Visualisation

Good visualisation tells a story, removing the noise from the data and illuminating the useful information. We visualise to present information to a lay audience; it also helps interpretation and discussion within the survey team. Geospatial visualisation is shown on the Maps page.

Diagrams, charts, figures are ubiquitous; do we still notice them? When a diagram is good it can have great influence, as shown in 100 Diagrams That Changed the World (Christianson 2014). Visualisation is the best way of communicating quantitative data and there are many modern examples on how to this; from the ground breaking work of Edward Tufte (Tufte 1983) (Tufte 2020) through to Alan Smith, at the Financial Times, with How Charts Work (Smith 2022). The Royal Statistical Society has published Best Practices for Data Visualisation providing insights, advice, and examples (with code) to make data outputs more readable, accessible, and impactful. Other examples of data visualisation are: Information is Beautiful (McCandless 2012); Data Points - Visualization that means something (Yau 2013); and, Visualize This - The Flowing Data Guide to Design, Visualization, and Statistics (Yau 2011).

1 Bat Colours

Colour can enhance visualisation, the choice and combination of colours is important to the communication (Crameri, Shephard, and Heron 2020). A consistent colour applied to individual bat species and a group of colours accessible to people with a visual impairment, helps make the visualisations readable. Unfortunately, accessibility is challenging with more than 8 or 10 colours; if this is the case consider separating the visualisation into different components, creating more than one graph or just highlighting the key species with one colour, with the remaining species shaded as grey.

Table 1 lists the set of colours used in the iBats::bat_colours_default function and are used for most of the visualisations on this page. The iBats::bat_colours() function allows any colour to be associated with any bat name (including common names); Section 5.2 gives an example chart with common bat names. The colour palett resource provided by coolors lends the ability to create and export a suite of colours; that could be assigned to individual species of bat.

Code

bat_colours_sci <- c("Barbastella barbastellus" = "#1f78b4", 
                     "Myotis alcathoe" = "#a52a2a",
                     "Myotis bechsteinii" = "#7fff00",
                     "Myotis brandtii" = "#b2df8a", 
                     "Myotis mystacinus" = "#6a3d9a", 
                     "Myotis nattereri" = "#ff7f00", 
                     "Myotis daubentonii" = "#a6cee3", 
                      "Myotis spp." = "#bcee68",
                      "Plecotus auritus" = "#8b0000", 
                      "Plecotus spp." = "#8b0000", 
                      "Plecotus austriacus" = "#000000",
                      "Pipistrellus pipistrellus" = "#ffff99", 
                      "Pipistrellus nathusii" = "#8a2be2", 
                      "Pipistrellus pygmaeus" = "#b15928", 
                      "Pipistrellus spp." = "#fdbf6f", 
                      "Rhinolophus ferrumequinum" = "#e31a1c", 
                      "Rhinolophus hipposideros" = "#33a02c", 
                      "Nyctalus noctula" = "#cab2d6", 
                      "Nyctalus leisleri" = "#fb9a99", 
                      "Nyctalus spp." = "#eee8cd", 
                      "Eptesicus serotinus" = "#008b8b")

bat_colour_table <- tibble(names(bat_colours_sci), unname(bat_colours_sci))

colnames(bat_colour_table) <- c("Scientific Name", "Colour (Hex)")

bat_colour_table %>% 
  # Table made with library(flextable)
    flextable(col_keys = colnames(.)) %>% 
    bold(part = "header") %>% 
    autofit() %>% 
    #autofit(add_w = 0.1, add_h = 0.1) %>% 
    #bg(bg = "black", part = "header") %>% 
    bg(j = 2, i = 1, bg = bat_colour_table$`Colour (Hex)`[1], part = "body") %>% 
    bg(j = 2, i = 2, bg = bat_colour_table$`Colour (Hex)`[2], part = "body") %>% 
    bg(j = 2, i = 3, bg = bat_colour_table$`Colour (Hex)`[3], part = "body") %>% 
    bg(j = 2, i = 4, bg = bat_colour_table$`Colour (Hex)`[4], part = "body") %>% 
    bg(j = 2, i = 5, bg = bat_colour_table$`Colour (Hex)`[5], part = "body") %>% 
    bg(j = 2, i = 6, bg = bat_colour_table$`Colour (Hex)`[6], part = "body") %>% 
    bg(j = 2, i = 7, bg = bat_colour_table$`Colour (Hex)`[7], part = "body") %>% 
    bg(j = 2, i = 8, bg = bat_colour_table$`Colour (Hex)`[8], part = "body") %>% 
    bg(j = 2, i = 9, bg = bat_colour_table$`Colour (Hex)`[9], part = "body") %>% 
    bg(j = 2, i = 10, bg = bat_colour_table$`Colour (Hex)`[10], part = "body") %>% 
    bg(j = 2, i = 11, bg = bat_colour_table$`Colour (Hex)`[11], part = "body") %>% 
    color(j = 2, i = 11, color = "white", part = "body") %>% 
    bg(j = 2, i = 12, bg = bat_colour_table$`Colour (Hex)`[12], part = "body") %>% 
    bg(j = 2, i = 13, bg = bat_colour_table$`Colour (Hex)`[13], part = "body") %>% 
    bg(j = 2, i = 14, bg = bat_colour_table$`Colour (Hex)`[14], part = "body") %>% 
    bg(j = 2, i = 15, bg = bat_colour_table$`Colour (Hex)`[15], part = "body") %>% 
    bg(j = 2, i = 16, bg = bat_colour_table$`Colour (Hex)`[16], part = "body") %>% 
    bg(j = 2, i = 17, bg = bat_colour_table$`Colour (Hex)`[17], part = "body") %>% 
    bg(j = 2, i = 18, bg = bat_colour_table$`Colour (Hex)`[18], part = "body") %>% 
    bg(j = 2, i = 19, bg = bat_colour_table$`Colour (Hex)`[19], part = "body") %>% 
    bg(j = 2, i = 20, bg = bat_colour_table$`Colour (Hex)`[20], part = "body") %>% 
    bg(j = 2, i = 21, bg = bat_colour_table$`Colour (Hex)`[21], part = "body") %>% 
   # color(color = "white", part = "header") %>% 
    align(align = "center", part = "header" ) %>% 
    align(j = 2, align = "right", part = "body") %>% 
    italic(j = 1, italic = TRUE, part = "body") 

Table 1:

Bat Name and Colour

Scientific Name	Colour (Hex)
Barbastella barbastellus	#1f78b4
Myotis alcathoe	#a52a2a
Myotis bechsteinii	#7fff00
Myotis brandtii	#b2df8a
Myotis mystacinus	#6a3d9a
Myotis nattereri	#ff7f00
Myotis daubentonii	#a6cee3
Myotis spp.	#bcee68
Plecotus auritus	#8b0000
Plecotus spp.	#8b0000
Plecotus austriacus	#000000
Pipistrellus pipistrellus	#ffff99
Pipistrellus nathusii	#8a2be2
Pipistrellus pygmaeus	#b15928
Pipistrellus spp.	#fdbf6f
Rhinolophus ferrumequinum	#e31a1c
Rhinolophus hipposideros	#33a02c
Nyctalus noctula	#cab2d6
Nyctalus leisleri	#fb9a99
Nyctalus spp.	#eee8cd
Eptesicus serotinus	#008b8b

2 Time Bats Were Active

Figure 1 illustrates bat activity through one night near an Oak tree in the River Tavy valley, Devon. Rather than depicting a bat pass the time the bat was present is shown. The y-axis is expanded to spread the bat activity making the graph more readable.

The data used TavyOak is from the iBats package. The bat species colour for the graph were made using the iBats::bat_colours() function; this provided the colour values used by ggplot's manual scale function scale_fill_manual().

Code

bat_colours_sci <- c(
  "Barbastella barbastellus" = "#1f78b4",
  "Myotis alcathoe" = "#a52a2a",
  "Myotis bechsteinii" = "#7fff00",
  "Myotis brandtii" = "#b2df8a",
  "Myotis mystacinus" = "#6a3d9a",
  "Myotis nattereri" = "#ff7f00",
  "Myotis daubentonii" = "#a6cee3",
  "Myotis spp." = "#bcee68",
  "Plecotus auritus" = "#8b0000",
  "Plecotus spp." = "#8b0000",
  "Plecotus austriacus" = "#000000",
  "Pipistrellus pipistrellus" = "#ffff99",
  "Pipistrellus nathusii" = "#8a2be2",
  "Pipistrellus pygmaeus" = "#b15928",
  "Pipistrellus spp." = "#fdbf6f",
  "Rhinolophus ferrumequinum" = "#e31a1c",
  "Rhinolophus hipposideros" = "#33a02c",
  "Nyctalus noctula" = "#cab2d6",
  "Nyctalus leisleri" = "#fb9a99",
  "Nyctalus spp." = "#eee8cd",
  "Eptesicus serotinus" = "#008b8b"
)

# graph anotation
graph_sunrise <- TavyOak$sunrise[1]
graph_sunset <- TavyOak$sunset[1]

# graph time limits x-axis
graph_limit1 <- TavyOak$sunset[1] - lubridate::hours(1)
graph_limit2 <- TavyOak$sunrise[1] + lubridate::hours(1)

# colour values used by scale_fill_manual()
graph_bat_colours <- iBats::bat_colours(TavyOak$Species, colour_vector = bat_colours_sci)

ggplot(TavyOak, aes(y = 1, x = DateTime, fill = Species, size = bat_time)) +
  geom_jitter(shape = 21, alpha = 0.7) +
  geom_vline(
    xintercept = graph_sunset,
    colour = "brown1",
    linetype = "dashed",
    linewidth = 1,
    alpha = 0.8
  ) +
  geom_vline(
    xintercept = graph_sunrise,
    colour = "mediumblue",
    linetype = "dashed",
    linewidth = 1,
    alpha = 0.8
  ) +
  annotate("text",
    x = graph_sunset - lubridate::minutes(20),
    y = 1,
    label = "Sunset",
    color = "brown1",
    angle = 270
  ) +
  annotate("text",
    x = graph_sunrise + lubridate::minutes(20),
    y = 1,
    label = "Sunrise",
    color = "mediumblue",
    angle = 270
  ) +
  scale_fill_manual(values = graph_bat_colours) +
  scale_size_area(max_size = 12) +
  scale_x_datetime(
    date_labels = "%H:%M hrs",
    date_breaks = "1 hour",
    limits = c(graph_limit1, graph_limit2)
  ) +
  labs(
    fill = "Species",
    size = "Time Bat Was Present\n(seconds)",
    y = "For clarity activity is spread across the verstical scale"
  ) +
  theme_bw() +
  theme(
    legend.position = "right",
    panel.grid.major.x = element_line(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 10, angle = 270),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    strip.text = element_text(size = 12, face = "bold", colour = "white"),
    legend.text = element_text(face = "italic"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size = 10)
  ) +
  #Make the point size larger on the legend to show the colour
  guides(fill = guide_legend(override.aes = list(size=4)))

Figure 1: Time Bats Were Active Through The Night

3 Emergence Time of Bats

Figure 2 illustrates emergence times for UK bats based on the work of (Andrews and Pearson 2022).

Code

### Libraries Used
library(tidyverse) # Data Science packages - see https://www.tidyverse.org/

# Install devtools if not installed 
# devtools is used to install the iBats package from GitHub
if(!require(devtools)){
  install.packages("devtools")
}

# If iBats not installed load from Github
if(!require(iBats)){
  devtools::install_github("Nattereri/iBats")
}
library(iBats)

# colour values used by scale_fill_manual()
graph_bat_colours <- iBats::bat_colours_default(Andrews$Species)

ggplot(Andrews) +
  geom_segment(aes(x = reorder(Species, -meanExit), xend = Species, y = firstExit95, yend = lastExit95), color = "grey", size = 2) +
  geom_point(aes(x = reorder(Species, -meanExit), y = meanExit, fill = Species), color = "black", size = 5, shape = 21) +
  scale_y_continuous(breaks = c(-20, -10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)) +
  geom_hline(yintercept = 0, linetype = "dashed", colour = "purple", size = 1) +
  scale_fill_manual(values = graph_bat_colours) +
  coord_flip() +
  labs(
    y = "Minutes After Sunset",
    caption = "adapted from (Andrews and Pearson, 2022)\ngrey bar is indicative of the emergence time range\ncoloured point is the mean emergence time"
  ) +
  theme_bw() +
  theme(
    legend.position = "none",
    axis.text.y = element_text(face = "italic"),
    axis.title.y = element_blank(),
    panel.grid = element_blank()
  )

Figure 2: Roost Emergence Times After Sunset

4 Graphing a Count of Bats

One of the problems with presenting a count of bat passes observed in the UK is the relative abundance of the Common Pipistrelle (Pipistrellus pipistrellus). Figure 3 tries to resolve this issue by using a log scale, not a friendly scale to the lay reader but some mitigation is achieved by placing the actual count of bat passes on the graph. Figure 3 shows the count of bat species in the statics data.

4.1 Dot Graph

Code

### Libraries Used 
library(tidyverse) # Data Science packages - see https://www.tidyverse.org/
library(broman) # useful add_commas function - 
# see https://cran.r-project.org/web/packages/broman/index.html
library(glue) # for joining text and variables
library(ggrepel) # for tidy graph labels


# Install devtools if not installed 
# devtools is used to install the iBats package from GitHub
if(!require(devtools)){
  install.packages("devtools")
}

# If iBats not installed load from Github
if(!require(iBats)){
  devtools::install_github("Nattereri/iBats")
}
library(iBats)

graph_data <- statics %>% #statics is a bat survey data set from the iBats package
  group_by(Species) %>%
  count() %>%
  # Add a graph species label; commas added with library(broman)
  mutate(
    total = add_commas(n),
    label = glue("{Species}: {total}")
  )

# colour values used by scale_fill_manual()
graph_bat_colours <- iBats::bat_colours_default(graph_data$Species)


ggplot(graph_data, aes(y = reorder(Species, n), x = n, fill = Species)) +
  geom_point(colour = "black", size = 5) +
  geom_label_repel(
    data = graph_data, aes(label = label),
    nudge_y = -0.25,
    nudge_x = ifelse(graph_data$n < 100, 0.33, -0.33),
    alpha = 0.7
  ) +
  scale_fill_manual(values = graph_bat_colours) +
  scale_x_log10(sec.axis = dup_axis()) +
  annotation_logticks(sides = "tb") +
  labs(
    x = "Bat Observations (Number of Passes)",
    caption = "Note: Log scale used"
  ) +
  theme_bw() +
  theme(
    legend.position = "none",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 12, face = "bold"),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    strip.text = element_text(size = 12, face = "bold", colour = "white"),
    axis.title.x = element_text(size = 12, face = "bold"),
    axis.title.y = element_blank()
  )

Figure 3: Count of All Species Observations

4.2 Tree Map Description

Code

### Libraries Used 
library(tidyverse) # Data Science packages - see https://www.tidyverse.org/
library(treemapify) # extension to ggplot for plotting treemaps - 
# see https://cran.r-project.org/web/packages/treemapify/vignettes/introduction-to-treemapify.html

# Install devtools if not installed 
# devtools is used to install the iBats package from GitHub
if(!require(devtools)){
  install.packages("devtools")
}

# If iBats not installed load from Github
if(!require(iBats)){
  devtools::install_github("Nattereri/iBats")
}
library(iBats)

graph_data <- statics %>% #statics is a bat survey data set from the iBats package
  group_by(Species, Description) %>%
  tally()

# colour values used by scale_fill_manual()
graph_bat_colours <- iBats::bat_colours_default(graph_data$Species)

ggplot(graph_data, aes(area = n, fill = Species, label = Species, subgroup = Description)) +
  scale_fill_manual(values = graph_bat_colours) +
  geom_treemap(colour = "white", size = 2, alpha = 0.9) +
  geom_treemap_subgroup_border(colour = "black", size = 5, alpha = 0.9) +
  geom_treemap_subgroup_text(
    place = "centre", grow = T, alpha = 0.9, colour =
      "grey20", min.size = 0
  ) +
  geom_treemap_text(
    colour = "grey90", place = "topleft", fontface = "italic",
    reflow = T, min.size = 0, alpha = 0.9
  ) +
  theme_bw() +
  theme(legend.position = "none") # No legend

Figure 4: Species and Location with the Area as the Number of Passes

4.3 Tree Map Month

Code

### Libraries Used 
library(tidyverse) # Data Science packages - see https://www.tidyverse.org/
library(treemapify) # extension to ggplot for plotting treemaps - 
# see https://cran.r-project.org/web/packages/treemapify/vignettes/introduction-to-treemapify.html
library(ggthemes) # for colour pallet "Tableau 10"

# Install devtools if not installed 
# devtools is used to install the iBats package from GitHub
if(!require(devtools)){
  install.packages("devtools")
}

# If iBats is not installed load from Github
if(!require(iBats)){
  devtools::install_github("Nattereri/iBats")
}
library(iBats)

# Add data and time information to the iBats statics bat survey data set using the iBats::date_time_info
statics_plus <- iBats::date_time_info(statics)

graph_data <- statics_plus %>%
  group_by(Species, Month) %>%
  tally()


ggplot(graph_data, aes(area = n, fill = Month, label = Species, subgroup = Month)) +
  scale_fill_tableau(palette = "Tableau 10") + # 
  geom_treemap(colour = "white", size = 2, alpha = 0.9) +
  geom_treemap_subgroup_border(colour = "black", size = 5, alpha = 0.9) +
  geom_treemap_subgroup_text(
    place = "centre", grow = T, alpha = 0.9, colour =
      "grey20", min.size = 0
  ) +
  geom_treemap_text(
    colour = "grey90", place = "topleft", fontface = "italic",
    reflow = T, min.size = 0, alpha = 0.9
  ) +
  theme_bw() +
  theme(legend.position = "none") # No legend

Figure 5: Species and Month with the Area as the Number of Passes

5 Identifying Roosts

5.1 Evening Bats and Roost Potential

Code

# Add data and time information to the statics data using the iBats::date_time_info
statics_plus <- iBats::date_time_info(statics)

# Add sun and night time metrics to the statics data using the iBats::sun_night_metrics() function.
statics_plus <- iBats::sun_night_metrics(statics_plus)

# Add roost emergence times adapted from (Andrews and Pearson, 2022)
statics_plus <- dplyr::left_join(statics_plus, Andrews, by = "Species")

# Graph text
yLab <- "Time before and after sunset (mins)"
Caption <- "Vertical dotted line represents sunset \nHorizontal bars are emergence times from (Andrews and Pearson, 2022)"

# Just choose Observations 90 mins or less after sunset
graph_data <- statics_plus %>%
  filter(post_set_min <= 90)

# colour values used by scale_fill_manual()
graph_bat_colours <- iBats::bat_colours_default(graph_data$Species)

ggplot(graph_data, aes(x = Species, y = post_set_min, fill = Species)) +
  geom_linerange(aes(x = Species, ymin = firstExit95, ymax = lastExit95),
    size = 3, colour = "floralwhite"
  ) +
  geom_jitter(size = 3, alpha = 0.8, shape = 21) +
  geom_hline(yintercept = 0, linetype = "dotted", colour = "floralwhite", linewidth = 2) +
  facet_grid(Description ~ ., scales = "free_y") +
  scale_fill_manual(values = graph_bat_colours) +
  labs(
    y = yLab,
    caption = Caption
  ) +
  scale_y_continuous(breaks = c(-30, -15, 0, 15, 30, 45, 60, 75, 90, 105), limits = c(-30, 105)) +
  coord_flip() +
  theme_bw() +
  theme(
    legend.position = "none",
    plot.caption = element_text(colour = "grey30", face = "bold"), # white
    axis.title.y = element_blank(),
    axis.title.x = element_text(colour = "grey30", size = 12),
    axis.text.x = element_text(hjust = 1, colour = "grey30", face = "bold"),
    axis.text.y = element_text(colour = "grey30", face = "bold.italic"),
    strip.text = element_text(size = 12, face = "bold", colour = "white"), # Bold facet names
    panel.background = element_rect(fill = "midnightblue"),
    panel.grid.major.x = element_line(colour = "transparent", linetype = "dotted"), # grey70
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    plot.background = element_rect(fill = "transparent"), # grey70
    axis.ticks = element_blank(),
    strip.background = element_rect(fill = "black")
  )

Figure 6: Bats Observed Around Dusk and Roost Potential

5.2 Dawn Bats and Roost Potential

Code

# List of bat common names and the scientific names
BatCommon <- c(
  "Barbastella barbastellus" = "Barbastelle",
  "Myotis alcathoe" = "Alcathoe",
  "Myotis bechsteinii" = "Bechstein's",
  "Myotis brandtii" = "Brandt's",
  "Myotis daubentonii" = "Daubenton's",
  "Myotis mystacinus" = "Whiskered",
  "Myotis spp." = "Myotis",
  "Rhinolophus ferrumequinum" = "Greater horseshoe",
  "Rhinolophus hipposideros" = "Lesser horseshoe",
  "Nyctalus leisleri" = "Leisler's",
  "Plecotus auritus" = "Brown long-eared",
  "Plecotus austriacus" = "Grey long-eared",
  "Pipistrellus nathusii" = "Nathusius pipistrelle",
  "Myotis nattereri" = "Natterer's",
  "Nyctalus noctula" = "Noctule",
  "Nyctalus spp." = "Noctule or Leisler's",
  "Eptesicus serotinus" = "Serotine",
  "Pipistrellus pipistrellus" = "Common pipistrelle",
  "Pipistrellus pygmaeus" = "Soprano pipistrelle",
  "Pipistrellus spp." = "Pipistrelle",
  "Plecotus spp." = "Long-eared")

# From Scientific name create a Common Name Vector 
statics$Common <-  unname(BatCommon[statics$Species])

# Add data and time information to the statics data using theiBats::date_time_info
statics_plus <- iBats::date_time_info(statics)

# Add sun and night time metrics to the statics data using the iBats::sun_night_metrics() function.
statics_plus <- iBats::sun_night_metrics(statics_plus)

# Add roost emergence times adapted from (Andrews and Pearson, 2022)
statics_plus <- dplyr::left_join(statics_plus, Andrews, by = "Species")

# From Scientific name create a Common Name Vector 
statics_plus$Common <-  unname(BatCommon[statics_plus$Species])

# Graph text
yLab <- "Time before and after sunrise (mins)"
Caption <- "Vertical dotted line represents sunrise"

# Just choose Observations 90 mins or less after sunset
graph_data <- statics_plus %>%
  filter(pre_rise_min <= 90) %>%
  mutate(pre_rise_min = pre_rise_min * (-1)) # For correct orientation on the graph

# colour values used by scale_fill_manual()
graph_bat_colours <- iBats::bat_colours(graph_data$Species, colour_vector = bat_colours_sci)

ggplot(graph_data, aes(x = Common, y = pre_rise_min, fill = Species)) +
  geom_jitter(size = 3, alpha = 0.8, shape = 21) +
  geom_hline(yintercept = 0, linetype = "dotted", colour = "floralwhite", linewidth = 2) +
  facet_grid(Description ~ ., scales = "free_y") +
  scale_fill_manual(values = graph_bat_colours) +
  labs(
    y = yLab,
    caption = Caption
  ) +
  scale_y_continuous(breaks = c(-90, -75, -60, -45, -30, -15, 0, 15), limits = c(-90, 15)) +
  coord_flip() +
  theme_bw() +
  theme(
    legend.position = "none",
    plot.caption = element_text(colour = "grey30", face = "bold"), # white
    axis.title.y = element_blank(),
    axis.title.x = element_text(colour = "grey30", size = 12),
    axis.text.x = element_text(hjust = 1, colour = "grey30", face = "bold"),
    axis.text.y = element_text(colour = "grey30", face = "bold.italic"),
    strip.text = element_text(size = 12, face = "bold", colour = "white"), # Bold facet names
    panel.background = element_rect(fill = "midnightblue"),
    panel.grid.major.x = element_line(colour = "transparent", linetype = "dotted"), # grey70
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    plot.background = element_rect(fill = "transparent"), # grey70
    axis.ticks = element_blank(),
    strip.background = element_rect(fill = "black")
  )

Figure 7: Bats Observed Around Dawn and Roost Potential

6 Standard Graphs

Bar charts (Figure 8 (a)) and pie charts (Figure 8 (b)) are part of the standard repertoire for reporting bat surveys. These flat graphs can be hard to interpret when there are a large number of variables to display, as in this case with the number of different species in Figure 8; and/or a high value, such as the Pipistrellus pipistrellus species in Figure 8, that can hide other values. These charts are more effective when they are interactive; example pie chart and bar charts are demonstrated on the Interactive Reports page.

Code

graph_data <- statics %>%
  group_by(Species) %>%
  count()

# colour values used by scale_fill_manual()
graph_bat_colours <- iBats::bat_colours_default(graph_data$Species)


g1 <- ggplot(graph_data, aes(x = "", y = n, fill = Species)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar(theta = "y") +
  scale_fill_manual(values = graph_bat_colours) +
  labs(
    y = "Bat Pass Observations (Nr)",
    fill = "Species"
  ) +
  theme_bw() +
  theme(
    legend.position = "right",
    legend.text = element_text(face = "italic"),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    strip.text = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank(),
    panel.border = element_blank()
  )


g2 <- ggplot(graph_data, aes(x = reorder(Species, n), y = n, fill = Species)) +
  geom_col() +
  scale_y_continuous(label = comma) +
  coord_flip() +
  scale_fill_manual(values = graph_bat_colours) +
  theme_bw() +
  theme(
    legend.position = "none", # No legend
    axis.text.x = element_text(size = 12, angle = 0, face = "bold"),
    axis.text.y = element_text(size = 12, face = "bold.italic"), # bat names italic
    axis.title.y = element_blank(), # no y title (just bat names)
    axis.title.x = element_blank(), # no x title
    panel.grid.major = element_blank(), # remove grid lines
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    panel.grid.major.x = element_line(colour = "grey20", linewidth = 0.1, linetype = "dashed")
  )

g1
g2

(a) Pie Chart

(b) Bar Chart

Figure 8: Conventional Graphs used for Reporting Bat Surveys

References

Andrews, Henry, and Louis Pearson. 2022. “Review of Empirical Data in Respect of Emergence and Return Times Reported for the UK’s Native Bat Species Version 6.” https://drive.google.com/file/d/1DeGHxyr9-p5XH6R6CRimsmquVD188WY8/view.

Christianson, Scott. 2014. 100 Diagrams That Changed the World. Batsford.

Crameri, Fabio, Grace E Shephard, and Philip J Heron. 2020. “The Misuse of Colour in Science Communication.” Nature Communications 11 (5444). https://doi.org/10.1038/s41467-020-19160-7.

McCandless, David. 2012. Information Is Beautiful. William Collins.

Smith, Alan. 2022. How Charts Work: Understand and Explain Data with Confidence. FT Publishing.

Tufte, Edward R. 1983. The Visual Display of Quantitative Information. Graphics Press.

———. 2020. Seeing with Fresh Eyes: Meaning, Space, Data, Truth. Graphics Press.

Yau, Nathan. 2011. Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics. John Wiley & Sons.

———. 2013. Data Points: Visualization That Means Something. Wiley.