Sådan arbejder du med datarammer og CSV-filer i R - En detaljeret introduktion med eksempler

Velkommen! Hvis du vil begynde at dykke ned i datavidenskab og statistik, vil datarammer, CSV-filer og R være vigtige værktøjer for dig. Lad os se, hvordan du kan bruge deres fantastiske muligheder.

I denne artikel lærer du:

  • Hvad CSV-filer er, og hvad de bruges til.
  • Sådan oprettes CSV-filer ved hjælp af Google Sheets.
  • Sådan læses CSV-filer i R.
  • Hvad datarammer er, og hvad de bruges til.
  • Sådan får du adgang til elementerne i en dataramme.
  • Sådan ændres en dataramme.
  • Sådan tilføjes og slettes rækker og kolonner.

Vi bruger RStudio, en open source IDE (Integrated Development Environment) til at køre eksemplerne.

Lad os begynde! ✨

? Introduktion til CSV-filer

CSV-filer (kommaseparerede værdier) kan betragtes som en af ​​byggestenene til dataanalyse, fordi de bruges til at gemme data repræsenteret i form af en tabel.

I denne fil adskilles værdier med kommaer for at repræsentere de forskellige kolonner i tabellen, som i dette eksempel:

Vi genererer denne fil ved hjælp af Google Sheets.

? Sådan oprettes en CSV-fil ved hjælp af Google Sheets

Lad os oprette din første CSV-fil ved hjælp af Google Sheets.

Trin 1: Gå til Google Sheets-webstedet, og klik på "Gå til Google Sheets":

? Tip: Du kan få adgang til Google Sheets ved at klikke på knappen øverst til højre på Googles startside:

Hvis vi zoomer ind, ser vi knappen "Ark":

? Tip: For at bruge Google Sheets skal du have en Gmail-konto. Alternativt kan du oprette en CSV-fil ved hjælp af MS Excel eller en anden regnearkeditor.

Du vil se dette panel:

Trin 2: Opret et tomt regneark ved at klikke på knappen "+".

Nu har du et nyt tomt regneark:

Trin 3: Skift navnet på regnearket til students_data. Vi bliver nødt til at bruge navnet på filen til at arbejde med datarammer. Skriv det nye navn, og klik på Enter for at bekræfte ændringen.

Trin 4: Skriv titlerne på kolonnerne i den første række i regnearket.

Når du importerer en CSV-fil i R, kaldes kolonnens titler variabler . Vi vil definere seks variable: first_name, last_name, age, num_siblings, num_pets, og eye_color, som du kan se lige her nedenfor:

? Tip: Bemærk, at navnene er skrevet med små bogstaver, og ord er adskilt med en understregning. Dette er ikke obligatorisk, men da du bliver nødt til at få adgang til disse navne i R, er det meget almindeligt at bruge dette format.

Trin 5: Indtast dataene for hver af kolonnerne.

Når du læser filen i R, kaldes hver række en observation , og den svarer til data taget fra et individ, dyr, objekt eller enhed, som vi indsamlede data fra.

I dette tilfælde svarer hver række til en studerendes data:

Trin 6: Download CSV-filen ved at klikke på File -> Download -> Comma-separated values, som du kan se nedenfor:

Trin 7: Omdøb filen CSV-fil. Du bliver nødt til at fjerne "Sheet1" fra standardnavnet, fordi Google Sheet automatisk tilføjer dette til filens navn.

Flot arbejde! Nu har du din CSV-fil, og det er tid til at begynde at arbejde med den i R.

Sådan læses en CSV-fil i R

I RStudio er det første trin inden læsning af en CSV-fil at sikre, at din nuværende arbejdsmappe er den mappe, hvor CSV-filen er placeret.

? Tip: Hvis dette ikke er tilfældet, skal du bruge den fulde sti til filen.

Skift nuværende arbejdsmappe

Du kan ændre din nuværende arbejdsmappe i dette panel:

If we zoom in, you can see the current path (1) and select the new one by clicking on the ellipsis (...) button to the right (2):

? Tip: You can also check your current working directory with getwd() in the interactive console.

Then, click "More" and "Set As Working Directory".

Read the CSV File

Once you have your current working directory set up, you 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 access the data of the CSV file with this variable. In R, we can separate words using dots ., underscores _, UpperCamelCase, or lowerCamelCase.

After running this command, you will see this in the top right panel:

Now you have a variable defined in the environment!Let's see what data frames are and how they are closely related to CSV files.

? Introduction to Data Frames

Data frames are the standard digital format used to store statistical data in the form of a table. When you read a CSV file in R, a data frame is generated.

We can confirm this by checking the type of the variable with the class function:

> class(students_data) [1] "data.frame"

It makes sense, right? CSV files contain data represented in the form of a table and data frames represent that tabular data in your code, so they are deeply connected.

If you enter this variable in the interactive console, you will see the content of the CSV file:

> students_data first_name last_name age num_siblings num_pets eye_color 1 Emily Dawson 15 2 5 BLUE 2 Rose Patterson 14 5 0 GREEN 3 Alexander Smith 16 0 2 BROWN 4 Nora Navona 16 4 10 GREEN 5 Gino Sand 17 3 8 BLUE

More Information About the Data Frame

You have several different alternatives to see the number of variables and observations of the data frame:

  • Your first option is to look at the top right panel that shows the variables that are currently defined in the environment. This data frame has 5 observations (rows) and 6 variables (columns):
  • Another alternative is to use the functions nrow and ncol in the interactive console or in your program, passing the data frame as argument. We get the same results: 5 rows and 6 columns.
> nrow(students_data) [1] 5 > ncol(students_data) [1] 6
  • You can also see more information about the data frame using the str function:
> str(students_data) 'data.frame': 5 obs. of 6 variables: $ first_name : Factor w/ 5 levels "Alexander","Emily",..: 2 5 1 4 3 $ last_name : Factor w/ 5 levels "Dawson","Navona",..: 1 3 5 2 4 $ age : int 15 14 16 16 17 $ num_siblings: int 2 5 0 4 3 $ num_pets : int 5 0 2 10 8 $ eye_color : Factor w/ 3 levels "BLUE","BROWN",..: 1 3 2 3 1

This function (applied to a data frame) tells you:

  • The number of observations (rows).
  • The number of variables (columns).
  • The names of the variables.
  • The data types of the variables.
  • More information about the variables.

You can see that this function is really great when you want to know more about the data that you are working with.

? Tip: In R, a "Factor" is a qualitative variable, which is a variable whose values represent categories. For example, eye_color has the values "BLUE", "BROWN", "GREEN" which are categories, so as you can see in the output of str above, this variable is automatically defined as a "factor" when the CSV file is read in R.

? Data Frames: Key Operations and Functions

Now you know how to see more information about the data frame. But the magic of data frames lies in the amazing capabilities and functionality that they offer, so let's see this in more detail.

How to Access A Value of a Data Frame

Data frames are like matrices, so you can access individual values using two indices surrounded by square brackets and separated by a comma to indicate which rows and which columns you would like to include in the result, like this:

For example, if we want to access the value of eye_color (column 6) of the fourth student in the data (row 4):

We need to use this command:

> students_data[4, 6]

? Tip: In R, indices start at 1 and the first row with the names of the variables is not counted.

This is the output:

[1] GREEN Levels: BLUE BROWN GREEN

You can see that the value is "GREEN". Variables of type "factor" have "levels" that represent the different categories or values that they can take. This output tells us the levels of the variable eye_color.

How to Access Rows and Columns of a Data Frame

We can also use this syntax to access a range of rows and columns to get a portion of the original matrix, like this:

For example, if we want to get the age and number of siblings of the third, fourth, and fifth student in the list, we would use:

> students_data[3:5, 3:4] age num_siblings 3 16 0 4 16 4 5 17 3

? Tip: The basic syntax to define an interval in R is :. Note that these indices are inclusive, so the third and fifth elements are included in the example above when we write 3:5.

If we want to get all the rows or columns, we simply 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 BROWN 4 Nora Navona 16 4 10 GREEN 5 Gino Sand 17 3 8 BLUE

We did not include an interval for the columns after the comma in students_data[3:5,], so we 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 we omit the rows:

> students_data[, 1:3] first_name last_name age 1 Emily Dawson 15 2 Rose Patterson 14 3 Alexander Smith 16 4 Nora Navona 16 5 Gino Sand 17

? Tip: Notice that you still need to include the comma in both cases.

How to Access a Column

There are three ways to access an entire column:

  • Option #1: to access a column and return it as a data frame, you can use 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 column as a vector (sequence), you can use this syntax:

? Tip: Notice the use of the $ symbol.

For example:

> students_data$first_name [1] Emily Rose Alexander Nora Gino Levels: Alexander Emily Gino Nora Rose
  • Option #3: You can also use this syntax to get the column as a vector (see 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 conditions.

For this, we use this syntax, passing the condition as the first element within square brackets, then a comma, and finally leaving the second element empty.

For example, to get all rows for which students_data$age > 16, we would use:

> students_data[students_data$age > 16,] first_name last_name age num_siblings num_pets eye_color 5 Gino Sand 17 3 8 BLUE

We  get a data frame with the rows that meet this condition.

Filter Rows and Choose Columns

You can combine this condition with a range of columns:

> students_data[students_data$age > 16, 3:6] age num_siblings num_pets eye_color 5 17 3 8 BLUE

We get the rows that meet the condition and the columns in the range 3:6.

? How to Modify Data Frames

You can modify individual values of a data frame, add columns, add rows, and remove them. Let's see how you can do this!

How to Change A Value

To change an individual value of the data frame, you need to use this syntax:

For example, if we want to change the value that is currently at row 4 and column 6, denoted in blue right here:

We need to use this line of code:

students_data[4, 6] <- "BROWN"

? Tip: You can also use = as the assignment operator.

This is the output. The value was changed successfully.

? Tip: Remember that the first row of the CSV file is not counted as the first row because it has the names of the variables.

How to Add Rows to a Data Frame

To add a row to a data frame, you need to use the rbind function:

This function takes two arguments:

  • The data frame that you want to modify.
  • A list with the data of the new row. To create the list, you can use the list() function with each value separated by a comma.

This is an example:

> rbind(students_data, list("William", "Smith", 14, 7, 3, "BROWN"))

The output is:

 first_name last_name age num_siblings num_pets eye_color 1 Emily Dawson 15 2 5 BLUE 2 Rose Patterson 14 5 0 GREEN 3 Alexander Smith 16 0 2 BROWN 4 Nora Navona 16 4 10 BROWN 5 Gino Sand 17 3 8 BLUE 6  Smith 14 7 3 BROWN

But wait! A warning message was displayed:

Warning message: In `[<-.factor`(`*tmp*`, ri, value = "William") : invalid factor level, NA generated

And notice the first value of the sixth row, it is :

6  Smith 14 7 3 BROWN

This occurred because the variable first_name was defined automatically as a factor when we read the CSV file and factors have fixed "categories" (levels).

You cannot add a new level (value - "William") to this variable unless you read the CSV file with the value FALSE for the parameter stringsAsFactors, as 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  students_data first_name last_name age num_siblings num_pets eye_color 1 Emily Dawson 15 2 5 BLUE 2 Rose Patterson 14 5 0 GREEN 3 Alexander Smith 16 0 2 BROWN 4 Nora Navona 16 4 10 GREEN 5 Gino Sand 17 3 8 BLUE 6 William Smith 14 7 3 BROWN

? Tip: Note that if you read the CSV file again and assign it to the same variable, all the changes made previously will be removed and you will see the original data frame. You 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. You need to use this syntax:

For example:

> students_data$GPA <- c(4.0, 3.5, 3.2, 3.15, 2.9, 3.0)

? Tip: The number of elements has to be equal to the number of rows of the data 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 1 Emily Dawson 15 2 5 BLUE 4.00 2 Rose Patterson 14 5 0 GREEN 3.50 3 Alexander Smith 16 0 2 BROWN 3.20 4 Nora Navona 16 4 10 GREEN 3.15 5 Gino Sand 17 3 8 BLUE 2.90 6 William Smith 14 7 3 BROWN 3.00

How to Remove Columns

To remove columns from a data frame, you need to use 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 column, 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 2 5 BLUE 4.00 2 Rose Patterson 5 0 GREEN 3.50 3 Alexander Smith 0 2 BROWN 3.20 4 Nora Navona 4 10 GREEN 3.15 5 Gino Sand 3 8 BLUE 2.90 6 William Smith 7 3 BROWN 3.00

How to Remove Rows

To remove rows from a data frame, you can use indices and ranges. For example, to remove the first row of a data frame:

The [-1,] takes a portion of the data frame that doesn't include the first 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 is the row that you want to remove:

? Tip: Notice the - sign before the row number.

For example, if we want to remove row 4 from this data frame:

The output is:

As you can see, row 4 was successfully removed.

? In Summary

  • CSV files are Comma-Separated Values Files used to represent data in the form of a table. 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 store the data.
  • You can access and modify the values, rows, and columns of a data frame.

I really hope that you liked my article and found it helpful.Now you can work with data frames and CSV files in R.

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