Castingfox101

1. R Zoo Cheat Sheet 2020
2. R Zoo Cheat Sheet 2019
3. R Zoo Cheat Sheet Download

Learning Objectives

• Produce scatter plots, boxplots, and time series plots using ggplot.
• Set universal plot settings.
• Describe what faceting is and apply faceting in ggplot.
• Modify the aesthetics of an existing ggplot plot (including axis labels and color).
• Build complex and customized plots from data in a data frame.

We start by loading the required packages. ggplot2 is included in the tidyverse package.

If not still in the workspace, load the data we saved in the previous lesson.

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Plotting with ggplot2

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 plots work best with data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built layer by layer by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a ggplot, we will use the following basic template that can be used for different types of plots:

• use the ggplot() function and bind the plot to a specific data frame using the data argument

• define an aesthetic mapping (using the aesthetic (aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g., as x/y positions or characteristics such as size, shape, color, etc.

• add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones today, including:

  • geom_point() for scatter plots, dot plots, etc.
  • geom_boxplot() for, well, boxplots!
  • geom_line() for trend lines, time series, etc.

To add a geom to the plot use + operator. Because we have two continuous variables, let’s use geom_point() first:

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot “templates” and conveniently explore different types of plots, so the above plot can also be generated with code like this:

Notes

• Anything you put in the ggplot() function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis you set up in aes().
• You can also specify aesthetics for a given geom independently of the aesthetics defined globally in the ggplot() function.
• The + sign used to add layers must be placed at the end of each line containing a layer. If, instead, the + sign is added in the line before the other layer, ggplot2 will not add the new layer and will return an error message.
• You may notice that we sometimes reference ‘ggplot2’ and sometimes ‘ggplot’. To clarify, ‘ggplot2’ is the name of the most recent version of the package. However, any time we call the function itself, it’s just called ‘ggplot’.

Challenge (optional)

Scatter plots can be useful exploratory tools for small datasets. For data sets with large numbers of observations, such as the surveys_complete data set, overplotting of points can be a limitation of scatter plots. One strategy for handling such settings is to use hexagonal binning of observations. The plot space is tessellated into hexagons. Each hexagon is assigned a color based on the number of observations that fall within its boundaries. To use hexagonal binning with ggplot2, first install the R package hexbin from CRAN:

Then use the geom_hex() function:

• What are the relative strengths and weaknesses of a hexagonal bin plot compared to a scatter plot? Examine the above scatter plot and compare it with the hexagonal bin plot that you created.

Building your plots iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to avoid overplotting:

We can also add colors for all the points:

Or to color each species in the plot differently, you could use a vector as an input to the argument color. ggplot2 will provide a different color corresponding to different values in the vector. Here is an example where we color with species_id:

Challenge

Use what you just learned to create a scatter plot of weight over species_id with the plot types showing in different colors. Is this a good way to show this type of data?

Boxplot

We can use boxplots to visualize the distribution of weight within each species:

By adding points to the boxplot, we can have a better idea of the number of measurements and of their distribution:

Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot in front of the points such that it’s not hidden?

Challenges

Boxplots are useful summaries, but hide the shape of the distribution. For example, if there is a bimodal distribution, it would not be observed with a boxplot. An alternative to the boxplot is the violin plot (sometimes known as a beanplot), where the shape (of the density of points) is drawn.

• Replace the box plot with a violin plot; see geom_violin().

In many types of data, it is important to consider the scale of the observations. For example, it may be worth changing the scale of the axis to better distribute the observations in the space of the plot. Changing the scale of the axes is done similarly to adding/modifying other components (i.e., by incrementally adding commands). Try making these modifications:

• Represent weight on the log₁₀ scale; see scale_y_log10().

So far, we’ve looked at the distribution of weight within species. Try making a new plot to explore the distribution of another variable within each species.

• Create boxplot for hindfoot_length. Overlay the boxplot layer on a jitter layer to show actual measurements.

• Add color to the data points on your boxplot according to the plot from which the sample was taken (plot_id).

Hint: Check the class for plot_id. Consider changing the class of plot_id from integer to factor. Why does this change how R makes the graph?

Plotting time series data

Let’s calculate number of counts per year for each genus. First we need to group the data and count records within each group:

Timelapse data can be visualized as a line plot with years on the x-axis and counts on the y-axis:

Unfortunately, this does not work because we plotted data for all the genera together. We need to tell ggplot to draw a line for each genus by modifying the aesthetic function to include group = genus:

We will be able to distinguish genera in the plot if we add colors (using color also automatically groups the data):

Integrating the pipe operator with ggplot2

In the previous lesson, we saw how to use the pipe operator %>% to use different functions in a sequence and create a coherent workflow. We can also use the pipe operator to pass the data argument to the ggplot() function. The hard part is to remember that to build your ggplot, you need to use + and not %>%.

The pipe operator can also be used to link data manipulation with consequent data visualization.

Faceting

ggplot has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to make a time series plot for each genus:

Now we would like to split the line in each plot by the sex of each individual measured. To do that we need to make counts in the data frame grouped by year, genus, and sex:

We can now make the faceted plot by splitting further by sex using color (within a single plot):

We can also facet both by sex and genus:

You can also organise the panels only by rows (or only by columns):

Note:ggplot2 before version 3.0.0 used formulas to specify how plots are faceted. If you encounter facet_grid/wrap(...) code containing ~, please read https://ggplot2.tidyverse.org/news/#tidy-evaluation.

ggplot2 themes

Usually plots with white background look more readable when printed. Every single component of a ggplot graph can be customized using the generic theme() function, as we will see below. However, there are pre-loaded themes available that change the overall appearance of the graph without much effort.

For example, we can change our previous graph to have a simpler white background using the theme_bw() function:

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at https://ggplot2.tidyverse.org/reference/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

The ggthemes package provides a wide variety of options.

Challenge

Use what you just learned to create a plot that depicts how the average weight of each species changes through the years.

Customization

Take a look at the ggplot2 cheat sheet, and think of ways you could improve the plot.

Now, let’s change names of axes to something more informative than ‘year’ and ‘n’ and add a title to the figure:

The axes have more informative names, but their readability can be improved by increasing the font size. This can be done with the generic theme() function:

Note that it is also possible to change the fonts of your plots. If you are on Windows, you may have to install the extrafont package, and follow the instructions included in the README for this package.

After our manipulations, you may notice that the values on the x-axis are still not properly readable. Let’s change the orientation of the labels and adjust them vertically and horizontally so they don’t overlap. You can use a 90 degree angle, or experiment to find the appropriate angle for diagonally oriented labels. We can also modify the facet label text (strip.text) to italicize the genus names:

If you like the changes you created better than the default theme, you can save them as an object to be able to easily apply them to other plots you may create:

Challenge

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio ggplot2 cheat sheet for inspiration.

Here are some ideas:

• See if you can change the thickness of the lines.
• Can you find a way to change the name of the legend? What about its labels?
• Try using a different color palette (see http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).

Arranging plots

Faceting is a great tool for splitting one plot into multiple plots, but sometimes you may want to produce a single figure that contains multiple plots using different variables or even different data frames. The patchwork package allows us to combine separate ggplots into a single figure while keeping everything aligned properly. Like most R packages, we can install patchwork from CRAN, the R package repository:

After you have loaded the patchwork package you can use + to place plots next to each other, / to arrange them vertically, and plot_layout() to determine how much space each plot uses:

You can also use parentheses () to create more complex layouts. There are many useful examples on the patchwork website

Exporting plots

R Zoo Cheat Sheet 2020

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters. The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi):

Note: The parameters width and height also determine the font size in the saved plot.

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Dataframe to xts

R for Economics and Finance: Data Manipulation, ” constructor function for creating timeseries object from raw data inputs. I'm trying to convert a data frame to xts object using the as.xts()-method. Here is my input dataframe q: q t x 1 2006-01-01 00:00:00 1 2 2006-01-01 01:00:00 2 3 2

Converting a data frame to xts, This is clearly documented --- xts and zoo objects are formed by supplying two arguments, a vector or matrix carrying data and Date , POSIXct xts provides methods to convert all of the major objects you are likely to come across. Suitable native R types like matrix, data.frame, and ts are supported, as well as contributed ones such as timeSeries, fts and of course zoo. as.xts() is the workhorse function to do the conversions to xts, and similar functions will provide the reverse

xts Cheat Sheet: Time Series in R, Get started on time series in R with this xts cheat sheet, with code examples. Even though the data.frame object is one of the core objects to Details. A simple and reliable way to convert many different objects into a uniform format for use within R.. It is possible with a call to as.xts to convert objects of class timeSeries, ts, matrix, data.frame, and zoo.

Xts to data.table in r

as.data.table.xts: Efficient xts to as.data.table conversion in data , frame`. Description Usage Arguments See Also Examples. View source: R/xts.R. Description. Efficient conversion xts to data.table. Converting to a data.table is easy with the built-in as.data.table() function. Note that this function has an xts-specific method! You'll need to do a little manipulation of names, but otherwise the conversion is straightforward. A sample xts object called nickelXTS has been loaded into the session for you.

as.xts.data.table: Efficient data.table to xts conversion in , frame`. Description Usage Arguments See Also Examples. View source: R/xts.R. Description. Efficient conversion of data.table to xts x: xts to convert to data.table. keep.rownames: keep xts index as index column in result data.table. key: Character vector of one or more column names which is passed to setkeyv.

R xts and data.table, Just to resolve an open question. As Vincent point in the comment there is no issue about that. It is included in data.table 1.9.5. Below is the similar content: data.table to convert to xts, must have POSIXct or Date in the first column. All others non-numeric columns will be omitted with warning. All others non-numeric columns will be omitted with warning. ignored, just for consistency with generic method.

Error in view cannot coerce class c xts zoo to a data frame

Manipulating Time Series Data in R with xts & zoo, It is for this reason that xts extends the popular zoo class. Load xts library(xts) # View the structure of ex_matrix str(ex_matrix) # Extract the 3rd Suitable native R types like matrix, data.frame, and ts are supported, as well as contributed ones You might also encounter an observation that is in error, yet expected to be as.data.frame is a generic function with many methods, and users and packages can supply further methods. For classes that act as vectors, often a copy of as.data.frame.vector will work as the method. If a list is supplied, each element is converted to a column in the data frame. Similarly, each column of a matrix is converted separately.

R convert between zoo object and data frame, results inconsistent , To convert from data frame to zoo use read.zoo : library(zoo) z <- read.zoo(df). Also note the availability of the drop and other arguments in as.data.frame is a generic function with many methods, and users and packages can supply further methods. For classes that act as vectors, often a copy of as.data.frame.vector will work as the method. If a list is supplied, each element is converted to a column in the data frame. Similarly, each column of a matrix is converted separately.

R Zoo Cheat Sheet 2019

[PDF] Working with Financial Time Series Data in R, b. POSIXt classes c. Working with dates and times using the lubridate package d. The core data object for holding data in R is the data.frame object. However, these classes cannot adequately represent more general irregularly spaced Time series data represented by timeSeries, zoo and xts objects To go back from a zoo object to a data frame in the original format, it's not enough to use as.data.frame since it won't include a Date column (the dates end up in the rownames): more work is needed.

As xts data table

Open Data, Crafting open data policy is hard in a complex era. SIIA's blog has more. Because of quantmod, it is common to have an xts with the symbol embedded in all the column names. (e.g. 'SPY.Open', 'SPY.High', etc.). So, here is an alternative to Jan's as.data.table.xts that puts the symbol in a separate column, which is more natural in data.tables (since you're probably going to rbind a bunch of these before doing any analysis).

as.xts.data.table: Efficient data.table to xts conversion in , data.table to convert to xts, must have POSIXct or Date in the first column. All others non-numeric columns will be omitted with warning. ignored, just for Arguments x. data.table to convert to xts, must have POSIXct or Date in the first column. All others non-numeric columns will be omitted with warning. … ignored, just for consistency with generic method.

as.data.table.xts: Efficient xts to as.data.table conversion in data , x. xts to convert to data.table. keep.rownames. Default is TRUE . If TRUE , adds the xts input's index as a separate column named 'index' . keep.rownames = 'id'​ Converting to a data.table is easy with the built-in as.data.table() function. Note that this function has an xts-specific method! You'll need to do a little manipulation of names, but otherwise the conversion is straightforward. A sample xts object called nickelXTS has been loaded into the session for you.

Ggplot xts

Plotting an xts object using ggplot2, The autoplot.zoo method in zoo (zoo is automatically pulled in by xts) will create plots using ggplot2 for xts objects too. It supports ggplot2's + The autoplot.zoo method in zoo (zoo is automatically pulled in by xts) will create plots using ggplot2 for xts objects too. It supports ggplot2's + if you need additional geoms.

Plotting ts objects, After loading {ggfortify} , you can use ggplot2::autoplot function for ts objects. library(xts) autoplot(as.xts(AirPassengers), ts.colour = 'green') library(timeSeries)​ SHORT HOW-TO ON USING XTS AND GGPLOT FOR TIME SERIES DATA. XTS is a very helpful package when working with time series data. I work with temperature and flow data frequently, so the ability to work with timeseries, and particularly to shift intervals (from 15 min to hourly or daily) can be very handy. Once data has been re-worked, ggplot2 is a

ggplot: Create a new ggplot plot from time series data in ggpmisc , ggplot() initializes a ggplot object. TRUE, environment = parent.frame() ) ## S3 method for class 'xts' ggplot( data, mapping = NULL, , time.resolution = 'day', xts object. y. NULL, not used … any passthrough graphical arguments for lines and points. subset. character vector of length one of the subset range using subsetting as in xts. panels. character vector of expressions to plot as panels. multi.panel. TRUE/FALSE or an integer less than or equal to the number of columns in the data set.

Time series cheat sheet r

RStudio Cheatsheets, dplyr provides a grammar for manipulating tables in R. This cheatsheet will guide you through the Impute missing data in time series by Steffen Moritz. R For Data Science Cheat Sheet: xts. eXtensible Time Series (xts) is a powerful package that provides an extensible time series class, enabling uniform handling of many R time series classes by extending zoo. Load the package as follows: library(xts) Xts Objects. xts objects have three main components: coredata: always a matrix for xts objects

R Zoo Cheat Sheet Download

[PDF] Cheat sheet xts R.indd, eXtensible Time Series (xts) is a powerful package that provides an extensible time series class, enabling uniform handling of many R time R For Data Science Cheat Sheet xts Learn R for data science Interactively at www.DataCamp.com xts DataCamp Learn R for Data Science Interactively eXtensible Time Series (xts) is a powerful package that provides an extensible time series class, enabling uniform handling of many R time series classes by extending zoo. Import From Files

xts Cheat Sheet: Time Series in R | by Karlijn Willems, You'll find yourself wanting a more flexible time series class in R that offers a variety of methods to manipulate your data. xts or the Extensible Time Series Cheat Sheet in R 4 minute read Getting started using the forecast package for time series data in R, as quickly as possible and no explanations. Source: Forecasting: Principles and Practice. Data Prep. Coerce your data to ts format:

Xts to ts

xts_to_ts: Converting 'xts' object to 'ts' object in TSstudio: Functions , xts.obj. A univariate 'xts' object. frequency. A character, optional, if not NULL (​default) set the frequency of the series. start. A Date or POSIXct/lt I have xts time-series object for 10 days of data. The data is sampled at minutes frequency. Therefore, for each day, I have 1440 observations. I need to coerce xts to ts object so that I can use stl

xts, zoo, or ts – which one to use?, It depends.There is no doubt that out of the three objects we have introduced so far (ts, zoo, and xts), the xts class is the most advanced and friendly to use. Converting 'xts' object to 'ts' object Arguments xts.obj. A univariate 'xts' object. frequency. A character, optional, if not NULL (default) set the frequency of the series

xts object to ts object / seasonal dummies for xts, To use seasonal dummy variables, the period of seasonality must be an integer. When you convert a to a time series using as.ts(a) , the period Converting 'xts' object to 'ts' object. xts.obj: A univariate 'xts' object. frequency: A character, optional, if not NULL (default) set the frequency of the series

Convert time series to dataframe in r

[R] convert time series to data.frame, Try this: x <- ts(11:30, start = 2000, freq = 12) data. frame(x = c(x), time = c(time(x))) The time will be in years plus fraction of a year. I want to convert above data into time series format. (start date is 3/14/2013 and end date is 3/13/2015) Converting data frame into Time Series using R. 0.

Data Conversion to Time Series in R, Check the Date column type if it is a character you need to convert it into the date format and then change the dataframe to time series object Hello everyone, I'm very new to R and I'm having a bit of difficulty with my data. I have 11 Economic variables a single country over a 21 year time span (from 1992 to 2013). I'm reading the data from csv file and then trying to define it as time series data using the ts() function. For some reason my figures are completely converted when I do so and I can't seem to figure out why. Below is a

Time Series, Creating a time series. The ts() function will convert a numeric vector into an R time series object. The format is ts(vector, start=, end= Dear R gurus I would like to take a monthly time series and convert it to a data frame without losing the tsp items, pleae I've tried as.data.frame and data.frame but I get the series without the time element.

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