> For the complete documentation index, see [llms.txt](https://ar-riyaz.gitbook.io/r-for-bioinformatics/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ar-riyaz.gitbook.io/r-for-bioinformatics/load-data.md).

# Load Data

## Load CSV Files in R

```r
ObjectName <- read.csv("path-to-file/filename.csv", header = TURE)
```

{% hint style="info" %}
**Code Breakdown:**

* `read.csv` used to load **csv** files.
* `header = TURE` argument will consider the first row as the header or column names.
  {% endhint %}

## Get Data from the URL

**-> Option 1: Directly save as an object.**

Let's say the data is in **csv** format. We can use `read.csv` function to directly parse the data and save it as a DataFrame.

{% tabs %}
{% tab title="Code" %}

<pre class="language-r" data-overflow="wrap"><code class="lang-r"><strong>polls &#x3C;- read.csv("https://raw.githubusercontent.com/ds4stats/r-tutorials/master/tidying-data/data/rcp-polls.csv",
</strong>na.strings = "--", as.is = TRUE)
</code></pre>

{% hint style="info" %}
**Code Breakdown:**

* We have to put the URL inside the double quote under `read.csv` function.
* <mark style="color:red;">`na.strings = "--"`</mark> : This dataset denotes missing data as <mark style="color:red;">`--`</mark>. But R doesn't understand that. So we converted the <mark style="color:red;">`--`</mark> into <mark style="color:red;">`NA`</mark>.
* <mark style="color:red;">`as.is = TRUE`</mark> : Normally R converts the character column into a factor. By this argument, we specified not to do this conversion.
  {% endhint %}
  {% endtab %}
  {% endtabs %}

\
**-> Option 2: First save as a file and then load as an object.**

{% tabs %}
{% tab title="Code" %}
{% code overflow="wrap" %}

```r
# Set the URL as an object
url <- "https://raw.githubusercontent.com/ds4stats/r-tutorials/master/tidying-data/data/rcp-polls.csv"

# Download the file
download.file(url, "poll_dataset.csv")

# Load as an object
polls <- read.csv("poll_dataset.csv", header = TRUE, na.strings = "--",
as.is = TRUE)

```

{% endcode %}

{% hint style="info" %}
**Code Breakdown:**

* At first, the URL was specified by an object <mark style="color:red;">`url`</mark>.
* In the second code, <mark style="color:red;">`download.file`</mark> function downloaded the dataset.
* At 3rd code, we loaded the dataset as a **csv** file.
  {% endhint %}
  {% endtab %}
  {% endtabs %}

## Load TSV Files in R

To load a **TSV** file in R, we can use either the <mark style="color:red;">`read.delim()`</mark> function or the <mark style="color:red;">`read_tsv()`</mark> function from the <mark style="color:red;">`readr`</mark> package.

### Using the <mark style="color:red;">`read.delim()`</mark> function

The <mark style="color:red;">`read.delim()`</mark> function is a general function for reading delimited text files. To read a **TSV** file, you need to specify the delimiter as <mark style="color:red;">`"\t"`</mark>.

```r
# Load the TSV file
tsv_data <- read.delim("path/to/tsv_file.tsv", sep = "\t")

# Print the head of the data frame
head(tsv_data)

```

### Using the `read_tsv()` function

The <mark style="color:red;">`read_tsv()`</mark> function is a specific function for reading **TSV** files. It is more efficient than using the <mark style="color:red;">`read.delim()`</mark> function for TSV files.

To use the <mark style="color:red;">`read_tsv()`</mark> function, you need to install the `readr` package first.

```r
# Install the readr package
install.packages("readr")

# Load the readr package
library(readr)

# Load the TSV file
tsv_data <- read_tsv("path/to/tsv_file.tsv")

# Print the head of the data frame
head(tsv_data)

```

{% hint style="info" %}
When you observe the outputs, the basic difference between both methods is <mark style="color:red;">`read_tsv()`</mark> function returns the dataframe with columns by specifying the type of it `[ Student_Id – double, Student_Name – Character ],` when it comes to <mark style="color:red;">`read.delim()`</mark> method it simply returns the data present in the tsv file.

However, the <mark style="color:red;">`read_tsv()`</mark> function is more efficient and easier to use for TSV files.
{% endhint %}

## Load Excel Files in R

To load an <mark style="color:red;">**`.xlsx`**</mark> file in R, you can use the <mark style="color:red;">`read_excel()`</mark> function from the <mark style="color:red;">`readxl`</mark> package.

```r
# Load the the readxl package
library("readxl")

# Read the XLSX file
xlsx_data <- read_excel("data.xlsx")

# Print the head of the data frame to see the first few rows of data
head(xlsx_data)
```

**Loading a specific sheet from an XLSX file**

If you want to load a specific sheet from an XLSX file, you can use the <mark style="color:red;">`sheet`</mark> argument to the <mark style="color:red;">`read_excel()`</mark> function.

For example, to load the sheet named "<mark style="color:red;">`Sheet1`</mark>" from the XLSX file <mark style="color:red;">`data.xlsx`</mark>, you would use the following code:

```
# load the data
xlsx_data <- read_excel("data.xlsx", sheet = "Sheet1")

# loading multiple sheets
xlsx_data <- read_excel("data.xlsx", sheets = c("Sheet1", "Sheet2"))
```