First, I would like to divide the intergers from 1 to n equally into m groups.
Second, I want to generate random intergers without replacement in each group by Julia.
Third, I would like to combine all the random integers.
For example, n=10000, m=2. Then the julia code would be
using Distributions
n=10000
order1 = sample(1:5000, 5000, replace = false)
order2 = sample(5001:10000, 5000, replace = false)
order=[order1;order2]
For example, n=10000, m=5. Then the julia code would be
using Distributions
n=10000
order1 = sample(1:2000, 2000, replace = false)
order2 = sample(2001:4000, 2000, replace = false)
order3 = sample(4001:6000, 2000, replace = false)
order4 = sample(6001:8000, 2000, replace = false)
order5 = sample(8001:10000, 2000, replace = false)
order=[order1;order2;order3;order4;order5]
I am just wondering if I can improve the julia code above. If m=100. then my code will be extremely long. There must be an eaiser way to do this.
Assume that you have eg. n=30 and m=5 (note that n % m == 0).
Than you can create the place to hold the results named x:
x = collect(1:n);
And now you can do this one liner (this requires using Random):
shuffle!.(eachcol(reshape(x, n ÷ m, m)));
Let us see the result (you have 5 groups and within each group values are randomly ordered without repetitions):
julia> x'
1×30 adjoint(::Vector{Int64}) with eltype Int64:
1 5 3 2 4 6 12 10 8 9 7 11 16 18 13 14 15 17 24 21 23 22 20 19 26 25 27 30 29 28
Related
I have a dataframe with 10 columns and 60 rows.
The first 2 columns contain treatment size (sq. m) and observation time (min), the other 8 show the 8 observed species as count data. All are integers.
I want to apply a correction to the observations:
Divide all observation counts by the root of the treatment size
Divide all observation counts by the square root of the observation time
Probably easily done with apply() and function() but I am not very familiar... Anyone who could help?
Some exemplary code (just 2 species here):
dftot <- data.frame(matrix(NA, nrow = 60, ncol = 4))
colnames(dftot)<- c("size", "time", "guppy", "barracuda")
set.seed(123)
dftot$size <- sample(60, size = 60, replace = TRUE)
dftot$time <- sample(60, size = 60, replace = TRUE)
dftot$guppy <- sample(60, size = 60, replace = TRUE)
dftot$barracuda <- sample(60, size = 60, replace = TRUE)
Is this what you want?
dftot %>%
mutate(across(guppy:last_col(), ~.x/sqrt(size))) %>%
mutate(across(guppy:last_col(), ~.x/sqrt(time)))
Output:
size time guppy barracuda
1 31 12 2.7997696 1.76281788
2 15 13 4.1534663 3.43735140
3 51 18 0.4950738 0.42906394
4 14 33 0.9770084 0.88396000
5 3 57 2.8294607 4.28242703
6 42 27 0.2375655 1.54417606
I'm trying to generate some sample insurance claims data that is meaningful instead of just random numbers.
Assuming I have two columns Age and Injury, I need meaningful values for ClaimAmount based on certain conditions:
ClaimantAge | InjuryType | ClaimAmount
---------------------------------------
35 Bruises
55 Fractures
. .
. .
. .
I want to generate claim amounts that increase as age increases, and then plateaus at around a certain age, say 65.
Claims for certain injuries need to be higher than claims for other types of injuries.
Currently I am generating my samples in a random manner, like so:
amount <- sample(0:100000, 2000, replace = TRUE)
How do I generate more meaningful samples?
There are many ways that this could need to be adjusted, as I don't know the field. Given that we're talking about dollar amounts, I would use the poisson distribution to generate data.
set.seed(1)
n_claims <- 2000
injuries <- c("bruises", "fractures")
prob_injuries <- c(0.7, 0.3)
sim_claims <- data.frame(claimid = 1:n_claims)
sim_claims$age <- round(rnorm(n = n_claims, mean = 35, sd = 15), 0)
sim_claims$Injury <- factor(sample(injuries, size = n_claims, replace = TRUE, prob = prob_injuries))
sim_claims$Amount <- rpois(n_claims, lambda = 100 + (5 * (sim_claims$age - median(sim_claims$age))) +
dplyr::case_when(sim_claims$Injury == "bruises" ~ 50,
sim_claims$Injury == "fractures" ~ 500))
head(sim_claims)
claimid age Injury Amount
1 1 26 bruises 117
2 2 38 bruises 175
3 3 22 bruises 102
4 4 59 bruises 261
5 5 40 fractures 644
6 6 23 bruises 92
In "Zero frequent items" when using the eclat to mine frequent itemsets, the OP is interested in the groupings/clusterings based on how frequent they are ordered together. This grouping can be inspected by the arules::inspect function.
library(arules)
dataset <- read.transactions("8GbjnHK2.txt", sep = ";", rm.duplicates = TRUE)
f <- eclat(dataset,
parameter = list(
supp = 0.001,
maxlen = 17,
tidLists = TRUE))
inspect(head(sort(f, by = "support"), 10))
The data set can be downloaded from https://pastebin.com/8GbjnHK2.
However, the output cannot be easily saved to another object as a data frame.
out <- inspect(f)
So how do we capture the output of inspect(f) for use as data frame?
We can use the methods labels to extract the associations/groupings and quality to extract the quality measures (support and count). We can then use cbind to store these into a data frame.
out <- cbind(labels = labels(f), quality(f))
head(out)
# labels support count
# 1 {3031093,3059242} 0.001010 16
# 2 {3031096,3059242} 0.001073 17
# 3 {3060614,3060615} 0.001010 16
# 4 {3022540,3072091} 0.001010 16
# 5 {3061698,3061700} 0.001073 17
# 6 {3031087,3059242} 0.002778 44
Coercing the itemsets to a data.frame also creates the required output.
> head(as(f, "data.frame"))
items support count
1 {3031093,3059242} 0.001010101 16
2 {3031096,3059242} 0.001073232 17
3 {3060614,3060615} 0.001010101 16
4 {3022540,3072091} 0.001010101 16
5 {3061698,3061700} 0.001073232 17
6 {3031087,3059242} 0.002777778 44
I am trying to create a new column conditional on another column, a bit like a moving average or moving window but based on distance between points. Take for example row 2 with a CO2 of 399.935. I would like to have the mean of all the points within 100 m (traveled) of that point. In my example (looking at column CumDist), rows 1, 3, 4, 5 would be selected to calculate the mean. The column CumDist (*100,000 to have the units in meters) consists of cumulative distance traveled. I have 5000 points and obviously the width (or the number of rows) of the moving window will vary.
I tested over() from the sp package, but it's problematic if the same road is taken more than once. I looked on the web for other solutions and I did not find anything that could help me.
dput(DF)
structure(list(CO2 = c(399.9350305, 399.9350305, 399.9350305,
400.0320031, 400.0320031, 400.0320031, 399.7718229, 399.7718229,
399.7718229, 399.3855075, 399.3855075, 399.3855075, 399.4708139,
399.4708139, 399.4708139, 400.0362474, 400.0362474, 400.0362474,
399.7556753, 399.7556753), lon = c(-103.7093538, -103.709352,
-103.7093492, -103.7093467, -103.7093455, -103.7093465, -103.7093482,
-103.7093596, -103.7094074, -103.7094625, -103.7094966, -103.709593,
-103.709649, -103.7096717, -103.7097349, -103.7097795, -103.709827,
-103.7099007, -103.709924, -103.7099887), lat = c(49.46972027,
49.46972153, 49.46971675, 49.46971533, 49.46971307, 49.4697124,
49.46970636, 49.46968214, 49.46960921, 49.46955984, 49.46953621,
49.46945809, 49.46938994, 49.46935281, 49.46924309, 49.46918635,
49.46914762, 49.46912566, 49.46912407, 49.46913321),distDiff = c(0.000342016147509882,
0.000191466419697602, 0.000569046320857002, 0.000240367540492089,
0.000265977754839834, 0.000103953049523505, 0.000682968856240796,
0.0028176007969857, 0.00882013898948418, 0.00678966015562509,
0.00360774024245839, 0.011149423290729, 0.00859796340323456,
0.00444526066124642, 0.0130344010874029, 0.00709037369666853,
0.00551435348701512, 0.00587377717110946, 0.00169806309901329,
0.00479849401022625), CumDist = c(0.000342016147509882, 0.000533482567207484,
0.00110252888806449, 0.00134289642855657, 0.00160887418339641,
0.00171282723291991, 0.00239579608916071, 0.00521339688614641,
0.0140335358756306, 0.0208231960312557, 0.0244309362737141, 0.0355803595644431,
0.0441783229676777, 0.0486235836289241, 0.0616579847163269, 0.0687483584129955,
0.0742627119000106, 0.08013648907112, 0.0818345521701333, 0.0866330461803596
)), .Names = c("X12CO2_dry", "coords.x1", "coords.x2", "V1",
"CumDist"), row.names = 2:21, class = "data.frame")
thanks, Martin
Man you beat me to it with a cleaner solution mra68.
Here's mine using a few loops.
####################
for (j in 1:nrow(DF)){#Loop through all rows of your dataset
CO2list<-NULL ##Need to make a variable before storing to it in the loop
for(i in 1:nrow(DF)){##Loop through all distances in the table
if ((abs(DF$CumDist[i]-DF$CumDist[j]))<=0.001) {
##Check to see if difference in CumDist<=100/100000 for all entries
#CumDist[j] is point with the 100 meter window around it
CO2list<-c(CO2list,DF$X12CO2_dry[i])
##Store your CO2 entries that are within the 100 meter window to a vector
}
}
DF$CO2AVG[j]<-mean(CO2list)
#Get the mean of your list and store it to column named CO2AVG
}
The window that belongs to the i-th row starts at n[i] and ends at m[i]-1. Hence the sum of the CO2-values in the i-th window is CumCO2[m[i]]-CumCO2[n[i]]. (Notice that the indices in CumCO2 are shifted by 1, because of the leading 0.) Dividing this CO2-sum by the window size m[i]-n[i] gives the values meanCO2 for the new column:
n <- sapply( df$CumDist,
function(x){
which.max( df$CumDist >= x-0.001 )
}
)
m <- sapply( df$CumDist,
function(x){
which.max( c(df$CumDist,Inf) > x+0.001 )
}
)
CumCO2 <- c( 0, cumsum(df$X12CO2) )
meanCO2 <- ( CumCO2[m] - CumCO2[n] ) / (m-n)
.
> n
[1] 1 1 1 2 3 3 5 8 9 10 11 12 13 14 15 16 17 18 19 20
> m
[1] 4 5 7 7 8 8 8 9 10 11 12 13 14 15 16 17 18 19 20 21
> meanCO2
[1] 399.9350 399.9593 399.9835 399.9932 399.9606 399.9606 399.9453 399.7718 399.7718 399.3855 399.3855 399.3855 399.4708 399.4708 399.4708 400.0362
[17] 400.0362 400.0362 399.7557 399.7557
>
I have a R dataframe which describes the evolution of the sales of a product in approx. 2000 shops in a quarterly basis, with 5 columns (ie. 5 periods of time). I'd like to know how to analyse it with R.
I've already tried to make some basic analysis, that is to say to determine the average sales for the 1st period, the 2nd period, etc. and then determine the average for each period and then to compare the evolution of each shop relatively to this general evolution. For instance, there is a total of 50 000 sales for the 1st period and 35 000 for the 5th, so I assume that for each shop the normal sale in the 5th period is to be 35/55=0.63*the amount of the 1st period's sale: if the shop X has sold 100 items in the first period, I assume that it should normally sell 63 items in the 5th period.
Obviously, this is an easy-to-do method, but it is not statistically relevant.
I would like a method which would enable me to determine a trend curb which miminizes my R-square. My objective is to be able to analyse the sales of the shops by neutralizing the general trend: I'd like to know precisely what are the underperforming shops and what are the overperforming shops, with a statistically correct approach.
My dataframe is structured in this way :
shopID | sum | qt1 | qt2 | qt3 | qt4 | qt5
000001 | 150 | 45 | 15 | 40 | 25 | 25
000002 | 100 | 20 | 20 | 20 | 20 | 20
000003 | 500 | 200 | 0 | 100 | 100 | 100
... (2200 rows)
I've tried to put my timeserie in a list, which is successful, with the following functon:
reversesales=t(data.frame(sales$qt1,sales$qt2,sales$qt3,sales$qt4,sales$qt5))
# I reverse rows and columns of the frame in order that the time periods be the rows
timeser<-ts(reversesales,start=1,end=5, deltat=1/4)
# deltat=1/4 because it is a quarterly basis, 1 and 5 because I have 5 quarters
Still, I am unable to do anything with this variable. I can't do any plot (with the "plot" function) as there are 2200 rows (and so R wants to make me 2200 successive plots, obviously this is not what I want).
In addition, I don't know how to determine the theoretical trend and the theoretical value of the sales for each period for each shop...
Thank you for your help! (and merry Christmas)
An implementation of mixed model:
install.packages("nlme")
library("nlme")
library(dplyr)
# Generating some data with a structure like yours:
start <- round(sample(10:100, 50, replace = TRUE)*runif(50))
df <- data_frame(shopID = 1:50, qt1 = start, qt2 =round(qt1*runif(50, .5, 2)) ,qt3 = round(qt2*runif(50, .5, 2)), qt4 = round(qt3*runif(50, .5, 2)), qt5 = round(qt4*runif(50, .5, 2)))
df <- as.data.frame(df)
# Converting in into the long format:
df <- reshape(df, idvar = "shopID", varying = names(df)[-1], direction = "long", sep = "")
Estimating the model:
mod <- lme(qt ~ time, random = ~ time | shopID, data = df)
# Extract the random effects for comparison:
random.effects(mod)
(Intercept) time
1 74.0790805 3.7034172
2 7.8713699 4.2138001
3 -8.0670810 -5.8754060
4 -16.5114428 16.4920663
5 -16.7098229 6.4685228
6 -11.9630688 -8.0411504
7 -12.9669777 21.3071366
8 -24.1099280 32.9274361
9 8.5107335 -9.7976905
10 -13.2707679 -6.6028927
11 3.6206163 -4.1017784
12 21.2342886 -6.7120725
13 -14.6489512 11.6847109
14 -14.7291647 2.1365768
15 10.6791941 3.2097199
16 -14.1524187 -1.6933291
17 5.2120647 8.0119320
18 -2.5172933 -6.5011416
19 -9.0094366 -5.6031271
20 1.4857512 -5.9913865
21 -16.5973442 3.5164298
22 -26.7724763 27.9264081
23 49.0764631 -12.9800871
24 -0.1512509 2.3589947
25 15.7723150 -7.9295698
26 2.1955489 11.0318875
27 -8.0890346 -5.4145977
28 0.1338790 -8.3551182
29 9.7113758 -9.5799588
30 -6.0257683 42.3140432
31 -15.7655545 -8.6226255
32 -4.1450984 18.7995079
33 4.1510104 -1.6384103
34 2.5107652 -2.0871890
35 -23.8640815 7.6680185
36 -10.8228653 -7.7370976
37 -14.1253093 -8.1738468
38 42.4114024 -9.0436585
39 -10.7453627 2.4590883
40 -12.0947901 -5.2763010
41 -7.6578305 -7.9630013
42 -14.9985612 -0.4848326
43 -13.4081771 -7.2655456
44 -11.5646620 -7.5365387
45 6.9116844 -10.5200339
46 70.7785492 -11.5522014
47 -7.3556367 -8.3946072
48 27.3830419 -6.9049164
49 14.3188079 -9.9334156
50 -15.2077850 -7.9161690
I would interpret the values as follows: consider them as a deviation from zero, so that positive values are positive deviations from the average, whereas negative values are negative deviation from the average. The averages of the two columns are zero, as is checked below:
round(apply(random.effects(mod), 2, mean))
(Intercept) time
0 0
library(zoo)
#Reconstructing the data with four quarter columns (instead of five quarters as in your example)
shopID <- c(1, 2, 3, 4, 5)
sum <- c(150, 100, 500, 350, 50)
qt1 <- c(40, 10, 130, 50, 10)
qt2 <- c(40, 40, 110, 100, 15)
qt3 <- c(50, 30, 140, 150, 10)
qt4 <- c(20, 20, 120, 50, 15)
myDF <- data.frame(shopID, sum, qt1, qt2, qt3, qt4)
#The ts() function converts a numeric vector into an R time series object
ts1 <- ts(as.numeric((myDF[1,3:6])), frequency=4)
ts2 <- ts(as.numeric((myDF[2,3:6])), frequency=4)
ts3 <- ts(as.numeric((myDF[3,3:6])), frequency=4)
ts4 <- ts(as.numeric((myDF[4,3:6])), frequency=4)
ts5 <- ts(as.numeric((myDF[5,3:6])), frequency=4)
#Merge time series objects
tsm <- merge(a = as.zoo(ts1), b = as.zoo(ts2), c = as.zoo(ts3), d = as.zoo(ts4), e = as.zoo(ts5))
#Plotting the Time Series
plot.ts(tsm, plot.type = "single", lty = 1:5, xlab = "Time", ylab = "Sales")
The code is not optimized, and can be improved. More about time series analysis can be read here. Hope this gives some direction.