Distributions

The distribution of any variable, as well as groups of variables, in a coin can be visualised quickly using the plot_dist() function. The simplest case is to plot the distribution of a single indicator.

plot_dist(coin, dset = "Raw", iCodes = "CO2")

To do this, as usual, we have to specify the data set (dset) and the indicator (iCodes) to plot. The data selection for plot_dist() is powered by get_data(), which means we can plot subsets of indicator and units. Commonly, with distribution plots, it might be interesting to plot the distributions of all indicators belonging to a particular group - let’s plot all indicator distributions in the “P2P” pillar:

plot_dist(coin, dset = "Raw", iCodes = "P2P", Level = 1, type = "Violindot")

This plots all eight indicators belonging to that group, and we also specified to plot as “violin-dot” plots. Optionally, data can also be normalised before plotting using the normalise argument. See plot_dist() for more details and further options.

A similar function, plot_dot() also plots a single indicator using dots but is rather used for highlighting individual units rather than as a statistical plot of the distribution. See Dot plots below.

Correlations

Correlation plots are very useful for understanding relationships between indicators. COINr’s plot_corr() function is a flexible tool for plotting correlations between almost any variables in a coin, and visualising them according to the structure of the index.

One thing to keep in mind from the outset is the directionality of your indicators: if some are negative then this will probably be reflected in the correlation plots, unless you normalise the data first. With that in mind, we will build the full example coin including the normalisation step and then plot some correlations:

coin <- build_example_coin(quietly = TRUE)

Now let’s do a basic plot of correlations within a group:

plot_corr(coin, dset = "Normalised", iCodes = list("Physical"), Levels = 1)

Notice the syntax: we have to specify iCodes as a list here, and specify the level to get data from. In this case we have specified that we want the indicators (level 1) of the “Physical” group to be correlated against each other. As usual these arguments are passed to get_data().

The reason that iCodes is specified as a list is that we can pass two character vectors to it, possibly from different levels:

plot_corr(coin, dset = "Aggregated",
          iCodes = list(c("Flights", "LPI"), c("Physical", "P2P")), Levels = c(1,2))

The point being that we can select any set of indicators or aggregates, and correlate them with any other set. We can also pass further arguments to get_data() such as groupings and unit selection, if needed.

Other useful features include the possibility to correlate a set of indicators with only its parent groups - this is done by setting withparent = "family". Here we also set to a discrete colour scheme using flagcolours = TRUE.

plot_corr(coin, dset = "Aggregated", iCodes = list("Sust"), withparent = "family", flagcolours = T)

Notice that boxes are drawn around aggregation groups in this case. As a final example, we show how boxes and groups can be used to show subsets of correlation matrices. Typically the most interesting correlations are within aggregation groups because weak correlations cause less information to be transferred to the aggregate. We can only show in-group correlations using the grouplev argument, which takes an aggregation level to group indicators at:

plot_corr(coin, dset = "Normalised", iCodes = list("Sust"),
          grouplev = 2, flagcolours = T)

This can also be done with the box_level argument, which can be used additionally to highlight groupings at different levels:

plot_corr(coin, dset = "Normalised", grouplev = 3, box_level = 2, showvals = F)

In this case we have also disabled correlation values themselves. Other options include using different types of correlations, and changing colours. For details, see the help page of plot_corr(). It is also worth mentioning that underneath, plot_corr() calls get_corr(), so if you are interested in correlation matrices rather than plots, use that.

Bar

A simple way to look at a set of scores for an indicator is with a bar chart:

plot_bar(coin, dset = "Raw", iCode = "CO2")

The plot_bar() function returns a bar chart of single indicator, sorted from high to low values. We can also colour this by any of the grouping variables found in the coin

plot_bar(coin, dset = "Raw", iCode = "CO2", by_group = "GDPpc_group", axes_label = "iName")

Here we have also set axes_label = "iName" to output indicator names rather than codes. Several other options are available, including a log scale, and colouring options. Here we just show one more thing, which is the possibility to break bars down into underlying component scores. This only works if we are plotting an aggregate score (i.e. level 2 or higher), rather than an indicator, because it looks for the underlying scores used to calculate each aggregate score. For example, we can see how the Sustainability scores break down into their three underlying components, for each country:

plot_bar(coin, dset = "Aggregated", iCode = "Sust", stack_children = TRUE)

Dot plots

COINr’s dot plot is pretty similar to a distribution plot but is intended for showing the position of a particular unit or units relative to its peers. This means that to make it useful, you should also select one or more units to highlight.

plot_dot(coin, dset = "Raw", iCode = "LPI", usel = c("JPN", "ESP"))

Here we have plotted the “LPI” indicator and highlighted Spain and Japan. We can also add a statistic of this indicator, such as the median:

plot_dot(coin, dset = "Raw", iCode = "LPI", usel = c("JPN", "ESP"), add_stat = "median",
         stat_label = "Median", plabel = "iName+unit")

Here we have also labelled the statistic using stat_label, and labelled the x-axis using the indicator name and unit which are taken from the indicator metadata found within the coin.

Scatter

The plot_scatter() function gives a quick way to plot scatter plots between any indicators or any variables in a coin.

plot_scatter(coin, dsets = "Raw", iCodes = c("Goods", "Services"), point_label = "uCode")

Variables can come from different data sets (including unit metadata), and we can also colour by groups:

plot_scatter(coin, dsets = c("uMeta", "Raw"), iCodes = c("Population", "Flights"),
             by_group = "GDPpc_group", log_scale = c(TRUE, FALSE))

Here we have also converted the x-axis to a log scale since population is highly skewed. Other options can be found in the help page of plot_scatter(), and all plots can be further modified using ggplot2 commands.