I'm making word frequency tables with R and the preferred output format would be a JSON file. sth like
{
"word" : "dog",
"frequency" : 12
}
Is there any way to save the table directly into this format? I've been using the write.csv() function and convert the output into JSON but this is very complicated and time consuming.
set.seed(1)
( tbl <- table(round(runif(100, 1, 5))) )
## 1 2 3 4 5
## 9 24 30 23 14
library(rjson)
sink("json.txt")
cat(toJSON(tbl))
sink()
file.show("json.txt")
## {"1":9,"2":24,"3":30,"4":23,"5":14}
or even better:
set.seed(1)
( tab <- table(letters[round(runif(100, 1, 26))]) )
a b c d e f g h i j k l m n o p q r s t u v w x y z
1 2 4 3 2 5 4 3 5 3 9 4 7 2 2 2 5 5 5 6 5 3 7 3 2 1
sink("lets.txt")
cat(toJSON(tab))
sink()
file.show("lets.txt")
## {"a":1,"b":2,"c":4,"d":3,"e":2,"f":5,"g":4,"h":3,"i":5,"j":3,"k":9,"l":4,"m":7,"n":2,"o":2,"p":2,"q":5,"r":5,"s":5,"t":6,"u":5,"v":3,"w":7,"x":3,"y":2,"z":1}
Then validate it with http://www.jsonlint.com/ to get pretty formatting. If you have multidimensional table, you'll have to work it out a bit...
EDIT:
Oh, now I see, you want the dataset characteristics sink-ed to a JSON file. No problem, just give us a sample data, and I'll work on a code a bit. Practically, you need to carry out the data into desirable format, hence convert it to JSON. list should suffice. Give me a sec, I'll update my answer.
EDIT #2:
Well, time is relative... it's a common knowledge... Here you go:
( dtf <- structure(list(word = structure(1:3, .Label = c("cat", "dog",
"mouse"), class = "factor"), frequency = c(12, 32, 18)), .Names = c("word",
"frequency"), row.names = c(NA, -3L), class = "data.frame") )
## word frequency
## 1 cat 12
## 2 dog 32
## 3 mouse 18
If dtf is a simple data frame, yes, data.frame, if it's not, coerce it! Long story short, you can do:
toJSON(as.data.frame(t(dtf)))
## [1] "{\"V1\":{\"word\":\"cat\",\"frequency\":\"12\"},\"V2\":{\"word\":\"dog\",\"frequency\":\"32\"},\"V3\":{\"word\":\"mouse\",\"frequency\":\"18\"}}"
I though I'll need some melt with this one, but simple t did the trick. Now, you only need to deal with column names after transposing the data.frame. t coerces data.frames to matrix, so you need to convert it back to data.frame. I used as.data.frame, but you can also use toJSON(data.frame(t(dtf))) - you'll get X instead of V as a variable name. Alternatively, you can use regexp to clean the JSON file (if needed), but it's a lousy practice, try to work it out by preparing the data.frame.
I hope this helped a bit...
These days I would typically use the jsonlite package.
library("jsonlite")
toJSON(mydatatable, pretty = TRUE)
This turns the data table into a JSON array of key/value pair objects directly.
RJSONIO is a package "that allows conversion to and from data in Javascript object notation (JSON) format". You can use it to export your object as a JSON file.
library(RJSONIO)
writeLines(toJSON(anobject), "afile.JSON")
Related
I am using BehaviorSpace to run the model hundreds of times with different parameters. But I need to know the locations of all turtles as a result instead of only the number of turtles. How can I achieve it with BehaviorSpace?
Currently, I output the results in a csv file by this code:
to-report get-locations
report (list xcor ycor)
end
to generate-output
file-open "model_r_1.0_locations.csv"
file-print csv:to-row get-locations
file-close
end
but all results are popped into same csv file, so I can't tell the condition of each running.
Seth's suggestion of incorporating behaviorspace-run-number in the filename of your csv output is one alternative. It would allow you to associate that file with the summary data in your main BehaviorSpace output file.
Another option is to include list reporters as "measures" in your behavior space experiment definition. For example, in your case:
map [ t -> [ xcor ] of t ] sort turtles
map [ t -> [ ycor ] of t ] sort turtles
You can then parse the resulting list "manually" in your favourite data analysis language. I've used the following function for this before, in Julia:
parselist(strlist, T = Float64) = parse.(T, split(strlist[2:end-1]))
I'm sure you can easily write some equivalent code in Python or R or whatever language you're using.
In the example above, I've outputted separate lists for the xcor and the ycor of turtles. You could also output a single "list of lists", but the parsing would be trickier.
Edit: How to do this using the csv extension and R
Coincidentally, I had to do something similar today for a different project, and I realized that a combination of the csv extension and R can make this very easy.
The general idea is the following:
In NetLogo, use csv:to-string to encode list data into a string and then write that string directly in the BehaviorSpace output.
In R, use purrr::map and readr::read_csv, followed by tidyr::unnest, to unpack everything in a neat "one observation per row" dataframe.
In other words: we like CSV, so we put CSV in our CSV so we can parse while we parse.
Here is a full-fledged example. Let's say we have the following NetLogo model:
extensions [ csv ]
to setup
clear-all
create-turtles 2 [ move-to one-of patches ]
reset-ticks
end
to go
ask turtles [ forward 1 ]
tick
end
to-report positions
let coords [ (list who xcor ycor) ] of turtles
report csv:to-string fput ["who" "x" "y"] coords
end
We then define the following tiny BehaviorSpace experiment, with only two repetitions and a time limit of two, using our positions reporter as an output:
The R code to process this is pleasantly straightforward:
library(tidyverse)
df <- read_csv("experiment-table.csv", skip = 6) %>%
mutate(positions = map(positions, read_csv)) %>%
unnest()
Which results in the following dataframe, all neat and tidy:
> df
# A tibble: 12 x 5
`[run number]` `[step]` who x y
<int> <int> <int> <dbl> <dbl>
1 1 0 0 16 10
2 1 0 1 10 -2
3 1 1 1 9.03 -2.24
4 1 1 0 -16.0 10.1
5 1 2 1 8.06 -2.48
6 1 2 0 -15.0 10.3
7 2 0 1 -14 1
8 2 0 0 13 15
9 2 1 0 14.0 15.1
10 2 1 1 -13.7 0.0489
11 2 2 0 15.0 15.1
12 2 2 1 -13.4 -0.902
The same thing in Julia:
using CSV, DataFrames
df = CSV.read("experiment-table.csv", header = 7)
cols = filter(col -> col != :positions, names(df))
df = by(df -> CSV.read(IOBuffer(df[:positions][1])), df, cols)
I have a sqlite database file with several columns. One of the columns has a JSON dictionary (with two keys) embedded in it. I want to extract the JSON column to a data frame in R that shows each key in a separate column.
I tried rjson::fromJSON, but it reads only the first item. Is there a trick that I'm missing?
Here's an example that mimics my problem:
> eg <- as.vector(c("{\"3x\": 20, \"6y\": 23}", "{\"3x\": 60, \"6y\": 50}"))
> fromJSON(eg)
$3x
[1] 20
$6y
[1] 23
The desired output is something like:
# a data frame for both variables
3x 6y
1 20 23
2 60 50
or,
# a data frame for each variable
3x
1 20
2 60
6y
1 23
2 50
What you are looking for is actually a combination of lapply and some application of rbind or related.
I'll extend your data a little, just to have more than 2 elements.
eg <- c("{\"3x\": 20, \"6y\": 23}",
"{\"3x\": 60, \"6y\": 50}",
"{\"3x\": 99, \"6y\": 72}")
library(jsonlite)
Using base R, we can do
do.call(rbind.data.frame, lapply(eg, fromJSON))
# X3x X6y
# 1 20 23
# 2 60 50
# 3 99 72
You might be tempted to do something like Reduce(rbind, lapply(eg, fromJSON)), but the notable difference is that in the Reduce model, rbind is called "N-1" times, where "N" is the number of elements in eg; this results in a LOT of copying of data, and though it might work alright with small "N", it scales horribly. With the do.call option, rbind is called exactly once.
Notice that the column labels have been R-ized, since data.frame column names should not start with numbers. (It is possible, but generally discouraged.)
If you're confident that all substrings will have exactly the same elements, then you may be good here. If there's a chance that there will be a difference at some point, perhaps
eg <- c(eg, "{\"3x\": 99}")
then you'll notice that the base R solution no longer works by default.
do.call(rbind.data.frame, lapply(eg, fromJSON))
# Error in (function (..., deparse.level = 1, make.row.names = TRUE, stringsAsFactors = default.stringsAsFactors()) :
# numbers of columns of arguments do not match
There may be techniques to try to normalize the elements such that you can be assured of matches. However, if you're not averse to a tidyverse package:
library(dplyr)
eg2 <- bind_rows(lapply(eg, fromJSON))
eg2
# # A tibble: 4 × 2
# `3x` `6y`
# <int> <int>
# 1 20 23
# 2 60 50
# 3 99 72
# 4 99 NA
though you cannot call it as directly with the dollar-method, you can still use [[ or backticks.
eg2$3x
# Error: unexpected numeric constant in "eg2$3"
eg2[["3x"]]
# [1] 20 60 99 99
eg2$`3x`
# [1] 20 60 99 99
When I try to run predict() on the dataset, it keeps giving me error -
Error in eval(expr, envir, enclos) : object 'LoanRange' not found
Here is the part of dataset -
LoanRange Loan.Type N WAFICO WALTV WAOrigRev WAPTValue
1 0-99999 Conventional 109 722.5216 63.55385 6068.239 0.6031879
2 0-99999 FHA 30 696.6348 80.00100 7129.650 0.5623650
3 0-99999 VA 13 698.6986 74.40525 7838.894 0.4892977
4 100000-149999 Conventional 860 731.2333 68.25817 6438.330 0.5962638
5 100000-149999 FHA 285 673.2256 82.42225 8145.068 0.5211495
6 100000-149999 VA 125 704.1686 87.71306 8911.461 0.5020074
7 150000-199999 Conventional 1291 738.7164 70.08944 8125.979 0.6045117
8 150000-199999 FHA 403 672.0891 84.65318 10112.192 0.5199632
9 150000-199999 VA 195 694.1885 90.77495 10909.393 0.5250807
10 200000-249999 Conventional 1162 740.8614 70.65027 8832.563 0.6111419
11 200000-249999 FHA 348 667.6291 85.13457 11013.856 0.5374226
12 200000-249999 VA 221 702.9796 91.76759 11753.642 0.5078298
13 250000-299999 Conventional 948 742.0405 72.22742 9903.160 0.6106858
Following is the code used for predicting count data N after determining the overdispersion-
model2=glm(N~Loan.Type+WAFICO+WALTV+WAOrigRev+WAPTValue, family=quasipoisson(link = "log"), data = DF)
summary(model2)
This is what I have done to create a sequence of count and use predict function-
countaxis <- seq (0,1500,150)
Y <- predict(model2, list(N=countaxis, type = "response")
At this step, I get the error -
Error in eval(expr, envir, enclos) : object 'LoanRange' not found
Can someone please point me where is the problem here.
Think about what exactly you are trying to predict. You are providing the predict function values of N (via countaxis), but in fact the way you set up your model, N is your response variable and the remaining variables are the predictors. That's why R is asking for LoanRange. It actually needs values for LoanRange, Loan.Type, ..., WAPTValue in order to predict N. So you need to feed predict inputs that let the model try to predict N.
For example, you could do something like this:
# create some fake data to predict N
newdata1 = data.frame(rbind(c("0-99999", "Conventional", 722.5216, 63.55385, 6068.239, 0.6031879),
c("150000-199999", "VA", 12.5216, 3.55385, 60.239, 0.0031879)))
colnames(newdata1) = c("LoanRange" ,"Loan.Type", "WAFICO" ,"WALTV" , "WAOrigRev" ,"WAPTValue")
# ensure that numeric variables are indeed numeric and not factors
newdata1$WAFICO = as.numeric(as.character(newdata1$WAFICO))
newdata1$WALTV = as.numeric(as.character(newdata1$WALTV))
newdata1$WAPTValue = as.numeric(as.character(newdata1$WAPTValue))
newdata1$WAOrigRev = as.numeric(as.character(newdata1$WAOrigRev))
# make predictions - this will output values of N
predict(model2, newdata = newdata1, type = "response")
I have a data.table where one of the columns contains JSON. I am trying to extract the content so that each variable is a column.
library(jsonlite)
library(data.table)
df<-data.table(a=c('{"tag_id":"34","response_id":2}',
'{"tag_id":"4","response_id":1,"other":4}',
'{"tag_id":"34"}'),stringsAsFactors=F)
The desired result, that does not refer to the "other" variable:
tag_id response_id
1 "34" 2
2 "4" 1
3 "34" NA
I have tried several versions of:
parseLog <- function(x){
if (is.na(x))
e=c(tag_id=NA,response_id=NA)
else{
j=fromJSON(x)
e=c(tag_id=as.integer(j$tag_id),response_id=j$response_id)
}
e
}
that seems to work well to retrieve a list of vectors (or lists if c is replaced by list) but when I try to convert the list to data.table something doesn´t work as expected.
parsed<-lapply(df$a,parseLog)
rparsed<-do.call(rbind.data.frame,parsed)
colnames(rparsed)<-c("tag_id","response_id")
Because of the missing value in the third row. How can I solve it in a R-ish clean way? How can I make that my parse method returns an NA for the missing value. Alternative, Is there a parameter "fill" like there is for rbind that can be used in rbind.data.frame or analogous method?
The dataset I am using has 11M rows so performance is important.
Additionally, there is an equivalent method to rbind.data.frame to obtain a data.table. How would that be used? When I check the documentation it refers me to rbindlist but it complains the parameter is not used and if call directly(without do.call it complains about the type of parsed):
rparsed<-do.call(rbindlist,fill=T,parsed)
EDIT: The case I need to cover is more general, in a set of 11M records all the possible circumstances happen:
df<-data.table(a=c('{"tag_id":"34","response_id":2}',
'{"trash":"34","useless":2}',
'{"tag_id":"4","response_id":1,"other":4}',
NA,
'{"response_id":"34"}',
'{"tag_id":"34"}'),stringsAsFactors=F)
and the output should only contain tag_id and response_id columns.
There might be a simpler way but this seems to be working:
library(data.table)
library(jsonlite)
df[, json := sapply(a, fromJSON)][, rbindlist(lapply(json, data.frame), fill=TRUE)]
#or if you need all the columns :
#df[, json := sapply(a, fromJSON)][,
# c('tag_id', 'response_id') := rbindlist(lapply(json, data.frame), fill=TRUE)]
Output:
> df[, json := sapply(a, fromJSON)][, rbindlist(lapply(json, data.frame), fill=TRUE)]
tag_id response_id
1: 34 2
2: 4 1
3: 34 NA
EDIT:
This solution comes after the edit of the question with additional requests.
There are lots of ways to do this but I find the simplest one is at the creation of the data.frame like this:
df[, json := sapply(a, fromJSON)][,
rbindlist(lapply(json, function(x) data.frame(x)[-3]), fill=TRUE)]
# tag_id response_id
#1: 34 2
#2: 4 1
#3: 34 NA
I wanted to import a .txt file in R but the format is really special and it's looks like a json format but I don't know how to import it. There is an example of my data:
{"datetime":"2015-07-08 09:10:00","subject":"MMM","sscore":"-0.2280","smean":"0.2593","svscore":"-0.2795","sdispersion":"0.375","svolume":"8","sbuzz":"0.6026","lastclose":"155.430000000","companyname":"3M Company"},{"datetime":"2015-07-07 09:10:00","subject":"MMM","sscore":"0.2977","smean":"0.2713","svscore":"-0.7436","sdispersion":"0.400","svolume":"5","sbuzz":"0.4895","lastclose":"155.080000000","companyname":"3M Company"},{"datetime":"2015-07-06 09:10:00","subject":"MMM","sscore":"-1.0057","smean":"0.2579","svscore":"-1.3796","sdispersion":"1.000","svolume":"1","sbuzz":"0.4531","lastclose":"155.380000000","companyname":"3M Company"}
To deal with this is used this code:
test1 <- read.csv("C:/Users/test1.txt", header=FALSE)
## Import as 5 observations (5th is all empty) of 1700 variables
#(in fact 40 observations of 11 variables). In fact when I imported the
#.txt file, it's having one line (5th obs) empty, and 4 lines of data and
#placed next to each other 4 lines of data of 11 variables.
# Get the different lines
part1=test1[1:10]
part2=test1[11:20]
part3=test1[21:30]
part4=test1[31:40]
...
## Remove the empty line (there were an empty line after each)
part1=part1[-5,]
part2=part2[-5,]
part3=part3[-5,]
...
## Rename the columns
names(part1)=c("Date Time","Subject","Sscore","Smean","Svscore","Sdispersion","Svolume","Sbuzz","Last close","Company name")
names(part2)=c("Date Time","Subject","Sscore","Smean","Svscore","Sdispersion","Svolume","Sbuzz","Last close","Company name")
names(part3)=c("Date Time","Subject","Sscore","Smean","Svscore","Sdispersion","Svolume","Sbuzz","Last close","Company name")
...
## Assemble data to have one dataset
data=rbind(part1,part2,part3,part4,part5,part6,part7,part8,part9,part10)
## Formate Date Time
times <- as.POSIXct(data$`Date Time`, format='{datetime:%Y-%m-%d %H:%M:%S')
data$`Date Time` <- times
## Keep only the Date
data$Date <- as.Date(times)
## Formate data - Remove text
data$Subject <- gsub("subject:", "", data$Subject)
data$Sscore <- gsub("sscore:", "", data$Sscore)
...
So My code is working to reinstate the data but it's maybe very difficult and more long I know there is better ways to do it, so if you could help me with that I would be very grateful.
There are many packages that read JSON, e.g. rjson, jsonlite, RJSONIO (they will turn in up a google search) - just pick one and give it a go.
e.g.
library(jsonlite)
json.text <- '{"datetime":"2015-07-08 09:10:00","subject":"MMM","sscore":"-0.2280","smean":"0.2593","svscore":"-0.2795","sdispersion":"0.375","svolume":"8","sbuzz":"0.6026","lastclose":"155.430000000","companyname":"3M Company"},{"datetime":"2015-07-07 09:10:00","subject":"MMM","sscore":"0.2977","smean":"0.2713","svscore":"-0.7436","sdispersion":"0.400","svolume":"5","sbuzz":"0.4895","lastclose":"155.080000000","companyname":"3M Company"},{"datetime":"2015-07-06 09:10:00","subject":"MMM","sscore":"-1.0057","smean":"0.2579","svscore":"-1.3796","sdispersion":"1.000","svolume":"1","sbuzz":"0.4531","lastclose":"155.380000000","companyname":"3M Company"}'
x <- fromJSON(paste0('[', json.text, ']'))
datetime subject sscore smean svscore sdispersion svolume sbuzz lastclose companyname
1 2015-07-08 09:10:00 MMM -0.2280 0.2593 -0.2795 0.375 8 0.6026 155.430000000 3M Company
2 2015-07-07 09:10:00 MMM 0.2977 0.2713 -0.7436 0.400 5 0.4895 155.080000000 3M Company
3 2015-07-06 09:10:00 MMM -1.0057 0.2579 -1.3796 1.000 1 0.4531 155.380000000 3M Company
I paste the '[' and ']' around your JSON because you have multiple JSON elements (the rows in the dataframe above) and for this to be well-formed JSON it needs to be an array, i.e. [ {...}, {...}, {...} ] rather than {...}, {...}, {...}.