I'm generating random numbers for one variable and repeating the process several times. I want to calculate the mean of value for each group (group1, group2, group3) in each iteration of the loop. I want to store the result so I afterward can estimate the mean share for each group across all iterations of the loop.
require(tidyverse)
set.seed(21)
group1 <- sample(c("A1", "A2", "B1", "B2", "C1", "C2"), 1000, TRUE)
group2 <- sample(c("G1", "G2", "G4"), 1000, TRUE)
group3 <- sample(c("D1", "D2"), 1000, TRUE)
prob <- runif(1000, 0, 1)
df <- as.data.frame(cbind(group1, group2, group3, prob))
df$prob <- as.numeric(df$prob)
for (i in 1:15) {
df <- df %>%
mutate(value = rbinom(nrow(df), 1, prob = prob))
# [INSERT CALCULATION OF MEAN FOR EACH GROUP VARIABLE AND STORE IT]
}
# [INSERT CALCULATION OF MEAN ACROSS ALL ITERATIONS]
My main issue is how to estimate the mean of value across several variables in an efficient way and store the result in a smooth way.
Thanks in advance.
To clarify:
I want the end result to look something like this:
col "group1_A1" "Group1_A2" "group1_B1" "group1_B2" "group1_C1" "group1_C2" "group2_G1" "group2_G2" "group2_G4" "group3_D1" "group3_D2"
x1 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x2 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x3 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x4 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x4 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x5 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x6 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x7 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x8 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x9 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
x10 "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar" "x_bar"
Where the three groups subgroups mean is replacing "x_bar and each row is one iteration calculated means. An easy solution would be to use dplyr's group_by but I want to find a solution so I go through all the three grouping variables.
To put this in a context: imagine that the variable prob is the probability of dying. group1 is a variable indicating 6 age groups, group2 is indicating on socioeconomic status, and group3 is gender. I then want to see who is most likely to die. To do so I randomly generate a Bernoulli variable that is dependent on the probability of prob. To remove some stochastic I repeat this process 15 times and then want to see how big a share of each sociodemographic group that died (received a value of 1 on the variable value. For each iteration, I want to calculate the group belonging of those who died (so how many males died, how many old people died). I'm sorry for not coming up with a more joyful example.
Here is an approach using some tidyverse functions.
library(dplyr)
library(tidyr)
df2 <- df %>%
pivot_longer(starts_with("group") ) %>%
mutate(group = paste0(name, "_", value)) %>%
select(group)
for (i in 1:15) {
df2 <- df %>%
mutate(value = rbinom(nrow(df), 1, prob = prob)) %>%
pivot_longer(starts_with("group"), values_to = "val" ) %>%
mutate(group = paste0(name, "_", val)) %>%
group_by(group) %>%
summarise(mean = mean(value, na.rm = TRUE)) %>%
rename_with(.cols = mean, .fn = ~ paste0("mean", i)) %>%
inner_join(df2, by = c("group" = "group"))
}
df2 %>%
pivot_longer(starts_with("mean"), names_to = "trial", names_prefix = "mean") %>%
distinct() %>%
pivot_wider(id_cols = mean, names_from = "group", values_from = "value")
# A tibble: 15 x 12
trial group1_A1 group1_A2 group1_B1 group1_B2 group1_C1 group1_C2 group2_G1 group2_G2 group2_G4 group3_D1 group3_D2
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 15 0.519 0.514 0.516 0.519 0.551 0.533 0.529 0.542 0.507 0.518 0.533
2 14 0.481 0.486 0.503 0.536 0.487 0.550 0.526 0.493 0.507 0.495 0.520
3 13 0.506 0.541 0.477 0.470 0.572 0.556 0.575 0.499 0.496 0.486 0.552
4 12 0.519 0.534 0.497 0.557 0.604 0.509 0.549 0.522 0.548 0.548 0.531
5 11 0.5 0.568 0.458 0.481 0.497 0.562 0.542 0.496 0.496 0.467 0.548
6 10 0.525 0.466 0.503 0.503 0.535 0.580 0.581 0.490 0.496 0.488 0.548
7 9 0.494 0.547 0.490 0.448 0.610 0.598 0.578 0.504 0.519 0.501 0.560
8 8 0.538 0.554 0.471 0.530 0.599 0.538 0.545 0.516 0.559 0.565 0.518
9 7 0.525 0.588 0.548 0.475 0.535 0.568 0.601 0.499 0.522 0.507 0.565
10 6 0.513 0.527 0.529 0.546 0.561 0.503 0.562 0.513 0.522 0.510 0.550
11 5 0.462 0.493 0.503 0.508 0.513 0.568 0.513 0.493 0.522 0.507 0.510
12 4 0.506 0.5 0.452 0.481 0.599 0.556 0.545 0.516 0.496 0.520 0.516
13 3 0.525 0.466 0.503 0.525 0.567 0.556 0.529 0.550 0.499 0.497 0.552
14 2 0.462 0.554 0.471 0.514 0.519 0.574 0.536 0.516 0.499 0.520 0.512
15 1 0.506 0.541 0.497 0.470 0.519 0.544 0.510 0.519 0.507 0.510 0.514
This gets you your first part - a data.frame where each row is a trial with means per group.
Your second part is as follows:
df2 %>%
pivot_longer(starts_with("mean"), names_to = "trial", names_prefix = "mean") %>%
distinct() %>%
group_by(group) %>%
summarize(mean = mean(value))
# A tibble: 11 x 2
group mean
<chr> <dbl>
1 group1_A1 0.505
2 group1_A2 0.525
3 group1_B1 0.495
4 group1_B2 0.504
5 group1_C1 0.551
6 group1_C2 0.553
7 group2_G1 0.548
8 group2_G2 0.511
9 group2_G4 0.513
10 group3_D1 0.509
11 group3_D2 0.535