I need a monthly Frequency

[Vittoriodale]

Hi,
I'm doing a project by r.
i need to convert a data that i downloaded with r from daily to monthly:

first.date<-as.Date("2002-01-02")
last.date<-as.Date("2020-10-30")

tickers = c("^GSPC")
l.out = BatchGetSymbols(tickers = tickers,
first.date = first.date, last.date = last.date,
cache.folder = file.path(tempdir(),"BGS_Cache"))
View(l.out$df.tickers)
How can i convert in monthly deadlines?

[siddharthprabhu]

Hi Vittoriodale, welcome to RStudio Community.

It's hard for us to help since we cannot reproduce your data. To facilitate this, you need to provide a reproducible example (or reprex for short). You can learn how to make one by following this guide.

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[Vittoriodale]

Thanks siddharthprabhu for your help.
My problem is not a error. I downloaded a data in days from 2002 Gen to 2020 october, i want to download a monthly frequency in the same period.
For example i used this code:
library(readxl)
library(BatchGetSymbols)
library(rvest)
library(xml2)
library(quantmod)
library(foreign)
library(xts)
library(ggplot2)
library(rsq)
library(pastecs)

first.date<-as.Date("2002-01-02")
last.date<-as.Date("2020-10-30")

#S&P500.rm
tickers = c("^GSPC")
l.out = BatchGetSymbols(tickers = tickers,
first.date = first.date, last.date = last.date,
cache.folder = file.path(tempdir(),"BGS_Cache"))
View(l.out$df.tickers)

this have a daily frequency i need a monthly frequency.
I hope you understand, sorry for my english.

[siddharthprabhu]

Reprexes are not just for errors. They allow others to reconstruct a sample of your data. That is very handy when it comes to figuring out an appropriate solution.

Since I don't know what your data looks like, I'll make up some of my own and illustrate how to aggregate it. You can adapt it to your needs.

library(dplyr, warn.conflicts = FALSE)
library(lubridate, warn.conflicts = FALSE)

df <- tibble::tribble(~date, ~value,
                      "2002-01-02", 20,
                      "2002-01-05", 50,
                      "2002-01-07", 35,
                      "2002-02-03", 10,
                      "2002-02-08", 70,
                      "2003-01-16", 10,
                      "2003-01-25", 90) %>% 
  mutate(date = as.Date(date))

df %>% 
  group_by(year = year(date), month = month(date, label = TRUE)) %>% 
  summarise(total_value = sum(value))
#> `summarise()` regrouping output by 'year' (override with `.groups` argument)
#> # A tibble: 3 x 3
#> # Groups:   year [2]
#>    year month total_value
#>   <dbl> <ord>       <dbl>
#> 1  2002 Jan           105
#> 2  2002 Feb            80
#> 3  2003 Jan           100

^{Created on 2020-11-20 by the reprex package (v0.3.0)}

[Vittoriodale]

I should use the first day of every month from 2002 to 2020.
2002-01-01
2002-02-01
2002-03-01
...
...
2020-08-01
2020-09-01
2020-10-01

[siddharthprabhu]

Sorry, I didn't understand your intent. Do you want to show the first value of each month or do you want to aggregate?

[Vittoriodale]

I want to show the first value of each month.

Thanks for you support and sorry for my bad english!!

[siddharthprabhu]

No worries. You could use dplyr::slice_min() to do that.

library(dplyr, warn.conflicts = FALSE)
library(lubridate, warn.conflicts = FALSE)

df <- tibble::tribble(~date, ~value,
                      "2002-01-02", 20,
                      "2002-01-05", 50,
                      "2002-01-07", 35,
                      "2002-02-03", 10,
                      "2002-02-08", 70,
                      "2003-01-16", 10,
                      "2003-01-25", 90) %>% 
  mutate(date = as.Date(date))

df %>% 
  group_by(year = year(date), month = month(date, label = TRUE)) %>% 
  slice_min(date)
#> # A tibble: 3 x 4
#> # Groups:   year, month [3]
#>   date       value  year month
#>   <date>     <dbl> <dbl> <ord>
#> 1 2002-01-02    20  2002 Jan  
#> 2 2002-02-03    10  2002 Feb  
#> 3 2003-01-16    10  2003 Jan

^{Created on 2020-11-20 by the reprex package (v0.3.0)}