Data Visualization

The tl;dr about Coast FIRE … Coast FIRE is when you have enough in your retirement accounts that without any additional contributions, your net worth will grow to support retirement at a traditional retirement age. I encourage everyone to read up on this very interesting personal finance concept!

Once in awhile I find some great charts which make me stop and think. Below is one example of this: 1% better every day is all it takes to completely change your life.

My original exploratory analysis on the topic can be found at Firearm Sales: How are Americans coping with 2020? This post is a quick #rstats follow-up to visualize the final tally for 2020 data.

Objective: plot the comparison of total Facebook spend between image and video creatives for a small sample of DTC brands. The original piece without any visualization (e.

Wildfires are raging across California (again). Always knew I would end up in hell but I imagined it was more of a spontaneous combustion type of event rather than a gradual descent into the infernal #everythingisfine pic.

The SEM industry has published a lot of information about the importance of improving quality score to lower average cost per click (CPC). Most of those articles, however, just share a table with quality score in one column and its associated % increase/decrease to average CPC in the other.

Objective: recreate and visualize the 500K sampling distribtuion of means from this intro to bootstrapping in statistics post using R. Load libraries library(tidyverse) library(rsample) Download data df <- read_csv("https://statisticsbyjim.

A couple weeks ago I shared an #rstats script to track global coronavirus cases by country. The New York Times also released COVID-19 data for new cases in the United States, both at the state and county level.

Changelog Originally published on September 10th, 2019 Built a Shiny app for this Full code can be found on GitHub One of my favorite online marketers, (the) Glen Allsopp , tweeted the following: Over the past few weeks I've went through every site in the Inc. 5000.

Aside from the base bid, Google SEM campaign performance can be influenced by contextual signals from the customer. These include but are not limited to: device, location, gender, parental status, household income, etc.

If you are like me then it’s very likely you share your Netflix account with multiple users. If you are also like me then it’s very likely you would be curious about how your Netflix viewing activity coompares and contrasts to all the parasites on your account!

Google Trends is great for understanding relative search popularity for a given keyword or phrase. However, if we wanted to explore the topics some more it is quite clunky to retrieve that data within the web interface.

After two politically-charged years, Robert Mueller finally concluded his investigation on Russian interference with the 2016 presidential elections. The outcome was a 440+ page report on their findings - the perfect candidate for some text mining.

I came across this tweet and its corresponding graph a few days ago: Did you know? 🧐 1‐word keywords account for only 2. 8% of all the keywords people search for in the United States.

Aggregated data is misleading. Let’s read that again: aggregated data is misleading . Why? Because the homogenized set buries the meaningful insights away.

I abhor tabulated data for a number of reasons: Quite difficult on the human eye to spot trends Puts a burden on the end user to spend extra time digesting the information True insights get lost because the devil is in the details In fact, individuals who join Square’s SEO team ( I’m hiring by the way ) are required to read this book on how to visualize data before making any presentations - an SEO bible, if you…

TF-IDF, or Term Frequency-Inverse Document Frequency, has long been utilized by search engines to score and rank a document’s relevance for any given search query. In spite of this, I think it continues to be a misunderstood or under-the-radar concept in the broader SEO world due to 1) “keyword density” being much easier to explain and 2) it’s like a word salad when you read it for the very first time.

In the first week of January 2012, I conducted a small research project where I manually extracted the text from SEO job postings on LinkedIn and plugged them into a word cloud generator called Wordle . I was curious to know what hiring companies nowadays are looking for when they want to fill an SEO position in terms of perception (job description) and qualifying factors (job experience).