placebo | Make boto3 calls that look real but have no effect | AWS library

I have two tables that I'd like do a full outer join where the resulting view separates the values table into two separate columns with one row for each name_id. I have made one approach with a CASE expression to select by type and then use it with pandas to fill in the values and return distinct name_ids.

Name Table

Values Table

This is a condensed version. In my full table, I need to do this twice with two separate value tables sorted by type, red/blue and control/placebo.

Simple Join

I'm using for loop to iterate through .txt files in a directory and grab specified rows from the files. Afterwards the output is passed to pr command in order to print it as a table. Everything works fine, however I'm manually specifying the number of columns that the table should contain. This is cumbersome when the number of files is not constant.

The command I'm using:

who will advise me on the %T>%,

the code is

As I am just starting to learn the R, I want to re write the below code into the pipe operator way. however, the setting rowname and colname blocked me. I will be grateful if some one can help me, which is highly appreciated!

The original code is detailed as below,

I have data of Troponin (TRP) levels (a type of protein found in the muscles of heart) which are measured in more than 200 patients. The data is divided into Placebo and Drug-X group. For every patient, the TRP levels are measured in Day 1, 3, 7, 14, 21 and 28. Incase the patient is discharged before 28 days, one extra measurement , i.e TRP_on_discharge_day is taken and that can in any day between 1-28. NOTE: not all the subjects have all the measurements as some patients get discharged in between.

I have two questions:

Firstly, I want to study the average TRP level in placebo and Drug-X group and whether they are significantly different. As data has multiple measurements for each subject, I think, a normal t test cannot be done. So as I read elsewhere, I am trying a linear mixed model using lme4::lmer function. And my model is lmer( measurement ~ Randomization + (1|id)). where measurement is the TRP level, Randomization is the group (Placebo or Drug-X) and id is the id of subject. Is this correct way to model my question?

Secondly I want to study the pattern and rate of change in TRP in Placebo group and Drug-X group and test whether they are different. Can you please give me some help on how to do this, what technique to use.

I am dputing a sample data here.. any kind of help is highly appreciated.

As mentioned in my previous question from a couple of days ago (Pairwise t test loop through dataframes contained in a list) , I have a large dataframe which can be mimicked by:

I have a very large dataframe which is built as follows: Originaldf

I want to perform a pairwise t test within item A, comparing the measured value within the condition groups. So I would like to see if for all observations pertaining to item A, there is a difference between the measured values of the control group, test group, and placebo group (Condition).

The first thing I did was to split the dataframe into a list using dplyr's filter function.

Listdf <- split(originaldf, Item)

This worked and I got a list containing 82 elements with one dataframe corresponding to each item in the original dataframe.

I now am trying to perform the pairwise.t.test function on each element of the list. I am relatively new to R and think that writing a loop for this process, though inefficient, would help me understand what is going on the background. I know there is also the option to use the lapply function. I tried this on the Listdf with the following code, which I know is most likely much too simple but was worth a try. lapply(Listdf, pairwise.t.test(Value, Condition))

However, I get the error Error in factor(g) : object 'Condition' not found. Not sure if there is a way to more specifically reference Condition so that it can be found. I've performed an individual pairwise.t.test on one of the items which worked with the following code.

pairwise.t.test(List$ItemA$Value, List$ItemA$Condition, p.adjust.method = "none")

However, I assume this would not work within the lapply function because I want it to perform the t.test for ItemA, ItemB, ItemC etc...

The loop I have tried so far is as follows:

I have a dataset with categorical data (let's use Arthritis from vcd package for exmaple purposes).

I want to obtain a barplot where for two variables and colouring by a third one.

You can find a RepEx below:

For two categorical variables inside a dataframe I want to compute the fisher test based on the user selection for the variables, specific factors of these variables (and also filtering by another column).

For this, I need to obtain the contingency table, and then apply the fisher.test function.

Just to visualize it, here is how it can be done in R base: