Introduction

Welcome to the February 2026 roundup! Similar to last time, here I document anything interesting I come across each month, from articles and books to skills and beyond. This is more for my personal reference and benefit but may also help others.

Summary

Articles

Tutorials

Books

Skills

I really enjoy learning new practical skills in a principled manner. This way, even the most routine tasks become easy and, most importantly, fun 🤹.

Here are some of the cool skills I picked up this month. Try them out!

Concluding Thoughts

Overall, this was quite an eventful month of activities for me. Also it took all my energy to fire up the blog after a four year hiatus 😬. Hopefully I’ll keep adding to it more regularly this year!

Recap

In this article we learned the following about Theorem 1:

• It gives an alternative way to characterize the triangle inequality for norm-like functions.
• Using this characterization we can prove the triangle inequality for such norm-like functions using the convexity of the unit ba induced by such functions.
• This is usuay easier since we have lots of tools from convex analysis to help us prove the convexity of .
• We saw this in action since Theorem 1 enabled us to write very short proofs of Minkowski’s inequality in and .

In summary, if you have a norm-like function for which you are trying to establish the triangle inequality, try out Theorem 1 💯!

Interesting Articles

• Came across this nice article on R error handling. See here for the original source.

  Key Takeaways

  • This the latest post from the ACLU Tech & Analytics blog. This post explains the key focus of the ACLU analytics team on having clean data pipelines and using testing and assertions to facilitate this process

  • Using functions like assertthat::noNA(df$source) will return a FALSE if in fact there are no NA values in the df$source column. This seems like a very useful function to use in %>% operations in my pipelines!

  • Used in combination with assertthat::noNA(df$source) will return the actual observations that have NA values, which is super useful!

  • These operations are %>% friendly and can be used to verify join operations perform as expected, for example:

    Does the join have the same number of rows as the original left-hand table or did the data structure of the right-hand table create new rows?

    How much of the right-hand table of the join falls away in the left join?

  • These checks are notably useful for the ACLU to also check for missingness in their data pipelines. They note:

    A helpful check to assess whether missingness grossly misrepresents our results is to quantify the severity of the problem. What level of missingness are we willing to live with?

  • This definitely seems useful to me, as I use ad-hoc approaches to these same issues e.g. na.omit without doing thorough assertions. Perhaps using this with tidylog will be useful in doing EDA. Let’s try and revisit this.

• This is a really insightful article on A regular person’s guide to outbreak preparedness, by Prof. Eleanor Murray:

  Key Takeaways

  • In the event of well publicized (ready mass hysteria) virus spreads such as the recent Coronavirus outbreak, it is important to listen to level-headed healthcare professionals. In this case it is Prof. Elie Murray, an epidimiologist from Boston University

  • Since I’m definitely a newbie to understanding this epidemic, I found this to be a very pragmatic guide to help prepare and prevent any further spread of such diseases

  • In terms of general healthy practices in the outbreak the key takeaways are to:

    • Wash your hands regularly
    • Focus on improving the immune system
    • Try to not catch other infections, and ensure you recover well from any existing infections
    • Don’t panic

  • In terms of good practices to do in the event that you are sick, the key takeaways are to:

    • Stay home and recover
    • Cover your mouth e.g. sneeze into the inner elbow
    • Call a medical professional if you or a relative was in Wuhan recently
    • Seek urgent medical care if you feel really sick

  • Please read the article on more detail on each of the above points and also to considerations for high risk individuals, a sick family member/friend, and if you are a healthcare responder

  • Overall, great practical advice! It is great to see statisticians such as Prof. Murray take the lead and address the community at large with their knowledge and expertise, when we live in an era of misinformation

  • I enjoyed the emphasisis on solving this challenge (like many others) as a community

Teaching

• I’m a TA for the STAT 36-350, the undergraduate statiscal computing course at CMU. This is a welcome change of pace from my previous TA assignment taught by Prof. Freeman, with whom I had the pleasure of teaching the course in Spring 2019.
• This time we have around 150 students and, as Head-TA, I have the pleasure of managing a motivated team of 9 graduate and undergraduate TAs. Here’s to a wonderful teaching and research semester 💯.

Interesting Articles

• Came across this amazing guide to reading an R help file. Experienced R users can use this fantastic pedagogical tool by Kieran Healy to teach new R users on how to read internal R help documentation.

• Came across this fascinating article on how calorie burning being a chess grandmaster is by Aishwarya Kumar

  Key Takeaways

  • Article notes that chess grandmasters can burn as much as 6000 calories individually during an intense day of sedentary chess playing!

  • Initial focus is on Fabiano Caruana, an American grandmaster in chess, and current world No. 2. Caruana has to maintain a very strict diet and exercise routine, particularly during tournaments

  • Primary reasons for the calorie loss are heavy mental stress of the tournament, constantly thinking of chess, thus leaving limited time to think about and consume food

  • Interesting quote:

  …India’s first grandmaster, Viswanathan Anand, does two hours of cardio each night to tire himself out so he doesn’t dream about chess

  • Magnus Carlsen, reigning No. 1, for example consulted a professional nutritionist who recommended that he stop drinking orange juice (to avoid sugar spikes) and replace it with a less sugary regular/chocolate milk blend

  • Carlsen has also optimized sitting. This is quite amazing and something to think about personally as someone who spends many hours daily in front of a screen

  • Carlsen has also undertaken load management (minimizing competitions participated) to increase the amount of recuperation time between tournaments

  • In short, there are a lot of parallels to the research life which I undertake, and a lot of useful tips to optimize energy and time spent doing what I enjoy for longer

• This is a really insightful interview with Hadley Wickham, a recent COPSS award winner, on the future of the R programming language:

  Key Takeaways

  • Wickham notes that R vs Python language wars are not constructive in moving data science and other fields forward.

  • I agree wholeheartedly on this and firmly believe in using the best tools for the statistical job at hand. What should matter are more critical aspects like code readability, usability, and reproducibility in light of the given task

  • Interestingly Wickham notes:

  A pattern that I see is that the data science team in a company uses R and the data engineering team uses Python

  • Wickham also has focused on bridging divides within the R community itself, namely in developing the dtplyr package to convert dplyr code to the alternative data.table package syntax. This is a promising direction ahead where tidyverse and data.table users can collaborate much more easily
  • There is also a focus on encouraging diversity in R usage and actively developing communities. He asks:

  Can we take the R-Ladies model and help other groups that are currently underserved?

  • Overall it is good that Wickham was recognized recently with the famous COPSS medal in statistics and that the community is embracing software development and design as a key aspect of our profession. It seems that the future is bright for statistics!

• This is a nice blogpost on making modular Rmarkdown files. In fact this modular Rmd approach is now used in my blog for common footer files. Did not realize how nicely the here package works with references in Rmd chunks.

Interesting Presentations

• This thoughtful presentation on Design at Quora by Rebecca Cox (VP of design at Quora). I’ve summarized what I feel are the key points from this important presentation below.

  Key Takeaways

  • Cox notes that it is a “great time to be a designer” because design has proven again and again to be a clear competitive advantage in tech
  • She notes her awareness of Quora’s apparent minimalist design interface i.e. dark, red, and text heavy
  • Cox asks - what is Design? Some say it is the visual style, for some the user interaction, and for others “it begins and ends with the logo”
  • For Cox, her definition is abstract, and summarized as:

  The set of decisions about a product

  • Not just an interface, logo etc. Designing is about making product decisions
  • Benefits of this broad decision-driven definition for Quora are:
    • A clear relationship between the product and the interface i.e. why should a dropdown even exist?
    • Concentrates attention on where it matters most i.e. company goals
    • Enables a role within Quora that balances authority and responsibility i.e. Designers should do more than “apply a coat of paint to a feature at the end”
  • To Cox:

  Great design is all the work you don’t ask people who use your products to do

  • There are a lot of deep direct applications to my statistics research i.e. ensure all theoretical and empirical tools are seamlessly able to be conveyed to end users in science, industry, or academia.

  • I will be coming back to this over time periodically and reflect if I have undertaken this definition of design and applied it in my work and daily life

• This is a really inspiring presentation on how data science is used at the ACLU specifically in the recent border immigration policies by Brooke Watson Madubuonwu.

  Key Takeaways

  • Utilizing tidyverse to sort through messy data linkage issues in a consistent framework is well thought out by the team with useful packaged functions created for use by the wider ACLU team
  • Here both statistics and law are used to tackle a major humanitarian issue i.e. child border separation. This is deeply inspiring and the kind of applied work that I would like to contribute to meaningfully in the future
  • I particularly appreciated the general data source skepticism showed by the ACLU team. As a statistician it is important to not only explore data but be very skeptical of the source quality i.e. competing legal bodies may not provide the ACLU unbiased data!

Teaching

• I’m a TA for the amazing graduate statiscal computing course at CMU. This is an intensive (but very rewarding) programming course designed by CMU statistics professors Alex Reinhart and Chris Genovese. I highly recommend checking out this course website as a general programming reference in daily work/research. I know that I certainly will be 😄.

Interesting Articles

• Came across this exhaustive BibTeX example file which will be a really handy reference going forward for documents
• The document is written by the Oren Patashnik, the co-creator of BibTeX (😮) and the co-author of the great book (Graham, Knuth, and Patashnik 1994)

Personal Blogging

Besides this post 😄 the main things I got up to on the personal blogging front were:

• Updating the distill blog settings, with a detailed step-by-step guide
• Wrote another fun blogpost on using the tidyverse to reproduce a plot on the survivorship of the Titanic. Always so cool to be able to reproduce such famous plots using modern tools.

Concluding Thoughts

Overall August 2019 was the end of summer and the start of a new year of graduate school - yay!

Please feel free to leave a comment if you found any useful articles, lectures, books, papers etc which I may find interesting.

The central problem

In a custom macro file I needed to generate several sequential newcommand entries of the form:

\newcommand{\bfa}{\mathbf{a}}
\newcommand{\bfA}{\mathbf{A}}

Where using $\bfa$ produces and using $\bfA$ produces i.e the lowecase/uppercase mathbf commands respectively.

Specifically I needed to construct 52 such combined sequential entries for both lowercase/uppercase letter versions of these newcommand macros. Rather than do this manually, I realized that this would be another fun scripting exercise with using the tidyverse packages glue, purrr, and stringr similar to this similar previous post here.

Goal: Create 52 such lowercase/uppercase newcommand entries and print to the console to directly-copy paste to my macros file.

The tidy approach

First step is to write a function that takes as an input the following:

• a single letter (case-sensitive) e.g. "a"
• the macro shortcut command prefix you prefer e.g "bf" (for bold font in case you were wondering!)
• the specific command that we are creating a macro shortcut for i.e. "mathbf" in this case

The function then outputs a single newcommand entry for that lecture i.e \newcommand{\bfa}{\mathbf{a}} in this case. Let’s do it!

# Load required libraries
library(tidyverse)
library(glue)

# Create LaTeX macro newcommand
get_lec_newcmd <- function(inp_letr, mac_type, mac_ref){
    out_str <- glue('\\newcommand{\\<mac_type><inp_letr>}{\\<mac_ref>{<inp_letr>}}',
                    .open = "<", .close = ">")
    return(out_str)
}

Let’s just test this out quickly:

c("a", "A") %>%
  map_chr(.x = ., .f = ~get_lec_newcmd(inp_letr = .x,
                                       mac_type = "bf",
                                       mac_ref = "mathbf")) %>%
  cat(., sep = "\n")

\newcommand{\bfa}{\mathbf{a}}
\newcommand{\bfA}{\mathbf{A}}

Great - looks like it is working as required!

Note that we can easily generate other macros like follows

c("a", "A") %>%
  map_chr(.x = ., .f = ~get_lec_newcmd(inp_letr = .x,
                                       mac_type = "mc",
                                       mac_ref = "mathcal")) %>%
  cat(., sep = "\n")

\newcommand{\mca}{\mathcal{a}}
\newcommand{\mcA}{\mathcal{A}}

Which generates the corresponding mathcal macros for and respectively.

So finally we can generate all 52 letter macros at time by simply replacing c("a", "A") with c(letters, LETTERS) which uses the input lowercase/uppercase letters/LETTERS vectors in base R:

\newcommand{\bfa}{\mathbf{a}}
\newcommand{\bfb}{\mathbf{b}}
\newcommand{\bfc}{\mathbf{c}}
\newcommand{\bfd}{\mathbf{d}}
\newcommand{\bfe}{\mathbf{e}}
\newcommand{\bff}{\mathbf{f}}
\newcommand{\bfg}{\mathbf{g}}
\newcommand{\bfh}{\mathbf{h}}
\newcommand{\bfi}{\mathbf{i}}
\newcommand{\bfj}{\mathbf{j}}
\newcommand{\bfk}{\mathbf{k}}
\newcommand{\bfl}{\mathbf{l}}
\newcommand{\bfm}{\mathbf{m}}
\newcommand{\bfn}{\mathbf{n}}
\newcommand{\bfo}{\mathbf{o}}
\newcommand{\bfp}{\mathbf{p}}
\newcommand{\bfq}{\mathbf{q}}
\newcommand{\bfr}{\mathbf{r}}
\newcommand{\bfs}{\mathbf{s}}
\newcommand{\bft}{\mathbf{t}}
\newcommand{\bfu}{\mathbf{u}}
\newcommand{\bfv}{\mathbf{v}}
\newcommand{\bfw}{\mathbf{w}}
\newcommand{\bfx}{\mathbf{x}}
\newcommand{\bfy}{\mathbf{y}}
\newcommand{\bfz}{\mathbf{z}}
\newcommand{\bfA}{\mathbf{A}}
\newcommand{\bfB}{\mathbf{B}}
\newcommand{\bfC}{\mathbf{C}}
\newcommand{\bfD}{\mathbf{D}}
\newcommand{\bfE}{\mathbf{E}}
\newcommand{\bfF}{\mathbf{F}}
\newcommand{\bfG}{\mathbf{G}}
\newcommand{\bfH}{\mathbf{H}}
\newcommand{\bfI}{\mathbf{I}}
\newcommand{\bfJ}{\mathbf{J}}
\newcommand{\bfK}{\mathbf{K}}
\newcommand{\bfL}{\mathbf{L}}
\newcommand{\bfM}{\mathbf{M}}
\newcommand{\bfN}{\mathbf{N}}
\newcommand{\bfO}{\mathbf{O}}
\newcommand{\bfP}{\mathbf{P}}
\newcommand{\bfQ}{\mathbf{Q}}
\newcommand{\bfR}{\mathbf{R}}
\newcommand{\bfS}{\mathbf{S}}
\newcommand{\bfT}{\mathbf{T}}
\newcommand{\bfU}{\mathbf{U}}
\newcommand{\bfV}{\mathbf{V}}
\newcommand{\bfW}{\mathbf{W}}
\newcommand{\bfX}{\mathbf{X}}
\newcommand{\bfY}{\mathbf{Y}}
\newcommand{\bfZ}{\mathbf{Z}}

Hope you have fun using this to quickly generate your newcommand macros ✌️.

Next Steps

In terms of core distill blog settings, these are the main options that I’m happy to implement for now. For me the next steps are more about customizing my own blog workflow. This will involve setting up utilities to automatically:

• Wrap Rmd files to 80 characters for consistency
• Quickly delete unused files e.g. DS_Store files on mac
• Clear knitr cache for all posts and thoroughly re-render the site

I expect to do this using a combination of R functions/Makefile workflow, but do stay tuned!

Concluding Thoughts

As it can be seen it is quite easy to customize distill for commonly required features. Really great work by the RStudio team in making such customizations so user-friendly 👍.

Interesting Articles

• Came across this exhaustive BibTex example file which will be a really handy reference going forward for documents

• This Pimp my RMD: a few tips for R Markdown document is a very good site to help deal with really slick features of editing Rmd documents.

  • One nice feature I noticed was to use HTML to center an image

Interesting Papers

• Particularly enjoyed reading this paper on the mathematical description of the carbon cycle (Rothman 2014). Really useful to get a good idea of the simple but powerful use of simple stochastic differential equations used to model climate change processes at various global scales.

Personal Blogging

Besides this post 😄 the main things I got up to on the personal blogging front were:

• Setting up this very blog, with a detailed step-by-step guide on the fun setup process with the RStudio distill package and also Netlify and Google Domains
• Wrote a fun blogpost on solving a couple of quick text-based tasks using the tidyverse including generating Oxford Comma triples and also Generating numbered BibTeX entries

Concluding Thoughts

Overall July 2019 was a somewhat productive summer month. Though things are never quite as productive as I like them to be in graduate school, but always got to keep trying.

I liked documenting this personal July 2019 roundup. Normally I’m not the type to share much about my personal life, but I thought documenting what I get up to gives me more stake in the respective activities and forces me to commit to them enthusiastically. I also like sharing cool stuff that I come across with friends, so dual benefit.

I think I’ll keep these monthly roundups going for a while. Let’s see how this experiment goes when semester starts 😄.

Please post in the comments any questions/feedback you may have or anything interesting resources you came across in July 2019 👍.

Collecting and cleaning the data

First let’s begin by loading our required data cleaning and plotting packages. First we will load the required libraries needed for the analysis.

In the article the authors cite several resources for collecting the data for this task. Per the article we note that the data is already pre-baked into R and located in datasets::Titanic when R loads, which is convenient 😎.

We can source the data and start cleaning it for our exploration, using the handy clean_names function for column name cleaning and converting various categorical variables (age, sex, survivorship, and passenger class) to factors for easy plotting later.

# Basic cleaning of names and filtering out non-zero counts
t1 <- datasets::Titanic %>%
        as_tibble() %>%
        clean_names(dat = .) %>%
        filter(.data = ., n != 0) %>%
        mutate(.data = .,
                      new_sex = ifelse(age == "Child", age, sex),
                      n_sgnd = ifelse(survived == "No", -1*n, n)) %>%
        select(class, new_sex, survived, n_sgnd)

# Passenger (non-crew) level aggregation
t2 <- t1 %>%
        filter(class != "Crew") %>%
        mutate(class = "Pass.") %>%
        group_by(class, new_sex, survived) %>%
        summarize(n_sgnd = sum(n_sgnd))

`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by class, new_sex, and survived.
ℹ Output is grouped by class and new_sex.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(class, new_sex, survived))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.

# Crew (non-passenger) level aggregation
t3 <- t1 %>%
        mutate(class = "Pass.\nCrew") %>%
        group_by(class, new_sex, survived) %>%
        summarize(n_sgnd = sum(n_sgnd))

`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by class, new_sex, and survived.
ℹ Output is grouped by class and new_sex.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(class, new_sex, survived))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.

# Combined cleaned plotting dataset
ttnc_cln <- t1 %>%
              bind_rows(t2) %>%
              bind_rows(t3) %>%
              mutate(.data = .,
                            class = as.factor(class),
                            new_sex = as.factor(new_sex),
                            survived = as.factor(survived))

# Display first 8 rows in a nice centered table
ttnc_cln %>%
  slice(.data = ., 1:8) %>%
  kable(x = ., align = 'c')

Looks nice. As you can see, the data cleaning was done in stages where 3 datasets t1, t2, t3 were built up. Essentially by staring at the plot it is clear that plots are split by class i.e.  Class, Class etc. This is the cleaned t1 data frame. However there are aggregate versions of these classes at combined Passenger level and Passenger and Crew level which are the t2 and t3 tibbles respectively. Finally we concatenate them together into ttnc_cln and ensure our categorical variables are cast as factors.

Next step - plotting!

Plotting the Data

The main chart object is a barplot by sex and adult status and faceted by passenger class i.e. first class, second class etc. Great, let’s do it!

out_plot <- ttnc_cln %>%
              ggplot(data = .,
                              aes(x = new_sex, y = n_sgnd, fill = survived)) +
              geom_bar(stat = "identity") +
              facet_wrap(~ class, ncol = 1,
                                  strip.position = "right",
                                  scales = "free_y") +
              coord_flip() +
              scale_fill_manual(values=c("#3C4144", "#D2D3D1")) +
              theme_bw() +
              theme(panel.background = element_rect(fill = "#969898"),
                             panel.grid.major = element_blank(),
                             panel.grid.minor = element_blank(),
                             axis.title.x = element_blank(),
                             axis.title.y = element_blank(),
                             strip.text.y = element_text(angle = 360),
                             legend.position = "none") +
              scale_y_continuous(breaks=seq(-1500,600,150)) +
              labs(title = 'The LOSS of the "TITANIC"',
                            subtitle = glue::glue("The Results Analyzed and Shown",
                                                  'in a special "Sphere" Diagram',
                                                  .sep = " "),
                            caption = glue::glue("Note: The Black color indicates",
                                                 "Passengers and Crew NOT SAVED.",
                                                 "The White color indicates SAVED.",
                                                  .sep = " "))

out_plot

Conclusion

• Overall looks like the plot was able to be reproduced to a decent level of accuracy
• To get the colors to be close to the plot, I simply opened the article online and used the Colorzilla for Chrome addin to select the color manually. This is a really nice tool to use for reproducing colors viewed through a browser
• I don’t quite like that the non-survivors here are shown on a negative scale, but this was the quick hack I could perform to get bars flipped for non-survivors vs. survivors
• Summary: Overall this was a really fun challenge and I learned a lot about old-school data visualization using the glorius modern tidyverse ecosystem we have at our fingertips. Will do a similar reproducibility challenge again for sure ✌️. Have fun playing around with the above and please post in the comments any questions/feedback you may have 👍.

Conclusion

This post was for me to document and serve as a guide to automating a couple of fun text-based tasks that I came across in my work (and social life!). Using the tidy framework can be a fun way to solve these tasks (but certainly not the only way in R). Have fun playing around with the above and please post in the comments any questions/feedback you may have 👍.

Stay tuned for more blogposts solving more such tasks.

Step 2: Clone the repo locally

With the github repo created, I switched locally on my mac to iterm2 terminal and cloned the repo locally using the following command:

git clone git@github.com:shamindras/ss_personal_distill_blog.git

And the cloning quickly finished with the following output

Cloning into 'ss_personal_distill_blog'...
remote: Enumerating objects: 4, done.
remote: Counting objects: 100% (4/4), done.
remote: Compressing objects: 100% (4/4), done.
remote: Total 4 (delta 0), reused 0 (delta 0), pack-reused 0
Receiving objects: 100% (4/4), done.

Now I simply changed into the newly cloned blog directory by running the following terminal command:

cd ss_personal_distill_blog

I then ran the following terminal command:

tree

This resulted in the following directory structure so far:

.
├── .git
├── .gitignore
└── README.md

Great, now the repo was setup locally. At this stage there is just a simple README.md which will get added to a bit later, but the main focus is to start creating the distill blog within this directory locally.

Step 6: Commit and push changes to github

Note that all our changes are so far in our local git repo. We need to get this blog online! A first step is to commit and push them to our github repo. I did this in my local directory in the terminal as follows: in git at my terminal as follows:

git add -A # Add all new changes
git commit -m "ENH: Created welcome post with date prefix, deleted default post"
git push origin master

And the changes are now reflected in the github master branch!

Step 7: Buy a Domain name (optional)

Although the blog was our blog contents are in a public online place i.e. github, I just needed to link it to a service that deploys websites from github. But first I needed to go buy a domain name for my blog. I went to Google Domains and bought www.shamindras.com for about $15/yr.

There are free alternatives e.g. Github Pages, but I wanted to have ownership on my page and found the annual fee to be reasonable with Google Domains.

Step 9: Patiently wait for deployment

With everything setup and configured on github/Netlify the deployment should be near instantaneous. But after about 20 mins my blog appeared at www.shamindras.com. So effectively Netlify and github were now talking to each other and site is setup!

Step 10: Future additions and extras

Now that the blog/site is created there are a number of features I’d like to add. The most important being more blogposts and personal content. However it would also be nice to have the following features:

• Documenting a general distill blogging workflow
• Setting up Disqus to enable user comments on blogposts
• How to setup Blog Gallery for featured posts
• How to setup an email subscription service for this blog
• How to setup Google Analytics service for basic user activity tracking

I will make sure to document the setup process as part of a series of future blog posts

Concluding Thoughts

If you managed to read this far, then I sincerely thank you. I hope to make even better technical and personal blogposts going forward. Please feel free to leave a friendly comment below for any questions you may have or any feedback for future blogposts.

Credits

The credit to starting this blog goes to the following people. I hope to not disappoint and create some useful content here 🖖.

• The Rstudio team - for co-developing the fantastic quarto package upon which this site is based.
• Rachel Thomas for posting this fantastic blog post encouraging people like me to (finally!) create a blog
• My parents for encouraging to communicate my passion for statistics. I secretly think that this is their way of minimizing my passionate rants about the bootstrap in our regular Skype chats (the rants will still continue though…😄).