Chapter6.3.qmd

# Ch. 6 – Diagnostics

This notebook demonstrates Data Diagnostics and Summaries.

## Preliminaries

Load or install the necessary R packages.

```{r}
#| label: libraries1
#| eval: true
#| echo: true
#| warning: false
library(tidyverse,quiet=TRUE)
library(ggplot2,quiet=TRUE)
#install.packages("cowplot",quiet=TRUE)
library(cowplot,quiet=TRUE)
options(repr.plot.width = 7, repr.plot.height = 5)
```

```{r}
#| eval: false
#| label: libraries2
#| warning: false
if (!require(devtools)) install.packages("devtools",quiet=TRUE)
devtools::install_github("tuomaseerola/MusicScienceData@main",quiet=TRUE)
library(MusicScienceData,quiet=TRUE)
```

```{r}
#| eval: true
#| label: libraries3
#| echo: false
library(MusicScienceData,quiet=TRUE)
```


## Code 6.1

```{r}
#| label: 6-1
#| warning: false
print(MusicScienceData::sadness[1:4,1:7])
```

## Code 6.2

```{r}
#| label: 6-2
#| warning: false
print(MusicScienceData::priming[1:3,1:6])
```

## Code 6.3

Figure 6.1. A histogram showing the distribution of responses to a particular question (no. 25) in Attitudes towards Sad Music (ASM) instrument.

```{r}
#| label: 6-3
#| warning: false
sadness <- MusicScienceData::sadness
g1 <- sadness %>%
  drop_na(ASM25) %>%   # drop missing values
  ggplot(aes(x = ASM25))+
  geom_histogram(bins=7,fill="grey50", colour='black')+
  scale_x_continuous(breaks = seq(1,7,by=1))+
  ylab('Count')+
  xlab('1 = Strongly disagree, 7 = Strongly agree')+
  theme_MusicScience()
g1
```

## Code 6.5

Figure 6.4. A box plot showing the distribution of responses to a particular question (no. 23) in Attitudes towards Sad Music (ASM) instrument split across gender.

```{r}
#| label: 6-5
#| warning: false
g5 <- sadness %>%
  drop_na(ASM25) %>%   # drop missing values
  ggplot(aes(y = ASM25,fill=gender))+
  geom_boxplot()+
  scale_y_continuous(breaks = seq(1,7,by=1))+
  scale_x_discrete()+
  scale_fill_grey(start = .4,end = .8,name='Gender')+
  ylab('1 = Strongly disagree, 7 = Strongly agree')+
  theme_MusicScience()
print(g5)
```

## Code 6.6

Figure 6.5. Alternative visualisations of data. A: density plot across gender, B: multiple boxplots, C: boxplot overlaid with original data, D: violin plot with mean and median overlaid.

```{r}
#| label: 6-6
#| warning: false
options(repr.plot.width = 12, repr.plot.height = 10)
d <- MusicScienceData::priming

g1<-ggplot(d,aes(x=RT))+
  geom_histogram(binwidth=100,colour='grey50',fill='white')+
  ggtitle('Bin width 100')+
  ylab('Count')+
  xlab('Reaction time (ms)')+
  scale_x_continuous(breaks=seq(0,2000,by=400))+
  theme_MusicScience()


g2<-ggplot(d,aes(x=RT))+
  geom_histogram(binwidth=10,colour='grey50',fill='white')+
  ggtitle('Bin width 10')+
  ylab('Count')+
  xlab('Reaction time (ms)')+
  scale_x_continuous(breaks=seq(0,2000,by=400))+
  theme_MusicScience()

g3<-ggplot(dplyr::filter(d,RT>200 & RT<1500),aes(x=RT))+
  geom_histogram(binwidth=10,colour='grey50',fill='white')+
  ggtitle('Bin width 10 with trimming')+
  ylab('Count')+
  xlab('Reaction time (ms)')+
  scale_x_continuous(breaks=seq(200,1500,by=200),limits = c(0,2000))+
  geom_vline(xintercept = c(200,1500),linetype='dashed')+
  theme_MusicScience()


g4<-ggplot(dplyr::filter(d,RT>200 & RT<1500),aes(x=RT))+
  geom_histogram(binwidth=10,colour='grey50',fill='white')+
  geom_density(aes(y=10 * after_stat(count)),alpha=0.5,colour='black',fill=NA)+
  ggtitle('Bin width 10 density with trimming')+
  ylab('Count')+
  xlab('Reaction time (ms)')+
  scale_x_continuous(breaks=seq(200,1500,by=200))+
  theme_MusicScience()

G1 <- plot_grid(g1, g2, g3, g4, nrow = 2)
print(G1)
```

## Code 6.7

Table 6.1: The means of the ASM question 20 across the age.

```{r}
#| label: 6-7
#| warning: false
library(Hmisc,quietly = TRUE)
table1 <- MusicScienceData::sadness %>%
        drop_na(ASM20) %>%   # drop missing values
        group_by(age) %>%
        summarise(n=n(),mean_cl_normal(ASM20))

colnames(table1) <- c('Age','N','M','95% CI LL','95% CI UL')
knitr::kable(table1,digits = 2, format='simple',
             caption = 'The means of the ASM question 20 across the age.')
```

## Code 6.8

```{r}
#| label: 6-8
#| warning: false
mean(MusicScienceData::sadness$ASM20, na.rm=TRUE) # Mean (ignore missing values)
sd(MusicScienceData::sadness$ASM20,na.rm=TRUE)
```

## Code 6.9

Figure 6.6. A bar graph showing the means of the responses to the question no. 20 in Attitudes towards Sad Music (ASM) instrument across gender.

```{r}
#| label: 6-9
#| warning: false
g6 <- sadness %>%
  drop_na(ASM20) %>%   # drop missing values
  group_by(gender) %>%
  summarise(mean= mean(ASM20),ci = mean_cl_normal(ASM20)) %>%
  ggplot(aes(x = gender,y = mean,fill=gender))+
  geom_col(colour='black',show.legend = FALSE)+
  geom_errorbar(aes(ymin=ci$ymin,ymax=ci$ymax),width=0.5)+
  scale_y_continuous(breaks = seq(1,7,by=1), expand = c(0,0))+
  scale_fill_grey(start=.25,end=.75)+
  coord_cartesian(ylim = c(1, 7)) +
  ylab('Mean ± 95% CI')+
  xlab('Gender')+
  theme_MusicScience()
print(g6)
```

## Code 6.10

Figure 6.7. A bar graph showing the means of the responses to the question no. 6 in Attitudes towards Sad Music (ASM) instrument across musical expertise.

```{r}
#| label: 6-10
#| warning: false
g1 <- MusicScienceData::sadness %>%
  drop_na(ASM1) %>%   # drop missing values
  ggplot(aes(x= ASM1,color=gender))+
  geom_density(adjust=1.25)+
  scale_color_grey(name='Gender')+
  scale_x_continuous(breaks = seq(1,7,by=1))+
  ggtitle(sadness_ASM_labels[1])+
  ylab('Density')+
  theme_bw()+
  theme(legend.justification=c(1,0), legend.position=c(0.95,0.75))+
  theme(plot.title = element_text(size=11))

tmp<-as_tibble(MusicScienceData::sadness)
tmp2<-tmp[,c(3,7:10)]
dfl <- pivot_longer(tmp2,cols = c(2:5))

g2 <- dfl %>%
  drop_na(value) %>%   # drop missing values
  ggplot(aes(x=name,y = value,fill=gender))+
  geom_boxplot(outlier.shape ="")+
  scale_y_continuous(breaks = seq(1,7,by=1))+
  scale_x_discrete()+
  scale_fill_grey(start = .75, end=.25, name="Gender")+
  ggtitle('ASM items 1 to 4')+
  ylab('1 = Strongly disagree, 7 = Strongly agree')+
  xlab('Item')+
  theme_bw()+
  theme(legend.justification=c(1,0), legend.position=c(0.95,0.70))

g3 <- MusicScienceData::sadness %>%
  drop_na(ASM12) %>%   # drop missing values
  ggplot(aes(x=1,y = ASM12))+
  geom_boxplot(fill='gray70')+
  geom_jitter(alpha=0.13,colour='black', width = 0.33)+
  scale_y_continuous(breaks = seq(1,7,by=1))+
  scale_x_discrete()+
  ggtitle(sadness_ASM_labels[12])+
  ylab('1 = Strongly disagree, 7 = Strongly agree')+
  xlab('ASM12')+
  theme_bw()

g4 <- MusicScienceData::sadness %>%
  drop_na(ASM13) %>%   # drop missing values
  ggplot(aes(x=1,y = ASM13))+
  geom_violin(fill='grey70',adjust=1.2,alpha=0.50)+
  scale_y_continuous(breaks = seq(1,7,by=1))+
  scale_x_discrete()+
  stat_summary(fun = median, fun.min = median, fun.max = median,
               geom = "crossbar", width = 0.9)+
  stat_summary(fun = mean, fun.min = mean, fun.max = mean,
               geom = "crossbar", width = 0.9,colour='gray50')+
  ggtitle(sadness_ASM_labels[13])+
  annotate("text",x=1.6,y=mean(MusicScienceData::sadness$ASM13,na.rm = TRUE),label='Mean',hjust=0)+
  annotate("text",x=1.6,y=median(MusicScienceData::sadness$ASM13,na.rm = TRUE),label='Median',hjust=0)+
  ylab('1 = Strongly disagree, 7 = Strongly agree')+
  xlab('ASM13')+
  theme_bw()

G2 <- plot_grid(g1,g2,g3,g4,labels = c("A", "B", "C", "D"),ncol = 2, nrow = 2)
print(G2)
```

## Code 6.11

Figure 6.8. A scatterplot showing the means of the ratings to 110 film soundtrack excerpts using scales tension and valence in Eerola and Vuoskoski (2011).

```{r}
#| label: 6-11
#| warning: false
g9 <- ggplot(soundtrack) +
  aes(x = Valence, y = Tension, colour = TARGET_EMOTION,
      label=Number,
      shape= TARGET_FRAMEWORK) +
  geom_point(size=4,alpha=0.80,show.legend=FALSE) +
  coord_fixed(ratio = 1)+
  geom_smooth(aes(shape = NULL,colour=NULL),method="lm",
              formula='y ~x',se=FALSE, fullrange=TRUE,
              level=0.95, colour='grey50', # adds trendline
              linetype='dashed',show.legend = FALSE)+
  geom_text(show.legend=FALSE,color='white',size=1.7)+ # labels
  scale_colour_grey(name='Emotion',start = .6,end = 0)+
  scale_shape(name='Framework')+
  scale_x_continuous(breaks=seq(1,9,by=2),limits=c(1,9))+
  scale_y_continuous(breaks=seq(1,9,by=2),limits=c(1,9))+
  theme_MusicScience()
print(g9)
```

## References

- Eerola, T., & Peltola, H.-R. (2016). Memorable experiences with sad music - reasons, reactions and mechanisms of three types of experiences. _PloS ONE, 11(6)_, e0157444. <https://doi.org/http://dx.doi.org/10.1371/journal.pone.0157444>

- Eerola, T., & Vuoskoski, J. K. (2011). A comparison of the discrete and dimensional models of emotion in music. _Psychology of Music, 39(1)_, 18–49.