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introdataviz

Installation

You can install introdataviz with:

# you may have to install devtools first with 
# install.packages("devtools")

devtools::install_github("psyteachr/introdataviz")
library(introdataviz)

Workbook functions

Open a local copy of the book:

introdataviz::book()

Save the workbook and data file to your working directory and open the workbook in RStudio:

introdataviz::workbook()

Data functions

ldt_data is the main data set we use in the tutorials. It is simulated lexical decision task data for monolingual and bilingual participants. DVs are mean reaction time (rt) and accuracy (acc) for words and non-words.

id age language rt_word rt_nonword acc_word acc_nonword
S001 22 1 379.4585 516.8176 99 90
S002 33 1 312.4513 435.0404 94 82
S003 23 1 404.9407 458.5022 96 87
S004 28 1 298.3734 335.8933 92 76
S005 26 1 316.4250 401.3214 91 83
S006 29 1 357.1710 367.3355 96 78

ldt_long is ldt_data converted to a different (longer) format for plotting and converting the numeric value for language into words.

id age language condition rt acc
S001 22 monolingual word 379.4585 99
S001 22 monolingual nonword 516.8176 90
S002 33 monolingual word 312.4513 94
S002 33 monolingual nonword 435.0404 82
S003 23 monolingual word 404.9407 96
S003 23 monolingual nonword 458.5022 87

Plotting functions

This package also provides two functions (geom_split_violin() and geom_flat_violin()) used to make split-violin and raincloud plots (modified from code by Allen et al., 2021).

Split-violin plots

Split-violin plots remove the redundancy of mirrored violin plots and make it easier to compare the distributions between multiple conditions.

colours <- c("dodgerblue2", "darkorange")

ggplot(ldt_long, aes(x = condition, y = rt, fill = language)) +
  introdataviz::geom_split_violin(alpha = .4) +
  geom_boxplot(width = .2, alpha = .6, show.legend = FALSE) +
  stat_summary(fun.data = "mean_se", geom = "pointrange", show.legend = F, 
               position = position_dodge(.175)) +
  scale_x_discrete(name = "Condition", labels = c("Non-word", "Word")) +
  scale_y_continuous(name = "Reaction time (ms)") +
  scale_fill_manual(values = colours, name = "Language group") +
  theme_minimal()
Split-violin plot

Split-violin plot

Raincloud plots

Raincloud plots combine a density plot, boxplot, raw data points, and any desired summary statistics for a complete visualisation of the data. They are so called because the density plot plus raw data is reminiscent of a rain cloud.

rain_height <- .1

ggplot(ldt_long, aes(x = "", y = rt, fill = language)) +
  # clouds
  introdataviz::geom_flat_violin(trim=FALSE, alpha = 0.4,
    position = position_nudge(x = rain_height+.05)) +
  # rain
  geom_point(aes(colour = language), size = 2, alpha = .5, show.legend = FALSE, 
              position = position_jitter(width = rain_height, height = 0)) +
  # boxplots
  geom_boxplot(width = rain_height, alpha = 0.5, show.legend = FALSE, 
               outlier.shape = NA,
               position = position_nudge(x = -rain_height*2)) +
  # mean and SE point in the cloud
  stat_summary(fun.data = mean_se, mapping = aes(color = language), show.legend = FALSE,
               position = position_nudge(x = rain_height * 3)) +
  # adjust layout
  scale_x_discrete(name = "", expand = c(rain_height*3, 0, 0, 0.7)) +
  scale_y_continuous(name = "Reaction time (ms)",
                     breaks = seq(200, 800, 100), 
                     limits = c(200, 800)) +
  coord_flip() +
  facet_wrap(~factor(condition, 
                     levels = c("word", "nonword"), 
                     labels = c("Words", "Non-Words")), 
             nrow = 2) +
  # custom colours and theme
  scale_fill_manual(values = colours, name = "Language group") +
  scale_colour_manual(values = colours) +
  theme_minimal() +
  theme(panel.grid.major.y = element_blank(),
        legend.position = c(0.8, 0.8),
        legend.background = element_rect(fill = "white", color = "white"))
#> Warning: Removed 10 rows containing missing values (violinist).
Raincloud plot

Raincloud plot