For demonstration we’ll load and subset the knz_bison data from the lterdatasampler package (hyperlink includes install instructions). This time we will ensure to duplicate rows.
#Load packagelibrary("lterdatasampler")#Subset of knz_bison tibble for demonstrationbison_tbl <- lterdatasampler::knz_bison |>#Convert to tibble tibble::as_tibble() |>#Slice rows to get 1:5 duplicated once dplyr::slice(c(1,1,2,2,3,3,4,4,5,5))bison_tbl
# A tibble: 10 × 8
data_code rec_year rec_month rec_day animal_code animal_sex animal_weight
<chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
1 CBH01 1994 11 8 813 F 890
2 CBH01 1994 11 8 813 F 890
3 CBH01 1994 11 8 834 F 1074
4 CBH01 1994 11 8 834 F 1074
5 CBH01 1994 11 8 B-301 F 1060
6 CBH01 1994 11 8 B-301 F 1060
7 CBH01 1994 11 8 B-402 F 989
8 CBH01 1994 11 8 B-402 F 989
9 CBH01 1994 11 8 B-403 F 1062
10 CBH01 1994 11 8 B-403 F 1062
# ℹ 1 more variable: animal_yob <dbl>
Extract unique rows
Extract the unique rows.
dplyr::distinct(bison_tbl)
# A tibble: 5 × 8
data_code rec_year rec_month rec_day animal_code animal_sex animal_weight
<chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
1 CBH01 1994 11 8 813 F 890
2 CBH01 1994 11 8 834 F 1074
3 CBH01 1994 11 8 B-301 F 1060
4 CBH01 1994 11 8 B-402 F 989
5 CBH01 1994 11 8 B-403 F 1062
# ℹ 1 more variable: animal_yob <dbl>