I am trying to solve a problem where I take a random sample based on probability with 5 observations per row where I observe a color out of a possible set of colors, exclude the observed color from the next observation and repeat. Colors can repeat in any given column, but not in the same row.
Here is how I have approached the problem:
library(tidyverse)
data <- tibble(obsId = 1:100)
colors <- tibble(color = c('red', 'blue', 'white', 'yellow', 'green', 'orange',
'gray', 'brown', 'purple', 'black', 'pink', 'navy',
'maroon'),
prob = c(0.85, 0.85, 0.75, 0.75, 0.65, 0.5, 0.5, 0.5, 0.4,
0.4, 0.25, 0.15, 0.15))
data <- data %>%
mutate(color1 = sample(x = colors$color, size = n(),
prob = colors$prob, replace = T),
color2 = sample(x = colors$color, size = n(),
prob = colors$prob, replace = T),
color3 = sample(x = colors$color, size = n(),
prob = colors$prob, replace = T),
color4 = sample(x = colors$color, size = n(),
prob = colors$prob, replace = T),
color5 = sample(x = colors$color, size = n(),
prob = colors$prob, replace = T)
The issue I have is that color 2 will be equal to color 1 (and so forth) in certain rows. Is there any easy way to resolve this?
CodePudding user response：
How about replicating (nrow(data) times) the sampling of 5 items:
setNames(
cbind(
data,
t(replicate(nrow(data), sample(colors$color, 5, prob=colors$prob)))
), c("obsID", paste0("color",1:5))
)
And here is a data.table version:
library(data.table)
f <- function(c, p,n=5) setNames(as.list(sample(c,n,prob=p)), paste0("color",1:n))
setDT(data)[, f(colors$color, colors$prob), obsId]
Output:
obsID color1 color2 color3 color4 color5
1 1 yellow gray white red green
2 2 blue white red black orange
3 3 white blue red purple yellow
4 4 orange blue maroon red yellow
5 5 blue yellow gray green brown
6 6 green black gray orange white
7 7 yellow white red blue green
8 8 yellow black green pink white
9 9 orange purple red white blue
10 10 pink yellow white orange blue
11 11 white red blue orange purple
12 12 red green brown yellow purple
13 13 red blue brown green white
14 14 red orange green blue gray
15 15 white blue red pink yellow
16 16 red blue white purple green
17 17 blue orange brown white green
18 18 black gray brown yellow purple
19 19 pink blue green orange gray
20 20 purple pink yellow brown red
21 21 green black white blue pink
22 22 black blue pink gray maroon
23 23 blue red brown orange yellow
24 24 gray red purple brown orange
25 25 purple brown green orange yellow
26 26 blue white orange green red
27 27 red blue orange white gray
28 28 white black yellow navy red
29 29 orange blue purple brown green
30 30 orange blue purple green white
31 31 blue red purple yellow white
32 32 white navy orange purple brown
33 33 orange blue purple gray yellow
34 34 red pink blue yellow green
35 35 brown blue yellow gray white
36 36 gray green yellow purple red
37 37 green orange yellow navy white
38 38 brown green purple black orange
39 39 gray black green pink white
40 40 white green yellow blue red
41 41 orange black gray maroon white
42 42 blue white pink brown red
43 43 yellow purple gray navy green
44 44 white red green yellow pink
45 45 red green pink yellow black
46 46 orange white red gray yellow
47 47 brown purple pink black red
48 48 pink blue green brown white
49 49 blue green brown orange yellow
50 50 red black green orange blue
51 51 gray red green white yellow
52 52 pink black yellow red brown
53 53 white green navy black blue
54 54 red orange yellow white green
55 55 brown gray purple yellow red
56 56 brown yellow black purple orange
57 57 pink black gray white orange
58 58 blue brown black purple green
59 59 red green gray white blue
60 60 orange maroon yellow green gray
61 61 pink white blue orange gray
62 62 brown red white gray blue
63 63 black red blue yellow navy
64 64 green maroon red black blue
65 65 brown yellow red purple green
66 66 gray brown blue green yellow
67 67 blue yellow maroon purple orange
68 68 red gray blue black white
69 69 gray yellow white brown orange
70 70 brown red white yellow maroon
71 71 black red white orange yellow
72 72 navy blue green gray black
73 73 gray orange brown blue red
74 74 black pink red yellow blue
75 75 gray black blue green yellow
76 76 blue yellow green pink red
77 77 yellow blue red green orange
78 78 brown white gray navy orange
79 79 brown blue white green navy
80 80 white gray yellow brown green
81 81 gray brown white yellow red
82 82 orange yellow blue white green
83 83 gray blue brown white green
84 84 orange blue green brown gray
85 85 pink orange blue brown red
86 86 red orange green yellow blue
87 87 black brown navy white yellow
88 88 white black green purple blue
89 89 yellow green red gray maroon
90 90 purple maroon brown gray yellow
91 91 white pink blue red black
92 92 blue black red pink white
93 93 red white brown black green
94 94 red pink gray blue purple
95 95 orange green white gray red
96 96 red gray green yellow purple
97 97 white brown purple yellow blue
98 98 purple red orange yellow green
99 99 black white gray yellow red
100 100 yellow white blue purple gray
CodePudding user response：
Here is a base/dplyr solution.
library(tidyverse)
data <- tibble(obsId = 1:100)
colors <- tibble(color = c('red', 'blue', 'white', 'yellow', 'green', 'orange',
'gray', 'brown', 'purple', 'black', 'pink', 'navy',
'maroon'),
prob = c(0.85, 0.85, 0.75, 0.75, 0.65, 0.5, 0.5, 0.5, 0.4,
0.4, 0.25, 0.15, 0.15))
sample_fun <- function(X, size) {
sample(X$color, size = size, prob = X$prob)
}
data <- data %>%
bind_cols(
t(replicate(nrow(data), sample_fun(colors, 5)))
)
#> New names:
#> • `` -> `...2`
#> • `` -> `...3`
#> • `` -> `...4`
#> • `` -> `...5`
#> • `` -> `...6`
names(data)[-1] <- paste0("color", 1:5)
head(data)
#> # A tibble: 6 × 6
#> obsId color1 color2 color3 color4 color5
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 blue white orange green yellow
#> 2 2 yellow maroon brown red pink
#> 3 3 brown white maroon orange gray
#> 4 4 brown gray blue purple pink
#> 5 5 green navy black gray yellow
#> 6 6 red yellow black green blue
Created on 2022-07-31 by the reprex package (v2.0.1)
CodePudding user response：
Maybe, building the dataframe by row may be easier, but I don't know if the distribution of the colors are good
# IS IT CORRECT WITH RESPECT TO THE DISTRIBUTION OF THE COLORS VECTOR
data <- data.frame(color1 = character(nrow(data)), color2 = character(nrow(data)), color3 = character(nrow(data)), color4 = character(nrow(data)), color5 = character(nrow(data)))
for(i in 1:nrow(data)){
data[i, ] <- sample(x = colors$color, size = 5, prob = colors$prob, replace = F)
}