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https://github.com/loukesio/plots
This is a public repository which shows how to do fancy plots in R with simple data
https://github.com/loukesio/plots
Last synced: 4 days ago
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This is a public repository which shows how to do fancy plots in R with simple data
- Host: GitHub
- URL: https://github.com/loukesio/plots
- Owner: loukesio
- License: mit
- Created: 2023-01-19T09:41:35.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-29T16:08:57.000Z (26 days ago)
- Last Synced: 2024-11-29T17:21:28.145Z (26 days ago)
- Language: R
- Size: 7.45 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PLOTS
This is a public repository which shows how to do fancy plots in R with simple data
### 1. Make a ternary plot with ggtern
This is how the data looks like
```
# > head(data)
# # A tibble: 6 × 4
# x y z time
#
# 1 0.893 0.268 0.672 1
# 2 0.141 0.448 0.863 2
# 3 0.00621 0.589 0.624 3
# 4 0.479 0.204 0.385 4
# 5 0.209 0.471 0.417 5
# 6 0.756 0.973 0.674 6
```
code
``` r
#______________________________________________________________________________________________________
# ████████╗███████╗██████╗ ███╗ ██╗ █████╗ ██████╗ ██╗ ██╗ ██████╗ ██╗ ██████╗ ████████╗
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# ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═══╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝ ╚═════╝ ╚═╝
#______________________________________________________________________________________________________
#_________________________
# ┬ ┬┌┐ ┬─┐┌─┐┬─┐┬┌─┐┌─┐
# │ │├┴┐├┬┘├─┤├┬┘│├┤ └─┐
# ┴─┘┴└─┘┴└─┴ ┴┴└─┴└─┘└─┘
#_________________________
suppressPackageStartupMessages(library(ggtern)) # library for loading the ggtern pacakge
library(tidyverse) # library for loading the tidyverse package for data analysis
library(here) # define the directory you want to go
#________________
# ┌┬┐┌─┐┌┬┐┌─┐
# ││├─┤ │ ├─┤
# ─┴┘┴ ┴ ┴ ┴ ┴
#________________
data=tibble(x=runif(200), y = runif(200), z = runif(200), time=rep(seq(1:10),20))
#______________
# ┌─┐┬ ┌─┐┌┬┐
# ├─┘│ │ │ │
# ┴ ┴─┘└─┘ ┴
#______________
ggtern(data, aes(x,y,z)) + # This line creates a ternary plot using the ggtern package. The data is mapped to the x, y, and z aesthetics.
geom_point() + # This line adds points to the ternary plot
theme_rgbw() + # This line applies the theme_rgbw() theme to the plot
stat_density_tern(geom = 'polygon', # This line adds a density polygon to the plot, with the fill and alpha aesthetics mapped to the density level
aes(fill = after_stat(level),
alpha = after_stat(level)), bdl=0.001) +
scale_fill_gradient(low = "blue",high = "red") + # This line scales the fill color of the density polygon from blue to red
guides(color = "none", fill = "none", alpha = "none") # This line removes the color, fill, and alpha guides from the plot
#___________________________
# ┌─┐┌┐┌┬┌┬┐┌─┐┌┬┐┬┌─┐┌┐┌
# ├─┤│││││││├─┤ │ ││ ││││
# ┴ ┴┘└┘┴┴ ┴┴ ┴ ┴ ┴└─┘┘└┘
#___________________________
library(gganimate) # this line loads the gganimate library, which allows animation to be added to ggplots
library(magick) # this line loads the magick library, which provides functionality to handle image files
# This loop iterates 20 times
for(i in 1:20) {
# This line creates a ternary plot with data filtered by the current iteration value, and applies the same aesthetics and formatting as the previous plot
p <- ggtern(data[data$time == i, ], aes(x, y, z)) +
geom_point() +
theme_rgbw() +
stat_density_tern(geom = 'polygon',
aes(fill = ..level.., alpha = ..level..), bdl = 0.001) +
scale_fill_gradient(low = "blue",high = "red") +
guides(color = "none", fill = "none", alpha = "none")
ggsave(paste0('ggtern', i, '.png'), p) # This line saves the current plot as a PNG file with a file name that includes the iteration number
}
list.files(pattern = 'ggtern\\d+\\.png', full.names = TRUE) %>% # This line uses the magick library to read the PNG files that were created in the loop
image_read() %>%
image_join() %>% # This line joins the images together to form an animation
image_animate(fps=4) %>% # This line sets the animation speed to 4 frames per second
image_write("plots/ggtern.gif")
```
### 2. Make a voronoi diagram
Cluster cars based on the gear
```
data(mtcars)
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb car_name
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda\nRX4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Mazda\nRX4\nWag
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Datsun\n710
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet\n4\nDrive
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Hornet\nSportabout
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 Valiant
```
code
```
#___________________________________________________________________________
#
# ╦ ╦┌─┐┬┌─┐┬ ┬┌┬┐┌─┐┌┬┐╔╦╗┬─┐┌─┐┌─┐┌┬┐┌─┐┌─┐┌─┐ ┌─┐┌─┐┌─┐┬┌─┌─┐┌─┐┌─┐
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# ╚╩╝└─┘┴└─┘┴ ┴ ┴ └─┘─┴┘ ╩ ┴└─└─┘└─┘┴ ┴┴ ┴┴ └─┘ ┴ ┴ ┴└─┘┴ ┴┴ ┴└─┘└─┘
#____________________________________________________________________________
library(WeightedTreemaps) #use a weightedTreemap that contains a lots of shapes https://github.com/m-jahn/WeightedTreemaps
library(ltc)
data(mtcars) #use the mtcars data
mtcars$car_name = gsub(" ", "\n", row.names(mtcars)) #add a new rows with the car names
mtcars %>% #shows how many different gears exist
distinct(gear)
tm <- voronoiTreemap( #use the voronoiTreemap function to
data = mtcars, #add the data
levels = c("gear", "car_name"), #it will split the area into the 3 gears (4,5,6) and in each of them will add the cars
cell_size = "wt",
shape = "rounded_rect",
seed = 123
)
pal=ltc("dora",100,"continuous") #add the color palette that you might need
palettes #see all palettes from ltc and choose the one you like
drawTreemap( #the function draws the Treemap
tm,
color_palette = pal,
color_type = "cell_size",
color_level = 2,
#label_level = c(1,2),
label_size = 3,
#label_color = grey(0.5),
border_color = "black", #add color for the borders
#layout = c(1, 2),
position = c(1, 1), #I am not sure what it does
title = "Cars per gear"
)
```
### 3. Raincloud plot
The data shows body characteristics of penguings in different islands
```
# A tibble: 342 × 9
# Groups: species [3]
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year bill_ratio
1 Adelie Torgersen 39.1 18.7 181 3750 male 2007 2.09
2 Adelie Torgersen 39.5 17.4 186 3800 female 2007 2.27
3 Adelie Torgersen 40.3 18 195 3250 female 2007 2.24
4 Adelie Torgersen 36.7 19.3 193 3450 female 2007 1.90
5 Adelie Torgersen 39.3 20.6 190 3650 male 2007 1.91
6 Adelie Torgersen 38.9 17.8 181 3625 female 2007 2.19
7 Adelie Torgersen 39.2 19.6 195 4675 male 2007 2
8 Adelie Torgersen 34.1 18.1 193 3475 NA 2007 1.88
9 Adelie Torgersen 42 20.2 190 4250 NA 2007 2.08
10 Adelie Torgersen 37.8 17.1 186 3300 NA 2007 2.21
```
code
```
#_____________________________________________________________________________________________________________________
#
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#_____________________________________________________________________________________________________________________
library(palmerpenguins) # Importing the palmerpenguins library
library(ggtext) # Importing the ggtext library
library(colorspace) # Importing the colorspace library
library(ragg) # Importing the ragg library
library(showtext) # Importing the showtext library
font_add_google("Roboto Mono", "Roboto Mono") # Adding the Roboto Mono font
font_add_google("Zilla Slab", "Zilla Slab") # Adding the Zilla Slab font
showtext_auto() # with this command you register the font
#___________________________________
# ┬─┐┌─┐┌─┐┌┬┐┌─┐┬─┐ ┬┌┬┐┌─┐┌─┐┌─┐
# ├┬┘├─┤└─┐ │ ├┤ ├┬┘ ││││├─┤│ ┬├┤
# ┴└─┴ ┴└─┘ ┴ └─┘┴└─ ┴┴ ┴┴ ┴└─┘└─┘
#___________________________________
# Defining the url of the image to be plotted
url <- "https://education.rstudio.com/blog/2020/07/palmerpenguins-cran/gorman-penguins.jpg"
# Reading the image
img <- magick::image_read((url))
# Creating a rasterGrob object of the image
pic <- grid::rasterGrob(img, interpolate = TRUE)
# Displaying the image
img
# Defining a color palette
pal <- c("#FF8C00", "#A034F0", "#159090")
pal2 <- c("#DC4434","#F7AA00","#00A9A6")
# Defining a function to add sample size to the plot
# the function is called add_sample and
add_sample <- function(x){ # it takes the argument x, which a column of numbers it creates two columns, aka two variables,
return(c(y = max(x) + .025, # the first column finds the max of the x and adds 0.25
label = length(x))) # the second column the label, finds the length of x and adds it, it tells you actually the sample size
}
# Grouping the data by species, creating a new variable 'bill_ratio', filtering missing values, and plotting the data
penguins %>%
group_by(species) %>% # group data by species
mutate(bill_ratio = # add the bill ratio column
bill_length_mm / bill_depth_mm) %>%
filter(!is.na(bill_ratio)) # remove NA values
ggplot(aes(x = fct_rev(species), y = bill_ratio)) + # plot species over bill ratio. fct_rev(), is reversing the order of the species variable so that it is displayed in descending order on the x-axis of the plot
# ggdist
ggdist::stat_halfeye( # Adding the stat_halfeye layer
aes(color = species,
fill = after_scale(lighten(color, .5))), # increases the lightness of the fill color by 0.5
adjust = .5, # function that controls the position of the median line in relation to the box.
width = .75, # the width of the dist schematic. A value of 0.75 will make the box wider.
.width = 0, # adds a line. controls the width of the line separating the top and bottom of the box. A value of 0 will make this line disappear.
justification = -.3, # justification is a parameter of the stat_halfeye() function that controls the position of the box relative to the x-axis. A negative value of -0.4 will shift the box to the left.
point_color = NA) + # it shows the point where the median is
geom_boxplot(
aes(color = species,
color = after_scale(darken(color, .1, space = "HLS")), # you can use after_scale to modify the the scale of the color after you apply it. Here you darken it by 0.1 and HLS means Hue Lightness Saturation
fill = after_scale(desaturate(lighten(color, .8), .4))),
width = .42,
outlier.shape = NA) +
# geom_point
geom_point(
aes(color = species,
color = after_scale(darken(color, .1, space = "HLS"))),
fill = "white",
shape = 21,
stroke = .4,
size = 2,
position = position_jitter(seed = 1, width = .12)
) +
stat_summary( # This function is used to add summary statistics to a plot
geom = "text",
fun = "median", # This function calculates the median of the variable y
aes(label = round(..y.., 2), # This function rounds the median value of the variable y to 2 decimal places and labels it
color = species,
color = after_scale(darken(color, .1, space = "HLS"))), # This function darkens the color of the median text by .1 in the HLS color space
family = "Roboto Mono", # This function sets the font family to Roboto Mono
fontface = "bold", # This function sets the fontface of the median text to bold
size = 4.5, # This function sets the size of the median text to 4.5
vjust = -3.5 # This function sets the vertical justification of the median text to -3.5
) +
stat_summary(
geom = "text", # this function uses a stat_summary and gives a geom_text
fun.data = add_sample, # the function that uses is calles add_sample
aes(label = paste("n =", ..label..), # the function pastes the label together with the letter n=
color = species,
color = after_scale(darken(color, .1, space = "HLS"))), # after you set the color you make a bit darker
#family = "Roboto Mono",
size = 4, # add the size
hjust = 0 # adjust horizontically the text
) +
coord_flip(xlim = c(1.2, NA), clip = "off") + # here flips the plot, sets the axis limits, and turns off clipping
annotation_custom(pic, ymin = 2.9, ymax = 3.85, xmin = 2.7, xmax = 4.7) + # here adds the images in the graph
scale_y_continuous(
limits = c(1.57, 3.8),
breaks = seq(1.6, 3.8, by = .2),
expand = c(.001, .001) # sets a small expansion of the scales to ensure that the data points do not touch the edge of the plot.
) +
scale_color_manual(values = pal2, guide = "none") +
scale_fill_manual(values = pal2, guide = "none") +
theme_minimal(base_family = "Zilla Slab", base_size = 15) +
theme(
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_text(color = rev(darken(pal2, .1, space = "HLS")), size = 18), # you can even dark the color from the element text
axis.title.x = element_text(margin = margin(t = 10), size = 16),
plot.title = element_markdown(face = "bold", size = 21),
plot.subtitle = element_text(color = "grey40", hjust = 0, margin = margin(0, 0, 20, 0)),
plot.title.position = "plot",
plot.caption = element_markdown(color = "grey40", lineheight = 1.2, margin = margin(20, 0, 0, 0)),
plot.margin = margin(15, 15, 10, 15)
) +
labs(
x = NULL,
y = "Bill ratio",
title = "Bill Ratios of Brush–Tailed Penguins (*Pygoscelis* spec.)",
subtitle = "Distribution of bill ratios, estimated as bill length divided by bill depth."
)
ggsave("plots/rainclound.png", width = 30, height = 25, units = "cm")
```
### 4. Chord Diagram
```
# A tibble: 147 × 3
# Groups: from [13]
from to value
1 Germany France 306
2 Italy France 268
3 Germany Belgium 235
4 Belgium France 200
5 France Belgium 190
6 Italy Spain 170
7 Spain France 152
8 United Kingdom France 131
9 Poland France 125
10 Spain Italy 123
# … with 137 more rows
```
code
```
#__________________________________________________________________________________________________________________
#
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#_________________________________________________________________________________________________________________
library(countrycode) # pacakge for converting country names, ISO codes, and other country-related information
library(tidyverse) # data manipulation, visualization, and analysis
library(circlize) # package for creating circular plots, heatmaps, and chord diagrams
library(cowplot) # functions for creating and arranging complex plots, including multi-panel figures.
library(grid) # its a graphics engine for packages like ggplot2
library(ggplotify) # a package for converting complex plots into 'ggplot2' objects, making it easier to customize and modify the appearance of the plots.
#import data from tidytuesday github
df <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-08/erasmus.csv')
#which countries sends the most students abroad?
top_sending<-df%>%
filter(sending_country_code!=receiving_country_code)%>%
group_by(sending_country_code)%>%
summarise(students=sum(participants))%>%
arrange(-students)%>%
head(10)
top_sending
#which countries receive the most students?
top_receiving<-df%>%
filter(sending_country_code!=receiving_country_code)%>%
group_by(receiving_country_code)%>%
summarise(students=sum(participants))%>%
arrange(-students)%>%
head(10)
top_receiving
#create a list of countries that are either in the top 10 sending or top 10 receiving
#convret list of country codes to country names. manually plug in "UK", not part of iso code list
top_country_codes<-unique(c(top_sending$sending_country_code, top_receiving$receiving_country_code))
top_countries<-countrycode(top_country_codes , origin="iso2c", destination="iso.name.en")
top_countries[5]<-"United Kingdom"
data<-df%>%
#use the countrycode function to convert codes to country names for both receiving and sending countries
mutate(
to= countrycode(receiving_country_code, origin="iso2c", destination="iso.name.en"),
from= countrycode(sending_country_code , origin="iso2c", destination="iso.name.en"),
)%>%
#United Kingdom and Greece not translated with ISO country codes, override with dplyr "replace"
mutate(
to = replace(to, receiving_country_code=="UK","United Kingdom"),
from = replace(from, sending_country_code=="UK","United Kingdom"),
to = replace(to, receiving_country_code=="EL","Greece"),
from = replace(from, sending_country_code=="EL","Greece")
)%>%
#summarise number of participants by sending and receiving country code
group_by(from, to)%>%
summarise(value=sum(participants))%>%
arrange(-value)
#subset data to simplify chord diagram
#filter out records where country "from" is the same as country "to". select countries part of top countries list
chord_data<-data%>%
filter(from!=to)%>%
filter(from %in% top_countries & to %in% top_countries)%>%
arrange(-value)
#replace Netherlands (the) with Netherlands for all occurences (from and to)
chord_data[chord_data=="Netherlands (the)"]<-"Netherlands"
#create custom color palette, sites like https://coolors.co/ make it easy
pal<-c("#002765","#0061fd","#1cc6ff","#00b661","#5bf34d","#ffdd00","#ff7d00","#da2818","#ff006d","#8f00ff","#453435","black","grey80")
pal2=c("#f94144", "#f3722c","#f8961e","#f9844a","#f9c74f","#90be6d","#43aa8b","#4d908e","#577590", "#277da1","#453435","black","grey80")
#based on tutorial from https://jokergoo.github.io/circlize_book/book/the-chorddiagram-function.html
chordDiagram(chord_data, grid.col = pal2)
#convert chordDiagram (base plot) to grid plot to combine with ggplot annotations and theme details
p<-recordPlot() # records the previous chord diagram and saves it as `p`. This is useful for creating complex plots that involve multiple layers, or for creating a base plot that can be modified later.
as.ggplot(ggdraw(p))+ # you can convert now chord diagram `p` to a ggplot object
labs(title="ERASMUS STUDENT MOBILITY",
subtitle="Graphic depicts movement of participants between top participating countries from 2014 to 2020",
caption="Data from Data.Europa")+
theme(text=element_text(family="Arial"),
plot.title=element_text(hjust=0.5, face="bold", size=18),
plot.subtitle=element_text(hjust=0.5, size=12, margin=margin(t=10)), # horizontal justification = 0.5, top margin to 10 units
plot.caption=element_text(size=10, hjust=0.95, margin=margin(b=12)), # sets the bottom maring to 12 units
plot.margin=margin(t=20)). # sets the top margin to 20 units
ggsave("erasmus.jpeg", height=9, width=9)
```
### 5. Grouped dot plot
``` r
# load required packages
library(tidyverse) # for data wrangling and visualization
library(palmerpenguins) # for the penguins dataset
head(penguins)
#> # A tibble: 6 × 8
#> species island bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex year
#>
#> 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
#> 2 Adelie Torgersen 39.5 17.4 186 3800 fema… 2007
#> 3 Adelie Torgersen 40.3 18 195 3250 fema… 2007
#> 4 Adelie Torgersen NA NA NA NA 2007
#> 5 Adelie Torgersen 36.7 19.3 193 3450 fema… 2007
#> 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
#> # … with abbreviated variable names ¹flipper_length_mm, ²body_mass_g
```
Created on 2023-04-03 with [reprex v2.0.2](https://reprex.tidyverse.org)
**Plot A**
code
```
# Load the dataset and remove rows with missing values
penguins %>%
drop_na() %>%
# Initialize the ggplot with species and bill length, grouped by sex
ggplot(aes(species, bill_length_mm, group = sex)) +
# Add jittered points to visualize data distribution, colored by sex
geom_jitter(
aes(color = sex), # Map colors to the 'sex' variable
size = 5, # Set the size of the points
alpha = 0.8, # Adjust transparency of points
position = position_jitterdodge() # Jitter points within groups
) +
# Customize the color palette for the sexes
scale_color_manual(
values = c("darkorange", "purple") # Specify colors for the groups
) +
# Add error bars to represent mean ± standard deviation
stat_summary(
fun.data = "mean_sdl", # Function to calculate mean and SD
fun.args = list(mult = 1), # Use 1 standard deviation
position = position_dodge(width = 0.75), # Align error bars for grouped data
geom = "errorbar", # Add error bars
linewidth = 1.2, # Set the thickness of error bar lines
width = 0.1 # Set the width of the error bars
) +
# Add points to represent the mean for each group
stat_summary(
fun = "mean", # Calculate the mean
geom = "point", # Represent mean with points
position = position_dodge(width = 0.75), # Align mean points with error bars
size = 6, # Set the size of mean points
shape = 21, # Use a filled circle for the mean
fill = "#333333", # Fill color for the mean point
stroke = 0 # Border thickness for mean point
) +
# Apply a custom theme (assumed to be defined elsewhere in your script)
custom_theme() +
# Add labels for the axes and title
labs(
x = "Species", # Label for the x-axis
y = "Bill Length (mm)", # Label for the y-axis
title = "Bill Length by Species and Sex" # Title of the plot
) +
# Place the legend at the bottom of the plot
theme(legend.position = "bottom")
```