Recent content by trinker

Hmm would have thought airquality[,0:4] would throw an error

I'd recommend the readxl package to read in most xlsx files. So this will install the package: install.packages('readxl') Then when you want to use it: library(readxl) mydata <- read_excel("S:\\CIU\\Testfolder\\DataforTS2.xlsx")

Importing files trips a lot of people up but R has nice tools to do this with little lift. Once you get it down it really makes it easy to automate tasks to make plots/reports. If you can post the code you tried and the error it gave that could be a start to getting help.

Also ggfortify must be in quotes but that'll just throw an error

See what this gives you: names(Mytsdata) That's because a ts object doesn't use names. This is usefule beyond str() to see what the object looks like. dput(Mytsdata) What were you trying to do here: names(Mytsdata)<-c("m","sp") > head(Mytsdata) the last command shows nothing changed.

> Mytsdata<-ts(mydata,frequency=12,c(2014,12)) does not. So for my data I have to use the above formulation. This is because you passed the whole dataframe into the data argument, and usually you likely only want to pass a vector from the data frame. > Why are there two sets of [] around...

mydata[['Spend']] This is grabbing the Spend column using column indexing. Almost identical to mydata[, 2] but safer in that it's gauranteed to return the vector not a list. Error in C(2014, 12) : object not interpretable as a factor R is case sensitive C is not c

It does but it's subtly using indexing as in [, 2] So this will likely work: ts(mydata[['Spend']],frequency=12) But you need to specify start = c(YOUR_START_YEAR, YOUR_START_MONTH) The dollar signs in the head you show above indicate character. R stores numeric/floats without dollar signs.

usnim_2002[, 2] Above you grabed 1 column, a vector. Do the same for this new data set with Spend start = c(2002, 1) Above you specified a date start (year, month) but don't this time. Also I think your data is character. Maybe use lapply(mydata, class) to see.

> stringsAsFactors=T had to go at some point! Agreed, but there was some pain (though minimal)

It's hard to help because we don't have the data. If you can make a minimal working example you're sure to get a quicker response.

I like dplyr and tidyr for analysis...for programming they're a *****...mostly because the API is constantly changing. You can't make data science pipelines with an unstable API.

Look at the check.names argument?

I take it back PSPP is the way forward: https://www.gnu.org/software/pspp/

There was a crappy open source version of spss I tried once upon a time before R...pspp