The solutions given below are just one way of obtaining the desired plots! There are probably several different ways you could code to get the same plots.

ggplot basics

Exercise I and II

For these exercises the solutions are already part of the material, all you need to do is to replace the filename counts_raw.txt with each of the different files to look at the differences between different normalization methods.

Exercise III

Task 3.1

gc_raw <- read.table(file = "../data/counts_raw.txt", sep = "\t", header = T)
gc_filt <- read.table(file = "../data/counts_filtered.txt", sep = "\t", header = T)
gc_vst <- read.table(file = "../data/counts_vst.txt", sep = "\t", header = T)
gc_deseq <- read.table(file = "../data/counts_deseq2.txt", sep = "\t", header = T)
md <- read.table("../data/metadata.csv", header = T, sep = ";")
rownames(md) <- md$Sample_ID
se <- function(x) sqrt(var(x)/length(x))

gene_counts_all <- 
  gc_raw %>% gather(Sample_ID, Raw, -Gene) %>%
  full_join(gc_filt %>% gather(Sample_ID, Filtered, -Gene), by = c("Gene", "Sample_ID")) %>%
  full_join(gc_vst %>% gather(Sample_ID, VST, -Gene), by = c("Gene", "Sample_ID")) %>%
  full_join(gc_deseq %>% gather(Sample_ID, DESeq2, -Gene), by = c("Gene", "Sample_ID")) %>%
  gather(Method, count, Raw:DESeq2) %>%
  filter(!is.na(count)) %>%
  full_join(md, by = "Sample_ID")

gene_counts_all$Time <- factor(gene_counts_all$Time, levels = c("t0","t2","t6","t24"))
gene_counts_all$Replicate <- factor(gene_counts_all$Replicate, levels = c("A","B","C"))
gene_counts_all$Method <- factor(gene_counts_all$Method, levels = c("Raw","Filtered","DESeq2","VST"))

gene_counts_all %>% 
  group_by(Time, Replicate, Method) %>% 
  summarise(mean=mean(log10(count +1)),se=se(log10(count +1))) %>%
  ggplot(aes(x= Time, y= mean, fill = Replicate)) + 
  geom_bar(position = position_dodge2(), stat = "identity") +
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), position = position_dodge2(.9, padding = .6)) +
  facet_wrap(~Method, scales = "free")

Task 3.2

gene_counts_all %>% 
  group_by(Time, Replicate, Method) %>% 
  ggplot() +
  geom_boxplot(mapping = aes(x = Sample_Name, y = log10(count + 1), fill = Time)) +
  facet_wrap(~Method*Replicate, ncol = 3, scales = "free")

Advanced ggplot

Exercise I

gc_long <- gc_raw %>%
  gather(Sample_ID, count, -Gene) %>% 
  full_join(md, by = "Sample_ID") %>% 
  select(Sample_ID, everything()) %>% 
  select(-c(Gene,count), c(Gene,count)) 
gc_long$Sample_Name <- factor(gc_long$Sample_Name, levels = c("t0_A","t0_B","t0_C","t2_A","t2_B","t2_C","t6_A","t6_B","t6_C","t24_A","t24_B","t24_C"))
gc_long$Time <- factor(gc_long$Time, levels = c("t0","t2","t6","t24"))
gc_long$Replicate <- factor(gc_long$Replicate, levels = c("A","B","C"))

gc_long %>% 
  group_by(Time, Replicate) %>% 
  summarise(mean=mean(log10(count +1)),se=se(log10(count +1))) %>%
  ggplot(aes(x=Time, y=mean, color = Replicate)) +
  facet_wrap(~Replicate) +
  geom_line(aes(group=1), stat= "identity", size = 2) +
  scale_x_discrete(limits= c("t0", "t2", "t24")) +
  scale_y_continuous(limits = c(0.4,0.8), breaks = seq(0.4,0.8,0.05)) +
  guides(color="none") +
  ylab(label = "mean(log10(count + 1))") +
  theme_light() +
  theme(axis.text = element_text(face="bold", size=12),
        axis.title = element_text(face="bold", color = "#C84DF9", size=14),
        axis.ticks = element_blank())

Exercise II

library(ggpubr)
p4 <- ggplot(data=iris,mapping=aes(x=Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3, alpha = 0.6) +
  theme_classic(base_size = 12) +
  border() 

d1 <- ggplot(data=iris,mapping=aes(Sepal.Length, fill = Species)) +
  geom_density(alpha = 0.6) +
  theme_classic() +
  clean_theme() +
  theme(legend.position = "none")

d2 <- ggplot(data=iris,mapping=aes(Sepal.Width, fill = Species)) +
  geom_density(alpha = 0.6) +
  theme_classic() +
  clean_theme() +
  theme(legend.position = "none") +
  rotate()

ggarrange(d1, NULL, p4, d2, 
          ncol = 2, nrow = 2,  align = "hv", 
          widths = c(3, 1), heights = c(1, 3),
          common.legend = TRUE)

PCA and Gene Expression

Exercise I

row.names(gc_vst) <- gc_vst$Gene
gc_vst$Gene <- NULL
gc_dist <- dist(t(gc_vst))
gc_mds <- cmdscale(gc_dist,eig=TRUE, k=2) 
Eigenvalues <- gc_mds$eig
Variance <- Eigenvalues / sum(Eigenvalues) 
Variance1 <- 100 * signif(Variance[1], 3)
Variance2 <- 100 * signif(Variance[2], 3)

gc_mds_long <- gc_mds$points %>%
  as.data.frame() %>%
  rownames_to_column("Sample_ID") %>%
  full_join(md, by = "Sample_ID")

gc_mds_long$Sample_Name <- factor(gc_mds_long$Sample_Name, levels = c("t0_A","t0_B","t0_C","t2_A","t2_B","t2_C","t6_A","t6_B","t6_C","t24_A","t24_B","t24_C"))
gc_mds_long$Time <- factor(gc_mds_long$Time, levels = c("t0","t2","t6","t24"))
gc_mds_long$Replicate <- factor(gc_mds_long$Replicate, levels = c("A","B","C"))

ggplot(gc_mds_long, aes(x=V1, y=V2, color = Time)) +
  geom_point(size = 3, aes(shape = Replicate)) +
  xlab(paste("PCO1: ", Variance1, "%")) +
  ylab(paste("PCO2: ", Variance2, "%")) +
  geom_vline(xintercept = 0, linetype=2) +
  geom_hline(yintercept = 0, linetype=2) +
  theme_bw() +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank())

Exercise II

Task 2.1

t2_vs_t0 <- read.table("../data/Time_t2_vs_t0.txt", sep = "\t", header = TRUE, row.names = 1)

t2_vs_t0 %>%
  ggplot() +
  geom_point(aes(x = baseMean, y = log2FoldChange), color = "grey70") +
  geom_point(data=filter(t2_vs_t0, padj < 0.05), aes(x = baseMean, y = log2FoldChange), color = "blue") +
  geom_hline(yintercept = 0) +
  scale_x_log10("Mean of normalized counts") +
  ylab("log fold change") +
  theme_bw(base_size = 14)

Task 2.2

library(ggrepel)
gene_info <- read.table("../data/human_biomaRt_annotation.csv", sep = ";", header = TRUE, row.names = 1)
names(gene_info) <- c('gene_id', 'gene_name')

de_w_names <- t2_vs_t0 %>%
  rownames_to_column('gene_id') %>%
  left_join(gene_info, by = "gene_id")

ggplot(de_w_names, aes(x = log2FoldChange, y = -log10(padj))) +
  geom_point(color = "grey70") +
  geom_text_repel(data=filter(de_w_names, padj < 0.05 & abs(log2FoldChange) > 1.5), aes(x = log2FoldChange, y = -log10(padj), label=gene_name)) +
  geom_hline(yintercept = 1.3, linetype = 2) +
  geom_point(data=filter(t2_vs_t0, padj < 0.05), color = "red") +
  ylab("Adjusted P-values in -log10") +
  theme_bw(base_size = 14)

Dynamic Plotting

Exercise 2.1

library(palmerpenguins)
library(leaflet)

leaflet() %>%
  addTiles() %>%
  #addProviderTiles(providers$Esri.WorldImagery) %>%
  setView(lng = -65.5, lat = -65.5, zoom = 6) %>%
  addCircleMarkers(data = island_coordinates, popup = c("Biscoe", "Dream", "Torgersen"), color = "green")

Exercise 3.1

library(ggplot2)
library(plotly)

plot_ly(msleep, x = ~sleep_rem, y = ~sleep_total, 
    color = ~vore, colors = "Set1", size=2, type = "scatter", mode = "markers")

plot_ly(msleep, x = ~sleep_rem, y = ~sleep_total, 
    color = ~vore, colors = "Set1", size=2, type = "scatter", mode = "lines", line = list(width = 4))

library(ggplot2)
library(plotly)

p1 <- plot_ly(msleep, x = ~vore, color = ~vore, colors = c("#88CCEE", "#CC6677", "#DDCC77", "#117733")) %>%
  add_histogram()

p2 <- plot_ly(msleep,  x = ~vore, y = ~sleep_total, color = ~vore, type = "box", size=2, colors = c("#88CCEE", "#CC6677", "#DDCC77", "#117733"), showlegend = F)

fig <- subplot(p1, p2)

fig %>% 
    layout(title = "Mammals Sleep Data",
    xaxis = list(title = "Diet"), 
    yaxis = list(title = "Number of mammals"),
    xaxis2 = list(title = "Diet"), 
    yaxis2 = list(title = "Total sleep (hours)"), 
    legend=list(title=list(text="Diet")))

Exercise 4.1

library(ggiraph)
library(ggplot)

PlotData <- ggplot(msleep,aes(x = vore, y = sleep_total, color = vore, tooltip = vore)) +
    geom_boxplot_interactive() +
    theme_minimal() +
    scale_color_manual(values = c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#999999")) + 
    labs(title = "Mammals Sleep Data", x = "Diet", y = "Total Sleep (hours)", color = "Diet")
  
girafe(ggobj = PlotData)

Exercise 5.1

library(DT)

gc <- read.table("../../data/counts_raw.txt", header = T, row.names = 1, sep = "\t")
md <- read.table("../../data/metadata.csv", header = T, sep = ";")

datatable(gc, options = list(pageLength = 10), caption = "Table 1: Raw gene counts") %>% 
  formatStyle(
    'Sample_1',
    background = styleColorBar(gc$Sample_1, 'steelblue'),
    backgroundSize = '100% 90%',
    backgroundRepeat = 'no-repeat',
    backgroundPosition = 'center') %>%
  formatStyle('Sample_3', color = styleInterval(c(8500, 15000), c('white', 'blue', 'green')),
    backgroundColor = styleInterval(11000, c('gray', 'orange')))

datatable(md, options = list(pageLength = 10, order = list(list(4, 'desc'))), caption = "Table 1: Metadata")

Exercise 6.1

library(plotly)
library(ggplot2)
library(crosstalk)

shared_msleep <- SharedData$new(msleep)

gg_plot1 <- ggplot(data = shared_msleep, aes(x = sleep_rem, y = sleep_total, color = vore)) +
  geom_point() + theme_bw()

plotly_plot1 <- ggplotly(gg_plot1)

bscols(widths = c(3, NA),
    list(
        filter_checkbox("vore", "Diet", shared_msleep, ~vore, inline = TRUE),
        filter_slider("sleep_total", "Total Sleep (hours)", shared_msleep, ~sleep_total, width = "100%"),
        filter_select("conservation", "Conservation Status", shared_msleep, ~conservation)
    ),
    plotly_plot1)

Exercise 7.1

---
title: "OJS example"
author: "John Doe"
date: last-modified
date-format: "YYYY-MMM-DD"
format: html
---

```{r}
library(ggplot2)
```

```{r}
dt_sleep <- msleep
ojs_define(ojsd = dt_sleep)
```

```{ojs}
ojsdata = transpose(ojsd)
```

```{ojs}
viewof raw_table = Inputs.table(ojsdata)
```

```{ojs}
viewof diets = Inputs.checkbox(
  ["herbi", "carni", "omni", "insecti"], 
  { value: ["herbi", "carni"], 
    label: "Diet:"
  }
)
```

```{ojs}
filtered = ojsdata.filter(function(mammal) {
    return diets.includes(mammal.vore);
})
```

```{ojs}
Plot.plot({
  marks: [
    Plot.dot(filtered, {
      x:"sleep_rem",
      y: "sleep_total",
      fill: "vore"
    })
  ],
  grid: true
})

--- title: "Solutions" subtitle: "Workshop on Advanced Data Visualization" author: "Lokesh Mano" format: html: code-tools: true font-size: 10 number-sections: false code-annotations: hover --- ```{r, eval= FALSE, echo=FALSE, warning=FALSE, message=FALSE} library(dplyr) library(ggplot2) library(reshape2) library(tidyverse) setwd("./topics") ``` The solutions given below are just one way of obtaining the desired plots! There are probably several different ways you could code to get the same plots. # ggplot basics ## Exercise I and II For these exercises the solutions are already part of the material, all you need to do is to replace the filename `counts_raw.txt` with each of the different files to look at the differences between different normalization methods. ## Exercise III ### Task 3.1 ```{r, eval=FALSE} gc_raw <- read.table(file = "../data/counts_raw.txt", sep = "\t", header = T) gc_filt <- read.table(file = "../data/counts_filtered.txt", sep = "\t", header = T) gc_vst <- read.table(file = "../data/counts_vst.txt", sep = "\t", header = T) gc_deseq <- read.table(file = "../data/counts_deseq2.txt", sep = "\t", header = T) md <- read.table("../data/metadata.csv", header = T, sep = ";") rownames(md) <- md$Sample_ID se <- function(x) sqrt(var(x)/length(x)) gene_counts_all <- gc_raw %>% gather(Sample_ID, Raw, -Gene) %>% full_join(gc_filt %>% gather(Sample_ID, Filtered, -Gene), by = c("Gene", "Sample_ID")) %>% full_join(gc_vst %>% gather(Sample_ID, VST, -Gene), by = c("Gene", "Sample_ID")) %>% full_join(gc_deseq %>% gather(Sample_ID, DESeq2, -Gene), by = c("Gene", "Sample_ID")) %>% gather(Method, count, Raw:DESeq2) %>% filter(!is.na(count)) %>% full_join(md, by = "Sample_ID") gene_counts_all$Time <- factor(gene_counts_all$Time, levels = c("t0","t2","t6","t24")) gene_counts_all$Replicate <- factor(gene_counts_all$Replicate, levels = c("A","B","C")) gene_counts_all$Method <- factor(gene_counts_all$Method, levels = c("Raw","Filtered","DESeq2","VST")) gene_counts_all %>% group_by(Time, Replicate, Method) %>% summarise(mean=mean(log10(count +1)),se=se(log10(count +1))) %>% ggplot(aes(x= Time, y= mean, fill = Replicate)) + geom_bar(position = position_dodge2(), stat = "identity") + geom_errorbar(aes(ymin=mean-se, ymax=mean+se), position = position_dodge2(.9, padding = .6)) + facet_wrap(~Method, scales = "free") ``` ### Task 3.2 ```{r, eval=FALSE} gene_counts_all %>% group_by(Time, Replicate, Method) %>% ggplot() + geom_boxplot(mapping = aes(x = Sample_Name, y = log10(count + 1), fill = Time)) + facet_wrap(~Method*Replicate, ncol = 3, scales = "free") ``` # Advanced ggplot ## Exercise I ```{r, eval=FALSE} gc_long <- gc_raw %>% gather(Sample_ID, count, -Gene) %>% full_join(md, by = "Sample_ID") %>% select(Sample_ID, everything()) %>% select(-c(Gene,count), c(Gene,count)) gc_long$Sample_Name <- factor(gc_long$Sample_Name, levels = c("t0_A","t0_B","t0_C","t2_A","t2_B","t2_C","t6_A","t6_B","t6_C","t24_A","t24_B","t24_C")) gc_long$Time <- factor(gc_long$Time, levels = c("t0","t2","t6","t24")) gc_long$Replicate <- factor(gc_long$Replicate, levels = c("A","B","C")) gc_long %>% group_by(Time, Replicate) %>% summarise(mean=mean(log10(count +1)),se=se(log10(count +1))) %>% ggplot(aes(x=Time, y=mean, color = Replicate)) + facet_wrap(~Replicate) + geom_line(aes(group=1), stat= "identity", size = 2) + scale_x_discrete(limits= c("t0", "t2", "t24")) + scale_y_continuous(limits = c(0.4,0.8), breaks = seq(0.4,0.8,0.05)) + guides(color="none") + ylab(label = "mean(log10(count + 1))") + theme_light() + theme(axis.text = element_text(face="bold", size=12), axis.title = element_text(face="bold", color = "#C84DF9", size=14), axis.ticks = element_blank()) ``` ## Exercise II ```{r, eval=FALSE} library(ggpubr) p4 <- ggplot(data=iris,mapping=aes(x=Sepal.Length, y = Sepal.Width, color = Species)) + geom_point(size = 3, alpha = 0.6) + theme_classic(base_size = 12) + border() d1 <- ggplot(data=iris,mapping=aes(Sepal.Length, fill = Species)) + geom_density(alpha = 0.6) + theme_classic() + clean_theme() + theme(legend.position = "none") d2 <- ggplot(data=iris,mapping=aes(Sepal.Width, fill = Species)) + geom_density(alpha = 0.6) + theme_classic() + clean_theme() + theme(legend.position = "none") + rotate() ggarrange(d1, NULL, p4, d2, ncol = 2, nrow = 2, align = "hv", widths = c(3, 1), heights = c(1, 3), common.legend = TRUE) ``` # PCA and Gene Expression ## Exercise I ```{r, eval=FALSE} row.names(gc_vst) <- gc_vst$Gene gc_vst$Gene <- NULL gc_dist <- dist(t(gc_vst)) gc_mds <- cmdscale(gc_dist,eig=TRUE, k=2) Eigenvalues <- gc_mds$eig Variance <- Eigenvalues / sum(Eigenvalues) Variance1 <- 100 * signif(Variance[1], 3) Variance2 <- 100 * signif(Variance[2], 3) gc_mds_long <- gc_mds$points %>% as.data.frame() %>% rownames_to_column("Sample_ID") %>% full_join(md, by = "Sample_ID") gc_mds_long$Sample_Name <- factor(gc_mds_long$Sample_Name, levels = c("t0_A","t0_B","t0_C","t2_A","t2_B","t2_C","t6_A","t6_B","t6_C","t24_A","t24_B","t24_C")) gc_mds_long$Time <- factor(gc_mds_long$Time, levels = c("t0","t2","t6","t24")) gc_mds_long$Replicate <- factor(gc_mds_long$Replicate, levels = c("A","B","C")) ggplot(gc_mds_long, aes(x=V1, y=V2, color = Time)) + geom_point(size = 3, aes(shape = Replicate)) + xlab(paste("PCO1: ", Variance1, "%")) + ylab(paste("PCO2: ", Variance2, "%")) + geom_vline(xintercept = 0, linetype=2) + geom_hline(yintercept = 0, linetype=2) + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) ``` ## Exercise II ### Task 2.1 ```{r, eval=FALSE} t2_vs_t0 <- read.table("../data/Time_t2_vs_t0.txt", sep = "\t", header = TRUE, row.names = 1) t2_vs_t0 %>% ggplot() + geom_point(aes(x = baseMean, y = log2FoldChange), color = "grey70") + geom_point(data=filter(t2_vs_t0, padj < 0.05), aes(x = baseMean, y = log2FoldChange), color = "blue") + geom_hline(yintercept = 0) + scale_x_log10("Mean of normalized counts") + ylab("log fold change") + theme_bw(base_size = 14) ``` ### Task 2.2 ```{r, eval=FALSE} library(ggrepel) gene_info <- read.table("../data/human_biomaRt_annotation.csv", sep = ";", header = TRUE, row.names = 1) names(gene_info) <- c('gene_id', 'gene_name') de_w_names <- t2_vs_t0 %>% rownames_to_column('gene_id') %>% left_join(gene_info, by = "gene_id") ggplot(de_w_names, aes(x = log2FoldChange, y = -log10(padj))) + geom_point(color = "grey70") + geom_text_repel(data=filter(de_w_names, padj < 0.05 & abs(log2FoldChange) > 1.5), aes(x = log2FoldChange, y = -log10(padj), label=gene_name)) + geom_hline(yintercept = 1.3, linetype = 2) + geom_point(data=filter(t2_vs_t0, padj < 0.05), color = "red") + ylab("Adjusted P-values in -log10") + theme_bw(base_size = 14) ``` # Dynamic Plotting ## Exercise 2.1 ```{r} #| eval: false library(palmerpenguins) library(leaflet) leaflet() %>% addTiles() %>% #addProviderTiles(providers$Esri.WorldImagery) %>% setView(lng = -65.5, lat = -65.5, zoom = 6) %>% addCircleMarkers(data = island_coordinates, popup = c("Biscoe", "Dream", "Torgersen"), color = "green") ``` ## Exercise 3.1 ```{r} #| eval: false library(ggplot2) library(plotly) plot_ly(msleep, x = ~sleep_rem, y = ~sleep_total, color = ~vore, colors = "Set1", size=2, type = "scatter", mode = "markers") plot_ly(msleep, x = ~sleep_rem, y = ~sleep_total, color = ~vore, colors = "Set1", size=2, type = "scatter", mode = "lines", line = list(width = 4)) ``` ```{r} #| eval: false library(ggplot2) library(plotly) p1 <- plot_ly(msleep, x = ~vore, color = ~vore, colors = c("#88CCEE", "#CC6677", "#DDCC77", "#117733")) %>% add_histogram() p2 <- plot_ly(msleep, x = ~vore, y = ~sleep_total, color = ~vore, type = "box", size=2, colors = c("#88CCEE", "#CC6677", "#DDCC77", "#117733"), showlegend = F) fig <- subplot(p1, p2) fig %>% layout(title = "Mammals Sleep Data", xaxis = list(title = "Diet"), yaxis = list(title = "Number of mammals"), xaxis2 = list(title = "Diet"), yaxis2 = list(title = "Total sleep (hours)"), legend=list(title=list(text="Diet"))) ``` ## Exercise 4.1 ```{r} #| eval: false library(ggiraph) library(ggplot) PlotData <- ggplot(msleep,aes(x = vore, y = sleep_total, color = vore, tooltip = vore)) + geom_boxplot_interactive() + theme_minimal() + scale_color_manual(values = c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#999999")) + labs(title = "Mammals Sleep Data", x = "Diet", y = "Total Sleep (hours)", color = "Diet") girafe(ggobj = PlotData) ``` ## Exercise 5.1 ```{r} #| eval: false library(DT) gc <- read.table("../../data/counts_raw.txt", header = T, row.names = 1, sep = "\t") md <- read.table("../../data/metadata.csv", header = T, sep = ";") datatable(gc, options = list(pageLength = 10), caption = "Table 1: Raw gene counts") %>% formatStyle( 'Sample_1', background = styleColorBar(gc$Sample_1, 'steelblue'), backgroundSize = '100% 90%', backgroundRepeat = 'no-repeat', backgroundPosition = 'center') %>% formatStyle('Sample_3', color = styleInterval(c(8500, 15000), c('white', 'blue', 'green')), backgroundColor = styleInterval(11000, c('gray', 'orange'))) datatable(md, options = list(pageLength = 10, order = list(list(4, 'desc'))), caption = "Table 1: Metadata") ``` ## Exercise 6.1 ```{r} #| eval: false library(plotly) library(ggplot2) library(crosstalk) shared_msleep <- SharedData$new(msleep) gg_plot1 <- ggplot(data = shared_msleep, aes(x = sleep_rem, y = sleep_total, color = vore)) + geom_point() + theme_bw() plotly_plot1 <- ggplotly(gg_plot1) bscols(widths = c(3, NA), list( filter_checkbox("vore", "Diet", shared_msleep, ~vore, inline = TRUE), filter_slider("sleep_total", "Total Sleep (hours)", shared_msleep, ~sleep_total, width = "100%"), filter_select("conservation", "Conservation Status", shared_msleep, ~conservation) ), plotly_plot1) ``` ## Exercise 7.1 ```` --- title: "OJS example" author: "John Doe" date: last-modified date-format: "YYYY-MMM-DD" format: html --- ```{r} library(ggplot2) ``` ```{r} dt_sleep <- msleep ojs_define(ojsd = dt_sleep) ``` ```{ojs} ojsdata = transpose(ojsd) ``` ```{ojs} viewof raw_table = Inputs.table(ojsdata) ``` ```{ojs} viewof diets = Inputs.checkbox( ["herbi", "carni", "omni", "insecti"], { value: ["herbi", "carni"], label: "Diet:" } ) ``` ```{ojs} filtered = ojsdata.filter(function(mammal) { return diets.includes(mammal.vore); }) ``` ```{ojs} Plot.plot({ marks: [ Plot.dot(filtered, { x:"sleep_rem", y: "sleep_total", fill: "vore" }) ], grid: true }) ````