Has anyone used 'tidytextmining' for sentiment analysis in R?
Tidytextmining
I am using R V 3.4.1 and I am getting the following error for this piece of code.
library(tidytext)
library(dplyr)
get_sentiments("afinn")
Error - Error in get_sentiments("afinn") :
could not find function "get_sentiments"
I have the right package installed and the library reference. What am I missing?
I tried your code and it's working just fine. Are you sure you have the right library reference? I would double-check!
Related
can someone please help me understand this:
I encountered an error when calling a function from a library, specifically "steinertree" from the "SteinerNet" package. When stepping into the function with debug(steinertree), I see that the error occurs, when the function in turn calls "steinertree3". When I try debug(steinertree3), I get "object 'steinertree3' not found". Similarly, I can get the code for 'steinertree' by typing it in the terminal, but not for 'steinertree3'.
So it seems to me that there are some "higher-level" functions and "hidden" functions in packages. I did eventually find the error by finding a file "steinertree.R" in the package at CRAN, which contains both 'steinertree' and 'steinertree3', but I`m wondering how to properly go about debugging such "hidden" functions.
Here is a simple example:
library(igraph)
library(SteinerNet)
set.seed(1)
g= erdos.renyi.game(n=10,p.or.m=0.2)
plot(g)
steinertree(type= 'KB', terminals= c(1,3), graph= g)
Thank you!
Use triple colon ::: to execute a function that is not exported by the package/namespace:
package:::hidden_function()
I'm quite new to R and I got an assignment that includes a sourcecode.
Part of the source code includes the following line:
library(preprocessCore)
Then I have in my source code a definition of the following function:
quantile.normalize.raw.gtex <- function(edata.mat)
{
norm_edata = normalize.quantiles(as.matrix(edata.mat))
rownames(norm_edata) = rownames(edata.mat)
colnames(norm_edata) = colnames(edata.mat)
return(norm_edata)
}
Finally, I have an object being initialized to the output of this function, after sending a predefined parameter:
tissue.edata.qn = quantile.normalize.raw.gtex(tissue.edata)
From what I understand, the library function is supposed to include the function normalize.quantiles, which is called in the function that is defined in my source code.
However, when I run the line library(preprocessCore) I get the following error:
Error in library(preprocessCore) :
there is no package called ‘preprocessCore’
I also tried to run the rest of the code and got the error:
Error in normalize.quantiles(as.matrix(edata.mat)) :
could not find function "normalize.quantiles"
I looked for the preprocessCore online and eventually I tried to write install.packages("preprocessCore"), but I got a warning message that this package is only available in version 3.6.0 of R, even though I checked and this is the version that I have.
If somebody has any idea what the problem is, I will appreciate your help.
Thanks in advance
The preprocessCore package is available in Bioconductor. So, to install it, you need the following lines:
source("http://bioconductor.org/biocLite.R")
biocLite("preprocessCore")
After that, you can load the package using library(preprocessCore)
Hope it helps.
I am trying to get the zip demographics data of the year 2015. When I tried the codes below, an error message returns.
library(devtools)
install_github('arilamstein/choroplethrZip#v1.4.0')
library(choroplethrZip)
df_zip_2015 = get_zip_demographics(2015,5)
When the data tries to read in, I first got a few NAs introduced by coercion warnings, but then an error message returns
Error in choroplethr:::convert_acs_obj_to_df("zip", age, 1) :
argument "include_moe" is missing, with no default
Is there a way I can fix this?
It looks like you are installing choroplethrZip version 1.4.0. However, if you go to the choroplethrZip github page you will see that the latest version is actually 1.5.0.
When I run this code:
library(devtools)
install_github('arilamstein/choroplethrZip#v1.5.0')
library(choroplethrZip)
df_zip_2015 = get_zip_demographics(2015,5)
I do not get the error you describe.
I tried to run the code in Chapter 7 Data mining with R learning with case study book but I got an error in following line:
rankWorkflows(svm, maxs = TRUE)
The error was:
Error in as.character.default(X[[i]], ...) : no method for coercing
this S4 class to a vector
Then I searched on the internet and found following solution:
importMethodsFrom(GenomicRanges, as.data.frame)
and again again I got a new error:
Error: could not find function "importMethodFrom"
I searched a lot but I got nothing :(
You can try using library(sos) to find the packages where your function is located.
library(sos)
findFn("replaceherewithyourfunction")
Based on the answer of #Bea, there does not seem to be a importMethodsFrom anywhere in R. My guess is you found the call in a NAMESPACE file. Those files have different syntax than normal R scripts.
If you want to load a specific function from an R package (rather than all functions from a package), you can use libraryname::functionname instad of functionname in your code. In your case, replace as.data.frame with GenomicRanges::as.data.frame
If this does not work (for example because you don't have as.data.frame anywhere in your code), you can also load the whole GenomicRanges library with library(GenomicRanges)
I'm using h2o package and trying to create a learner using the below given code
install.packages("h2o")
library("h2o")
h2o.learner <- makeLearner("regr.h2o.deeplearning",predict.type = "response")
But I'm getting this error
> h2o.learner <- makeLearner("regr.h2o.deeplearning",predict.type = "response")
Error: could not find function "makeLearner"
Note: Few months back I used this code without any problem.
Any idea what could be possible thing for this error?
The correct code for this is simply
library(mlr)
h2o.learner = makeLearner("regr.h2o.deeplearning")
The makeLearner() is not part of H2O. It appears to be part of the mlr package. It also seems that mlr does have h2o support, so it might be as simple as adding a library(mlr) to the top of your script? (Making sure that the mlr package has been installed, already, of course.)