来自 pivot_wider 的奇怪输出。生成的 Tibble 与 View(df) 输出不同。 （包括输出和代码！）
Strange output from pivot_wider. Tibble produced is different from the View(df) output. (includes dput and code!)
我正在尝试将以下数据集转换为更宽的格式，其中 Full_name 中的物种名称成为列 headers，counts500 中的值进入每一列。 ID 列不应重复。
Year
Location
Site
Depth
Transect
ID
Full_name
counts500
2019
Tela
AD
10
1
2019_Tela_AD_1_10
Stegastes planifrons
0
2019
Tela
AD
10
1
2019_Tela_AD_1_10
Anisotremus virginicus
1
2019
Tela
AD
10
1
2019_Tela_AD_1_10
Stegastes adustus
0
2019
Tela
AD
10
1
2019_Tela_AD_1_10
Stegastes partitus
0
2019
Tela
AD
10
1
2019_Tela_AD_1_10
Stegastes partitus
0
2019
Tela
AD
10
1
2019_Tela_AD_1_10
Stegastes partitus
0
我使用的代码是：
> dfnew <- pivot_wider(df, names_from = Full_name, values_from = counts500, values_fn = list) ###Pivoting dataset wider to give species as columns
这个 returns 一个看起来正确的数据集（在 View(df) 中），但是当我在控制台中输入 'df' 时出现错误，它的值不正确。会不会是它在计算每个 ID 每个物种的出现次数，而不是从 counts500 列中获取值？
这是我的顶部 table 的输出，其中包含更多行：
structure(list(Year = c(2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L), Location = c("Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela"), Site = c("AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD"), Depth = c(10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L), Transect = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), ID = c("2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10"
), Full_name = c("Stegastes planifrons", "Anisotremus virginicus",
"Stegastes adustus", "Stegastes partitus", "Stegastes partitus",
"Stegastes partitus", "Stegastes partitus", "Stegastes partitus",
"Stegastes adustus", "Stegastes partitus", "Stegastes adustus",
"Stegastes adustus", "Stegastes adustus", "Stegastes variabilis",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Serranus tortugarum", "Serranus tortugarum", "Stegastes adustus",
"Stegastes partitus", "Stegastes adustus", "Stegastes partitus",
"Microspathodon chrysurus", "Abudefduf saxatilis", "Microspathodon chrysurus",
"Haemulon carbonarium", "Microspathodon chrysurus", "Abudefduf saxatilis",
"Acanthurus coeruleus", "Haemulon chrysargyreum", "Anisotremus virginicus",
"Anisotremus virginicus", "Sparisoma viride", "Caranx ruber",
"Scarus vetula", "Halichoeres bivittatus", "Scarus iseri", "Sparisoma viride",
"Anisotremus virginicus", "Sparisoma viride", "Scarus iseri",
"Scarus iseri", "Scarus iseri", "Scarus vetula", "Stegastes partitus",
"Stegastes adustus", "Stegastes adustus", "Stegastes adustus",
"Scarus iseri", "Microspathodon chrysurus", "Stegastes adustus",
"Stegastes adustus", "Microspathodon chrysurus", "Microspathodon chrysurus",
"Stegastes adustus", "Stegastes adustus", "Scarus iseri", "Stegastes diencaeus",
"Stegastes adustus", "Haemulon sciurus", "Stegastes adustus",
"Stegastes adustus", "Anisotremus virginicus", "Lutjanus mahogoni",
"Scarus vetula", "Stegastes partitus", "Stegastes adustus", "Holacanthus tricolor",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Stegastes partitus", "Stegastes partitus", "Halichoeres maculipinna",
"Stegastes partitus", "Stegastes variabilis", "Microspathodon chrysurus",
"Stegastes adustus", "Stegastes variabilis", "Stegastes adustus",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Stegastes adustus", "Stegastes diencaeus", "Stegastes partitus",
"Thalassoma bifasciatum", "Chaetodon capistratus", "Haemulon carbonarium",
"Haemulon carbonarium", "Stegastes adustus", "Thalassoma bifasciatum",
"Stegastes adustus", "Stegastes partitus", "Stegastes partitus",
"Stegastes partitus"), counts500 = c(0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 2, 0, 0, 1, 1, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, 100L), class = "data.frame")))
谢谢！
据推测，如果有多行都相同，您希望将计数加在一起，并且在观察到的物种中有 none 的地方您想要一个零。既然如此，你可以这样做：
tidyr::pivot_wider(exfibi500, names_from = Full_name, values_from = counts500,
values_fn = sum, values_fill = 0)
#> # A tibble: 3 x 29
#> Year Location Site Depth Transect ID `Stegastes pla~` `Anisotremus v~`
#> <int> <chr> <chr> <int> <int> <chr> <dbl> <dbl>
#> 1 2019 Tela AD 10 1 2019_Te~ 0 4
#> 2 2019 Tela AD 10 2 2019_Te~ 0 0
#> 3 2019 Tela AD 10 3 2019_Te~ 0 0
#> # ... with 21 more variables: `Stegastes adustus` <dbl>,
#> # `Stegastes partitus` <dbl>, `Stegastes variabilis` <dbl>,
#> # `Serranus tortugarum` <dbl>, `Microspathodon chrysurus` <dbl>,
#> # `Abudefduf saxatilis` <dbl>, `Haemulon carbonarium` <dbl>,
#> # `Acanthurus coeruleus` <dbl>, `Haemulon chrysargyreum` <dbl>,
#> # `Sparisoma viride` <dbl>, `Caranx ruber` <dbl>, `Scarus vetula` <dbl>,
#> # `Halichoeres bivittatus` <dbl>, `Scarus iseri` <dbl>, ...
由 reprex package (v2.0.1)
于 2022-04-12 创建
我们可以用distinct然后pivot_wider
library(dplyr)
library(tidyr)
pivot_wider(distinct(df), names_from = Full_name, values_from = counts500)
-输出
# A tibble: 3 × 29
Year Location Site Depth Transect ID `Stegastes plan… `Anisotremus vi… `Stegastes adus… `Stegastes part… `Stegastes vari… `Serranus tortu…
<int> <chr> <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2019 Tela AD 10 1 2019_Tela_AD_1_10 0 1 0 0 0 0
2 2019 Tela AD 10 2 2019_Tela_AD_2_10 NA 0 0 0 NA NA
3 2019 Tela AD 10 3 2019_Tela_AD_3_10 NA NA 0 0 0 NA
# … with 17 more variables: Microspathodon chrysurus <dbl>, Abudefduf saxatilis <dbl>, Haemulon carbonarium <dbl>, Acanthurus coeruleus <dbl>,
# Haemulon chrysargyreum <dbl>, Sparisoma viride <dbl>, Caranx ruber <dbl>, Scarus vetula <dbl>, Halichoeres bivittatus <dbl>, Scarus iseri <dbl>,
# Stegastes diencaeus <dbl>, Haemulon sciurus <dbl>, Lutjanus mahogoni <dbl>, Holacanthus tricolor <dbl>, Halichoeres maculipinna <dbl>,
# Thalassoma bifasciatum <dbl>, Chaetodon capistratus <dbl>
我正在尝试将以下数据集转换为更宽的格式，其中 Full_name 中的物种名称成为列 headers，counts500 中的值进入每一列。 ID 列不应重复。
| Year | Location | Site | Depth | Transect | ID | Full_name | counts500 |
|---|---|---|---|---|---|---|---|
| 2019 | Tela | AD | 10 | 1 | 2019_Tela_AD_1_10 | Stegastes planifrons | 0 |
| 2019 | Tela | AD | 10 | 1 | 2019_Tela_AD_1_10 | Anisotremus virginicus | 1 |
| 2019 | Tela | AD | 10 | 1 | 2019_Tela_AD_1_10 | Stegastes adustus | 0 |
| 2019 | Tela | AD | 10 | 1 | 2019_Tela_AD_1_10 | Stegastes partitus | 0 |
| 2019 | Tela | AD | 10 | 1 | 2019_Tela_AD_1_10 | Stegastes partitus | 0 |
| 2019 | Tela | AD | 10 | 1 | 2019_Tela_AD_1_10 | Stegastes partitus | 0 |
我使用的代码是：
> dfnew <- pivot_wider(df, names_from = Full_name, values_from = counts500, values_fn = list) ###Pivoting dataset wider to give species as columns
这个 returns 一个看起来正确的数据集（在 View(df) 中），但是当我在控制台中输入 'df' 时出现错误，它的值不正确。会不会是它在计算每个 ID 每个物种的出现次数，而不是从 counts500 列中获取值？
这是我的顶部 table 的输出，其中包含更多行：
structure(list(Year = c(2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L), Location = c("Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela", "Tela",
"Tela"), Site = c("AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD",
"AD", "AD", "AD", "AD"), Depth = c(10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L), Transect = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), ID = c("2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_1_10", "2019_Tela_AD_1_10",
"2019_Tela_AD_1_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_2_10",
"2019_Tela_AD_2_10", "2019_Tela_AD_2_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10",
"2019_Tela_AD_3_10", "2019_Tela_AD_3_10", "2019_Tela_AD_3_10"
), Full_name = c("Stegastes planifrons", "Anisotremus virginicus",
"Stegastes adustus", "Stegastes partitus", "Stegastes partitus",
"Stegastes partitus", "Stegastes partitus", "Stegastes partitus",
"Stegastes adustus", "Stegastes partitus", "Stegastes adustus",
"Stegastes adustus", "Stegastes adustus", "Stegastes variabilis",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Serranus tortugarum", "Serranus tortugarum", "Stegastes adustus",
"Stegastes partitus", "Stegastes adustus", "Stegastes partitus",
"Microspathodon chrysurus", "Abudefduf saxatilis", "Microspathodon chrysurus",
"Haemulon carbonarium", "Microspathodon chrysurus", "Abudefduf saxatilis",
"Acanthurus coeruleus", "Haemulon chrysargyreum", "Anisotremus virginicus",
"Anisotremus virginicus", "Sparisoma viride", "Caranx ruber",
"Scarus vetula", "Halichoeres bivittatus", "Scarus iseri", "Sparisoma viride",
"Anisotremus virginicus", "Sparisoma viride", "Scarus iseri",
"Scarus iseri", "Scarus iseri", "Scarus vetula", "Stegastes partitus",
"Stegastes adustus", "Stegastes adustus", "Stegastes adustus",
"Scarus iseri", "Microspathodon chrysurus", "Stegastes adustus",
"Stegastes adustus", "Microspathodon chrysurus", "Microspathodon chrysurus",
"Stegastes adustus", "Stegastes adustus", "Scarus iseri", "Stegastes diencaeus",
"Stegastes adustus", "Haemulon sciurus", "Stegastes adustus",
"Stegastes adustus", "Anisotremus virginicus", "Lutjanus mahogoni",
"Scarus vetula", "Stegastes partitus", "Stegastes adustus", "Holacanthus tricolor",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Stegastes partitus", "Stegastes partitus", "Halichoeres maculipinna",
"Stegastes partitus", "Stegastes variabilis", "Microspathodon chrysurus",
"Stegastes adustus", "Stegastes variabilis", "Stegastes adustus",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Stegastes partitus", "Stegastes adustus", "Stegastes adustus",
"Stegastes adustus", "Stegastes diencaeus", "Stegastes partitus",
"Thalassoma bifasciatum", "Chaetodon capistratus", "Haemulon carbonarium",
"Haemulon carbonarium", "Stegastes adustus", "Thalassoma bifasciatum",
"Stegastes adustus", "Stegastes partitus", "Stegastes partitus",
"Stegastes partitus"), counts500 = c(0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 2, 0, 0, 1, 1, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, 100L), class = "data.frame")))
谢谢！
据推测，如果有多行都相同，您希望将计数加在一起，并且在观察到的物种中有 none 的地方您想要一个零。既然如此，你可以这样做：
tidyr::pivot_wider(exfibi500, names_from = Full_name, values_from = counts500,
values_fn = sum, values_fill = 0)
#> # A tibble: 3 x 29
#> Year Location Site Depth Transect ID `Stegastes pla~` `Anisotremus v~`
#> <int> <chr> <chr> <int> <int> <chr> <dbl> <dbl>
#> 1 2019 Tela AD 10 1 2019_Te~ 0 4
#> 2 2019 Tela AD 10 2 2019_Te~ 0 0
#> 3 2019 Tela AD 10 3 2019_Te~ 0 0
#> # ... with 21 more variables: `Stegastes adustus` <dbl>,
#> # `Stegastes partitus` <dbl>, `Stegastes variabilis` <dbl>,
#> # `Serranus tortugarum` <dbl>, `Microspathodon chrysurus` <dbl>,
#> # `Abudefduf saxatilis` <dbl>, `Haemulon carbonarium` <dbl>,
#> # `Acanthurus coeruleus` <dbl>, `Haemulon chrysargyreum` <dbl>,
#> # `Sparisoma viride` <dbl>, `Caranx ruber` <dbl>, `Scarus vetula` <dbl>,
#> # `Halichoeres bivittatus` <dbl>, `Scarus iseri` <dbl>, ...
由 reprex package (v2.0.1)
于 2022-04-12 创建我们可以用distinct然后pivot_wider
library(dplyr)
library(tidyr)
pivot_wider(distinct(df), names_from = Full_name, values_from = counts500)
-输出
# A tibble: 3 × 29
Year Location Site Depth Transect ID `Stegastes plan… `Anisotremus vi… `Stegastes adus… `Stegastes part… `Stegastes vari… `Serranus tortu…
<int> <chr> <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2019 Tela AD 10 1 2019_Tela_AD_1_10 0 1 0 0 0 0
2 2019 Tela AD 10 2 2019_Tela_AD_2_10 NA 0 0 0 NA NA
3 2019 Tela AD 10 3 2019_Tela_AD_3_10 NA NA 0 0 0 NA
# … with 17 more variables: Microspathodon chrysurus <dbl>, Abudefduf saxatilis <dbl>, Haemulon carbonarium <dbl>, Acanthurus coeruleus <dbl>,
# Haemulon chrysargyreum <dbl>, Sparisoma viride <dbl>, Caranx ruber <dbl>, Scarus vetula <dbl>, Halichoeres bivittatus <dbl>, Scarus iseri <dbl>,
# Stegastes diencaeus <dbl>, Haemulon sciurus <dbl>, Lutjanus mahogoni <dbl>, Holacanthus tricolor <dbl>, Halichoeres maculipinna <dbl>,
# Thalassoma bifasciatum <dbl>, Chaetodon capistratus <dbl>