I'm a beginner in R. I'm working in a data to expand my knowledge especially in data manipulation.
The task is to split my data set based on a parameter(column). Then to calculate the standard deviation for each group, then to provide some graphs. I did split my data set to about 3000 list, but I'm stuck in converting the lists into separate data sets so I can collect the SD for each data set. Or if there is an efficient way to do it in one code.
this is what I did so far.
xx <- read.table("NetworkRail1.csv",sep=",",header=TRUE)
selected <- select(xx,ID, Location, Top70m)
splitNR <- split(selected, selected$Location %% 0.125)
If your problem is to save the different components of the list separately, you can try this:
list2env(splitNR, envir=.GlobalEnv)
Related
I have data that I want to separate by date, I have managed to do this manually through:
tsssplit <- split(tss, tss$created_at)
and then creating dataframes for each list which I then use.
t1 <- tsssplit[[1]]
t2 <- tsssplit[[2]]
But I don't know how many splits I will need, as sometimes the og data frame may may have 6 dates to split up by, and sometimes it may have 5, etc. So I want to create a for loop.
Within the for loop, I want to incorporate this code, which connects to a function:
bscore3 <- score.sentiment(t3$cleaned_text,pos.words,neg.words,.progress='text')
score3 <- as.integer(bscore3$score[[1]])
Then I want to be able to create a new data frame that has the scores for each list.
So essentially I want the for loop to:
split the data into lists using split
split each list into a separate data frames for each different day
Come out with a score for each data frame
Put that into a new data frame
It doesn't have to be exactly like this as long as I can come up with a visualisation of the scores at the end.
Thanks!
It is not recommended to create separate dataframes in the global environment, they are difficult to keep track of. Put them in a list instead. You have started off well by using split and creating list of dataframes. You can then iterate over each dataframe in the list and apply the function on each one of them.
Using by this would look like as :
by(tss, tss$created_at, function(x) {
bscore3 <- score.sentiment(x$cleaned_text,pos.words,neg.words,.progress='text')
score3 <- as.integer(bscore3$score[[1]])
return(score3)
}) -> result
result
I am having a bit of trouble with trying to script a code in R so that it separates a data frame based on the character in a data frame column without manually specifying a subset command. Below is the script for reproduction in R:
a=c("Model_A","R1",358723.0,171704.0,1.0,36.818500,4.0222700,1.38895000)
b=c("Model_A","R2",358723.0,171704.0,2.6,36.447300,4.0116100,1.37479000)
c=c("Model_A","R3",358723.0,171704.0,5.0,35.615400,3.8092600,1.34301000)
d=c("Model_B","R1",358723.0,171704.0,1.0,39.818300,2.4475600,1.50384000)
e=c("Model_B","R2",358723.0,171704.0,2.6,39.391600,2.4209900,1.48754000)
f=c("Model_B","R3",358723.0,171704.0,5.0,38.442700,2.3618400,1.45126000)
g=c("Model_C","R1",358723.0,171704.0,1.0,31.246400,2.2388000,1.30652000)
h=c("Model_C","R2",358723.0,171704.0,2.6,30.911600,2.2144800,1.29234000)
i=c("Model_C","R3",358723.0,171704.0,5.0,30.166700,2.1603000,1.26077000)
df=data.frame(a,b,c,d,e,f,g,h,i)
df=t(df)
df=data.frame(df)
col_list=list("Model","Receptor.name","X(m.)","Y(m.)","Z(m.)",
"nox","PM10","PM2.5")
colnames(df)=col_list
Essentially what I am trying is to separate the data frame (df) by the Model names ("Model_A", "Model_B", and "Model_C") and store them in new and different data frames. I have been trying to use the following command
df_test=split(df,with(df,interaction(Model,Model)), drop = TRUE)
This command separates the data frame but stores them in lists, and I don't know how to extract the lists individually and store them as data frames. Is there a simpler solution (avoiding the subset command if possible as I need the script to be dynamic and relative) or does anyone know how to use the last command shown above to separate the lists into individual data frames? Also if possible, is it possible to name the data frame after the model?
I apologize if these are a lot of questions but any help would be hugely appreciated! Thank you!
list2env(split(df, df$Model), envir = .GlobalEnv) will give you three dataframes in your global environment, named after the models, containing the relevant rows.
> Model_A
Model Receptor.name X(m.) Y(m.) Z(m.) nox PM10 PM2.5
a Model_A R1 358723 171704 1 36.8185 4.02227 1.38895
b Model_A R2 358723 171704 2.6 36.4473 4.01161 1.37479
c Model_A R3 358723 171704 5 35.6154 3.80926 1.34301
Although I would just keep the list of three dataframes by only using dflist <- split(df, df$Model).
Why a list? Lists allow you the use of lapply - a looping function that applies an operation over every list element. A quick example: Let's say you'd want to get a frequency table for both PM variables in your data for all three datasets.
For single elements in your global environment this would be
table(Model_A$PM10)
table(Model_A$PM2.5)
...
table(Model_C$PM2.5)
With a list, it would be
lapply(dflist, function(x) table(x["PM10"]))
lapply(dflist, function(x) table(x["PM2.5"]))
Right now, it seems to only save some lines of code, but better yet, the output of lapply is again a list, which you can store in an object and further use for different operations. Due to this, you can have a global environment with only a few objects in it, each being lists which contain certain similar objects, like dataframes, tables, summaries or even plots.
I cannot for the life of me figure out where the simple error is in my for loop to perform the same analyses over multiple data frames and output each iteration's new data frame utilizing the variable used along with extra string to identify the new data frame.
Here is my code:
john and jane are 2 data frames among many I am hoping to loop over and compare to bcm to find duplicate results in rows.
x <- list(john,jane)
for (i in x) {
test <- rbind(bcm,i)
test$dups <- duplicated(test$Full.Name,fromLast=T)
test$dups2 <- duplicated(test$Full.Name)
test <- test[which(test$dups==T | test$dups2==T),]
newname <- paste("dupl",i,sep=".")
assign(newname, test)
}
Thus far, I can either get the naming to work correctly without including the x data or the loop to complete correctly without naming the new data frames correctly.
Intended Result: I am hoping to create new data frames dupl.john and dupl.jane to show which rows are duplicated in comparison to bcm.
I understand that lapply() might be better to use and am very open to that form of solution. I could not figure out how to use it to solve my problem, so I turned to the more familiar for loop.
EDIT:
Sorry if I'm not being more clear. I have about 13 data frames in total that I want to run the same analysis over to find the duplicate rows in $Full.Name. I could do the first 4 lines of my loop and then dupl.john <- test 13 times (for each data frame), but I am purposely trying to write a for loop or lapply() to gain more knowledge in R and because I'm sure it is more efficient.
If I understand correctly based on your intended result, maybe using the match_df could be an option.
library(plyr)
dupl.john <- match_df(john, bcm)
dupl.jane <- match_df(jane, bcm)
dupl.john and dupl.jane will be both data frames and both will have the rows that are in these data frames and bcm. Is this what you are trying to achieve?
EDITED after the first comment
library(plyr)
l <- list(john, jane)
res <- lapply(l, function(x) {match_df(x, bcm, on = "Full.Name")} )
dupl.john <- as.data.frame(res[1])
dupl.jane <- as.data.frame(res[2])
Now, res will have a list of the data frames with the matches, based on the column "Full.Name".
I'm writing a function in R that plots some data submitted by the user. The plot area has some polygons defined by a data frame that is constant, does not depend on the submitted data. The dataframe is read from a csv file that has 26 rows and 13 columns.
To make the R file as portable as possible I decided to keep the data frame within the file. As there are quite a lot columns, I come up with the following idea:
csv_data <- c(
"h1,h2,h3
v11,v21,v31
v12,v22,v32
v13,v23,v33"
)
write(csv_data, file="temp.csv")
df <- read.csv("temp.csv",header=T)
OK, I know this is kind of disgusting. but I don't want to reorganize the original csv to make the data frame in the conventional way, as the dataset is quite big:
h1 <- c(v11, v12, v13)
h2 <- c(v21, v22, v23)
h3 <- c(v31, v32, v33)
df <- data.frame(h1,h2,h3)
So, is there any more appropriate way to achieve this? Thank you very much!
If want to make a data.frame from an array of character variables, how about
df<-read.csv(text=csv_data, header=T)
At least that way you don't need the write.table.
I have got a technical problem which, as it seems, I am not able to solve by myself. I ran an estimation with the mcmcglmm package. By results$Sol I get access to the estimated posterior distributions. Applying class() tells me that the object is of class "mcmc". Using as.data.frame() results in a nested data frame which contains other data frames (one data frame which contains many other data frames). I would like to rbind() all data frames within the main data frame in order to produce one data frame (or rather a vector) with all values of all posterior distributions and the name of the (secondary) data frame as a rowname., Any ideas? I would be grateful for every hint!
Update: I didn't manage to produce a useful data set for the purpose of stackoverflow, with all these sampling chains these data sets would be always too large. If you want to help me, please consider to run the following (exemplaric) model
require(MCMCglmm)
data(PlodiaPO)
result <- MCMCglmm(PO ~ plate + FSfamily, data = PlodiaPO, nitt = 50, thin = 2, burn = 10, verbose = FALSE)
result$Sol (an mcmc object) is where all the chains are stored. I want to rbind all chains in order to have a vector with all values of all posterior distributions and the variable names as rownames (or since no duplicated rownames are allowed, as an additional character vector).
I can't (using the example code from MCMCglmm) construct an example where as.data.frame(model$Sol) gives me a dataframe of dataframes. So although there's probably a simple answer I can't check it very easily.
That said, here's an example that might help. Note that if your child dataframes don't have the same colnames then this won't work.
# create a nested data.frame example to work on
a.df <- data.frame(c1=runif(10),c2=runif(10))
b.df <- data.frame(c1=runif(10),c2=runif(10))
full.df <- data.frame(1:10)
full.df$a <- a.df
full.df$b <- b.df
full.df <- full.df[,c("a","b")]
# the solution
res <- do.call(rbind,full.df)
EDIT
Okay, using your new example,
require(MCMCglmm)
data(PlodiaPO)
result<- MCMCglmm(PO ~ plate + FSfamily, data=PlodiaPO,nitt=50,thin=2,burn=10,verbose=FALSE)
melt(do.call(rbind,(as.data.frame(result$Sol))))