Selamat Datang! Sekiranya anda ingin mula menyelami sains dan statistik data, maka bingkai data, fail CSV, dan R akan menjadi alat penting untuk anda. Mari lihat bagaimana anda boleh menggunakan kemampuan mereka yang luar biasa.
Dalam artikel ini, anda akan belajar:
- Apa fail CSV dan untuk apa ia digunakan.
- Cara membuat fail CSV menggunakan Helaian Google.
- Cara membaca fail CSV di R.
- Apa itu Bingkai Data dan untuk apa ia digunakan.
- Cara mengakses elemen kerangka data.
- Cara mengubah bingkai data.
- Cara menambah dan menghapus baris dan lajur.
Kami akan menggunakan RStudio, IDE sumber terbuka (Integrated Development Environment) untuk menjalankan contohnya.
Mari kita mulakan! ✨
? Pengenalan Fail CSV
Fail CSV (Nilai yang dipisahkan dengan koma) dapat dianggap sebagai salah satu blok bangunan analisis data kerana digunakan untuk menyimpan data yang diwakili dalam bentuk tabel.
Dalam fail ini, nilai dipisahkan dengan koma untuk mewakili lajur jadual yang berbeza, seperti dalam contoh ini:
Kami akan menghasilkan fail ini menggunakan Helaian Google.
? Cara Membuat Fail CSV Menggunakan Helaian Google
Mari buat fail CSV pertama anda menggunakan Helaian Google.
Langkah 1: Pergi ke Laman Web Helaian Google dan klik "Pergi ke Helaian Google":
? Petua: Anda boleh mengakses Helaian Google dengan mengklik butang yang terletak di tepi kanan atas Laman Utama Google:
Sekiranya kita mengezum masuk, kita akan melihat butang "Helaian":
? Petua: Untuk menggunakan Helaian Google, anda perlu mempunyai akaun Gmail. Sebagai alternatif, anda boleh membuat fail CSV menggunakan MS Excel atau editor hamparan lain.
Anda akan melihat panel ini:
Langkah 2: Buat hamparan kosong dengan mengklik butang "+".
Sekarang anda mempunyai hamparan kosong baru:
Langkah 3: Tukar nama hamparan menjadi students_data. Kita perlu menggunakan nama fail untuk bekerja dengan bingkai data. Tulis nama baru dan klik enter untuk mengesahkan perubahan.
Langkah 4: Pada baris pertama hamparan, tulis tajuk lajur.
Apabila anda mengimport fail CSV dalam R, tajuk lajur disebut pemboleh ubah . Kami akan menentukan enam pembolehubah: first_name, last_name, age, num_siblings, num_pets, dan eye_color, seperti yang anda boleh lihat di sini di bawah:
? Petua: Perhatikan bahawa nama ditulis dengan huruf kecil dan kata-kata dipisahkan dengan garis bawah. Ini tidak wajib, tetapi kerana anda perlu mengakses nama-nama ini dalam R, sangat biasa menggunakan format ini.
Langkah 5: Masukkan data untuk setiap lajur.
Ketika Anda membaca file dalam R, setiap baris disebut pengamatan , dan sesuai dengan data yang diambil dari individu, binatang, objek, atau entitas tempat kami mengumpulkan data.
Dalam kes ini, setiap baris sesuai dengan data pelajar:
Langkah 6: Muat turun fail CSV dengan mengklik File -> Download -> Comma-separated values, seperti yang anda lihat di bawah:
Langkah 7: Namakan semula fail CSV fail. Anda perlu mengeluarkan "Sheet1" dari nama lalai kerana Google Sheet akan menambahkannya secara automatik ke nama fail.
Kerja hebat! Sekarang anda mempunyai fail CSV anda dan tiba masanya untuk mula menggunakannya di R.
? Cara Membaca fail CSV di R
Di RStudio, langkah pertama sebelum membaca fail CSV adalah memastikan bahawa direktori kerja semasa anda adalah direktori tempat fail CSV berada.
? Petua: Sekiranya ini tidak berlaku, anda perlu menggunakan jalan penuh ke fail.
Tukar Direktori Kerja Semasa
Anda boleh mengubah direktori kerja semasa anda di panel ini:
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 BLUEMore 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
nrowandncolin 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
strfunction:
> 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 1This 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 GREENYou 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 BLUEWe 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$first_name [1] Emily Rose Alexander Nora Gino Levels: Alexander Emily Gino Nora Rose- Option #3: You can also use this syntax to get the column as a vector (see below). This is equivalent to the previous syntax:
> students_data[["first_name"]] [1] Emily Rose Alexander Nora Gino Levels: Alexander Emily Gino Nora RoseHow to Filter Rows of a Data Frame
You can filter the rows of a data frame to get a portion of the matrix that meets certain conditions.
For this, we use this syntax, passing the condition as the first element within square brackets, then a comma, and finally leaving the second element empty.
For example, to get all rows for which students_data$age > 16, we would use:
> students_data[students_data$age > 16,] first_name last_name age num_siblings num_pets eye_color 5 Gino Sand 17 3 8 BLUEWe get a data frame with the rows that meet this condition.
Filter Rows and Choose Columns
You can combine this condition with a range of columns:
> students_data[students_data$age > 16, 3:6] age num_siblings num_pets eye_color 5 17 3 8 BLUEWe 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 BROWNBut wait! A warning message was displayed:
Warning message: In `[<-.factor`(`*tmp*`, ri, value = "William") : invalid factor level, NA generatedAnd notice the first value of the sixth row, it is :
6 Smith 14 7 3 BROWNThis 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$GPA <- c(4.0, 3.5, 3.2, 3.15, 2.9, 3.0)? Tip: The number of elements has to be equal to the number of rows of the data frame.
The output shows the data frame with the new GPA column:
> students_data first_name last_name age num_siblings num_pets eye_color GPA 1 Emily Dawson 15 2 5 BLUE 4.00 2 Rose Patterson 14 5 0 GREEN 3.50 3 Alexander Smith 16 0 2 BROWN 3.20 4 Nora Navona 16 4 10 GREEN 3.15 5 Gino Sand 17 3 8 BLUE 2.90 6 William Smith 14 7 3 BROWN 3.00How to Remove Columns
To remove columns from a data frame, you need to use this syntax:
When you assign the value Null to a column, that column is removed from the data frame automatically.
For example, to remove the age column, we use:
> students_data$age <- NULLThe 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 3.00How 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.
If you liked this article, considerenrolling in my new online course"Introduction to Statistics in R - A Practical Approach"