R Subset Multiple Conditions

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In R, to subset the data frame based on multiple conditions, you can use the df[] notation, the subset() function from the base package, or the filter() function from the dplyr package. I will explore multiple ways to subset the R data frame in this article by various conditions.

Create Dataframe

Let’s build an R data frame, execute these examples, and analyze the results.


# Create DataFrame
df <- data.frame(
  id = c(10,11,12,13,14,15,16,17),
  name = c('sai','ram','deepika','sahithi','kumar','scott','Don','Lin'),
  gender = c('M','M',NA,'F','M','M','M','F'),
  dob = as.Date(c('1990-10-02','1981-3-24','1987-6-14','1985-8-16',
                  '1995-03-02','1991-6-21','1986-3-24','1990-8-26')),
  state = c('CA','NY',NA,NA,'DC','DW','AZ','PH'),
  row.names=c('r1','r2','r3','r4','r5','r6','r7','r8')
)
df

Yields below output. 


   id    name gender        dob state
r1 10     sai      M 1990-10-02    CA
r2 11     ram      M 1981-03-24    NY
r3 12 deepika   <NA> 1987-06-14  <NA>
r4 13 sahithi      F 1985-08-16  <NA>
r5 14   kumar      M 1995-03-02    DC
r6 15   scott      M 1991-06-21    DW
r7 16     Don      M 1986-03-24    AZ
r8 17     Lin      F 1990-08-26    PH

Subset Rows by Multiple Conditions

The subset() is an R base function that extracts specific observations and variables from a data frame (DataFrame) by submitting with multiple conditions. It is also used to get a subset of vectors and a subset of matrices.

This subset() function takes a syntax subset(x, subset, select, drop = FALSE, …) the first argument is the input object, the second is the subset expression, and the third specifies the variables to select.


# subset by multiple conditions using |
subset(df, gender == 'M' | state == 'PH')

# subset by multiple conditions using &
subset(df, gender == 'M' & state %in% c('CA','NY'))

Yields below output.

r subset multiple conditions

Using df[] Notation

By using bracket notation df[] on the R data frame we can also get data frame by multiple conditions.


# Select Rows by Checking multiple conditions
df[df$gender == 'M' | df$state == 'PH',]

df[df$gender == 'M' & df$state %in% c('CA','NY'),]

Yields the same output as above.

Using filter() Function

Similarly, you can also subset the data frame by multiple conditions using the filter() function from dplyr package. To use this, you first need to install it with install.packages('dplyr') and then load it with library(dplyr)


library(dplyr)
# Using dplyr::filter
df %>% filter(gender == 'M' | state == 'PH')
df %>% filter(gender == 'M' & state %in% c('CA','NY') )

Yields the same output as above.

Complete Example


# Create DataFrame
df <- data.frame(
  id = c(10,11,12,13,14,15,16,17),
  name = c('sai','ram','deepika','sahithi','kumar','scott','Don','Lin'),
  gender = c('M','M',NA,'F','M','M','M','F'),
  dob = as.Date(c('1990-10-02','1981-3-24','1987-6-14','1985-8-16',
                  '1995-03-02','1991-6-21','1986-3-24','1990-8-26')),
  state = c('CA','NY',NA,NA,'DC','DW','AZ','PH'),
  row.names=c('r1','r2','r3','r4','r5','r6','r7','r8')
)
df

# subset by multiple conditions using |
subset(df, gender == 'M' | state == 'PH')

# subset by multiple conditions using &
subset(df, gender == 'M' & state %in% c('CA','NY'))

# Select Rows by Checking multiple conditions
df[df$gender == 'M' | df$state == 'PH',]

df[df$gender == 'M' & df$state %in% c('CA','NY'),]

library(dplyr)
# Using dplyr::filter
df %>% filter(gender == 'M' | state == 'PH')
df %>% filter(gender == 'M' & state %in% c('CA','NY') )

Conclusion

In this article, I have explained how to subset the data frame by multiple conditions in R by using the subset() function, filter() from dplyr package, and using df[] notation.

References