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I am currently trying to make my code dryer by rewriting some parts with the help of functions. One of the functions I am using is:
datasetperuniversity<-function(university,year){assign(paste("data",university,sep=""),subset(get(paste("originaldata",year,sep="")),get(paste("allcollaboration",university,sep=""))==1))}
Executing the function datasetperuniversity("Harvard","2000") would result within the function in something like this:
dataHarvard=subset(originaldata2000,allcollaborationHarvard==1)
The function runs nearly perfectly, except that it does not store a the results in dataHarvard. I read that this is normal in functions, and using the <<- instead of the = could solve this issue, however since I am making use of the assign function this is not really possible, since the = is just the outcome of the assign function.
Here some data:
sales = c(2, 3, 5,6)
numberofemployees = c(1, 9, 20,12)
allcollaborationHarvard = c(0, 1, 0,1)
originaldata = data.frame(sales, numberofemployees, allcollaborationHarvard)
Generally, it's best not to embed data/a variable into the name of an object. So instead of using assign to dataHarvard, make a list data with an element called "Harvard":
# enumerate unis, attaching names for lapply to use
unis = setNames(, "Harvard")
# make a table for each subset with lapply
data = lapply(unis, function(x)
originaldata[originaldata[[ paste0("allcollaboration", x) ]] == 1, ]
)
which gives
> data
$Harvard
sales numberofemployees allcollaborationHarvard
2 3 9 1
4 6 12 1
As seen here, you can use DF[["column name"]] to access a column instead of get as in the OP. Also, see the note in ?subset:
Warning
This is a convenience function intended for use interactively. For programming it is better to use the standard subsetting functions like [, and in particular the non-standard evaluation of argument subset can have unanticipated consequences.
Generally, it's also better not to embed data in column names if possible. If the allcollaboration* columns are mutually exclusive, they can be collapsed to a single categorical variable with values like "Harvard", "Yale", etc. Alternately, it might make sense to put the data in long form.
For more guidance on arranging data, I recommend Hadley Wickham's tidy data paper.
I am trying to create a column which has the mean of a variable according to subsectors of my data set. In this case, the mean is the crime rate of each state calculated from county observations, and then assigning this number to each county relative to the state they are located in. Here is the function wrote.
Create the new column
Data.Final$state_mean <- 0
Then calculate and assign the mean.
for (j in range[1:3136])
{
state <- Data.Final[j, "state"]
Data.Final[j, "state_mean"] <- mean(Data.Final$violent_crime_2009-2014,
which(Data.Final[, "state"] == state))
}
Here is the following error
Error in range[1:3137] : object of type 'builtin' is not subsettable
Very much appreciated if you could, take a few minutes to help a beginner out.
You've got a few problems:
range[1:3136] isn't valid syntax. range(1:3136) is valid syntax, but the range() function just returns the minimum and maximum. You don't need anything more than 1:3136, just use
for (j in 1:3136) instead.
Because of the dash, violent_crime_2009-2014 isn't a standard column name. You'll need to use it in backticks, Data.Final$\violent_crime_2009-2014`` or in quotes with [: Data.Final[["violent_crime_2009-2014"]] or Data.Final[, "violent_crime_2009-2014"]
Also, your code is very inefficient - you re-calculate the mean on every single time. Try having a look at the
Mean by Group R-FAQ. There are many faster and easier methods to get grouped means.
Without using extra packages, you could do
Data.Final$state_mean = ave(x = Data.Final[["violent_crime_2009-2014"]],
Data.Final$state,
FUN = mean)
For friendlier syntax and greater efficiency, the data.table and dplyr packages are popular. You can see examples using them at the link above.
Here is one of many ways this can be done (I'm sure someone will post a tidyverse answer soon if not before I manage to post):
# Data for my example:
data(InsectSprays)
# Note I have a response column and a column I could subset on
str(InsectSprays)
# Take the averages with the by var:
mn <- with(InsectSprays,aggregate(x=list(mean=count),by=list(spray=spray),FUN=mean))
# Map the means back to your data using the by var as the key to map on:
InsectSprays <- merge(InsectSprays,mn,by="spray",all=TRUE)
Since you mentioned you're a beginner, I'll just mention that whenever you can, avoid looping in R. Vectorize your operations when you can. The nice thing about using aggregate, and merge, is that you don't have to worry about errors in your mapping because you get an index shift while looping and something weird happens.
Cheers!
All, I'm very new to R, and can't find anything in the existing questions database that fits my exact issue. I'm running a loop of several regressions (200), and am trying to bind the results/coefficients into a single dataframe that I can export to Excel, with one set of headers. All variables in each regression are identical. The regression part of my loop looks like,
getreg<-OutChg~USInput
stepreg<-lm(getreg,data=mydata)
I'm trying use a "master" dataframe to bind everything together, such as,
master<-data.frame()
master<-rbind(master,stepreg$coefficients)
But I get the response Error in stepreg$coefficients : $ operator is invalid for atomic vectors. Ideally, I'd like to have something where I don't even have to define master<-data.frame().
Any advice is much appreciated. Thank you!
Try using getreg <- as.formula(OutChg ~ USInput) or just put that in the for the lm() function.
If you use str(stepreg) you will probably find that it is not a list but some other data type (in this case an atomic vector).
In order to use rbind() the variable "master" has to exist (as something)
Using data.table
datandfit <- function(x) {
USInput <- rnorm(100, 0, 5)
OutChg <- USInput*5 + 10 + rnorm(100, 0, 5)
mydata <- data.table(USInput, OutChg)
stepreg <- lm(OutChg ~ USInput, data = mydata)
data.table(t(stepreg$coefficients))
}
This will generate some random data, fit a model to it, and return a data.table of the results. You would skip the first three lines of the function, since you would already have data. Then, you can lapply over the function, which will return a list of 200 data.tables, and use rbindlist to combine them all into one data.table.
rbindlist(lapply(1:200, datandfit))
(Intercept) USInput
1: 9.979968 4.909842
2: 10.086159 5.083225
3: 10.285307 4.873432
4: 10.457751 4.905266
5: 9.108176 5.005555
---
196: 10.715356 4.846002
197: 9.938905 4.966180
198: 9.968473 5.073163
199: 10.098703 5.065169
200: 9.538539 4.946085
All, I finally figured this out! As a new user and non-programmer, figuring out how the different R objects work together is cumbersome, but using master<-list() before doing any of the "binding" got it to work...took me a minute or two to realize I can't have the loop designate the master as a list every time or it erases previous aggregation, too...thanks all for your help!
I've got this dataset
install.packages("combinat")
install.packages("quantmod")
library(quantmod)
library(combinat)
library(utils)
getSymbols("AAPL",from="2012-01-01")
data<-AAPL
p1<-4
dO<-data[,1]
dC<-data[,4]
emaO<-EMA(dO,n=p1)
emaC<-EMA(dC,n=p1)
Pos_emaO_dO_UP<-emaO>dO
Pos_emaO_dO_D<-emaO<dO
Pos_emaC_dC_UP<-emaC>dC
Pos_emaC_dC_D<-emaC<dC
Pos_emaC_dO_D<-emaC<dO
Pos_emaC_dO_UP<-emaC>dO
Pos_emaO_dC_UP<-emaO>dC
Pos_emaO_dC_D<-emaO<dC
Profit_L_1<-((lag(dC,-1)-lag(dO,-1))/(lag(dO,-1)))*100
Profit_L_2<-(((lag(dC,-2)-lag(dO,-1))/(lag(dO,-1)))*100)/2
Profit_L_3<-(((lag(dC,-3)-lag(dO,-1))/(lag(dO,-1)))*100)/3
Profit_L_4<-(((lag(dC,-4)-lag(dO,-1))/(lag(dO,-1)))*100)/4
Profit_L_5<-(((lag(dC,-5)-lag(dO,-1))/(lag(dO,-1)))*100)/5
Profit_L_6<-(((lag(dC,-6)-lag(dO,-1))/(lag(dO,-1)))*100)/6
Profit_L_7<-(((lag(dC,-7)-lag(dO,-1))/(lag(dO,-1)))*100)/7
Profit_L_8<-(((lag(dC,-8)-lag(dO,-1))/(lag(dO,-1)))*100)/8
Profit_L_9<-(((lag(dC,-9)-lag(dO,-1))/(lag(dO,-1)))*100)/9
Profit_L_10<-(((lag(dC,-10)-lag(dO,-1))/(lag(dO,-1)))*100)/10
which are given to this frame
frame<-data.frame(Pos_emaO_dO_UP,Pos_emaO_dO_D,Pos_emaC_dC_UP,Pos_emaC_dC_D,Pos_emaC_dO_D,Pos_emaC_dO_UP,Pos_emaO_dC_UP,Pos_emaO_dC_D,Profit_L_1,Profit_L_2,Profit_L_3,Profit_L_4,Profit_L_5,Profit_L_6,Profit_L_7,Profit_L_8,Profit_L_9,Profit_L_10)
colnames(frame)<-c("Pos_emaO_dO_UP","Pos_emaO_dO_D","Pos_emaC_dC_UP","Pos_emaC_dC_D","Pos_emaC_dO_D","Pos_emaC_dO_UP","Pos_emaO_dC_UP","Pos_emaO_dC_D","Profit_L_1","Profit_L_2","Profit_L_3","Profit_L_4","Profit_L_5","Profit_L_6","Profit_L_7","Profit_L_8","Profit_L_9","Profit_L_10")
There is vector with variables for later usage
vector<-c("Pos_emaO_dO_UP","Pos_emaO_dO_D","Pos_emaC_dC_UP","Pos_emaC_dC_D","Pos_emaC_dO_D","Pos_emaC_dO_UP","Pos_emaO_dC_UP","Pos_emaO_dC_D")
I made all possible combination with 4 variables of the vector (there are no depended variables)
comb<-as.data.frame(combn(vector,4))
comb
and get out the ,,nonsense" combination (where are both possible values of variable)
rc<-comb[!sapply(comb, function(x) any(duplicated(sub('_D|_UP', '', x))))]
rc
Then I prepare the first combination to later subseting
var<-paste(rc[,1],collapse=" & ")
var
and subset the frame (with all DVs)
kr<-eval(parse(text=paste0('subset(frame,' , var,')' )))
kr
Now I have the subseted df by the first combination of 4 variables.
Then I used the evaluation function on it
evaluation<-function(x){
s_1<-nrow(x[x$Profit_L_1>0,])/nrow(x)
s_2<-nrow(x[x$Profit_L_2>0,])/nrow(x)
s_3<-nrow(x[x$Profit_L_3>0,])/nrow(x)
s_4<-nrow(x[x$Profit_L_4>0,])/nrow(x)
s_5<-nrow(x[x$Profit_L_5>0,])/nrow(x)
s_6<-nrow(x[x$Profit_L_6>0,])/nrow(x)
s_7<-nrow(x[x$Profit_L_7>0,])/nrow(x)
s_8<-nrow(x[x$Profit_L_8>0,])/nrow(x)
s_9<-nrow(x[x$Profit_L_9>0,])/nrow(x)
s_10<-nrow(x[x$Profit_L_10>0,])/nrow(x)
n_1<-nrow(x[x$Profit_L_1>0,])/nrow(frame)
n_2<-nrow(x[x$Profit_L_2>0,])/nrow(frame)
n_3<-nrow(x[x$Profit_L_3>0,])/nrow(frame)
n_4<-nrow(x[x$Profit_L_4>0,])/nrow(frame)
n_5<-nrow(x[x$Profit_L_5>0,])/nrow(frame)
n_6<-nrow(x[x$Profit_L_6>0,])/nrow(frame)
n_7<-nrow(x[x$Profit_L_7>0,])/nrow(frame)
n_8<-nrow(x[x$Profit_L_8>0,])/nrow(frame)
n_9<-nrow(x[x$Profit_L_9>0,])/nrow(frame)
n_10<-nrow(x[x$Profit_L_10>0,])/nrow(frame)
pr_1<-sum(kr[,"Profit_L_1"])/nrow(kr[,kr=="Profit_L_1"])
pr_2<-sum(kr[,"Profit_L_2"])/nrow(kr[,kr=="Profit_L_2"])
pr_3<-sum(kr[,"Profit_L_3"])/nrow(kr[,kr=="Profit_L_3"])
pr_4<-sum(kr[,"Profit_L_4"])/nrow(kr[,kr=="Profit_L_4"])
pr_5<-sum(kr[,"Profit_L_5"])/nrow(kr[,kr=="Profit_L_5"])
pr_6<-sum(kr[,"Profit_L_6"])/nrow(kr[,kr=="Profit_L_6"])
pr_7<-sum(kr[,"Profit_L_7"])/nrow(kr[,kr=="Profit_L_7"])
pr_8<-sum(kr[,"Profit_L_8"])/nrow(kr[,kr=="Profit_L_8"])
pr_9<-sum(kr[,"Profit_L_9"])/nrow(kr[,kr=="Profit_L_9"])
pr_10<-sum(kr[,"Profit_L_10"])/nrow(kr[,kr=="Profit_L_10"])
mat<-matrix(c(s_1,n_1,pr_1,s_2,n_2,pr_2,s_3,n_3,pr_3,s_4,n_4,pr_4,s_5,n_5,pr_5,s_6,n_6,pr_6,s_7,n_7,pr_7,s_8,n_8,pr_8,s_9,n_9,pr_9,s_10,n_10,pr_10),ncol=3,nrow=10,dimnames=list(c(1:10),c("s","n","pr")))
df<-as.data.frame(mat)
return(df)
}
result<-evaluation(kr)
result
And I need to help in several cases.
1, in evaluation function the way the matrix is made is wrong (s_1,n_1,pr_1 are starting in first column but I need to start the order by rows)
2, I need to use some loop/lapply function to go trough all possible combinations (not only the first one like in this case (var<-paste(rc[,1],collapse=" & ")) and have the understandable output where is evaluation function used on every combination and I will be able to see for which combination of variables is the evaluation done (understand I need to recognize for what is this evaluation made) and compare evaluation results for each combination.
3, This is not main point, BUT I generally want to evaluate all possible combinations (it means for 2:n number of variables and also all combinations in each of them) and then get the best possible combination according to specific DV (Profit_L_1 or Profit_L_2 and so on). And I am so weak in looping now, so, if it this possible, keep in mind what am I going to do with it later.
Thanks, feel free to update, repair or improve the question (if there is something which could be done way more easily, effectively - do it - I am open for every senseful advice.
As you would expect from a DSL aimed at data analysis, R handles missing/incomplete data very well, for instance:
Many R functions have an na.rm flag that when set to TRUE, remove the NAs:
>>> v = mean( c(5, NA, 6, 12, NA, 87, 9, NA, 43, 67), na.rm=T)
>>> v
(5, 6, 12, 87, 9, 43, 67)
But if you want to deal with NAs before the function call, you need to do something like this:
to remove each 'NA' from a vector:
vx = vx[!is.na(a)]
to remove each 'NA' from a vector and replace it w/ a '0':
ifelse(is.na(vx), 0, vx)
to remove entire each row that contains 'NA' from a data frame:
dfx = dfx[complete.cases(dfx),]
All of these functions permanently remove 'NA' or rows with an 'NA' in them.
Sometimes this isn't quite what you want though--making an 'NA'-excised copy of the data frame might be necessary for the next step in the workflow but in subsequent steps you often want those rows back (e.g., to calculate a column-wise statistic for a column that has missing rows caused by a prior call to 'complete cases' yet that column has no 'NA' values in it).
to be as clear as possible about what i'm looking for: python/numpy has a class, masked array, with a mask method, which lets you conceal--but not remove--NAs during a function call. Is there an analogous function in R?
Exactly what to do with missing data -- which may be flagged as NA if we know it is missing -- may well differ from domain to domain.
To take an example related to time series, where you may want to skip, or fill, or interpolate, or interpolate differently, ... is that just the (very useful and popular) zoo has all these functions related to NA handling:
zoo::na.approx zoo::na.locf
zoo::na.spline zoo::na.trim
allowing to approximate (using different algorithms), carry-forward or backward, use spline interpolation or trim.
Another example would be the numerous missing imputation packages on CRAN -- often providing domain-specific solutions. [ So if you call R a DSL, what is this? "Sub-domain specific solutions for domain specific languages" or SDSSFDSL? Quite a mouthful :) ]
But for your specific question: no, I am not aware of a bit-level flag in base R that allows you to mark observations as 'to be excluded'. I presume most R users would resort to functions like na.omit() et al or use the na.rm=TRUE option you mentioned.
It's a good practice to look at the data, hence infer about the type of missing values: is it MCAR (missing complete and random), MAR (missing at random) or MNAR (missing not at random)? Based on these three types, you can study the underlying structure of missing values and conclude whether imputation is at all applicable (you're lucky if it's not MNAR, 'cause, in that case, missing values are considered non-ignorable, and are related to some unknown underlying influence, factor, process, variable... whatever).
Chapter 3. in "Interactive and Dynamic Graphics for Data Analyst with R and GGobi" by Di Cook and Deborah Swayne is great reference regarding this topic.
You'll see norm package in action in this chapter, but Hmisc package has data imputation routines. See also Amelia, cat (for categorical missings imputation), mi, mitools, VIM, vmv (for missing data visualisation).
Honestly, I still don't quite understand is your question about statistics, or about R missing data imputation capabilities? I reckon that I've provided good references on second one, and about the first one: you can replace your NA's either with central tendency (mean, median, or similar), hence reduce the variability, or with random constant "pulled out" of observed (recorded) cases, or you can apply regression analysis with variable that contains NA's as criteria, and other variables as predictors, then assign residuals to NA's... it's an elegant way to deal with NA's, but quite often it would not go easy on your CPU (I have Celeron on 1.1GHz, so I have to be gentle).
This is an optimization problem... there's no definite answer, you should decide what/why are you sticking with some method. But it's always good practice to look at the data! =)
Be sure to check Cook & Swayne - it's an excellent, skilfully written guide. "Linear Models with R" by Faraway also contains a chapter about missing values.
So there.
Good luck! =)
The function na.exclude() sounds like what you want, although it's only an option for some (important) functions.
In the context of fitting and working with models, R has a family of generic functions for dealing with NAs: na.fail(), na.pass(), na.omit(), and na.exclude(). These are, in turn, arguments for some of R's key modeling functions, such as lm(), glm(), and nls() as well as functions in MASS, rpart, and survival packages.
All four generic functions basically act as filters. na.fail() will only pass the data through if there are no NAs, otherwise it fails. na.pass() passes all cases through. na.omit() and na.exclude() will both leave out cases with NAs and pass the other cases through. But na.exclude() has a different attribute that tells functions processing the resulting object to take into account the NAs. You could see this attribute if you did attributes(na.exclude(some_data_frame)). Here's a demonstration of how na.exclude() alters the behavior of predict() in the context of a linear model.
fakedata <- data.frame(x = c(1, 2, 3, 4), y = c(0, 10, NA, 40))
## We can tell the modeling function how to handle the NAs
r_omitted <- lm(x~y, na.action="na.omit", data=fakedata)
r_excluded <- lm(x~y, na.action="na.exclude", data=fakedata)
predict(r_omitted)
# 1 2 4
# 1.115385 1.846154 4.038462
predict(r_excluded)
# 1 2 3 4
# 1.115385 1.846154 NA 4.038462
Your default na.action, by the way, is determined by options("na.action") and begins as na.omit() but you can set it.