`geom_line()`

Lines in ggplot2

Geoms for lines

geom_path()

geom_line()

geom_step()

Packages
Data

PACKAGES:

Install packages.

Code

install.packages("ggplot2")
library(ggplot2)

DATA:

Load the ggplot2::economics data.

Code

economics <- ggplot2::economics
dplyr::glimpse(economics)

Rows: 574
Columns: 6
$ date     <date> 1967-07-01, 1967-08-01, 1967-09-01, 1967-10-01, 1967-11-01, …
$ pce      <dbl> 506.7, 509.8, 515.6, 512.2, 517.4, 525.1, 530.9, 533.6, 544.3…
$ pop      <dbl> 198712, 198911, 199113, 199311, 199498, 199657, 199808, 19992…
$ psavert  <dbl> 12.6, 12.6, 11.9, 12.9, 12.8, 11.8, 11.7, 12.3, 11.7, 12.3, 1…
$ uempmed  <dbl> 4.5, 4.7, 4.6, 4.9, 4.7, 4.8, 5.1, 4.5, 4.1, 4.6, 4.4, 4.4, 4…
$ unemploy <dbl> 2944, 2945, 2958, 3143, 3066, 3018, 2878, 3001, 2877, 2709, 2…

`geom_line()`

geom_line() connects them in order of the variable on the x axis

BASICS:

Code

ggp2_line_base <- ggplot(data = economics)
ggp2_line_base +
    geom_line(mapping = aes(x = date, 
                            y = unemploy))

AESTHETICS:

The required aesthetics are: x and y positions

Code

ggp2_line_base <- ggplot(economics) 
ggp2_line_base + 
    geom_line(aes(x = date, 
                  y = unemploy))

OPTIONAL AESTHETICS:

Optional aesthetics include:

group
color
linewidth
alpha
linetype

`group`

To explore group, we’ll load some data on Building Permit Data for United States from Texas A & M’s Real Estate Research Center.

Code

permits_raw <- readr::read_csv(file = "../data/dataPermit_full.csv", 
    na = "null")
dplyr::glimpse(permits_raw)

Rows: 111,784
Columns: 14
$ area         <chr> "Abilene, TX", "Abilene, TX", "Abilene, TX", "Abilene, TX…
$ date         <chr> "01/1980", "02/1980", "03/1980", "04/1980", "05/1980", "0…
$ f1units      <dbl> 24, 39, 38, 29, 29, 42, 48, 67, 53, 80, 44, 65, 55, 4, 70…
$ f1change     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 129.2, -8…
$ f1value      <dbl> 67900, 75900, 78000, 66500, 77600, 66500, 67600, 69000, 6…
$ f1valchange  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -2.5, -48…
$ f24units     <dbl> 4, 0, 4, 0, 0, 0, 18, 0, 2, 2, 0, 4, 6, 0, 0, 0, 8, 20, 8…
$ f24change    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50.0, 0.0…
$ f24value     <dbl> 46200, 0, 37000, 0, 0, 0, 24400, 0, 31200, 23800, 0, 5350…
$ f24valchange <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -13.6, 0.…
$ f5units      <dbl> 200, 0, 0, 0, 0, 0, 0, 0, 0, 152, 0, 0, 0, 0, 0, 0, 12, 2…
$ f5change     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -100, 0, …
$ f5value      <dbl> 12800, 0, 0, 0, 0, 0, 0, 0, 0, 22700, 0, 0, 0, 0, 0, 0, 4…
$ f5valchange  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -100, 0, …

First we’ll create the yr_shift and yr_mnth variables, and limit the data to 2010 - 2020.

Code

permits <- permits_raw |> 
    tidyr::separate(col = date, 
                  into = c("month", "year"), 
                  sep = "/", 
                  convert = TRUE, 
                  remove = FALSE) |> 
    dplyr::mutate(
        yr_shift = year + (month - 1) / 12,
        yr_mnth = paste0(year, "-", month),
        yr_mnth = lubridate::ym(yr_mnth)) |> 
    dplyr::filter(year >= 2010 & year <= 2020) |> 
    dplyr::select(year, yr_shift, date, 
        yr_mnth, area, contains("units")) 
utils::head(permits, 10)

yr_shift leaves 01/2010 as 2010.000, but 02/2010 becomes 2010.083, and 03/2010 shifts a little bit more to 2010.167, and so on.

If we try to view the change in f1units using geom_line() with yr_shift and yr_mnth, we see the following:

Code

ggplot(data = permits, 
    mapping = aes(x = yr_shift, y = f1units)) + 
    geom_line() + 
    labs(title = "yr_shift = year + (month - 1) / 12")
ggplot(data = permits, 
    mapping = aes(x = yr_mnth, y = f1units)) + 
    geom_line() + 
    labs(title = "yr_mnth = lubridate::ym(yr_mnth)")

We can see the lines aren’t acting like we expected, but if we map the area variable to group…

Code

ggplot(data = permits, 
    mapping = aes(x = yr_shift, y = f1units)) + 
    geom_line(aes(group = area))
ggplot(data = permits, 
    mapping = aes(x = yr_mnth, y = f1units)) + 
    geom_line(aes(group = area))

We can see the lines are now separated by area.

`color`

The color aesthetic can be set or mapped, and is useful for differentiating categorical levels.

Code

ggp2_line_base <- ggplot(economics) 
ggp2_line_base_long <- ggplot(economics_long)
# mapped color
ggp2_line_base_long + 
    geom_line(aes(x = date, 
        y = value01, 
        color = variable))
# set color
ggp2_line_base + 
    geom_line(aes(x = date, 
        y = uempmed),
        color = "#007bff")

`linewidth`

The linewidth determines the size of the line.

Code

# color, linewidth
ggp2_line_base_long + 
    geom_line(aes(x = date, 
        y = value01, 
        color = variable),
        linewidth = 0.75)

`alpha`

The alpha sets the opacity of the line (and is useful for overlapping lines).

Code

# color, alpha, linewidth
ggp2_line_base_long + 
    geom_line(aes(x = date, 
        y = value01, 
        color = variable), 
        alpha = 1 / 2,
        linewidth = 0.75)

`linetype`

The linetype can be mapped or set with values from ggplot2-specs

Code

# map group, color, linetype, with facets 
ggp2_line_base_long + 
    geom_line(aes(x = date, 
        y = value01, 
        color = variable, 
        group = variable,
        linetype = variable)) + 
    facet_wrap(. ~ variable, ncol = 2) + 
    theme(legend.position = "none")

ARGUMENTS:

We’ve defined two functions for displaying the arguments in geom_path(): path_linetype() displays the different linetype options, and path_lines() displays the options for lineend and linejoin.

`linetype`

We’ve defined line_linetype(), a function that lets us quickly specify the linetype aesthetic for geom_line():

Code

line_linetype <- function(type = "solid") {
    df <- data.frame(x = 1:5, y = c(5, 1, 4, 2, 7))
    ggplot2::ggplot(data = df,
        mapping = aes(x = x, y = y)) +
            xlim(0.5, 5.5) +
            ylim(0, 10) + 
        ggplot2::geom_line(
            linewidth = 15,
            lineend = "round",
            linetype = "solid") + 
        ggplot2::geom_line(
            linewidth = 1.5,
            color = "#ffffff",
            linetype = type) + 
        ggplot2::labs(subtitle = type)
}

The white line displays the linetype against a black backdrop to enhance visibility. See the lineend argument in the section below:

Code

# manually set linetype
# type = one of: "solid", "dashed", "dotted", "dotdash",
# "longdash", "twodash"
line_linetype(type = "dashed")
line_linetype(type = "dotted")
line_linetype(type = "dotdash")
line_linetype(type = "longdash")
line_linetype(type = "twodash")
line_linetype(type = "solid")

Below the define line_lines(), which builds line graphs with geom_line()’s lineend and linejoin arguments set to "round", "butt", or "square" and "bevel", "round", and "mitre".

The linewidth (lw) and linemitre (lnmtr) can be adjusted to exaggerate their effects.

We’ve added white lines/points to illustrate the data points.

Code

line_lines <- function(lw = 5, lend = 'r', ljoin = 'r', lnmtr = NULL) {
    lineends <- c('r' = 'round', 'b' = 'butt', 's' = 'square')
    linejoins <- c('r' = 'round', 'm' = 'mitre', 'b' = 'bevel')
    ljoin <- linejoins[ljoin]
    lend <- lineends[lend]
    df <- data.frame(x = 1:5, y = c(5, 1, 4, 2, 7))
    ggplot2::ggplot(data = df,
        mapping = aes(x = x, y = y)) +
        xlim(0.5, 5.5) +
        ylim(0, 10) +
        ggplot2::geom_line(
            linewidth = lw,
            lineend = lend,
            linejoin = ljoin,
            linemitre = lnmtr
        ) + 
        geom_line(color = "#ffffff") +
        geom_point(size = 3, color = "#ffffff") +
        ggplot2::labs(title = paste0(
            "lineend = ", lend,
            " / ", 
            "linejoin = ", ljoin), 
            subtitle = paste0("linewidth = ", lw))
}

Lineend: `round`

A “round” line ending extends the line width slightly and produces curved caps at the start and end of the line (with the intersecting values at the center).

The white line/points are the data points

Code

line_lines(lw = 15, lend = "r", ljoin = "r") # round/round
line_lines(lw = 15, lend = "r", ljoin = "m") # round/mitre 
line_lines(lw = 15, lend = "r", ljoin = "b") # round/bevel

Linejoin: `round`

A “round” line join produces a curve at the intersection of points on the line.

The white line/points are the data points

Code

line_lines(lw = 15, lend = "r", ljoin = "r") # round/round
line_lines(lw = 15, lend = "b", ljoin = "r") # butt/round
line_lines(lw = 15, lend = "s", ljoin = "r") # square/round

Lineend: `butt`

A “butt” line end produces an abrupt ending at the start and end of the data points along the line.

The white line/points are the data points

Code

line_lines(lw = 15, lend = "b", ljoin = "r") # butt/round
line_lines(lw = 15, lend = "b", ljoin = "m") # butt/mitre 
line_lines(lw = 15, lend = "b", ljoin = "b") # butt/bevel

Linejoin: `mitre`

A “mitre” line join extends the area of the line width between intersecting points, giving each connecting point a straight edge.

The white line/points are the data points

Code

line_lines(lw = 15, lend = "b", ljoin = "m") # butt/mitre 
line_lines(lw = 15, lend = "s", ljoin = "m") # square/mitre 
line_lines(lw = 15, lend = "r", ljoin = "m") # round/mitre

Lineend: `square`

A “square” line ending extends the line width slightly and produces a square cap at the start and end of the line (with the intersecting values at the center).

The white line/points are the data points

Code

line_lines(lw = 15, lend = "s", ljoin = "r") # square/round
line_lines(lw = 15, lend = "s", ljoin = "m") # square/mitre 
line_lines(lw = 15, lend = "s", ljoin = "b") # square/bevel

Linejoin: `bevel`

A “bevel” line join ‘shaves’ the line width at the intersection of data points and curves the line with by producing two additional angles (with the intersecting values at the center of the two angles).

The white line/points are the data points

Code

line_lines(lw = 15, lend = "b", ljoin = "b") # butt/bevel 
line_lines(lw = 15, lend = "s", ljoin = "b") # square/bevel
line_lines(lw = 15, lend = "r", ljoin = "b") # round/bevel

`geom_line()` vs. `geom_step()`

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

Code

recent_econ <- dplyr::filter(economics, 
    date > lubridate::as_date("2013-01-01"))
ggplot(data = recent_econ, 
    mapping = aes(x = date, y = unemploy)) + 
    geom_line() + 
    labs(title = "geom_line()")
ggplot(data = recent_econ, 
    mapping = aes(x = date, y = unemploy)) + 
    geom_step() + 
    labs(title = "geom_step()")

--- title: "`geom_line()`" format: html: toc: true code-fold: show df-print: paged execute: warning: false message: false --- ```{r} #| label: setup #| message: false #| warning: false #| include: false library(tidyverse) library(lubridate) library(scales) library(knitr) library(kableExtra) library(colorblindr) library(downlit) library(patchwork) library(faux) # options ---- options( repos = "https://cloud.r-project.org", dplyr.print_min = 6, dplyr.print_max = 6, scipen = 9999) # fonts ---- library(extrafont) library(sysfonts) # import font extrafont::font_import( paths = "../assets/Ubuntu/", prompt = FALSE) # add font sysfonts::font_add( family = "Ubuntu", regular = "../assets/Ubuntu/Ubuntu-Regular.ttf") # use font showtext::showtext_auto() # add theme source("../R/theme_ggp2g.R") # set theme ggplot2::theme_set(theme_ggp2g( base_size = 16)) # install data packages ---- install.packages("palmerpenguins") ``` :::: {.callout-note collapse="false" icon=false} ## Lines in `ggplot2` ::: {style="color: #043b67;"} #### Geoms for lines ::: ```{r} #| label: full_code_display #| eval: true #| echo: false #| warning: false #| message: false #| out-height: '55%' #| out-width: '55%' #| fig-align: right library(palmerpenguins) library(ggplot2) library(patchwork) economics <- ggplot2::economics seals <- ggplot2::seals ggp2_line_base <- ggplot(data = economics, aes(x = date, y = unemploy)) # geom_path ---- pth <- ggp2_line_base + geom_path() + theme_void() + labs(title = "geom_path()") # geom_line ---- ln <- ggp2_line_base + geom_line(color = "#E74A2F") + theme_void() + labs(title = "geom_line()") # geom_step ---- stp <- ggp2_line_base + geom_step() + theme_void() + labs(title = "geom_step()") # path, line, step ---- pth + ln + stp + patchwork::plot_layout(ncol = 2) ``` - `geom_path()` :::{style="font-size: 0.90em; color: #E74A2F;"} - **geom_line()** ::: - `geom_step()` :::: :::: {.panel-tabset} ### Packages ::: {.column-margin} ::: {style="font-size: 0.75em;"} ![](../www/ggplot2.png){fig-align="right" width="40%" height="40%"} ::: ::: ::: {style="font-size: 1.15em; color: #1e83c8;"} **PACKAGES:** ::: ::: {style="font-size: 0.85em;"} Install packages. ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: pkg_code_lines #| eval: true #| echo: true #| warning: false #| message: false #| results: hide install.packages("ggplot2") library(ggplot2) ``` ::: ### Data ::: {style="font-size: 1.15em; color: #1e83c8;"} **DATA:** ::: ::: {style="font-size: 0.85em;"} Load the `ggplot2::economics` data. ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: data_lines #| eval: true #| echo: true economics <- ggplot2::economics dplyr::glimpse(economics) ``` ::: :::: ## `geom_line()` :::: {.panel-tabset} ### Basic Use ::: {.column-margin} ::: {style="font-size: 0.85em;"} [`geom_line()`](https://ggplot2.tidyverse.org/reference/geom_path.html) *connects them in order of the variable on the x axis* ::: ::: ::: {style="font-size: 1.15em; color: #1e83c8;"} **BASICS:** ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: basic_line #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 1 ggp2_line_base <- ggplot(data = economics) ggp2_line_base + geom_line(mapping = aes(x = date, y = unemploy)) ``` ::: ### Aesthetics ::: {style="font-size: 1.15em; color: #1e83c8;"} **AESTHETICS:** ::: ::: {style="font-size: 0.85em;"} The required aesthetics are: `x` and `y` positions ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_line #| eval: true #| echo: true #| warning: false #| message: false ggp2_line_base <- ggplot(economics) ggp2_line_base + geom_line(aes(x = date, y = unemploy)) ``` ::: ::: {style="font-size: 1.15em; color: #1e83c8;"} **OPTIONAL AESTHETICS:** ::: ::: {style="font-size: 0.85em;"} Optional aesthetics include: - `group` - `color` - `linewidth` - `alpha` - `linetype` ::: #### `group` ::: {style="font-size: 0.85em;"} To explore `group`, we'll load some data on *Building Permit Data for United States* from [Texas A & M's Real Estate Research Center](https://www.recenter.tamu.edu/data/building-permits#!/state/United_States). ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_line_group_data #| eval: true #| echo: true #| code-fold: show #| warning: false #| message: false permits_raw <- readr::read_csv(file = "../data/dataPermit_full.csv", na = "null") dplyr::glimpse(permits_raw) ``` ::: ::: {style="font-size: 0.85em;"} First we'll create the `yr_shift` and `yr_mnth` variables, and limit the data to `2010` - `2020`. ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_line_group_yr_shift #| eval: true #| echo: true #| code-fold: show #| warning: false #| message: false permits <- permits_raw |> tidyr::separate(col = date, into = c("month", "year"), sep = "/", convert = TRUE, remove = FALSE) |> dplyr::mutate( yr_shift = year + (month - 1) / 12, yr_mnth = paste0(year, "-", month), yr_mnth = lubridate::ym(yr_mnth)) |> dplyr::filter(year >= 2010 & year <= 2020) |> dplyr::select(year, yr_shift, date, yr_mnth, area, contains("units")) utils::head(permits, 10) ``` ::: ::: {style="font-size: 0.85em;"} `yr_shift` leaves `01/2010` as `2010.000`, but `02/2010` becomes `2010.083`, and `03/2010` shifts a little bit more to `2010.167`, and so on. If we try to view the change in `f1units` using `geom_line()` with `yr_shift` and `yr_mnth`, we see the following: ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_line_group_yr_shift_mnth_f1units #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 ggplot(data = permits, mapping = aes(x = yr_shift, y = f1units)) + geom_line() + labs(title = "yr_shift = year + (month - 1) / 12") ggplot(data = permits, mapping = aes(x = yr_mnth, y = f1units)) + geom_line() + labs(title = "yr_mnth = lubridate::ym(yr_mnth)") ``` ::: ::: {style="font-size: 0.85em;"} We can see the lines aren't acting like we expected, but if we map the `area` variable to `group`... ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_path_group_yr_shift_f1units #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 ggplot(data = permits, mapping = aes(x = yr_shift, y = f1units)) + geom_line(aes(group = area)) ggplot(data = permits, mapping = aes(x = yr_mnth, y = f1units)) + geom_line(aes(group = area)) ``` ::: ::: {style="font-size: 0.85em;"} We can see the lines are now separated by `area`. ::: #### `color` ::: {style="font-size: 0.85em;"} The `color` aesthetic can be set or mapped, and is useful for differentiating categorical levels. ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: permits_long #| eval: false #| echo: false #| code-fold: true #| warning: false #| message: false permits_long <- tidyr::pivot_longer(data = permits, cols = contains("units"), names_to = "fkey", values_to = "units") ``` ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_line_color #| eval: true #| echo: true #| code-fold: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 ggp2_line_base <- ggplot(economics) ggp2_line_base_long <- ggplot(economics_long) # mapped color ggp2_line_base_long + geom_line(aes(x = date, y = value01, color = variable)) # set color ggp2_line_base + geom_line(aes(x = date, y = uempmed), color = "#007bff") ``` ::: #### `linewidth` ::: {style="font-size: 0.85em;"} The `linewidth` determines the size of the line. ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_line_linewidth #| eval: true #| echo: true #| code-fold: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 1 # color, linewidth ggp2_line_base_long + geom_line(aes(x = date, y = value01, color = variable), linewidth = 0.75) ``` ::: #### `alpha` ::: {style="font-size: 0.85em;"} The alpha sets the opacity of the line (and is useful for overlapping lines). ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_path_alpha #| eval: true #| echo: true #| code-fold: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 1 # color, alpha, linewidth ggp2_line_base_long + geom_line(aes(x = date, y = value01, color = variable), alpha = 1 / 2, linewidth = 0.75) ``` ::: #### `linetype` ::: {style="font-size: 0.85em;"} The `linetype` can be mapped or set with values from [ggplot2-specs](https://ggplot2.tidyverse.org/articles/ggplot2-specs.html#lines) ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: aes_line_linetype #| eval: true #| echo: true #| code-fold: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 1 # map group, color, linetype, with facets ggp2_line_base_long + geom_line(aes(x = date, y = value01, color = variable, group = variable, linetype = variable)) + facet_wrap(. ~ variable, ncol = 2) + theme(legend.position = "none") ``` ::: ### Arguments ::: {style="font-size: 1.15em; color: #1e83c8;"} **ARGUMENTS:** ::: ::: {style="font-size: 0.85em;"} We've defined two functions for displaying the arguments in `geom_path()`: `path_linetype()` displays the different `linetype` options, and `path_lines()` displays the options for `lineend` and `linejoin`. ::: #### `linetype` ::: {style="font-size: 0.85em;"} We've defined `line_linetype()`, a function that lets us quickly specify the `linetype` aesthetic for `geom_line()`: ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: line_linetype #| eval: true #| echo: true #| code-fold: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' line_linetype <- function(type = "solid") { df <- data.frame(x = 1:5, y = c(5, 1, 4, 2, 7)) ggplot2::ggplot(data = df, mapping = aes(x = x, y = y)) + xlim(0.5, 5.5) + ylim(0, 10) + ggplot2::geom_line( linewidth = 15, lineend = "round", linetype = "solid") + ggplot2::geom_line( linewidth = 1.5, color = "#ffffff", linetype = type) + ggplot2::labs(subtitle = type) } ``` ::: ::: {style="font-size: 0.85em;"} The white line displays the `linetype` against a black backdrop to enhance visibility. See the `lineend` argument in the section below: ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: draw_linetype_use #| eval: true #| echo: true #| code-fold: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 # manually set linetype # type = one of: "solid", "dashed", "dotted", "dotdash", # "longdash", "twodash" line_linetype(type = "dashed") line_linetype(type = "dotted") line_linetype(type = "dotdash") line_linetype(type = "longdash") line_linetype(type = "twodash") line_linetype(type = "solid") ``` ::: ::: {style="font-size: 0.85em;"} Below the define `line_lines()`, which builds line graphs with `geom_line()`'s `lineend` and `linejoin` arguments set to `"round"`, `"butt"`, or `"square"` and `"bevel"`, `"round"`, and `"mitre"`. The `linewidth` (`lw`) and `linemitre` (`lnmtr`) can be adjusted to exaggerate their effects. We've added white lines/points to illustrate the data points. ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: line_lines #| code-fold: true #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 line_lines <- function(lw = 5, lend = 'r', ljoin = 'r', lnmtr = NULL) { lineends <- c('r' = 'round', 'b' = 'butt', 's' = 'square') linejoins <- c('r' = 'round', 'm' = 'mitre', 'b' = 'bevel') ljoin <- linejoins[ljoin] lend <- lineends[lend] df <- data.frame(x = 1:5, y = c(5, 1, 4, 2, 7)) ggplot2::ggplot(data = df, mapping = aes(x = x, y = y)) + xlim(0.5, 5.5) + ylim(0, 10) + ggplot2::geom_line( linewidth = lw, lineend = lend, linejoin = ljoin, linemitre = lnmtr ) + geom_line(color = "#ffffff") + geom_point(size = 3, color = "#ffffff") + ggplot2::labs(title = paste0( "lineend = ", lend, " / ", "linejoin = ", ljoin), subtitle = paste0("linewidth = ", lw)) } ``` ::: #### Lineend: `round` ::: {style="font-size: 0.85em;"} A "round" line ending extends the line width slightly and produces curved caps at the start and end of the line (with the intersecting values at the center). *The white line/points are the data points* ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: args_line_lend_round #| code-fold: true #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 line_lines(lw = 15, lend = "r", ljoin = "r") # round/round line_lines(lw = 15, lend = "r", ljoin = "m") # round/mitre line_lines(lw = 15, lend = "r", ljoin = "b") # round/bevel ``` ::: #### Linejoin: `round` ::: {style="font-size: 0.85em;"} A "round" line join produces a curve at the intersection of points on the line. *The white line/points are the data points* ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: args_line_ljoin_round #| code-fold: true #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 line_lines(lw = 15, lend = "r", ljoin = "r") # round/round line_lines(lw = 15, lend = "b", ljoin = "r") # butt/round line_lines(lw = 15, lend = "s", ljoin = "r") # square/round ``` ::: #### Lineend: `butt` ::: {style="font-size: 0.85em;"} A "butt" line end produces an abrupt ending at the start and end of the data points along the line. *The white line/points are the data points* ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: args_line_lend_butt #| code-fold: true #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 line_lines(lw = 15, lend = "b", ljoin = "r") # butt/round line_lines(lw = 15, lend = "b", ljoin = "m") # butt/mitre line_lines(lw = 15, lend = "b", ljoin = "b") # butt/bevel ``` ::: #### Linejoin: `mitre` ::: {style="font-size: 0.85em;"} A "mitre" line join extends the area of the line width between intersecting points, giving each connecting point a straight edge. *The white line/points are the data points* ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: args_line_ljoin_mitre #| code-fold: true #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 line_lines(lw = 15, lend = "b", ljoin = "m") # butt/mitre line_lines(lw = 15, lend = "s", ljoin = "m") # square/mitre line_lines(lw = 15, lend = "r", ljoin = "m") # round/mitre ``` ::: #### Lineend: `square` ::: {style="font-size: 0.85em;"} A "square" line ending extends the line width slightly and produces a square cap at the start and end of the line (with the intersecting values at the center). *The white line/points are the data points* ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: args_line_lend_square #| code-fold: true #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 line_lines(lw = 15, lend = "s", ljoin = "r") # square/round line_lines(lw = 15, lend = "s", ljoin = "m") # square/mitre line_lines(lw = 15, lend = "s", ljoin = "b") # square/bevel ``` ::: #### Linejoin: `bevel` ::: {style="font-size: 0.85em;"} A "bevel" line join 'shaves' the line width at the intersection of data points and curves the line with by producing two additional angles (with the intersecting values at the center of the two angles). *The white line/points are the data points* ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: args_line_ljoin_bevel #| code-fold: true #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 line_lines(lw = 15, lend = "b", ljoin = "b") # butt/bevel line_lines(lw = 15, lend = "s", ljoin = "b") # square/bevel line_lines(lw = 15, lend = "r", ljoin = "b") # round/bevel ``` ::: ### Examples #### `geom_line()` vs. `geom_step()` ::: {style="font-size: 0.85em;"} [`geom_step()` *is useful when you want to highlight exactly when the `y` value changes*](https://ggplot2.tidyverse.org/reference/geom_path.html) ::: ::: {style="font-size: 0.75em;"} ```{r} #| label: example_line_vs_step #| eval: true #| echo: true #| warning: false #| message: false #| out-height: '100%' #| out-width: '100%' #| layout-ncol: 2 recent_econ <- dplyr::filter(economics, date > lubridate::as_date("2013-01-01")) ggplot(data = recent_econ, mapping = aes(x = date, y = unemploy)) + geom_line() + labs(title = "geom_line()") ggplot(data = recent_econ, mapping = aes(x = date, y = unemploy)) + geom_step() + labs(title = "geom_step()") ``` ::: ::::

Geoms for lines

geom_line()

group

color

linewidth

alpha

linetype

linetype

Lineend: round

Linejoin: round

Lineend: butt

Linejoin: mitre

Lineend: square

Linejoin: bevel

geom_line() vs. geom_step()

`geom_line()`

`group`

`color`

`linewidth`

`alpha`

`linetype`

`linetype`

Lineend: `round`

Linejoin: `round`

Lineend: `butt`

Linejoin: `mitre`

Lineend: `square`

Linejoin: `bevel`

`geom_line()` vs. `geom_step()`