Learn SCI thesis drawing skills (f)

brief introduction

In the process of consulting the literature , I saw some very good published pictures , Today, I'll study with Xiaobian , How they use R Drawn out .

Today is mainly about Picture 6 （F) —— Four dimensional scatter diagram , This figure is also commonly used in scientific research drawings . It can display data of multiple dimensions .

The detailed code introduction of the first five figures can be seen ： be based on R Detailed explanation of drawing skills of scientific research papers in language （4）、 be based on R Detailed explanation of drawing skills of scientific research papers in language （3） be based on R Detailed explanation of drawing skills of scientific research papers in language （2） be based on R Detailed explanation of drawing skills of scientific research papers in language （1） . The last picture will be introduced later , Readers in the process of learning , You can apply the internal knowledge points to your own graphics drawing . Twitter has listed the main knowledge points , It is more conducive for readers to learn and consult .

So let's see , How does the author realize this function , There are many knowledge points in this article , Let's study patiently , Suggest that you practice . The corresponding code 、 Data can be found in GitHub - marco-meer/scifig_plot_examples_R: Scientific publication figure plotting examples with R Find .

mapping

Load package

First, load some packages that need to be used .

library(ggplot2) 
library(viridis) #  Use color styles 

Set the theme

Next , For convenience , The author set the theme before drawing , And name the function my_theme.

This part is in First tweet give , The complete code will be given at the end of the text .

Manually modify most panels , For details, please refer to this chapter article . Or watch me B Station issued 《R Language visualization course 》, There are also some simple topic settings .

Import data

use first read.csv() Import four-dimensional data , front 6 The row data is as follows .

data_F = read.csv("./data_F.csv")
head(data_F)
# K n amplitude duration
1 32.586025 3.065018  21.95444 2.3242144
2  9.012644 2.358836  60.16122 1.0029049
3 10.814589 1.829281  25.99396 0.5406435
4 23.560813 1.709909  61.18815 2.7394397
5  7.626436 1.566929  54.41945 0.7152433
6 15.295503 2.276775  39.79195 0.8423647

Drawing graphics

Draw a simple scatter diagram geom_point(),amplitude To set shape size ,duration As the filling basis .

base_size = 12 
ggplot(data=data_F,
                  aes(x=K,y=n,
                      size=amplitude,
                      fill=duration))+
  geom_point(pch=21) 

Add theme , And modify it x、y Axis scales and labels .

Be careful ：x Axis into logarithmic scale (trans = 'log10').

my_theme() +
  scale_x_continuous(expand = c(0, 0),
                     trans = 'log10',
                     labels=c(1,10,100),
                     breaks=c(1,10,100),
                     limits = c(1,100)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,4,by=0.5)),
                     limits = c(0,4)) + +   
  xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
  ylab("Hill coefficient n") 

Use annotation_logticks() Add logarithmic scale .scale_size(range = c(1, 3)) Modify the size range of scatter points . It also uses viridis Color styles in the package scale_fill_viridis(option="D"). Finally, modify the legend position theme(legend.position = c(0.9,0.35)).

  annotation_logticks(sides='b') + # Add logarithmic scale 
  scale_size(range = c(1, 3)) + # The size range of the scatter 
  scale_fill_viridis(option="D") + # Color style 
  theme(legend.position = c(0.9,0.35))  # Change the legend position 

however , There is a point at bottom that is not clearly shown , Here the author uses a trick （ The last article also used ）, Show these points clearly . Let's see the effect of this code ：

coord_cartesian(clip = "off")

Complete code

# Panel F ----
library(ggplot2) 
library(vi)
base_size = 12 
my_theme <-  function() {
    
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
}

data_F = read.csv("./data_F.csv")
head(data_F)
panel_F <- ggplot(data=data_F,
                  aes(x=K,y=n,
                      size=amplitude,
                      fill=duration))+
  geom_point(pch=21) +
  my_theme() + 
  scale_x_continuous(expand = c(0, 0),
                     trans = 'log10',
                     labels=c(1,10,100),
                     breaks=c(1,10,100),
                     limits = c(1,100)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,4,by=0.5)),
                     limits = c(0,4)) +   
  xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
  ylab("Hill coefficient n") +
  annotation_logticks(sides='b') +
  scale_size(range = c(1, 3)) +
  scale_fill_viridis(option="D") + # a color palette from the viridis package
  theme(legend.position = c(0.9,0.35)) +
  coord_cartesian(clip = "off")

panel_F

Small make up has words

The main knowledge points learned in this paper are as follows ：

1. Use annotation_logticks() Add logarithmic scale .
2. Use scale_size() Modify the size range of scatter points ;
3. Use viridis Color styles in the package scale_fill_viridis();
4. Use theme(legend.position = ) Change the legend position .

After reading this article , Believe that your skill pack has more things . Remember to practice ！ Reading it doesn't mean you will ～ If you find the content useful , Prepared by Xiaowen with intention . Give me a cup of coffee ！