R Combine Multiple Rows of DataFrame by creating new columns and union values
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I have a dataframe in R that looks like this
ID APPROVAL_STEP APPROVAL_STATUS APPROVAL_DATE APPROVER
1234 STEP_A APPROVED 23-Jan-2019 John Smith
1234 STEP_B APPROVED 21-Jan-2019 Jane Doe
I need it to look like this
ID STEP_A_STATUS STEP_A_APPROVAL_DATE STEP_A_APPROVER STEP_B_STATUS STEP_B_APPROVAL_DATE STEP_B_APPROVER
1234 APPROVED 23-Jan-2019 John Smith APPROVED 21-Jan-2019 Jane Doe
And of course, with the original dataframe, any of APPROVAL_STATUS, APPROVAL_DATE, or APPROVER can be NA.
What is the most elegant way to do this? I know how to do it by looping through the unique IDs, grabbing each row, creating new columns, etc.; but is there any way to do this in a more elegant way (e.g., using tidyverse)?
r data-cleaning data-wrangling
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add a comment |
$begingroup$
I have a dataframe in R that looks like this
ID APPROVAL_STEP APPROVAL_STATUS APPROVAL_DATE APPROVER
1234 STEP_A APPROVED 23-Jan-2019 John Smith
1234 STEP_B APPROVED 21-Jan-2019 Jane Doe
I need it to look like this
ID STEP_A_STATUS STEP_A_APPROVAL_DATE STEP_A_APPROVER STEP_B_STATUS STEP_B_APPROVAL_DATE STEP_B_APPROVER
1234 APPROVED 23-Jan-2019 John Smith APPROVED 21-Jan-2019 Jane Doe
And of course, with the original dataframe, any of APPROVAL_STATUS, APPROVAL_DATE, or APPROVER can be NA.
What is the most elegant way to do this? I know how to do it by looping through the unique IDs, grabbing each row, creating new columns, etc.; but is there any way to do this in a more elegant way (e.g., using tidyverse)?
r data-cleaning data-wrangling
$endgroup$
add a comment |
$begingroup$
I have a dataframe in R that looks like this
ID APPROVAL_STEP APPROVAL_STATUS APPROVAL_DATE APPROVER
1234 STEP_A APPROVED 23-Jan-2019 John Smith
1234 STEP_B APPROVED 21-Jan-2019 Jane Doe
I need it to look like this
ID STEP_A_STATUS STEP_A_APPROVAL_DATE STEP_A_APPROVER STEP_B_STATUS STEP_B_APPROVAL_DATE STEP_B_APPROVER
1234 APPROVED 23-Jan-2019 John Smith APPROVED 21-Jan-2019 Jane Doe
And of course, with the original dataframe, any of APPROVAL_STATUS, APPROVAL_DATE, or APPROVER can be NA.
What is the most elegant way to do this? I know how to do it by looping through the unique IDs, grabbing each row, creating new columns, etc.; but is there any way to do this in a more elegant way (e.g., using tidyverse)?
r data-cleaning data-wrangling
$endgroup$
I have a dataframe in R that looks like this
ID APPROVAL_STEP APPROVAL_STATUS APPROVAL_DATE APPROVER
1234 STEP_A APPROVED 23-Jan-2019 John Smith
1234 STEP_B APPROVED 21-Jan-2019 Jane Doe
I need it to look like this
ID STEP_A_STATUS STEP_A_APPROVAL_DATE STEP_A_APPROVER STEP_B_STATUS STEP_B_APPROVAL_DATE STEP_B_APPROVER
1234 APPROVED 23-Jan-2019 John Smith APPROVED 21-Jan-2019 Jane Doe
And of course, with the original dataframe, any of APPROVAL_STATUS, APPROVAL_DATE, or APPROVER can be NA.
What is the most elegant way to do this? I know how to do it by looping through the unique IDs, grabbing each row, creating new columns, etc.; but is there any way to do this in a more elegant way (e.g., using tidyverse)?
r data-cleaning data-wrangling
r data-cleaning data-wrangling
asked 2 days ago
user69420
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