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

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