnorm to create fictional samples eg c(rnorm(9710, mean = 156958.8, sd = 364368)), but the problem with this approach is that in complex sampling methods like stratification, the effective n is usually smaller than the nominal n, and so I am unsure what to put as n. Additionally, this method feels a bit contrived, as I would be fitting the data to a particular type of distribution.

Finally, I tried writing out the equation for a t-statistic myself, however, in calculating the "standard error of the difference of means" involving a complex survey design, I also run into problems related to the "effective sample size."

Is there another approach that would work for both the t-test between srsmean, mean_weighted AND the t-test between mean_weighted, mean_strat?

How do I add label names in the white boxes to each of the three axis on the ggalluvial sankey plot from the dataframe? (see image)

Column "Country" has observations for multiple countries. In the example below I am focused on Ireland which has the label "IE". How do I harvest the dataframe for the Country name using the ggplot code below? I will also be doing this task for the "Vaccine" and the "TargetGroup" names so if the solution can include these columns too, I would be grateful. Thank you

Dataframe code and sankey colours - data is from the EU ECDC

I have a dataset named "drugs" and I created a weighted dataset named "weighted_data".

I have a dataset

I am using carData::Rossi data to estimate a survival curve. This is the code I used to generate the plot: