I have a little problem with my code. I hope you can help me :)
I used a function apply to create a list of 20 data frames (data about stock index returns, grouped by year and index - about three companies and the stock, for 5 years). And now I want to use function with two arguments (it calculates proportion of covariance of the returns for selected company and the stock to variance (for every year) - this is why I'm trying to group the data. How to do it... automatically, without manual typing code for every year and company?
I don't have any idea if I should use for loop or there is any other way...?
And the other thing is in which way can I delete uneccesary columns from list of data frames?
I'll be thankful for your help.
And sorry for my English :D
You may consider purrr::map_dfr(). The first argument will be your list of data frames, and the second the action to do with that data frame. The final result will be a single data frame uniting the result of all of the above. Your code will likely look something like this:
purrr::map_dfr(list_of_dataframes, function(x) {...})
Within the bracketes, instead of ... insert your logic. In that context, x will be the same as list_of_dataframes[[1]], and then list_of_dataframes[[2]], etc.
You may want to consult the documentation of the package purrr for further details.
Related
It is hard to explain this without just showing what I have, where I am, and what I need in terms of data structure:
What structure I had:
Where I have got to with my transformation efforts:
What I need to end up with:
Notes:
I've not given actual names for anything as the data is classed as sensitive, but:
Metrics are things that can be measured- for example, the number of permanent or full-time jobs. The number of metrics is larger than presented in the test data (and the example structure above).
Each metric has many years of data (whilst trying to do the code I have restricted myself to just 3 years. The illustration of the structure is based on this test). The number of years captured will change overtime- generally it will increase.
The number of policies will fluctuate, I've just labelled them policy 1, 2 etc for sensitivity reasons and limited the number whilst testing the code. Again, I have limited the number to make it easier to check the outputs.
The source data comes from a workbook of surveys with a tab for each policy. The initial import creates a list of tibbles consisting of a row for each metric, and 4 columns (the metric names, the values for 2024, the values for 2030, and the values for 2035). I converted this to a dataframe, created a vector to be a column header and used cbind() to put this on top to get the "What structure I had" data.
To get to the "Where I have got to with my transformation efforts" version of the table, I removed all the metric columns, created another vector of metrics and used rbind() to put this as the first column.
The idea in my head was to group the data by policy to get a vector for each metric, then transpose this so that the metric became the column, and the grouped data would become the row. Then expand the data to get the metrics repeated for each year. A friend of mine who does coding (but has never used R) has suggested using loops might be a better way forward. Again, I am not sure of the best approach so welcome advice. On Reddit someone suggested using pivot_wider/pivot_longer but this appears to be a summarise tool and I am not trying to summarise the data rather transform its structure.
Any suggestions on approaches or possible tools/functions to use would be gratefully received. I am learning R whilst trying to pull this data together to create a database that can be used for analysis, so, if my approach sounds weird, feel free to suggest alternatives. Thanks
I have used the "htmltab" library to get data on the NFL draft and combine. The data has been selected fine but they are lists at the moment. I intend to merge them and perform analysis the data. at the moment it looks like this:
image List of combine 2016 1
Whenever I try use the unlist method I lose the headers of the columns and they are still remaining as a list.
any suggestions on this?
urlcom16 <- "http://nflcombineresults.com/nflcombinedata.php?
year=2016&pos=&college="
com16 <- htmltab(doc=urlcom16, which=1)
Try as.data.frame(com16). If it doesn't work, you might not have the same vector length in each list entry.
This is a question of a general approach in R, I'm trying to find a way into R language but the data types and loop approaches (apply, sapply, etc) are a bit unclear to me.
What is my target:
Query data from API with parameters from a config list with multiple parameters. Return the data as aggregated data.frame.
First I want to define a list of multiple vectors (colums)
site segment id
google.com Googleuser 123
bing.com Binguser 456
How to manage such a list of value groups (row by row)? data.frames are column focused, you cant write a data.frame row by row in an R script. So the only way I found to define this initial config table is a csv, which is really an approach I try to avoid, but I can't find a way to make it more elegant.
Now I want to query my data, lets say with this function:
query.data <- function(site, segment, id){
config <- define_request(site, segment, id)
result <- query_api(config)
return result
}
This will give me a data.frame as a result, this means every time I query data the same columns are used. So my result should be one big data.frame, not a list of similar data.frames.
Now sapply allows to use one parameter-list and multiple static parameters. The mapply works, but it will give me my data in some crazy output I cant handle or even understand exactly what it is.
In principle the list of data.frames is ok, the data is correct, but it feels cumbersome to me.
What core concepts of R I did not understand yet? What would be the approach?
If you have a lapply/sapply solution that is returning a list of dataframes with identical columns, you can easily get a single large dataframe with do.call(). do.call() inputs each item of a list as arguments into another function, allowing you to do things such as
big.df <- do.call(rbind, list.of.dfs)
Which would append the component dataframes into a single large dataframe.
In general do.call(rbind,something) is a good trick to keep in your back pocket when working with R, since often the most efficient way to do something will be some kind of apply function that leaves you with a list of elements when you really want a single matrix/vector/dataframe/etc.
I have a list of five data frames full of user responses to a survey.
In each of these data frames, the second column is the user id number. Some of the users took the survey multiple times, and I am trying to weed out the duplicate responses and just keep the first record.
The naming conventions are fairly standard, so the column in the first data frame is called akin to survey1_id and the second is survey2_id, etc. with the exception being that the column in the third data frame is called survey3a_id.
So basically what I tried to do was this:
for (i in seq(1,5)) {
newdata <- distinct(survey_list[[i]], grep(names("^survey.*_id$", survey_list[[i]]), value = TRUE))
}
But this doesn't work.
I originally thought it was just because the grep output had quotes around it, but I tried to strip them with noquote() and that didn't work. I then realized that distinct() doesn't actually evaluate the second argument, it just takes it literally, so I tried to force it to evaluate using eval(), but that didn't work. (Not sure I really expected it to.)
So now I'm kind of stuck. I don't know if the best solution is just to write five individual lines of code or, for a more generalizable solution, to sort and compare item-by-item in a loop? Was just hoping for a cleaner solution. I'm kind of new to this stuff.
For a project at work, I need to generate a table from a list of proposal ids, and a table with more data about some of those proposals (called "awards"). I'm having trouble with the match() function; the data in the "awards" table often has several rows that use the same ID, while the proposals frame has only one copy of each ID. From what I've tried, R ignores multiple rows and only returns the first match, when I need all of them. I haven't been able to find anything in documentation or through searches that helps me, though I have been having difficulty phrasing the right question.
Here's what I have so far:
#R CODE to add awards data on proposals to new data spreadsheet
#read tab delimited files
Awards=read.delim("O:/testing.txt",as.is=T)
Proposals=read.delim("O:/test.txt",as.is=T)
#match IDs from both spreadsheets
Proposals$TotalAwarded=Awards$TotalAwarded([match(Proposals$IDs,Awards$IDs)]),
write.table(Proposals,"O:/tested.txt",quote=F,row.names=F,sep="\t")
This does exactly what I want, except that only the first match is encapsulated.
What's the best way to go forward? How do I make R utilize all of the matches available?
Thanks
See help on merge: ?merge
merge( Proposals, Awards, by=ID, all.y=TRUE )
But I cannot believe this hasn't been asked on SO before.