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) ##  68 16 colnames(emperors) ##  "index" "name" "name_full" "birth" "death" ##  "birth_cty" "birth_prv" "rise" "reign_start" "reign_end" ##  "cause" "killer" "dynasty" "era" "notes" ##  "verif_who" emperors %>% filter(birth_prv !
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.
Introduction Today’s coding practice is based on the following article and data source (there is literally a “Get the Data” link):
Here’s a List of Colleges’ Plans for Reopening in the Fall library("geofacet") library("rvest") library("tidyverse") # load data df_raw <- read_csv("data-w8lLG.csv") colnames(df_raw) ##  "Institution" "Control" "State" "Category" Data Wrangling # filter out Excel artifacts (trivial, empty rows) df <- df_raw %>% filter(Institution != "#REF!") #States as factors states_alphabetically <- sort(unique(df$State)) df$State_factor <- factor(df$State, levels = states_alphabetically) #extracting text from urls (rvest!
If you are planning to do the R assignments on your own computer (recommended), then here is a quick outline for obtaining the software.
There are two separate software programs. Most people find it easier to use RStudio. than just R, but you need to install R first before installing RStudio (analogously speaking: you need an cell phone before you can use an cell phone case). If you have R and RStudio from a previous course, you still need to update to the current versions!
Today I am going to try to make a geofacet graph using the GAI (Global Acceptability Index) data. My goal is to show trends in LGBT acceptance in Europe between the years 2003 and 2017.
Sources and tools Social Acceptance of LGBT People in 174 Countries publication from the UCLA School of Law Williams Institute geofacet R package List of European nations Convert Town to convert spreadsheet columns to comma-separated lists library("geofacet") library("tidyverse") Data raw_data <- read_csv("gai.