stock_reco<- read.csv("C:\\temp\\Migration.csv")
migrate<-as.matrix(stock_reco)
title<-colnames(migrate)
dt1<-migrate[,3]
dt2<-as.Date(dt1, format= "%d/%m/%Y")
reco1<-migrate[,6]
reco<-as.matrix(reco1)
for(i in 1:4099)
{
if((migrate[i,1]== migrate[i+1,1]) && (migrate[i,2]== migrate[i+1,2]))
{
k<-difftime(dt2[i+1],dt2[i],units = "days")
if((k <=180) && (reco[i] == reco[i+1]))
print (migrate[i,])
print (migrate[i+1,])
print ("----------------------------------------------------")
}
}
Last two print statements give my final output in the image attached below. I want the whole Final output in an excel file. So how do I import this output from R to an excel file?
I want the output in this form
Comp Name Brokerage House Date CMP Target Recomendation
Yes Bank Motilal Oswal 14/6/2011 294 420 Buy
Yes Bank Motilal Oswal 22/9/2011 285 400 Buy
The above is just one set where if conditions are satisfied and there are several such pairs. I need all of these in an Excel sheet.
When I run my code I get this in zig zag/ random form in R there are no errors in the code.
Please let me know how to write this output to an excel sheet in detail.
Thanks
#iceiceice you could try this
stock_reco<- read.csv("C:\\temp\\Migration.csv")
migrate<-as.matrix(stock_reco)
title<-colnames(migrate)
dt1<-migrate[,3]
dt2<-as.Date(dt1, format= "%d/%m/%Y")
reco1<-migrate[,6]
reco<-as.matrix(reco1)
for (i in 1:4099) {
if((migrate[i,1]== migrate[i+1,1]) && (migrate[i,2]== migrate[i+1,2]))
{
k<-difftime(dt2[i+1],dt2[i],units = "days")
if((k <=180) && (reco[i] == reco[i+1]))
d <- rbind(d, data.frame(migrate[i,], migrate[i+1,]))
}
}
write.csv(file=fileName, x='d')
Related
I just have a column "methods_discussed" in CSV (link is https://github.com/pandas-dev/pandas/files/3496001/multiple_responses.zip)
multi<- read.csv("multiple_responses.csv", header = T)
This file having values name of family planning methods in the column name like:
methods_discussed
emergency female_sterilization male_sterilization iud NaN injectables male_condoms -77 male_condoms female_sterilization male_sterilization injectables iud male_condoms
I have created a vector of all but not -77 and NAN of 8 family planning methods as:
method_names = c('female_condoms', 'emergency', 'male_condoms', 'pill', 'injectables', 'iud', 'male_sterilization', 'female_sterilization')
I want to create new indicator variable based on the names of vector (method_names) in the existing data frame multi2, for this I used (I)
for (abc in method_names) {
multi2[abc]<- as.integer(str_detect(multi2$methods_discussed, fixed(abc)))
}
(II)
for (abc in method_names) {
multi2[abc]<- as.integer(str_contains(abc,multi2$methods_discussed))
}
(III) I also tried
for (abc in method_names) {
multi2[abc]<- as.integer(stri_detect_fixed(multi2$methods_discussed, abc))
}
but the output is not matching as expected. Probably male_sterilization is a substring of female_sterilization and it shows 1(TRUE) for male_sterilization for female_sterlization also. It is shown below in the Actual output at row 2. It must show 0 (FALSE) as female_sterilization is in the method_discussed column at row 2. I also don't want to generate any thing like 0/1 (False/True) (should be blank) corresponding to -77 and blank in method_discussed (All are highlighted in Expected output.
Actual Output
Expected Output
No error in code but only in the output.
You can add word boundaries to fix that issue.
multi<- read.csv("multiple_responses.csv", header = T)
method_names = c('female_condoms', 'emergency', 'male_condoms', 'pill', 'injectables', 'iud', 'male_sterilization', 'female_sterilization')
for (abc in method_names) {
multi[abc]<- as.integer(grepl(paste0('\\b', abc, '\\b'), multi$methods_discussed))
}
multi[multi$methods_discussed %in% c('', -77), method_names] <- ''
I'd like to import a textfile as a list into R but don't know how to tell R the desired format. The source list is a txt file and is formatted as followes:
Column1=a
Date=21.01.2020
Column2=1|2|3
Subtable_Column1=a|2|3
Subtable_Column2=c|d|e
[2]
Column1=b
Date=21.02.2020
Column2=1|2|4
Subtable_Column1=a|2|3
Subtable_Column2=c|d|e
Subtable_Column3=c|d|e
In the end, each [n] should be the list index containing the named vectors (e.g. Column 1, Date etc.)
How's that possible in R?
Thanks for your help!
Here is a way to pre-format the data.
varval.dat
Column1=a
Date=21.01.2020
Column2=1|2|3
Subtable_Column1=a|2|3
Subtable_Column2=c|d|e
[2]
Column1=b
Date=21.02.2020
Column2=1|2|4
Subtable_Column1=a|2|3
Subtable_Column2=c|d|e
Subtable_Column3=c|d|e
It puts each dataset into a row of a data-frame:
awk -F '=' 'BEGIN{i=1}
!NF{i++; j=0; next}
/=/{ j++; cname[0,j]=$1; content[i,j]=$2; rows=i; cols[i]++ }
END{ m=0; for(k in cols){ if(cols[k]>m){ m=cols[k] } };
for(j=1;j<m;j++){ printf("%s,",cname[0,j]) }; print cname[0,m];
for(i=1;i<=rows;i++){
for(j=1;j<m;j++){
printf("%s,",content[i,j]) } print content[i,m] } }' varval.dat > varval.csv
R:
a <- read.csv("varval.csv", na.strings="")
# Column1 Date Column2 Subtable_Column1 Subtable_Column2 Subtable_Column3
# 1 a 21.01.2020 1|2|3 a|2|3 c|d|e <NA>
# 2 b 21.02.2020 1|2|4 a|2|3 c|d|e c|d|e
I'm sure this is not exactly what you wanted but may serve as a starting point.
I would like to do some statistical analysis with Python on the live casino game called Crazy Time from Evolution Gaming. There is a website that has the data to do this: https://tracksino.com/crazytime. I want the data of the lowest table 'Spin History' to be imported into excel. However, I do not now how this can be done. Could anyone give me an idea where to start?
Thanks in advance!
Try the below code:
import json
import requests
from urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
import csv
import datetime
def scrap_history():
csv_headers = []
file_path = '' #mention your system where you have to save the file
file_name = 'spin_history.csv' # filename
page_number = 1
while True:
#Dynamic URL fetching data in chunks of 100
url = 'https://api.tracksino.com/crazytime_history?filter=&sort_by=&sort_desc=false&page_num=' + str(page_number) + '&per_page=100&period=24hours'
print('-' * 100)
print('URL created : ',url)
response = requests.get(url,verify=False)
result = json.loads(response.text) # loading data to convert in JSON.
history_data = result['data']
print(history_data)
if history_data != []:
with open(file_path + file_name ,'a+') as history:
#Headers for file
csv_headers = ['Occured At','Slot Result','Spin Result','Total Winners','Total Payout',]
csvwriter = csv.DictWriter(history, delimiter=',', lineterminator='\n',fieldnames=csv_headers)
if page_number == 1:
print('Writing CSV header now...')
csvwriter.writeheader()
#write exracted data in to csv file one by one
for item in history_data:
value = datetime.datetime.fromtimestamp(item['when'])
occured_at = f'{value:%d-%B-%Y # %H:%M:%S}'
csvwriter.writerow({'Occured At':occured_at,
'Slot Result': item['slot_result'],
'Spin Result': item['result'],
'Total Winners': item['total_winners'],
'Total Payout': item['total_payout'],
})
print('-' * 100)
page_number +=1
print(page_number)
print('-' * 100)
else:
break
Explanation:
I have implemented the above script using python requests way. The API url https://api.tracksino.com/crazytime_history?filter=&sort_by=&sort_desc=false&page_num=1&per_page=50&period=24hours extarcted from the web site itself(refer screenshot). In the very first step script will take the dynamic URL where page number is dynamic and changed upon on every iteration. For ex:- first it will be page_num = 1 then page_num = 2 and so on till all the data will get extracted.
I complete my first back testing scripts with help of great people in Stackoverflow. However, when I try to run this by using the data of my local stock market (Hong Kong) it got an error. I cannot find out where the problem is. Please give me a hand to take a look my coding. thanks.
library(quantmod)
library(lubridate)
library(xlsx)
stock0<-getSymbols("^HSI",src="yahoo",from="1988-01-01",auto.assign=F)
stock0 <- to.weekly(stock0)
stock1<-na.locf(stock0)
stock1$SMA1<-SMA(Cl(stock1),n=1)
stock1$SMA30<-SMA(Cl(stock1),n=30)
stock1$SMACheck<-ifelse(stock1$SMA1>stock1$SMA30,1,0)
stock1$SMA_CrossOverUp<-ifelse(diff(stock1$SMACheck)==1,1,0)
stock1$SMA_CrossOverDown<-ifelse(diff(stock1$SMACheck)==-1,-1,0)
stock1<-stock1[index(stock1)>="1998-01-01",]
stock1_df<-data.frame(index(stock1),coredata(stock1))
colnames(stock1_df)<-c("Date","Open","High","Low","Close","Volume","Adj","SMA1","SMA30","EMACheck","EMACheck_up","EMACheck_down")
#To calculate the number of crossoverup transactions during the duration from 2016-01-01
sum(stock1_df$SMACheck_up==1 & index(stock1)>="2010-01-01",na.rm=T)
stock1_df$Date[stock1_df$SMACheck_up==1 & index(stock1)>="2010-01-01"]
sum(stock1_df$SMACheck_down==-1 & index(stock1)>="2010-01-01",na.rm=T)
stock1_df$Date[stock1_df$SMACheck_down==-1 & index(stock1)>="2010-01-01"]
stock1_df
#To generate the transcation according to the strategy
transaction_dates<-function(stock2,Buy,Sell)
{
Date_buy<-c()
Date_sell<-c()
hold<-F
stock2[["Hold"]]<-hold
for(i in 1:nrow(stock2)) {
if(hold == T) {
stock2[["Hold"]][i]<-T
if(stock2[[Sell]][i] == -1) {
#stock2[["Hold"]][i]<-T
hold<-F
}
} else {
if(stock2[[Buy]][i] == 1) {
hold<-T
stock2[["Hold"]][i]<-T
}
}
}
stock2[["Enter"]]<-c(0,ifelse(diff(stock2[["Hold"]])==1,1,0))
stock2[["Exit"]]<-c(ifelse(diff(stock2[["Hold"]])==-1,-1,0),0)
Buy_date <- stock2[["Date"]][stock2[["Enter"]] == 1]
Sell_date <- stock2[["Date"]][stock2[["Exit"]] == -1]
if (length(Sell_date)<length(Buy_date)){
#Sell_date[length(Sell_date)+1]<-tail(stock2[["Date"]],n=2)[1]
Buy_date<-Buy_date[1:length(Buy_date)-1]
}
return(list(DatesBuy=Buy_date,DatesSell=Sell_date))
}
#transaction dates generate:
stock1_df <- na.locf(stock1_df)
transactionDates<-transaction_dates(stock1_df,"SMACheck_up","SMACheck_down")
transactionDates
num_transaction1<-length(transactionDates[[1]])
Open_price<-function(df,x) {
df[which(df[["Date"]]==x)+1,][["Open"]]
}
transactions_date<-function(df,x) {
df[which(df[["Date"]]==x)+1,][["Date"]]
}
transactions_generate<-function(df,num_transaction)
{
price_buy<-sapply(1:num_transaction,function(x) {Open_price(df,transactionDates[[1]][x])})
price_sell<-sapply(1:num_transaction,function(x) {Open_price(df,transactionDates[[2]][x])})
Dates_buy<-as.Date(sapply(1:num_transaction,function(x) {transactions_date(df,transactionDates[[1]][x])}))
Dates_sell<-as.Date(sapply(1:num_transaction,function(x) {transactions_date(df,transactionDates[[2]][x])}))
transactions_df<-data.frame(DatesBuy=Dates_buy,DatesSell=Dates_sell,pricesBuy=price_buy,pricesSell=price_sell)
#transactions_df$return<-100*(transactions_df$pricesSell-transactions_df$pricesBuy)/transactions_df$pricesBuy
transactions_df$Stop_loss<-NA
return(transactions_df)
}
transaction_summary<-transactions_generate(stock1_df,num_transaction1)
transaction_summary$Return<-100*(transaction_summary$pricesSell-transaction_summary$pricesBuy)/transaction_summary$pricesBuy
transaction_summary
I complete my first back testing scripts with help of great people in Stackoverflow. However, when I try to run this by using the data of my local stock market (Hong Kong) it got an error. I cannot find out where the problem is. Please give me a hand to take a look my coding. thanks.
I want to call .exe program (spi_sl_6.exe) using a command of R (system), however I can't input parameters to the program using "system". The followwing is my command and parameters:system("D:\\working\spi_sl_6.exe")
I am searching for a long time on net. But no use. Please help or try to give some ideas how to achieve this. Thanks in advance.
This is using the Standardized Precipitation Index software from
http://drought.unl.edu/MonitoringTools/DownloadableSPIProgram.aspx.
This seems to give a working solution using Windows (but not without warnings!)
Fisrt download the software and example files
# Create directory to download software
mydir <- "C:\\Users\\david\\spi"
dir.create(mydir)
url <- "http://drought.unl.edu/archive/Programs/SPI"
download.file(file.path(url, "spi_sl_6.exe"), file.path(mydir, "spi_sl_6.exe"), mode="wb")
# Download example files
download.file(file.path(url, "SPI_samplefiles.zip"), file.path(mydir, "SPI_samplefiles.zip"))
# extract one example file, and write out
temp <- unzip(file.path(mydir, "SPI_samplefiles.zip"), "wymo.cor")
dat <- read.table(temp)
# Use this file as an example input
write.table(dat, file.path(mydir,"wymo.cor"), col.names = FALSE, row.names = FALSE)
From page 3 of the help file basic-spi-program-information.pdf at the above link the command line code should be of the form spi 3 6 12 <infile.dat >outfile.dat, however,
neither of the following worked (just from command line not in R), and various iterations of how to pass parameters.
C:\Users\david\spi\spi_sl_6 3 <C:\Users\david\spi\wymo.cor >C:\Users\david\spi\out.dat
cd C:\Users\david\spi && spi_sl_6 3 <wymo.cor >out.dat
However, using the accepted answer from Running .exe file with multiple parameters in c#
seems to work. That is again from the command line
cd C:\Users\david\spi && (echo 2 && echo 3 && echo 6 && echo wymo.cor && echo out1.dat) | spi_sl_6
So to run this in R you can wrap this in a shell (you will need to change the path to where you have saved the exe)
shell("cd C:\\Users\\david\\spi && (echo 2 && echo 3 && echo 6 && echo wymo.cor && echo out2.dat) | spi_sl_6", intern=TRUE)
out1.dat and out2.dat should be the same.
This throws warning messages, I think from the echo (in R but not from command line) but the output file is produced.
Suppose you can automate all the echo calls sligtly, so all you need to do is input the time parameters.
timez <- c(2, 3, 6)
stime <- paste("echo", timez, collapse =" && ")
infile <- "wymo.cor"
outfile <- "out3.dat"
spiCall <- paste("cd", mydir, "&& (" , stime, "&& echo", infile, "&& echo", outfile, " ) | spi_sl_6")
shell(spiCall)
You can construct the command using sprintf :
cmd_name <- "D:\\working\spi_sl_6.exe"
param1 <- "a"
param2 <- "b"
system2(sprintf("%s %s %s",cmd_name,param1,param2))
Or using system2( I prefer this option):
system2(cmd_name, args = c(param1,param2))