How Do I Select a Column in a Data File I Uploaded to R
Welcome! If you want to outset diving into data science and statistics, so data frames, CSV files, and R will exist essential tools for you lot. Let's see how you tin can use their amazing capabilities.
In this article, y'all will learn:
- What CSV files are and what they are used for.
- How to create CSV files using Google Sheets.
- How to read CSV files in R.
- What Information Frames are and what they are used for.
- How to admission the elements of a data frame.
- How to modify a data frame.
- How to add and delete rows and columns.
We will use RStudio, an open-source IDE (Integrated Development Environment) to run the examples.
Let's begin! ✨
🔹 Introduction to CSV Files
CSV (Comma-separated Values) files can be considered i of the building blocks of data analysis considering they are used to store information represented in the form of a table.
In this file, values are separated past commas to represent the different columns of the tabular array, like in this case:
We will generate this file using Google Sheets.
🔸 How to Create a CSV File Using Google Sheets
Let's create your first CSV file using Google Sheets.
Step 1: Go to the Google Sheets Website and click on "Go to Google Sheets":
💡 Tip: You can access Google Sheets by clicking on the button located at the top-correct edge of Google's Home Page:
If we zoom in, nosotros see the "Sheets" button:
💡 Tip: To use Google Sheets, you need to accept a Gmail account. Alternatively, y'all tin create a CSV file using MS Excel or another spreadsheet editor.
You will run into this panel:
Pace 2: Create a bare spreadsheet past clicking on the "+" button.
Now you have a new empty spreadsheet:
Stride three: Change the name of the spreadsheet to students_data. We will need to utilise the proper noun of the file to work with data frames. Write the new name and click enter to confirm the change.
Step iv: In the showtime row of the spreadsheet, write the titles of the columns.
When you import a CSV file in R, the titles of the columns are chosen variables. Nosotros will ascertain vi variables: first_name, last_name, age, num_siblings, num_pets, and eye_color, as y'all can encounter right here below:
💡 Tip: Notice that the names are written in lowercase and words are separated with an underscore. This is not mandatory, but since you will need to access these names in R, information technology's very common to use this format.
Step 5: Enter the data for each 1 of the columns.
When you lot read the file in R, each row is chosen an observation, and it corresponds to data taken from an individual, animal, object, or entity that we nerveless data from.
In this example, each row corresponds to the information of a student:
Step 6: Download the CSV file by clicking on File -> Download -> Comma-separated values, as you can see beneath:
Stride 7: Rename the file CSV file. You will need to remove "Sheet1" from the default proper noun considering Google Sheet will automatically add this to the proper name of the file.
Bully piece of work! Now y'all take your CSV file and it's time to start working with it in R.
🔹 How to Read a CSV file in R
In RStudio, the get-go footstep before reading a CSV file is making sure that your electric current working directory is the directory where the CSV file is located.
💡 Tip: If this is not the case, you lot will demand to utilize the full path to the file.
Change Current Working Directory
You can change your current working directory in this panel:
If nosotros zoom in, you tin see the current path (1) and select the new ane by clicking on the ellipsis (...) button to the right (two):
💡 Tip: You can also check your current working directory with getwd() in the interactive console.
And then, click "More" and "Set As Working Directory".
Read the CSV File
Once y'all have your current working directory ready upwards, you lot tin can read the CSV file with this command:
In R code, we have this:
> students_data <- read.csv("students_data.csv") 💡 Tip: We assign it to the variable students_data to admission the data of the CSV file with this variable. In R, we can carve up words using dots ., underscores _, UpperCamelCase, or lowerCamelCase.
After running this command, you will run across this in the top right panel:
At present y'all have a variable divers in the surround! Let's run into what data frames are and how they are closely related to CSV files.
🔸 Introduction to Information Frames
Data frames are the standard digital format used to store statistical data in the form of a tabular array. When you read a CSV file in R, a data frame is generated.
Nosotros can confirm this by checking the blazon of the variable with the class function:
> class(students_data) [1] "data.frame" Information technology makes sense, right? CSV files contain data represented in the course of a table and data frames represent that tabular data in your code, so they are securely connected.
If yous enter this variable in the interactive console, you will see the content of the CSV file:
> students_data first_name last_name historic period num_siblings num_pets eye_color 1 Emily Dawson xv 2 v Blueish ii Rose Patterson 14 v 0 GREEN three Alexander Smith 16 0 2 BROWN iv Nora Navona sixteen 4 ten Light-green 5 Gino Sand 17 3 8 BLUE More Information Virtually the Information Frame
You have several different alternatives to see the number of variables and observations of the data frame:
- Your showtime selection is to look at the acme right panel that shows the variables that are currently divers in the environs. This data frame has 5 observations (rows) and 6 variables (columns):
- Another alternative is to utilise the functions
nrowandncolin the interactive console or in your programme, passing the data frame as argument. We get the same results: 5 rows and 6 columns.
> nrow(students_data) [1] 5 > ncol(students_data) [i] 6 - You can also see more information virtually the data frame using the
strfunction:
> str(students_data) 'information.frame': 5 obs. of six variables: $ first_name : Factor w/ 5 levels "Alexander","Emily",..: 2 5 1 4 3 $ last_name : Cistron w/ 5 levels "Dawson","Navona",..: 1 3 5 two 4 $ historic period : int xv 14 16 16 17 $ num_siblings: int 2 five 0 4 3 $ num_pets : int five 0 two 10 8 $ eye_color : Factor w/ 3 levels "BLUE","Dark-brown",..: 1 3 2 3 one This function (applied to a data frame) tells y'all:
- The number of observations (rows).
- The number of variables (columns).
- The names of the variables.
- The information types of the variables.
- More information about the variables.
You tin see that this function is really great when you desire to know more most the information that y'all are working with.
💡 Tip: In R, a "Factor" is a qualitative variable, which is a variable whose values stand for categories. For example, eye_color has the values "BLUE", "BROWN", "GREEN" which are categories, and then every bit you can run across in the output of str above, this variable is automatically divers as a "gene" when the CSV file is read in R.
🔹 Data Frames: Central Operations and Functions
Now you know how to see more information about the information frame. But the magic of data frames lies in the amazing capabilities and functionality that they offer, and then let's run across this in more than detail.
How to Access A Value of a Data Frame
Data frames are like matrices, so you lot can access private values using two indices surrounded by foursquare brackets and separated by a comma to indicate which rows and which columns you would like to include in the event, like this:
For case, if we desire to access the value of eye_color (column 6) of the quaternary educatee in the data (row 4):
Nosotros need to utilise this command:
> students_data[four, 6] 💡 Tip: In R, indices start at i and the kickoff row with the names of the variables is not counted.
This is the output:
[1] GREEN Levels: Blueish BROWN GREEN You can run into that the value is "Dark-green". Variables of type "factor" accept "levels" that represent the unlike categories or values that they can take. This output tells usa the levels of the variable eye_color.
How to Access Rows and Columns of a Information Frame
We can also use this syntax to admission a range of rows and columns to get a portion of the original matrix, like this:
For case, if we desire to become the age and number of siblings of the third, fourth, and 5th pupil in the listing, we would use:
> students_data[3:five, three:4] age num_siblings 3 16 0 four 16 four 5 17 3 💡 Tip: The basic syntax to ascertain an interval in R is <start>:<end>. Notation that these indices are inclusive, so the third and 5th elements are included in the example in a higher place when we write 3:5.
If we want to get all the rows or columns, we merely omit the interval and include the comma, like this:
> students_data[3:5,] first_name last_name age num_siblings num_pets eye_color 3 Alexander Smith 16 0 2 Chocolate-brown 4 Nora Navona 16 4 ten GREEN 5 Gino Sand 17 3 8 BLUE Nosotros did non include an interval for the columns after the comma in students_data[3:v,], so nosotros get all the columns of the data frame for the three rows that we specified.
Similarly, we can get all the rows for a specific range of columns if nosotros omit the rows:
> students_data[, 1:3] first_name last_name age ane Emily Dawson fifteen two Rose Patterson 14 3 Alexander Smith 16 4 Nora Navona 16 five Gino Sand 17 💡 Tip: Detect that you still need to include the comma in both cases.
How to Admission a Column
There are three ways to access an entire column:
- Selection #1: to access a column and return it every bit a data frame, you can utilise this syntax:
For example:
> students_data["first_name"] first_name 1 Emily 2 Rose 3 Alexander 4 Nora 5 Gino - Option #2: to get a cavalcade as a vector (sequence), you lot tin can use this syntax:
💡 Tip: Observe the use of the $ symbol.
For case:
> students_data$first_name [1] Emily Rose Alexander Nora Gino Levels: Alexander Emily Gino Nora Rose - Option #3: You tin too use this syntax to get the column as a vector (come across below). This is equivalent to the previous syntax:
> students_data[["first_name"]] [1] Emily Rose Alexander Nora Gino Levels: Alexander Emily Gino Nora Rose How to Filter Rows of a Data Frame
You can filter the rows of a data frame to get a portion of the matrix that meets certain atmospheric condition.
For this, we use this syntax, passing the condition as the first element within square brackets, then a comma, and finally leaving the second chemical element empty.
For case, to get all rows for which students_data$age > 16, we would utilize:
> students_data[students_data$historic period > 16,] first_name last_name age num_siblings num_pets eye_color five Gino Sand 17 three 8 Blue We get a data frame with the rows that meet this status.
Filter Rows and Cull Columns
Y'all can combine this condition with a range of columns:
> students_data[students_data$age > xvi, iii:six] age num_siblings num_pets eye_color 5 17 three 8 BLUE We get the rows that meet the condition and the columns in the range 3:6.
🔸 How to Modify Data Frames
You tin can modify individual values of a data frame, add columns, add rows, and remove them. Allow'southward see how you tin do this!
How to Modify A Value
To change an individual value of the information frame, yous demand to use this syntax:
For case, if we want to change the value that is currently at row iv and cavalcade 6, denoted in blue right here:
We need to use this line of lawmaking:
students_data[4, six] <- "BROWN" 💡 Tip: You tin likewise utilize = as the consignment operator.
This is the output. The value was changed successfully.
💡 Tip: Think that the starting time row of the CSV file is not counted as the kickoff row because it has the names of the variables.
How to Add Rows to a Data Frame
To add together a row to a data frame, you lot demand to use the rbind function:
This function takes two arguments:
- The data frame that you want to change.
- A list with the data of the new row. To create the listing, you can use the
listing()role with each value separated by a comma.
This is an example:
> rbind(students_data, listing("William", "Smith", fourteen, seven, 3, "BROWN")) The output is:
first_name last_name historic period num_siblings num_pets eye_color one Emily Dawson 15 two 5 Blueish 2 Rose Patterson xiv 5 0 GREEN 3 Alexander Smith 16 0 2 Brownish 4 Nora Navona 16 four 10 Brown 5 Gino Sand 17 iii 8 Blue 6 <NA> Smith 14 7 3 BROWN But wait! A warning message was displayed:
Warning message: In `[<-.gene`(`*tmp*`, ri, value = "William") : invalid cistron level, NA generated And notice the first value of the sixth row, information technology is <NA>:
6 <NA> Smith fourteen 7 3 Dark-brown This occurred considering the variable first_name was divers automatically as a factor when nosotros read the CSV file and factors have fixed "categories" (levels).
You lot cannot add a new level (value - "William") to this variable unless you read the CSV file with the value FALSE for the parameter stringsAsFactors, equally shown below:
> students_data <- read.csv("students_data.csv", stringsAsFactors = FALSE) 
Now, if we try to add this row, the data frame is modified successfully.
> students_data <- rbind(students_data, listing("William", "Smith", xiv, seven, 3, "Dark-brown")) > students_data first_name last_name age num_siblings num_pets eye_color 1 Emily Dawson xv ii 5 BLUE 2 Rose Patterson 14 5 0 GREEN three Alexander Smith 16 0 2 Dark-brown iv Nora Navona 16 4 x Light-green 5 Gino Sand 17 3 8 BLUE half-dozen William Smith xiv 7 3 BROWN 💡 Tip: Note that if you lot read the CSV file again and assign information technology to the same variable, all the changes made previously will be removed and you volition come across the original data frame. Yous need to add this argument to the first line of code that reads the CSV file and then make changes to it.
How to Add Columns to a Data Frame
Adding columns to a data frame is much simpler. Yous need to use this syntax:
For instance:
> students_data$GPA <- c(4.0, 3.v, 3.2, 3.15, two.9, 3.0) 💡 Tip: The number of elements has to be equal to the number of rows of the information frame.
The output shows the data frame with the new GPA column:
> students_data first_name last_name age num_siblings num_pets eye_color GPA one Emily Dawson 15 2 v BLUE 4.00 ii Rose Patterson 14 five 0 GREEN three.50 3 Alexander Smith 16 0 2 BROWN 3.xx four Nora Navona 16 4 10 GREEN iii.xv 5 Gino Sand 17 iii viii BLUE 2.xc 6 William Smith 14 7 3 Chocolate-brown 3.00 How to Remove Columns
To remove columns from a data frame, yous need to apply this syntax:
When you assign the value Null to a column, that column is removed from the data frame automatically.
For example, to remove the age cavalcade, we use:
> students_data$age <- Null The output is:
> students_data first_name last_name num_siblings num_pets eye_color GPA 1 Emily Dawson ii 5 BLUE four.00 ii Rose Patterson 5 0 GREEN three.l three Alexander Smith 0 2 BROWN three.20 4 Nora Navona 4 10 GREEN iii.xv 5 Gino Sand 3 8 BLUE 2.90 6 William Smith 7 3 Dark-brown iii.00 How to Remove Rows
To remove rows from a information frame, you can utilise indices and ranges. For case, to remove the outset row of a data frame:
The [-i,] takes a portion of the data frame that doesn't include the offset row. Then, this portion is assigned to the same variable.
If we have this data frame and we want to delete the first row:
The output is a data frame that doesn't include the first row:
In general, to remove a specific row, you need to use this syntax where <row_num> is the row that you want to remove:
💡 Tip: Notice the - sign before the row number.
For instance, if we desire to remove row 4 from this data frame:
The output is:
As you tin run across, row four was successfully removed.
🔹 In Summary
- CSV files are Comma-Separated Values Files used to stand for data in the form of a tabular array. These files can be read using R and RStudio.
- Data frames are used in R to represent tabular data. When you read a CSV file, a data frame is created to shop the data.
- You can access and modify the values, rows, and columns of a data frame.
I really promise that you liked my article and found information technology helpful. At present you tin piece of work with information frames and CSV files in R.
If you lot liked this article, consider enrolling in my new online course "Introduction to Statistics in R - A Practical Arroyo "
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