I am trying to change my data from long to wide format. It is a factorial design with one between subject and two within subject variables.
My data:
https://drive.google.com/file/d/0B9lnMw6dkH9KZUZKQkh4M3BIbGM/view?usp=sharing
When I try
library(reshape2)
data.wide<- dcast(correct.anal,group+subnum~speed+int, value.var="corr")
on the data, it says
Aggregation function missing: defaulting to length
I do not have duplicate values though so I do not understand what I need to do.
What I want to achieve is to get from my current data to output one line per subject with 22 columns (subnum, group and the twenty combinations).
Can anyone help with that?
Perhaps this can help:
data.wide<- dcast(correct.anal,group+subnum~speed+int,fun.aggregate=mean, value.var="corr")
I just add the fun.aggregate=mean to average the duplicates.
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 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.
I need to subset my data depending on the content of one factor variable.
I tried to do it with subset:
new <- subset(data, original$Group1=="SALAD")
data is already a subset from a bigger data frame, in original I have the factor variable which should identify the wanted rows.
This works perfectly for one level of the factor variable, but (and I really don´t understand why!!) when I do it with the other factor level "BREAD" it creates the data frame but says "no data available" - so it is empty. I´ve imported the data from SPSS, if this matters. I´ve already checked the factor levels, but the naming should be right!
Would be really grateful for help, I spent 3 hours on this problem and wasn´t able to find a solution.
I´ve also tried other ways to subset my data (e.g. split), but I want a data frame as output.
Do you have advice in general, what is the best way to subset a data frame if I want e.g. 3 columns of this data frame and these should be extracted depending on the level of a factor (most Code examples are only for one or all columns..)
The entire point of the subset function (as I understand it) is to look inside the data frame for the right variable - so you can type
subset(data, var1 == "value")
instead of
data[data$var1 == "value,]
Please correct me anyone if that is incorrect.
Now, in you're case, you are explicitly taking Group1 from the data frame original and using that to subset data - which you say is a subset of original. Based on this, I see no reason to believe (and every reason not to believe) that the elements of original$Group1 will align with the rows of data. If Group1 is defined within data, why not just use the copy defined there - which is aligned correctly? If not, you need to be very explicit about what you are trying to accomplish, so that you can ensure that things are aligned correctly.
I think my problem is less difficult than it seems to me right now:
In R, I have a data frame with several columns. Two of them are called "PlotId" and "Landuse":
PlotId Landuse
---------------------
000AEG01 Wiese
000AEG02 Weide
000AEG03 Maehweide
000AEG04 ...
"PlotId" contains 50 rows with values from "000AEG01" to "000AEG50", "Landuse" contains three levels: "Wiese", "Weide" and "Maehweide".
For another problem, which I try to solve, I simply need to get the "Landuse"-value for the corresponding "PlotId"-row. SO, I need a command that gives me the information: "Plot 000AEG01 corresponds to Landuse Wiese." And so on for all the other rows, so I probably need to write a loop for that. This information, I would like to get as an object, which I can use then within another loop. I hope, you get what I mean and can help me!
Thanks a lot in advance!
I am currently analysing a rather large dataset (22k+records) and am having some trouble getting the data into a wide format (with one row corresponding to each observation, and columns representing variables).
The data came in two CSV files, one giving demographics and the other giving participants probability ratings to a number of questions. Both of these CSV files were in long format.
I have used the reshape (and reshape2 for speed) packages to attempt to solve my problem. The specific issue i am having is the following.
I have the participants probability ratings in the following form (after one successful reshape).
dtf <- read.csv("http://dl.dropbox.com/u/8566396/foobar.csv")
Now, the format i would like my data to be in is as follows:
User ID Qid1, ....Qid255 Time, with the probabilities for each question in the questions corresponding column.
I have tried a loop and apply to put the values into a new data frame, and many variations of melt and cast. I have also tried the base reshape function, but all to no avail.
In the past, i've always edited my CSV files directly, but this is not an option with the size of this file (my laziness when it comes to data manipulation within R has come back to haunt me).
Any advice or solution you can give to avoid me having to do this by hand would be greatly appreciated.
Your dataset has 6 rows, 3 of which have the column "variable" equal to "probability" and 3 of which have that column equal to "time". You want to have probability be the value of each, and time be added onto the right.
I think there's a difficulty in making this work for you because what you want to do isn't clear. You have values for each UID-Time-X### cell, and values for each UID-Prob-X### cell. Therefore, you have to discard information to get it into your preferred format (UID-Time-X### with probabilities as the values). It seems to me like you're treating time as an ID variable, but it's storing values like a content variable.
To avoid discarding any data, your output would have to look something like:
UID Time1 Time2 Time3 Prob1 Prob2 Prob3
Which is simply reshaped wide.