Part 2: Piping and producing single-panel ggplot figures

by Vikram B. Baliga, Andrea Gaede and Shreeram Senthivasan

Last updated on 2019-11-29 09:54:38

Introduction

This vignette is Part 2 of 3 for an R workshop created for BIOL 548L, a graduate-level course on data visualization taught at the University of British Columbia.

When the workshop runs, we split students into three groups with successively increasing levels of difficulty. To make sense of what follows, we recommend working through (or at least looking at) Part 1’s vignette.

The goal of Part 2’s vignette is to learn how to structure data to produce single plots ready for presentations and publications

Part 3’s vignette (which we recommend going through only after completing this page) can be found here.

All code and contents of this vignette were written together by Vikram B. Baliga, Andrea Gaede and Shreeram Senthivasan.

Learning Objectives:

1. Articulate the advantages of literate programming
2. Employ piping to restructure data for tidyverse
3. Describe the grammar of graphics that underlies plotting in ggplot
4. Manipulate plots to improve clarity and maximize the data : ink ratio

Load or install&load packages

We’ll first load packages that will be necessary. The package.check() function below is designed to first see if each package is already installed. If any aren’t, the function then installs them from CRAN. Then all the packages are loaded.

The code block is modified from this blog post

#library(ubcBIOL548L)

## Modified from: 
## https://vbaliga.github.io/verify-that-r-packages-are-installed-and-loaded/

## First specify the packages of interest
packages <-
  c("gapminder",
    "ggplot2",
    "tidyr",
    "dplyr",
    "tibble",
    "readr",
    "forcats",
    "readxl",
    "ggthemes",
    "magick",
    "grid",
    "cowplot",
    # ggmap and maps are optional; needed for creating maps
    "ggmap",
    "maps")
## Now load or install&load all packages
package.check <- lapply(
  packages,
  FUN = function(x)
  {
    if (!require(x, character.only = TRUE))
    {
      install.packages(x, dependencies = TRUE,
                       repos = "http://cran.us.r-project.org")
      library(x, character.only = TRUE)
    }
  }
)

Data sets

You can get all data used in this vignette (and the other two!) by downloading this zip file.

What is literate programming?

Literate programming is a coding paradigm that rethinks the “grammar” of traditional code to more closely resemble writing in a human language, like English.

The primary goal of literate programming is to move away from coding “for computers” (ie, code that simplifies compilation) and instead to write in a way that more resembles how we think.

In addition to making for much more readable code even with fewer comments, a good literate coding language should make it easier for you to translate ideas into code.

In the tidyverse, we will largely use the analogy of variables as nouns and functions as verbs that operate on the nouns.

The main way we will make our code literate however is with the pipe: %>%

What is a pipe?

A pipe, or %>% , is an operator that takes everything on the left and plugs it into the function on the right.

In short: x %>% f(y) is equivalent to f(x, y)

# So:
filter(gapminder, continent == 'Asia')
#> # A tibble: 396 x 6
#>    country     continent  year lifeExp      pop gdpPercap
#>    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#>  1 Afghanistan Asia       1952    28.8  8425333      779.
#>  2 Afghanistan Asia       1957    30.3  9240934      821.
#>  3 Afghanistan Asia       1962    32.0 10267083      853.
#>  4 Afghanistan Asia       1967    34.0 11537966      836.
#>  5 Afghanistan Asia       1972    36.1 13079460      740.
#>  6 Afghanistan Asia       1977    38.4 14880372      786.
#>  7 Afghanistan Asia       1982    39.9 12881816      978.
#>  8 Afghanistan Asia       1987    40.8 13867957      852.
#>  9 Afghanistan Asia       1992    41.7 16317921      649.
#> 10 Afghanistan Asia       1997    41.8 22227415      635.
#> # ... with 386 more rows

# Can be re-written as:
gapminder %>%
  filter(continent == 'Asia')
#> # A tibble: 396 x 6
#>    country     continent  year lifeExp      pop gdpPercap
#>    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#>  1 Afghanistan Asia       1952    28.8  8425333      779.
#>  2 Afghanistan Asia       1957    30.3  9240934      821.
#>  3 Afghanistan Asia       1962    32.0 10267083      853.
#>  4 Afghanistan Asia       1967    34.0 11537966      836.
#>  5 Afghanistan Asia       1972    36.1 13079460      740.
#>  6 Afghanistan Asia       1977    38.4 14880372      786.
#>  7 Afghanistan Asia       1982    39.9 12881816      978.
#>  8 Afghanistan Asia       1987    40.8 13867957      852.
#>  9 Afghanistan Asia       1992    41.7 16317921      649.
#> 10 Afghanistan Asia       1997    41.8 22227415      635.
#> # ... with 386 more rows

In RStudio you can use the shortcut CTRL/CMD + SHIFT + M to insert a pipe

Why bother with pipes?

To see how pipes can make code more readable, let’s translate this simple cake recipe into pseudo R code:

1. Cream together sugar and butter
2. Beat in eggs and vanilla
3. Mix in flour and baking powder
4. Stir in milk
5. Bake
6. Frost

Saving Intermediate Steps:
One way you might approach coding this is by defining a lot of variables:

batter_1   <- cream(sugar, butter)  
batter_2   <- beat_in(batter_1, eggs, vanilla)  
batter_3   <- mix_in(batter_2, flour, baking_powder)  
batter_4   <- stir_in(batter_3, milk)  
cake       <- bake(batter_3)  
cake_final <- frost(cake)  

The info is all there, but there’s a lot of repetition and opportunity for typos. You also have to jump back and forth between lines to make sure you’re calling the right variables.

A similar approach would be to keep overwriting a single variable:

cake <- cream(sugar, butter)  
cake <- beat_in(cake, eggs, vanilla)  
cake <- mix_in(cake, flour, baking_powder)  
cake <- stir_in(cake, milk)  
cake <- bake(cake)  
cake <- frost(cake)  

But it’s still not as clear as the original recipe…And if anything goes wrong you have to re-run the whole pipeline.

Function Composition:
If we don’t want to save intermediates, we can just do all the steps in one line:

frost(bake(stir_in(mix_in(beat_in(cream(sugar, butter), eggs, vanilla), flour, baking_powder), milk)))  

Or with better indentation:

frost(  
    bake(  
      stir_in(  
        mix_in(  
          beat_in(  
            cream(sugar, butter),  
            eggs,  
            vanilla  
          ),  
          flour, baking_powder  
        ),  
      milk)  
    )  
  )  

It’s pretty clear why this is no good

Piping

Now for the piping approach:

cake <-  
    cream(sugar, butter) %>%  
    beat_in(eggs, vanilla) %>%  
    mix_in(flour, baking_powder) %>%  
    stir_in(milk) %>%  
    bake() %>%  
    frost()  

When you are reading code, you can replace pipes with the phrase “and then.”

And remember, pipes just take everything on the left and plug it into the function on the right. If you step through this code, this chunk is exactly the same as the function composition example above, it’s just written for people to read.

When you chain together multiple manipulations, piping makes it easy to focus on what’s important at every step. One caveat is that you need the first argument of every function in the pipe to be the object you are manipulating. Tidyverse functions all follow this convention, as do many other functions.

Where do you find new verbs?

The RStudio cheat sheets are a whole lot more useful once you start piping – here are a few:

Data transformation

Data import + restructuring

Other cheat sheets

Some prep work for the coding part of the lesson:

In this section our main goal will be to reproduce as closely as possible all the individual plots that make up Figure 3 in the Gaede et al. 2017 paper.

The one exception to that will be the legends and icons, which are easier to align and arrange when organizing the plots into a single multipanel figure, which is left for Group 3.

Let’s start by opening up the paper and setting up some variables we will be using throughout the plotting.

Colours for the different birds

col_hb <- "#ED0080"
col_zb <- "#F48D00"
col_pg <- "#4AA4F2"

Anatomy of a ggplot

ggplot is built around the idea of a “Grammar of Graphics” – a way of describing (almost) any graphic.

There are three key components to any graphic under the Grammar of Graphics:

• Data: the information to be conveyed
• Geometries: the things you actually draw on the plot (lines, points, polygons, etc…)
• Aesthetic mapping: the connection between relevant parts of the data and the aesthetics (size, colour, position, etc…) of the geometries

Any ggplot you make will at the very minimum require these three things and will usually look something like this:

ggplot(data = my_data, mapping = aes(x = var1, y = var2)) +
  geom_point()

Or, equivalently:

my_data %>%
  ggplot(aes(x = var1, y = var2)) +
  geom_point()

But how do you know what geometries are available or what aesthetic mappings you can use for each?
Use the plotting with ggplot2 cheat sheet

Other possibly relevant componenets include:

• Coordinate systems (eg cartesian vs polar plots)
• Scales (eg mapping specifc colours to groups)
• Facets (subpanels of similar plots)

Build a boxplot with data superimposed (Figure 3 Panel D):

data_D <- read_csv("Fig3D_data.csv")
#> Parsed with column specification:
#> cols(
#>   species = col_character(),
#>   perc_firing = col_double(),
#>   num_bins = col_double()
#> )

# Visualize data
data_D
#> # A tibble: 2,400 x 3
#>    species perc_firing num_bins
#>    <chr>         <dbl>    <dbl>
#>  1 calan          0.5         4
#>  2 calan          0.55        4
#>  3 calan          0.6         4
#>  4 calan          0.65        4
#>  5 calan          0.7         3
#>  6 calan          0.75        3
#>  7 calan          0.8         1
#>  8 calan          0.85        1
#>  9 calan          0.9         1
#> 10 calan          0.95        1
#> # ... with 2,390 more rows

Let’s make a ggplot!

data_D %>%
  ggplot(mapping = aes(x = perc_firing, y = num_bins, colour = species))

Add points:

data_D %>%
  ggplot(mapping = aes(x = perc_firing, y = num_bins, colour = species)) +
    geom_point(mapping = aes(shape = species))

Separate points:

data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, colour = species)) +
    geom_point(aes(shape = species), position = 'jitter')

Add summary statistics:

data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, colour = species)) +
    geom_point(aes(shape = species), position = 'jitter') +
    geom_boxplot()

Fix x axis:

data_D <- read_csv("Fig3D_data.csv") %>%
  mutate(
    perc_firing = perc_firing * 100,
    perc_firing = as_factor(perc_firing)
  )
#> Parsed with column specification:
#> cols(
#>   species = col_character(),
#>   perc_firing = col_double(),
#>   num_bins = col_double()
#> )

Re-plot with new x:

data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, colour = species)) +
    geom_point(aes(shape = species), position = 'jitter') +
    geom_boxplot()

Swap colour to fill:

data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, fill = species)) +
    geom_point(aes(shape = species), position = 'jitter') +
    geom_boxplot()

Fix colours:

# browseURL("http://www.sthda.com/english/wiki/ggplot2-point-shapes")
data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, fill = species)) +
    geom_point(aes(shape = species), position = 'jitter') +
    geom_boxplot() +
    scale_fill_manual(values = c(col_hb, col_zb)) +
    scale_shape_manual(values = c(24, 21))

De-emphasize boxplots, remove outliers

data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, fill = species)) +
    geom_boxplot(alpha = 0.5, outlier.size = 0) +
    geom_point(aes(shape = species), position = 'jitter') +
    scale_fill_manual(values = c(col_hb, col_zb)) +
    scale_shape_manual(values = c(24, 21))

Tune jitter:

data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, fill = species)) +
    geom_boxplot(alpha = 0.5, outlier.size = 0) +
    geom_point(
      aes(shape = species),
      position = position_jitterdodge(jitter.height = 0.3, jitter.width = 0.2)
    ) +
    scale_fill_manual(values = c(col_hb, col_zb)) +
    scale_shape_manual(values = c(24, 21))

Titles and theming and save:

fig_D <- data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, fill = species)) +
    geom_boxplot(alpha = 0.5, outlier.size = 0) +
    geom_point(
      aes(shape = species),
      position = position_jitterdodge(jitter.height = 0.3, jitter.width = 0.2)
    ) +
    scale_fill_manual(values = c(col_hb, col_zb)) +
    scale_shape_manual(values = c(24, 21)) +
    labs(
      x = "Percent of maximum firing rate (threshold)",
      y = "Number of speed bins above threshold"
    ) +
    # theme_bw()
    # theme_light()
    # theme_dark()
    theme_classic()
fig_D

Let’s create a theme:

theme_548 <- theme_classic() +
    theme(
      axis.title = element_text(size = 13),
      axis.text = element_text(size = 13, colour = "black"),
      axis.line = element_blank()
    )

Let’s see what it looks like

fig_D + theme_548

Let’s create a theme:

theme_548 <- theme_classic() +
    theme(
      # Text
      axis.title = element_text(size = 13),
      axis.text = element_text(size = 13, colour = "black"),
      axis.text.x = element_text(margin = margin(t = 10, unit = "pt")),
      axis.text.y = element_text(margin = margin(r = 10)),
      # Axis line
      axis.line = element_blank(),
      axis.ticks.length = unit(-5,"pt"),
      # Legend
      legend.position = 'none',
      # Background transparency
        # Background of panel
      panel.background = element_rect(fill = "transparent"),
        # Background behind actual data points
      plot.background = element_rect(fill = "transparent", color = NA)
    )

fig_D + theme_548

Draw in x and y axes:

data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, fill = species)) +
    geom_boxplot(alpha = 0.5, outlier.size = 0) +
    geom_point(
      aes(shape = species),
      position = position_jitterdodge(jitter.height = 0.3, jitter.width = 0.2)
    ) +
    scale_fill_manual(values = c(col_hb, col_zb)) +
    scale_shape_manual(values = c(24, 21)) +
    labs(
      x = "Percent of maximum firing rate (threshold)",
      y = "Number of speed bins above threshold"
    ) +
    theme_548 +
    ggthemes::geom_rangeframe()

Draw in x and y axes:

fig_D <- data_D %>%
  ggplot(aes(x = perc_firing, y = num_bins, fill = species)) +
    geom_boxplot(alpha = 0.5, outlier.size = 0) +
    geom_point(
      aes(shape = species),
      position = position_jitterdodge(jitter.height = 0.3, jitter.width = 0.2)
    ) +
    scale_fill_manual(values = c(col_hb, col_zb)) +
    scale_shape_manual(values = c(24, 21)) +
    labs(
      x = "Percent of maximum firing rate (threshold)",
      y = "Number of speed bins above threshold"
    ) +
    theme_548 +
    geom_rangeframe() +
    annotate(geom = "segment", x = 0, xend = 0, y = 2.5, yend = 10)
fig_D

Build a grouped barplot (Figure 3 Panel E):

Visualize the data:

read_csv("./Fig3E_data.csv")
#> Parsed with column specification:
#> cols(
#>   `0.1` = col_double(),
#>   `0.24` = col_double(),
#>   `0.5` = col_double(),
#>   `1` = col_double(),
#>   `1.5` = col_double(),
#>   `2` = col_double(),
#>   `4` = col_double(),
#>   `8` = col_double(),
#>   `16` = col_double(),
#>   `24` = col_double(),
#>   `32` = col_double(),
#>   `48` = col_double(),
#>   `64` = col_double(),
#>   `80` = col_double(),
#>   `128` = col_double(),
#>   `256` = col_double()
#> )
#> # A tibble: 3 x 16
#>   `0.1` `0.24`  `0.5`    `1`  `1.5`     `2`     `4`    `8`   `16`   `24`
#>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
#> 1     0 0.0357 0.0179 0      0      0.107   0.0357  0.0357 0.0536 0.0893
#> 2     0 0      0      0.0187 0      0.00935 0.00935 0.0935 0.168  0.121 
#> 3     0 0      0.0947 0.0526 0.0105 0.126   0.126   0.0842 0.147  0     
#> # ... with 6 more variables: `32` <dbl>, `48` <dbl>, `64` <dbl>,
#> #   `80` <dbl>, `128` <dbl>, `256` <dbl>

Restructure data for plotting:

# Add the missing info
read_csv("./Fig3E_data.csv") %>%
  mutate(species = c("hb", "zb", "pg"))
#> Parsed with column specification:
#> cols(
#>   `0.1` = col_double(),
#>   `0.24` = col_double(),
#>   `0.5` = col_double(),
#>   `1` = col_double(),
#>   `1.5` = col_double(),
#>   `2` = col_double(),
#>   `4` = col_double(),
#>   `8` = col_double(),
#>   `16` = col_double(),
#>   `24` = col_double(),
#>   `32` = col_double(),
#>   `48` = col_double(),
#>   `64` = col_double(),
#>   `80` = col_double(),
#>   `128` = col_double(),
#>   `256` = col_double()
#> )
#> # A tibble: 3 x 17
#>   `0.1` `0.24`  `0.5`    `1`  `1.5`     `2`     `4`    `8`   `16`   `24`
#>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
#> 1     0 0.0357 0.0179 0      0      0.107   0.0357  0.0357 0.0536 0.0893
#> 2     0 0      0      0.0187 0      0.00935 0.00935 0.0935 0.168  0.121 
#> 3     0 0      0.0947 0.0526 0.0105 0.126   0.126   0.0842 0.147  0     
#> # ... with 7 more variables: `32` <dbl>, `48` <dbl>, `64` <dbl>,
#> #   `80` <dbl>, `128` <dbl>, `256` <dbl>, species <chr>

# Pull the y-values down from the header
data_E <- read_csv("./Fig3E_data.csv") %>%
  mutate(species = c("hb", "zb", "pg")) %>%
  gather(key = speed, value = prop, -species)
#> Parsed with column specification:
#> cols(
#>   `0.1` = col_double(),
#>   `0.24` = col_double(),
#>   `0.5` = col_double(),
#>   `1` = col_double(),
#>   `1.5` = col_double(),
#>   `2` = col_double(),
#>   `4` = col_double(),
#>   `8` = col_double(),
#>   `16` = col_double(),
#>   `24` = col_double(),
#>   `32` = col_double(),
#>   `48` = col_double(),
#>   `64` = col_double(),
#>   `80` = col_double(),
#>   `128` = col_double(),
#>   `256` = col_double()
#> )
data_E
#> # A tibble: 48 x 3
#>    species speed   prop
#>    <chr>   <chr>  <dbl>
#>  1 hb      0.1   0     
#>  2 zb      0.1   0     
#>  3 pg      0.1   0     
#>  4 hb      0.24  0.0357
#>  5 zb      0.24  0     
#>  6 pg      0.24  0     
#>  7 hb      0.5   0.0179
#>  8 zb      0.5   0     
#>  9 pg      0.5   0.0947
#> 10 hb      1     0     
#> # ... with 38 more rows

Plot data:

data_E %>%
  ggplot(aes(x = speed, y = prop, fill = species)) +
    geom_col()

# But wait, the x-axis and species are out of order!
data_E
#> # A tibble: 48 x 3
#>    species speed   prop
#>    <chr>   <chr>  <dbl>
#>  1 hb      0.1   0     
#>  2 zb      0.1   0     
#>  3 pg      0.1   0     
#>  4 hb      0.24  0.0357
#>  5 zb      0.24  0     
#>  6 pg      0.24  0     
#>  7 hb      0.5   0.0179
#>  8 zb      0.5   0     
#>  9 pg      0.5   0.0947
#> 10 hb      1     0     
#> # ... with 38 more rows

data_E <- data_E %>%
  mutate(
    species = fct_relevel(species, c("hb","zb","pg")),
    speed = as_factor(as.numeric(speed))
  )
data_E
#> # A tibble: 48 x 3
#>    species speed   prop
#>    <fct>   <fct>  <dbl>
#>  1 hb      0.1   0     
#>  2 zb      0.1   0     
#>  3 pg      0.1   0     
#>  4 hb      0.24  0.0357
#>  5 zb      0.24  0     
#>  6 pg      0.24  0     
#>  7 hb      0.5   0.0179
#>  8 zb      0.5   0     
#>  9 pg      0.5   0.0947
#> 10 hb      1     0     
#> # ... with 38 more rows

Plot data again!

data_E %>%
  ggplot(aes(x = speed, y = prop, fill = species)) +
    geom_col()

# Unstack columns and add black outline
data_E %>%
  ggplot(aes(x = speed, y = prop, fill = species)) +
    geom_col(position = 'dodge', colour = "black")

# Space out column groups, thin out the columns, thin the outline
data_E %>%
  ggplot(aes(x = speed, y = prop, fill = species)) +
    geom_col(
      position = position_dodge(0.75),
      width = 0.75,
      size = 0.2,
      colour = "black"
    )

Pick colours, add labels, add theme:

data_E %>%
  ggplot(aes(x = speed, y = prop, fill = species)) +
    geom_col(
      position = position_dodge(0.75),
      width = 0.75,
      size = 0.2,
      colour = "black"
    ) +
    scale_fill_manual(values = c(col_hb, col_zb, col_pg)) +
    labs(
      x = "Speed bins (degrees/s)",
      y = "Proportion of LM\ncell population"
    )+
    theme_548

Fix x axis, add y axis line:

fig_E <- data_E %>%
  ggplot(aes(x = speed, y = prop, fill = species)) +
    # Plot data
    geom_col(
      position = position_dodge(0.75),
      width = 0.75,
      size = 0.2,
      colour = "black"
    ) +
    # Pick colours
    scale_fill_manual(values = c(col_hb, col_zb, col_pg)) +
    # Text
    labs(
      x = "Speed bins (degrees/s)",
      y = "Proportion of LM\ncell population"
    )+
    # Theme
    theme_548 +
    # Axes
    theme(
      axis.ticks.x = element_blank(),
      axis.text.x = element_text(margin = margin(t = 0))
    ) +
    geom_rangeframe(sides = 'l')
fig_E

Build a line plot with error bars and other graphical features (Figure 3 Panel B):

Visualize data:

read_csv("Fig3B_data.csv")
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 239 x 23
#>    trial direction    speed  bin1  bin2  bin3  bin4  bin5  bin6  bin7  bin8
#>    <dbl>     <dbl>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1     1       135   1.90   12.0   4.00  4.00  0     4.00  0     0     0   
#>  2     2       NaN NaN       0     4.00  0     4.00  0     0     0     0   
#>  3     3       315  -0.696   0     4.00  0     0     0     0     4.00  0   
#>  4     4       NaN NaN       0     0     0     0     0     4.00  0     0   
#>  5     5       135  -0.0867  0     0     0     4.00  0     0     0     0   
#>  6     6       NaN NaN       0     0     0     0     0     0     0     0   
#>  7     7       135   0.821  16    24    16    28    36     8.00  8.00  8.00
#>  8     8       NaN NaN       4.00  0     0     0     0     0     0     0   
#>  9     9       315   1.63    0     0     0     0     0     0     0     0   
#> 10    10       NaN NaN       0     4.00 12     0     4.00  0     0     4.00
#> # ... with 229 more rows, and 12 more variables: bin9 <dbl>, bin10 <dbl>,
#> #   bin11 <dbl>, bin12 <dbl>, bin13 <dbl>, bin14 <dbl>, bin15 <dbl>,
#> #   bin16 <dbl>, bin17 <dbl>, bin18 <dbl>, bin19 <dbl>, bin20 <dbl>

Gather all the bin data into one column:

read_csv("Fig3B_data.csv") %>%
  gather(key = bin, value = count, starts_with("bin"))
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 4,780 x 5
#>    trial direction    speed bin   count
#>    <dbl>     <dbl>    <dbl> <chr> <dbl>
#>  1     1       135   1.90   bin1  12.0 
#>  2     2       NaN NaN      bin1   0   
#>  3     3       315  -0.696  bin1   0   
#>  4     4       NaN NaN      bin1   0   
#>  5     5       135  -0.0867 bin1   0   
#>  6     6       NaN NaN      bin1   0   
#>  7     7       135   0.821  bin1  16   
#>  8     8       NaN NaN      bin1   4.00
#>  9     9       315   1.63   bin1   0   
#> 10    10       NaN NaN      bin1   0   
#> # ... with 4,770 more rows

Group the data by trial, direction speed:

read_csv("Fig3B_data.csv") %>%
  gather(key = bin, value = count, starts_with("bin")) %>%
  group_by(trial, direction, speed)
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 4,780 x 5
#> # Groups:   trial, direction, speed [239]
#>    trial direction    speed bin   count
#>    <dbl>     <dbl>    <dbl> <chr> <dbl>
#>  1     1       135   1.90   bin1  12.0 
#>  2     2       NaN NaN      bin1   0   
#>  3     3       315  -0.696  bin1   0   
#>  4     4       NaN NaN      bin1   0   
#>  5     5       135  -0.0867 bin1   0   
#>  6     6       NaN NaN      bin1   0   
#>  7     7       135   0.821  bin1  16   
#>  8     8       NaN NaN      bin1   4.00
#>  9     9       315   1.63   bin1   0   
#> 10    10       NaN NaN      bin1   0   
#> # ... with 4,770 more rows

Calculate firing rate for each trial:

read_csv("Fig3B_data.csv") %>%
  gather(key = bin, value = count, starts_with("bin")) %>%
  group_by(trial, direction, speed) %>%
  summarize(
    raw_firing_rate = mean(count)
  )
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 239 x 4
#> # Groups:   trial, direction [239]
#>    trial direction    speed raw_firing_rate
#>    <dbl>     <dbl>    <dbl>           <dbl>
#>  1     1       135   1.90             2.00 
#>  2     2       NaN NaN                0.400
#>  3     3       315  -0.696            0.800
#>  4     4       NaN NaN                0.400
#>  5     5       135  -0.0867           0.600
#>  6     6       NaN NaN                0    
#>  7     7       135   0.821           11.4  
#>  8     8       NaN NaN                0.200
#>  9     9       315   1.63             0    
#> 10    10       NaN NaN                1.20 
#> # ... with 229 more rows

Finally let’s ungroup and store this unnormalized data:

data_B_raw <- read_csv("Fig3B_data.csv") %>%
  gather(key = bin, value = count, starts_with("bin")) %>%
  group_by(trial, direction, speed) %>%
  summarize(
    raw_firing_rate = mean(count)
  ) %>%
  ungroup()
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
data_B_raw
#> # A tibble: 239 x 4
#>    trial direction    speed raw_firing_rate
#>    <dbl>     <dbl>    <dbl>           <dbl>
#>  1     1       135   1.90             2.00 
#>  2     2       NaN NaN                0.400
#>  3     3       315  -0.696            0.800
#>  4     4       NaN NaN                0.400
#>  5     5       135  -0.0867           0.600
#>  6     6       NaN NaN                0    
#>  7     7       135   0.821           11.4  
#>  8     8       NaN NaN                0.200
#>  9     9       315   1.63             0    
#> 10    10       NaN NaN                1.20 
#> # ... with 229 more rows

Now we need to normalize the data:

# First calculate a baseline
tail(data_B_raw)
#> # A tibble: 6 x 4
#>   trial direction    speed raw_firing_rate
#>   <dbl>     <dbl>    <dbl>           <dbl>
#> 1   234       NaN NaN                0.400
#> 2   235       315  -0.0867           4.00 
#> 3   236       NaN NaN                0    
#> 4   237       135  -0.696            0.600
#> 5   238       NaN NaN                0.600
#> 6   239       315   0.821            0.200

data_B_raw %>%
  filter(direction == 'NaN')
#> # A tibble: 119 x 4
#>    trial direction speed raw_firing_rate
#>    <dbl>     <dbl> <dbl>           <dbl>
#>  1     2       NaN   NaN           0.400
#>  2     4       NaN   NaN           0.400
#>  3     6       NaN   NaN           0    
#>  4     8       NaN   NaN           0.200
#>  5    10       NaN   NaN           1.20 
#>  6    12       NaN   NaN           0.600
#>  7    14       NaN   NaN           0.800
#>  8    16       NaN   NaN           0.6  
#>  9    18       NaN   NaN           0.200
#> 10    20       NaN   NaN           0.400
#> # ... with 109 more rows

data_B_raw %>%
  filter(direction == 'NaN') %>%
  pull(raw_firing_rate)
#>   [1]  0.400010  0.400010  0.000000  0.200005  1.200015  0.600015  0.800020
#>   [8]  0.600000  0.200005  0.400010  0.200005  1.000020  4.600060  1.800045
#>  [15]  2.200045  0.000000 11.000040  1.200030  1.400035  0.400010  1.600010
#>  [22]  0.600030  0.000000  0.000000  0.000000  0.200005  0.000000  0.000000
#>  [29]  0.200005  0.400010  0.200005  0.000000  0.200005  0.200005  0.600015
#>  [36]  0.000000  0.000000  1.600035  3.600060  0.200005  0.200000  0.200005
#>  [43]  0.800020  0.000000  0.400010  0.200005  0.800020  0.800020  0.800020
#>  [50]  0.200005  0.200000  0.600015  0.200005  0.600015  0.600015  0.000000
#>  [57]  0.200005  0.000000  0.200005  0.000000  0.000000  0.000000  0.000000
#>  [64]  0.000000  0.200005  0.400005  1.400020  0.000000  0.000000  0.000000
#>  [71]  0.000000 12.800070  0.000000  0.000000  0.000000  0.000000  1.600070
#>  [78]  0.400010  4.200115  1.200015  0.000000  0.000000  0.000000  0.200005
#>  [85]  0.000000  0.400010  0.400010  0.400010  0.200005  1.400035  0.400010
#>  [92]  0.200005  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
#>  [99]  4.600050  0.200005  0.000000  0.400010  1.800045  0.600015  0.200005
#> [106]  0.000000  0.200005  0.200005  0.000000  0.000000  0.200005  0.000000
#> [113]  0.000000  0.200005  0.000000  0.400005  0.400010  0.000000  0.600015

baseline <- data_B_raw %>%
  filter(direction == 'NaN') %>%
  pull(raw_firing_rate) %>%
  mean
baseline
#> [1] 0.6806833

# Use the baseline to normalize the data
data_B_raw %>%
  filter(direction != 'NaN') %>%
  mutate(
    norm_firing_rate = raw_firing_rate - baseline,
    norm_firing_rate = norm_firing_rate / max(abs(norm_firing_rate))
  )
#> # A tibble: 120 x 5
#>    trial direction   speed raw_firing_rate norm_firing_rate
#>    <dbl>     <dbl>   <dbl>           <dbl>            <dbl>
#>  1     1       135  1.90             2.00           0.0676 
#>  2     3       315 -0.696            0.800          0.00611
#>  3     5       135 -0.0867           0.600         -0.00413
#>  4     7       135  0.821           11.4            0.549  
#>  5     9       315  1.63             0             -0.0349 
#>  6    11       135 -0.401            0.200         -0.0246 
#>  7    13       315  1.90             0             -0.0349 
#>  8    15       135  1.63            20.2            1      
#>  9    17       315 -0.0867           0.200         -0.0246 
#> 10    19       135  1.13            13.2            0.641  
#> # ... with 110 more rows

Next let’s find the mean and SE of firing rate across like trials:

data_B_raw %>%
  filter(direction != 'NaN') %>%
  mutate(
    norm_firing_rate = raw_firing_rate - baseline,
    norm_firing_rate = norm_firing_rate / max(abs(norm_firing_rate))
  ) %>%
  group_by(direction, speed) %>%
  summarize(
    firing_rate = mean(norm_firing_rate),
    sem = sd(norm_firing_rate) / sqrt(n())
  )
#> # A tibble: 24 x 4
#> # Groups:   direction [2]
#>    direction   speed firing_rate     sem
#>        <dbl>   <dbl>       <dbl>   <dbl>
#>  1       135 -0.696      -0.0185 0.00832
#>  2       135 -0.401      -0.0246 0.00561
#>  3       135 -0.0867     -0.0164 0.00753
#>  4       135  0.214      -0.0103 0.0132 
#>  5       135  0.524       0.0287 0.0252 
#>  6       135  0.821       0.148  0.101  
#>  7       135  1.13        0.412  0.0661 
#>  8       135  1.30        0.578  0.0902 
#>  9       135  1.43        0.654  0.0572 
#> 10       135  1.63        0.717  0.0812 
#> # ... with 14 more rows

Finally ungroup the data and convert direction to a factor:

data_B <- data_B_raw %>%
  filter(direction != 'NaN') %>%
  mutate(
    norm_firing_rate = raw_firing_rate - baseline,
    norm_firing_rate = norm_firing_rate / max(abs(norm_firing_rate))
  ) %>%
  group_by(direction, speed) %>%
  summarize(
    firing_rate = mean(norm_firing_rate),
    sem = sd(norm_firing_rate) / sqrt(n())
  ) %>%
  ungroup() %>%
  mutate(direction = as_factor(direction))
data_B
#> # A tibble: 24 x 4
#>    direction   speed firing_rate     sem
#>    <fct>       <dbl>       <dbl>   <dbl>
#>  1 135       -0.696      -0.0185 0.00832
#>  2 135       -0.401      -0.0246 0.00561
#>  3 135       -0.0867     -0.0164 0.00753
#>  4 135        0.214      -0.0103 0.0132 
#>  5 135        0.524       0.0287 0.0252 
#>  6 135        0.821       0.148  0.101  
#>  7 135        1.13        0.412  0.0661 
#>  8 135        1.30        0.578  0.0902 
#>  9 135        1.43        0.654  0.0572 
#> 10 135        1.63        0.717  0.0812 
#> # ... with 14 more rows

Plot the data!

data_B %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    geom_point(aes(colour = direction, shape = direction)) +
    geom_line(aes(colour = direction))

Add error bars and threshold line:

data_B %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    geom_point(aes(colour = direction, shape = direction)) +
    geom_line(aes(colour = direction)) +
    geom_errorbar(
      aes(
        colour = direction,
        ymin = firing_rate - sem,
        ymax = firing_rate + sem
      ),
      width = 0.02
    ) +
    geom_hline(
      yintercept = 0.8 * max(data_B$firing_rate),
      colour = 'grey70',
      linetype = 'dashed'
    )

Add a line segment indicating points over threshold:

data_B %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    geom_point(aes(colour = direction, shape = direction)) +
    geom_line(aes(colour = direction)) +
    geom_errorbar(
      aes(
        colour = direction,
        ymin = firing_rate - sem,
        ymax = firing_rate + sem
      ),
      width = 0.02
    ) +
    geom_hline(
      yintercept = 0.8 * max(data_B$firing_rate),
      colour = 'grey70',
      linetype = 'dashed'
    ) +
    annotate(
      geom = 'segment',
      x = 1.3,
      xend = 1.78,
      y = 1.2,
      yend = 1.2,
      size = 0.7,
      arrow = arrow(ends = 'both', length = unit(2, "pt"), angle = 90)
    )

Set colours, shapes, labels, and themes:

data_B %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    geom_point(aes(colour = direction, shape = direction)) +
    geom_line(aes(colour = direction)) +
    geom_errorbar(
      aes(
        colour = direction,
        ymin = firing_rate - sem,
        ymax = firing_rate + sem
      ),
      width = 0.02
    ) +
    geom_hline(
      yintercept = 0.8 * max(data_B$firing_rate),
      colour = 'grey70',
      linetype = 'dashed'
    ) +
    annotate(
      geom = 'segment',
      x = 1.3, xend = 1.78,
      y = 1.2, yend = 1.2,
      size = 0.7,
      arrow = arrow(ends = 'both', length = unit(2, "pt"), angle = 90)
    ) +
    scale_colour_manual(values = c("black", "grey50")) +
    scale_shape_manual(values = c(15, 18)) +
    labs(y = "Normalized\nfiring rate", x = "") +
    theme_548

Make the diamonds bigger, reorder layers to emphasize points, remove x axis title:

data_B %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    geom_hline(
      yintercept = 0.8 * max(data_B$firing_rate),
      colour = 'grey70',
      linetype = 'dashed'
    ) +
    geom_line(aes(colour = direction)) +
    geom_errorbar(
      aes(
        colour = direction,
        ymin = firing_rate - sem,
        ymax = firing_rate + sem
      ),
      width = 0.02
    ) +
    geom_point(aes(colour = direction, shape = direction, size = direction)) +
    annotate(
      geom = 'segment',
      x = 1.3, xend = 1.78,
      y = 1.2, yend = 1.2,
      size = 0.7,
      arrow = arrow(ends = 'both', length = unit(2, "pt"), angle = 90)
    ) +
    scale_colour_manual(values = c("black", "grey50")) +
    scale_shape_manual(values = c(15, 18)) +
    scale_size_manual(values = c(2, 3)) +
    labs(y = "Normalized\nfiring rate", x = "") +
    theme(axis.title.x = element_blank()) +
    theme_548

Add axis lines:

data_B %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    geom_hline(
      yintercept = 0.8 * max(data_B$firing_rate),
      colour = 'grey70',
      linetype = 'dashed'
    ) +
    geom_line(aes(colour = direction)) +
    geom_errorbar(
      aes(
        colour = direction,
        ymin = firing_rate - sem,
        ymax = firing_rate + sem
      ),
      width = 0.02
    ) +
    geom_point(aes(colour = direction, shape = direction, size = direction)) +
    annotate(
      geom = 'segment',
      x = 1.3, xend = 1.78,
      y = 1.2, yend = 1.2,
      size = 0.7,
      arrow = arrow(ends = 'both', length = unit(2, "pt"), angle = 90)
    ) +
    scale_colour_manual(values = c("black", "grey50")) +
    scale_shape_manual(values = c(15, 18)) +
    scale_size_manual(values = c(2, 3)) +
    labs(y = "Normalized\nfiring rate", x = "") +
    theme(axis.title.x = element_blank()) +
    theme_548 +
    geom_rangeframe()

Note the line lengths, y limits, and x breaks are all wrong
To fudge the lines, let’s make another dataset:

fudge_axis_BC <- tibble(speed = c(-0.5,2), firing_rate = c(-1,2))
fudge_axis_BC
#> # A tibble: 2 x 2
#>   speed firing_rate
#>   <dbl>       <dbl>
#> 1  -0.5          -1
#> 2   2             2

fig_B <- data_B %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    # Plot data and threshold
    geom_hline(
      yintercept = 0.8 * max(data_B$firing_rate),
      colour = 'grey70',
      linetype = 'dashed'
    ) +
    geom_line(aes(colour = direction)) +
    geom_errorbar(
      aes(
        colour = direction,
        ymin = firing_rate - sem,
        ymax = firing_rate + sem
      ),
      width = 0.02
    ) +
    geom_point(aes(colour = direction, shape = direction, size = direction)) +
    # Annotate points over threshold
    annotate(
      geom = 'segment',
      x = 1.3, xend = 1.78,
      y = 1.2, yend = 1.2,
      size = 0.7,
      arrow = arrow(ends = 'both', length = unit(2, "pt"), angle = 90)
    ) +
    # Select colours and shapes
    scale_colour_manual(values = c("black", "grey50")) +
    scale_shape_manual(values = c(15, 18)) +
    scale_size_manual(values = c(2, 3)) +
    # Text
    labs(y = "Normalized\nfiring rate", x = "") +
    # Theme
    theme_548 +
    # Axes
    geom_rangeframe(data = fudge_axis_BC) +
    lims(y = c(-1,2)) +
    scale_x_continuous(breaks = -1:4 / 2)
fig_B

Build another grouped barplot (Figure 3 Panel F):

Visualize data:

read_csv("./Fig3F_data.csv")
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 240 x 33
#>     bird   trk  site  cell pd.sum species test.dir1 dir1.area test.dir2
#>    <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>     <dbl>     <dbl>     <dbl>
#>  1   194     1  3007     1   11.1       2         0      25.1       180
#>  2   194     1  3007     2  343.        2         0      41.4       180
#>  3   194     1  3062     1  298.        2        NA      NA          NA
#>  4   194     1  3062     2  358.        2         0      46.9       180
#>  5   194     1  3062     3  287.        2        NA      NA          NA
#>  6   194     3  3459     3  188.        2        NA      NA          NA
#>  7   194     5  3516     1  117.        2       135      20.9       315
#>  8   194     5  3516     2  176.        2       135      20.2       315
#>  9   194     5  3516     3  192.        2        NA      NA          NA
#> 10   194     5  3516     4  155.        2        NA      NA          NA
#> # ... with 230 more rows, and 24 more variables: dir2.area <dbl>,
#> #   pref.speed <dbl>, sp80.1 <dbl>, sp80.2 <dbl>, sp80.3 <dbl>,
#> #   sp80.4 <dbl>, sp80.5 <dbl>, sp80.6 <dbl>, sp80.7 <dbl>, sp80.8 <dbl>,
#> #   sp80.9 <dbl>, sp80.10 <dbl>, sp80.11 <dbl>, sp80.12 <dbl>,
#> #   tune50 <dbl>, tune55 <dbl>, tune60 <dbl>, tune65 <dbl>, tune70 <dbl>,
#> #   tune75 <dbl>, tune80 <dbl>, tune85 <dbl>, tune90 <dbl>, tune95 <dbl>
speeds_F <- c(0.24,0.5,1,2,4,8,16,24,32,48,64,80)

Remove any rows without data (dir1.area is NA):

read_csv("./Fig3F_data.csv") %>%
  filter(!is.na(dir1.area))
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 163 x 33
#>     bird   trk  site  cell pd.sum species test.dir1 dir1.area test.dir2
#>    <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>     <dbl>     <dbl>     <dbl>
#>  1   194     1  3007     1   11.1       2         0     25.1        180
#>  2   194     1  3007     2  343.        2         0     41.4        180
#>  3   194     1  3062     2  358.        2         0     46.9        180
#>  4   194     5  3516     1  117.        2       135     20.9        315
#>  5   194     5  3516     2  176.        2       135     20.2        315
#>  6   198     3  2990     1  320.        2       315    -12.9        135
#>  7   198     3  2990     2  340.        2       315     30.4        135
#>  8   198     5  3345     2  147.        2       135     -2.81       315
#>  9   198     5  3345     3  141.        2       135    -22.7        315
#> 10   199     1  2383     1   92.4       2        90      1.67       270
#> # ... with 153 more rows, and 24 more variables: dir2.area <dbl>,
#> #   pref.speed <dbl>, sp80.1 <dbl>, sp80.2 <dbl>, sp80.3 <dbl>,
#> #   sp80.4 <dbl>, sp80.5 <dbl>, sp80.6 <dbl>, sp80.7 <dbl>, sp80.8 <dbl>,
#> #   sp80.9 <dbl>, sp80.10 <dbl>, sp80.11 <dbl>, sp80.12 <dbl>,
#> #   tune50 <dbl>, tune55 <dbl>, tune60 <dbl>, tune65 <dbl>, tune70 <dbl>,
#> #   tune75 <dbl>, tune80 <dbl>, tune85 <dbl>, tune90 <dbl>, tune95 <dbl>

Remove unnecessary columns:

read_csv("./Fig3F_data.csv") %>%
  filter(!is.na(dir1.area))  %>%
  select(starts_with("sp"))
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 163 x 13
#>    species sp80.1 sp80.2 sp80.3 sp80.4 sp80.5 sp80.6 sp80.7 sp80.8 sp80.9
#>      <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#>  1       2      0      0      0      0      0      0      0      0      0
#>  2       2      0      0      0      0      0      0      0      0      1
#>  3       2      0      0      0      0      0      0      0      0      1
#>  4       2      0      0      0      0      0      0      0      0      0
#>  5       2      0      0      0      0      1      0      0      0      0
#>  6       2      0      0      0      1      0      0      0      0      0
#>  7       2      0      0      0      0      0      0      0      0      0
#>  8       2      0      0      0      0      0      0      0      0      0
#>  9       2      1      0      0      0      0      0      0      0      0
#> 10       2      0      1      0      0      0      0      0      0      0
#> # ... with 153 more rows, and 3 more variables: sp80.10 <dbl>,
#> #   sp80.11 <dbl>, sp80.12 <dbl>

Rename columns using speeds_F, change species to words:

read_csv("./Fig3F_data.csv") %>%
  filter(!is.na(dir1.area))  %>%
  select(starts_with("sp")) %>%
  rename_all(~c("species",as.character(speeds_F))) %>%
  mutate(
    species = case_when(
      species == 1 ~ "zb",
      species == 2 ~ "hb"
    )
  )
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 163 x 13
#>    species `0.24` `0.5`   `1`   `2`   `4`   `8`  `16`  `24`  `32`  `48`
#>    <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 hb           0     0     0     0     0     0     0     0     0     1
#>  2 hb           0     0     0     0     0     0     0     0     1     1
#>  3 hb           0     0     0     0     0     0     0     0     1     0
#>  4 hb           0     0     0     0     0     0     0     0     0     1
#>  5 hb           0     0     0     0     1     0     0     0     0     1
#>  6 hb           0     0     0     1     0     0     0     0     0     0
#>  7 hb           0     0     0     0     0     0     0     0     0     0
#>  8 hb           0     0     0     0     0     0     0     0     0     0
#>  9 hb           1     0     0     0     0     0     0     0     0     0
#> 10 hb           0     1     0     0     0     0     0     0     0     0
#> # ... with 153 more rows, and 2 more variables: `64` <dbl>, `80` <dbl>

Reshape data for plotting:

read_csv("./Fig3F_data.csv") %>%
  filter(!is.na(dir1.area))  %>%
  select(starts_with("sp")) %>%
  rename_all(~c("species",as.character(speeds_F))) %>%
  mutate(
    species = case_when(
      species == 1 ~ "zb",
      species == 2 ~ "hb"
    )
  ) %>%
  gather(key = speed, value = prop, -species)
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 1,956 x 3
#>    species speed  prop
#>    <chr>   <chr> <dbl>
#>  1 hb      0.24      0
#>  2 hb      0.24      0
#>  3 hb      0.24      0
#>  4 hb      0.24      0
#>  5 hb      0.24      0
#>  6 hb      0.24      0
#>  7 hb      0.24      0
#>  8 hb      0.24      0
#>  9 hb      0.24      1
#> 10 hb      0.24      0
#> # ... with 1,946 more rows

Summarize proporions by species and speed:

read_csv("./Fig3F_data.csv") %>%
  filter(!is.na(dir1.area))  %>%
  select(starts_with("sp")) %>%
  rename_all(~c("species",as.character(speeds_F))) %>%
  mutate(
    species = case_when(
      species == 1 ~ "zb",
      species == 2 ~ "hb"
    )
  ) %>%
  gather(key = speed, value = prop, -species) %>%
  group_by(species, speed) %>%
  summarise(prop = mean(prop)) %>%
  ungroup
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
#> # A tibble: 24 x 3
#>    species speed   prop
#>    <chr>   <chr>  <dbl>
#>  1 hb      0.24  0.0357
#>  2 hb      0.5   0.0179
#>  3 hb      1     0     
#>  4 hb      16    0.143 
#>  5 hb      2     0.125 
#>  6 hb      24    0.232 
#>  7 hb      32    0.339 
#>  8 hb      4     0.0357
#>  9 hb      48    0.446 
#> 10 hb      64    0.5   
#> # ... with 14 more rows

Finally fix the factor levels as before:

data_F <- read_csv("./Fig3F_data.csv") %>%
  filter(!is.na(dir1.area))  %>%
  select(starts_with("sp")) %>%
  rename_all(~c("species",as.character(speeds_F))) %>%
  mutate(
    species = case_when(
      species == 1 ~ "zb",
      species == 2 ~ "hb"
    )
  ) %>%
  gather(key = speed, value = prop, -species) %>%
  group_by(species, speed) %>%
  summarise(prop = mean(prop)) %>%
  ungroup %>%
  mutate(
    species = fct_relevel(species, c('hb', 'zb')),
    speed = as_factor(as.numeric(speed))
  )
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
data_F
#> # A tibble: 24 x 3
#>    species speed   prop
#>    <fct>   <fct>  <dbl>
#>  1 hb      0.24  0.0357
#>  2 hb      0.5   0.0179
#>  3 hb      1     0     
#>  4 hb      16    0.143 
#>  5 hb      2     0.125 
#>  6 hb      24    0.232 
#>  7 hb      32    0.339 
#>  8 hb      4     0.0357
#>  9 hb      48    0.446 
#> 10 hb      64    0.5   
#> # ... with 14 more rows

Plot data as before:

fig_F <- data_F %>%
  ggplot(aes(x = speed, y = prop, fill = species)) +
    # Plot data
    geom_col(
      position = position_dodge(0.75),
      width = 0.75,
      size = 0.2,
      colour = "black"
    ) +
    # Pick colours
    scale_fill_manual(values = c(col_hb, col_zb)) +
    # Text
    labs(
      x = "Speed bins (degrees/s)",
      y = "Proportion of LM\ncell population"
    ) +
    # Theme
    theme_548 +
    # Axes
    theme(
      axis.ticks.x = element_blank(),
      axis.text.x = element_text(margin = margin(t = 0))
    ) +
    geom_rangeframe(sides = 'l')
fig_F

Build another line plot (Figure 3 Panel C):

Reorganize raw data:

data_C_raw <- read_csv("Fig3C_data.csv") %>%
  gather(key = bin, value = count, starts_with("bin")) %>%
  group_by(trial, direction, speed) %>%
  summarize(
    raw_firing_rate = mean(count)
  ) %>%
  ungroup()
#> Parsed with column specification:
#> cols(
#>   .default = col_double()
#> )
#> See spec(...) for full column specifications.
data_C_raw
#> # A tibble: 239 x 4
#>    trial direction   speed raw_firing_rate
#>    <dbl>     <dbl>   <dbl>           <dbl>
#>  1     1       180   0.821            7.99
#>  2     2       NaN NaN                5.20
#>  3     3         0   1.30            24.8 
#>  4     4       NaN NaN                2.80
#>  5     5       180   1.77             3.00
#>  6     6       NaN NaN                4.00
#>  7     7         0   0.214           14.8 
#>  8     8       NaN NaN                2.80
#>  9     9       180   1.63             3.00
#> 10    10       NaN NaN                4.60
#> # ... with 229 more rows

Calculate baseline:

baseline <- data_C_raw %>%
  filter(direction == 'NaN') %>%
  pull(raw_firing_rate) %>%
  mean
baseline
#> [1] 3.852074

Normalize data and calculate mean, SE:

data_C <- data_C_raw %>%
  filter(direction != 'NaN') %>%
  mutate(
    norm_firing_rate = raw_firing_rate - baseline,
    norm_firing_rate = norm_firing_rate / max(abs(norm_firing_rate))
  ) %>%
  group_by(direction, speed) %>%
  summarize(
    firing_rate = mean(norm_firing_rate),
    sem = sd(norm_firing_rate) / sqrt(n())
  ) %>%
  ungroup() %>%
  mutate(direction = as_factor(direction))
data_C
#> # A tibble: 24 x 4
#>    direction   speed firing_rate     sem
#>    <fct>       <dbl>       <dbl>   <dbl>
#>  1 0         -0.696      -0.0109 0.0164 
#>  2 0         -0.401       0.0133 0.00324
#>  3 0         -0.0867      0.138  0.0561 
#>  4 0          0.214       0.475  0.100  
#>  5 0          0.524       0.734  0.0500 
#>  6 0          0.821       0.872  0.0443 
#>  7 0          1.13        0.810  0.0794 
#>  8 0          1.30        0.746  0.0507 
#>  9 0          1.43        0.811  0.0452 
#> 10 0          1.63        0.772  0.0796 
#> # ... with 14 more rows

Plot data:

fig_C <- data_C %>%
  ggplot(aes(x = speed, y = firing_rate)) +
    # Plot data and threshold
    geom_hline(
      yintercept = 0.8 * max(data_C$firing_rate),
      colour = 'grey70', linetype = 'dashed'
    ) +
    geom_line(aes(colour = direction)) +
    geom_errorbar(
      aes(
        colour = direction,
        ymin = firing_rate - sem,
        ymax = firing_rate + sem
      ),
      width = 0.02
    ) +
    geom_point(aes(colour = direction, shape = direction, size = direction)) +
    # Annotate points over threshold
    annotate(
      geom = "segment",
      x = 0.5, xend = 1.9,
      y = 1.2, yend = 1.2,
      size = 0.7,
      arrow = arrow(ends = 'both', length = unit(2, "pt"), angle = 90)
    ) +
    # Select colours and shapes
    scale_colour_manual(values = c("black", "grey50")) +
    scale_shape_manual(values = c(15, 18)) +
    scale_size_manual(values = c(2, 3)) +
    # Text
    labs(y = "Normalized\nfiring rate", x = "log(degrees/s)") +
    #theme(axis.title.x = element_blank()) +
    # Theme
    theme_548 +
    # Axes
    geom_rangeframe(data = fudge_axis_BC) +
    lims(y = c(-1,2)) +
    scale_x_continuous(breaks = -1:4 / 2)
fig_C