Hello R, meet plotly!

Stacked Area Chart

As you can see that previous example was not really different from the regular line plot. However, there is a modification of area chart called stacked area chart that is way more useful. It is used to show the difference when two or more labels are included in the plot. When multiple attributes are included, the first attribute is plotted as a line with color fill followed by the second attribute, and so on. For example, let’s take a look on how amount of awards differs among men and women for each year.

However, this requires some manual manipulation with the data frame.

# select count for women by all years
female_df <- nobel_df %>% 
  filter(Sex == "Female") %>% 
  group_by(Year) %>% 
  summarise(female = n())

# select count for men by all years
male_df <- nobel_df %>% 
  filter(Sex == "Male") %>% 
  group_by(Year) %>% 
  summarise(male = n())

## since there migh be years when just women/just men got the award
## we need to join previous dfs with *all* years available in the data set
## and replace missing values by 0s.
joint_df <- nobel_df %>% 
  select(Year) %>% 
  distinct(Year) %>% 
  left_join(female_df, by = "Year") %>% 
  left_join(male_df, by = "Year") %>% 
  mutate(female = replace_na(female, 0),
         male = replace_na(male, 0))

kable(head(joint_df))

fig1 <- joint_df %>% 
  plot_ly(
    x = ~Year, y = ~male, 
    name = 'Male', stackgroup = 'one', # specify that this is stacked chart
    type = 'scatter', mode = "none",
    legendgroup = 'Male', # legend group name
    fillcolor = 'rgba(158,202,225, 0.7)',
    hovertemplate = paste('<i>Year</i>: %{x}',
                          '<br><i>Awards</i>: %{y}'))

fig1 <- fig1 %>% 
  add_trace(
    y = ~female, name = 'Female',  
    fillcolor = 'rgba(255, 188, 101, 0.8)',
    legendgroup = 'Female', # legend group name
    hovertemplate = paste('<i>Year</i>: %{x}',
                          '<br><i>Awards</i>: %{y}'))

Also, stacked chart can be used to show the normalized data (ratio) foe each label. It can be done by simply adding groupnorm = 'percent' to plot_ly function.

fig2 <- joint_df %>% 
  plot_ly(
    x = ~Year, y = ~male, 
    name = 'Male', stackgroup = 'one', 
    type = 'scatter', mode = "none", showlegend = FALSE,
    groupnorm = 'percent', legendgroup = 'Male',
    fillcolor = 'rgba(158,202,225, 0.7)',
    hovertemplate = paste('<i>Year</i>: %{x}',
                          '<br><i>Ratio</i>: %{y: .2f}%'))

fig2 <- fig2 %>% 
  add_trace(y = ~female, name = 'Female',  
            legendgroup = 'Female', showlegend = FALSE,
            fillcolor = 'rgba(255, 188, 101, 0.8)',
            hovertemplate = paste('<i>Year</i>: %{x}',
                                  '<br><i>Ratio</i>: %{y: .2f}%'))

Now we have two objects fig1 (stacked area chart) and fig2 (normalized stacked area chart). We can show them on one plot using subplots. The reason why we used showlegend = FALSE in fig2 object is that we grouped two legends for subplot by adding legendgroup and we don’t need to show the same legend twice.

# `shareX` means that `x` axis will be the same for both plots
subplot(fig1, fig2, nrows = 2, shareX = TRUE) %>% 
  layout(title = "<b>Total Awards by Year by Gender</b>",
         xaxis = list(title = "<b>Year</b>"),
         yaxis = list(title = "<b>Number of Awards</b>"),
         yaxis2 = list(title = "<b>Ratio of Awards (%)</b>"),
         margin = list(t = 70))

Summary:

The idea for area chart stays the same as for line chart - x variable should be continuous:

• x axis - numerical variable (usually time series)
• y axis - numerical variable

You can add more categorical variables as new area charts.

What if we wanted to see how the number of awards by Category (categorical variable) on x axis rather then by Year? We couldn’t use line chart, however, we could use the bar chart.

Grouped Bar chart

In a grouped bar chart, for each categorical group there are two or more bars. These bars are color-coded to represent a particular grouping. example, a business owner with two stores might make a grouped bar chart with different colored bars to represent each store: the horizontal axis would show the months of the year and the vertical axis would show the revenue. [4]

This is useful for side-by-side comparison among categories.

Stacked Bar Chart

The stacked bar chart stacks bars that represent different groups on top of each other. The height of the resulting bar shows the combined result of the groups. However, stacked bar charts are not suited to data sets where some groups have negative values. In such cases, grouped bar chart are preferable.

< Grouped bar graphs usually present the information in the same order in each grouping. Stacked bar graphs present the information in the same sequence on each bar. [4]

The idea is similar to stacked area chart. For each x value (or y if we use horizontal bar chart) we stack a value of the label on top of each other.

grouped_bc <- plot_ly()
grouped_bc <- grouped_bc %>% 
  add_trace(
    ## some manipulations with the data:
    ## first plotly object will have the count of 
    ## awards by categories for men only
    data = nobel_df %>%
      filter(Sex == "Male") %>% 
      group_by(Category) %>% 
      summarise(awards = n()),
    x = ~Category, y = ~awards,
    name = "Men", type = "bar", legendgroup = 'Male',
    # change the color of bar
    marker = list(color = 'rgba(158,202,225, 0.9)'),
    hovertemplate = paste('<i>Category</i>: %{x}',
                          '<br><i>Awards</i>: %{y}')) %>% 
  add_trace(
    ## second plotly object will have the count of 
    ## awards by categories for women only
    data = nobel_df %>%
      filter(Sex == "Female") %>% 
      group_by(Category) %>% 
      summarise(awards = n()),
    x = ~Category, y = ~awards,
    name = "Women", type = "bar", legendgroup = 'Female',
    marker = list(color = 'rgba(255, 188, 101, 0.9)'),
    hovertemplate = paste('<i>Category</i>: %{x}',
                          '<br><i>Awards</i>: %{y}')) %>% 
  layout(title = paste0("<b>Total awards by Category and Gender</b><br>",
                        "<i>Grouped Bar Chart</i>"),
         barmode = 'group', # set the type of bar chart
         xaxis = list(title = "<b>Category</b>"),
         yaxis = list(title = "<b>Number of Awards</b>"),
         margin = list(t = 70))

grouped_bc

stacked_bc <- plot_ly()

stacked_bc <- stacked_bc %>% 
  add_trace(
    data = nobel_df %>%
      filter(Sex == "Male") %>% 
      group_by(Category) %>% 
      summarise(awards = n()),
    x = ~Category, y = ~awards,
    name = "Men", type = "bar", legendgroup = 'Male',
    marker = list(color = 'rgba(158,202,225, 0.9)'),
    hovertemplate = paste('<i>Category</i>: %{x}',
                          '<br><i>Awards</i>: %{y}')) %>% 
  add_trace(
    data = nobel_df %>%
      filter(Sex == "Female") %>% 
      group_by(Category) %>% 
      summarise(awards = n()),
    x = ~Category, y = ~awards,
    name = "Female", type = "bar", legendgroup = 'Female',
    marker = list(color = 'rgba(255, 188, 101, 0.9)'),
    hovertemplate = paste('<i>Category</i>: %{x}',
                          '<br><i>Awards</i>: %{y}')) %>% 
  layout(title = paste0("<b>Total awards by Category and Gender</b><br>",
                        "<i>Stacked Bar Chart</i>"),
         barmode = 'stack', # set the type of bar chart
         xaxis = list(title = "<b>Category</b>"),
         yaxis = list(title = "<b>Number of Awards</b>"),
         margin = list(t = 70))

stacked_bc

Note, that it would be still ok to plot total awards by year using bar chart.

Summary:

• For horizontal bar chart:
  • x axis - numerical variable
  • y axis - categorical variable
• For vertical bar chart:
  • x axis - categorical variable
  • y axis - numerical variable
• For side-by-side comparison among different categorical variables you can use grouped bar chart.
• If you are interested in total proportion of each categorical variable for each x values (for vertical bar chart) you can use stacked bar chart (or normalized stacked bar chart).

Bubble Chart

A bubble chart is a type of chart that displays three dimensions of data. Each entity with its triplet of associated data is plotted as a disk that expresses two of the vi values through the disk’s xy location and the third through its size. Bubble charts can facilitate the understanding of social, economical, medical, and other scientific relationships. [7]

We will slightly modify previous plot to add unemployment rate as a third variable:

edindex_df %>% 
  plot_ly(
    x = ~GDP, y = ~OOCP,
    color = ~UNEMP,  colors = "Blues", # adding colors
    text = ~`Country Name`, name = "",
    type = "scatter", mode = 'markers',
    marker = list(size = ~UNEMP), # add `UNEMP` variable as a size of a point
    hovertemplate = paste0(
      '<b>Country</b>: %{text}',
      '<br><b>Gross domestic product</b>: %{y}<br>',
      '<b>Out-of-school children of Primary School</b>: %{x}<br>')) %>% 
  layout(
    title = "<b>GDP vs Out-of-school children of Primary School</b>",
    xaxis = list(title = "<b>Gross domestic product</b>"),
    yaxis = list(title = "<b>Out-of-school children of Primary School</b>"),
    margin = list(t = 70)) %>% 
  # change name of color scale
  colorbar(title = '<b>Unemployment<br>Rate</b>')

We could also add a third categorical variable, for example we could add a column pop_size with three labels - less than 10m, less than 20m, greater than 20m.

edindex_df %>% 
  mutate(pop_size = case_when(PPT < 10*10^6 ~ "less than 10m",
                              PPT < 20*10^6 ~ "less than 20m",
                              TRUE ~ "greater than 20m")) %>% 
  plot_ly(
    x = ~GDP, y = ~OOCP,
    color = ~pop_size,  
    text = ~`Country Name`, 
    marker = list(size = ~UNEMP),
    type = "scatter", mode = 'markers',
    hovertemplate = paste0(
      '<b>Country</b>: %{text}',
      '<br><b>Gross domestic product</b>: %{y}<br>',
      '<b>Out-of-school children of Primary School</b>: %{x}<br>')) %>% 
  layout(
    title = "<b>GDP vs Out-of-school children of Primary School</b>",
    xaxis = list(title = "<b>Gross domestic product</b>"),
    yaxis = list(title = "<b>Out-of-school children of Primary School</b>"),
    margin = list(t = 70))

Summary:

• x axis - numerical variable
• y axis - numerical variable
• Third continuous varialbe can be added as a size of a point.
• Third categorical varialbe can be added as a color/shape of a point.