Line Graphs

Load data and Libraries

https://r4ds.had.co.nz/data-visualisation.html

if (!("ggplot2" %in% installed.packages())) {
    install.packages('ggplot2')
}
library('ggplot2')
if (!("devtools" %in% installed.packages())) {
    install.packages('devtools')
}
if (!("tidyr" %in% installed.packages())) {
    install.packages('tidyr')
}
library(tidyr)
if (!("dplyr" %in% installed.packages())) {
    install.packages('dplyr')
}
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#devtools::install_github('neuhausi/canvasXpress')
#devtools::install_local("~/git/canvas/R/canvasXpress.tar.gz")
#devtools::install_local("~/git/canvas/R/canvasXpress.tar.gz", build_manual = TRUE, upgrade = "always")
library('canvasXpress')

geom_line() is suitable for time series

g <- ggplot(economics, aes(date, unemploy)) + geom_line()
g

canvasXpress(g)

geom_line() is suitable for time series – by color

eco <- economics_long
colnames(eco)[2] <- "vari"
g <- ggplot(eco, aes(date, value01, colour = vari)) + geom_line()
g

canvasXpress(g)

geom_step() is useful when you want to highlight exactly when the y value changes

recent <- economics[economics$date > as.Date("2013-01-01"), ]
g <- ggplot(recent, aes(date, unemploy)) + geom_line()
g