Load packages

title	Assignment 2 R
author	Daniel Moraes
date	2024-03-06
output	html_document

R Assignment 2

The files created are organized in the folders: maize, teosinte and plots

Part I: Data Inspection and Data Processing

Load packages

library(dbplyr)
library(plyr)
library(tidyverse)
library(ggplot2)
library(reshape2)

Importing data

here is my snippet of code used for data importing: #For fang_et_al_genotypes.txt file:

## Importing data
#For fang_et_al_genotypes.txt file:
fang <- read_tsv("./fang_et_al_genotypes.txt")
print(fang)

Data Inspection

here is my snippet of code used for data inspection:

view(fang)
head(fang)
str(fang)
summary(fang)
dim(fang)
# Get information about the file
file_info <- file.info('fang_et_al_genotypes.txt')

# Extract the size of the file
file_size_fang <- file_info$size

# Print the size of the file
cat("Size of the file:", file_size_fang, "bytes\n")

#By inspecting this file (fang) I learned that:

The number of lines is 2782, and the columns is 986
The Size of the file: 11051939 bytes
There are multiple commands to understand the data as str, view, summary

#For snp_position.txt file:

snp <- read_tsv("./snp_position.txt")
print(snp)

Data Inspection

here is my snippet of code used for data inspection:

head(snp)
str(snp)
summary(snp)
dim(snp)
# Get information about the file
file_info <- file.info('snp_position.txt')

# Extract the size of the file
file_size_snp <- file_info$size

# Print the size of the file
cat("Size of the file:", file_size_snp, "bytes\n")

#By inspecting this file (snp) I learned that:

The number of lines is 983, and the columns is 15
The Size of the file: 82763 bytes
There are multiple commands to understand the data as str, view, summary

Data Processing

here is my snippet of code used for data processing:

Organize and Sort the data

snp_final <- select(snp, SNP_ID, Chromosome, Position)
snp_sort <- arrange(snp_final, Position)
maize <- filter(fang, Group %in% c("ZMMIL", "ZMMLR", "ZMMMR"))
teosinte<- filter(fang, Group %in% c("ZMPBA", "ZMPIL", "ZMPJA"))

Organize and Transpose the data

# Transposing the files
transpose_maize <- t(maize)
transpose_teosinte <- t(teosinte)
# Final data for maize and teosinte

snp_final <- select(snp, SNP_ID, Chromosome, Position)
snp_sort <- arrange(snp_final, Position)
maize <- filter(fang, Group %in% c("ZMMIL", "ZMMLR", "ZMMMR"))
teosinte<- filter(fang, Group %in% c("ZMPBA", "ZMPIL", "ZMPJA"))

Organize and Transpose the data

#Rename the first column in SNP file to merge 
rename(snp_sort, Sample_ID = SNP_ID) -> snp_f
#Transposing the files
transpose_maize <- t(maize)
transpose_teosinte <- t(teosinte)
#Final data for maize and teosinte
maize_final<- data.frame(SNP_ID=rownames(transpose_maize), transpose_maize, row.names=NULL, check.names=FALSE)
teosinte_final <- data.frame(SNP_ID=rownames(transpose_teosinte), transpose_teosinte, row.names=NULL, check.names=FALSE)

Organize and Merge the data

# Merging
maize_snp<- merge(snp_final, maize_final)
teosinte_snp <- merge(snp_final, teosinte_final)

# For sorting into chromosomes using a loop for maize
for(i in 1:10){
  data <- filter(maize_snp, Chromosome == i)
  write.csv(data, paste("maize_chr",i,".csv",sep=""),row.names=F)
}
# For sorting into chromosomes using a loop for teosinte 
for(i in 1:10){
  data <- filter(teosinte_snp, Chromosome == i)
  write.csv(data, paste("teosinte_chr",i,".csv",sep=""),row.names=F)
}

Load the data from R repository-maize

# Load the data from R repository
maize_chr1 <- read.csv("maize_chr1.csv")
maize_chr2 <- read.csv("maize_chr2.csv")
maize_chr3 <- read.csv("maize_chr3.csv")
maize_chr4 <- read.csv("maize_chr4.csv")
maize_chr5 <- read.csv("maize_chr5.csv")
maize_chr6 <- read.csv("maize_chr6.csv")
maize_chr7 <- read.csv("maize_chr7.csv")
maize_chr8 <- read.csv("maize_chr8.csv")
maize_chr9 <- read.csv("maize_chr9.csv")
maize_chr10 <- read.csv("maize_chr10.csv")

Load the data from R repository-teosinte

# Load the data from R repository
teosinte_chr1 <- read.csv("teosinte_chr1.csv")
teosinte_chr2 <- read.csv("teosinte_chr2.csv")
teosinte_chr3 <- read.csv("teosinte_chr3.csv")
teosinte_chr4 <- read.csv("teosinte_chr4.csv")
teosinte_chr5 <- read.csv("teosinte_chr5.csv")
teosinte_chr6 <- read.csv("teosinte_chr6.csv")
teosinte_chr7 <- read.csv("teosinte_chr7.csv")
teosinte_chr8 <- read.csv("teosinte_chr8.csv") 
teosinte_chr9 <- read.csv("teosinte_chr9.csv")
teosinte_chr10 <- read.csv("teosinte_chr10.csv")

Increasing files- maize

# Maize- 1 to 10
data <- arrange(maize_chr1, Position) 
write.csv(data, paste("maize_inc_1",".csv",sep=""),row.names=F)
data <- arrange(maize_chr2, Position) 
write.csv(data, paste("maize_inc_2",".csv",sep=""),row.names=F)
data <- arrange(maize_chr3, Position) 
write.csv(data, paste("maize_inc_3",".csv",sep=""),row.names=F)
data <- arrange(maize_chr4, Position) 
write.csv(data, paste("maize_inc_4",".csv",sep=""),row.names=F)
data <- arrange(maize_chr5, Position) 
write.csv(data, paste("maize_inc_5",".csv",sep=""),row.names=F)
data <- arrange(maize_chr6, Position) 
write.csv(data, paste("maize_inc_6",".csv",sep=""),row.names=F)
data <- arrange(maize_chr7, Position) 
write.csv(data, paste("maize_inc_7",".csv",sep=""),row.names=F)
data <- arrange(maize_chr8, Position) 
write.csv(data, paste("maize_inc_8",".csv",sep=""),row.names=F)
data <- arrange(maize_chr9, Position) 
write.csv(data, paste("maize_inc_9",".csv",sep=""),row.names=F)
data <- arrange(maize_chr10, Position) 
write.csv(data, paste("maize_inc_10",".csv",sep=""),row.names=F)

Decreasing files- maize

# Maize- 1 to 10 
data <- arrange(maize_chr1, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_1",".csv",sep=""),row.names=F)

data <- arrange(maize_chr2, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_2",".csv",sep=""),row.names=F)

data <- arrange(maize_chr3, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_3",".csv",sep=""),row.names=F)

data <- arrange(maize_chr4, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_4",".csv",sep=""),row.names=F)

data <- arrange(maize_chr5, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_5",".csv",sep=""),row.names=F)

data <- arrange(maize_chr6, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_6",".csv",sep=""),row.names=F)

data <- arrange(maize_chr7, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_7",".csv",sep=""),row.names=F)

data <- arrange(maize_chr8, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_8",".csv",sep=""),row.names=F)

data <- arrange(maize_chr9, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_9",".csv",sep=""),row.names=F)

data <- arrange(maize_chr10, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("maize_dec_10",".csv",sep=""),row.names=F)

Organizing the increasing and decreasing files in a folder called maize

# Create the maize folder
if (!file.exists("maize")) {
  dir.create("maize")
}

# Move the files into the maize folder
for (i in 1:10) {
  file.rename(paste0("maize_inc_", i, ".csv"), paste0("maize/maize_inc_", i, ".csv"))
  file.rename(paste0("maize_dec_", i, ".csv"), paste0("maize/maize_dec_", i, ".csv"))
}

Increasing files- teosinte

# Teosinte- 1 to 10
data <- arrange(teosinte_chr1, Position) 
write.csv(data, paste("teosinte_inc_1",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr2, Position) 
write.csv(data, paste("teosinte_inc_2",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr3, Position) 
write.csv(data, paste("teosinte_inc_3",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr4, Position) 
write.csv(data, paste("teosinte_inc_4",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr5, Position) 
write.csv(data, paste("teosinte_inc_5",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr6, Position) 
write.csv(data, paste("teosinte_inc_6",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr7, Position) 
write.csv(data, paste("teosinte_inc_7",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr8, Position) 
write.csv(data, paste("teosinte_inc_8",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr9, Position) 
write.csv(data, paste("teosinte_inc_9",".csv",sep=""),row.names=F)
data <- arrange(teosinte_chr10, Position) 
write.csv(data, paste("teosinte_inc_10",".csv",sep=""),row.names=F)

Decreasing files- teosinte

# Teosinte- 1 to 10
data <- arrange(teosinte_chr1, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_1",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr2, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_2",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr3, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_3",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr4, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_4",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr5, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_5",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr6, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_6",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr7, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_7",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr8, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_8",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr9, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_9",".csv",sep=""),row.names=F)

data <- arrange(teosinte_chr10, desc(Position)) 
data1 <- data.frame(sapply(data,function(x) {x <- gsub("?","-",x,fixed=TRUE)}))
write.csv(data1, paste("teosinte_dec_10",".csv",sep=""),row.names=F)

Organizing the increasing and decreasing files in a folder called teosinte

# Create the teosinte folder
if (!file.exists("teosinte")) {
  dir.create("teosinte")
}

# Move the files into the teosinte folder
for (i in 1:10) {
  file.rename(paste0("teosinte_inc_", i, ".csv"), paste0("teosinte/teosinte_inc_", i, ".csv"))
  file.rename(paste0("teosinte_dec_", i, ".csv"), paste0("teosinte/teosinte_dec_", i, ".csv"))
}

Organizing the Other files that were used for Part I in a folder called Other

# Create the "Other" folder if it doesn't exist
if (!file.exists("Other")) {
  dir.create("Other")
}

# Move the files into the "Other" folder
for (i in 1:10) {
  file.rename(paste0("maize_chr", i, ".csv"), paste0("Other/maize_chr", i, ".csv"))
  file.rename(paste0("teosinte_chr", i, ".csv"), paste0("Other/teosinte_chr", i, ".csv"))
}

Part II Visualization

Data organization


# Transposing the fang file 
t(fang) -> t_fang

# Organizing 
fang_final <- data.frame(SNP_ID=rownames(t_fang), t_fang, row.names=NULL, check.names=FALSE)

# Merging fang with snp
fang_snp<- merge( snp_final, fang_final, by="SNP_ID", all=TRUE)

Visualization: SNPs per chromosome

fang_snp$Chromosome <- factor(fang_snp$Chromosome, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "unknown", "multiple", "NA"))
Graph_total<- ggplot(fang_snp,aes(x = Chromosome, fill=Chromosome)) + geom_bar(position ="stack")+
  ggtitle(" Total SNPs per Chromosome")
Graph_total
ggsave("Graph_total.tiff", plot = Graph_total, device = "tiff", width = 8, height = 6, units = "in")

maize_snp$Chromosome <- factor(maize_snp$Chromosome, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "unknown", "multiple", "NA"))
Graph_maize<- ggplot(maize_snp,aes(x = Chromosome, fill=Chromosome)) + geom_bar(position ="stack")+
  ggtitle("Maize SNPs per Chromosome")
Graph_maize
ggsave("Graph_maize.tiff", plot = Graph_maize, device = "tiff", width = 8, height = 6, units = "in")
teosinte_snp$Chromosome <- factor(teosinte_snp$Chromosome, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "unknown", "multiple", "NA"))
Graph_teosinte<- ggplot(teosinte_snp,aes(x = Chromosome, fill=Chromosome)) + geom_bar(position ="stack")+
  ggtitle("Teosinte SNPs per Chromosome")
Graph_teosinte
ggsave("Graph_teosinte.tiff", plot = Graph_teosinte, device = "tiff", width = 8, height = 6, units = "in")

Data organization- part 2

# Creating the groups
fang_new <- fang %>%
  mutate_if(is.character, str_replace_all, pattern = "A/A", replacement = "Homozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "T/T", replacement = "Homozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "G/G", replacement = "Homozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "C/C", replacement = "Homozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "A/T", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "A/G", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "A/C", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "T/A", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "T/G", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "T/C", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "G/A", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "G/T", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "G/C", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "C/A", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "C/T", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "C/G", replacement = "Heterozygous") %>%
  mutate_if(is.character, str_replace_all, pattern = "\\?\\/?", replacement = "Missing")

Final Data visualization- Plotting the proportion heterozygous and homozygous

#Subsetting SNP columns
data <- colnames(fang_new)[-c(1:3)]
data1 <- gather(fang_new, SNP, Genotype, data)

# Plotting the proportion heterozygous and homozygous SNPs with missing data
Proportion <- ggplot(data = data1, aes(x = Sample_ID, fill = Genotype)) + 
  geom_bar(position = "fill") +
  labs(x = "Sample ID", y = "Proportion") +
  theme(axis.text.x = element_blank())+
  ggtitle("Proportion heterozygous and homozygous SNPs with missing data")
Proportion
ggsave("Proportion.tiff", plot = Proportion, device = "tiff", width = 8, height = 6, units = "in")

# Plotting the proportion heterozygous and homozygous SNPs with missing data by Groups
Proportion_group <- ggplot(data = data1, aes(x = Group, fill = Genotype)) + 
  geom_bar(position = "fill") +
  labs(x = "Group", y = "Proportion") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +  # Rotate x-axis labels by 45 degrees
  ggtitle("Proportion heterozygous and homozygous by Group")
Proportion_group
ggsave("Proportion_group.tiff", plot = Proportion_group, device = "tiff", width = 8, height = 6, units = "in")

My Own Graph: Distribution of SNP by group

# Plot: Distribution of SNP 
Distribution_SNP<- ggplot(data = data1, aes(x = Group)) +
  geom_density(aes(fill = Genotype), alpha = 0.5) +
  facet_wrap(~ Genotype)+
  ggtitle(" Distribution of SNP by Group")
Distribution_SNP
ggsave("Distribution_SNP.tiff", plot = Distribution_SNP, device = "tiff", width = 8, height = 6, units = "in")

Oganize the plots in a folder called plots

# Create a folder named "plots" if it doesn't exist
if (!file.exists("plots")) {
  dir.create("plots")
}

# List of files to be moved
files_to_move <- c("Graph_total.tiff","Graph_maize.tiff","Graph_teosinte.tiff","Proportion.tiff","Proportion_group.tiff","Distribution_SNP.tiff" )

# Move each file to the "plots" folder
for (file in files_to_move) {
  file.rename(file, paste0("plots/", file))
}

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
Other		Other
maize		maize
plots		plots
teosinte		teosinte
.gitignore		.gitignore
Assignment 2 R.Rmd		Assignment 2 R.Rmd
Assignment-2-R.html		Assignment-2-R.html
Greene_Review.Rmd		Greene_Review.Rmd
R Assignment 2.R		R Assignment 2.R
README.md		README.md
fang_et_al_genotypes.txt		fang_et_al_genotypes.txt
snp_position.txt		snp_position.txt

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R Assignment 2

Part I: Data Inspection and Data Processing

Load packages

Importing data

Data Inspection

Data Inspection

Data Processing

Organize and Sort the data

Organize and Transpose the data

Organize and Transpose the data

Organize and Merge the data

Load the data from R repository-maize

Load the data from R repository-teosinte

Increasing files- maize

Decreasing files- maize

Organizing the increasing and decreasing files in a folder called maize

Increasing files- teosinte

Decreasing files- teosinte

Organizing the increasing and decreasing files in a folder called teosinte

Organizing the Other files that were used for Part I in a folder called Other

Part II Visualization

Data organization

Visualization: SNPs per chromosome

Data organization- part 2

Final Data visualization- Plotting the proportion heterozygous and homozygous

My Own Graph: Distribution of SNP by group

Oganize the plots in a folder called plots

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Repository files navigation

R Assignment 2

Part I: Data Inspection and Data Processing

Load packages

Importing data

Data Inspection

Data Inspection

Data Processing

Organize and Sort the data

Organize and Transpose the data

Organize and Transpose the data

Organize and Merge the data

Load the data from R repository-maize

Load the data from R repository-teosinte

Increasing files- maize

Decreasing files- maize

Organizing the increasing and decreasing files in a folder called maize

Increasing files- teosinte

Decreasing files- teosinte

Organizing the increasing and decreasing files in a folder called teosinte

Organizing the Other files that were used for Part I in a folder called Other

Part II Visualization

Data organization

Visualization: SNPs per chromosome

Data organization- part 2

Final Data visualization- Plotting the proportion heterozygous and homozygous

My Own Graph: Distribution of SNP by group

Oganize the plots in a folder called plots

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