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R is a powerful programming language and environment for statistical computing and graphics. Developed in the 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland, R has become a standard tool in data science, statistical analysis, and machine learning. It is open-source and boasts a vast ecosystem of packages, making it highly extensible and adaptable to various data-related tasks. R excels in data manipulation, visualization, and complex statistical analyses. Its strengths lie in its statistical and graphical techniques, including linear and nonlinear modeling, time-series analysis, classification, and clustering. R's flexibility allows it to integrate with other languages and tools, making it an essential part of many data science workflows. With active community support and continuous development, R remains at the forefront of statistical computing and data analysis.

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R is a programming language and environment for statistical analysis and graphics, primarily used by statisticians and data researchers. It enables the performance of statistical computations, data modeling, and visualization. R is particularly valued for its rich library of packages that can be used for a variety of data analyses, including exploratory analysis, statistical tests, regression, classification, and processing large datasets. It is an open-source tool, meaning it is available for free, and its source code can be freely modified and distributed.

One of the main advantages of R is its extensive and active user community, which continuously develops new packages and tools that enable the use of the latest data analysis techniques. R also has advanced graphical capabilities that allow for the creation of high-quality data visualizations, which is an invaluable asset for analyzing and presenting results.

R is used in various fields such as science, business, medicine, engineering, and many others, each time offering tools suitable for the specific needs of those fields.

One of the main advantages of R is its extensive and active user community, which continuously develops new packages and tools that enable the use of the latest data analysis techniques. R also has advanced graphical capabilities that allow for the creation of high-quality data visualizations, which is an invaluable asset for analyzing and presenting results.

R is used in various fields such as science, business, medicine, engineering, and many others, each time offering tools suitable for the specific needs of those fields.

In the R language, a vector can be created using the `c()` function, which stands for "combine." This function allows you to combine multiple elements into a single vector. The data type in a vector can vary, but it is most commonly numeric, logical, or character. Here is an example of how to create a vector containing different data types:

Remember that all elements in a vector must be of the same type. If different data types are combined into a single vector, R will automatically coerce the types to the most general type that can store all the data. For example, mixing numbers and strings results in a vector of strings.

```
# Creating a numeric vector
numbers <- c(1, 2, 3, 4, 5)
print(numbers)
# Creating a logical vector
logical_values <- c(TRUE, FALSE, TRUE, FALSE)
print(logical_values)
# Creating a character vector
characters <- c("ala", "ma", "kota")
print(characters)
```

Remember that all elements in a vector must be of the same type. If different data types are combined into a single vector, R will automatically coerce the types to the most general type that can store all the data. For example, mixing numbers and strings results in a vector of strings.

We can merge two data frames in R using the `merge()` function. This function allows us to combine data based on one or more common columns between the two data sets. If no specific column is specified, R will attempt to merge the data frames based on all columns with matching names in both data sets.

Example of using the `merge()` function to combine two data frames:

In the above example, `merged_data_frames` will contain data from both data frames that have been combined based on the 'ID' column. The resulting data frame will only include records that have a match in both data sets (an inner join operation). To change the type of join, you can use the arguments `all`, `all.x`, `all.y`; for instance, `merge(data_frame1, data_frame2, by = "ID", all = TRUE)` will result in an outer join.

Example of using the `merge()` function to combine two data frames:

```
# Creating the first data frame
data_frame1 <- data.frame(
ID = c(1, 2, 3),
Name = c("Anna", "Jan", "Paweł"),
Age = c(25, 30, 22)
)
# Creating the second data frame
data_frame2 <- data.frame(
ID = c(2, 3, 4),
City = c("Kraków", "Warszawa", "Gdańsk")
)
# Merging the data
merged_data_frames <- merge(data_frame1, data_frame2, by = "ID")
```

In the above example, `merged_data_frames` will contain data from both data frames that have been combined based on the 'ID' column. The resulting data frame will only include records that have a match in both data sets (an inner join operation). To change the type of join, you can use the arguments `all`, `all.x`, `all.y`; for instance, `merge(data_frame1, data_frame2, by = "ID", all = TRUE)` will result in an outer join.

A Factor in R is a data type used for storing categorical variables. These types of variables are very important in statistics and data analysis because they allow for modeling dependencies for discrete data.

You can create a factor using the factor() function. You pass a vector to it, which will be converted into a factor. You can also specify the levels (categories) and labels for these levels.

In the example above, `data_factor` is now a factor that stores information about the fruits with levels automatically generated based on the unique values of the input vector.

Factors are particularly useful in statistical modeling because R treats each level as a separate group, which makes it easier to statistically analyze differences between groups. With factors, it is also easier to create graphs and comparative charts that require grouping categorical data.

Another important aspect of factors is the ability to set the order of the levels. By default, R sets the levels in alphabetical order, but this can be changed, which is particularly useful when the categories have a natural order, like 'low', 'medium', 'high'.

In this example, we create a factor with a specific order of levels, so R will treat the variable as ordered (ordinal). This changes how other R functions (e.g., statistical models) can utilize this information for proper data analysis.

You can create a factor using the factor() function. You pass a vector to it, which will be converted into a factor. You can also specify the levels (categories) and labels for these levels.

```
data_vector <- c("apple", "banana", "cherry", "banana", "apple")
data_factor <- factor(data_vector)
```

In the example above, `data_factor` is now a factor that stores information about the fruits with levels automatically generated based on the unique values of the input vector.

Factors are particularly useful in statistical modeling because R treats each level as a separate group, which makes it easier to statistically analyze differences between groups. With factors, it is also easier to create graphs and comparative charts that require grouping categorical data.

Another important aspect of factors is the ability to set the order of the levels. By default, R sets the levels in alphabetical order, but this can be changed, which is particularly useful when the categories have a natural order, like 'low', 'medium', 'high'.

`ordered_factor <- factor(data_vector, levels = c("cherry", "apple", "banana"), ordered = TRUE)`

In this example, we create a factor with a specific order of levels, so R will treat the variable as ordered (ordinal). This changes how other R functions (e.g., statistical models) can utilize this information for proper data analysis.

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