Generate Data Frame from Count Data - r

Question

I am trying to create an unsummarized data frame from a data frame of count data.
I have had some experience creating sample datasets but I am having some trouble trying to get a specific number of rows and proportion for each state/person without coding each of them separately and then combining them. I was able to do it using the following code but I feel like there is a better way.
set.seed(2312)
dragon <- sample(c(1),3,replace=TRUE)
Maine <- sample(c("Maine"),3,replace=TRUE)
Maine1 <- data.frame(dragon, Maine)
dragon <- sample(c(0),20,replace=TRUE)
Maine <- sample(c("Maine"),20,replace=TRUE)
Maine2 <- data.frame(dragon, Maine)
Maine2
library(dplyr)
maine3 <- bind_rows(Maine1, Maine2)
Is there a better way to generate this dataset then the code above?
I am trying to create a data frame from the following count data:
+-------------+--------------+--------------+
| | # of dragons | # no dragons |
+-------------+--------------+--------------+
| Maine | 3 | 20|
| California | 1 | 10|
| Jocko | 28 | 110515 |
| Jessica Day | 17 | 26122 |
| | 14 | 19655 |
+-------------+--------------+--------------+
And I would like it to look like this:
+-----------------------+---------------+
| | Dragons (1/0) |
+-----------------------+---------------+
| Maine | 1 |
| Maine | 1 |
| Maine | 1 |
| Maine | 0 |
| Maine….(2:20) | 0…. |
| California | 1 |
| California….(2:10) | 0… |
| Ect.. | |
+-----------------------+---------------+
I do not want the code written for me but would love with ideas on function or examples that you think might be helpful.

Answer 1

I am not completely sure what does sampling have to do with this problem?
It looks to me like you are looking for untable.
Here is an example
data:
set.seed(1)
no_drag = sample(1:5, 5)
drag = sample(15:25, 5)
df <- data.frame(names = LETTERS[1:5],
drag,
no_drag)
names drag no_drag
1 A 24 2
2 B 25 5
3 C 20 4
4 D 23 3
5 E 15 1
library(reshape)
library(tidyverse)
df %>%
gather(key, value, 2:3) %>% #convert to long format
{untable(.,num = .$value)} %>% #untable by value column
mutate(value = ifelse(key == "drag", 0, 1)) %>% #convert values to 0/1
select(-key) %>% #remove unwanted column
arrange(names) #optional
#part of output
names value
1 A 0
2 A 0
3 A 0
4 A 0
5 A 0
6 A 0
7 A 0
8 A 0
9 A 0
10 A 0
11 A 0
12 A 0
13 A 0
14 A 0
15 A 0
16 A 0
17 A 0
18 A 0
19 A 0
20 A 0
21 A 0
22 A 0
23 A 0
24 A 0
25 A 1
26 A 1
27 B 0
28 B 0
29 B 0
30 B 0
there are other ways to tackle the problem here is one:
One is like #Frank mentioned in the comment:
df %>%
gather(key, val, 2:3) %>%
mutate(v = Map(rep, key == "drag", val)) %>%
unnest %>%
select(-key, -val)
Another:
df <- gather(df, key, value, 2:3)
df <- df[rep(seq_len(nrow(df)), df$value), 1:2]
df$key[df$key == "drag"] <- FALSE
df$key[df$key != "drag"] <- TRUE

Answer 2

One can use tidyr::expand to expand rows in desired format.
The solution using df used by #missuse can be shown as:
library(tidyverse)
df %>% gather(key,value,-names) %>%
mutate(key = ifelse(key=="drag", 1, 0)) %>%
group_by(names,key) %>%
expand(value = 1:value) %>%
select(names, value = key) %>%
as.data.frame()
# names value
# 1 A 0
# 2 A 0
# 3 A 1
# 4 A 1
# 5 A 1
# 6 A 1
# 7 A 1
# 8 A 1
# 9 A 1
# 10 A 1
# ...so on
# 117 E 1
# 118 E 1
# 119 E 1
# 120 E 1
# 121 E 1
# 122 E 1