BIN510 UNIT 1: Lab 1 PART II

Loading dplyr and manipulating data

Introduction:

Part 1 had you follow my instructions for loading and working with data. This part will have you dive into a VERY useful package in R called “dplyr”, this package allows you to easily manipulate and rework data. To complete this lab, you will be following the instructions on this website:

https://datacarpentry.org/R-genomics/04-dplyr.html

***You will need to bring the “metadata.csv” file into R (found on canvas) NOTE: in order for the codes you use later on to work, you NEED to name your dataframe “metadata”

Follow this guide step-by-step. This will be as simple as copying and pasting code from the site. You WILL need to perform the challenge outlined!

Place all of your code for this assignment below and knit this to an HTML to save and upload to canvas.

#Install dplyr, load the package, and read in file

install.packages("dplyr") ## install
library("dplyr")          ## load
metadata<- read.csv('Ecoli_metadata.csv')

#Perform common dplyr functions

##select data columns 
select(metadata, sample, clade, cit, genome_size)

##To choose rows 
filter(metadata, cit == "plus")

#Use pipes to select and filter

#Use pipes to send the metadata data set first through filter, to keep rows where 'cit' was equal to 'plus', and then through select to keep the sample and generation and clade columns 
metadata %>%
  filter(cit == "plus") %>%
  select(sample, generation, clade)

#Create a new object with this smaller version of the data we could do so by assigning it a new name
meta_citplus <- metadata %>%
  filter(cit == "plus") %>%
  select(sample, generation, clade)

meta_citplus

#Challenge

#Using pipes, subset the data to include rows where the clade is ‘Cit+’. Retain columns sample, cit, and genome_size.
filtered_data <- metadata %>%
  filter(clade == 'Cit+') %>%
  select(sample, cit, genome_size)

#Mutate

#To create a new column of genome size in bp:
metadata %>%
  mutate(genome_bp = genome_size *1e6)

#If this runs off your screen and you just want to see the first few rows, you can use a pipe to view the head() of the data
metadata %>%
  mutate(genome_bp = genome_size *1e6) %>%
  head

#Remove rows that have NA values for clade 
metadata %>%
  mutate(genome_bp = genome_size *1e6) %>%
  filter(!is.na(clade)) %>%
  head

#Split-apply-combine data analysis

#Split the data into groups, apply some analysis to each group, and then combine the results
metadata %>%
  group_by(cit) %>%
  summarize(n())

#Ignore NA
metadata %>%
  group_by(cit) %>%
  summarize(mean_size = mean(genome_size, na.rm = TRUE))

#Group by multiple columns
metadata %>%
  group_by(cit, clade) %>%
  summarize(mean_size = mean(genome_size, na.rm = TRUE))

#Discard rows with missing information 
metadata %>%
  group_by(cit, clade) %>%
  summarize(mean_size = mean(genome_size, na.rm = TRUE)) %>%
  filter(!is.na(clade))

#Summarize multiple varaible at the same time 
metadata %>%
  group_by(cit, clade) %>%
  summarize(mean_size = mean(genome_size, na.rm = TRUE),
            min_generation = min(generation))