Converting monthly data into daywise table in R

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I have a dataframe like this in R:-

NO_OF_EMPLOYMENT   MONTH    YEAR
127                 9        2017
125                 10       2017
120                 11       2017
130                 12       2017
110                 1        2018
125                 2        2018

I need to convert MONTH data to day wise data and my data frame should look like this:-

    NO_OF_EMPLOYMENT   MONTH     YEAR  DAY
    127                 9        2017   1
    127                 9        2017   2
    127                 9        2017   3
    127                 9        2017   4
    .
    .

    .
    127                 9        2017   30
    125                 10       2017    1
    125                 10       2017    2

and so on for every month. I tried finding a similar question but it didn't work.

data

df <- read.table(text="
NO_OF_EMPLOYMENT   MONTH    YEAR
127                 9        2017
125                 10       2017
120                 11       2017
130                 12       2017
110                 1        2018
125                 2        2018", h = T)

Another way using tidyverse and lubridate -

library(tidyverse)
library(lubridate)

df %>% 
  uncount(
    weights = days_in_month(make_date(YEAR, MONTH)),
    .id = "Day"
  )

Converting monthly data into daywise table in R - r, I have a dataframe like this in R:- NO_OF_EMPLOYMENT MONTH YEAR 127 9 2017 125 10 2017 120 11 2017 130 12 2017 110 1 2018 125 2 2018 I need to  I have day-wise data of interest rate of 15 years from 01-01-2000 to 01-01-2015. I want to convert this data to monthly data, which only having month and year. I want to take mean of the values of all the days in a month and make it one value of that month.

Another lubdridate / tidyverse way :

library(tidyverse)
library(lubridate)
df %>%
  mutate(DAY = map2(
    YEAR, MONTH, ~seq(days_in_month(as.Date(str_c(.x,"-",.y,"-",1)))))) %>%
  unnest
#    NO_OF_EMPLOYMENT MONTH YEAR day
# 1               127     9 2017   1
# 2               127     9 2017   2
# 3               127     9 2017   3
# 4               127     9 2017   4
# ...

Getting monthly data into daily/weekly data - dplyr, Now I need to convert it into days and then calender weeks by the really how to start, as I am very new to R and I have a tight schedule for my  When plotting time series data, you might want to bin the values so that each data point corresponds to the sum for a given month or week. This post will show an easy way to use cut and ggplot2‘s stat_summary to plot month totals in R without needing to reorganize the data into a second data frame.

Here is a simple way to do it:

     mydata<-data.frame(NO_Empl=c(127,125,124),Month=c(9,8,7),Year=c(2017,2018,2017))
library(lubridate)
library(tidyverse)
as.tibble(mydata) %>% 
  mutate(Date=make_date(Year,Month)) %>% 
  select(-Month,-Year) %>% 
  mutate(Day=wday(Date),Day_date=month(Date))

The result:

# A tibble: 3 x 4
  NO_Empl Date         Day Day_date
    <dbl> <date>     <dbl>    <dbl>
1     127 2017-09-01     6        9
2     125 2018-08-01     4        8
3     124 2017-07-01     7        7

Group data by month in R, I often analyze time series data in R — things like daily expenses or webserver statistics. And just as often I want to aggregate the data by  When you are trying to create tables from a matrix in R, you end up with trial.table. The object trial.table looks exactly the same as the matrix trial, but it really isn’t. The difference becomes clear when you transform these objects to a data frame. Take a look at the outcome of this code: > …

0 dependency, 2x faster base R solution:

read.table(text="NO_OF_EMPLOYMENT   MONTH    YEAR
    127                 9        2017
    125                 10       2017
    120                 11       2017
    130                 12       2017
    110                 1        2018
    125                 2        2018", header=TRUE) -> xdf

do.call(
  rbind.data.frame,
  lapply(1:nrow(xdf), function(idx) {
    time <- as.POSIXlt(as.Date(sprintf("%s-%02s-01", xdf$YEAR[idx], xdf$MONTH[idx])))
    time$mday[] <- time$sec[] <- time$min <- time$hour <- 0
    time$mon <- time$mon + 1
    data.frame(
      YEAR = xdf$YEAR[idx],
      MONTH = xdf$MONTH[idx],
      DAY = seq(1:as.POSIXlt(as.POSIXct(time))$mday),
      NO_OF_EMPLOYMENT = xdf$NO_OF_EMPLOYMENT[idx]
    )  
  })
)

Subset a data frame based on date, Utility function to make it easier to select periods from a data frame before sending to a function Can either be numeric e.g. month = 1:6 to select months 1​-6 (January to Selecting date/times in R format can be intimidating for new users. All options are applied in turn making it possible to select quite complex dates  Converting monthly data to quarterly data Dear R users, I have a dataframe where column is has countries, column 2 is dates (monthly) for each countrly, the next 10 columns are my factors where I have measurements for each country and for each date.

Converting monthly data into daywise table in R, Converting monthly data into daywise table in R. Multi tool use. up vote 2 down vote favorite. I have a dataframe like this in R:-. Using the pivot table for the convert time scale of data in excel. Using the pivot table for the convert time scale of data in excel. The used file in this video is downloadable on: https

Summarize Time Series Data by Month or Year Using Tidyverse , Learn how to summarize time series data by day, month or year with on data where you've already converted the date into a date class that R  Data comes to us in many forms, and often our biggest challenge is translating it from the form it came in into the form we need it in. Date-based data is especially challenging – there are days of the week, weekly totals, months with different numbers of days, and holidays that land on different weekdays each year.

How to Convert Weekly Data into Monthly Data in Excel, Building the Monthly Total Formula, Part 1. To begin, we need to set up a monthly table. Next to your data table, build a row of month calendar headings using  Convert to Bi-Monthly Date Sometimes (again, company pay periods are a good example), rather than using a bi-weekly calendar, you want to use a bi-monthly calendar — every date from the 1st through the 14th should be converted to the first day of the month, and every day from the 15th through the end of the month should be coded as the 15th.

Comments
  • Welcome to SO Supriya, and congratulations on your first question. I notice someone has downvoted your question already without providing feedback, which is a bit mean. You might want to edit your question and fill in some detail about what you have researched so far. You may find this guide helpful stackoverflow.com/help/how-to-ask
  • emp_data <- emp_data%>%uncount(weights = days_in_month(as.Date(str_c(Year,"-",Month,"-",1))),.id="Day")
  • No problem. Consider up-voting or marking this as correct using the tick mark at top left of this answer.
  • It gave below result: NO_OF_EMPLOYMENT Month Year day 1 127.24 9 2017 1 2 127.24 9 2017 2 3 127.24 9 2017 3 4 127.24 9 2017 4 5 127.24 9 2017 5 6 127.24 9 2017 6 7 127.24 9 2017 7 8 127.24 9 2017 8 9 127.06 10 2017 1 10 127.06 10 2017 2 It was going till Day 8 only . I guess it is taking the total months:8 and giving sequence till that.
  • This worked for me:- emp_data <- emp_data%>%uncount(weights = days_in_month(as.Date(str_c(Year,"-",Month,"-",1))),.id="Day").... Thank you so much :)