I have this data frame in R:
steps day month
4758 Tuesday December
9822 Wednesday December
10773 Thursday December
I want to iterate over the data frame and apply a function to the steps column based on the value in the month column. I'm trying to work out the average number of steps per weekday for each month.
I want to output to a new data frame like so where the week days repeat but I only have the average values per day:
average.steps day month
4500 Tuesday December
9000 Wednesday December
1000 Thursday December
I can work out how to work out the averages for the data frame as a whole, but want to use a for loop to apply it just for step values from the same month.
avgsteps <- ddply(DATA, "day", summarise, msteps = mean(steps))
My basic idea for the for function was:
f <- function(m in month) {ddply(DATA, "day", summarise, msteps = mean(steps))}
But it won't process it and throws the error:
Error: unexpected 'in' in "f <- function(m in"
Any help would be greatly appreciated!
EDIT:
SO I've tried #agstudy's suggested fix (below) and it gets the right data structure (single value for each weekday for each month), but the value assigned to each day is identical. I'm a bit confused what could be going wrong.
steps.month.day.avg <- ddply(steps.month.day, .(fitbit.day,fitbit.month), summarise, msteps = mean(steps))
No need to loop here , you should just change the variables to split data frame by,
ddply(DATA, .(day,month), summarise, msteps = mean(steps))
Related
I have a dataframe with Precipitation data for every day from January 1961 to December 2017 that looks like this:
DF=data.frame(Years,Month,Day,Precipitation Value)
I want to create periods of 30 days starting with 1th of January of 1961 so the first period will be 1st january to 30th January 1961 and want R to calculate the number of days without rain (Precipitation Value=0). Then, I want to do the same with the next day: 2th January so the period will be 2nd january-31st January, etc. After that, I need R to create a data frame with all the results for the year 1961. So it should be a data frame with of only one column with values (those values will be the number of days without rain in every period).
Then I need to do the same thing with all the years. Which means I will end up with 56 dataframes (1 for each year) and after that I could make a matrix with all of them (putting each data frame as a row).
The thing is I DO NOT KNOW how to start. I have no idea about how making the loop. I know it should be really easy, but I am having trouble with doing it. Specially i do not know how to tell R to stop every different year and start over and make a NEW data frame/vector with values.
Please provide a reproducible subset of your data so others can help you more effectively. While I cannot teach you how to create a loop from scratch here is some code that I think will help. This code simply calculates the moving 30 day average of precipitation using a simple for loop. You can use dplyr to filter these moving averages by year and create data frames doing that. Note I'm not counting the number of no precipitation days here but you can modify the loop easily to do that if needed
df<-data.frame(year = rep(1967:2002, each =12*30),
month = rep(rep(1:12, each = 30), 36),
day = rep(seq(1,30, by = 1), 432),
precipitation = sample(1:2000, 12*36))
df
#create a column that goes from 1 to however long your dataframe is
df$marker <- 1:nrow(df)
#'Now we create a simple loop to calculate the mean precipitation for
#'every 30 day window. You can modify this to count the number of days with
#'0 precipitation
#'the new column moving precip will tell you the mean precipitation for the
#' past 30 days relative to its postion. So if your on row 55, it will give
#' you the mean precipitation from row 25 to 55
df$movingprecip<-NA
for(i in 1:nrow(df)){
start = i #this says we start at i
end = i + 30 #we end 30 days later from i
if(end > nrow(df)){
#here I tell R to print this if there is not enough days
#in the dataset (30 days) to calculate the 30 day window mean
#this happens at the beginning of the dataset because we need to get to the
#30th row to start calculating means
print("not able to calculate, not 30 days into the data yet")
}else{
#Here I calculate the mean the of the past 30 days of precip
df$movingprecip[end] = mean(df[start:end,4])}
}
I have two data frames: rainfall data collected daily and nitrate concentrations of water samples collected irregularly, approximately once a month. I would like to create a vector of values for each nitrate concentration that is the sum of the previous 5 days' rainfall. Basically, I need to match the nitrate date with the rain date, sum the previous 5 days' rainfall, then print the sum with the nitrate data.
I think I need to either make a function, a for loop, or use tapply to do this, but I don't know how. I'm not an expert at any of those, though I've used them in simple cases. I've searched for similar posts, but none get at this exactly. This one deals with summing by factor groups. This one deals with summing each possible pair of rows. This one deals with summing by aggregate.
Here are 2 example data frames:
# rainfall df
mm<- c(0,0,0,0,5, 0,0,2,0,0, 10,0,0,0,0)
date<- c(1:15)
rain <- data.frame(cbind(mm, date))
# b/c sums of rainfall depend on correct chronological order, make sure the data are in order by date.
rain[ do.call(order, list(rain$date)),]
# nitrate df
nconc <- c(15, 12, 14, 20, 8.5) # nitrate concentration
ndate<- c(6,8,11,13,14)
nitrate <- data.frame(cbind(nconc, ndate))
I would like to have a way of finding the matching rainfall date for each nitrate measurement, such as:
match(nitrate$date[i] %in% rain$date)
(Note: Will match work with as.Date dates?) And then sum the preceding 5 days' rainfall (not including the measurement date), such as:
sum(rain$mm[j-6:j-1]
And prints the sum in a new column in nitrate
print(nitrate$mm_sum[i])
To make sure it's clear what result I'm looking for, here's how to do the calculation 'by hand'. The first nitrate concentration was collected on day 6, so the sum of rainfall on days 1-5 is 5mm.
Many thanks in advance.
You were more or less there!
nitrate$prev_five_rainfall = NA
for (i in 1:length(nitrate$ndate)) {
day = nitrate$ndate[i]
nitrate$prev_five_rainfall[i] = sum(rain$mm[(day-6):(day-1)])
}
Step by step explanation:
Initialize empty result column:
nitrate$prev_five_rainfall = NA
For each line in the nitrate df: (i = 1,2,3,4,5)
for (i in 1:length(nitrate$ndate)) {
Grab the day we want final result for:
day = nitrate$ndate[i]
Take the rainfull sum and it put in in the results column
nitrate$prev_five_rainfall[i] = sum(rain$mm[(day-6):(day-1)])
Close the for loop :)
}
Disclaimer: This answer is basic in that:
It will break if nitrate's ndate < 6
It will be incorrect if some dates are missing in the rain dataframe
It will be slow on larger data
As you get more experience with R, you might use data manipulation packages like dplyr or data.table for these types of manipulations.
#nelsonauner's answer does all the heavy lifting. But one thing to note, in my actual data my dates are not numerical like they are in the example above, they are dates listed as MM/DD/YYYY with the appropriate as.Date(nitrate$date, "%m/%d/%Y").
I found that the for loop above gave me all zeros for nitrate$prev_five_rainfall and I suspected it was a problem with the dates.
So I changed my dates in both data sets to numerical using the difference in number of days between a common start date and the recorded date, so that the for loop would look for a matching number of days in each data frame rather than a date. First, make a column of the start date using rep_len() and format it:
nitrate$startdate <- rep_len("01/01/1980", nrow(nitrate))
nitrate$startdate <- as.Date(all$startdate, "%m/%d/%Y")
Then, calculate the difference using difftime():
nitrate$diffdays <- as.numeric(difftime(nitrate$date, nitrate$startdate, units="days"))
Do the same for the rain data frame. Finally, the for loop looks like this:
nitrate$prev_five_rainfall = NA
for (i in 1:length(nitrate$diffdays)) {
day = nitrate$diffdays[i]
nitrate$prev_five_rainfall[i] = sum(rain$mm[(day-5):(day-1)]) # 5 days
}
Hi I am looking at data to do with prices of commodities throughout a period of a few years. I want to summarize prices by work weeks, not weeks defined by seven day periods since Jan 1st. When I tried:
data <- mutate(data, week = week(strptime(Date, "%m/%d/%Y")))
The lubridate week() function counts "1/13/10" (mdy) as week 2 and "1/14/10" as week 3. I want those to be in the same week. Basically any run of mon-fri in the same week. If the year starts on a wednesday I want week1 to be wed-fri, week2 to start the next monday. I have no data on any weekends. Any thoughts? Thanks
This will give you week number assuming Date column is in Date format (you can use as.Date() to convert):
data <- mutate(data, week = format(Date, '%U'))
If you want week and year, you can use:
data <- mutate(data, week = format(Date, '%Y-%U'))
It will correctly number partial weeks.
Note: week number starts with 00 (but, that should be no problem).
You can also do it WITHOUT dplyr and it's mutate, like this:
data$week <- format(data$Date, '%U')
I have a two variable dataframe (df) in R of daily sales for a ten year period from 2004-07-09 through 2014-12-31. Not every single date is represented in the ten year period, but pretty much most days Monday through Friday.
My objective is to aggregate sales by quarter, convert to a time series object, and run a seasonal decomposition and other time series forecasting.
I am having trouble with the conversion, as ulitmately I receive a error:
time series has no or less than 2 periods
Here's the structure of my code.
# create a time series object
library(xts)
x <- xts(df$amount, df$date)
# create a time series object aggregated by quarter
q.x <- apply.quarterly(x, sum)
When I try to run
fit <- stl(q.x, s.window = "periodic")
I get the error message
series is not periodic or has less than two periods
When I try to run
q.x.components <- decompose(q.x)
# or
decompose(x)
I get the error message
time series has no or less than 2 periods
So, how do I take my original dataframe, with a date variable and an amount variable (sales), aggregate that quarterly as a time series object, and then run a time series analysis?
I think I was able to answer my own question. I did this. Can anyone confirm if this structure makes sense?
library(lubridate)
# add a new variable indicating the calendar year.quarter (i.e. 2004.3) of each observation
df$year.quarter <- quarter(df$date, with_year = TRUE)
library(plyr)
# summarize gift amount by year.quarter
new.data <- ddply(df, .(year.quarter), summarize,
sum = round(sum(amount), 2))
# convert the new data to a quarterly time series object beginning
# in July 2004 (2004, Q3) and ending in December 2014 (2014, Q4)
nd.ts <- ts(new.data$sum, start = c(2004,3), end = c(2014,4), frequency = 4)
I've seen a lot of solutions to working with groups of times or date, like aggregate to sum daily observations into weekly observations, or other solutions to compute a moving average, but I haven't found a way do what I want, which is to pluck relative dates out of data keyed by an additional variable.
I have daily sales data for a bunch of stores. So that is a data.frame with columns
store_id date sales
It's nearly complete, but there are some missing data points, and those missing data points are having a strong effect on our models (I suspect). So I used expand.grid to make sure we have a row for every store and every date, but at this point the sales data for those missing data points are NAs. I've found solutions like
dframe[is.na(dframe)] <- 0
or
dframe$sales[is.na(dframe$sales)] <- mean(dframe$sales, na.rm = TRUE)
but I'm not happy with the RHS of either of those. I want to replace missing sales data with our best estimate, and the best estimate of sales for a given store on a given date is the average of the sales 7 days prior and 7 days later. E.g. for Sunday the 8th, the average of Sunday the 1st and Sunday the 15th, because sales is significantly dependent on day of the week.
So I guess I can use
dframe$sales[is.na(dframe$sales)] <- my_func(dframe)
where my_func(dframe) replaces every stores' sales data with the average of the store's sales 7 days prior and 7 days later (ignoring for the first go around the situation where one of those data points is also missing), but I have no idea how to write my_func in an efficient way.
How do I match up the store_id and the dates 7 days prior and future without using a terribly inefficient for loop? Preferably using only base R packages.
Something like:
with(
dframe,
ave(sales, store_id, FUN=function(x) {
naw <- which(is.na(x))
x[naw] <- rowMeans(cbind(x[naw+7],x[naw-7]))
x
}
)
)