state_arima_spaghetti: state_arima_spaghetti: Use the output of state_arima to...

In tlcaputi/gtrendR: An R Complement to the gtrends Python package

state_arima_spaghetti: Use the output of state_arima to create a state-level spaghetti plot of differences between the ARIMA model and actual searches

Description

state_arima_spaghetti: Use the output of state_arima to create a state-level spaghetti plot of differences between the ARIMA model and actual searches

Usage

state_arima_spaghetti(
  state_arima_list,
  beginplot,
  endplot,
  interrupt = NA,
  ylim = NULL,
  title = NULL,
  xlab = "Date",
  ylab = "Actual Versus Model-Fitted\nSearch Queries (% Diff.)",
  linelabel = "Interruption",
  lbreak = "1 week",
  lwd = 0.4,
  right_margin = 0.1,
  xfmt = date_format("%d %b"),
  states_with_labels = c("CA", "NY", "US", "IL", "TX"),
  states_to_exclude = c(),
  hicol = NA,
  locol = NA,
  nucol = NA,
  opcol = NA,
  colorscheme = "red",
  spaghettialpha = 0.25,
  extend = F,
  save = T,
  width = 6,
  height = 4,
  outfn = "./output/fig.png"
)

Arguments

state_arima_list	A dataframe including time as timestamp and searches for your given geography in one column.
beginplot	When the plot begins
endplot	When the plot ends
interrupt	The date where things change. ARIMA will be predicted on all days before the interrupt.
ylim	A 2-item vector for the limits of the y axis
title	Title of the figure
xlab	x-axis label, default is "Date"
ylab	y-axis label, default is "Actual Versus Model-Fitted Search Queries (Pct Diff.)"
linelabel	label next to interruption line
lbreak	space between tick marks on x axis
lwd	line width
xfmt	date format for x axis
states_with_labels	vector of states that you want to have labels
states_to_exclude	vector of states not to include in the figure
save	default T, if false does not save
width	width in inches
height	height in inches
outfn	output filename

Examples

state_arima_spaghetti(
  state_list,
  interrupt = "2019-03-01",
  title = NULL,
  xlab = "Date",
  ylab = "Actual Versus Model-Fitted Search Queries (Pct Diff.)",
  data = read.csv("./temp/data.csv", header = T, stringsAsFactor = F),
  linelabel = "COVID-19 Outbreak",
  lbreak = "1 week",
  lwd = 0.4,
  beginplot = ymd("2019-03-01")-(7*1),
  endplot = ymd("2019-03-18"),
  states_with_labels = c("CA", "NY", "US", "IA"),
  states_to_exclude = c("IA"),
  save = T,
  width = 6,
  height = 4,
  outfn = "./output/fig.png"
)

tlcaputi/gtrendR documentation built on Nov. 3, 2022, 10:46 p.m.