Personal Website https://anthonyjruiz.github.io Wed, 12 Jun 2019 00:00:00 -0700 https://anthonyjruiz.github.io/2019-06-12-gowers_clustering/ https://anthonyjruiz.github.io/2019-06-12-gowers_clustering/ Using data from the CDC Testing Gif health.data <- read.csv("https://data.cdc.gov/api/views/bi63-dtpu/rows.csv?accessType=DOWNLOAD") health.data <- health.data %>% clean_names("lower_camel") %>% rename("detailedCause" = "x113CauseName") Plotting leading cause of death by year health.data %>% filter(causeName != 'All causes') %>% group_by(year, causeName) %>% summarize(totalDeaths = sum(deaths)) %>% ggplot() + geom_bar(aes(causeName, totalDeaths), stat = 'identity') + labs(title =... Tue, 09 Apr 2019 00:00:00 -0700 https://anthonyjruiz.github.io/2019-04-09-gganimate_example/ https://anthonyjruiz.github.io/2019-04-09-gganimate_example/ I’ve seen a few people do quick examples of price optimization problems, but one thing I havent seen done is using calculus to obtain the optimal price for a good. In this extremely quick example im going to demonstrate how to do this… Package Load library(dplyr) library(ggplot2) library(stats) #optimization library(broom)... Tue, 26 Mar 2019 00:00:00 -0700 https://anthonyjruiz.github.io/2019-03-26-price_optimization/ https://anthonyjruiz.github.io/2019-03-26-price_optimization/ Linear regression is probably one of the most widely used statistical techniques in all of econometrics/data science. There are a couple reasons for this; its relatively easy and intuitive to interpret the model output, it’s not computationally expensive… etc. In order to properly utilize this technique there are a certain... Sat, 26 Jan 2019 00:00:00 -0800 https://anthonyjruiz.github.io/2019-01-26-linear_regression_assumptions/ https://anthonyjruiz.github.io/2019-01-26-linear_regression_assumptions/