Consistency between charts

A collection of common dataviz caveats by Data-to-Viz.com




Let’s consider a small report where you present several graphics to your audience. The report is composed of:


# Libraries
library(tidyverse)
library(hrbrthemes)
library(patchwork)
library(viridis)

# create dummy data
data <- data.frame(
  product = LETTERS[1:5],
  value = c(10, 14, 18, 6, 15)
)

# Plot
a <- data %>%
  ggplot( aes(x=product, y=value, fill=product)) +
    geom_bar(stat="identity", width=0.5) +
    scale_fill_viridis(discrete=TRUE) +
    ggtitle("How much the products cost") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=12),
      legend.position = "none"
    ) +
    xlab("") +
    ylab("Cost in M$")

# create dummy data
data <- data.frame(
  year = rep( seq(1,10), 5 ),
  product = rep(LETTERS[1:5], each=10 ),
  value = c(
    seq(1,10) + sample( 1:3, 10, replace = TRUE),
    seq(5,14) + sample( 4:10, 10, replace=TRUE),
    seq(10,1)*2 + sample( 3:5, 10, replace=TRUE),
    seq(20,11) + sample( 12:17, 10, replace=TRUE),
    seq(1,10) + sample( 40:10, 10))
)

# Plot
b <- data %>%
  ggplot( aes(x=year, y=value, color=product)) +
    geom_line() +
    scale_color_discrete() +
    scale_x_continuous(breaks=c(1:10)) +
    ggtitle("How much the products make") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=12)
    ) +
    xlab("Year") +
    ylab("Benefit in M$")

# Show
a + b

Here, it is very hard to make the link between the two charts, since the color palette used for each chart is different. Product A is in dark purple on the left graphic, and in red on the right one. It takes time to make the link.

Be consistent.

When you have several graphic, use the same color for the same item throughout the report:

# Libraries
library(tidyverse)
library(hrbrthemes)
library(patchwork)
library(viridis)

# create dummy data
data <- data.frame(
  product = LETTERS[1:5],
  value = c(10, 14, 18, 6, 15)
)

# Plot
a <- data %>%
  ggplot( aes(x=product, y=value, fill=product)) +
    geom_bar(stat="identity", width=0.5) +
    scale_fill_viridis(discrete=TRUE) +
    ggtitle("How much the products cost") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=12),
      legend.position = "none"
    ) +
    xlab("") +
    ylab("Cost in M$")

# create dummy data
data <- data.frame(
  year = rep( seq(1,10), 5 ),
  product = rep(LETTERS[1:5], each=10 ),
  value = c(
    seq(1,10) + sample( 1:3, 10, replace = TRUE),
    seq(5,14) + sample( 4:10, 10, replace=TRUE),
    seq(10,1)*2 + sample( 3:5, 10, replace=TRUE),
    seq(20,11) + sample( 12:17, 10, replace=TRUE),
    seq(1,10) + sample( 40:10, 10))
)

# Plot
b <- data %>%
  ggplot( aes(x=year, y=value, color=product)) +
    geom_line() +
    scale_color_viridis(discrete=TRUE) +
    scale_x_continuous(breaks=c(1:10)) +
    ggtitle("How much the products make") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=12)
    ) +
    xlab("Year") +
    ylab("Benefit in M$")

# Show
a + b

That’s better, isn’t it?


Dataviz decision tree

Data To Viz is a comprehensive classification of chart types organized by data input format. Get a high-resolution version of our decision tree delivered to your inbox now!


High Resolution Poster
 

A work by Yan Holtz for data-to-viz.com