I wanted to extract data from a package in R called Rdota2. However, when trying to use write.csv on my function, it displays
Error in as.data.frame.default(heroes) : cannot coerce class ""dota_api"" to a data.frame.
Is there any way I can convert the file directly using any other package?
The output of my function is displayed on my console. The only thing I am concerned is to save the data in CSV or as text.
The data frame is stored in an element of heroes...
write.csv(heroes$content, "heroes.csv")
Related
I do not have any expertise on R and I have to convert RData files to CSV to analyze the data. I followed the following links to do this: Converting Rdata files to CSV and "filename.rdata" file Exploring and Converting to CSV. The second option seemed to be a simpler as I failed to understand the first one. This is what I have tried till now and the results along with it:
>ddata <- load("input_data.RData")
>print(ddata)
[1] "input_data"
> print(ddata[[1]])
[1] "input_data"
> write.csv(ddata,"test.csv")
From the first link I learnt that we can see the RData type and when I did str(ddata) I found out that it is a List of size 1. Hence, I checked to see if print(ddata[[1]]) would print anything apart from just "input_data". With the write.csv I was able to write it to a csv without any errors but it has just the following 2 lines inside the CSV file:
"","x"
"1","input_data"
Can you please help me understand what am I doing wrong and show a way to get all the details in a csv?
The object ddata contains the name of the object(s) that load() created. Try typing the command ls(). That should give you the names of the objects in your environment. One of them should be input_data. That is the object. If it is a data frame (str(input_data)), you can create the csv file with
write.csv(input_data, "test.csv")
I have an Excel file containing a column of 10000 numbers that I wish to import into R.
However, no matter the method I use, the resulting object is either a list of 1, or 10000 obs. of 1 variable (I have used read.csv on the .csv version of the file, read_xlsx on the .xlsx version). If this is expected, how can I work these objects into ordinary arrays?
I have tried importing the same files into matlab and everything is working normally there (it's immediately an ordinary array).
If it's an excel file you might want to try the readxl package.
library("readxl")
dt <- read_excel("your_file_path")
link
Found an easy method:
convert data to a dataframe, and then convert it to an array;
my_data<-data.frame(my_data)
my_data<-data.matrix(my_data)
I am a novice in R and I have been having some trouble trying to get R and Excel to cooperate.
I have written a code that makes it able to compare two vectors with each other and determine the differences between them:
data.x<-read.csv(file.choose(), header=T)
data.y<-read.csv(file.choose(), header=T)
newdata.x<-grep("DAG36|G379",data.x,value=TRUE,invert=TRUE)
newdata.x
newdata.y<-grep("DAG36|G379",data.y,value=TRUE,invert=TRUE)
newdata.y
setdiff(newdata.x,newdata.y)
setdiff(newdata.y,newdata.x)
The data I want to transfer from Excel to R is a long row of numbers placed as so:
“312334-2056”, “457689-0932”, “857384-9857”,….,
There are about 350 of these numbers placed in their own separate cell along a single row.
I used the command: = """" & A1 & """" To put double quotes around every number in order for R to read it properly.
At first I tried to simply copy/paste the data directly into a vector in R, but it's as if R won’t read it as a single row of data and therefore splits it up.
I also tried to save the excel file as a CSV file but that didn’t work either.
Lastly I tried to open it directly in to R using the command:
data.x<- read.csv(file.choose(), header=T)
But as I type in: data.x and press enter it simply says:
<0 rows> (or 0-lenghts row.names)
I simply can’t figure out what I’m doing wrong. Any help would be greatly appreciated.
It's hard to access without a reproducible example, but you should be able to transpose the Excel file into a single column. Then import using read_csv from the readr package. Take a look at the tidyverse package, which will contain some great tools to import and work with this type of data.
I use https://github.com/tidyverse/readxl/. It makes it easy to maintain formatting from excel into type safe tibbles.
If you can share some sample data a working solution can be generated.
I often export data.frame's in R but run into the problem when I try to import them back in, lose all of the formatting into date/numeric/logical/factor values and get it all back as character variables instead. It gets kind of old to have to run a cleaning/formatting script over and over again on the same file so I was wondering if there is a way or a parameter on write.table that allows one to conserve all of this?
Use saveRDS and readRDS. These will save and load your data frames into exactly the same format.
If it's tabular data, you can use the new feather format to save your data. That way you can even read it into Python without losing column type information.
I had a .csv file that I wanted to read into Octave (originally tried to use csvread). It was taking too long, so I tried to use R to workaround: How to read large matrix from a csv efficiently in Octave
This is what I did in R:
forest_test=read.csv('forest_test.csv')
library(R.matlab)
writeMat("forest_test.mat", forest_test_data=forest_test)
and then I went back to Octave and did this:
forest_test = load('forest_test.mat')
This is not giving me a matrix, but a struct. What am I doing wrong?
To answer your exact question, you are using the load function wrong. You must not assign it's output to a variable if you just want the variables on the file to be inserted in the workspace. From Octave's load help text:
If invoked with a single output argument, Octave returns data
instead of inserting variables in the symbol table. If the data
file contains only numbers (TAB- or space-delimited columns), a
matrix of values is returned. Otherwise, 'load' returns a
structure with members corresponding to the names of the variables
in the file.
With examples, following our case:
## inserts all variables in the file in the workspace
load ("forest_test.mat");
## each variable in the file becomes a field in the forest_test struct
forest_test = load ("forest_test.mat");
But still, the link you posted about Octave being slow with CSV files makes referece to Octave 3.2.4 which is a quite old version. Have you confirmed this is still the case in a recent version (last release was 3.8.2).
There is a function designed to convert dataframes to matrices:
?data.matrix
forest_test=data.matrix( read.csv('forest_test.csv') )
library(R.matlab)
writeMat("forest_test.mat", forest_test_data=forest_test)