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I'm attempting to prepare a dataframe of time series data (panel data) for dtwclust::tsclust. My current code looks like this:
library(dtwclust)
library(parallel)
library(dplyr)
library(zoo)
library(data.table)
Panel_VisitPages = data.table(
RecordID = c(1,1,1,1,2,3,3,5,5,5,5,5,5),
VisitPages = c(5,3,5,6,3,100,5,3,5,22,3,22,11),
DayDif = (0,-1,-2,-15,0,0,-5,0,-3,-5,-15,-20)
)
Panel_VisitPages = #df_view_ts[, TotalVisitPages:=sum(VisitPages),by=list(RecordID, DayDif)]
df_view_ts %>%group_by(RecordID, DayDif)%>%summarise(TotalVisitPages= sum(VisitPages))
groups = unique(Panel_VisitPages$RecordID)
set.seed(1)
groups = sample(groups, 3)
Panel_VisitPages = Panel_VisitPages[Panel_VisitPages$RecordID %in% groups,]
PageSeries = read.zoo(Panel_VisitPages
#, format =
, split='RecordID'
, index='DayDif'
,FUN = identity
)
PageSeries = na.fill(PageSeries, fill=0)
# Cluster Test------------------------------------------------------------------
workers <- makeCluster(detectCores()-4)
# load dtwclust in each one, and make them use 1 thread per worker
invisible(clusterEvalQ(workers, {
library(dtwclust)
library(bigmemory)
RcppParallel::setThreadOptions(1L)
}))
# register your workers, e.g. with doParallel
require(doParallel)
registerDoParallel(workers)
clustTest = tsclust(
series = t(PageSeries)
, type = 'partitional'
, k = 2L:10L
, distance = 'SBD'
, seed = 1
, trace = 1
)
The first line is of course much slower than it would be if I were able to use the data.table command I have commented out, but when I reach the tsclust command I get
Error in merge.zoo(--blocked out--, :
series cannot be merged with non-unique index entries in a series
Can anyone tell me how the data.table code at the top of this block differs from the similar dplyr code and what I can do to get it to perform the same as the dplyr line? Thanks!
I am trying to work on a for loop to make running a function I've developed more efficient.
However, when I put it in a for loop, it is overwriting columns that it should not be and returning incorrect results.
Edit: The error is that in the resulting dataframe MiSeq_Bord_Outliers_table0, the resulting columns containing label Outlier_type is returning incorrect outputs.
As per the Outlier_Hunter function, when Avg_Trim_Cov and S2_Total_Read_Pairs_Processed are below their
respective Q1 Thresholds their respective Outlier_type columns should read "Lower_Outlier", if between Q1 & Q3 Threshold, "Normal" and if above Q3 Threshold then "Upper_outlier". But when the for loop is executed, only "Upper_outlier" is shown in the Outlier_type columns.
Edit: The inputs have been simplified and tested on the different computer with a clean console. If there were any artifacts there before, they should have been eliminated now, and there should be no errors here now. It is important to run the outlier_results_1var part first. If you test run this code and get errors, please let me know which part failed.
Edit: MiSeq_Bord_Outliers_table0_error is the error that is being reproduced. This is the error result, not an input.
Can someone please tell me why is it returning these incorrect results and what I can do to fix it? I will upload the relevant code below. Or is there another way to do this without a for loop?
#libraries used
library(tidyverse)
library(datapasta)
library(data.table)
library(janitor)
library(ggpubr)
library(labeling)
#2.) Outlier_Hunter Function
#Function to Generate the Outlier table
#Outlier Hunter function takes 4 arguments: the dataset, column/variable of interest,
#Q1 and Q3. Q1 and Q3 are stored in the results of Quartile_Hunter.
#Input ex: MiSeq_Bord_final_report0, Avg_Trim_Cov, MiSeq_Bord_Quartiles_ATC$First_Quartile[1], MiSeq_Bord_Quartiles_ATC$Third_Quartile[1]
#Usage ex: Outlier_Hunter(MiSeq_Bord_final_report0, Avg_Trim_Cov,
#MiSeq_Bord_Quartiles_ATC$First_Quartile[1], MiSeq_Bord_Quartiles_ATC$Third_Quartile[1])
#Here is the Function to get the Outlier Table
Outlier_Hunter <- function(Platform_Genus_final_report0, my_col, Q1, Q3) {
#set up and generalize the variable name you want to work with
varname <- enquo(my_col)
#print(varname) #just to see what variable the function is working with
#get the outliers
Platform_Genus_Variable_Outliers <- Platform_Genus_final_report0 %>%
select(ReadID, Platform, Genus, !!varname) %>%
#Tell if it is an outlier, and if so, what kind of outlier
mutate(
Q1_Threshold = Q1,
Q3_Threshold = Q3,
Outlier_type =
case_when(
!!varname < Q1_Threshold ~ "Lower_Outlier",
!!varname >= Q1_Threshold & !!varname <= Q3_Threshold ~ "Normal",
!!varname > Q3_Threshold ~ "Upper_Outlier"
)
)
}
#MiSeq_Bord_Quartiles entries
MiSeq_Bord_Quartiles <- data.frame(
stringsAsFactors = FALSE,
row.names = c("Avg_Trim_Cov", "S2_Total_Read_Pairs_Processed"),
Platform = c("MiSeq", "MiSeq"),
Genus = c("Bord", "Bord"),
Min = c(0.03, 295),
First_Quartile = c(80.08, 687613.25),
Median = c(97.085, 818806.5),
Third_Quartile = c(121.5625, 988173.75),
Max = c(327.76, 2836438)
)
#Remove the hashtag below to test if what you have is correct
#datapasta::df_paste(head(MiSeq_Bord_Quartiles, 5))
#dataset entry
MiSeq_Bord_final_report0 <- data.frame(
stringsAsFactors = FALSE,
ReadID = c("A005_20160223_S11_L001","A050_20210122_S6_L001",
"A073_20210122_S7_L001",
"A076_20210426_S11_L001",
"A080_20210426_S12_L001"),
Platform = c("MiSeq","MiSeq",
"MiSeq","MiSeq","MiSeq"),
Genus = c("Bordetella",
"Bordetella","Bordetella",
"Bordetella","Bordetella"),
Avg_Raw_Read_bp = c(232.85,241.09,
248.54,246.99,248.35),
Avg_Trimmed_Read_bp = c(204.32,232.6,
238.56,242.54,244.91),
Avg_Trim_Cov = c(72.04,101.05,
92.81,41.77,54.83),
Genome_Size_Mb = c(4.1, 4.1, 4.1, 4.1, 4.1),
S1_Input_reads = c(1450010L,
1786206L,1601542L,710792L,925462L),
S1_Contaminant_reads = c(12220L,6974L,
7606L,1076L,1782L),
S1_Total_reads_removed = c(12220L,6974L,
7606L,1076L,1782L),
S1_Result_reads = c(1437790L,
1779232L,1593936L,709716L,923680L),
S2_Read_Pairs_Written = c(712776L,882301L,
790675L,352508L,459215L),
S2_Total_Read_Pairs_Processed = c(718895L,889616L,
796968L,354858L,461840L)
)
MiSeq_Bord_final_report0
#Execution for 1 variable
outlier_results_1var <- Outlier_Hunter(MiSeq_Bord_final_report0, Avg_Trim_Cov,
MiSeq_Bord_Quartiles$First_Quartile[1], MiSeq_Bord_Quartiles$Third_Quartile[1])
#Now do it with a for loop
col_var_outliers <- row.names(MiSeq_Bord_Quartiles)
#col_var_outliers <- c("Avg_Trim_Cov", "S2_Total_Read_Pairs_Processed")
#change line above to change input of variables few into Outlier Hunter Function
outlier_list_MiSeq_Bord <- list()
for (y in col_var_outliers) {
outlier_results0 <- Outlier_Hunter(MiSeq_Bord_final_report0, y, MiSeq_Bord_Quartiles[y, "First_Quartile"], MiSeq_Bord_Quartiles[y, "Third_Quartile"])
outlier_results1 <- outlier_results0
colnames(outlier_results1)[5:7] <- paste0(y, "_", colnames(outlier_results1[, c(5:7)]), sep = "")
outlier_list_MiSeq_Bord[[y]] <- outlier_results1
}
MiSeq_Bord_Outliers_table0 <- reduce(outlier_list_MiSeq_Bord, left_join, by = c("ReadID", "Platform", "Genus"))
#the columns containing label Outlier_type is where the code goes wrong.
#When Avg_Trim_Cov and S2_Total_Read_Pairs_Processed are below their
#respective Q1 Thresholds their respective Outlier_type columns should read
#"Lower_Outlier", if between Q1 & Q3 Threshold, "Normal" and if above Q3
#Threshold then "Upper_outlier". But when the for loop is executed, only
"Upper_outlier" is shown in the Outlier_type columns.
datapasta::df_paste(head(MiSeq_Bord_Outliers_table0, 5))
MiSeq_Bord_Outliers_table0_error <- data.frame(
stringsAsFactors = FALSE,
ReadID = c("A005_20160223_S11_L001",
"A050_20210122_S6_L001",
"A073_20210122_S7_L001","A076_20210426_S11_L001",
"A080_20210426_S12_L001"),
Platform = c("MiSeq",
"MiSeq","MiSeq","MiSeq",
"MiSeq"),
Genus = c("Bordetella","Bordetella","Bordetella",
"Bordetella","Bordetella"),
Avg_Trim_Cov = c(72.04,
101.05,92.81,41.77,54.83),
Avg_Trim_Cov_Q1_Threshold = c(80.08,
80.08,80.08,80.08,80.08),
Avg_Trim_Cov_Q3_Threshold = c(121.5625,
121.5625,121.5625,121.5625,
121.5625),
Avg_Trim_Cov_Outlier_type = c("Upper_Outlier","Upper_Outlier",
"Upper_Outlier","Upper_Outlier",
"Upper_Outlier"),
S2_Total_Read_Pairs_Processed = c(718895L,
889616L,796968L,354858L,
461840L),
S2_Total_Read_Pairs_Processed_Q1_Threshold = c(687613.25,
687613.25,687613.25,
687613.25,687613.25),
S2_Total_Read_Pairs_Processed_Q3_Threshold = c(988173.75,
988173.75,988173.75,
988173.75,988173.75),
S2_Total_Read_Pairs_Processed_Outlier_type = c("Upper_Outlier","Upper_Outlier",
"Upper_Outlier","Upper_Outlier",
"Upper_Outlier")
)
For use in a loop like you do, it would be more useful to write your Outlier_Hunter() function to take the target column as a character string rather than an expression.
To do that, try replacing all instances of !!varname in your function with .data[[my_col]], and remove the enquo() line altogether.
Note that with these changes, you also need to change how you call the function when you don't have the column name in a variable. For example, your single execution would become:
Outlier_Hunter(
MiSeq_Bord_final_report0,
"Avg_Trim_Cov",
MiSeq_Bord_Quartiles$First_Quartile[1],
MiSeq_Bord_Quartiles$Third_Quartile[1]
)
For more info about programming with tidy evaluation functions, you may find this rlang vignette useful.
Let's take a look at the example in ?sql_variant:
We define a new translator function for aggregated functions, expanded from the default one:
postgres_agg <- sql_translator(.parent = base_agg,
cor = sql_prefix("corr"),
cov = sql_prefix("covar_samp"),
sd = sql_prefix("stddev_samp"),
var = sql_prefix("var_samp")
)
We then define a new variant, which is made from translation functions of the 3 different types (here 2):
postgres_var <- sql_variant(
base_scalar,
postgres_agg
)
translate_sql(cor(x, y), variant = postgres_var)
# <SQL> COR("x", "y")
translate_sql(sd(income / years), variant = postgres_var)
# <SQL> SD("income" / "years")
These don't look translated to me, shouldn't they be "CORR" and "STDDEV_SAMP" ?
# Original comment:
# Any functions not explicitly listed in the converter will be translated
# to sql as is, so you don't need to convert all functions.
translate_sql(regr_intercept(y, x), variant = postgres_var)
# <SQL> REGR_INTERCEPT("y", "x")
This one behaves as expected, which is just like the other 2.
On the other hand default translated functions work, see:
translate_sql(mean(x), variant = postgres_var)
#<SQL> avg("x") OVER ()
It's a bug right ? or am I missing something ?
My goal is to create some variants for Oracle and use it in the following fashion,then for more complicated functions (example with SQLite to be reproducible):
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
copy_to(con, cars, "cars")
con %>% tbl("cars") %>% summarize(dist = group_concat(dist)) # works as expected, as we're stealing the keyword from sqlite directly
sqlite_variant <- sql_variant(aggregate=sql_translator(.parent = base_agg,gpc = sql_prefix("group_concat")))
con %>% tbl("cars") %>% summarize(dist = gpc(dist)) # how do I make this work ?
EDIT:
One bounty later still no solution, I've cross posted the issue in the dplyr/dbplyr github page directly where I'm not sure if it has or will get attention, but in case I (or someone else) don't update this in time, check this url : https://github.com/tidyverse/dplyr/issues/3117
This is what Hadley Wickham answered on provided github link:
translate_sql() doesn't have a variant argument any more
Indeed the variant argument is not documented, though the examples use it, I suppose it will be corrected for next version.
Asked how to define custom SQL translations he had this to offer:
Have a look at http://dbplyr.tidyverse.org/articles/new-backend.html
and http://dbplyr.tidyverse.org/articles/sql-translation.html
I guess another option is to get the older version of dbplyr::sql_variant.
I have a R code that I am trying to run in a server. But it is stopping in the middle/get frozen probably because of memory limitation. The data files are huge/massive (one has 20 million lines) and if you look at the double for loop in the code, length(ratSplit) = 281 and length(humanSplit) = 36. The data has specific data of human and rats' genes and human has 36 replicates, while rat has 281. So, the loop is basically 281*36 steps. What I want to do is to process data using the function getGeneType and see how different/independent are the expression of different replicate combinations. Using Fisher's test. The data rat_processed_7_25_FDR_05.out looks like this :
2 Sptbn1 114201107 114200202 chr14|Sptbn1:114201107|Sptbn1:114200202|reg|- 2 Thymus_M_GSM1328751 reg
2 Ndufb7 35680273 35683909 chr19|Ndufb7:35680273|Ndufb7:35683909|reg|+ 2 Thymus_M_GSM1328751 rev
2 Ndufb10 13906408 13906289 chr10|Ndufb10:13906408|Ndufb10:13906289|reg|- 2 Thymus_M_GSM1328751 reg
3 Cdc14b 1719665 1719190 chr17|Cdc14b:1719665|Cdc14b:1719190|reg|- 3 Thymus_M_GSM1328751 reg
and the data fetal_output_7_2.out has the form
SPTLC2 78018438 77987924 chr14|SPTLC2:78018438|SPTLC2:77987924|reg|- 11 Fetal_Brain_408_AGTCAA_L006_R1_report.txt reg
EXOSC1 99202993 99201016 chr10|EXOSC1:99202993|EXOSC1:99201016|rev|- 5 Fetal_Brain_408_AGTCAA_L006_R1_report.txt reg
SHMT2 57627893 57628016 chr12|SHMT2:57627893|SHMT2:57628016|reg|+ 8 Fetal_Brain_408_AGTCAA_L006_R1_report.txt reg
ZNF510 99538281 99537128 chr9|ZNF510:99538281|ZNF510:99537128|reg|- 8 Fetal_Brain_408_AGTCAA_L006_R1_report.txt reg
PPFIBP1 27820253 27824363 chr12|PPFIBP1:27820253|PPFIBP1:27824363|reg|+ 10 Fetal_Brain_408_AGTCAA_L006_R1_report.txt reg
Now I have few questions on how to make this more efficient. I think when I run this code, R takes up lots of memory that ultimately causes problems. I am wondering if there is any way of doing this more efficiently
Another possibility is the usage of double for-loop'. Will sapply help? In that case, how should I apply sapply?
At the end I want to convert result into a csv file. I know this is a bit overwhelming to put code like this. But any optimization/efficient coding/programming will be A LOT! I really need to run the whole thing at least one to get the data soon.
#this one compares reg vs rev
date()
ratRawData <- read.table("rat_processed_7_25_FDR_05.out",col.names = c("alignment", "ratGene", "start", "end", "chrom", "align", "ratReplicate", "RNAtype"), fill = TRUE)
humanRawData <- read.table("fetal_output_7_2.out", col.names = c("humanGene", "start", "end", "chrom", "alignment", "humanReplicate", "RNAtype"), fill = TRUE)
geneList <- read.table("geneList.txt", col.names = c("human", "rat"), sep = ',')
#keeping only information about gene, alignment number, replicate and RNAtype, discard other columns
ratRawData <- ratRawData[,c("ratGene", "ratReplicate", "alignment", "RNAtype")]
humanRawData <- humanRawData[, c( "humanGene", "humanReplicate", "alignment", "RNAtype")]
#function to capitalize
capitalize <- function(x){
capital <- toupper(x) ## capitalize
paste0(capital)
}
#capitalizing the rna type naming for rat. So, reg ->REG, dup ->DUP, rev ->REV
#doing this to make data manipulation for making contingency table easier.
levels(ratRawData$RNAtype) <- capitalize(levels(ratRawData$RNAtype))
#spliting data in replicates
ratSplit <- split(ratRawData, ratRawData$ratReplicate)
humanSplit <- split(humanRawData, humanRawData$humanReplicate)
print("done splitting")
#HyRy :when some gene has only reg, rev , REG, REV
#HnRy : when some gene has only reg,REG,REV
#HyRn : add 1 when some gene has only reg,rev,REG
#HnRn : add 1 when some gene has only reg,REG
#function to be used to aggregate
getGeneType <- function(types) {
types <- as.character(types)
if ('rev' %in% types) {
return(ifelse(('REV' %in% types), 'HyRy', 'HyRn'))
}
else {
return(ifelse(('REV' %in% types), 'HnRy', 'HnRn'))
}
}
#logical function to see whether x is integer(0) ..It's used the for loop bellow in case any one HmYn is equal to zero
is.integer0 <- function(x) {
is.integer(x) && length(x) == 0L
}
result <- data.frame(humanReplicate = "human_replicate", ratReplicate = "rat_replicate", pvalue = "p-value", alternative = "alternative_hypothesis",
Conf.int1 = "conf.int1", Conf.int2 ="conf.int2", oddratio = "Odd_Ratio")
for(i in 1:length(ratSplit)) {
for(j in 1:length(humanSplit)) {
ratReplicateName <- names(ratSplit[i])
humanReplicateName <- names(humanSplit[j])
#merging above two based on the one-to-one gene mapping as in geneList defined above.
mergedHumanData <-merge(geneList,humanSplit[[j]], by.x = "human", by.y = "humanGene")
mergedRatData <- merge(geneList, ratSplit[[i]], by.x = "rat", by.y = "ratGene")
mergedHumanData <- mergedHumanData[,c(1,2,4,5)] #rearrange column
mergedRatData <- mergedRatData[,c(2,1,4,5)] #rearrange column
mergedHumanRatData <- rbind(mergedHumanData,mergedRatData) #now the columns are "human", "rat", "alignment", "RNAtype"
agg <- aggregate(RNAtype ~ human+rat, data= mergedHumanRatData, FUN=getGeneType) #agg to make HmYn form
HmRnTable <- table(agg$RNAtype) #table of HmRn ie RNAtype in human and rat.
#now assign these numbers to variables HmYn. Consider cases when some form of HmRy is not present in the table. That's why
#is.integer0 function is used
HyRy <- ifelse(is.integer0(HmRnTable[names(HmRnTable) == "HyRy"]), 0, HmRnTable[names(HmRnTable) == "HyRy"][[1]])
HnRn <- ifelse(is.integer0(HmRnTable[names(HmRnTable) == "HnRn"]), 0, HmRnTable[names(HmRnTable) == "HnRn"][[1]])
HyRn <- ifelse(is.integer0(HmRnTable[names(HmRnTable) == "HyRn"]), 0, HmRnTable[names(HmRnTable) == "HyRn"][[1]])
HnRy <- ifelse(is.integer0(HmRnTable[names(HmRnTable) == "HnRy"]), 0, HmRnTable[names(HmRnTable) == "HnRy"][[1]])
contingencyTable <- matrix(c(HnRn,HnRy,HyRn,HyRy), nrow = 2)
# contingencyTable:
# HnRn --|--HyRn
# |------|-----|
# HnRy --|-- HyRy
#
fisherTest <- fisher.test(contingencyTable)
#make new line out of the result of fisherTest
newLine <- data.frame(t(c(humanReplicate = humanReplicateName, ratReplicate = ratReplicateName, pvalue = fisherTest$p,
alternative = fisherTest$alternative, Conf.int1 = fisherTest$conf.int[1], Conf.int2 =fisherTest$conf.int[2],
oddratio = fisherTest$estimate[[1]])))
result <-rbind(result,newLine) #append newline to result
if(j%%10 = 0) print(c(i,j))
}
}
write.table(result, file = "compareRegAndRev.csv", row.names = FALSE, append = FALSE, col.names = TRUE, sep = ",")
Referring to the accepted answer to Monitor memory usage in R, the amount of memory used by R can be tracked with gc().
If the script is, indeed, running short of memory (which would not surprise me), the easiest way to resolve the problem would be to move the write.table() from the outside to the inside of the loop, to replace the rbind(). It would just be necessary to create a new file name for the CSV file that is written from each output, e.g. by:
csvFileName <- sprintf("compareRegAndRev%03d_%03d.csv",i,j)
If the CSV files are written without headers, they could then be concatenated separately outside R (e.g. using cat in Unix) and the header added later.
While this approach might succeed in creating the CSV file that is sought, it is possible that file might be too big to process subsequently. If so, it may be preferable to process the CSV files individually, rather than concatenating them at all.
I need to define blocks of actions - so I want to group together all actions for a single id that take place less than 30 days since the last action. If it's more than 30 days since the last action, then I'd increment the label by one (so label 2, 3, 4...). Every new id would start at 1 again.
Here's the data:
dat = data.frame(cbind(
id = c(rep(1,2), rep(16,3), rep(17,24)),
##day_id is the action date in %Y%m%d format - I keep it as numeric but could potentially turn to a date.
day_id = c(20130702, 20130121, 20131028, 20131028, 20130531, 20140513, 20140509,
20140430, 20140417, 20140411, 20140410, 20140404,
20140320, 20140313, 20140305, 20140224, 20140213, 20140131, 20140114,
20130827, 20130820, 20130806, 20130730, 20130723,
20130719, 20130716, 20130620, 20130620, 20130614 ),
###diff is the # of days between actions/day_ids
diff =c(NA,162,NA,0,150,NA,4,9,13,6,1,6,15,7,8,9,11,13,17,140,7,14,
7,7,4,3,26,0,6),
###Just a flag to say whether it's a new id
new_id = c(1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
))
I've done it with a for loop and managed to avoid loops within loops (see below) but can't seem to get rid of that outer loop. Of course, it gets extremely slow with thousands of ids. In the example below, 'call_block' is what I'm trying to reproduce but without the for loop. Can anyone help me get this out of a loop??
max_days = 30
r = NULL
for(i in unique(dat$id)){
d = dat$diff[dat$id==i]
w = c(1,which(d>=max_days) , length(d)+1)
w2 = diff(w)
r = c(r,rep(1:(length(w)-1), w2))
}
dat$call_block = r
Thank you!
Posting #alexis_laz's answer here to close out the question
library(data.table)
f = function(x){
ret = c(1, cumsum((x >= 30)[-1]) + 1)
return(ret = ret)
}
df = data.table(dat)
df2 = df[,list(call_block= f(diff)), by = id]