I'm using IPUMS CPS data and created a binary variable for disability from the six different variables within IPUMS for disability (https://cps.ipums.org/cps-action/variables/group/core_dis)
bias <- bias %>%
mutate(DISABLED= if_else(.$DIFFCARE ==2|
.$DIFFMOB == 2 |
.$DIFFEYE ==2 |
.$DIFFREM == 2 |
.$DIFFPHYS == 2|
.$DIFFMOB == 2 |
.$DIFFCARE ==2 |
.$DIFFANY == 2|
.$DIFFHEAR == 2,1, 0))
If the respondent replied yes to any of the various disabilities, they were categorized under the new "Disabled" variable above with a "1". For not disabled, I'm looking for survey respondents to have selected "no disability" among the six variables. I believe the DISABLED code above, when coded as "0," may signify they only had categorized as not having a disability in one or more of the disability categories; however, I need them to be not disabled in all the categories.
So, I created this variable below, but I'm not sure this captures what I want.
bias <- bias %>%
mutate(NOTDISABLED= if_else(.$DIFFCARE == 1|
.$DIFFMOB == 1 |
.$DIFFEYE == 1 |
.$DIFFREM == 1 |
.$DIFFPHYS == 1|
.$DIFFMOB == 1 |
.$DIFFCARE == 1 |
.$DIFFANY == 1|
.$DIFFHEAR == 1, 1, 0))
Please advise. Thank you again for reading!
As Sirius said, the key is using &:
bias <- bias %>%
mutate(NOTDISABLED= if_else(DIFFCARE == 1|
DIFFMOB == 1 |
DIFFEYE == 1 |
DIFFREM == 1 |
DIFFPHYS == 1|
DIFFMOB == 1 |
DIFFCARE == 1 |
DIFFANY == 1|
DIFFHEAR == 1, 1, 0),
DISABLED= if_else(DIFFCARE == 1 &
DIFFMOB == 1 &
DIFFEYE == 1 &
DIFFREM == 1 &
DIFFPHYS == 1 &
DIFFMOB == 1 &
DIFFCARE == 1 &
DIFFANY == 1 &
DIFFHEAR == 1, 1, 0))
Code simplified (removed .$s, they are unnecessary). See the documentation for more information on things like & and | (Boolean operators)