# ETL
library(tidyverse)
# Charts
library(highcharter)
library(RColorBrewer)16 Classic Charts with highcharter
Let’s start by uploading the libraries we will use : tidyverse for the ETL, and highcharter and RColorBrewer for the charts.
17 Introduction
This section is dedicated to the library highcharter.
Within this package, the functions highchart() and hchart() are both used to create charts, but they serve different purposes and have distinct use cases. Let’s present both of them.
The highchart() function :
it’s a general function that initializes an empty Highcharts object.
This function is useful when you want to have fine-grained control over the chart’s configuration and need to build the chart step-by-step by adding series, axes, titles, and other components.
The hchart() function :
it’s a higher-level function that provides a simpler and quicker way to create a chart from a given data structure.
It is designed to be more user-friendly and automatically handles many of the chart configuration details based on the input data. This function is particularly useful when you want to create a chart quickly with minimal configuration.
Key Differences Level of Control:
highchart(): provides a blank chart and allows detailed customization by adding components step-by-step.hchart(): provides a quick way to create a chart with sensible defaults based on the input data.
Ease of Use:
highchart(): requires more lines of code and a deeper understanding of Highcharts configuration options.hchart(): requires fewer lines of code and is easier to use for creating standard charts.
Flexibility:
highchart(): offers more flexibility and is suitable for complex and highly customized charts.hchart(): offers less flexibility but is ideal for quickly generating common types of charts.
In summary, use highchart() when you need full control over the chart’s configuration, and use hchart() when you want to quickly create a chart with minimal effort.
Now, we’re ready to start discovering and using both functions!
18 Create demo data frames
Now, let’s create 2 demo data frames that we will use to practice.
18.1 1 product
It’s a data frame with 6 variables :
5 dimensions :
a calendar period.
- and 3 related variables : calendar year, calendar month and a calendar month abbreviation.
a product description.
1 measure : sales quantity.
# create Sales Qty
sales_qty <- c(1370,1528,1101,738,1229,1451,879,1505,1375,1146,1325,1156,1081,1258,894,700,1289,1207,926,1476,1254,1111,1175,881,916,1225,682,739,1056,1117,727,1233,839,862,862,702,740,934,603,468,903,901)
# create period of time
calendar_period <- c("10/1/2016","11/1/2016","12/1/2016","1/1/2017","2/1/2017","3/1/2017","4/1/2017","5/1/2017","6/1/2017","7/1/2017","8/1/2017","9/1/2017","10/1/2017","11/1/2017","12/1/2017","1/1/2018","2/1/2018","3/1/2018","4/1/2018","5/1/2018","6/1/2018","7/1/2018","8/1/2018","9/1/2018","10/1/2018","11/1/2018","12/1/2018","1/1/2019","2/1/2019","3/1/2019","4/1/2019","5/1/2019","6/1/2019","7/1/2019","8/1/2019","9/1/2019","10/1/2019","11/1/2019","12/1/2019","1/1/2020","2/1/2020","3/1/2020")
# combine into a data frame
df1 <- data.frame(calendar_period, sales_qty)
# add Product Description
df1$product_description <- "Product A"
# Format calendar_period as a date
df1$calendar_period <- as.Date(df1$calendar_period, format = '%m/%d/%Y')
# Get additional Calendar Fields: Month and Year
# add the Calendar Year
df1$calendar_year <- year(df1$calendar_period)
# add the Calendar.Month
df1$calendar_month <- month(df1$calendar_period)
# transform the Month number into a month abbreviation
# note that month.abb comes with [ and not (
df1$calendar_month_abb <- month.abb[df1$calendar_month]
# create factor
df1$calendar_month_abb <- factor(df1$calendar_month_abb,
levels= c("Jan","Feb","Mar","Apr","May","Jun",
"Jul","Aug","Sep", "Oct", "Nov", "Dec"))
# Get Results
sales_data <- df1
glimpse(df1)Rows: 42
Columns: 6
$ calendar_period <date> 2016-10-01, 2016-11-01, 2016-12-01, 2017-01-01, 2…
$ sales_qty <dbl> 1370, 1528, 1101, 738, 1229, 1451, 879, 1505, 1375…
$ product_description <chr> "Product A", "Product A", "Product A", "Product A"…
$ calendar_year <dbl> 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017, 20…
$ calendar_month <dbl> 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,…
$ calendar_month_abb <fct> Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun, Jul, …18.2 2 products
The structure is similar to the previous one, but with 2 products (Product A and B).
#-----------------------------------
# create data frame for Product A
#-----------------------------------
# get Sales Qty
sales_qty <- c(1370,1528,1101,738,1229,1451,879,1505,1375,1146,1325,1156,1081,1258,894,700,1289,1207,926,1476,1254,1111,1175,881,916,1225,682,739,1056,1117,727,1233,839,862,862,702,740,934,603,468,903,901)
# get Period
calendar_period <- c("10/1/2016","11/1/2016","12/1/2016","1/1/2017","2/1/2017","3/1/2017","4/1/2017","5/1/2017","6/1/2017","7/1/2017","8/1/2017","9/1/2017","10/1/2017","11/1/2017","12/1/2017","1/1/2018","2/1/2018","3/1/2018","4/1/2018","5/1/2018","6/1/2018","7/1/2018","8/1/2018","9/1/2018","10/1/2018","11/1/2018","12/1/2018","1/1/2019","2/1/2019","3/1/2019","4/1/2019","5/1/2019","6/1/2019","7/1/2019","8/1/2019","9/1/2019","10/1/2019","11/1/2019","12/1/2019","1/1/2020","2/1/2020","3/1/2020")
# combine into a dataframe
df1<- data.frame(calendar_period, sales_qty)
# add Product Description
df1$product_description <- "Product A"
#-----------------------------------
# create data frame for Product B
#-----------------------------------
# get Sales Qty
sales_qty <- c(1623,1522,2306,1416,2214,2538,1841,1982,2749,1929,626,1280,1215,2693,1564,1836,1949,1923,1810,2181,2296,1671,1049,1229,1880,2041,1935,1617,1821,1940,1905,1699,2255,1853,522,1682,2261,1523,3210,1764,2230,1605)
# combine into a dataframe
df2<- data.frame(calendar_period, sales_qty)
# add Product Description
df2$product_description <- "Product B"
# stack data
df1 <- rbind(df1,df2)
#-----------------------------------
# work on dates
#-----------------------------------
# Format calendar_period as a date
df1$calendar_period <- as.Date(df1$calendar_period, format = '%m/%d/%Y')
# Get additional Calendar Fields: Month and Year
# add the Calendar Year
df1$calendar_year <- year(df1$calendar_period)
# add the Calendar.Month
df1$calendar_month <- month(df1$calendar_period)
# transform the Month number into a month abbreviation
# note that month.abb comes with [ and not (
df1$calendar_month_abb <- month.abb[df1$calendar_month]
# create factor
df1$calendar_month_abb <- factor(df1$calendar_month_abb,
levels= c("Jan","Feb","Mar","Apr","May","Jun",
"Jul","Aug","Sep", "Oct", "Nov", "Dec"))
# Get Results
sales_data2 <- df1
glimpse(df1)Rows: 84
Columns: 6
$ calendar_period <date> 2016-10-01, 2016-11-01, 2016-12-01, 2017-01-01, 2…
$ sales_qty <dbl> 1370, 1528, 1101, 738, 1229, 1451, 879, 1505, 1375…
$ product_description <chr> "Product A", "Product A", "Product A", "Product A"…
$ calendar_year <dbl> 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017, 20…
$ calendar_month <dbl> 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,…
$ calendar_month_abb <fct> Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun, Jul, …19 Classic Charts
19.1 Basic line chart
Let’s use the demo data frame sales_data and create a simple line chart. We need 2 variables :
1 dimension : a calendar period.
1 measure : a sales quantity.
We display the measure using the function hc_add_series(), giving it a name, a color, and linking it to the variable sales_qty.
# set working dataframe
df1 <- sales_data
# chart
highchart() |>
hc_title(text = "Sales Volumes") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$calendar_period) |>
hc_add_series(name = "Sales",
color = "steelblue",
data = df1$sales_qty)Now, you can change the theme and the color of the line. For example replace :
the theme with :
hc_theme_538().the color with :
gold.
# set working dataframe
df1 <- sales_data
# chart
highchart() |>
hc_title(text = "Sales Volumes") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_538()) |>
hc_xAxis(categories = df1$calendar_period) |>
hc_add_series(name = "Sales",
color = "gold",
data = df1$sales_qty)If we want to name the y axis, we can add a line of code : hc_yAxis(title = list(text = "Sales (units)")) .
Note : displaying the title of the y axis is often optional, as the info is already captured inside the chart’s title.
# set working dataframe
df1 <- sales_data
# chart
highchart() |>
hc_title(text = "Sales Volumes") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$calendar_period) |>
hc_yAxis(title = list(text = "Sales (units)")) |> # we can add this line
hc_add_series(name = "Sales",
color = "steelblue",
data = df1$sales_qty)If we want to display the values, we can add one more line of code inside the hc_add_series() :
# set working dataframe
df1 <- sales_data
# chart
highchart() |>
hc_title(text = "Sales Volumes") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$calendar_period) |>
hc_add_series(name = "Sales",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE), # we can add this line
data = df1$sales_qty)19.2 MTM line chart
Now, let’s create a Month To Month (MTM) line chart.
This is very useful to :
compare the monthly evolution between 2 (or more) years.
identify easily any seasonality pattern.
First, let’s prepare the data. We want to have a data frame with :
1 dimension : the calendar month.
3 measures : the calendar years 2018, 2019 and 2020.
- and the monthly sales of each calendar year.
# set working dataframe
df1 <- sales_data
# keep only the needed columns
df1 <- df1 |> select(calendar_month_abb, calendar_year, sales_qty)
# spread data
df1 <- df1 |> spread(calendar_year, sales_qty)
# replace zero by NA
df1[df1 == 0] <- NA
# display
df1 calendar_month_abb 2016 2017 2018 2019 2020
1 Jan NA 738 700 739 468
2 Feb NA 1229 1289 1056 903
3 Mar NA 1451 1207 1117 901
4 Apr NA 879 926 727 NA
5 May NA 1505 1476 1233 NA
6 Jun NA 1375 1254 839 NA
7 Jul NA 1146 1111 862 NA
8 Aug NA 1325 1175 862 NA
9 Sep NA 1156 881 702 NA
10 Oct 1370 1081 916 740 NA
11 Nov 1528 1258 1225 934 NA
12 Dec 1101 894 682 603 NANow, let’s create our chart.
We’re going to display 3 calendar years : 2018, 2019 and 2020, and then will create 3 different series, with 3 different colors, using the function hc_add_series() .
highchart() |>
hc_title(text = "MTM Actual Sales") |>
hc_subtitle(text = "in units") |>
hc_xAxis(categories = df1$calendar_month_abb) |>
hc_add_theme(hc_theme_google()) |>
hc_add_series(name = "2018",
color = "mediumseagreen",
data = df1$`2018`) |>
hc_add_series(name = "2019",
color = "skyblue",
data = df1$`2019`) |>
hc_add_series(name = "2020",
color = "gold",
data = df1$`2020`)19.3 Column & bar chart
19.3.1 column
In this example, we’re going to display some YTD sales. We will look at the first quarter, so the YTD March.
Let’s notice the work flow below :
1) Get Data.
2) Filter.
3) Transform.
4) Chart.
It’s the work flow illustrated during the introduction of this chapter.
Besides, before performing the aggregation, we make sure that there are no missing sales values, replacing any missing values (NA) by zero.
We use the syntax : df1$sales_qty <- df1$sales_qty |> replace_na(0) .
By doing this, we ensure to have a correct aggregation (a sum in this case).
This type of chart is called “column”, and we define it by adding at the end the syntax : hc_chart(type = "column").
The chart shows some vertical columns.
#------------------
# Get data
#------------------
# set working dataframe
df1 <- sales_data
#------------------
# Filters
#------------------
# Select months
df1 <- df1 |> filter(calendar_month <= 3)
#------------------
# Transform
#------------------
# replace missing values by zero, if any
df1$sales_qty <- df1$sales_qty |> replace_na(0)
# aggregate
df1 <- df1 |> group_by(calendar_year) |>
summarise(sales_qty = sum(sales_qty)
)
#------------------
# Chart
#------------------
highchart() |>
hc_title(text = "YTD Sales Volumes") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$calendar_year) |>
hc_add_series(name = "Sales",
color = "mediumseagreen",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$sales_qty) |>
hc_chart(type = "column") 19.3.2 bar
If at the end of the code, we change the chart type “column” by “bar”, we obtain the following chart, which is with horizontal bars.
#------------------
# Get data
#------------------
# set working dataframe
df1 <- sales_data
#------------------
# Filters
#------------------
# Select months
df1 <- df1 |> filter(calendar_month <= 3)
#------------------
# Transform
#------------------
# replace missing values by zero, if any
df1$sales_qty <- df1$sales_qty |> replace_na(0)
# aggregate
df1 <- df1 |> group_by(calendar_year) |>
summarise(sales_qty = sum(sales_qty)
)
#------------------
# Chart
#------------------
highchart() |>
hc_title(text = "YTD Sales Volumes") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$calendar_year) |>
hc_add_series(name = "Sales",
color = "mediumseagreen",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$sales_qty) |>
hc_chart(type = "bar") 19.4 Columns & Multiple Products
19.4.1 stacking
In this example, we will use the library RColorBrewer. We define a value called “cols”, which contains the HEX codes of 7 colors of the palette “Set2”.
Regarding the chart :
the syntax changes a bit and we now use the
hchart()function.we also use a notion of “group”, where we affect the variable “product_description”.
- Doing so, the series related to each product will be generated automatically, and with a color from the palette “Set2”.
Note :
We choose the option stacking = “normal” in the function
hc_plotOptions()This means that the series (here 2 series) are stacked
#------------------
# Get data
#------------------
# set working dataframe
df1 <- sales_data2
#------------------
# Filters
#------------------
# Select months
df1 <- df1 |> filter(calendar_month <= 3)
#------------------
# Transform
#------------------
# replace missing values by zero, if any
df1$sales_qty <- df1$sales_qty |> replace_na(0)
# aggregate
df1 <- df1 |> group_by(product_description, calendar_year) |>
summarise(sales_qty = sum(sales_qty)
)
#------------------
# Chart
#------------------
cols <- brewer.pal(7, "Set2")
hchart(df1, "column",
hcaes(x = calendar_year,
y = sales_qty,
group = product_description), # we introduce the notion of group
dataLabels = list(align = "center", enabled = TRUE)
) |>
hc_plotOptions(series = list(stacking = "normal")) |>
hc_title(text = "YTD Sales by Products") |>
hc_subtitle(text = "in units") |>
hc_colors(cols)19.4.2 without settings
Let’s now create the same chart but :
without the
RColorBrewerlibrary.without stacking the series.
- removing the part :
hc_plotOptions(series = list(stacking = "normal")).
- removing the part :
The chart is generated, with some by default colors.
#------------------
# Get data
#------------------
# set working dataframe
df1 <- sales_data2
#------------------
# Filters
#------------------
# Select months
df1 <- df1 |> filter(calendar_month <= 3)
#------------------
# Transform
#------------------
# replace missing values by zero, if any
df1$sales_qty <- df1$sales_qty |> replace_na(0)
# aggregate
df1 <- df1 |> group_by(product_description, calendar_year) |>
summarise(sales_qty = sum(sales_qty)
)
#------------------
# Chart
#------------------
hchart(df1, "column",
hcaes(x = calendar_year,
y = sales_qty,
group = product_description), # we introduce the notion of group
dataLabels = list(align = "center", enabled = TRUE)
) |>
#hc_plotOptions(series = list(stacking = "normal")) |>
hc_title(text = "YTD Sales by Products") |>
hc_subtitle(text = "in units") 19.4.3 self settings
We can define our own color for each serie, as below.
This time we will stack the series, and use the syntax : hc_plotOptions(series = list(stacking = "normal")) .
#------------------
# Get data
#------------------
# set working dataframe
df1 <- sales_data2
#------------------
# Filters
#------------------
# Select months
df1 <- df1 |> filter(calendar_month <= 3)
#------------------
# Transform
#------------------
# replace missing values by zero, if any
df1$sales_qty <- df1$sales_qty |> replace_na(0)
# aggregate
df1 <- df1 |> group_by(product_description, calendar_year) |>
summarise(sales_qty = sum(sales_qty)
)
# spread
df1 <- df1 |> spread(product_description, sales_qty)
#------------------
# Chart
#------------------
highchart() |>
hc_title(text = "YTD Sales by Products") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$calendar_year) |>
hc_add_series(name = "Product A",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Product A`) |>
hc_add_series(name = "Product B",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Product B`) |>
hc_chart(type = "column") |>
hc_plotOptions(series = list(stacking = "normal"))19.5 Area chart
This is a chart a bit less popular than the previous ones, but still useful sometimes. Here is the syntax to generate it. We mention “area” within the function hchart() .
# set working dataframe
df1 <- sales_data2
# aggregate
df1 <- df1 |> group_by(product_description, calendar_period) |>
summarise(sales_qty = sum(sales_qty)
)
# chart
cols <- brewer.pal(7, "Set2")
hchart(df1,
"area",
hcaes(x = calendar_period,
y = sales_qty,
group = product_description)
) |>
hc_plotOptions(series = list(stacking = "normal")) |>
hc_title(text = "Sales by Products") |>
hc_subtitle(text = "in units") |>
hc_colors(cols)19.6 Pie chart
Pie charts are getting less common nowadays, however, they are still a classic and simple way to visualize proportions.
Here is the syntax to generate it. We mention “pie” within the function hchart() .
# create a demo data frame
cities <- c("Taipei", "Kuala Lumpur", "Singapore")
pop <- c(2.6, 1.8, 5.6)
population_data <- data.frame(cities, pop)
# and now let’s create the pie
hchart(population_data,
"pie",
hcaes(name = cities,
y = pop)) |>
hc_title(text = "Population pie chart") |>
hc_colors(c("#0198f9", "#800000", "#ffcc33"))19.7 Tooltip customization
We can customize the charts, adding some tooltips, through the function hc_tooltip(), as described below.
When the mouse passes on top of the chart, a value is displayed.
it’s a good way to keep the chart light (without any value).
while displaying when needed the values with a customized info.
# let’s use the previous data frame
population_data <- population_data |> arrange(desc(pop))
# now let’s create a chart, with a tooltip
hchart(population_data,
"column",
hcaes(x = cities,
y = pop),
color = "#0198f9",
name = "Population") |>
hc_title(text = "Population by cities") |>
hc_xAxis(title = list(text = "Cities")) |>
hc_yAxis(title = list(text = "Population (millions)")) |>
hc_tooltip(formatter = JS("function(){return this.y + ' M inhabitants';}"))19.8 Box plot
A box plot is convenient representation to visualize the distribution of some values. Let’s take the example below :
we have 4 suppliers (A, B, C, D).
with different transit lead times, recorded through 10 observations.
We want to visualize those different transit lead times, and their distribution (and distance) versus a mean value.
First, let’s create a demo data frame :
# Create a demo data frame
set.seed(123) # For reproducibility
df1 <- data.frame(
supplier = rep(c("Supplier A", "Supplier B", "Supplier C", "Supplier D"),
each = 10),
lead_time = c(rnorm(10, mean = 37, sd = 1), # Lead times for Supplier A
rnorm(10, mean = 35, sd = 1.5), # Lead times for Supplier B
rnorm(10, mean = 33, sd = 0.5), # Lead times for Supplier C
rnorm(10, mean = 42, sd = 2)) # Lead times for Supplier D
)
glimpse(df1)Rows: 40
Columns: 2
$ supplier <chr> "Supplier A", "Supplier A", "Supplier A", "Supplier A", "Sup…
$ lead_time <dbl> 36.43952, 36.76982, 38.55871, 37.07051, 37.12929, 38.71506, …Now let’s create a box plat chart, using the library highcharter and the function hcboxplot() :
hcboxplot(
x = df1$lead_time,
var = df1$supplier,
name = "Transit time",
color = "#2980b9"
) |>
hc_chart(type = "column") |>
hc_title(text = "Transit time Distribution per Supplier") |>
hc_subtitle(text = "in days")20 Additional Features
20.1 Display of percentages
How about displaying some percentages?
First, let’s create a simple data frame.
In this example we look at some projected inventories (of a portfolio of products) and the projected percentage (e.g. the ratio) of overstocks inventories. We want to display this projected ratio in the coming months.
# create vectors
period <- c("2026-01-01", "2026-02-01", "2026-03-01", "2026-04-01")
ratio <- c(68, 52, 47, 44)
# combine into a data frame
df1 <- data.frame(period, ratio)
# format as Date
df1$period <- as.Date(df1$period, format = "%Y-%m-%d")
# display
df1 period ratio
1 2026-01-01 68
2 2026-02-01 52
3 2026-03-01 47
4 2026-04-01 44Now we can create the chart, using the syntax {point.y:.1f}% as format for the dataLabels, and also for the hc_tooltip .
Notes :
we also display and format here the
hc_yAxis.- using the syntax
hc_yAxis(labels = list(format = "{value}%")).
- using the syntax
the ratio in the data frame is expressed as 68 and not 0.68 .The conversion in percentage is done in the highchart.
highchart() |>
hc_title(text = "ratio of overstocks inventories") |>
hc_subtitle(text = "as % of total projected inventories") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$period) |>
hc_yAxis(labels = list(format = "{value}%")) |>
hc_add_series(name = "share of Overstocks",
color = "orange",
data = df1$ratio,
dataLabels = list(enabled = TRUE, format = '{point.y:.1f}%')
) |>
hc_tooltip(pointFormat = '{point.y:.1f}%')20.2 Horizontal reference lines
Having some reference lines can make the reading of a chart faster and more insightful, for example :
how is the observed value vs a min & max thresholds or vs an average value ?
are we frequently below or above those thresholds ?
are we significantly below or above those thresholds ?
do we need to take any actions ?
To illustrate the syntax, we will use the simple demo data frame below, and display a monthly production charge vs a minimum and maximum production capacity.
#---------------------
# create demo data frame
#---------------------
# create vectors
period <- c("M", "M+1", "M+2", "M+3", "M+5")
charge <- c(700, 800, 1000, 300, 600)
max_capacity <- c(800, 800, 800, 800, 800)
min_capacity <- c(300, 300, 300, 300, 300)
# combine into a data frame
df1 <- data.frame(period,
charge,
max_capacity,
min_capacity)
#---------------------
# chart
#---------------------
# chart
highchart() |>
hc_title(text = "Production Charge vs Capacity") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$period) |>
hc_add_series(name = "Charge",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
type ='column',
data = df1$charge) |>
hc_add_series(name = "Min Capacity",
color = "mediumseagreen",
data = df1$min_capacity) |>
hc_add_series(name = "Max Capacity",
color = "salmon",
data = df1$max_capacity)20.3 Vertical reference lines
Reference lines can also be vertical.
They can be used to display specific periods of time for example.
Let’s use the previous data frame “sales_data” :
# set working dataframe
df1 <- sales_data
# Convert the dates to milliseconds since epoch for plotLines
plot_line_date_1 <- as.numeric(as.POSIXct("2018-07-01")) * 1000
plot_line_date_2 <- as.numeric(as.POSIXct("2019-06-01")) * 1000
# chart
highchart() |>
hc_title(text = "Sales Volumes") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_add_series(data = df1,
type = "line",
hcaes(x = calendar_period, y = sales_qty),
name = "Sales") %>%
hc_xAxis(type = "datetime", # Ensure x-axis is treated as datetime
plotLines = list(
list(
value = plot_line_date_1,
color = "mediumseagreen",
width = 2,
label = list(text = "Beginning of Horizon")
),
list(
value = plot_line_date_2,
color = "red",
width = 2,
label = list(text = "End of Horizon")
)
))In this chart, we can for example :
highlight a specific horizon of time where something happened.
display a training horizon where we want to train our Statistical Forecasts model.
20.4 Background
To make the reading easier, we also can add a background to our chart.
In the example below, we will :
add a lightgray background.
label it as “Year-To-Date”, to mention the historical period of time until now.
#---------------------
# create demo data frame
#---------------------
# create vectors
calendar_month_abb <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
sales_qty <- c(700, 800, 1000, 300, 600, 800, 900, 500, 400, 1200, 1000, 800)
# combine into a data frame
df1 <- data.frame(calendar_month_abb,
sales_qty)
# create a factor
df1$calendar_month_abb <- factor(df1$calendar_month_abb,
levels = c("Jan","Feb","Mar","Apr","May","Jun",
"Jul","Aug","Sep", "Oct", "Nov", "Dec"))
#---------------------
# chart
#---------------------
highchart() |>
hc_add_series(name = "sales",
color = "mediumseagreen",
type = 'column',
dataLabels = list(align = "center", enabled = TRUE),
data = df1$sales_qty) |>
hc_title(text = "Monthly Demand") |>
hc_subtitle(text = "in units") |>
hc_xAxis(categories = df1$calendar_month_abb,
plotLines = list(
list(label = list(text = "End of YTD Period"),
color = "orange",
width = 3,
value = 5)
),
plotBands = list(
list(from = 0,
to = 5,
color = "lightgray",
label = list(text = "Year-To-Date")
)
)
) |> # close hc_xAxis
hc_add_theme(hc_theme_google()) There’s (much) more!
For more examples, let’s have a look at the website of the R package highcharter .