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7 Steps guide to make Publication Ready Bargraphs from Scratch (TWO-Way ANOVA)

12/12/2021

This guide will show you how to make publication ready barplots with TWO-WAY ANOVA from scratch.

Group barplot with TWO-WAy ANova in R

Install these packages if you have not and load them:

library(ggplot2)
library(ggthemes)
library(multcompView)
library(dplyr)
## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(stats)

1- Create/load a data set

We will use the following built-in dataset for this example:

data2 <- ToothGrowth
data2$dose = as.factor(data2$dose)

2- Calculating Two Way ANOVA

anova <- aov(len ~ supp*dose, data = data2) #two way anova
summary(anova) #represents ANOVA
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## supp         1  205.4   205.4  15.572 0.000231 ***
## dose         2 2426.4  1213.2  92.000  < 2e-16 ***
## supp:dose    2  108.3    54.2   4.107 0.021860 *  
## Residuals   54  712.1    13.2                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3- Multiple Mean Comparison Analysis (only if you have significant differences in Two-way anova)

tukey <- TukeyHSD(anova)
tukey
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = len ~ supp * dose, data = data2)
## 
## $supp
##       diff       lwr       upr     p adj
## VC-OJ -3.7 -5.579828 -1.820172 0.0002312
## 
## $dose
##         diff       lwr       upr   p adj
## 1-0.5  9.130  6.362488 11.897512 0.0e+00
## 2-0.5 15.495 12.727488 18.262512 0.0e+00
## 2-1    6.365  3.597488  9.132512 2.7e-06
## 
## $`supp:dose`
##                diff        lwr        upr     p adj
## VC:0.5-OJ:0.5 -5.25 -10.048124 -0.4518762 0.0242521
## OJ:1-OJ:0.5    9.47   4.671876 14.2681238 0.0000046
## VC:1-OJ:0.5    3.54  -1.258124  8.3381238 0.2640208
## OJ:2-OJ:0.5   12.83   8.031876 17.6281238 0.0000000
## VC:2-OJ:0.5   12.91   8.111876 17.7081238 0.0000000
## OJ:1-VC:0.5   14.72   9.921876 19.5181238 0.0000000
## VC:1-VC:0.5    8.79   3.991876 13.5881238 0.0000210
## OJ:2-VC:0.5   18.08  13.281876 22.8781238 0.0000000
## VC:2-VC:0.5   18.16  13.361876 22.9581238 0.0000000
## VC:1-OJ:1     -5.93 -10.728124 -1.1318762 0.0073930
## OJ:2-OJ:1      3.36  -1.438124  8.1581238 0.3187361
## VC:2-OJ:1      3.44  -1.358124  8.2381238 0.2936430
## OJ:2-VC:1      9.29   4.491876 14.0881238 0.0000069
## VC:2-VC:1      9.37   4.571876 14.1681238 0.0000058
## VC:2-OJ:2      0.08  -4.718124  4.8781238 1.0000000

4- Extract lettering from TWO-WAY ANOVA and Tukey’s Test:

# compact letter display
group_lettering <- multcompLetters4(anova, tukey) #
group_lettering
## $supp
##  OJ  VC 
## "a" "b" 
## 
## $dose
##   2   1 0.5 
## "a" "b" "c" 
## 
## $`supp:dose`
##   VC:2   OJ:2   OJ:1   VC:1 OJ:0.5 VC:0.5 
##    "a"    "a"    "a"    "b"    "b"    "c"

group_lettering2 <- data.frame(group_lettering$`supp:dose`$Letters)
group_lettering2
##        group_lettering..supp.dose..Letters
## VC:2                                     a
## OJ:2                                     a
## OJ:1                                     a
## VC:1                                     b
## OJ:0.5                                   b
## VC:0.5                                   c

5- Calculating and adding mean, sd and lettering columns to the data set:

mean_data2 <- data2 %>% 
  group_by(supp, dose) %>% 
  summarise(len_mean=mean(len), sd = sd(len)) %>% #to calculate mean and SD
  arrange(desc(len_mean)) #to arange in descending order
## `summarise()` has grouped output by 'supp'. You can override using the `.groups` argument.

tibble(mean_data2)
## # A tibble: 6 x 4
##   supp  dose  len_mean    sd
##   <fct> <fct>    <dbl> <dbl>
## 1 VC    2        26.1   4.80
## 2 OJ    2        26.1   2.66
## 3 OJ    1        22.7   3.91
## 4 VC    1        16.8   2.52
## 5 OJ    0.5      13.2   4.46
## 6 VC    0.5       7.98  2.75

mean_data2$group_lettering <- group_lettering2$group_lettering..supp.dose..Letters

6- Drawing Publication ready Barplots with TWO-WAY ANOVA

  • Draw Basic Barplot
ggplot(mean_data2, aes(x = dose, y = len_mean,group=supp))  +
  geom_bar(position=position_dodge(0.9),stat = "identity", aes(fill = supp), 
           show.legend = TRUE)

  • *Add erro Bars *(sd)* on the plot*
ggplot(mean_data2, aes(x = dose, y = len_mean,group=supp))  +
  geom_bar(position=position_dodge(0.9),stat = "identity", aes(fill = supp), show.legend = TRUE) + #barplot
  geom_errorbar(aes(ymin = len_mean-sd, ymax=len_mean+sd),width = 0.1, position=position_dodge(0.9))

  • Add lettering to error bars
ggplot(mean_data2, aes(x = dose, y = len_mean,group=supp))+
  geom_bar(position=position_dodge(0.9),stat = "identity", aes(fill = supp), show.legend = TRUE) + #barplot
  geom_errorbar(aes(ymin = len_mean-sd, ymax=len_mean+sd),width = 0.1, position=position_dodge(0.9)) + 
  geom_text(aes(label = group_lettering, y = len_mean + sd), vjust=-0.4, position=position_dodge(0.9)) #add letters

  • Final Publication Ready group bar plot with TWO-Way ANOVA (Type-1)
#grouped barplot
p1 <- ggplot(mean_data2, aes(x = dose, y = len_mean,group=supp))  +
  geom_bar(position=position_dodge(0.9),stat = "identity", aes(fill = supp), show.legend = TRUE) + #barplot
  geom_errorbar(aes(ymin = len_mean-sd, ymax=len_mean+sd),width = 0.1, position=position_dodge(0.9)) + 
  geom_text(aes(label = group_lettering, y = len_mean + sd), vjust=-0.4, position=position_dodge(0.9)) + #add letters
  scale_fill_brewer(palette = "BrBG", direction = 1) + #theme setting
  labs(#this will add labels 
    x = "Dose",
    y = "Length (cm)",
    title = "Publication Ready Group Barplot",
    subtitle = "Made by #RwithAammar",
    fill = "Supp"
  ) +
  #facet_wrap(~supp)+
  ylim(0,35)+ #change your yaxis limits based on the letters
  ggthemes::theme_par(); p1

  • Publication ready grouped barplot with TWO-Way ANOVA with Facets (Type-2)
p2 <- ggplot(mean_data2, aes(x = dose, y = len_mean,group=supp))  +
  geom_bar(position=position_dodge(0.9),stat = "identity", aes(fill = supp), show.legend = TRUE) + #barplot
  geom_errorbar(aes(ymin = len_mean-sd, ymax=len_mean+sd),width = 0.1, position=position_dodge(0.9)) + 
  geom_text(aes(label = group_lettering, y = len_mean + sd), vjust=-0.4, position=position_dodge(0.9)) + #add letters
  scale_fill_brewer(palette = "BrBG", direction = 1) + #theme setting
  labs(#this will add labels 
    x = "Dose",
    y = "Length (cm)",
    title = "Publication Ready Group Barplot",
    subtitle = "Made by #RwithAammar",
    fill = "Supp"
  ) +
  facet_wrap(~supp)+
  ylim(0,35)+ #change your yaxis limits based on the letters
  ggthemes::theme_par(); p2

7- Saving upto 4K barplots in R

  • First choose a working directory by pressing ctrl+shift+H and select a folder then run the following code to save in .tiff:
tiff('Barplot_G1.tiff', units="in", width=10, height=6, res=300, compression = 'lzw')
p1
dev.off()
## png 
##   2

tiff('Barplot_G2.tiff', units="in", width=10, height=6, res=300, compression = 'lzw')
p2
dev.off()
## png 
##   2

Best regards,

Muhammad Farooq

Google Certified Data Analyst

(Ask any questions!)