my question is a follow-up to this question on imputation by group using "mice":
multiple imputation and multigroup SEM in R
The code in the answer works fine as far as the imputation part goes. But afterwards I am left with a list of actually complete data but more than one set. The sample looks as follows:
'Set up data frame'
df.g1<-data.frame(ID=rep("A",5),x1=floor(runif(5,0,2)),x2=floor(runif(5,10,20)),x3=floor(runif(5,100,150)))
df.g2<-data.frame(ID=rep("B",5),x1=floor(runif(5,0,2)),x2=floor(runif(5,25,50)),x3=floor(runif(5,200,250)))
df.g3<-data.frame(ID=rep("C",5),x1=floor(runif(5,4,5)),x2=floor(runif(5,75,99)),x3=floor(runif(5,500,550)))
df<-rbind(df.g1,df.g2,df.g3)
'Introduce NAs'
df$x1[rbinom(15,1,0.1)==1]<-NA
df$x2[rbinom(15,1,0.1)==1]<-NA
df$x3[rbinom(15,1,0.1)==1]<-NA
df
'Impute values by group:'
df.clean<-lapply(split(df,df$ID), function(x) mice::complete(mice(df,m=5)))
df.clean
As you can see, df.clean is a list of 3. One element per group. But each element containing a complete data set I am looking for.
The original answer suggests to rbind() the obtained data in df.clean which leaves me with a new data set with 45 (3x the original size) observations.
Here is the original code for the last step:
imputed.both <- do.call(args = df.clean, what = rbind)
Which data is the "right" one? And why the last step?
Thanks a bunch!
There's a bug in the code, i have a edited version below that works:
#Set up data frame
set.seed(12345)
df.g1<-data.frame(ID=rep("A",5),x1=floor(runif(5,0,2)),x2=floor(runif(5,10,20)),x3=floor(runif(5,100,150)))
df.g2<-data.frame(ID=rep("B",5),x1=floor(runif(5,0,2)),x2=floor(runif(5,25,50)),x3=floor(runif(5,200,250)))
df.g3<-data.frame(ID=rep("C",5),x1=floor(runif(5,4,5)),x2=floor(runif(5,75,99)),x3=floor(runif(5,500,550)))
df<-rbind(df.g1,df.g2,df.g3)
#Introduce NAs
df$x1[rbinom(15,1,0.1)==1]<-NA
df$x2[rbinom(15,1,0.1)==1]<-NA
df$x3[rbinom(15,1,0.1)==1]<-NA
# check NAs
colSums(is.na(df))
#Impute values by group:
# here's the bug
df.clean<-lapply(split(df,df$ID), function(x) mice::complete(mice(x,m=5)))
imputed.both <- do.call(args = df.clean, what = rbind)
dim(imputed.both)
# returns 15,4
In the code in the question, you have
df.clean<-lapply(split(df,df$ID), function(x) mice::complete(mice(df,m=5)))
dim(do.call(rbind,df.clean))
#this returns 45,4
The function is specified with "x" but you call df from the global environment. Hence you impute on the complete df.
So to answer your question, if you do this step:
split(df,df$ID)
You split your data frame into a list of data.frames with only A,B or Cs. Then if you lapply through this list, you get
df.clean<-lapply(split(df,df$ID), function(x) mice::complete(mice(x,m=5)))
names(df.clean)
lapply(df.clean,dim)
each item of the list df.clean contains a subset of the original df, with ID being A, B or C. Now you combine this list together into a data.frame using:
imputed.both <- do.call(rbind,df.clean)
Related
Good morning everyone.
Please I do have a problem that I have not been able to solve for quite some time now.(please take a look at the image link to see a screen shot of my data set) https://i.stack.imgur.com/g2eTM.jpg
I have a column of data (status) containing two set of values (1 and 2). These are dummies representing two categories (or status) of dependent Variables (say Pp and Pt) that I need for a regression. their actual values are contained the last column Pp.Pt (Pp.Pt is just a name nothing more).
I need to run two separate regressions each using either Pp or Pt (meaning using their respective values in the Pp.Pt column (each value in the last column is either of status 1 or of status 2) . **My question is How do I separte them or group them into these two categories 1= Pp and 2 = Pt so that i could clearly identitify and group them.
https://i.stack.imgur.com/g2eTM.jpg
Thank you very much for your kind help.
Best
Ludovic
Split-Apply-Combine method :
# Using the mtcars dataset as an example:
df <- mtcars
# Allocate some memory for a list storing the split data.frame:
# df_list => empty list with the number of elements of the unique
# values of the cyl vector
df_list <- vector("list", length(unique(df$cyl)))
# Split the data.frame by the cyl vector:
df_list <- split(df, df$cyl)
# Apply the regression model, return the summary data:
lapply(df_list, function(x){
summary(lm(mpg ~ hp, data = x))
}
)
this approach can fix your issue
yourdata %>%
mutate(classofyourcolumn=ifelse(columntosplit<quantile(columntosplit,0.5),1,0))
I have a data set with Air Quality Data. The Data Frame is a matrix of 153 rows and 5 columns.
I want to find the mean of the first column in this Data Frame.
There are missing values in the column, so I want to exclude those while finding the mean.
And finally I want to do that using Control Structures (for loops and if-else loops)
I have tried writing code as seen below. I have created 'y' instead of the actual Air Quality data set to have a reproducible example.
y <- c(1,2,3,NA,5,6,NA,NA,9,10,11,NA,13,NA,15)
x <- matrix(y,nrow=15)
for(i in 1:15){
if(is.na(data.frame[i,1]) == FALSE){
New.Vec <- c(x[i,1])
}
}
print(mean(New.Vec))
I expected the output to be the mean. Though the error I received is this:
Error: object 'New.Vec' not found
One line of code, no need for for loop.
mean(data.frame$name_of_the_first_column, na.rm = TRUE)
Setting na.rm = TRUE makes the mean function ignore NAs.
Here, we can make use of na.aggregate from zoo
library(zoo)
df1[] <- na.aggregate(df1)
Assuming that 'df1' is a data.frame with all numeric columns and wanted to fill the NA elements with the corresponding mean of that column. na.aggregate, by default have the fun.aggregate as mean
can't see your data, but probably like this? the vector needed to be initialized. better to avoid loops in R when you can...
myDataFrame <- read.csv("hw1_data.csv")
New.Vec <- c()
for(i in 1:153){
if(!is.na(myDataFrame[i,1])){
New.Vec <- c(New.Vec, myDataFrame[i,1])
}
}
print(mean(New.Vec))
I have a data set of plant demographics from 5 years across 10 sites with a total of 37 transects within the sites. Below is a link to a GoogleDoc with some of the data:
https://docs.google.com/spreadsheets/d/1VT-dDrTwG8wHBNx7eW4BtXH5wqesnIDwKTdK61xsD0U/edit?usp=sharing
In total, I have 101 unique combinations.
I need to subset each unique set of data, so that I can run each through some code. This code will give me one column of output that I need to add back to the original data frame so that I can run LMs on the entire data set. I had hoped to write a for-loop where I could subset each unique combination, run the code on each, and then append the output for each model back onto the original dataset. My attempts at writing a subset loop have all failed to produce even a simple output.
I created a column, "SiteTY", with unique Site, Transect, Year combinations. So "PWR 832015" is site PWR Transect 83 Year 2015. I tried to use that to loop through and fill an empty matrix, as proof of concept.
transect=unique(dat$SiteTY)
ntrans=length(transect)
tmpout=matrix(NA, nrow=ntrans, ncol=2)
for (i in 1:ntrans) {
df=subset(dat, SiteTY==i)
tmpout[i,]=(unique(df$SiteTY))
}
When I do this, I notice that df has no observations. If I replace "i" with a known value (like PWR 832015) and run each line of the for-loop individually, it populates correctly. If I use is.factor() for i or PWR 832015, both return FALSE.
This particular code also gives me the error:
Error in [,-(*tmp*, , i, value=mean(df$Year)) : subscript out of bounds
I can only assume this happens because the data frame is empty.
I've read enough SO posts to know that for-loops are tricky, but I've tried more iterations than I can remember to try to make this work in the last 3 years to no avail.
Any tips on loops or ways to avoid them while getting the output I need would be appreciated.
Per your needs, I need to subset each unique set of data, run a function, take the output and calculate a new value, consider two routes:
Using ave if your function expects and returns a single numeric column.
Using by if your function expects a data frame and returns anything.
ave
Returns a grouped inline aggregate column with repeated value for every member of group. Below, with is used as context manager to avoid repeated dat$ references.
# BY SITE GROUPING
dat$New_Column <- with(dat, ave(Numeric_Column, Site, FUN=myfunction))
# BY SITE AND TRANSECT GROUPINGS
dat$New_Column <- with(dat, ave(Numeric_Column, Site, Transect, FUN=myfunction))
# BY SITE AND TRANSECT AND YEAR GROUPINGS
dat$New_Column <- with(dat, ave(Numeric_Column, Site, Transect, Year, FUN=myfunction))
by
Returns a named list of objects or whatever your function returns for each possible grouping. For more than one grouping, tryCatch is used due to possibly empty data frame item from all possible combinations where your myfunction can return an error.
# BY SITE GROUPING
obj_list <- by(dat, dat$Site, function(sub) {
myfunction(sub) # RUN ANY OPERATION ON sub DATA FRAME
})
# BY SITE AND TRANSECT GROUPINGS
obj_list <- by(dat, dat[c("Site", "Transect")], function(sub) {
tryCatch(myfunction(sub),
error = function(e) NULL)
})
# BY SITE AND TRANSECT AND YEAR GROUPINGS
obj_list <- by(dat, dat[c("Site", "Transect", "Year")], function(sub) {
tryCatch(myfunction(sub),
error = function(e) NULL)
})
# FILTERS OUT ALL NULLs (I.E., NO LENGTH)
obj_list <- Filter(length, obj_list)
# BUILDS SINGLE OUTPUT IF MATRIX OR DATA FRAME
final_obj <- do.call(rbind, obj_list)
Here's another approach using the dplyr library, in which I'm creating a data.frame of summary statistics for each group and then just joining it back on:
library(dplyr)
# Group by species (site, transect, etc) and summarise
species_summary <- iris %>%
group_by(Species) %>%
summarise(mean.Sepal.Length = mean(Sepal.Length),
mean.Sepal.Width = mean(Sepal.Width))
# A data.frame with one row per species, one column per statistic
species_summary
# Join the summary stats back onto the original data
iris_plus <- iris %>% left_join(species_summary, by = "Species")
head(iris_plus)
here is my code:
file.number <- c(1:29)
data <- setNames(lapply(paste0(file.number, ".csv"), read.csv), paste0(file.number, ".data"))
n <- c(1:3,10:15,21:26)
sw <- na.omit(data[[n]]$RT[data[[n]]$rep.sw=="sw"])
rep <-na.omit(data[[n]]$RT[data[[n]]$rep.sw=="rep"])
The problem is that 3rd line - if n = 1, it works, but if I include multiple numbers I get an error "recursive indexing fail." Is there a way I can access multiple indexes at once?
Thanks R Community! Any advice would be much appreciated!
Too long for a comment.
It looks like data is a list of data frames. The list elements are named, e.g. 1.data, 2.data, etc. and each data frame has, among other things, columns named RT and rep.sw. So, like this:
## representative example???
df <- data.frame(RT=1:100,rep.sw=sample(c("sw","rep"),100,replace=TRUE))
data <- setNames(lapply(1:29,function(i)df),paste0(1:29,".data"))
You seem to want to remove NA's from the RT column of each data frame for rows where res.sw=="sw" (or "rep").
If that is correct, then something like this should work:
sw <- lapply(data[n],function(df) with(df,na.omit(RT[rep.sw=="sw"])))
rep <- lapply(data[n],function(df) with(df,na.omit(RT[rep.sw=="rep"])))
This code will pass the data frames identified in n to the function one at a time, and for each of those return the rows of column RT for which rep.sw="sw", with NA's omitted. The result will be a list of vectors.
I notice that most of the columns are imported as factors, which is probably a bad idea. You might want to import using:
data <- setNames(lapply(paste0(file.number, ".csv"), read.csv, stringsAsFactors=FALSE),
paste0(file.number, ".data"))
Update: My NOAA GHCN-Daily weather station data functions have since been cleaned and merged into the rnoaa package, available on CRAN or here: https://github.com/ropensci/rnoaa
I'm designing a R function to calculate statistics across a data set comprised of multiple data frames. In short, I want to pull data frames by class based on a reference data frame containing the names. I then want to apply statistical functions to values for the metrics listed for each given day. In effect, I want to call and then overlay a list of data frames to calculate functions on a vector of values for every unique date and metric where values are not NA.
The data frames are iteratively read into the workspace from file based on a class variable, using the 'by' function. After importing the files for a given class, I want to rbind() the data frames for that class and each user-defined metric within a range of years. I then want to apply a concatenation of user-provided statistical functions to each metric within a class that corresponds to a given value for the year, month, and day (i.e., the mean [function] low temperature [class] on July 1st, 1990 [date] reported across all locations [data frames] within a given region [class]. I want the end result to be new data frames containing values for every date within a region and a year range for each metric and statistical function applied. I am very close to having this result using the aggregate() function, but I am having trouble getting reasonable results out of the aggregate function, which is currently outputting NA and NaN for most functions other than the mean temperature. Any advice would be much appreciated! Here is my code thus far:
# Example parameters
w <- c("mean","sd","scale") # Statistical functions to apply
x <- "C:/Data/" # Folder location of CSV files
y <- c("MaxTemp","AvgTemp","MinTemp") # Metrics to subset the data
z <- c(1970:2000) # Year range to subset the data
CSVstnClass <- data.frame(CSVstations,CSVclasses)
by(CSVstnClass, CSVstnClass[,2], function(a){ # Station list by class
suppressWarnings(assign(paste(a[,2]),paste(a[,1]),envir=.GlobalEnv))
apply(a, 1, function(b){ # Data frame list, row-wise
classData <- data.frame()
sapply(y, function(d){ # Element list
CSV_DF <- read.csv(paste(x,b[2],"/",b[1],".csv",sep="")) # Read in CSV files as data frames
CSV_DF1 <- CSV_DF[!is.na("Value")]
CSV_DF2 <- CSV_DF1[which(CSV_DF1$Year %in% z & CSV_DF1$Element == d),]
assign(paste(b[2],"_",d,sep=""),CSV_DF2,envir=.GlobalEnv)
if(nrow(CSV_DF2) > 0){ # Remove empty data frames
classData <<- rbind(classData,CSV_DF2) # Bind all data frames by row for a class and element
assign(paste(b[2],"_",d,"_bound",sep=""),classData,envir=.GlobalEnv)
sapply(w, function(g){ # Function list
# Aggregate results of bound data frame for each unique date
dataFunc <- aggregate(Value~Year+Month+Day+Element,data=classData,FUN=g,na.action=na.pass)
assign(paste(b[2],"_",d,"_",g,sep=""),dataFunc,envir=.GlobalEnv)
})
}
})
})
})
I think I am pretty close, but I am not sure if rbind() is performing properly, nor why the aggregate() function is outputting NA and NaN for so many metrics. I was concerned that the data frames were not being bound together or that missing values were not being handled well by some of the statistical functions. Thank you in advance for any advice you can offer.
Cheers,
Adam
You've tackled this problem in a way that makes it very hard to debug. I'd recommend switching things around so you can more easily check each step. (Using informative variable names also helps!) The code is unlikely to work as is, but it should be much easier to work iteratively, checking that each step has succeeded before continuing to the next.
paths <- dir("C:/Data/", pattern = "\\.csv$")
# Read in CSV files as data frames
raw <- lapply(paths, read.csv, str)
# Extract needed rows
filter_metrics <- c("MaxTemp", "AvgTemp", "MinTemp")
filter_years <- 1970:2000
filtered <- lapply(raw, subset,
!is.na(Value) & Year %in% filter_years & Element %in% filter_metrics)
# Drop any empty data frames
rows <- vapply(filtered, nrow, integer(1))
filtered <- filtered[rows > 0]
# Compute aggregates
my_aggregate <- function(df, fun) {
aggregate(Value ~ Year + Month + Day + Element, data = df, FUN = fun,
na.action = na.pass)
}
means <- lapply(filtered, my_aggregate, mean)
sds <- lapply(filtered, my_aggregate, sd)
scales <- lapply(filtered, my_aggregate, scale)