Slice

The dplyr:slice() function can be used to subset row.

Their are multiple slice commands:

slice(): Index rows by numeric index location.
slice_head(): Select rows from the top of the tibble.
slice_tail(): Select rows from the bottom of the tibble.
slice_sample(): Randomly select rows.
slice_min(): Select rows with smallest values of variable.
slice_max(): Select rows with largest values of a variable.

Dataset

For demonstration we’ll load the knz_bison data from the lterdatasampler package (hyperlink includes install instructions).

#Load package
library("lterdatasampler")
#knz_bison tibble for demonstration
bison_tbl <- lterdatasampler::knz_bison |>
    #Convert to tibble
    tibble::as_tibble()
bison_tbl

# A tibble: 8,325 × 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
 6 CBH01         1994        11       8 B-502       F                    978
 7 CBH01         1994        11       8 B-503       F                   1068
 8 CBH01         1994        11       8 B-504       F                   1024
 9 CBH01         1994        11       8 B-601       F                    978
10 CBH01         1994        11       8 B-602       F                   1188
# ℹ 8,315 more rows
# ℹ 1 more variable: animal_yob <dbl>

Slice

The base slice() function is incredibly similar to normal indexing of a tibble/data.frame. Its main advantage is that it can be more easily used with pipes.

Extract rows 1 to 5.

bison_tbl |> dplyr::slice(1:5)

# 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>

Head and tail

Extract the first 6 rows.

bison_tbl |> dplyr::slice_head(n=6)

# A tibble: 6 × 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
6 CBH01         1994        11       8 B-502       F                    978
# ℹ 1 more variable: animal_yob <dbl>

Extract the last 4 rows.

bison_tbl |> dplyr::slice_tail(n=4)

# A tibble: 4 × 8
  data_code rec_year rec_month rec_day animal_code animal_sex animal_weight
  <chr>        <dbl>     <dbl>   <dbl> <chr>       <chr>              <dbl>
1 CBH01         2020        10      29 P-080       F                    330
2 CBH01         2020        10      29 P-081       M                    350
3 CBH01         2020        10      29 P-082       F                    265
4 CBH01         2020        10      29 P-083       M                    370
# ℹ 1 more variable: animal_yob <dbl>

Max and min

Extract the 3 observations with the highest animal_weight.

bison_tbl |> dplyr::slice_max(animal_weight, n=3)

# A tibble: 3 × 8
  data_code rec_year rec_month rec_day animal_code animal_sex animal_weight
  <chr>        <dbl>     <dbl>   <dbl> <chr>       <chr>              <dbl>
1 CBH01         2006        11       6 W-927       M                   2050
2 CBH01         2005        11       7 W-927       M                   1894
3 CBH01         1995        11       6 114         M                   1892
# ℹ 1 more variable: animal_yob <dbl>

Extract the 9 observations with the lowest animal_weight.

bison_tbl |> dplyr::slice_min(animal_weight, n=9)

# A tibble: 9 × 8
  data_code rec_year rec_month rec_day animal_code animal_sex animal_weight
  <chr>        <dbl>     <dbl>   <dbl> <chr>       <chr>              <dbl>
1 CBH01         2003        11      13 Y-3100      F                     90
2 CBH01         2003        11      13 Y-3101      M                    100
3 CBH01         1997        10      31 W-706       F                    112
4 CBH01         1996        10      25 W-617       F                    118
5 CBH01         2019        10      24 O-9104      F                    120
6 CBH01         2004        11      18 Y-432       F                    126
7 CBH01         1994        11       8 W-437       F                    132
8 CBH01         2004        11      18 Y-417       F                    134
9 CBH01         2006        11       6 Y-646       M                    134
# ℹ 1 more variable: animal_yob <dbl>