I need to generate a table counting new levels of a factor per site. My code is like this
# Data creation
f = c("red", "green", "blue", "orange", "yellow")
f = factor(f)
d = data.frame(
site = 1:10,
color1= c(
"red", "red", "green", "green", "green",
"blue","green", "blue", "orange", "yellow"
),
color2= c(
"green", "green", "green", "blue","green",
"blue", "orange", "yellow","red", "red"
)
)
d$color1 = factor( d$color1 , levels = levels(f) )
d$color2 = factor( d$color2 , levels = levels(f) )
d
It shows me this table
I need to count how many new colors are in every new site. Only count first time appearing, not duplicated. Resulting a table like this one.
Counting not duplicated colors per site is in this figure.
Is there a dplyr way to find this output?
CodePudding user response:
You can do:
library(tidyverse)
d %>%
pivot_longer(cols = -site) %>%
mutate(newColors = duplicated(value)) %>%
group_by(site) %>%
mutate(newColors = sum(!newColors)) %>%
ungroup() %>%
pivot_wider()
which gives:
# A tibble: 10 x 4
site newColors color1 color2
<int> <int> <fct> <fct>
1 1 2 red green
2 2 0 red green
3 3 0 green green
4 4 1 green blue
5 5 0 green green
6 6 0 blue blue
7 7 1 green orange
8 8 1 blue yellow
9 9 0 orange red
10 10 0 yellow red
Note that this differs for row 9 where you have a 1
, but both colors (orange and red) already appeared in previous rows.