Package 'chartql'

ChartQL Chart Generator

Description

Generates a chart/plot using source_frame as the source dataframe and using cql_string as the query string. Options and structure of the query string are described below:

Usage

cql(source_frame, cql_string)

Arguments

source_frame	DataFrame object. Used as the source to build the plot.
cql_string	ChartQL query string. Used to specify options in terms of how to build the plot. This includes the type of plot, choice of x and y variables, axis titles and more. Full parameter list below.

Details

The ChartQL query language uses a fairly simple format to let users generate amazing plots but with minimal scripting, allowing for much faster prototyping. The ChartQL string uses the following general format:

CHART <chart_type> X <x_var> Y <y_var> (Options) <options>

The main variables that are required for all types of plots are:

Chart <chart_type>:				bar|scatter|hist|line
X <x_var>, <(x_facet)>:				X-axis variable. May include a second categorical
				variable (comma separated)
Y <y_var>:				Y-axis variable. *Not used for hist.

Optional Variables

X_label '<xlab>':				X-axis custom label. Defaults to <x_var>
Y_label '<ylab>':				Y-axis custom label. Defaults to <y_var>
Legend '<legend>':				Legend custom label. Defaults to <x_category>
Colorset '<clist>':				Custom set of colors for <x_category> levels.
				Must be comma-separated list.
AggFunc '<func>':				Summary function for bar type.
				Valid types: mean|median|count|sum.
				Defaults to mean.
Fit '<show_se>':				Valid types: true|false. Fit quadratic line.
				Show/Don't show standard error curves.
ConfInt '<interval>':				Show error bars. Value is confidence interval value. E.g. '.95'

Value

response	List object. Entries are the valid parameters and their values extracted from cql_string.
plot_obj	ggplot object. This is the resulting plot.

Note

1. Custom label parameters X_label, Y_label and Legend may contain newlines but should be escaped with an extra backslash as: "\\n".

2. The parameter names themselves are not case-sensitive (e.g. both "CHART bar" and "chart bar" are valid formatting).

3. You can hide the Legend by setting the value to "<>" (e.g. "Legend '<>'")

Examples

# Test data
dframe <- data.frame(
category = factor(c(rep("Sports",50), rep("Home", 150), rep("Fashion", 100))),
season = factor(c(rep(c("Fall","Winter"),150))),
sales = c(runif(100,min=0,max=100), runif(100,min=50,max=200), runif(100,min=50,max=80))
)
dframe$visitors <- dframe$sales * runif(300, min=0.5, max=1.5)


# Bar chart with product category on x-axis and total sales on y-axis
cql_str <- "CHART bar X category Y sales";
cql(dframe, cql_str);


# Bar chart but facet x-axis with both category and season of the year
cql_str <- "CHART bar X category, season Y sales X_label 'Product\\nCategory'";
cql(dframe, cql_str);


# Bar chart with 95% confidence interval error bars
cql_str <- "CHART bar X category, season Y sales ConfInt '.95'";
cql(dframe, cql_str);


# Bar chart but specify the colors for each season
cql_str <- "CHART bar X category, season Y sales Colorset '#FF9900, #990099'";
cql(dframe, cql_str);


# Scatter plot of number of visitors and sales
cql_str <- "CHART scatter X visitors Y sales";
cql(dframe, cql_str);


# Scatter plot but facet by season
cql_str <- "CHART scatter X visitors, season Y sales";
cql(dframe, cql_str);


# Scatter plot with fitted line (no SE curves)
cql_str <- "CHART scatter X visitors, season Y sales Fit 'false'";
cql(dframe, cql_str);

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