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GitHub is home to over 36 million developers working together to host and review code, manage projects, and build software together. Sign up code for Numerical Ecology with R. Data Manipulation using dplyr. Dplyr is a package for making data manipulation easier. Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like str or data.frame , come built into R; packages give you access to more of them.
Data Manipulation using dplyrBracket subsetting is handy, but it can be cumbersome and difficult to read, especially for complicated operations. Dplyr is a package for making data manipulation easier.Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like str or data.frame, come built into R; packages give you access to more of them.
Before you use a package for the first time you need to install it on your machine, and then you should import it in every subsequent R session when you need it. What is dplyr?The package dplyr provides easy tools for the most common data manipulation tasks. It is built to work directly with data frames.
The thinking behind it was largely inspired by the package plyr which has been in use for some time but suffered from being slow in some cases. Dplyr addresses this by porting much of the computation to C. An additional feature is the ability to work directly with data stored in an external database. The benefits of doing this are that the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of the query returned.This addresses a common problem with R in that all operations are conducted in memory and thus the amount of data you can work with is limited by available memory. The database connections essentially remove that limitation in that you can have a database of many 100s GB, conduct queries on it directly, and pull back just what you need for analysis in R.To learn more about dplyr after the workshop, you may want to check out this. PipesBut what if you wanted to select and filter at the same time? There are three ways to do this: use intermediate steps, nested functions, or pipes.
With the intermediate steps, you essentially create a temporary data frame and use that as input to the next function. This can clutter up your workspace with lots of objects. You can also nest functions (i.e. one function inside of another). This is handy, but can be difficult to read if too many functions are nested as the process from inside out. The last option, pipes, are a fairly recent addition to R.
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Pipes let you take the output of one function and send it directly to the next, which is useful when you need to do many things to the same data set. Pipes in R look like%% and are made available via the magrittr package installed as part of dplyr. Surveys%%filter(weight%select(speciesid, sex, weight)In the above we use the pipe to send the surveys data set first through filter, to keep rows where weight was less than 5, and then through select to keep the species and sex columns. When the data frame is being passed to the filter and select functions through a pipe, we don’t need to include it as an argument to these functions anymore.If we wanted to create a new object with this smaller version of the data we could do so by assigning it a new name. Is.na(weight))%%mutate( weightkg = weight / 1000)%%headis.na is a function that determines whether something is or is not an NA. Symbol negates it, so we’re asking for everything that is not an NA.
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ChallengeCreate a new dataframe from the survey data that meets the following criteria: contains only the speciesid column and a column that contains values that are half the hindfootlength values (e.g. a new column hindfoothalf). In this hindfoothalf column, there are no NA values and all values are. Surveys%%groupby(sex, speciesid)%%summarize( meanweight = mean(weight, na.rm = TRUE))When grouping both by “sex” and “speciesid”, the first rows are for individuals that escaped before their sex could be determined and weighted. You may notice that the last column does not contain NA but NaN (which refers to “Not a Number”). To avoid this, we can remove the missing values for weight before we attempt to calculate the summary statistics on weight.
Because the missing values are removed, we can omit na.rm=TRUE when computing the mean. Is.na(weight))%%groupby(sex, speciesid)%%summarize( meanweight = mean(weight))You may also have noticed, that the output from these calls don’t run off the screen anymore. That’s because dplyr has changed our data.frame to a tbldf.
This is a data structure that’s very similar to a data frame; for our purposes the only difference is that it won’t automatically show tons of data going off the screen, while displaying the data type for each column under its name. If you want to display more data on the screen, you can add the print function at the end with the argument n specifying the number of rows to display. Exporting dataNow that you have learned how to use dplyr to extract the information you need from the raw data, or to summarize your raw data, you may want to export these new datasets to share them with your collaborators or for archival.Similarly to the read.csv function used to read in CSV into R, there is a write.csv function that generates CSV files from data frames.Before using it, we are going to create a new folder, dataoutput in our working directory that will store this generated dataset. We don’t want to write generated datasets in the same directory as our raw data. It’s good practice to keep them separate.
The data folder should only contain the raw, unaltered data, and should be left alone to make sure we don’t delete or modify it; on the other end the content of dataoutput directory will be generated by our script, and we know that we can delete the files it contains because we have the script that can re-generate these files.In preparation for our next lesson on plotting, we are going to prepare a cleaned up version of the dataset that doesn’t include any missing data.Let’s start by removing observations for which the speciesid is missing. In this dataset, the missing species are represented by an empty string and not an NA. Let’s also remove observations for which weight and the hindfootlength are missing. This dataset will also only contain observations of animals for which the sex has been determined.
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Surveyscomplete%filter(speciesid!= ', # remove missing speciesid! Is.na(weight), # remove missing weight!
Is.na(hindfootlength), # remove missing hindfootlengthsex!= ') # remove missing sexBecause we are interested in plotting how species abundances have changed through time, we are also going to remove observations for rare species (i.e., that have been observed less than 50 times). We will do this in two steps: first we are going to create a dataset that counts how often each species has been observed, and filter out the rare species; then, we will extract only the observations for these more common species. ## Extract the most common speciesidspeciescounts%groupby(speciesid)%%tally%%filter(n = 50)%%select(speciesid)## Only keep the most common speciessurveyscomplete%filter(speciesid%in% speciescounts$speciesid)To make sure that everyone has the same dataset, check that surveyscomplete has 30463 rows and 13 columns by typing dim(surveyscomplete).Now that our dataset is ready, we can save it as a CSV file in our dataoutput folder. By default, write.csv includes a column with row names (in our case the names are just the row numbers), so we need to add row.names = FALSE so they are not included.