Welcome to Data Diction

Data Diction

Feed your data addiction.

• Data: things known or assumed as facts, making the basis of reasoning or calculation

• Diction: 1) the choice and use of words and phrases in speech or writing. 2) the choice of words especially with regard to correctness, clearness, or effectiveness.

If you believe data can and should be used in all facets of life, this blog is for you. Its goal is to describe interesting studies, questions, and stories in terms of the data involved.

In addition to the play on “Data Addiction”, Data Diction is also a play on the very commonly used term of “Data dictionary”, a term with which statistical practitioners should be familiar.

Objectives

• My initial goal is to post ~1 piece every other month. Most posts will involve data analysis, and all code used to pull and analyze data will be made available upon request, if not included in the post already.
• Damn it, Jim, I am a statistician, not a scientist. If you are an expert on a topic that you believe I’ve butchered, please leave a constructive comment with any corrections or caveats that should be made.
• This is a blog… not a peer-reviewed scientific journal. All content, though hopefully based on data in well-described and well-dictated ways, should be regarded as having come from a blog.

Logo

I created the logo using ozone data from Denver that I compiled for the post about Denver’s 2021 “Zero Fare for Cleaner Air”.

library(tidyverse)

df_ozone <- read_csv(here::here("posts/did-denver-zero-fare-policy-work/ozone_data-91-23.csv"))

df_ozone$daily_avg <- imputeTS::na_kalman(df_ozone$daily_avg)

ggplot(df_ozone, aes(x=date_local, y=daily_avg)) + 
  geom_line(col = "grey", alpha = .85) + 
  stat_smooth(fill = "lightblue4", level = .999999) + 
  ylab("Daily average Ozone in Denver") + 
  xlab("")