I still recall that damp evening I first encountered a classification tree in a conference presentation, and have done my best to farm that in my backyard since then!

I have grown a few so far, but here I will show one that was to become iris one day)))

All I needed to get my hands dirty were a few packages:

library(visNetwork)library(shinyWidgets)

and a one-line romance epic:

and boY o bOy… my hands looked cleaner than ever…

my favorite Stata packages

I do not know why I didn’t do this earlier, but as they say, better late then never!

Install all the packages in one go and enjoy:

ssc inst      bandplotssc inst     basetablessc inst      beamplotssc inst     betterbarssc inst        bihistssc…

A feature much craved, and yet rarely used. Now here is a quick and neat solution!

All we need is:

The sample dataset that I have looks like this:

and the unreadable columns (v007, v012…) can now be renamed to their corresponding labels by the following:

and here comes the et voilà moment ...

SAS has no straightforward way to print the column names, but this can be done using two simple tricks: transpose the table, and set observation number to zero.

Just copy and paste the color schemes in the ‘Preferences.tps’ file. Tableau should be relaunched for these schemes to appear on the list.

Enjoy!

<workbook>
<preferences>
<color-palette name='CYANo' type='regular'>
<color>#FFAEBC</color>
<color>#A0E7E5</color>
<color>#B4F8C8</color>
<color>#D4F1F4</color>
<color>#FBE7C6</color>
</color-palette>
<color-palette name='CYAN2' type='regular'>
<color>#05445E</color>
<color>#189AB4</color>
<color>#75E6DA</color>
<color>#D4F1F4</color>
<color>#4FC8D1</color>…

Given their ease of calculation and simplicity, odds ratios are used near universally in medical reporting. While many researchers still prefer the tabular format to report ORs, the odds ratio plots, or forest plots, have recently come into vogue. There are many ways to generate OR plot in R, but the one that I find to be most intuitive and least onerous is the ‘finalfit’ package.

Here is a quick demonstration:

Load the colon_s dataset

data(colon_s)

Define the dependent and independent variables:

now time to make the plot:

Here is the quick and sweet output:

All it takes is an R script with the desired processes:

# Run a linear model
model1 <- lm (mpg~ wt, data = df)
library (broom)
# Inspect the model summary table
tidy(model1)

saving it ‘PDF-report.R’, and a sweet click:

Cmd+Shft+k:

I choose HTML

and isn’t it sleek!

I thought increasing the disk size from VM Fusion settings will do the job.

Took quite a while to disillusion me…

To see the current disk sizes:

To enter the operational mode:

fdisk /dev/sda

Then you have to type ‘p’ for partition to see the disk profile again.

Comforting day, after many days of stressful experience in installing MongoDB in Ubuntu. Although RedHat has ceased supporting Centos, these little commands deserve a spot on notebok!

Step1: editing the bash file

and creating a repo:

Installlation:

Getting started:

Now to check status:

before the launching:

That mongod command has been the most frustrating experience after the pandemic during last two years, so the veer to Centos proved fruitful!

But that was not the end of the surprise…

returned already installed. nothing to do. Didn’t expect you to be this good my RedHat friend!

bigdata 101

Epidemiologist and data analyst by profession, photographer and writer by passsion…

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store