Recent Posts
Payrolls of the Cleveland Baseball Team
Today I made a quick graph of the player payrolls for the Cleveland baseball team to compare their projected payroll after today’s trade with the Mets (Lindor, Carrasco, etc.) to past seasons.
Sources:
(past) http://www.stevetheump.com/Payrolls.htm
(2021 projection) https://twitter.com/ZackMeisel/status/1347246681520295936
https://teamcolorcodes.com/cleveland-indians-color-codes/
years <- 2014:2021
payrolls <- c(82.5, 86.1, 86.3, 124.3, 134.4, 88.7, 37.6, 35)
df <- data.frame(years, payrolls)
df %>%
ggplot(aes(x = years, y = payrolls, label = payrolls)) +
geom_bar(stat = "identity", fill = "#E31937", color = "#0C2340") +
geom_label() +
labs(title = "Opening Day Payrolls of the Cleveland Baseball Team",
subtitle = "2016: World Series appearance\n2020: pandemic-shortened season\n2021: projected",
caption = "Sources: http://www.
Quick meme making in R
Source: Create Bart Simpson Blackboard Memes with R
knitr::opts_chunk$set(echo = TRUE)
library("meme")## Warning: package 'meme' was built under R version 4.0.3if (.Platform$OS.type == "windows") {
windowsFonts(Comic = windowsFont("Comic Sans MS"))
}bart <- "bart_simpson_chalkboard-5157.gif" # source: http://free-extras.com/images/bart_simpson_chalkboard-5157.htm
text <- paste(rep("I will not procrastinate \n in grant writing \n by making memes in R instead", 2), collapse = "\n")meme(bart, text, size = 2, font = "Comic")
Covid-19 Cases in the Central Valley
Data
Source: USA Facts — downloaded July 6, 2020
library("tidyverse")
library("zoo")
start_date <- "5/28/20"
end_date <- "7/5/20"
county_list <- c("Santa Clara", "Stanislaus", "Calaveras",
"San Benito", "Merced", "Tuolumne",
"Fresno", "Madera", "Mariposa")
lag <- 7 #number of days for rolling average
#loads files
cases_raw <- read_csv("covid_confirmed_usafacts.csv")
populations <- read_csv("covid_county_population_usafacts.csv")
Data Wrangling
raw_data_merged <- cases_raw %>%
full_join(populations, by = c("County Name", "State"))
# find column positions by date
column_names <- colnames(raw_data_merged)
start_loc <- match(start_date, column_names)
end_loc <- match(end_date, column_names)
cases_filtered <- cases_raw %>%
filter(State == "CA") %>%
select("County Name", all_of(start_loc:end_loc))
populations_filtered <- populations %>%
filter(State == "CA") %>%
select("County Name", "population")
df_merged <- cases_filtered %>%
full_join(populations_filtered, by = "County Name")
df_clean <- df_merged %>%
# avoids unallocated cases and the cruise ship!
TidyTuesday: Roman Emperors
Introduction
Today, for practice with ggplot2, I wish to replicate @JoshuaFeldman’s wonderful #TidyTuesday submission about the dataset of Roman emperors.
library("tidyverse")
TidyTuesday’s Roman Emperor dataset — posted on August 13, 2019
# TidyTuesday's given line of code to load the data
emperors <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-08-13/emperors.csv")
Exploring the Data
dim(emperors)
## [1] 68 16
colnames(emperors)
## [1] "index" "name" "name_full" "birth" "death" ## [6] "birth_cty" "birth_prv" "rise" "reign_start" "reign_end" ## [11] "cause" "killer" "dynasty" "era" "notes" ## [16] "verif_who"
emperors %>%
filter(birth_prv !
Recent Supreme Court Cases
Introduction
library("tidyverse")
Today, I am going to create an overly simplified view of the past 10 Supreme Court decisions for the sake of coding practice with the ggplot package.
data source: SCOTUS Blog
useful tool: Convert Town’s “Column to Comma Separated Values” function
Data
Just in case anyone actually uses my blog post, I will type out the data manually instead of load a separate CSV file so that anyone can copy-and-paste the code for replicability.