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The post Filter Using Multiple Conditions in R appeared first on Data Science Tutorials

Filter Using Multiple Conditions in R, Using the dplyr package, you can filter data frames by several conditions using the following syntax.

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Method 1: Using OR, filter by many conditions.

```library(dplyr)
df %>%
filter(col1 == 'A' | col2 > 50)```

Method 2: Filter by Multiple Conditions Using AND

```library(dplyr)
df %>%
filter(col1 == 'A' & col2 > 80)```

With the following data frame in R, the following example explains how to apply these methods in practice.

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Let’s create a data frame

```df <- data.frame(team=c('P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8'),
points=c(110, 120, 80, 16, 105, 185, 112, 112),
assists=c(133, 128, 131, 139, 134,55,66,135),
rebounds=c(18, 18, 14, 13, 12, 15, 17, 12))```

Now we can view the data frame

```df
team points assists rebounds
1   P1    110     133       18
2   P2    120     128       18
3   P3     80     131       14
4   P4     16     139       13
5   P5    105     134       12
6   P6    185      55       15
7   P7    112      66       17
8   P8    112     135       12```

## Method 1: Multiple Conditions Filter Using the OR

The code below demonstrates how to use the or (|) operator to filter the data frame by rows that satisfy one or more conditions.

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`library(dplyr)`

Find rows where the team is equivalent to ‘P1’ or the points total exceeds 90.

```df %>%
filter(team == 'P1' | points > 90)
team points assists rebounds
1   P1    110     133       18
2   P2    120     128       18
3   P5    105     134       12
4   P6    185      55       15
5   P7    112      66       17
6   P8    112     135       12```

The only rows that are returned are those in which the team is equal to ‘P1’ or the points total exceeds 90.

In the filter function, we can use as many “or” operators as we want.

`library(dplyr)`

Find rows where the team is equivalent to ‘P1’ or ‘P2’, or where the points are fewer than 90.

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```df %>%
filter(team == 'P1' | team == 'P2' | points < 90)
team points assists rebounds
1   P1    110     133       18
2   P2    120     128       18
3   P3     80     131       14
4   P4     16     139       13```

## Method 2: Filter by Multiple Conditions Using AND

The following code demonstrates how to use the and (&) operator to filter the data frame by rows that satisfy a number of criteria.

`library(dplyr)`

Find rows where the team is ‘P1’ and the points are larger than 90.

```df %>%
filter(team == 'P1' & points > 90)
team points assists rebounds
1   P1    110     133       18```

Only one entry in the filter function matched both conditions.

In the filter function, we may also use as many “and” operators as we want.

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`library(dplyr)`

Where the team is equivalent to ‘P1’, the points are greater than 100, and the assists are less than 150.

```df %>%
filter(team == 'P1' & points > 100 & assists < 150)
team points assists rebounds
1   P1    110     133       18```

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