You can install introdataviz with:
# you may have to install devtools first with
# install.packages("devtools")
devtools::install_github("psyteachr/introdataviz")library(introdataviz)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()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 |
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 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
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
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