All I need to do for this simple assignment, which I have been able to do with no problem before, is take the mean of a column from a .csv dataset uploaded to R.
Here is the code I have:
library(readr)
X1888B_PSet03_Dataset1 <-read_csv("Downloads/188B_PSet03_Dataset1.csv")
View(X188B_PSet03_Dataset1)
mean(FillerAcc)
and this is where I get the error message
object 'FillerAcc' not found
Meanwhile, there is literally a column table in my data called FillerAcc.
Call your column this way.
mean(X1888B_PSet03_Dataset1$FillerAcc)
Related
I'm fairly new to working with XML files within the R environment, but I have at least come further in making it work normally than I have with the specific file.
Quick background: I receive data in the attached format, but I cannot convert the data into a data frame (which I have succeeded in with other files.) Somehow my normal procedure doesn't work with this. My goal is to make the data into a data frame. Normally I would just use xmlToDataFrame(), but that provides me with the following error:
unable to find an inherited method for function ‘xmlToDataFrame’ for
signature ‘"xml_document", "missing", "missing", "missing", "missing"’
Then I tried the below sequence
data = read_xml("file.xml")
xmlimport = xmlTreeParse("file.xml")
topxml = xmlRoot(xmlimport)
topxml = xmlSApply(topxml,function(x) xmlSApply(x,xmlValue))
That provided me with the attached picture as output. All the data is contained within the cells, and I cannot seem to access the data. I feel like there is a really simple solution, but after working with the file for longer than I like to admit, I hope you can point something (hopefully) obvious out to me.
If you have the time to assist me in it, I've uploaded the file here
Hope that will do.
Thanks for taking the time to assist me.
Note: The data is a bank fee statement, and the data is completely fictional
Output result
I want to create a pivot table from my data set in excel to R. I have been following this tutorial on how to do this: http://excel2r.com/pivot-tables-in-r-basic-pivot-table-columns-and-metrics/ . I have used the codes mentioned in this tutorial by replacing it with my own data variables, but I keep getting an error message noting: Error: select() doesn't handle lists.
What does this error message mean and how I can I fix this?
The R-Script I have been using from the tutorial is:
library(dplyr)
library(tidyr)
pivot <- df %>%
select(Product.Category, Region, Customer.Segment, Sales)%>%
group_by(Product.Category, Region, Customer.Segment) %>%
summarise(TotalSales = sum(Sales))
Thank you in advance for the help!
By your error message: "select() doesn't handle lists.", I supose that your object called df isn't a dataframe.
Maybe you have a dataframe inside a list.
Try this in your R console:
class(df)
If the class is a list, you need take off the dataframe from the list. You can do this by the position. Probably in the first position. df[[1]]
The functions that you are using, works only for dataframes in general. (And tibbles, that is a another type of dataframe)
Like this example:
I hope it works for you.
And, for the next time, try to make an reproducible example.
You could at least print your dataframe original, before try to use these functions, that way I could help you efficiently.
I'm starting doing text mining in R and I've some problems. I have a csv with users comments about a page. Each row is a different comment. It only has 1 column, the one that has the comments. I was trying to use Tidy in R so I import the file (read.csv) and I get a data frame with n factor levels.
The next step is try to tokenize the rows
The csv looks like this
#load the data
prueba <- read.csv(file="C:/Users/Mr & Mrs Bean/Downloads/Prueba.csv", sep=";")
#trying to tokenize
ty_prueba <- tidy(prueba)
Error in UseMethod("tidy") :
no applicable method for 'tidy' applied to an object of class "factor"
As you can see, I get that error. I've also try to convert to character that column but I get the same error. Every example I look has a text prepare to work, so it's difficult to see how the raw texts are prepared.
It's a rookie problem, so any advice will be appreciated.
I have found a solution. As someone post here now I've used read_excel (library readxl) instead of read.csv. It works for me. I suppose that it's something related to how R reads the file.
I am trying to take R code, stored in cells of the content column of a dataframe, and analyze the functions used by applying the Tidycode package. However, I first need to convert the data to a Matahari tibble before applying an unnest_calls() function.
Here is the data:
data <- read.csv("https://github.com/making-data-science-count/TidyTuesday-Analysis/raw/master/db-tmp/cleaned%20database.csv")
I have tried doing this in a number of different ways, including extracting each row (in the content column ) as an Rfile and then reading it back in with Tidycode calls, for example:
tmp<-data$content2[1])
writeLines(tmp, "tmp.R") #I've also used save() and write()
rfile<-tidycode::read_rfiles("tmp.R")
But, I keep getting errors such as: "Error in parse(text = x) : <text>:1:14: unexpected symbol
1: library(here)library"
Ultimately, what I would like to do is analyze the different types of code per file, and keep that linked with the other data in the data dataframe, such as date and username.
Any help would be greatly appreciated!
I'm brand new to R and am having difficulty with something very basic. I'm importing data from an excel file like this:
data1 <- read.csv(file.choose(), header=TRUE)
When I try to look at the data in the table by column, R doesn't recognize the column headers as objects. This is what it looks like
summary(Square.Feet)
Error in summary(Square.Feet) : object 'Square.Feet' not found
I need to run a regression and I'm having the same problem. Any help would be much appreciated.
Yes it recognizes, you have to tell R to select the dataframe so:
summary(data1$Square.Feet)
Where "data" is the name of your dataframe, and after the dollar goes the name of the variable
Hope it helps
UPDATE
As suggested below, you can use the following:
data1 <- read.csv(file.choose(), header=TRUE)
attach(data1)
This way, by doing "attach", you avoid to write everytime the name of the dataset, so we would go from
summary(data1$Square.Feet)
To this point after attaching the data:
summary(Square.Feet)
However I DO NOT recommend to do it, because if you load other datasets you may mess everything as it's quite common that variables have the same names, among other major problems, see here (Thanks Ben Bolker for your contribution): here , here, here and
here
if you want a summary of all data fields, then
summary(data1)
or you can use the 'with' helper function
with(data1, summary(Square.Feet))