Publications by Karol Orozco
Exploratory Data Analysis- Project
Exploratory Data Analysis Karol Orozco, Jullian Schrup, Maribel Mendez 10/19/2021 The Data This data came from: https://www.kaggle.com/imakash3011/customer-personality-analysis Big Idea We plan to earn customer loyalty and increase revenue through machine-readable marketing data, which allows us to create a user behavior profile and more accur...
1232 sym R (13770 sym/25 pcs) 12 img
Dynamic Viz
fert<-read_csv("https://raw.githubusercontent.com/kitadasmalley/FA2020_DataViz/main/data/gapminderFert.csv") head(fert) ## # A tibble: 6 x 6 ## Country year life fert pop continent ## <chr> <dbl> <dbl> <dbl> <dbl> <chr> ## 1 Afghanistan 1962 33.0 7.67 9.3 Asia ## 2 Afghanistan 1963 33.5 7.67 9.5 Asia ...
1113 sym R (6437 sym/14 pcs) 2 img
MarketingData- Viz
Marketing Data ###library(dplyr) ###library(ggplot2) ###library(readxl) ###library(tidyverse) ###library(plotly) ###library(viridis) ###library(colorspace) ###library(ggthemes) Big Idea We plan to earn customer loyalty and increase revenue through machine-readable marketing data, which allows us to create a user behavior profile and more...
741 sym R (6355 sym/10 pcs) 4 img
Maps- Practice
library(gbm) ## Loaded gbm 2.1.8 library(tidyverse) ## -- Attaching packages --------------------------------------- tidyverse 1.3.1 -- ## v ggplot2 3.3.5 v purrr 0.3.4 ## v tibble 3.1.4 v dplyr 1.0.7 ## v tidyr 1.1.3 v stringr 1.4.0 ## v readr 2.0.1 v forcats 0.5.1 ## -- Conflicts -------------------------------------...
2931 sym R (15036 sym/78 pcs) 7 img
segmentation
Customer segmentation: the basics In the absence of appropriate data for an RFM analysis, We will use the three features that are actually pretty similar: total number of orders per customer; average lag (in days) between orders per customer; and average size of orders (in products) per customer ( the quantity is implied in orders. You have the q...
501 sym R (3899 sym/15 pcs) 3 img
Ensemble Graphics
title: “DataViz: Ensemble Graphics ” author: “Karol Orozco” date: “12/1/2021” output: html_document str(coffee) ## 'data.frame': 43 obs. of 14 variables: ## $ Variety : num 1 1 1 1 1 1 1 1 1 1 ... ## $ Country : Factor w/ 29 levels "angola","brasile",..: 19 19 12 15 24 24 24 21 20 7 ... ## $ Water...
1794 sym R (12120 sym/69 pcs) 23 img 1 tbl