From the course: Learning the R Tidyverse
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Export .rdata objects for later - R Tutorial
From the course: Learning the R Tidyverse
Export .rdata objects for later
- [Announcer] Sometimes R objects can become quite complex. For instance, you may have a data frame which has been grouped and sorted using the tidyverse and this information is crucial to the representation of the data, at least so that it can be easily reused by other functions. Other examples of complex objects are geospatial data sets. For instance, an object containing the shape files of a number of different countries. Exporting these objects as flat files (i.e. as CSV files or tab separated files) will lose this important data and are therefore inappropriate data files for this kind of data. In these cases, it's efficient to store the object directly as a .rdata file which contains the true R representation of the object so that when it's loaded back into R, you have exactly the same object you exported. However, because this is a special file only R knows how to read, you should ensure that you keep your reproducible workflow to generate that .rdata file in your data-raw…
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Contents
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Separate raw and clean data folders2m 48s
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Import .xlsx files with readxl in R12m 19s
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Import .csv files with readr into R5m 38s
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Is it a data frame or a tibble?10m 43s
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Select and filter data9m 32s
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Convert strings to dates with mutate6m 40s
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Separating columns into multiple columns6m 36s
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Filter out NA values3m 35s
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Export .csv files with readr5m 6s
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Export .rdata objects for later4m 51s
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