How to use filter() method In R Language
The filter() method in the tidyverse package is used to apply a range of constraints and conditions to the column values of the data frame in data transformation in R. It filters the data and results in the smaller output returned by the column values satisfying the specified condition. The conditions are specified using the logical operators, and values are validated then. A data frame can be supplied with the pipe operator and then using the filter condition.
Syntax: filter(cond1, cond2)
Parameter:
cond1, cond2 – Condition to be applied on data.
The following code snippet indicates that all the column values are returned where the col1 value is greater than 4. Only those rows are returned in the output of the data frame.
R
# Importing tidyverselibrary(tidyverse)# Creating a data framedata_frame = data.frame( col1 = c(2,4,1,7,5,3,5,8), col2 = letters[1:8], col3 = c(0,1,1,1,0,0,0,0), col4 = c(9:16))print("Data Frame")# Printing data frameprint(data_frame)# Selecting values where # col1 value is greater than 4arr_data_frame <- data_frame %>% filter(col1>4)print("Selecting col1 >4 ")# Printing data frame after# applying filterprint(arr_data_frame) |
Output:
col1 col2 col3 col4
1 2 a 0 9
2 4 b 1 10
3 1 c 1 11
4 7 d 1 12
5 5 e 0 13
6 3 f 0 14
7 5 g 0 15
8 8 h 0 16
[1] "Selecting col1 >4 "
col1 col2 col3 col4
1 7 d 1 12
2 5 e 0 13
3 5 g 0 15
4 8 h 0 16
Explanation :
The output data frame contains the rows of the original data frame where the col1 value is greater than 4. The rows are fetched in the order in which they occur in the original data frame.
Multiple values can also be checked using the filter tag. For instance, the range of values to be checked is specified in the c() vector method. The following code snippet illustrates that the col3 value is checked either for the “there” or “this” value. A row satisfying any of these equality constraints is returned in the final output of the data frame.
R
# Importing tidyverselibrary(tidyverse)# Creating a data framedata_frame = data.frame( col1 = c(2,4,1,7,5,3,5,8), col2 = letters[1:8], col3 = c("this","that","there","here","there","this","that","here"), col4 = c(9:16))print("Data Frame")# Printing data frameprint(data_frame)# Selecting values where # col1 value is greater than 4arr_data_frame <- data_frame %>% filter(col3 == c("there","this"))print("Selecting col1>4 ")# Printing data frame after# applying filterprint(arr_data_frame) |
Output:
col1 col2 col3 col4
1 2 a this 9
2 4 b that 10
3 1 c there 11
4 7 d here 12
5 5 e there 13
6 3 f this 14
7 5 g that 15
8 8 h here 16
[1] "Selecting col3 value is either there or this"
col1 col2 col3 col4
1 1 c there 11
2 5 e there 13
3 3 f this 14
Explanation :
The output data frame contains the rows of the original data frame where the col3 value is either “this” or “there”. The rows are fetched in the order in which they occur in the original data frame.
Multiple conditions can also be checked in the filter method and combined using the comma using filter() method. For instance, the below code checks for the col3 value equal to “there” and col1 value equivalent to 5, respectively. The output data frame contains the rows of the original data frame where the col1 value is equivalent to 5 and the col3 value is equivalent to “there.” The rows are fetched in the order in which they occur in the original data frame.
R
# Importing tidyverselibrary(tidyverse)# Creating a data framedata_frame = data.frame( col1 = c(2,4,1,7,5,3,5,8), col2 = letters[1:8], col3 = c("this","that","there","here", "there","this","that","here"), col4 = c(9:16))print("Data Frame")print(data_frame)# Selecting values where # col3 value is there and col1 is 5arr_data_frame <- data_frame %>% filter(col3=="there",col1==5)print("Selecting col3 value is there and col1 is 5")print(arr_data_frame) |
Output:
col1 col2 col3 col4
1 2 a this 9
2 4 b that 10
3 1 c there 11
4 7 d here 12
5 5 e there 13
6 3 f this 14
7 5 g that 15
8 8 h here 16
[1] "Selecting col3 value is there and col1 is 5"
col1 col2 col3 col4
1 5 e there 13
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