Working With Data

Working With Data using R

One of R’s most powerful attributes is it’s ability to work with data, ranging from cleaning all the way through analysis. This is possible through R’s has a diversity of libraries and tools to efficiently work with datasets.

This guide will focus on how to move data into R and the basics on accessing data in a data set

Reading Data into R

In order to work with data in R, we need to first Read our data into R.

This is the process of taking a file that we have in our File System or from an Online URL, and entering a command that allows R to read the file contents and store them in our workspace.

Commonly Installed Files

File Abbreviation	File Name
CSV	Comma-Separated Values
TSV	Tab-Separated Values
XLS	Excel Spreadsheet (old)
XLSX	Excel Spreadsheet (New)
gsheet	Google Sheet File
FWF	Fixed-Width File
JSON	Java Script Object Notation

Base R Data Reading

R provides simeple commands that revolve around read. These are useful for simple file types such as CSV’s. However, we can access commands that are much more robust through our Library

Reading a CSV file from the file tree

read.csv("enter/your/file/path/here.csv")

Reading a CSV from a URL

read.csv(url(enter your url link here.csv))

Installing Packages

Installing packages can Widen the Scope of Your Data Reading Capabilities.

Package	Readable File Types
tidyVerse	CSV’s, TSV’s, Text, XLSX, Google Sheets,Fixed Width Files
readr	CSV’S, TSV’S, Text, XLSX, Google Sheets, FWF
readxl	XLX,XLSX

Installing the Tidyverse Package will be suitable for most file reading needs.

File Reading Procedures

Setting up your file directory

Make sure that your current working directory is set to your home directory, or the directory you want to work in.

> cd ~

#This will take you to the home directory in the console.

> cd "/Enter/Directory/Path/Here"

#This will take you to a specific working directory.

Download the Desired Files to Your Computer.

Make sure that you know where your storing them in your computer so you can pull them later with ease.

Installing your package of choice

Let’s use Tidyverse as an example. We first need to install Tidyverse.

install.packages("tidyverse")

We know tidyverse has been installed when we run this command and are given a long series of code; this is our computer downloading all the packages and programs that tidyverse uses.

Next, we will pull this package from our Library.

library(tidyverse)

This will allow us to use Commands in the tidyverse package

Reading in a file

For Tidyverse, we can read in a multitude of files like so

read_filetpe(This/is/the/path/to/your/file/file_name.filtype)

you will know the file has been successfully downloaded when it Appears in Your Environment in the top right panel, or no error signs are given.

Navigating your Data set

This guide will provide you the baseline tools for Accessing Rows, Columns, and Elements in your data set.

Accessing Columns

We can Access Columns in a data set by using the $ command as well as our [] syntax.

This code will allow us to create a Vector of a Dataframes Specific Column.

desired_column <- dataframe$column

This code will allow us to return a dataframes column as a Variable

desired_column_dS <- dataframe["column"]

To Manipulate Column Names, we can use a simple command.

names(dataframe)[names(dataframe) == "column"] <- "new column name"

We can also select Multiple Columns

Selected_Columns <- dataframe[c("colname1","colname2")]

Accessing Rows

We can access rows through Filtering for certain Values rows have in a column

Filtering rows

Filtered <- Dataframe(Dataframe$column = "Certain Value Within Columns Rows")

Accessing elements in rows

We can take this one step further, and access Specific Elements in matrices, vectors, lists, and datasets

Vectors

# we will use an example vector

vector <- c(10,20,25,200,1000)

#pulling the value 10

pulled_value <- vector[1]

10

# we can do the same to access multiple values 

multilpe_values <- vector[c(1,5)]

10 , 1000

Note: If working with other coding languages. When pulling elements, the order may start with 0 instead of 1. An example is Javascript. To pull the first element from a JavaScript Vector, you would enter vector[0].

Matrices

Because Matrices operate similar to a Two Dimensional Vector, we can access components through simple commands

# we will create an example matrix
 
example_matrix <- matrix(1:25, nrow = 5, ncol = 5) # this is a 5 by 5 matrix 

# This is how we want to pull a specific row from the matrix 

row_matrix <- example_matrix(2, )

This will print all the values of row 2

[6,7,8,9,10]

We can also pull specific row and column values

Specific_matrix <- example_matrix(2,4) # 2nd row, 4th column

[9]

We can access Multiple Values as well.

Specific_Rows_and_Columns <- example_matrix[c(1,2),C(4,5)]

[2][20]

We can also modify values

example_matrix[1,1] <- 100

# Let's pull the top row now

New_Row <- example_matrix[1][1]

print(New_Row)

[100,2,3,4,5]

Lists

Let’s look at how we can modify the elements within a list

example_list <- list(
age <- c(1,5,13,18)
name <- c("Julie","Richard","Will","Grant")
Status <- c("Baby","Toddler","Teenager"."Adult")
)

If we want to only know the age column we can do an operation such as this

example_list[[1]]

If we want to know the age and their status we can expand on our operation

example_list[[1]][3]

Finally, we can access specific elements within our list

example_list$name[2] 

Datasets

Datasets are similar to lists, only they commonly will contain far more information and can be organized in several different file formats.

Let’s create a dataset example

data_set_example <- df(
age <- c(1,5,13,18)
name <- c("Julie","Richard","Will","Grant")
Status <- c("Baby","Toddler","Teenager"."Adult")
))

# let's pull the name Julie from our dataset

dataset_element <- dataset_example[2,1]

We can run similar commands to access rows and columns

``` Row Access

dataset_row <- dataset_example[2,]

### Applications of Navigating Datasets

Being able to navigate through your dataframe,vector,list,or matrix quickly
will allow you to **clean your data** efficiently and provide a strong foundation
for future work. 

--------------------------------------------------------------------------------

## Using Dplyr

We will briefly touch on Dplyr and it's use in R. Dplyr is a package that provides
a multitude of functions that will make tedious cleaning procedures in base R 
**Simpler and Quicker** 

### Accessing Dplyr

Accessing Dplyr will require following the same procedure that was followed earlier 
for accessing Tidyverse

```r

install.packages("dplyr")
library(dplyr)

Common Dplyr commands

Filter

Filter allows you to select for only certain values in a column that match a desired value

Example_dataframe %>% 
filter(column name == "certain value")

This filter will provide only certain values from one column

other comparative operators can be used with this command such as >, <, ==, >=, and <= to select for values that are exceeding, equal, or are less then a certain value.

Select

Select is a streamlined command that allows you to select certain columns from your dataset

Selected_DF <- Dataframe |>
select("This Column", "This other column", "Ooh maybe this one!")

Mutate

This is a similar command to the one we did earlier, where we were able to change the value of a column The difference is that we are able to apply a function or changes to a column, which will create a new column with those changes applied

Mutated_DF <- Dataframe %>%
mutate(
new_column <- existing_column * 2 %>%
# we can also mutate a singular column
existing_column <- existing_column * 2 
)

Rename

This command allows us to rename a column to something cleaner

Dataframe |>
new_name <- old_name

Applying What we Have Learned

We can apply what we have learned to compute introductory data cleaning.

Prompt: For an introductory R class you have been assigned to create 2 new datasets from an existing dataset

Dataset 1

We want our raw dataset to only contain the first 2 columns, and the first 2 rows

Dataset 2

We want to only include rows that have an age greater then 20

Practice Code

raw_data <- df(
name_column <- c("Darnold","Matilda","Juan","Louise","Aaron","Elena")
age_column <- c(10,20,17,14,32,21)
school <- c("elementary","college","highschool","junior","postgrad","college")
grade <- c(4,N/A,11,8,N/A,N/A)
)

#First we will declare our first dataset
Dataset_1 <- raw_data(1:2, 1:2) |> 
Dataset_2 <- raw_data(raw_data$age_column > 20)
             

With only 2 lines of code, we organize our data in substantail ways

Best Practices

Use Clear Naming Conventions when you are naming datatypes
Never Change the Raw Data, it’s best to create a clean data copy of your raw data that you have manipulated
Document Your work so that you can come back to it later and understand what you have changed

Summary

This guide goes over the basics of accessing data files and beginning to learn to navigate them.

We recommend accessing the resources in the resource hub to learn more about using R to prepare you for future classwork and jobs that will require it!

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