I have a JSON file "adjFloatTest.data" .In R, i read the field "Volume" from that file using the following code:
json <- fromJSON("adjFloatTest.data")
volume <- json$volume
the value of the volume is as follows
> volume
$AAPL
$AAPL[[1]]
1980-12-12
16751200
$AAPL[[2]]
1980-12-15
100424081
$AAPL[[3]]
1980-12-16
0.1177374
$AAPL[[4]]
1980-12-17
7164476
$AAPL[[5]]
1980-12-18
5364366
Each elements corresponding to company,date,value. I want to store each dates into a list. How it is possible?
This will give you the list of dates
sapply(volume,names)
the following should work:
sapply(volume, function(x) lapply(x, "[[", 1))
but a reproducible example that could be copied+pasted would be helpful.
If the above doesnt work, please use something like dput(volume[1:2]) to offer some workable sample data.
Related
Good morning.
I want to use the the following rest: https://rest.ensembl.org/documentation/info/sequence_id_post
I have the vector object (ids) in R:
> ids
[1] "NM_007294.3:c.932_933insT" "NM_007294.3:c.1883C>T" "NM_007294.3:c.2183A>C"
[4] "NM_007294.3:c.2321C>T" "NM_007294.3:c.4585G>A" "NM_007294.3:c.4681C>A"
I have to put this vector(ids) with more than 200 variables in the body= ids variable (bellow), according to the example of code below, for it works:
Code:
library(httr)
library(jsonlite)
library(xml2)
server <- "https://rest.ensembl.org"
ext <- "/vep/human/hgvs"
r <- POST(paste(server, ext, sep = ""), content_type("application/json"), accept("application/json"), body = '{ "hgvs_notations" : ["NM_007294.3:c.932_933insT", "NM_007294.3:c.1883C>T"] }')
stop_for_status(r)
head(fromJSON(toJSON(content(r))))
I know it's a json format, but when I convert my variable ids to json it's not in the correct format.
Do you have any suggestions?
Thanks for any help.
Leandro
I think that NM_007294.3:c.2321C>T is not a valid query to /sequence/id REST endpoint. It contains a sequence id (NM_007294.3) and a variant (c.2321C>T) and if you understood this literally, you are asking the server a letter T, since this call returns sequences.
Valid query would contain only sequence ids and you can use it like that (provided you have your ids in a vector):
r <- POST(paste(server, ext, sep = ""), content_type("application/json"), accept("application/json"), body = paste0('{ "ids" :', jsonlite::toJSON(ids), ' }')
Depending on the downstream scenario, making your ids unique might help/speed things up.
I have a periodic process in R that yields me a data.frame.
I want to use this data.frame to create a dropdown selector with AngularJS.
My final data.frame will look more or less as follows (my real example might have a deeper hierarchical structure):
DF<-data.frame(hie1=c(rep("Cl1",2),"Cl2"),hie2=c("Cl1op1","Cl1op2","Clop1"),
hie3=c("/first.html","/second.html","/third.html"))
I need to convert that data.frame into a JSON with the following structure :
{
"Cl1":{"Cl1op1":"/first.html","Cl1op2": "/second.html"},
"Cl2":{"Cl2op1":"/third.html"}
}
So far, I have tried all the toJSON commands of the rjson and RJSONIO packages for the data.frame with and without column names:
library(rjson)
#library(RJSONIO)
DF2<-DF
colnames(DF2)<-NULL
cat(toJSON(DF))
cat(toJSON(DF2))
I thought about using reshape2's dcast function beforeusing toJSON, but I do not know what kind of structure I need to achieve my goal.
I also used the functions toJSON2 an toJSONArray from the rCharts with no success.
Is there an appropriate transformation in R to get the output I am looking for?
P.S. (I do not mind having [] instead of {})
EDIT:
I have created a couple of functions (included below) to fulfil my needs.
However, they are not too clean and I believe that there must be a better way to perform this transformation in R.
I keep this question open expecting a better solution.
linktwo<-function(V){
paste0(sapply(V,function(x) paste0("'",toString(x),"'")),collapse=":")
}
pastehier<-function(DF){
if(ncol(DF)==2){
return(paste0(apply(DF,1,linktwo),collapse=","))
}else{
u<-unique(DF[,1])
output=character()
for(i in u){
output<-append(output,paste0(paste0("'",i,"'"),":{",pastehier(DF[DF[,1]==i,-1]),
"}"))
}
return(paste0(output,collapse=","))
}
}
pastehier(DF)
I do not fully understand your request and maybe my solution is useless, but here is a try:
library(reshape2)
prova <- dcast(DF, hie1 ~ ... )
toJSON(prova, pretty = TRUE)
[
{
"hie1": "Cl1",
"Cl1op1": "/first.html",
"Cl1op2": "/second.html"
},
{
"hie1": "Cl2",
"Clop1": "/third.html"
}
]
where:
> prova
hie1 Cl1op1 Cl1op2 Clop1
1 Cl1 /first.html /second.html <NA>
2 Cl2 <NA> <NA> /third.html
I have a big json file, containing 18 fields, some of which contain some other subfields. I read the file in R in the following way:
json_file <- "daily_profiles_Bnzai_20150914_20150915_20150914.json"
data <- fromJSON(sprintf("[%s]", paste(readLines(json_file), collapse=",")))
This gives me a giant list with all the fields contained in the json file. I want to make it into a data.frame and do some operations in the meantime. For example if I do:
doc_length <- data.frame(t(apply(as.data.frame(data$doc_lenght_map), 1, unlist)))
os <- data.frame(t(apply(as.data.frame(data$operating_system), 1, unlist)))
navigation <- as.data.frame(data$navigation)
monday <- data.frame(t(apply(navigation[,grep("Monday",names(data$navigation))],1,unlist)))
Monday <- data.frame(apply(monday, 1, sum))
works fine, I get what I want, with all the right subfields and then I want to join them in a final data.frame that I will use to do other operations.
Now, I'd like to do something like that on the subset of fields where I don't need to do operations. So, for example, the days of the week contained in navigation are not included. I'd like to have something like (suppose I have a data.frame df):
for(name in names(data))
{
df <- cbind(df, data.frame(t(apply(as.data.frame(data$name), 1, unlist)))
}
The above loop gives me errors. So, what I want to do is finding a way to access all the fields of the list in an automatic way, as in the loop, where the iterator "name" takes on all the fields of the list, without having to call them singularly and then doing some operations with those fields. I tried even with
for(name in names(data))
{
df <- cbind(df, data.frame(t(apply(as.data.frame(data[name]), 1, unlist)))
}
but it doesn't take all of the subfields. I also tried with
data[, name]
but it doesn't work either. So I think I need to use the "$" operator.
Is it possible to do something like that?
Thank you a lot!
Davide
Like the other commenters, I am confused, but I will throw this out to see if it might point you in the right direction.
# make mtcars a list as an example
data <- lapply(mtcars,identity)
do.call(
cbind,
lapply(
names(data),
function(name){
data.frame(data[name])
}
)
)
This question is about a generic mechanism for converting any collection of non-cyclical homogeneous or heterogeneous data structures into a dataframe. This can be particularly useful when dealing with the ingestion of many JSON documents or with a large JSON document that is an array of dictionaries.
There are several SO questions that deal with manipulating deeply nested JSON structures and turning them into dataframes using functionality such as plyr, lapply, etc. All the questions and answers I have found are about specific cases as opposed to offering a general approach for dealing with collections of complex JSON data structures.
In Python and Ruby I've been well-served by implementing a generic data structure flattening utility that uses the path to a leaf node in a data structure as the name of the value at that node in the flattened data structure. For example, the value my_data[['x']][[2]][['y']] would appear as result[['x.2.y']].
If one has a collection of these data structures that may not be entirely homogeneous the key to doing a successful flattening to a dataframe would be to discover the names of all possible dataframe columns, e.g., by taking the union of all keys/names of the values in the individually flattened data structures.
This seems like a common pattern and so I'm wondering whether someone has already built this for R. If not, I'll build it but, given R's unique promise-based data structures, I'd appreciate advice on an implementation approach that minimizes heap thrashing.
Hi #Sim I had cause to reflect on your problem yesterday define:
flatten<-function(x) {
dumnames<-unlist(getnames(x,T))
dumnames<-gsub("(*.)\\.1","\\1",dumnames)
repeat {
x <- do.call(.Primitive("c"), x)
if(!any(vapply(x, is.list, logical(1)))){
names(x)<-dumnames
return(x)
}
}
}
getnames<-function(x,recursive){
nametree <- function(x, parent_name, depth) {
if (length(x) == 0)
return(character(0))
x_names <- names(x)
if (is.null(x_names)){
x_names <- seq_along(x)
x_names <- paste(parent_name, x_names, sep = "")
}else{
x_names[x_names==""] <- seq_along(x)[x_names==""]
x_names <- paste(parent_name, x_names, sep = "")
}
if (!is.list(x) || (!recursive && depth >= 1L))
return(x_names)
x_names <- paste(x_names, ".", sep = "")
lapply(seq_len(length(x)), function(i) nametree(x[[i]],
x_names[i], depth + 1L))
}
nametree(x, "", 0L)
}
(getnames is adapted from AnnotationDbi:::make.name.tree)
(flatten is adapted from discussion here How to flatten a list to a list without coercion?)
as a simple example
my_data<-list(x=list(1,list(1,2,y='e'),3))
> my_data[['x']][[2]][['y']]
[1] "e"
> out<-flatten(my_data)
> out
$x.1
[1] 1
$x.2.1
[1] 1
$x.2.2
[1] 2
$x.2.y
[1] "e"
$x.3
[1] 3
> out[['x.2.y']]
[1] "e"
so the result is a flattened list with roughly the naming structure you suggest. Coercion is avoided also which is a plus.
A more complicated example
library(RJSONIO)
library(RCurl)
json.data<-getURL("http://www.reddit.com/r/leagueoflegends/.json")
dumdata<-fromJSON(json.data)
out<-flatten(dumdata)
UPDATE
naive way to remove trailing .1
my_data<-list(x=list(1,list(1,2,y='e'),3))
gsub("(*.)\\.1","\\1",unlist(getnames(my_data,T)))
> gsub("(*.)\\.1","\\1",unlist(getnames(my_data,T)))
[1] "x.1" "x.2.1" "x.2.2" "x.2.y" "x.3"
R has two packages for dealing with JSON input: rjson and RJSONIO. If I understand correctly what you mean by "collection of non-cyclical homogeneous or heterogeneous data structures", I think either of these packages will import that sort of structure as a list.
You can then flatten that list (into a vector) using the unlist function.
If the list is suitably structured (a non-nested list where each element is the same length) then as.data.frame prvoides an alternative to convert the list to be a data frame.
An example:
(my_data <- list(x = list('1' = 1, '2' = list(y = 2))))
unlist(my_data)
The jsonlite package is a fork of RJSONIO specifically designed to make conversion between JSON and data frames easier. You don't provide any example json data, but I think this might be what you are looking for. Have a look at this blog post or the vignette.
Great answer with the flatten and getnames functions. Took a few minutes to figure out all the options needed to get from a vector of JSON strings to a data.frame, so I thought I'd record that here. Suppose jsonvec is a vector of JSON strings. The following builds a data.frame (data.table) where there is one row per string, and each column corresponds to a different possible leaf node of the JSON tree. Any string missing a particular leaf node is filled with NA.
library(data.table)
library(jsonlite)
parsed = lapply(jsonvec, fromJSON, simplifyVector=FALSE)
flattened = lapply(parsed, flatten) #using flatten from accepted answer
d = rbindlist(flattened, fill=TRUE)
I'm now a big fan of simply:
library(jsonlite)
library(tidyverse)
fromJSON("file_path.json") %>%
unlist() %>%
enframe()
And then potentially, depending on your data, piping that into
%>%
pivot_wider()
Once it's in a flat table shape, there are a load of tools in tidyverse and other R libraries more generally for wrangling things around and e.g., dealing with columns with similar prefixes (which will result from the above pipeline as the parent name of the children within a nested json chunk will be prefixed to the child's name).
I am trying to use \Sexpr{} to include values from my R objects in a LaTeX table. I am essentially trying to replicate the summary output of a lm object in R because xtable's built in methods xtable.lm and xtable.summary.lm don't seem to include the Fstats, adjusted R-squared, etc (all the stuff at the bottom of the summary printout of the lm object in R console) So I tried accomplishing this by building a matrix to replicate the xtable.summary.lm output then construct a data frame of the relevant info for the extra stuff so I can refer to the values using \Sexpr{}. I tried doing this by using add.to.row to append the \multicolumn{} command in order to merge all columns of the last row of the LaTeX table and then just pass all the information I need into that cell of the table.
The problem is that I get an "Undefined control sequence" for the \Sexpr{} expression in the \multicolumn{} expression. Are these two not compatible? If so, what am I doing wrong and if not does anyone know how to do what I am trying to do?
Thanks,
Here is the relevant part of my code:
<<Test, results=tex>>=
model1 <- lm(stndfnl ~ atndrte + frosh + soph)
# Build matrix to replicate xtable.summary.lm output
x <- summary(model1)
colnames <- c("Estimate", "Std. Error", "t value", "Pr(<|t|)")
rownames <- c("(Intercept)", attr(x$terms, "term.labels"))
fpval <- pf(x$fstatistic[1],x$fstatistic[2], x$fstatistic[3], lower.tail=FALSE)
mat1 <- matrix(coef(x), nrow=length(rownames), ncol=length(colnames), dimnames=list(rownames,colnames))
# Make a data frame for extra information to be called by \Sexpr in last row of table
residse <- x$sigma
degf <- x$df[2]
multr2 <- x$r.squared
adjr2 <- x$adj.r.squared
fstat <- x$fstatistic[1]
fstatdf1 <- x$fstatistic[2]
fstatdf2 <- x$fstatistic[3]
extradat <- data.frame(v1 = round(residse,4), v2 =degf, v3=round(multr2,4), v4=round(adjr2,4),v5=round(fstat,3), v6=fstatdf1, v7=fstatdf2, v8=round(fpval,6))
addtorow<- list()
addtorow$pos <-list()
addtorow$pos[[1]] <- dim(mat1)[1]
addtorow$command <-c('\\hline \\multicolumn{5}{l}{Residual standard error:\\Sexpr{extradat$v1}} \\\\ ')
print(xtable(mat1, caption="Summary Results for Regression in Equation \\eqref{model1} ", label="tab:model1"), add.to.row=addtorow, sanitize.text.function=NULL, caption.placement="top")
You don't need to have Sexpr in your R code; the R code can use the expressions directly. Sexpr is not a LaTeX command, even though it looks like one; it's an Sweave command, so it doesn't work to have it as output from R code.
Try
addtorow$command <-paste('\\hline \\multicolumn{5}{l}{Residual standard error:',
extradat$v1, '} \\\\ ')
Also, no need to completely recreate the matrix used by xtable, you can just build on the default output. Building on what you have above, something like:
mytab <- xtable(model1, caption="Summary Results", label="tab:model1")
addtorow$pos[[1]] <- dim(mytab)[1]
print(mytab, add.to.row=addtorow, sanitize.text.function=NULL,
caption.placement="top")
See http://people.su.se/~lundh/reproduce/sweaveintro.pdf for an example which you might be able to use as is.