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I currently have multiple data frames (named cont, cont2 .... cont7), and need to combine them
Each data frame has 2 columns; date and a mean temperature value (taken from a netcdf file)
The dates are monthly values, in
cont = 1951-1 to 1960-12
cont7 = 2011-1 to 2014-12
(basically monthly values split into groups of 10 years, from Jan 1951- Dec 2014)
How can I extent my data frame so all values are in 1 table? I want to make it continuous so as to plot a time series
Perhaps I do not understand your problem correctly, but would not rbind() do the Job?
cont_all <- rbind(cont1,cont2,cont3,cont4,con5,cont6,cont7)
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I have a dataset with monthly return of various stocks over a certain period of time where the months are already formatted to consecutively numbered month-ID. To compare those I have imported a .csv file with unique one-month interest rates during that time and saved it as a vector. Now, I want to add this vector to my datasaet. Problem is the difference in length.
My question is: how can I extend this vector to the length of my data by duplicating the elements such that every rate is correctly assigned to the corresponding month?
Say the stock dataset is called stocks with a variable called months. and the interest vector dataset is called interest. I assume they're both in the same order, and have the same months,
Then add the months to the interests with int_dat <- data.frame(months = unique(stocks$months), interest = interest). Add the months to the stocks data with stocks_new <- merge(stocks, int_dat, by = 'month', all = TRUE).
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I have a set of 20 column, each contains number value. I would like to have a function in excel or in r or somewhere else to extract the shared values among all the columns.
Several of the online Venn tools can visualize and list among up to 6 columns.
Any tool?
Thanks
in R, we can use intersect with Reduce to get the common values across all the columns
Reduce(intersect, dftest)
data
dftest <- data.frame(col1 = 1:5, col2 = 2:6, col3 = 3:7)
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How can I scale(x) only certain columns of a dataframe? I have a dataframe with 7 columns and I want to scale only column 3 and 6. The rest should stay as it is.
We can do this with lapply. Subset the columns of interest, loop through them with lapply, assign the output back to the subset of data. Here, we are using c because the outpuf of scale is a matrix with a single column. Using c or as.vector, it gets converted to vector
df[c(3,6)] <- lapply(df[c(3, 6), function(x) c(scale(x)))
Or another option is mutate_at from dplyr
library(dplyr)
df %>%
mutate_at(c(3,6), funs(c(scale(.))))
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I have tried to plot sales order against time. graph is as follows:
I want to replace number of days by dates:
which means replace 0 by 22/09/2016
50 by 11/11/2016
100 by 31/12/2016
150 by 19/02/2017
200 by 10/04/2017
I don't know how to proceed.
You will want to make use of the xts package which can handle daily values
library(xts)
v <- 0:200 # your data here
d <- seq.Date(as.Date('2016-09-22'), as.Date('2017-04-10'), length.out = 201)
plot(xts(v, order.by = as.POSIXct(d)))
Here is a great cheat sheet:
https://www.datacamp.com/community/blog/r-xts-cheat-sheet#gs.1XjXRyI
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I'm trying to calculate the number of retained students using R. The two variables I'm working with are 'registration_date' (mm/dd/yr) and 'date_of_last_login' (mm/dd/yr). A student is considered retained if they logged-in in the preceding 30 days.
ID 1 , 2, 3, 4, 5
registration_date 2/1/15, 2/1/15, 3/15/15, 2/10/15, 4/15/15
date_of_last_login 2/3/15, 3/15/15, 4/30/15, 4/25/15, 5/16/15
I imagine the idea is to create a new variable: 'retained students' but I am not sure how to set up the formula in R.
Assuming you mean the 30 days previous to today:
last_login <- c("2/3/15","3/15/15","4/30/15")
login <- as.Date(last_login, format = '%m/%d/%y')
retained_students <- (Sys.Date()-login < 30)
retained_students
retained_students is then a vector with either TRUE or FALSE for each login