I have the data:
transaction <- c(1,2,3);
date <- c("2010-01-31","2010-02-28","2010-03-31");
type <- c("debit", "debit", "credit");
amount <- c(-500, -1000.97, 12500.81);
oldbalance <- c(5000, 4500, 17000.81)
evolution <- data.frame(transaction, date, type, amount, oldbalance, row.names=transaction, stringsAsFactors=FALSE);
evolution$date <- as.Date(evolution$date, "%Y-%m-%d");
evolution <- transform(evolution, newbalance = oldbalance + amount);
evolution
If I want to create a table with the digits in amount just equal to 1 decimal place, does such a command work?
> tab.for <- formatC(evolution$amount,digits=1,format="f")
> tab.lat <- xtable(tab.for)
Error in UseMethod("xtable") :
no applicable method for 'xtable' applied to an object of class "character"
Thanks.
If you just want to reformat the amount column and present in an xtable, then you need to supply xtable with a dataframe, just supplying one column makes it looks like a character vector.
evolution$tab.for <- formatC(evolution$amount,digits=1,format="f")
evolutionxtable<-xtable(subset(evolution, select=c(date, type, tab.for)))
print(evolutionxtable)
Related
I am trying to use seqtime (https://github.com/hallucigenia-sparsa/seqtime) to analyze time-serie microbiome data, as follow:
meta = data.table::data.table(day=rep(c(15:27),each=3), condition =c("a","b","c"))
meta<- meta[order(meta$day, meta$condition),]
meta.ts<-as.data.frame(t(meta))
otu=matrix(1:390, ncol = 39)
oturar<-rarefyFilter(otu, min=0)
rarotu<-oturar$rar
time<-meta.ts[1,]
interp.otu<-interpolate(rarotu, time.vector = time,
method = "stineman", groups = meta$condition)
the interpolation returns the following error:
[1] "Processing group a"
[1] "Number of members 13"
intervals
0
12
[1] "Selected interval: 1"
[1] "Length of time series: 13"
[1] "Length of time series after interpolation: 1"
Error in stinepack::stinterp(time.vector, as.numeric(x[i, ]), xout = xout, :
The values of x must strictly increasing
I tried to change method to "hyman", but it returns the error below:
Error in interpolateSub(x = x, time.vector = time.vector, method = method) :
Time points must be provided in chronological order.
I am using R version 3.6.1 and I am a bit new to R.
Please can anyone tell me what I am doing wrong/ how to go around these errors?
Many thanks!
I used quite some time stumbling around trying to figure this out. It all comes down to the data structure of meta and the resulting time variable used as input for the time.vector parameter.
When meta.ts is being converted to a data frame, all strings are automatically converted to factors - this includes day.
To adjust, you can edit your code to the following:
library(seqtime)
meta <- data.table::data.table(day=rep(c(15:27),each=3), condition =c("a","b","c"))
meta <- meta[order(meta$day, meta$condition),]
meta.ts <- as.data.frame(t(meta), stringsAsFactors = FALSE) # Set stringsAsFactors = FALSE
otu <- matrix(1:390, ncol = 39)
oturar <- rarefyFilter(otu, min=0)
rarotu <- oturar$rar
time <- as.integer(meta.ts[1,]) # Now 'day' is character, so convert to integer
interp.otu <- interpolate(rarotu, time.vector = time,
method = "stineman", groups = meta$condition)
As a bonus, read this blogpost for information on the stringsAsFactors parameter. Strings automatically being converted to Factors is a common bewilderment.
I am doing some describtive sequence analysis using the "TraMineR" library. I want to report my findings via R-Markdown in html format. For formating tables I use "kable" and "kableExtra".
To get the frequency and propotions of the most common sequences I use seqtab(). The result is an stslist.freq object. When I try to coerce it to a dataframe, the dataframe is not containing any frequencies and proportions.
I tried to print the results of seqtab() and store this result again. This gives me the dataframe I desire. However there are two "problems" with that: (1) I don't understand what is happening here and it seems like a "dirty" trick, (2) as a result I also get the output of the print command in my final html document if I don't split the code in multiple chunks and disable the ouput in the specific chunk.
Here is some code to replicate the problem:
library("TraMineR")
#Data creation
data.long <- data.frame(
id=rep(1:50, each=4),
time = c(0,1,2,3),
status = sample(letters[1:2], 200, replace = TRUE),
weight=rep(runif(50, 0, 1), each=4)
)
#reshape
data.wide <- reshape(data.long, v.names = "status", idvar="id", direction="wide", timevar="time")
#sequence
sequence <- seqdef(data.wide,
var=c("status.0", "status.1", "status.2", "status.3"),
weights=data.wide$weight)
#frequencies of sequences
##doesn't work:
seqtab.df1 <- as.data.frame(seqtab(sequence))
##works:
seqtab.df2 <- print(seqtab(sequence))
I expect the dataframe to be the same as the one saved in seqtab.df2, however either without using the print command or with "silently" (no output printed) using the print command.
Thank you very much for your help and let me know if I forgot something to make answering the question possible!
If you look at the class() of the object returned by seqtab, it has the type
class(seqtab(sequence))
# [1] "stslist.freq" "stslist" "data.frame"
so if we look at exactly, what's happening in the print statement for such an object we can get a clue what's going on
TraMineR:::print.stslist.freq
# function (x, digits = 2, width = 1, ...)
# {
# table <- attr(x, "freq")
# print(table, digits = digits, width = width, ...)
# }
# <bytecode: 0x0000000003e831f8>
# <environment: namespace:TraMineR>
We see that what it's really giving you is the "freq" attribute. You can extract this directly and skip the print()
attr(seqtab(sequence), "freq")
# Freq Percent
# a/3-b/1 4.283261 20.130845
# b/1-a/1-b/2 2.773341 13.034390
# a/2-b/1-a/1 2.141982 10.067073
# a/1-b/1-a/1-b/1 1.880359 8.837476
# a/1-b/2-a/1 1.723489 8.100203
# b/1-a/2-b/1 1.418302 6.665861
# b/2-a/1-b/1 1.365099 6.415813
# a/1-b/3 1.241644 5.835586
# a/1-b/1-a/2 1.164434 5.472710
# a/2-b/2 1.092656 5.135360
I have a question regarding LDA in topicmodels in R.
I created a matrix with documents as rows, terms as columns, and the number of terms in a document as respective values from a data frame. While I wanted to start LDA, I got an Error Message stating "Error in !all.equal(x$v, as.integer(x$v)) : invalid argument type" . The data contains 1675 documents of 368 terms. What can I do to make the code work?
library("tm")
library("topicmodels")
data_matrix <- data %>%
group_by(documents, terms) %>%
tally %>%
spread(terms, n, fill=0)
doctermmatrix <- as.DocumentTermMatrix(data_matrix, weightTf("data_matrix"))
lda_head <- topicmodels::LDA(doctermmatrix, 10, method="Gibbs")
Help is much appreciated!
edit
# Toy Data
documentstoy <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16)
meta1toy <- c(3,4,1,12,1,2,3,5,1,4,2,1,1,1,1,1)
meta2toy <- c(10,0,10,1,1,0,1,1,3,3,0,0,18,1,10,10)
termstoy <- c("cus","cus","bill","bill","tube","tube","coa","coa","un","arc","arc","yib","yib","yib","dar","dar")
toydata <- data.frame(documentstoy,meta1toy,meta2toy,termstoy)
So I looked inside the code and apparently the lda() function only accepts integers as the input so you have to convert your categorical variables as below:
library('tm')
library('topicmodels')
documentstoy <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16)
meta1toy <- c(3,4,1,12,1,2,3,5,1,4,2,1,1,1,1,1)
meta2toy <- c(10,0,10,1,1,0,1,1,3,3,0,0,18,1,10,10)
toydata <- data.frame(documentstoy,meta1toy,meta2toy)
termstoy <- c("cus","cus","bill","bill","tube","tube","coa","coa","un","arc","arc","yib","yib","yib","dar","dar")
toy_unique = unique(termstoy)
for (i in 1:length(toy_unique)){
A = as.integer(termstoy == toy_unique[i])
toydata[toy_unique[i]] = A
}
lda_head <- topicmodels::LDA(toydata, 10, method="Gibbs")
I am trying to estimate the static yield curve for Brazil using termstrc package in R. I am using the function estim_nss.couponbonds and putting 0% coupon-rates and $0 cash-flows, except for the last one which is $1000 (the face-value at maturity) -- as far as I know this is the function to do this, because the estim_nss.zeroyields only calculates the dynamic curve. The problem is that I receive the following error message:
"Error in (pos_cf[i] + 1):pos_cf[i + 1] : NA/NaN argument In addition: Warning message: In max(n_of_cf) : no non-missing arguments to max; returning -Inf "
I've tried to trace the problem using trace(estim_nss.couponbons, edit=T) but I cannot find where pos_cf[i]+1 is calculated. Based on the name I figured it could come from the postpro_bondfunction and used trace(postpro_bond, edit=T), but I couldn't find the calculation again. I believe "cf" comes from cashflow, so there could be some problem in the calculation of the cashflows somehow. I used create_cashflows_matrix to test this theory, but it works well, so I am not sure the problem is in the cashflows.
The code is:
#Creating the 'couponbond' class
ISIN <- as.character(c('ltn_2017','ltn_2018', 'ltn_2019', 'ltn_2021','ltn_2023')) #Bond's identification
MATURITYDATE <- as.Date(c(42736, 43101, 43466, 44197, 44927), origin='1899-12-30') #Dates are in system's format
ISSUEDATE <- as.Date(c(41288,41666,42395, 42073, 42395), origin='1899-12-30') #Dates are in system's format
COUPONRATE <- rep(0,5) #Coupon rates are 0 because these are zero-coupon bonds
PRICE <- c(969.32, 867.77, 782.48, 628.43, 501.95) #Prices seen 'TODAY'
ACCRUED <- rep(0.1,5) #There is no accrued interest in the brazilian bond's market
#Creating the cashflows sublist
CFISIN <- as.character(c('ltn_2017','ltn_2018', 'ltn_2019', 'ltn_2021', 'ltn_2023')) #Bond's identification
CF <- c(1000,1000,1000,1000,1000)# The face-values
DATE <- as.Date(c(42736, 43101, 43466, 44197, 44927), origin='1899-12-30') #Dates are in system's format
CASHFLOWS <- list(CFISIN,CF,DATE)
names(CASHFLOWS) <- c("ISIN","CF","DATE")
TODAY <- as.Date(42646, origin='1899-12-30')
brasil <- list(ISIN,MATURITYDATE,ISSUEDATE,
COUPONRATE,PRICE,ACCRUED,CASHFLOWS,TODAY)
names(brasil) <- c("ISIN","MATURITYDATE","ISSUEDATE","COUPONRATE",
"PRICE","ACCRUED","CASHFLOWS","TODAY")
mybonds <- list(brasil)
class(mybonds) <- "couponbonds"
#Estimating the zero-yield curve
ns_res <-estim_nss.couponbonds(mybonds, 'brasil' ,method = "ns")
#Testing the hypothesis that the error comes from the cashflow matrix
cf_p <- create_cashflows_matrix(mybonds[[1]], include_price = T)
m_p <- create_maturities_matrix(mybonds[[1]], include_price = T)
b <- bond_yields(cf_p,m_p)
Note that I am aware of this question which reports the same problem. However, it is for the dynamic curve. Besides that, there is no useful answer.
Your code has two problems. (1) doesn't name the 1st list (this is the direct reason of the error. But if modifiy it, another error happens). (2) In the cashflows sublist, at least one level of ISIN needs more than 1 data.
# ...
CFISIN <- as.character(c('ltn_2017','ltn_2018', 'ltn_2019',
'ltn_2021', 'ltn_2023', 'ltn_2023')) # added a 6th element
CF <- c(1000,1000,1000,1000,1000, 1000) # added a 6th
DATE <- as.Date(c(42736,43101,43466,44197,44927, 44928), origin='1899-12-30') # added a 6th
CASHFLOWS <- list(CFISIN,CF,DATE)
names(CASHFLOWS) <- c("ISIN","CF","DATE")
TODAY <- as.Date(42646, origin='1899-12-30')
brasil <- list(ISIN,MATURITYDATE,ISSUEDATE,
COUPONRATE,PRICE,ACCRUED,CASHFLOWS,TODAY)
names(brasil) <- c("ISIN","MATURITYDATE","ISSUEDATE","COUPONRATE",
"PRICE","ACCRUED","CASHFLOWS","TODAY")
mybonds <- list(brasil = brasil) # named the list
class(mybonds) <- "couponbonds"
ns_res <-estim_nss.couponbonds(mybonds, 'brasil', method = "ns")
Note: the error came from these lines
bonddata <- bonddata[group] # prepro_bond()'s 1st line (the direct reason).
# cf <- lapply(bonddata, create_cashflows_matrix) # the additional error
create_cashflows_matrix(mybonds[[1]], include_price = F) # don't run
I am stuck with the following code.
For reference the code it is taken from the following website (http://gekkoquant.com/2013/01/21/statistical-arbitrage-trading-a-cointegrated-pair/), I am also compiling the code through R Studio.
library("quantmod")
startDate = as.Date("2013-01-01")
symbolLst<-c("WPL.AX","BHP.AX")
symbolData <- new.env()
getSymbols(symbolLst, env = symbolData, src = "yahoo", from = startDate)
stockPair <- list(
a =coredata(Cl(eval(parse(text=paste("symbolData$\"",symbolLst[1],"\"",sep="")))))
,b = coredata(Cl(eval(parse(text=paste("symbolData$\"",symbolLst[2],"\"",sep="")))))
,hedgeRatio = 0.70 ,name=title)
spread <- stockPair$a - stockPair$hedgeRatio*stockPair$b
I am getting the following error.
Error in stockPair$a - stockPair$hedgeRatio * stockPair$b :
non-conformable arrays
The reason these particular series don't match is because "WPL.AX" has an extra value (date:19-05-2014 - the matrix lengths are different) compared to "BHP". How can I solve this issue when loading data?
I have also tested other stock pairs such as "ANZ","WBC" with the source = "google" which produces two of the same length arrays.
> length(stockPair$a)
[1] 360
> length(stockPair$b)
[1] 359
Add code such as this prior to the stockPair computation, to trim each xts set to the intersection of dates:
common_dates <- as.Date(Reduce(intersect, eapply(symbolData, index)))
symbolData <- eapply(symbolData, `[`, i=common_dates)
Your code works fine if you don't convert your xts object to matrix via coredata. Then Ops.xts will ensure that only the rows with the same index will be subtracted. And fortune(106) applies.
fortunes::fortune(106)
# If the answer is parse() you should usually rethink the question.
# -- Thomas Lumley
# R-help (February 2005)
stockPair <- list(
a = Cl(symbolData[[symbolLst[1]]])
,b = Cl(symbolData[[symbolLst[2]]])
,hedgeRatio = 0.70
,name = "title")
spread <- stockPair$a - stockPair$hedgeRatio*stockPair$b
Here's an alternative approach:
# merge stocks into a single xts object
stockPair <- do.call(merge, eapply(symbolData, Cl))
# ensure stockPair columns are in the same order as symbolLst, since
# eapply may loop over the environment in an order you don't expect
stockPair <- stockPair[,pmatch(symbolLst, colnames(stockPair))]
colnames(stockPair) <- c("a","b")
# add hedgeRatio and name as xts attributes
xtsAttributes(stockPair) <- list(hedgeRatio=0.7, name="title")
spread <- stockPair$a - attr(stockPair,'hedgeRatio')*stockPair$b