I have a table that I routinely compute with R that has three dimensions. I would like to add some tables within the (here 5) marginal tables. What I usually do is like:
A=sample(LETTERS[1:5],100, rep=T)
b=sample(letters[1:2],100, rep=T)
numbers=sample(1:3,100, rep=T)
( tab=table(A,b,numbers) )
( tab1=ftable(addmargins(tab)) )
I would like to add the sum of the first few marginal tables and then the sum of the remaining tables at the bottom, then the grand total. I can imagine that in the resulting ftable I would want the As,Bs,Cs, then the sum of those three, then the Ds, Es, and the sum of those two, then the sum of all of the tables, like:
numbers 1 2 3 Sum
A b
A a 1 5 0 6
b 4 2 2 8
Sum 5 7 2 14
B a 2 6 6 14
b 5 4 5 14
Sum 7 10 11 28
C a 3 2 5 10
b 1 2 2 5
Sum 4 4 7 15
sumac a 6 13 11 30 #### how do i add these three lines
b ....
sum ....
D a 2 1 1 4
b 4 5 3 12
Sum 6 6 4 16
E a 5 3 4 12
b 4 3 8 15
Sum 9 6 12 27
sumde a 7 4 5 20 #### and these
b ....
sum ....
sumae a 13 17 16 46
b 18 16 20 54
Sum 31 33 36 100
As usual I think the solution is prolly many fewer lines than the question. Thanks
Seth Latimer
You could do something like this:
isABC<-ifelse(A %in% c("A", "B", "C"), "ABC", "CD")
( tab=table(isABC,A,b,numbers) )
( tab1=ftable(addmargins(tab)) )
Now you have a larger table which holds even more rows than you want, but those should be easy to drop...
Related
I have the following dataframe containing a variable "group" and a variable "number of elements per group"
group elements
1 3
2 1
3 14
4 10
.. ..
.. ..
30 5
then I have a bunch of numbers going from 1 to (let's say) 30
when summing "elements" I would get 900. what I want to obtain is to randomly select a number (from 0 to 30) from 1-30 and assign it to each group until I fill the number of elements for that group. Each of those should appear 30 times in total.
thus, for group 1, I want to randomly select 3 number from 0 to 30
for group 2, 1 number from 0 to 30 etc. until I filled all of the groups.
the final table should look like this:
group number(randomly selected)
1 7
1 20
1 7
2 4
3 21
3 20
...
any suggestions on how I can achieve this?
In base R, if you have df like this...
df
group elements
1 3
2 1
3 14
Then you can do this...
data.frame(group = rep(df$group, #repeat group no...
df$elements), #elements times
number = unlist(sapply(df$elements, #for each elements...
sample.int, #...sample <elements> numbers
n=30, #from 1 to 30
replace = FALSE))) #without duplicates
group number
1 1 19
2 1 15
3 1 28
4 2 15
5 3 20
6 3 18
7 3 27
8 3 10
9 3 23
10 3 12
11 3 25
12 3 11
13 3 14
14 3 13
15 3 16
16 3 26
17 3 22
18 3 7
Give this a try:
df <- read.table(text = "group elements
1 3
2 1
3 14
4 10
30 5", header = TRUE)
# reproducibility
set.seed(1)
df_split2 <- do.call("rbind",
(lapply(split(df, df$group),
function(m) cbind(m,
`number(randomly selected)` =
sample(1:30, replace = TRUE,
size = m$elements),
row.names = NULL
))))
# remove element column name
df_split2$elements <- NULL
head(df_split2)
#> group number(randomly selected)
#> 1.1 1 25
#> 1.2 1 4
#> 1.3 1 7
#> 2 2 1
#> 3.1 3 2
#> 3.2 3 29
The split function splits the df into chunks based on the group column. We then take those smaller data frames and add a column to them by sampling 1:30 a total of elements time. We then do.call on this list to rbind back together.
Yo have to generate a new dataframe repeating $group $element times, and then using sample you can generate the exact number of random numbers:
data<-data.frame(group=c(1,2,3,4,5),
elements=c(2,5,2,1,3))
data.elements<-data.frame(group=rep(data$group,data$elements),
number=sample(1:30,sum(data$elements)))
The result:
group number
1 1 9
2 1 4
3 2 29
4 2 28
5 2 18
6 2 7
7 2 25
8 3 17
9 3 22
10 4 5
11 5 3
12 5 8
13 5 26
I solved as follow:
random_sample <- rep(1:30, each=30)
random_sample <- sample(random_sample)
then I create a df with this variable and a variable containing one group per row repeated by the number of elements in the group itself
My data looks like this:
x y
1 1
2 2
3 2
4 4
5 5
6 6
7 6
8 8
9 9
10 9
11 11
12 12
13 13
14 13
15 14
16 15
17 14
18 16
19 17
20 18
y is a grouping variable. I would like to see how well this grouping went.
Because of this I want to extract a sample of n pairs of cases that are grouped together by variable y
and n pairs of cases that are not grouped together by variable y. In order to calculate the number of
false positives and false negatives (either falsly grouped or not). How do I extract a sample of grouped pairs
and a sample of not-grouped pairs?
I would like the samples to look like this (for n=6) :
Grouped sample:
x y
2 2
3 2
9 9
10 9
15 14
17 14
Not-grouped sample:
x y
1 1
2 2
6 8
6 8
11 11
19 17
How would I go about this in R?
I'm not entirely clear on what you like to do, partly because I feel there is some context missing as to what you're trying to achieve. I also don't quite understand your expected output (for example, the not-grouped sample contains an entry 6 8 that does not exist in your original data...)
That aside, here is a possible approach.
# Maximum number of samples per group
n <- 3;
# Set fixed RNG seed for reproducibility
set.seed(2017);
# Grouped samples
df.grouped <- do.call(rbind.data.frame, lapply(split(df, df$y),
function(x) if (nrow(x) > 1) x[sample(min(n, nrow(x))), ]));
df.grouped;
# x y
#2.3 3 2
#2.2 2 2
#6.6 6 6
#6.7 7 6
#9.10 10 9
#9.9 9 9
#13.13 13 13
#13.14 14 13
#14.15 15 14
#14.17 17 14
# Ungrouped samples
df.ungrouped <- df[sample(nrow(df.grouped)), ];
df.ungrouped;
# x y
#7 7 6
#1 1 1
#9 9 9
#4 4 4
#3 3 2
#2 2 2
#5 5 5
#6 6 6
#10 10 9
#8 8 8
Explanation: Split df based on y, then draw min(n, nrow(x)) samples from subset x containing >1 rows; rbinding gives the grouped df.grouped. We then draw nrow(df.grouped) samples from df to produce the ungrouped df.ungrouped.
Sample data
df <- read.table(text =
"x y
1 1
2 2
3 2
4 4
5 5
6 6
7 6
8 8
9 9
10 9
11 11
12 12
13 13
14 13
15 14
16 15
17 14
18 16
19 17
20 18", header = T)
I am trying to get all combinations of values per group. I want to prevent combination of values between different groups.
To create all combinations of values (no matter which group the value belongs) vaI can use:
expand.grid(value, value)
Awaited result should be the subset of result of previous command.
Example:
#base data
value = c(1,3,5, 1,5,7,9, 2)
group = c("a", "a", "a","b","b","b","b", "c")
base <- data.frame(value, group)
#creating ALL combinations of value
allComb <- expand.grid(base$value, base$value)
#awaited result is subset of allComb.
#Note: first colums shows the number of row from allComb.
#Empty rows are separating combinations per group and are shown only for clarification.
Var1 Var2
1 1 1
2 3 1
3 5 1
11 1 3
12 3 3
13 5 3
21 1 5
22 3 5
23 5 5
34 1 1
35 5 1
36 7 1
37 9 1
44 1 5
45 5 5
46 7 5
47 9 5
54 1 7
55 5 7
56 7 7
57 9 7
64 1 9
65 5 9
66 7 9
67 9 9
78 2 2
I am trying to create a table with random entries from a central hypergeometric distribution where the column and row totals are fixed.
However I can get the column sums to be fixed and equal but not the row sums. I have read other answers but none seem to talk specifically about how to do it, my R knowledge is pretty basic and could do with some help or a point in the right direction.
To get the values from a central hypergeometric distribution I am using the BiasedUrn package.
For example:
N <- 50
rand <- 10
n1 <- 25
odds0 <- rep(1,K)
m0 <- rep(N/K,K)
library(BiasedUrn)
i <- as.table(rMFNCHypergeo(nran=rand, n=n1, m=m0, odds=odds0))
addmargins(i)
A B C D E F G H I J Sum
A 5 3 5 7 5 5 6 6 5 5 52
B 8 7 4 5 5 6 3 4 5 4 51
C 3 6 4 4 4 5 6 8 5 4 49
D 4 4 6 3 6 4 5 3 3 5 43
E 5 5 6 6 5 5 5 4 7 7 55
Sum 25 25 25 25 25 25 25 25 25 25 250
Where I'm looking to keep all the column sums equal to 25, and all the row sums equal to another number which I can choose such as 50.
Are you looking for the r2dtable function from base R?
set.seed(101)
tt <- r2dtable(n=1,c=rep(25,6),r=rep(50,3))
addmargins(as.table(tt[[1]]))
## A B C D E F Sum
## A 7 9 7 11 9 7 50
## B 10 7 10 6 7 10 50
## C 8 9 8 8 9 8 50
## Sum 25 25 25 25 25 25 150
This is my first post at StackOverflow. I am relatively a newbie in programming and trying to work with the data.table in R, for its reputation in speed.
I have a very large data.table, named "Actions", with 5 columns and potentially several million rows. The column names are k1, k2, i, l1 and l2. I have another data.table, with the unique values of Actions in columns k1 and k2, named "States".
For every row in Actions, I would like to find the unique index for columns 4 and 5, matching with States. A reproducible code is as follows:
S.disc <- c(2000,2000)
S.max <- c(6200,2300)
S.min <- c(700,100)
Traces.num <- 3
Class.str <- lapply(1:2,function(x) seq(S.min[x],S.max[x],S.disc[x]))
Class.inf <- seq_len(Traces.num)
Actions <- data.table(expand.grid(Class.inf, Class.str[[2]], Class.str[[1]], Class.str[[2]], Class.str[[1]])[,c(5,4,1,3,2)])
setnames(Actions,c("k1","k2","i","l1","l2"))
States <- unique(Actions[,list(k1,k2,i)])
So if i was using data.frame, the following line would be like:
index <- apply(Actions,1,function(x) {which((States[,1]==x[4]) & (States[,2]==x[5]))})
How can I do the same with data.table efficiently ?
This is relatively simple once you get the hang of keys and the special symbols which may be used in the j expression of a data.table. Try this...
# First make an ID for each row for use in the `dcast`
# because you are going to have multiple rows with the
# same key values and you need to know where they came from
Actions[ , ID := 1:.N ]
# Set the keys to join on
setkeyv( Actions , c("l1" , "l2" ) )
setkeyv( States , c("k1" , "k2" ) )
# Join States to Actions, using '.I', which
# is the row locations in States in which the
# key of Actions are found and within each
# group the row number ( 1:.N - a repeating 1,2,3)
New <- States[ J(Actions) , list( ID , Ind = .I , Row = 1:.N ) ]
# k1 k2 ID Ind Row
#1: 700 100 1 1 1
#2: 700 100 1 2 2
#3: 700 100 1 3 3
#4: 700 100 2 1 1
#5: 700 100 2 2 2
#6: 700 100 2 3 3
# reshape using 'dcast.data.table'
dcast.data.table( Row ~ ID , data = New , value.var = "Ind" )
# Row 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27...
#1: 1 1 1 1 4 4 4 7 7 7 10 10 10 13 13 13 16 16 16 1 1 1 4 4 4 7 7 7...
#2: 2 2 2 2 5 5 5 8 8 8 11 11 11 14 14 14 17 17 17 2 2 2 5 5 5 8 8 8...
#3: 3 3 3 3 6 6 6 9 9 9 12 12 12 15 15 15 18 18 18 3 3 3 6 6 6 9 9 9...