tto | simple code generator written by golang , support multiple | Generator Utils library

I have these two functions:

Assignment is to create a magic 8 ball program. I have set up a random object and established all that. However, part of the assignment is for the program to continue until the user enters nothing into the console, and then it will exit the program.

I've tried using a control variable of a blank string "" to exit the console upon that entry, but I'm not sure what I'm missing. Anything I do in the MagicPCUI class hasn't worked yet so I'm not going to include those changes into the code, just the basic stuff I have set up already. But here is my main:

I have a large list and I am trying to create multiple data frames from the same. For that I'm filtering out data in various variables. Basic structure of most of code is same and all are working fine except for one.

This the list.

I have a dataset with about 50 columns (all indicators I got from World Bank), Country Code and Year. These 50 columns are not all complete, and I would like to fill in the missing values based on an lm fit for the column for that specific country. For example:

Doing this for a single country and a single column is absolutely fine when following these steps here: Filling NA using linear regression in R

However, I have over 180 different countries I want to do this to. And I want this to work for each indicator per country (so 50 columns total) So in a way, each country and each column would have its own linear regression model that fills out the missing values.

Here is how it looked after I did the steps above: This is the expected output for ONE column. I would like to do this for EVERY column by individual country groups.

However, the data looks like this:

There are numerous countries and columns that I want to perform this on just like the post above.

This is for a project I am working on for my data-mining / statistics class. Any help would be appreciated and thanks so much in advance!

EDIT

I tried this:

I have the following table:

I have a large number of csv data files that are located in many different subdirectories. The files all have the same name and are differentiated by the subdirectory name.

I'm trying to find a way to import them all into r in such a way that the subdirectory name for each file populates a column in the datafile.

I have generated a list of the files using list.files(), which I've called tto_refs.

head(tto_refs) 1 "210119/210115 2021-01-19 16-28-14/REF TTO-210119.D/REPORT01.CSV" "210122/210115 2021-01-22 14-49-41/REF TTO-210122.D/REPORT01.CSV"
[3] "210127/210127 2021-01-27 09-39-15/REF TTO-210127_1.D/REPORT01.CSV" "210127/210127 2021-01-27 09-39-15/REF TTO-210127_2.D/REPORT01.CSV"
[5] "210127A/210127 2021-01-28 15-57-40/REF TTO-210127A_1.D/REPORT01.CSV" "210127A/210127 2021-01-28 15-57-40/REF TTO-210127A_2.D/REPORT01.CSV"

I tried a few different methods to import the data into r, but they all had errors related to 'embedded nul(s)'.

For example, tbl <- tto_refs %>% map_df(~read.csv(.))

There were 50 or more warnings (use warnings() to see the first 50)

warnings() Warning messages: 1: In read.table(file = file, header = header, sep = sep, ... : line 1 appears to contain embedded nulls 2: In read.table(file = file, header = header, sep = sep, ... : line 2 appears to contain embedded nulls

etc.

How can I get this data into R?

Edit: the .csv files are generated from Agilent Chemstation analytical software.

The data looks like this:

I am trying to print object data in modal using jQuery. When I click the button it sends the Java object to jQuery and then prints it but it is printing in this format:

Trip [tid=1, tname=North, tplace=Ladhak, tpackage=12000, tfrom=2022-05-21, tto=2022-05-31, lastdate=2021-12-22, tinfo=XYZ]

I want to access data of the object and display it.

I am using Synth() package (see ftp://cran.r-project.org/pub/R/web/packages/Synth/Synth.pdf) in R.

This is a part of my data frame:

I would like to use SCOOP (Scalable COncurrent Operations in Python) in a high performance computing context. To test SCOOP, I installed the module in an Anaconda environment (4.10.3) with Python 2.7.18 on my Windows machine. When running this example script from the docs: