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
I am a R noob, and hope some of you can help me.
I have two data sets:
- store (containing store data, including location coordinates (x,y). The location are integer values, corresponding to GridIds)
- grid (containing all gridIDs (x,y) as well as a population variable TOT_P for each grid point)
What I want to achieve is this:
For each store I want loop over the grid date, and sum the population of the grid ids close to the store grid id.
I.e basically SUMIF the grid population variable, with the condition that
grid(x) < store(x) + 1 &
grid(x) > store(x) - 1 &
grid(y) < store(y) + 1 &
grid(y) > store(y) - 1
How can I accomplish that? My own take has been trying to use different things like merge, sapply, etc, but my R inexperience stops me from getting it right.
Thanks in advance!
Edit:
Sample data:
StoreName StoreX StoreY
Store1 3 6
Store2 5 2
TOT_P GridX GridY
8 1 1
7 2 1
3 3 1
3 4 1
22 5 1
20 6 1
9 7 1
28 1 2
8 2 2
3 3 2
12 4 2
12 5 2
15 6 2
7 7 2
3 1 3
3 2 3
3 3 3
4 4 3
13 5 3
18 6 3
3 7 3
61 1 4
25 2 4
5 3 4
20 4 4
23 5 4
72 6 4
14 7 4
178 1 5
407 2 5
26 3 5
167 4 5
58 5 5
113 6 5
73 7 5
76 1 6
3 2 6
3 3 6
3 4 6
4 5 6
13 6 6
18 7 6
3 1 7
61 2 7
25 3 7
26 4 7
167 5 7
58 6 7
113 7 7
The output I am looking for is
StoreName StoreX StoreY SUM_P
Store1 3 6 479
Store2 5 2 119
I.e for store1 it is the sum of TOT_P for Grid fields X=[2-4] and Y=[5-7]
One approach would be to use dplyr to calculate the difference between each store and all grid points and then group and sum based on these new columns.
#import library
library(dplyr)
#create example store table
StoreName<-paste0("Store",1:2)
StoreX<-c(3,5)
StoreY<-c(6,2)
df.store<-data.frame(StoreName,StoreX,StoreY)
#create example population data (copied example table from OP)
df.pop
#add dummy column to each table to enable cross join
df.store$k=1
df.pop$k=1
#dplyr to join, calculate absolute distance, filter and sum
df.store %>%
inner_join(df.pop, by='k') %>%
mutate(x.diff = abs(StoreX-GridX), y.diff=abs(StoreY-GridY)) %>%
filter(x.diff<=1, y.diff<=1) %>%
group_by(StoreName) %>%
summarise(StoreX=max(StoreX), StoreY=max(StoreY), tot.pop = sum(TOT_P) )
#output:
StoreName StoreX StoreY tot.pop
<fctr> <dbl> <dbl> <int>
1 Store1 3 6 721
2 Store2 5 2 119
I've been trying to figure a way to script this through R, but just can't get it. I have a dataset like this:
Trial Type Correct Latency
1 55 0 0
3 30 1 766
4 10 1 344
6 40 1 716
7 10 1 326
9 30 1 550
10 10 1 350
11 64 0 0
13 30 1 683
14 10 1 270
16 30 1 666
17 10 1 297
19 40 1 616
20 10 1 315
21 64 0 0
23 40 1 850
24 10 1 322
26 30 1 566
27 20 0 766
28 40 1 500
29 20 1 230
which goes for much longer(around 1000 rows).
From this one dataset, I would like to create 4 separate data.frames/tables I can export tables with as well as do my own calculations
I would like to have a data.frame (4 in total), one for each of these bullet points:
type 10 rows which are preceded by a type 30 row
type 10 rows which are preceded by a type 40 row
type 20 rows which are preceded by a type 30 row
type 20 rows which are preceded by a type 40 row
I would like for all the columns in the relevant rows to be placed into these new tables, but only including the column info of row types 10 or 20.
For example, the first table (type 10 preceded by type 30) would like this based on the sample data:
Trial Type Correct Latency
4 10 1 344
10 10 1 350
14 10 1 270
17 10 1 297
Second table (type 10 preceded by type 40):
Trial Type Correct Latency
7 10 1 326
20 10 1 315
24 10 1 322
Third table (type 20 preceded by type 30):
Trial Type Correct Latency
27 20 0 766
Fourth table (table 20 preceded by type 40):
Trial Type Correct Latency
29 20 1 230
I can subset just fine to get one table only of type 10 rows and another for type 20 rows, but I can't figure out how to create different tables for type 10 and 20 rows based on the previous type value. Also, an issue is that "Trials" is not in order (skips numbers).
Any help would be greatly appreciated. Thank you.
Also, is there a way to include the previous row as well, so the output for the fourth table would look something like this:
Fourth table (table 20 preceded by type 40):
Trial Type Correct Latency
28 40 1 500
29 20 1 230
For the fourth example, you could use which() in combination with lag() from dplyr, to attain the indices that meet your criteria. Then you can use these to subset the data.frame.
# Get indices of rows that meet condition
ind2 <- which(df$Type==20 & dplyr::lag(df$Type)==40)
# Get indices of rows before the ones that meet condition
ind1 <- which(df$Type==20 & dplyr::lag(df$Type)==40)-1
# Subset data
> df[c(ind1,ind2)]
Trial Type Correct Latency
1: 28 40 1 500
2: 29 20 1 230
Here is an example code if you always want to delete the first trials of your data.
var1 <- c(1,2,1,2,1,2,1,2,1,2)
var2 <- c(1,1,1,2,2,2,2,3,3,3)
dat <- data.frame(var1, var2)
var1 var2
1 1 1
2 2 1
3 1 1
4 2 2
5 1 2
6 2 2
7 1 2
8 2 3
9 1 3
10 2 3
#delete only this line directly
filter(dat,lag(var2)==var2)
var1 var2
1 1 1
2 2 1
3 1 1
6 2 2
7 1 2
10 2 3
#delete the first 2 trials
#make a list of all rows where var2[n-1]!=var2[n] --> using lag from dplyr
drops <- c(1,2,which(lag(dat$var2)!=dat$var2), which(lag(dat$var2)!=dat$var2)+1)
if (!identical(drops,numeric(0))) { dat <- dat[-drops,] }
var1 var2
3 1 1
6 2 2
7 1 2
10 2 3
I am having trouble summing select columns within a data frame, a basic problem that I've seen numerous similar, but not identical questions/answers for on StackOverflow.
With this perhaps overly complex data frame:
site<-c(223,257,223,223,257,298,223,298,298,211)
moisture<-c(7,7,7,7,7,8,7,8,8,5)
shade<-c(83,18,83,83,18,76,83,76,76,51)
sampleID<-c(158,163,222,107,106,166,188,186,262,114)
bluestm<-c(3,4,6,3,0,0,1,1,1,0)
foxtail<-c(0,2,0,4,0,1,1,0,3,0)
crabgr<-c(0,0,2,0,33,0,2,1,2,0)
johnson<-c(0,0,0,7,0,8,1,0,1,0)
sedge1<-c(2,0,3,0,0,9,1,0,4,0)
sedge2<-c(0,0,1,0,1,0,0,1,1,1)
redoak<-c(9,1,0,5,0,4,0,0,5,0)
blkoak<-c(0,22,0,23,0,23,22,17,0,0)
my.data<-data.frame(site,moisture,shade,sampleID,bluestm,foxtail,crabgr,johnson,sedge1,sedge2,redoak,blkoak)
I want to sum the counts of each plant species (bluestem, foxtail, etc. - columns 4-12 in this example) within each site, by summing rows that have the same site number. I also want to keep information about moisture and shade (these are consistant withing site, but may also be the same between sites), and want a new column that is the count of number of rows summed.
the result would look like this
site,moisture,shade,NumSamples,bluestm,foxtail,crabgr,johnson,sedge1,sedge2,redoak,blkoak
211,5,51,1,0,0,0,0,0,1,0,0
223,7,83,4,13,5,4,8,6,1,14,45
257,7,18,2,4,2,33,0,0,1,1,22
298,8,76,3,2,4,3,9,13,2,9,40
The problem I am having is that, my real data sets (and I have several of them) have from 50 to 300 plant species, and I want refer a range of columns (in this case, [5:12] ) instead of my.data$foxtail, my.data$sedge1, etc., which is going to be very difficult with 300 species.
I know I can start off by deleting the column I don't need (SampleID)
my.data$SampleID <- NULL
but then how do I get the sums? I've messed with the aggregate command and with ddply, and have seen lots of examples which call particular column names, but just haven't gotten anything to work. I recognize this is a variant of a commonly asked and simple type of question, but I've spent hours without resolving it on my own. So, apologies for my stupidity!
This works ok:
x <- aggregate(my.data[,5:12], by=list(site=my.data$site, moisture=my.data$moisture, shade=my.data$shade), FUN=sum, na.rm=T)
library(dplyr)
my.data %>%
group_by(site) %>%
tally %>%
left_join(x)
site n moisture shade bluestm foxtail crabgr johnson sedge1 sedge2 redoak blkoak
1 211 1 5 51 0 0 0 0 0 1 0 0
2 223 4 7 83 13 5 4 8 6 1 14 45
3 257 2 7 18 4 2 33 0 0 1 1 22
4 298 3 8 76 2 4 3 9 13 2 9 40
Or to do it all in dplyr
my.data %>%
group_by(site) %>%
tally %>%
left_join(my.data) %>%
group_by(site,moisture,shade,n) %>%
summarise_each(funs(sum=sum)) %>%
select(-sampleID)
site moisture shade n bluestm foxtail crabgr johnson sedge1 sedge2 redoak blkoak
1 211 5 51 1 0 0 0 0 0 1 0 0
2 223 7 83 4 13 5 4 8 6 1 14 45
3 257 7 18 2 4 2 33 0 0 1 1 22
4 298 8 76 3 2 4 3 9 13 2 9 40
Try following using base R:
outdf<-data.frame(site=numeric(),moisture=numeric(),shade=numeric(),bluestm=numeric(),foxtail=numeric(),crabgr=numeric(),johnson=numeric(),sedge1=numeric(),sedge2=numeric(),redoak=numeric(),blkoak=numeric())
my.data$basic = with(my.data, paste(site, moisture, shade))
for(b in unique(my.data$basic)) {
outdf[nrow(outdf)+1,1:3] = unlist(strsplit(b,' '))
for(i in 4:11)
outdf[nrow(outdf),i]= sum(my.data[my.data$basic==b,i])
}
outdf
site moisture shade bluestm foxtail crabgr johnson sedge1 sedge2 redoak blkoak
1 223 7 83 13 5 4 8 6 1 14 45
2 257 7 18 4 2 33 0 0 1 1 22
3 298 8 76 2 4 3 9 13 2 9 40
4 211 5 51 0 0 0 0 0 1 0 0