This produces the expected result for a full join with the sample data you have provided
library(dplyr)
dataa <- tibble::tribble(
~IDs, ~Age, ~Gender, ~race_eth, ~relationship_status,
"SS35", 22L, "Female", "White", "Single, not in a romantic relationship",
"SS34", 18L, "Female", "White", "Single, not in a romantic relationship",
"SS33", 20L, "Female", "White", "Widowed",
"SS02", 21L, "Female", "Multiracial", "Not married but in a relationship",
"SS08", 22L, "Female", "Hispanic", "Not married but in a relationship",
"SS10", 21L, "Female", "White", "Single, not in a romantic relationship",
"SS19", 19L, "Female", "White", "Not married but in a relationship",
"SS22", 20L, "Female", "Other", "Single, not in a romantic relationship",
"SS26", 21L, "Female", "Hispanic", "Single, not in a romantic relationship",
"SS27", 21L, "Female", "White", "Not married but in a relationship"
)
dataab <- tibble::tribble(
~IDs, ~StartStress, ~RetBase, ~HRT_00_00, ~HRT_00_01, ~HRT_00_02, ~HRT_00_03, ~HRT_00_04, ~HRT_00_05,
"SS34", "25_14", "21_20", 56L, 55L, 55L, 54L, 52L, 51L,
"SS35", "26_55", "22_15", 76L, 76L, 76L, 77L, 78L, 78L,
"SS33", "25_34", "21_25", 103L, 103L, 103L, 103L, 103L, 104L,
"SS02", "25_50", "22_20", 105L, 104L, 112L, 112L, 113L, 100L,
"SS26", "26_23", "21_45", 47L, 47L, 47L, 47L, 45L, 45L,
"SS27", "26_00", "22_35", 53L, 53L, 53L, 53L, 53L, 52L,
"SS08", "25_00", "19_40", 80L, 80L, 80L, 80L, 81L, 82L,
"SS19", "26_15", "22_50", 94L, 81L, 80L, 74L, 74L, 74L,
"SS10", "26_45", "22_05", 96L, 96L, 96L, 96L, 93L, 90L,
"SS22", "25_20", "21_15", 61L, 66L, 66L, 66L, 66L, 71L,
"CD11", "25_40", "22_00", 48L, 49L, 49L, 49L, 49L, 49L
)
full_join(dataa,dataab,by="IDs")
#> # A tibble: 11 × 13
#> IDs Age Gender race_eth relationship_st… StartStress RetBase HRT_00_00
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 SS35 22 Female White Single, not in … 26_55 22_15 76
#> 2 SS34 18 Female White Single, not in … 25_14 21_20 56
#> 3 SS33 20 Female White Widowed 25_34 21_25 103
#> 4 SS02 21 Female Multiracial Not married but… 25_50 22_20 105
#> 5 SS08 22 Female Hispanic Not married but… 25_00 19_40 80
#> 6 SS10 21 Female White Single, not in … 26_45 22_05 96
#> 7 SS19 19 Female White Not married but… 26_15 22_50 94
#> 8 SS22 20 Female Other Single, not in … 25_20 21_15 61
#> 9 SS26 21 Female Hispanic Single, not in … 26_23 21_45 47
#> 10 SS27 21 Female White Not married but… 26_00 22_35 53
#> 11 CD11 NA <NA> <NA> <NA> 25_40 22_00 48
#> # … with 5 more variables: HRT_00_01 <int>, HRT_00_02 <int>, HRT_00_03 <int>,
#> # HRT_00_04 <int>, HRT_00_05 <int>
Created on 2022-07-14 by the reprex package (v2.0.1)
Can you explain in which way this differs from the result you are expecting? ideally, Can you provide a proper REPRoducible EXample (reprex) illustrating your issue.