Learn to plot Data Using R and GGplot2: Import, manipulate , graph and customize the plot, graph
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This tutorial explains how to create publication-ready graphs using R programming and ggplot2. It covers everything from importing and handling data to plotting and customizing graphs. The session is focused on practical steps to visualize data effectively.
What You’ll Learn:
Setting up the working directory and importing data
Understanding data frames and summarizing data
Basic plotting with base R
Introduction to ggplot2 and its grammar of graphics
Creating scatter plots, adding trendlines, and customizing themes
Converting wide-format data to long-format for better analysis
Visualizing distributions with box plots and violin plots
Summarizing data with dplyr for cleaner plots
Who Should Watch:
Students and researchers working with data
Professionals needing clear and impactful visualizations
Anyone learning R for data analysis
If this tutorial is useful, like the video and subscribe to the channel for more R programming and data analysis content. Share it with others who want to improve their data visualization skills.
0:00 Introduction to the Session
0:16 Overview of the Data
1:41 Setting Up the Environment
2:46 Understanding the Data Frame
4:21 Basic Plotting with Base R
6:01 Installing and Loading ggplot2
7:21 Basic ggplot2 Plotting
10:01 Adding Trendlines and Customizations
13:01 Converting Data from Wide to Long Format
16:21 Advanced ggplot2 Customizations
19:31 Summarizing Data with dplyr
22:21 Box Plots and Violin Plots
25:11 Combining Geoms and Avoiding Overplotting
27:31 Final Customizations and Conclusion
Facebook page:
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Mail Id:
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• R programming tutorials
#Code used in this tutorial ( You can copy-paste from this downward.)
data file link : https://drive.google.com/file/d/1JPJu...
setwd("D:/Rworks/datatoplot") # Change working directory to directory where your data file is saved
getwd()
df = read.csv("polyphenolassay.csv")
df
summary(df)
str(df)
plot(df)
install.packages("ggplot2")
library(ggplot2)
ggplot(df, aes(conc,rep1))+
geom_point()+
geom_point(aes(y=rep2),color="red")+
geom_point(aes(y=rep3),color="green")+
geom_smooth(method="lm",formula=y~x-1,se=0)+
geom_smooth(aes(y=rep2),method="lm",formula=y~x-1,se=0,color="red")+
geom_smooth(aes(y=rep3),method="lm",formula=y~x-1,se=0,color="green")+
theme_classic()+
labs(title="Estimation of Polyphenol Content",subtitle="Folin Dennis Method",caption="Exepriment conducted as biochemistry lab",
x="Concentration of polyphenol in mcg/ml",y="OD795nm")
install.packages("tidyr")
library(tidyr)
df_long= pivot_longer(df,cols=2:4,names_to = "rep",values_to = "OD795")
df_long
str(df_long)
ggplot(df_long, aes(conc,OD795,color= rep))+
geom_point()+
geom_smooth(method="lm",formula=y~x-1,se=0)+
theme_classic()+
labs(title="Estimation of Polyphenol Content",subtitle="Folin Dennis Method",caption="Exepriment conducted as biochemistry lab",
x="Concentration of polyphenol in mcg/ml",y="OD795nm")
install.packages("dplyr")
library(dplyr)
I have removed the pipes as angled brackets are not allowed in description
df_summary= group_by(df, conc)
df_summary= summarise(df_summary, mean_OD795=mean(OD795))
ggplot(df_summary, aes(conc,mean_OD795))+
geom_point()+
geom_smooth(method="lm",formula=y~x-1,se=0)+
theme_classic()+
labs(title="Estimation of Polyphenol Content",subtitle="Folin Dennis Method",caption="Exepriment conducted as biochemistry lab",
x="Concentration of polyphenol in mcg/ml",y="OD795nm")
ggplot(df_long, aes(rep,OD795,color= rep))+
geom_boxplot()+
theme_classic()+
labs(title="Estimation of Polyphenol Content",subtitle="Folin Dennis Method",caption="Exepriment conducted as biochemistry lab",
x="Concentration of polyphenol in mcg/ml",y="OD795nm")
ggplot(df_long, aes(rep,OD795,color= rep))+
geom_violin()+
geom_jitter()+
theme_classic()+
labs(title="Estimation of Polyphenol Content",subtitle="Folin Dennis Method",caption="Exepriment conducted as biochemistry lab",
x="Concentration of polyphenol in mcg/ml",y="OD795nm")
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