# Filter Dataframe with Multiple Conditions Name Matching in R dplyr

## Table of Contents

- Introduction to dplyr
- Filtering Dataframes with dplyr
- Multiple Conditions Name Matching
- Combining Multiple Conditions
- Conclusion

## Introduction to dplyr

Dplyr is a part of the tidyverse, a collection of R packages designed for data science. It provides a set of functions that perform common data manipulation operations, making it easier to read and write code. The key functions in dplyr are:

`filter()`

: Subset rows using column values`select()`

: Subset columns using column names`mutate()`

: Create new columns using existing ones`summarise()`

: Collapse multiple values down to a single summary`arrange()`

: Reorder rows by column values

## Filtering Dataframes with dplyr

Filtering is a common operation in data analysis. It involves selecting a subset of rows in a dataframe that meet certain conditions. In dplyr, the `filter()`

function is used for this purpose.

Let’s start with a simple example. Suppose we have a dataframe `df`

with columns `x`

, `y`

, and `z`

. We want to filter the dataframe to include only rows where `x < 50`

and `z == TRUE`

. Here’s how we can do it:

```
library(dplyr)
# sample data
df=data.frame(x=c(12,31,4,66,78),
y=c(22.1,44.5,6.1,43.1,99),
z=c(TRUE,TRUE,FALSE,TRUE,TRUE))
# condition
filter(df, x<50 & z==TRUE)
```

The `filter()`

function takes a logical condition and returns a dataframe with rows where the condition is TRUE.

## Multiple Conditions Name Matching

Now, let’s say we want to filter the dataframe based on multiple conditions that involve matching names. For example, we want to include rows where `x`

is either 12, 4, or 66. We can use the `%in%`

operator for this:

```
filter(x %in% c(12, 4, 66))
```

The `%in%`

operator checks if a value is in a set of values. The `c()`

function combines its arguments into a vector.

## Combining Multiple Conditions

We can combine multiple conditions using logical operators. For example, if we want to include rows where `x`

is ‘12’, ‘4’, or ‘66’ and `y`

is greater than 25, we can do:

```
filter(x %in% c(12, 4, 66) & y > 25)
```

## Conclusion

The dplyr package in R provides a powerful and flexible way to manipulate data. The `filter()`

function, in particular, allows us to subset dataframes based on multiple conditions. By combining logical operators and the `%in%`

operator, we can filter dataframes based on multiple conditions name matching.

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