# Chapter 3 Improving visualisation I

Below we are creating data which are the results of a word-recognition-task experiment.

``````data <- data.frame(
sex = c("male", "male", "male", "male", "male",
"male", "male", "male", "male", "male",
"female", "female", "female", "female", "female",
"female", "female", "female", "female", "female"),
score = c(6.41, 6.34, 2.46, 3.93, 4.5, 6.47, 3.52, 5.4, 3.91, 7.06,
3.06, 2, 3.75, 3.18, 3.22, 9.94, 11.49, 11.19, 11.36, 10.94)
)``````

A researcher concludes on the basis of the below graph that women have higher verbal intelligence than men on the basis of this word-recognition-task. What do you think?

``````ggplot(data, aes(x = sex, y = score)) +
geom_bar(stat = "summary", fun = "mean")`````` ## 3.1 Above all else …

The above graph is far from showing all the table. In fact, all that fuss to represent two averages. That could have been expressed in one sentence! Let’s try showing all the data.

``````ggplot(data, aes(x = sex, y = score)) +
geom_bar(stat = "summary", fun = "mean") +
geom_point(colour = "blue")`````` Better! How do you feel about the conclusion now?

## 3.2 Do we (ever) need the bars?

``````ggplot(data, aes(x = sex, y = score)) +
geom_point(colour = "blue")`````` ## 3.3 But I want averages!

``````ggplot(data, aes(x = sex, y = score)) +
geom_point(colour = "blue") +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "purple", size = 7, alpha = 0.5)`````` ## 3.4 What about 95% CI!?

If you must stick to inferential statistics:

``````ggplot(data, aes(x = sex, y = score)) +
geom_point(colour = "blue") +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "purple", size = 7, alpha = 0.5) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
colour = "purple", alpha = 0.5) `````` ## 3.5 Onions have layers

``````ggplot(data, aes(x = sex, y = score)) +
geom_violin(aes(fill = sex)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "purple", size = 5, alpha = 0.5) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "purple", alpha = 0.5) +
geom_point(colour = "blue", size = 4, alpha = 0.3) `````` Oh my god, so much information, also oh my god my eyes hurt. And why is that legend there!?!

### 3.5.1 Let’s do better

Let’s try to improve, by making use of a ggplot-extension:

``````#install.packages("gghalves")
library(gghalves)``````
``````ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "l", colour = NA) +
geom_half_point(side = "r", transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none")`````` #### 3.5.1.1 Flip the script

Flipping the axes leads to a cloud-and-rain-plot:

``````ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "r", colour = NA) +
geom_half_point(side = "l", transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none") +
coord_flip()`````` ### 3.6.1 A grey background!?

Grey is the default theme because, believe it or not, contrasts work better on a grey background. That might be a pro, aesthetically, it’s a bit less nice. Let’s try a different theme (see also the section on themes!).

``````ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "r", colour = NA) +
geom_half_point(side = "l", transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none") +
coord_flip() +
theme_minimal()`````` ### 3.6.2 Give it some colour

Defaults colour are useful because they are chosen on how dissimilar they are. We might want to go for aesthetically pleasing colours. See the section on colours.

``````# install.packages("wesanderson")
library(wesanderson)

ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "r", colour = NA) +
geom_half_point(side = "l", colour = "grey", size = 4,
transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none") +
coord_flip() +
theme_minimal() +
scale_fill_manual(values = wes_palette("FantasticFox1"))`````` ``````ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "r", colour = NA) +
geom_half_point(side = "l", colour = "grey", size = 4,
transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none") +
coord_flip() +
theme_minimal() +
scale_fill_manual(values = wes_palette("FantasticFox1")) +
labs(x = NULL, y = "score on word-recognition-task") +
scale_x_discrete(labels = c("male" = "men", "female" = "women"))`````` ### 3.6.4 Titles are useful

``````ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "r", colour = NA) +
geom_half_point(side = "l", colour = "grey", size = 4,
transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none") +
coord_flip() +
theme_minimal() +
scale_fill_manual(values = wes_palette("FantasticFox1")) +
labs(x = NULL, y = "score on word-recognition-task",
title = "Women score higher than men on average on word-recognition-task",
subtitle = "based on 10 male and 10 female psychology students") +
scale_x_discrete(labels = c("male" = "men", "female" = "women"))`````` ### 3.6.5 Changing the axes

What if I told you the minimum possible score on the word-recognition-task is 1, and the maximum is 12. Perhaps we want to visualise that:

``````ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "r", colour = NA) +
geom_half_point(side = "l", colour = "grey", size = 4,
transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none") +
coord_flip() +
theme_minimal() +
scale_fill_manual(values = wes_palette("FantasticFox1")) +
labs(x = NULL, y = "score on word-recognition-task",
title = "Women score higher than men on average on word-recognition-task",
subtitle = "based on 10 male and 10 female psychology students") +
scale_x_discrete(labels = c("male" = "men", "female" = "women")) +
scale_y_continuous(limits = c(1, 12), breaks = seq(0, 12, 1))`````` ### 3.6.6 Changing theme elements

You can change many thematic elements in ggplot (see section on theme). Let’s see some at work.

``````ggplot(data, aes(x = sex, y = score)) +
geom_half_violin(aes(fill = sex), side = "r", colour = NA) +
geom_half_point(side = "l", colour = "grey", size = 4,
transformation = position_jitter(height = 0)) +
geom_point(stat ="summary", fun = "mean", shape = 15,
colour = "black", size = 3) +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96),
width = 0, colour = "black") +
guides(fill = "none") +
coord_flip() +
theme_minimal() +
scale_fill_manual(values = wes_palette("FantasticFox1")) +
labs(x = NULL, y = "score on word-recognition-task",
title = "Women score higher than men on average on word-recognition-task",
subtitle = "based on 10 male and 10 female psychology students") +
scale_x_discrete(labels = c("male" = "men", "female" = "women")) +
scale_y_continuous(limits = c(1, 12), breaks = seq(0, 12, 1)) +
theme(
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
axis.title = element_text(face = "bold", size = 14),
axis.text.y = element_text(face = "bold", size = 14),
plot.title = element_text(size = 14),
plot.subtitle = element_text(size = 12, colour = "grey")
)`````` 