Delete rows of Dataframe based on dates in R - r

Question

I have a data frame which has over 4000 columns and 3000 rows. Columns are companies and rows have daily stock closing price. The rows have daily observation data based on dates of the Month. Now, I want is to remove rows in between the last date of of each month i.e. I want to have data of only last day of month based on the avaiable date of month form my data frame. Last date of each month should be according to the date column in my data frame avaiable.
the main challenge and difference of my question to others is date of last month should be according to provided dates in my dataframe. Its a financial data and non trading days and no. of trading days differ from other types of sectors of industry
I illustrate some part of my dataframe.
Date A B
30/12/1999 1 3
04/01/2000 1 3
05/01/2000 1 3
06/01/2000 1 3
07/01/2000 1 3
10/01/2000 1 3
11/01/2000 1 3
12/01/2000 1 3
13/01/2000 1 3
14/01/2000 1 3
17/01/2000 1 3
18/01/2000 1 3
19/01/2000 1 3
20/01/2000 1 3
21/01/2000 1 3
24/01/2000 1 3
25/01/2000 1 3
26/01/2000 1 3
27/01/2000 1 3
28/01/2000 1 3
31/01/2000 1 3
01/02/2000 1 3
02/02/2000 1 3
03/02/2000 1 3
04/02/2000 1 3
07/02/2000 1 3
08/02/2000 1 3
09/02/2000 1 3
10/02/2000 1 3
11/02/2000 1 3
14/02/2000 1 3
15/02/2000 1 3
16/02/2000 1 3
17/02/2000 1 3
18/02/2000 1 3
21/02/2000 1 3
22/02/2000 1 3
23/02/2000 1 3
24/02/2000 1 3
25/02/2000 1 3
28/02/2000 1 3
29/02/2000 1 3
Desired output
Date A B
30/12/1999 1 3
31/01/2000 1 3
29/02/2000 1 3
I would really appreciate your help in this regard.

Answer 1

Using lubridate and dplyr, first parse Date
library(lubridate)
library(dplyr)
df$Date <- dmy(df$Date)
Now we can build a dplyr chain to filter:
df %>% group_by(month = month(Date), year = year(Date)) %>% filter(Date == max(Date))
where we group_by month and year columns we add, and then filter down to only the dates that are the max for each group. It returns
Source: local data frame [3 x 5]
Groups: month, year [3]
Date A B month year
(time) (int) (int) (dbl) (dbl)
1 1999-12-30 1 3 12 1999
2 2000-01-31 1 3 1 2000
3 2000-02-29 1 3 2 2000
You could, of course, do this all in base R if you prefer.
Edit: H/T #Jaap for recommending using group_by to add columns instead of a separate mutate. You could also use slice(which.max(Date)) instead of the filter term; it would likely be a hint faster, if that's a concern.

Answer 2

We can also use data.table
library(data.table)
library(lubridate)
setDT(df1)[, c('month', 'year', 'Date') :={tmp <- dmy(Date)
list(month= month(tmp), year= year(tmp), Date= tmp)}
][, .SD[ which.max(Date)] ,.(month, year)]
# month year Date A B
#1: 12 1999 1999-12-30 1 3
#2: 1 2000 2000-01-31 1 3
#3: 2 2000 2000-02-29 1 3

Answer 3

Here's another possibility:
month_year <- as.numeric(as.factor(sub("^[0-9]*/","",df1$Date)))
df1[!!c(diff(month_year),1),]
# Date A B
#1 30/12/1999 1 3
#21 31/01/2000 1 3
#42 29/02/2000 1 3
This solution does not change the format of the date in the original dataframe. However, it is assumed that the data is chronologically ordered like the data displayed in the OP.
data
df1 <- structure(list(Date = structure(c(41L, 4L, 6L, 7L, 8L, 12L, 14L,
16L, 17L, 18L, 22L, 24L, 26L, 27L, 28L, 32L, 34L, 36L, 37L, 38L,
42L, 1L, 2L, 3L, 5L, 9L, 10L, 11L, 13L, 15L, 19L, 20L, 21L, 23L,
25L, 29L, 30L, 31L, 33L, 35L, 39L, 40L), .Label = c("01/02/2000",
"02/02/2000", "03/02/2000", "04/01/2000", "04/02/2000", "05/01/2000",
"06/01/2000", "07/01/2000", "07/02/2000", "08/02/2000", "09/02/2000",
"10/01/2000", "10/02/2000", "11/01/2000", "11/02/2000", "12/01/2000",
"13/01/2000", "14/01/2000", "14/02/2000", "15/02/2000", "16/02/2000",
"17/01/2000", "17/02/2000", "18/01/2000", "18/02/2000", "19/01/2000",
"20/01/2000", "21/01/2000", "21/02/2000", "22/02/2000", "23/02/2000",
"24/01/2000", "24/02/2000", "25/01/2000", "25/02/2000", "26/01/2000",
"27/01/2000", "28/01/2000", "28/02/2000", "29/02/2000", "30/12/1999",
"31/01/2000"), class = "factor"), A = c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), B = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L
)), .Names = c("Date", "A", "B"), class = "data.frame", row.names = c(NA,
-42L))

Answer 4

I'd create a vector containing the end of month dates for your data like so:
library(dplyr)
df.dates = seq(as.Date("1999-01-01"),as.Date(Sys.Date()),by="months")-1
df.dates = as.data.frame(df.dates)
names(df.dates) = "Date"
df.joined = inner_join(df.dates, df)
This assumes that you have your data in a data frame with the Date column named "Date"
*Re-reading the question, this won't work if the last trading day isn't the last day of the month. #alistaire has a better solution using max(Date)