RDT | A library of Reversible Data Transforms

When running the code that I present, I get the following alert:

Column 2 ['t2'] of item 2 is missing in item 1. Use fill=TRUE to fill with NA (NULL for list columns), or use.names=FALSE to ignore column names. use.names='check' (default from v1.12.2) emits this message and proceeds as if use.names=FALSE for backwards compatibility. See news item 5 in v1.12.2 for options to control this message.

how to correct it?

I'm trying to display a data set using the DT package for R which lets you render javascript datatables. Two of the columns contain text that is quite long so my colleague wrote some JS to truncate the text while letting you see the whole text when you hover over the cell. We also want the user to be able hit a download button what the filter. BUT, when I add the code to make download buttons, it breaks the text truncation. I'd like to have someway to truncate the text AND download the data.

Here's the function:

I want to make use of a promising NN I found at towardsdatascience for my case study.

The data shapes I have are:

I have a two list one list contain default dtypes of the column in dataframe and second list contain changing dtypes list how to use which apporach to handel this problem .suppose Columns Name is ['NameID','Age','Address','DOB']

Here's the idea of the code: The code is required to make a random number then keepon spouting random numbers until it reaches that number, but its always the same. heres the code:

This is part of a project to switch from SPSS to R. While there are good tools to import SPSS files into R (expss) what this question is part of is attempting to get the benefits of SPSS style labeling when data originates from CSV sources. This is to help bridge the staff training gap between SPSS and R by providing a common format for data.tables irrespective of file format origin.

Whilst CSV does a reasonable job of storing data it is hopeless for providing meaningful data. This inevitably means variable and factor levels and labels have to come from somewhere else. In most short examples of this (e.g. in documentation) it is practical to simply hard code the meta data in. But for larger projects it makes more sense to store this meta data in a second csv file.

Example data file

ID,varone,vartwo,varthree,varfour,varfive,varsix,varseven,vareight,varnine,varten 1,1,34,1,,1,,1,1,4, 2,1,21,0,1,,1,3,14,3,2 3,1,54,1,,,1,3,6,4,4 4,2,32,1,1,1,,3,7,4, 5,3,66,0,,,1,3,9,3,3 6,2,43,1,,1,,1,12,2,1 7,2,26,0,,,1,2,11,1, 8,3,,1,1,,,2,15,1,4 9,1,34,1,,1,,1,12,3,4 10,2,46,0,,,,3,13,2, 11,3,39,1,1,1,,3,7,1,2 12,1,28,0,,,1,1,6,5,1 13,2,64,0,,1,,2,11,,3 14,3,34,1,1,,,3,10,1,1 15,1,52,1,,1,1,1,8,6,

Example metadata file

Rowlabels,ID,varone,vartwo,varthree,varfour,varfive,varsix,varseven,vareight,varnine,varten varlabel,,Question one,Question two,Question three,Question four,Question five,Question six,Question seven,Question eight,Question nine,Question ten varrole,Unique,Attitude,Unique,Filter,Filter,Filter,Filter,Attitude,Filter,Attitude,Attitude Missing,Error,Error,Ignored,Error,Unchecked,Unchecked,Unchecked,Error,Error,Error,Ignored vallable,,One,,No,Checked,Checked,Checked,x,One,A,Support vallable,,Two,,Yes,,,,y,Two,B,Neutral vallable,,Three,,,,,,z,Three,C,Oppose vallable,,,,,,,,,Four,D,Dont know vallable,,,,,,,,,Five,E, vallable,,,,,,,,,Six,F, vallable,,,,,,,,,Seven,G, vallable,,,,,,,,,Eight,, vallable,,,,,,,,,Nine,, vallable,,,,,,,,,Ten,, vallable,,,,,,,,,Eleven,, vallable,,,,,,,,,Twelve,, vallable,,,,,,,,,Thirteen,, vallable,,,,,,,,,Fourteen,, vallable,,,,,,,,,Fifteen,,

SO the common elements are the column names which are the key to both files

The first column of the metadata file describes the role of the row for the data file so varlabel provides the variable label for each column varrole describes the analytic purpose of the variable missing describes how to treat missing data varlabel describes the label for a factor level starting at one on up to as many labels as there are.

Right! Here's the code that works:

Applying labels is an important part of making survey data comprehensible when reported

So the best example I can find uses expss::apply_labels() e.g the famous mtcars example https://cran.r-project.org/web/packages/expss/vignettes/tables-with-labels.html

as input this requires a data.table and a list of comma separated assignment pairs e.g

I'm trying to make a dotplot with geom_segment() lines to show two different COVID infection rates: prison staffers and prison residents. I have observations for each state, and would like to produce two geom_segments and two geom_points per state. As you can see from the graph, I'm struggling with two different components: 1) I would like to have two segment lines per state (so two lines for Ohio, one for residents and one for staff, next to each other). Does anyone know how to do this? position_dodge only seems to move the geom_points and won't create two lines. 2) I would like to order the states by only prison resident infection counts. Currently, reorder(State,Count) is ordering them by the total sum of both resident and staff infections.

Here is the code I'm currently running (rdt=dataset, Count=infection count, State=observation grouping, Type=staff/resident infection count--data are stored long):

I'm trying to display all dates in a month, and also in the reservation detail, I only have check_in_date and check_out_date, so I have to create left join inside a left join, below is my script