How to import data and install packages. R programming for beginners.

Summary

This video tutorial from the 'SPAR Programming' series introduces viewers to the basics of creating and managing projects in R. It covers the importance of projects for organizing scripts, data, and outputs in a single directory. The instructor demonstrates how to import data, specifically using CSV files, and emphasizes the use of code for data importation to ensure automation. The video also touches on data manipulation techniques, such as selecting specific variables and filtering data based on conditions. Additionally, it highlights the utility of R packages, like 'tidyverse', for expanding functionality and facilitating data analysis. The tutorial aims to empower viewers with the skills to start analyzing their own data effectively.

Takeaways

• 📚 Start by creating a project in R to organize scripts, data, and outputs neatly within a single directory.
• 🔍 R projects help set the working directory, making it easier to manage file paths and outputs.
• 📁 Create a new project by using the 'Create a Project' button and naming the project for easy identification.
• 📈 Import data into R by writing code that automates the process, ensuring reproducibility.
• 🚫 Avoid using the 'Import Dataset' options in RStudio, as coding the import process is more efficient in the long run.
• 🔢 Use functions like `read.csv` to import data from files, and assign the imported data to an object for further analysis.
• 🔑 Learn to manipulate data using R functions such as `head()`, `tail()`, and `view()` to explore the dataset.
• 🎛️ Understand the concept of 'packages' in R, which are collections of functions that solve specific problems and extend R's capabilities.
• 🛠️ Install and load R packages using `install.packages()` and `library()` or `require()` functions to access additional commands and functions.
• 🔬 Demonstrated the use of the 'tidyverse' package for data analysis, emphasizing its ease of use and power.
• 📊 Explored data manipulation techniques such as selecting specific columns, filtering rows based on conditions, and arranging data.

Q & A

What is the main topic of the video?

The main topic of the video is how to get started with R programming, focusing on creating a project, importing data, installing packages, and manipulating data.

What are the four quadrants mentioned in the video?

The four quadrants refer to the layout of the RStudio environment, which includes the script editor, console, environment/workspace, and files/plots panels.

Why is it recommended to start a project in R?

Starting a project in R helps organize the work by setting the working directory, keeping scripts, data, and outputs neatly stored in one place, which is useful for managing and reproducing the analysis.

How does one create a new project in R according to the video?

To create a new project in R, click on the 'Create Project' button, choose to create a new directory, give the project a name, and then click 'Create Project'.

What is the purpose of the 'read.csv' function in R?

The 'read.csv' function is used to import data from a CSV (Comma Separated Values) file into the R environment, making it available for analysis.

How can one view the first six rows of a dataset in R?

To view the first six rows of a dataset in R, use the 'head' function followed by the dataset name, like 'head(mydata)'.

What is the significance of the pipe operator in R scripting?

The pipe operator (%>%) in R allows for chaining commands together, making it easier to read and write complex sequences of operations on data.

What does the 'install.packages' function do in R?

The 'install.packages' function is used to install additional packages in R that provide extra functions and capabilities for specific tasks or analyses.

How can one select specific variables from a dataset in R?

To select specific variables from a dataset in R, use the 'select' function from the 'dplyr' package, specifying the variables to include, like 'mydata %>% select(variable1, variable2)'.

What is the purpose of the 'filter' function in data manipulation?

The 'filter' function is used to subset a dataset to include only the rows that meet certain conditions, such as filtering by age or height as demonstrated in the video.

How can one arrange a dataset by a specific variable in R?

To arrange a dataset by a specific variable in R, use the 'arrange' function from the 'dplyr' package, specifying the variable to sort by, like 'mydata %>% arrange(variable)'.

Outlines

00:00

📂 Project Creation and Data Import Basics

This paragraph introduces the concept of a project in R programming, emphasizing the importance of organizing scripts, data, and outputs in one place for better workflow management. It guides viewers on how to create a new project in RStudio, set a working directory, and the benefits of doing so. The speaker also touches on the different options available for starting a new project and suggests ignoring certain features for now to focus on the essentials. The paragraph concludes with a demonstration of how to import data into the R environment by creating a new directory for the project and manually copying data into it.

05:01

🔍 Data Manipulation and Package Installation

The second paragraph delves into data manipulation within R, starting with the process of importing a CSV file into the R environment. It explains how to read a CSV file using the 'read.csv' function and the importance of assigning the imported data to an object for further use. The paragraph also introduces the 'head', 'tail', and 'view' functions for data examination. It then covers the extraction of specific data components using indexing and the dollar sign notation. The speaker highlights the utility of R packages, explaining what they are, how to install them using 'install.packages', and load them using 'library' or 'require'. The paragraph wraps up with a brief mention of the 'tidyverse' package and its role in simplifying data analysis.

10:02

📊 Data Analysis with the Tidyverse Package

In the final paragraph, the focus shifts to performing data analysis using the 'tidyverse' package in R. The speaker demonstrates how to select specific variables from a data frame using the 'select' function and the pipe operator '%>%' for chaining commands. It also shows how to filter data based on conditions such as age and height, and how to arrange the data by a specific variable, in this case, height. The paragraph concludes with an invitation for viewers to subscribe and enable notifications for further educational content on R programming and data analysis.

Keywords

💡Project

A 'Project' in the context of the video refers to a specific collection of scripts, data, and outputs organized within a designated directory in RStudio. It helps in maintaining a clean and organized workspace, making it easier to manage different aspects of data analysis. The script mentions creating a new project called 'test one' to demonstrate how R organizes everything neatly in one place.

💡Import Data

The term 'Import Data' pertains to the process of bringing external data into the R environment to work with it. In the video, the speaker discusses different methods of importing data, such as through the 'Import Dataset' option or by using code to automate the process, which is recommended for efficiency and organization.

💡Packages

In R programming, 'Packages' are collections of functions and data that extend the software's capabilities. They are used to solve specific problems and enhance the functionality of R scripts. The video emphasizes the importance of packages like 'tidyverse' for data manipulation and analysis, and demonstrates how to install and load them into a project.

💡CSV

The 'CSV' (Comma Separated Values) file is a simple file format used to store tabular data, where each line represents a row and commas separate the values. The speaker in the video suggests using CSV files for their simplicity and recommends saving Excel spreadsheets as CSVs for easy import into R.

💡Data Manipulation

Data Manipulation involves altering and organizing data to suit analysis needs. The video script covers basic data manipulation techniques in R, such as selecting specific columns ('name', 'age', 'height') and filtering data based on conditions (e.g., age less than 24) using functions from the 'tidyverse' package.

💡Working Directory

The 'Working Directory' is the current directory R is using for reading and writing files. The video explains that starting a project sets the working directory, which helps R to know where to look for data files and where to store outputs, thus maintaining an organized workflow.

💡R Script

An 'R Script' is a file containing R code that can be executed to perform data analysis tasks. The video mentions starting an R script from within a project, which allows for saving and organizing the code in a structured manner within the project's directory.

💡Data Frame

A 'Data Frame' in R is a data structure used to store data tables. It consists of rows (observations) and columns (variables). The video script describes how to create a data frame from a CSV file and how to manipulate it using functions like 'select', 'filter', and 'arrange' from the 'tidyverse' package.

💡Pipe Operator

The 'Pipe Operator' in R, represented by '%>%', is used to feed the result of one function as an argument to the next function. This makes the code more readable and modular. The video demonstrates the use of the pipe operator in a sequence of data manipulation steps.

💡Analysis

The term 'Analysis' in the video refers to the process of examining data to draw conclusions or insights. The script includes an example of a simple analysis where the speaker filters and sorts data based on specific criteria, showcasing how R can be used for data analysis.

Highlights

Introduction to the series and the goal of the video: creating a project in R and understanding its components.

Explanation of what a project is and its importance in organizing scripts, data, and outputs in R.

Overview of the RStudio interface and the importance of starting with a project.

Detailed steps on how to create a new project in RStudio, including naming and setting up the working directory.

How to organize files and directories for a project to keep everything neat and accessible.

Importing data into R: recommended methods and why scripting the import process is better than using GUI options.

Demonstration of the read.csv function for importing CSV files and converting them into data frames.

Explanation of functions and objects in R, using examples like head(), tail(), and view() to explore data.

Manipulating data frames: accessing specific rows and columns using indexing and dollar sign notation.

Introduction to packages in R: what they are, how to install them, and how to use them in scripts.

Example of installing and loading the tidyverse package and its significance in data analysis.

Using the pipe operator (%>%) to chain commands and perform multiple operations on data frames.

Demonstration of selecting specific columns from a data frame using the select() function.

Filtering data based on conditions using the filter() function and combining multiple conditions.

Sorting data by a specific column using the arrange() function to organize the data output.

Encouragement to subscribe and hit the notification bell to stay updated with future R programming videos.

Transcripts

welcome back to the spar programming

video series on how to get started with

our in this video we're gonna talk about

how to create a project and I'm gonna

explain to you what a project is we're

gonna talk about how to import data I'm

gonna teach you how to install packages

and you are going to love packages they

Rock and we're gonna talk a little bit

about manipulating data

I want you at the end of the session to

feel as if you can do something in our

okay so let's get started if you want to

learn about our programming then you

have come to the right place

on this YouTube channel we're creating

our programming videos on everything

right we're looking at our studio at

this point you've installed our you've

installed our studio if you're not

familiar with this environment there's

these four quadrants I've got a video

that goes through that and introduces

you to this environment so have a look

at that video I'm not gonna go through

it right now

you wanting to get going right so at the

top on the left you've got a little pull

down menu and you've got some options

things you can start there's an R script

or a notebook

we're gonna go through each of these in

detail in future videos I'm gonna

suggest ignore this these for now right

start off by starting a project so just

to the left you're gonna see create a

project button and the reason is if you

write your script in the context of a

project that you've started R will know

where to look for your daughter where to

put all of your outputs your graphics

etc etc it stores everything quite

neatly in one place it's sets watch of

what we call your working directory and

that's quite a useful thing you're gonna

find it more and more useful down the

line so my suggestion is right off the

get-go get into the habit of when you're

starting a project in our click on the

starter project button you've got an

option you and I'm gonna suggest create

a new directory a new project give the

project a name and I'm gonna call it

test one like that and say create

project ok so R is created a project we

can see the project down here at the

bottom on the right just so that you can

see what's happening at the same time on

my hard drive

if we let have a look at my hard drive R

is created a folder called test

one click on that folder and there we

can see that icon that represents the

project if all was closed and we went to

this place on my hard drive clicked on

that icon our would open up in that

project and we would see all of the

script and the data and the outputs from

that project all in one place it would

be very neat it would be lovely it would

be poetry you're gonna love it

okay so that's starting a project okay

so how do we get some data into our well

let's go back to our hard drive that's

the folder that was created when we

created our project go in there I'm

gonna cut and paste some data into that

folder if we go back into our we can see

that data sitting here now that dot it

hasn't been imported yet we still need

to do that but at least we know where to

find it now to bring that data into our

into our environment make it into an

object that we can use there's a few

things we can do and I'm gonna show you

the things not to do but just so that

you know that they exist if we ticked on

the start down here you've got the

option of import data set you can do

that that's fine but I'm gonna say don't

do that

there's other options we've got import

data set up here again don't do that the

best thing to do is to use your code get

that get your actual script to go and

fetch the data so that when you run your

code it's automated it automatically

goes fetches the data creates an object

puts it in your environment and you

never have to think about it again so

that's the way to do it and I'm going to

show you how right here we've got some

code and this code is going to import

some data and it's also going to do a

little bit of analysis and I'm going to

go through the code one step at a time

just to teach you how it is that I've

done this right just so that you know

when you write code here in the source

up on the lip top left if you when

you've written the code you put go to

file save as and you save it and it pops

down here into your project which is all

nicely and neatly kept and you're

working direct you down on the bottom

right okay so let's go through this one

step at a time okay we're gonna start

off by looking at the read CSV read dot

CSV function of course we can import any

kind of data we can import data straight

from Microsoft Excel we can import SPSS

files we can ports data files as CSV is

a nice and simple file if you've got an

Excel spreadsheet you can save it as a

CSV I usually save

as CSV is an import them that way

because it's uncomplicated and it's not

messy but we're gonna create videos that

look at each of these individually and

we'll go through them one at a time for

this video we're just going to stick

with a nice simple CSV file so we've got

a function that says read CSV in

brackets and in inverted commas that's

important we have the file name and the

final extension now if I didn't have

this little arrow over here if I just

did the function and it's a file it's

gonna look in our working directory I

push command and to run that or I click

on run over there so command enter and

down here in the console we can see

there's our data now that's not

particularly useful to us right now as

it is because we want that to be an

object that we can use so if I give that

a name and I say my data and create this

little arrow with the less than and -

which is kind of like an arrow it says

everything that's over here gets

assigned to that name push ctrl enter

and in our environment on the Left we

can see my daughter sits there we can

have a look at what the variable

variables within there are and that's

our daughter sitting and it's being read

in and it's within our and we can start

using it so we want to view our daughter

now for the most part our works with

functions and objects so we've got an

object my daughter sitting in our

environment of there

and we've got functions a function is

this function called head if we type in

head my daughter and push command into

or ctrl enter or run up there it's gonna

give us the first six rows of data if we

do tail my daughter it's gonna give us

the last six rows of our data and if we

do view my daughter it's gonna produce

the daughter in a little spreadsheet

that we can look at so let's have a look

at that right remember with this kind of

data this is a nice flat spreadsheet

we've got each row is an observation and

each column is a variable let's go back

to our four quadrants we can also view

the data if we looking at our script we

can also view the data by clicking on my

data we can click on the object and

it'll also bring it up over there now we

might want to extract specific

components of our data

so remember we've we've said that rows

observation columns are variables and if

we put my diet and we use these square

brackets to tell our way to look the

first number after the square bracket

tells it what row to look at and the

second number what column right so if we

run that it's gonna give us blue well

what is blue blue is the first row and

the third column the variable eye color

so we got this cell over here popped out

if we didn't put a row and we just put

comma 3 it's gonna do the entire column

so let's run that and there we go blue

brown blue blah-blah-blah-blah-blah

that's basically spitting out this

entire column and this column this

variable name is eye color so we can

also do my daughter dollar sign I color

and it does the same thing okay before

we start doing some analysis I just want

to talk to you a little bit about

packages because you're gonna find these

things tremendously useful right

packages are the program functions that

solve very specific problems they expand

the our vocabulary to install a package

you use this function install packages

right and then open brackets you need

the inverted commas you put the name of

the package close brackets you only ever

need to install a package once once its

installed on your computer it's there

but when you want to use it in your

script you need to include either

library or require either of those two

you don't need them both you put that

into your script it'll go and fetch that

package it'll use it and then from that

point onwards in your script you have

access to additional commands and

functions so of course I have previously

installed the tidy verse package at this

point I want to push command enter or

control enter to run this line of code

that uses it so bada-bing now I'm gonna

show you how to do an analysis in our

using some of this vocabulary that comes

to the tight abuse you're going to see

how easy and it should have been

straightforward it is when you see how

easy it is you're going to be really

excited about analyzing your own

daughter okay so the first thing we do

is we type in our daughter frame we

start off with my my daughter that's

that's our object okay if I push command

in total control in throw on a PC at

this point it brings up the whole dart

frame in that this is a small dot frame

okay

shift control to to have a closer look

at the console right this is our whole

dart frame now daughter frames are

usually much much bigger than this we

may have hundreds of variables what do

we do we want to select just a few of

them we might in this case what I select

just name age and height right shift

control zero to go back to all four

quadrants so we want to select that

before we select it I want to teach you

about a little thing called the pipe

operator right shift command M that's

the pipe operator right it's a percent

greater than percent it looks a little

bit like a pipe and what it means is

whatever you've done on the left hand

side whatever that line of code is gets

piped into the next line of code right

so if you've done some sort of change or

manipulation that change gets piped into

and you'll see more how that works as we

as we go through this example now you

would ordinary you could just carry on

typing to the right I like to after a

pipe operator go to the next line R will

see that as continuing on the same line

it doesn't really matter okay it looks

like an air is popping up there it's not

you can ignore that rid okay so we've

said my daughter we've got in a pipe

operator which just means and then so my

daughter and then right we've said we

want to select name age and height so it

is literally as simple as that

select open brackets name age height

command enter okay now we can see in our

console and I'm gonna zoom in and the

console with shift control - we can see

we originally had the entire data frame

we wanted to select a few of the

variables in this case name age and

height and there they are now we might

want to only look at people that are

less than 24 years old so another pipe

operator which is and then go to the

next time filter by those that are aged

less than 24 and we might want to say

let's let's make this a bit more

complicated to say age less than 24

and height greater than 1.78 for example

okay let's have a look at what that does

and voila

let's have a look at that will we don't

need to zoom in on the console because

we can see right here through just three

rows that met that criteria we might now

want to add here another pipe operator

and then arrange by height and it'll

arrange it by height come on to enter

and there we go voila so if you are

serious about learning how to analyze

data and you want to learn our

programming then hit the subscribe

button now and hit the little bell

notification if you want to get notified

of future videos

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