Filtering and summarizing but keeping 'zeros'
I have data collected at a number of Sites. At each site, species were identified (Species) and counted (Number). I also recorded teh distance they occurred from me (Distance). A sample data set is:
library(tidyverse)
library(dplyr)
Data <- data.frame(
Site = c("1", "1", "1", "1", "2", "3", "3"),
Species = c("abc", "bcd", "abc", "kjh", "jh", "abc", "gd"),
Number = c(10,1,1,1,1,1,1),
Distance = c("50m", "60m", "In", "In", "Out", "In", "In")
)
I wish to calculate: (A) the number of unique species and (B) the number of individuals of each Species at each Site. However, I wish to filter out all Distances == "Out". I tried the following filter:
Filtered <- Data %>%
filter(Distance %in% c(
"50m",
"60m",
"In"))
And then created my summary:
summary <- Filtered %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species), count = sum(Number))
summary
# A tibble: 2 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 3 2 2
But what I really need is:
# A tibble: 3 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 2 0 0
3 3 2 2
In other words, I don't want the 'Out" sites to be included in the summary calculations, but I want to show that there were 0 species at the 'non-Out' Distances.
Is there a better way that I am missing?
r
asked Nov 23 '18 at 5:01
SFM
174
add a comment |
I have data collected at a number of Sites. At each site, species were identified (Species) and counted (Number). I also recorded teh distance they occurred from me (Distance). A sample data set is:
library(tidyverse)
library(dplyr)
Data <- data.frame(
Site = c("1", "1", "1", "1", "2", "3", "3"),
Species = c("abc", "bcd", "abc", "kjh", "jh", "abc", "gd"),
Number = c(10,1,1,1,1,1,1),
Distance = c("50m", "60m", "In", "In", "Out", "In", "In")
)
I wish to calculate: (A) the number of unique species and (B) the number of individuals of each Species at each Site. However, I wish to filter out all Distances == "Out". I tried the following filter:
Filtered <- Data %>%
filter(Distance %in% c(
"50m",
"60m",
"In"))
And then created my summary:
summary <- Filtered %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species), count = sum(Number))
summary
# A tibble: 2 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 3 2 2
But what I really need is:
# A tibble: 3 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 2 0 0
3 3 2 2
In other words, I don't want the 'Out" sites to be included in the summary calculations, but I want to show that there were 0 species at the 'non-Out' Distances.
Is there a better way that I am missing?
r
asked Nov 23 '18 at 5:01
SFM
174
add a comment |
I have data collected at a number of Sites. At each site, species were identified (Species) and counted (Number). I also recorded teh distance they occurred from me (Distance). A sample data set is:
library(tidyverse)
library(dplyr)
Data <- data.frame(
Site = c("1", "1", "1", "1", "2", "3", "3"),
Species = c("abc", "bcd", "abc", "kjh", "jh", "abc", "gd"),
Number = c(10,1,1,1,1,1,1),
Distance = c("50m", "60m", "In", "In", "Out", "In", "In")
)
I wish to calculate: (A) the number of unique species and (B) the number of individuals of each Species at each Site. However, I wish to filter out all Distances == "Out". I tried the following filter:
Filtered <- Data %>%
filter(Distance %in% c(
"50m",
"60m",
"In"))
And then created my summary:
summary <- Filtered %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species), count = sum(Number))
summary
# A tibble: 2 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 3 2 2
But what I really need is:
# A tibble: 3 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 2 0 0
3 3 2 2
In other words, I don't want the 'Out" sites to be included in the summary calculations, but I want to show that there were 0 species at the 'non-Out' Distances.
Is there a better way that I am missing?
r
asked Nov 23 '18 at 5:01
SFM
174
I have data collected at a number of Sites. At each site, species were identified (Species) and counted (Number). I also recorded teh distance they occurred from me (Distance). A sample data set is:
library(tidyverse)
library(dplyr)
Data <- data.frame(
Site = c("1", "1", "1", "1", "2", "3", "3"),
Species = c("abc", "bcd", "abc", "kjh", "jh", "abc", "gd"),
Number = c(10,1,1,1,1,1,1),
Distance = c("50m", "60m", "In", "In", "Out", "In", "In")
)
I wish to calculate: (A) the number of unique species and (B) the number of individuals of each Species at each Site. However, I wish to filter out all Distances == "Out". I tried the following filter:
Filtered <- Data %>%
filter(Distance %in% c(
"50m",
"60m",
"In"))
And then created my summary:
summary <- Filtered %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species), count = sum(Number))
summary
# A tibble: 2 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 3 2 2
But what I really need is:
# A tibble: 3 x 3
Site richness count
<fct> <int> <dbl>
1 1 3 13
2 2 0 0
3 3 2 2
In other words, I don't want the 'Out" sites to be included in the summary calculations, but I want to show that there were 0 species at the 'non-Out' Distances.
Is there a better way that I am missing?
r
r
asked Nov 23 '18 at 5:01
SFM
174
asked Nov 23 '18 at 5:01
SFM
174
asked Nov 23 '18 at 5:01
SFM
174
asked Nov 23 '18 at 5:01
SFM
174
asked Nov 23 '18 at 5:01
SFM
174
174
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
We could group_by Site and filter "Out" entries after grouping in summarize step.
library(dplyr)
Data %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species[Distance != "Out"]),
count = sum(Number[Distance != "Out"]))
# Site richness count
# <fct> <int> <dbl>
#1 1 3 13
#2 2 0 0
#3 3 2 2
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
We could group_by Site and filter "Out" entries after grouping in summarize step.
library(dplyr)
Data %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species[Distance != "Out"]),
count = sum(Number[Distance != "Out"]))
# Site richness count
# <fct> <int> <dbl>
#1 1 3 13
#2 2 0 0
#3 3 2 2
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
add a comment |
We could group_by Site and filter "Out" entries after grouping in summarize step.
library(dplyr)
Data %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species[Distance != "Out"]),
count = sum(Number[Distance != "Out"]))
# Site richness count
# <fct> <int> <dbl>
#1 1 3 13
#2 2 0 0
#3 3 2 2
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
add a comment |
We could group_by Site and filter "Out" entries after grouping in summarize step.
library(dplyr)
Data %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species[Distance != "Out"]),
count = sum(Number[Distance != "Out"]))
# Site richness count
# <fct> <int> <dbl>
#1 1 3 13
#2 2 0 0
#3 3 2 2
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
We could group_by Site and filter "Out" entries after grouping in summarize step.
library(dplyr)
Data %>%
group_by(Site) %>%
summarize(richness = n_distinct(Species[Distance != "Out"]),
count = sum(Number[Distance != "Out"]))
# Site richness count
# <fct> <int> <dbl>
#1 1 3 13
#2 2 0 0
#3 3 2 2
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
answered Nov 23 '18 at 5:23
Ronak Shah
32.4k103753
32.4k103753
add a comment |
add a comment |
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