Streamlined Data Summary in R STUDIO: Enhancing Bar Graphs with Error Bars

When working with data in R, there are situations where you might need to examine summarized information, such as means, standard deviations, and more. Today, I will introduce the methods that can be employed for this purpose.

Let’s start by loading a dataset.

if(!require(readr)) install.packages("readr")
library(readr)

github= paste0("https://raw.githubusercontent.com/agronomy4future/",
                "raw_data_practice/refs/heads/main/",
                "fertilizer_treatment.csv")
dataA= data.frame(read_csv(url(github),show_col_types = FALSE))

print(head(dataA, 5))
    Genotype Block variable value
1 Genotype_A     I  Control  42.9
2 Genotype_A    II  Control  41.6
3 Genotype_A   III  Control  28.9
4 Genotype_A    IV  Control  30.8
5 Genotype_B     I  Control  53.3
.
.
.

As I engage in various tasks involving this data, I aim to summarize it. Therefore, I will introduce methods applicable to such situations.

1) using plyr package

First, install and activate the package.

if(!require(plyr)) install.packages("plyr")
library(plyr)

I want to summarize the average values for the ‘Genotype’ and ‘variable’ in the given dataset “dataA”. The summarized data will be named “dataB”.

dataB= ddply(dataA, c("Genotype","variable"), 
             summarise, mean=mean(value), 
             sd=sd(value), 
             n=length(value), 
             se=sd/sqrt(n))

print(head(dataB, 5))
    Genotype    variable   mean        sd n       se
1 Genotype_A     Control 36.050  7.220572 4 3.610286
2 Genotype_A Fertilizer1 50.625  6.733684 4 3.366842
3 Genotype_A Fertilizer2 45.850  6.943822 4 3.471911
4 Genotype_A Fertilizer3 37.300  7.266820 4 3.633410
5 Genotype_B     Control 50.850 14.552548 4 7.276274

The means, standard deviations (sd), and standard errors (se) of the values are compiled for each combination of Genotype and variable.

□ Utilizing R Studio for Data Grouping and Mean/Standard Error Calculation (feat ddply)

2) using dplyr package

This time, I will demonstrate how to create the same data using the dplyr package. First, let’s install the package.

if(!require(dplyr)) install.packages("dplyr")
library(dplyr)

I will create a dataset that compiles the means, standard deviations, and standard errors of value based on Genotype and variable in the given dataset “dataA”. The summarized data will be named “dataC”.

The %>% symbol can be generated automatically by pressing Ctrl + Shift + M, which eliminates the requirement for manual typing.

if(!require(dplyr)) install.packages("dplyr")
library(dplyr)

dataC = dataA %>%
       group_by(Genotype, variable) %>%
       dplyr::summarize(
       across(
        .cols= value,
        .fns= list(
         Mean= ~mean(., na.rm= TRUE),
         n= ~length(.),
         se= ~sd(., na.rm= TRUE) / sqrt(length(.)))),
        .groups= "drop") %>%
        as.data.frame()

print(head(dataC, 5))
    Genotype    variable value_Mean value_n value_se
1 Genotype_A     Control     36.050       4 3.610286
2 Genotype_A Fertilizer1     50.625       4 3.366842
3 Genotype_A Fertilizer2     45.850       4 3.471911
4 Genotype_A Fertilizer3     37.300       4 3.633410
5 Genotype_B     Control     50.850       4 7.276274
.
.
.

No matter which package is used, the data has been summarized by means. Now, let’s proceed to create a bar graph using this summarized mean data.

if(!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)

Fig1= ggplot(data=dataC, aes(x=Genotype, y=value_Mean, fill=variable))+
  geom_bar(stat="identity",position="dodge", width= 0.7) +
  geom_errorbar(aes(ymin= value_Mean-value_se, 
  ymax=value_Mean + value_se), 
  position=position_dodge(0.7), width=0.2, color='Black') +
  scale_fill_manual(values= c ("dark blue", "darkred", "blue", 
  "orange")) +
  scale_y_continuous(breaks = seq(0,100,20), limits = c(0,100)) +
  labs(x="Genotype", y="Yield") +
  theme_classic(base_size=20, base_family="serif")+
  theme(legend.position=c(0.90,0.9),,
        legend.title=element_blank(),
        legend.key.size=unit(0.5,'cm'),
        legend.key=element_rect(color=alpha("white",.05), 
        fill=alpha("white",.05)),
        legend.text=element_text(size=11),
        legend.background= element_rect(fill=alpha("white",.05)),
        panel.grid.major=element_line(colour="grey90", linewidth=0.5),
        axis.line=element_line(linewidth=0.5, colour="black"))

options(repr.plot.width=9, repr.plot.height=5)
print(Fig1)

ggsave("Fig1.png", plot= Fig1, width=9, height= 5, dpi= 300)

Since you used windows(), the graph will be displayed in a new window.

A bar graph with standard errors included has been successfully plotted.

Tip 1 > If you want to change legend titles:

In the above graph, you used the legend.title = element_blank() code to hide the legend title. Now, I want to display the legend title as “Treatment”.

First, I will change the code from legend.title = element_blank() to legend.title = element_text(face= "plain", family= "serif", size= 12, color= "Black"). Additionally, I will include name= "Treatment" in the scale_fill_manual(values= c("dark blue", "darkred", "blue", "orange")) code. In other words, the modified code will be scale_fill_manual(name= "Treatment", values= c("dark blue", "darkred", "blue", "orange")).

The complete code is as follows:

if(!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)

Fig2=ggplot(data=dataC, aes(x=Genotype, y=value_Mean, fill=variable))+
  geom_bar(stat="identity",position="dodge", width= 0.7) +
  geom_errorbar(aes(ymin= value_Mean-value_se, 
  ymax=value_Mean + value_se), 
  position=position_dodge(0.7), width=0.2, color='black') +
  scale_fill_manual(name="Treatment", 
  values= c("dark blue", "darkred", "blue", "orange")) +
  scale_y_continuous(breaks = seq(0,100,20), limits = c(0,100)) +
  labs (x="Genotype", y="Yield") +
  theme_classic(base_size=20, base_family="serif")+
  theme(legend.position=c(0.90,0.9),,
        legend.title= element_text(face= "plain", family="serif", 
        size= 12, color= "black"),
        legend.key.size=unit(0.5,'cm'),
        legend.key=element_rect(color=alpha("white",.05), 
        fill=alpha("white",.05)),
        legend.text=element_text(size=11),
        legend.background= element_rect(fill=alpha("white",.05)),
        panel.grid.major=element_line(colour="grey90", linewidth=0.5),
        axis.line=element_line(linewidth=0.5, colour="black")) 

options(repr.plot.width=9, repr.plot.height=5)
print(Fig2)

ggsave("Fig2.png", plot= Fig2, width=9, height= 5, dpi= 300)

Tip 2 > If you want to change legend labels:

Now, I want to change the legend label ‘Fertilizer 1 – 3’ to ‘Nitrogen,’ ‘Phosphorus,’ and ‘Potassium.’ How can I achieve this? While there are various methods to change variable names, for now, I’ll make the changes directly in the provided codes.

□ How to Rename Variables within Columns in R?

In the scale_fill_manual() function, I will add some code as shown below.

scale_fill_manual(name="Treatment", 
       values= c("dark blue", "darkred", "blue", "orange"),
       breaks=c("Control","Fertilizer1","Fertilizer2","Fertilizer3"), 
       labels=c("Control", "Nitrogen","Phosphorus","Potassium")) +

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