Generate a scatterplot.

Easily generate scatterplots using ggplot2 with a simplified customization interface for common modifications with static (ggplot) and interactive (plotly) output options. The static output is useful for producing static reports (e.g. for manuscripts) and is readily customized further using ggplot2 syntax. The interactive output is helpful for exploring the data and producing dynamic html reports. See this blog post for an introduction to ggplot2.

plot_scatter(
  data,
  y,
  x,
  jitter = FALSE,
  shape = 21,
  ...,
  fill_var = NULL,
  colour_var = NULL,
  shape_var = NULL,
  size_var = NULL,
  ylab = NULL,
  xlab = NULL,
  title = NULL,
  title_hjust = 0.5,
  caption = NULL,
  caption_hjust = 0,
  fill_var_title = NULL,
  colour_var_title = NULL,
  shape_var_title = NULL,
  size_var_title = NULL,
  ylim = c(NA, NA),
  ybreaks = ggplot2::waiver(),
  transform_y = FALSE,
  y_transformation = "log10",
  y_var_labs = ggplot2::waiver(),
  xlim = c(NA, NA),
  xbreaks = ggplot2::waiver(),
  transform_x = FALSE,
  x_transformation = "log10",
  x_var_labs = ggplot2::waiver(),
  size_lim = c(NA, NA),
  transform_size = FALSE,
  size_transformation = "log10",
  fill_var_order = NULL,
  colour_var_order = NULL,
  shape_var_order = NULL,
  fill_var_labs = NULL,
  colour_var_labs = NULL,
  shape_var_labs = NULL,
  fill_var_values = NULL,
  colour_var_values = NULL,
  shape_var_values = NULL,
  palette = c("plasma", "C", "magma", "A", "inferno", "B", "viridis", "D", "cividis",
    "E"),
  palette_direction = c("d2l", "l2d"),
  palette_begin = 0,
  palette_end = 0.8,
  regression_line = FALSE,
  regression_method = "gam",
  regression_formula = NULL,
  regression_se = FALSE,
  ci_level = 0.95,
  regression_geom = "smooth",
  regression_line_size = 1,
  regression_line_colour = NULL,
  regression_alpha = 0.5,
  regression_line_type = 1,
  regression_line_full_range = FALSE,
  regression_method_args = NULL,
  loess_span = 0.75,
  alpha = 0.6,
  greyscale = FALSE,
  theme = c("bw", "classic", "grey", "light", "dark", "minimal"),
  text_size = 14,
  font = c("sans", "serif", "mono"),
  facet_var = NULL,
  facet_var_order = NULL,
  facet_var_labs = NULL,
  facet_var_strip_position = c("top", "bottom"),
  facet_var_text_bold = TRUE,
  legend_position = c("right", "left", "top", "bottom"),
  omit_legend = FALSE,
  interactive = FALSE,
  aesthetic_options = FALSE
)

Arguments

data

A data frame or tibble containing the dependent measure "y", the independent measure "x", and any grouping variables or covariates.

y

A numeric variable you want to plot against x (quoted or unquoted), e.g. y = "variable" or y = variable.

x

A numeric variable you want to plot against y (quoted or unquoted), e.g. x = "variable" or x = variable.

jitter

Set to TRUE to slightly offset overlapping points in random directions. See geom_jitter for details.

shape

Point shape to use. Default is 21 for "circle filled". Use the "aesthetic_options" argument to view the options.

...

graphical parameters (not associated with variables) to be passed to geom_point, e.g. colour or fill, to be applied to all bars. To see some of the available options in a web browser, set the aesthetic_options argument to TRUE.

fill_var

Use if you want to assign a variable to the point fill colour, e.g. fill_var = "grouping_variable" or fill_var = grouping_variable. Produces separate sets of points for each level of the fill variable. See aes for details. Note: for geom_point, fill_var and fill only affect shapes 21-24. To split the data by a variable based on colour, it is therefore easier to use colour_var for this particular plot geometry. Also works with continuous variables.

colour_var

Use if you want to assign a variable to the point outline colour, e.g. colour_var = "grouping_variable" or colour_var = grouping_variable. Produces separate sets of points for each level of the colour variable. See aes for details.

shape_var

Use if you want to assign a variable to the point shape, e.g. shape_var = "grouping_variable" or shape_var = grouping_variable. Produces separate sets of points for each level of the shape variable. See aes for details.

size_var

Use if you want to assign a continuous variable to the point size, e.g. size_var = "covariate" or size_var = covariate. Adjusts point sizes according to the value of the covariate. See aes for details.

ylab

Specify/overwrite the y-axis label using a character string, e.g. "y-axis label"

xlab

Specify/overwrite the x-axis label using a character string, e.g. "x-axis label"

title

Add a main title to the plot using a character string, e.g. "scatterplot of y as a function of x"

title_hjust

Left-to-right/horizontal justification (alignment) of the main plot title. Accepts values from 0 (far left) to 1 (far right). Default is 0.5 (centre).

caption

Add a figure caption to the bottom of the plot using a character string.

caption_hjust

Left-to-right/horizontal justification (alignment) of the caption. Accepts values from 0 (far left) to 1 (far right). Default is 0 (left).

fill_var_title

If a variable has been assigned to fill using fill_var, this allows you to modify the variable label in the plot legend.

colour_var_title

If a variable has been assigned to colour using colour_var, this allows you to modify the variable label in the plot legend.

shape_var_title

If a variable has been assigned to shape using shape_var, this allows you to modify the variable label in the plot legend.

size_var_title

If a variable has been assigned to shape using shape_var, this allows you to modify the variable label in the plot legend.

ylim

specify the y-axis limits, e.g. ylim = c(lower_limit, upper_limit). Use NA for the existing minimum or maximum value of y, e.g. the default is ylim = c(NA, NA).

ybreaks

This allows you to change the break points to use for tick marks on the y-axis. seq is particularly useful here. See scale_y_continuous for details. If ybreaks is specified, then ylim should be also.

transform_y

Would you like to transform the y axis? (TRUE or FALSE)

y_transformation

If transform_y = TRUE, this determines the transformation to be applied. Common choices include "log10" (the default), "log2", "sqrt", or "exp". See scale_continuous for details.

y_var_labs

Allows you to modify the labels displayed with the y-axis tick marks. See scale_continuous for details.

xlim

specify the x-axis limits, e.g. xlim = c(lower_limit, upper_limit). Use NA for the existing minimum or maximum value of x, e.g. the default is xlim = c(NA, NA)

xbreaks

This allows you to change the break points to use for tick marks on the x-axis. seq is particularly useful here. See scale_x_continuous for details. If xbreaks is specified, then xlim should be also.

transform_x

Would you like to transform the x axis? (TRUE or FALSE)

x_transformation

If transform_x = TRUE, this determines the transformation to be applied. Common choices include "log10" (the default), "log2", "sqrt", or "exp". See scale_continuous for details.

x_var_labs

Allows you to modify the labels displayed with the x-axis tick marks. See scale_continuous for details.

size_lim

specify the size scale limits, e.g. size_lim = c(lower_limit, upper_limit). Use NA for the existing minimum or maximum value of x, e.g. the default is size_lim = c(NA, NA)

transform_size

Would you like to transform the size scale? (TRUE or FALSE)

size_transformation

If transform_size = TRUE, this determines the transformation to be applied. Common choices include "log10" (the default), "log2", "sqrt", or "exp". See scale_continuous for details.

fill_var_order

If a variable has been assigned to fill using fill_var, this allows you to modify the order of the variable groups, e.g. fill_var = grouping_variable, fill_var_order = c("group_2", "group_1"). See fct_relevel for details.

colour_var_order

If a variable has been assigned to colour using colour_var, this allows you to modify the order of the variable groups, e.g. colour_var = grouping_variable, fill_var_order = c("group_2", "group_1"). See fct_relevel for details.

shape_var_order

If a variable has been assigned to shape using shape_var, this allows you to modify the order of the variable groups, e.g. shape_var = grouping_variable, shape_var_order = c("group_2", "group_1"). See fct_relevel for details.

fill_var_labs

If a variable has been assigned to fill using fill_var, this allows you to modify the labels of the variable groups, e.g. fill_var = grouping_variable, fill_var_labs = c("group_1_new_label" = "group_1_old_label", "group_2_new_label" = "group_2_old_label"). See fct_recode for details.

colour_var_labs

If a variable has been assigned to colour using colour_var, this allows you to modify the labels of the variable groups, e.g. colour_var = grouping_variable, colour_var_labs = c("group_1_new_label" = "group_1_old_label", "group_2_new_label" = "group_2_old_label"). See fct_recode for details.

shape_var_labs

If a variable has been assigned to shape using shape_var, this allows you to modify the labels of the variable groups, e.g. shape_var = grouping_variable, shape_var_labs = c("group_1_new_label" = "group_1_old_label", "group_2_new_label" = "group_2_old_label"). See fct_recode for details.

fill_var_values

If a variable has been assigned to fill using fill_var, this allows you to modify the colours assigned to the fill of each of the variable groups, e.g. fill_var = grouping_variable, fill_var_values = c("blue", "red"). See scale_fill_manual for details. For the colour options available in base R, see colour_options.

colour_var_values

If a variable has been assigned to colour using colour_var, this allows you to modify the colours assigned to the outline of each of the variable groups, e.g. colour_var = grouping_variable, colour_var_values = c("blue", "red"). See scale_fill_manual for details. For the colour options available in base R, see colour_options.

shape_var_values

If a variable has been assigned to shape using shape_var, this allows you to modify the shapes assigned to each of the variable groups, e.g. colour_var = grouping_variable, shape_var_values = c(1, 2). See scale_shape_manual for details. For the available shape options, set aesthetic_options = TRUE.

palette

If a variable is assigned to fill_var or colour_var, this determines which viridis colour palette to use. Options include "plasma" or "C" (default), "magma" or "A", "inferno" or "B", "viridis" or "D", and "cividis" or "E". See this link for examples. You can override these colour palettes with fill_var_values or colour_var_values.

palette_direction

Choose "d2l" for dark to light (default) or "l2d" for light to dark.

palette_begin

Value between 0 and 1 that determines where along the full range of the chosen colour palette's spectrum to begin sampling colours. See scale_fill_viridis_d for details.

palette_end

Value between 0 and 1 that determines where along the full range of the chosen colour palette's spectrum to end sampling colours. See scale_fill_viridis_d for details.

regression_line

Set to TRUE if you want to add a regression line to the plot.

regression_method

This determines the type of regression line to use. Common options are "lm", "loess", & "gam". "gam" is the default, which fits a generalized additive model using a smoothing term for x. This method has a longer run time, but typically provides a better fit to the data than other options and uses an optimization algorithm to determine the optimal wiggliness of the line. If the relationship between y and x is linear, the output will be equivalent to fitting a linear model. See stat_smooth and gam for details.

regression_formula

Specify a formula to use with the chosen regression method, using the formula() interface. see stat_smooth and formula for details. Useful for plotting polynomials, e.g. regression_formula = y ~ poly(x, 2).

regression_se

Add a confidence envelope for the regression line? (TRUE or FALSE)

ci_level

Confidence level for the regression_line confidence envelope. Default is 0.95.

regression_geom

Not typically modified. See stat_smooth for more information

regression_line_size

Adjusts the thickness/size of regression lines

regression_line_colour

Adjusts the colour of regression lines

regression_alpha

Adjusts the transparency of the regression confidence envelope.

regression_line_type

Adjusts the line type of regression lines, e.g. "solid", "dashed", etc.

regression_line_full_range

Set to TRUE if you want regression lines to extend beyond the limits of the data to cover the full range of the plot.

regression_method_args

Additional arguments you would like passed to the regression_method modelling function, see method.args in stat_smooth.

loess_span

Affects the wiggliness of the loess regression line. See stat_smooth for details.

alpha

This adjusts the transparency/opacity of the graphical components of the plot, ranging from 0 = 100% transparent to 1 = 100% opaque.

greyscale

Set to TRUE if you want the plot converted to greyscale.

theme

Adjusts the theme using 1 of 6 predefined "complete" theme templates provided by ggplot2. Currently supported options are: "classic", "bw" (the elucidate default), "grey" (the ggplot2 default), "light", "dark", & "minimal". See theme_bw for more information.

text_size

This controls the size of all plot text. Default = 14.

font

This controls the font of all plot text. Default = "sans" (Arial). Other options include "serif" (Times New Roman) and "mono" (Courier New).

facet_var

Use if you want separate plots for each level of a grouping variable (i.e. a faceted plot), e.g. facet_var = "grouping_variable" or facet_var = grouping_variable. See facet_wrap for details.

facet_var_order

If a variable has been assigned for faceting using facet_var, this allows you to modify the order of the variable groups, e.g. facet_var = grouping_variable, facet_var_order = c("group_2", "group_1"). See fct_relevel for details.

facet_var_labs

If a variable has been assigned for faceting using facet_var, this allows you to modify the labels of the variable groups which will appear in the facet strips, e.g. facet_var = grouping_variable, facet_var_labs = c("group_1_new_label" = "group_1_old_label", "group_2_new_label" = "group_2_old_label"). See fct_recode for details.

facet_var_strip_position

If a variable has been assigned for faceting using facet_var, this allows you to modify the position of the facet strip labels. Sensible options include "top" (the default) or "bottom".

facet_var_text_bold

If a variable has been assigned for faceting using facet_var, this allows you to use boldface (TRUE/default or FALSE) for the facet strip label text.

legend_position

This allows you to modify the legend position. Options include "right" (the default), "left", "top", & "bottom".

omit_legend

Set to TRUE if you want to remove/omit the legends.

interactive

Determines whether a static ggplot object or an interactive html plotly object is returned. See ggplotly for details.

aesthetic_options

If set to TRUE, opens a web browser to the tidyverse online aesthetic options vignette.

Value

A ggplot object or plotly object depending on whether static or interactive output was requested.

References

Wickham, H. (2016). ggplot2: elegant graphics for data analysis. New York, N.Y.: Springer-Verlag.

See also

geom_point, geom_jitter, stat_smooth, ggplotly

Author

Craig P. Hutton, craig.hutton@gov.bc.ca

Examples

data(mtcars) #load the mtcars data

plot_scatter(mtcars, y = mpg, x = hp, colour = "blue")


# \donttest{
plot_scatter(mtcars, y = mpg, x = hp)


plot_scatter(mtcars, y = mpg, x = hp,
             colour_var = cyl, shape_var = am, size = 4)


plot_scatter(mtcars, y = mpg, x = hp, colour = "blue",
             shape_var_labs = c("manual" = "0", "automatic" = "1"),
             shape_var = am, theme = "bw")


#map colour, shape, and size to different variables

plot_scatter(mtcars, y = mpg, x = hp,
             colour_var = cyl, shape_var = am, size_var = wt)


#map colour and shape to a common variable
plot_scatter(mtcars, y = mpg, x = hp,
             shape_var = cyl, colour_var = cyl)


#add a regression line

#linear
 plot_scatter(mtcars, y = mpg, x = hp,
              regression_line = TRUE, regression_method = "lm")


#change the regression line colour
plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_method = "lm",
             regression_line_colour = "green")


#add standard error envelope
plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_method = "lm",
             regression_se = TRUE)


#adjust standard error envelope transparency
plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_method = "lm",
             regression_se = TRUE,
             regression_alpha = 0.8) #default is 0.5


#split by a grouping variable
plot_scatter(mtcars, y = mpg, x = hp, colour_var = cyl,
             regression_line = TRUE, regression_method = "lm")


#fit a polynomial regression line by specifying a regression_formula = formula()
plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_method = "lm", regression_se = TRUE,
             regression_formula = y ~ poly(x, 2))


plot_scatter(mtcars, y = mpg, x = hp, shape_var = cyl, colour_var = cyl,
             regression_line = TRUE, regression_method = "lm",
             regression_formula = y ~ poly(x, 3))



#fit a non-linear regression line using locally(-weighted) scatterplot smoothing (loess)
plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_se = TRUE,
             regression_method = "loess")



#fit a non-linear regression line using locally(-weighted) scatterplot smoothing (loess)
#& also adjust the span (default = 0.75).
#This controls how much of the data is used for the weighted smoothing.

plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_se = TRUE,
             regression_method = "loess", loess_span = 0.3)


#fit a non-linear regression line using a generalized additive model (gam), the default
plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_se = TRUE,
             regression_method = "gam")


#use a dashed regression line instead
plot_scatter(mtcars, y = mpg, x = hp,
             regression_line = TRUE, regression_se = TRUE, regression_line_type = "dashed")


#more complex example with overplotting
plot_scatter(pdata, y = y1, x = d, colour_var = g,
             regression_line = TRUE)


#option 1 for dealing with overplotting: add jittering to offset overlappping points
plot_scatter(pdata, y = y1, x = d, colour_var = g,
             jitter = TRUE,
             regression_line = TRUE)


#option 2: make overlapping values more transparent
plot_scatter(pdata, y = y1, x = d, colour_var = g,
             alpha = 0.2,
             regression_line = TRUE)


#option 3: do both and make it interactive
plot_scatter(pdata, y = y1, x = d, colour_var = g,
             jitter = TRUE, alpha = 0.2,
             regression_line = TRUE, interactive = TRUE)


#add a faceting variable
plot_scatter(pdata, y = y1, x = d,
             colour = "black", shape = 21, fill = "green4",
             jitter = TRUE, size = 4, alpha = 0.1,
             regression_line = TRUE, regression_se = TRUE,
             facet_var = g,
             ylab = "outcome",
             theme = "bw")

# }