Visual Basic for Applications Back in August, I learned that with Microsoft Office products (i.e. Word, Excel, Access) you can write your own macros to help automate repetative tasks or create your own needed office functions that are not standard with office products to improve your workflow. I give props to Microsoft, because I have definitely been frustrated at times and wishing for more functionality from their products, but I understand they reach a global market and it is not possible to program everything people need in their products.
Inspiration I was recently asked if I could help out with a project that involved parsing out JSON (JavaScript Object Notation) data. I am really grateful that I become involved on this because I had no prior experience with JSON structured data but the knowledge I gained from some of the challenges I ran into has been priceless. The project led to a major paradigm shift that has now helped me understand valuable programming ideas (i.
Background I don’t remember when I first heard of Nassim N. Taleb, but I started reading his work a little more than a year ago. I usually struggle to understand some of his ideas or technical work but his work has personally been hard to shake off. I keep returning to his work and over time they tend to start making more and more sense the more I study them.
Dashboards The package flexdashboard makes it easy to create dashboards. I used the mtcars dataset to create an example of a dashboard.
I first tried to host the dashboard embedded within this post but the output in the YAML is set to call flexdashboard instead of the html output file even though the flexdashboard produces the dashboard in html. When I first tried to host the dashboard here the format was thrown off.
Data Visualization in R & Labels I often treat building data visualizations in R as a fun puzzle. Prior to learning how to use ggplot2, I would do all of my graphs in excel. Making eye catching graphs in excel is often easier and quicker (especially if you follow Stephanie Evergreen). One feature that I really enjoy in excel is the ease of adding/removing data point labels and re-positioning them with a mouse to get them exactly where I want them, but excel is a tool designed by others for general use and has its own limitations.
Learning Python This post is a continuation of a learning python. In this post, I will extend the last post and focus on tidyverse equivalents to:
Mutate columns Summarize data (Groupby) Pipe functions (%>%) Setting up Python in Rmarkdown Load Modules and Data import pandas as pd import numpy as np url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-28/winemag-data-130k-v2.csv' dat = pd.read_csv(url, index_col=0) Settings for better output This blog is built through the blogdown package and uses Rmarkdown files that render to HTML documents.
Learning Python I often see a debate online between Python and R as programming languages for Data Science. I am not interested in continuing that debate here, but rather just want to continue to learn other programming languages and have another tool available to use. I often read online Python is better for general use programming while R was developed with statisticians in mind. If that is the case I think it would be helpful to start learning Python and see where I can use it for my advantage.
MyAnimelist.net This week TidyTuesday posted data from an anime and manga social networking and social cataloging website, MyAnimeList (MAL). This data will be interesting to examine because last summer I started watching anime shows and got hooked. I am a little embarrassed to admit it but I’ve seen a lot of different shows since then. I hope I get to find some new shows that I can watch later.
Working with Maps If you are interesting in learning about how to use R to work with geospatial data, I would first point you in the direction of reading Geocomputation in R. I previously thought that you had to use special software like ArcGIS in order to make maps but I found it not too difficult using certain R packages to make simple maps quickly.
Joining #TidyTuesday Every Tuesday, I often see submissions from the #rstats community on twitter about their #TidyTuesday projects. #TidyTuesdays are weekly data projects posted online by the R4DS (R for Data Science) community for anyone interested in working on developing their skills wrangling data within the R ecosystem. Feel free to check out the #tidytuesday repository on github for more information.
I felt it would be only natural for my first post to be joining this community and moving from being an observer to a participant.