3 Plots In R
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*3 Variable Plot In R
*Plot 3 Lines In R
*3 Plots In R V
*3 Plots In R H10 min read
Most of figures and plots that I find on research papers are 2-dimensional (i.e., x-axis y-axis), but sometimes, I prefer to visualize three valiables simultaneously and to know how they are related to each other. For this purpose, I found a -new to me- package named scatterplot3d. As usual, I will use the NHANES data. The plot function is used to draw points (markers) in a diagram. The function takes parameters for specifying points in the diagram. Parameter 1 specifies points on the x-axis. Parameter 2 specifies points on the y-axis. At its simplest, you can use the plot function to plot.2017/09/09Introduction
This is the tenth post in the series Data Visualization With R. In the previous post, we learnt how to add text annotations to plots. In this post, we will learn how to combine multiple plots. Often, it is useful to have multiple plots in the same frame as it allows us to get a comprehensive view of a particular variable or compare among different variables. The Graphics package offers two methods to combine multiple plots. par() can be used to set graphical parameters regarding plot layout using the mfcol and mfrow arguments. layout() serves the same purpose but offers more flexibility by allowing us to modify the height and width of rows and columns.
A boxplot provides a graphical view of the median, quartiles, maximum, and minimum of a data set. Here we provide examples using two different data sets. R par function. We can put multiple graphs in a single plot by setting some graphical parameters with the help of par function. R programming has a lot of graphical parameters which control the way our graphs are displayed. The par function helps us in setting or inquiring about these parameters. For example, you can look at all the. Combine 3 plots in 2 rows/2 columns filled by rows The magic of the layout function begins here. We want to combine 3 plots and the first plot should occupy both the columns in row 1 and the next 2 plots should be in row 2.
par() allows us to customize the graphical parameters(title, axis, font, color, size) for a particular session. For combining multiple plots, we can use the graphical parameters mfrow and mfcol. These two parameters create a matrix of plots filled by rows and columns respectively. Let us combine plots using both the above parameters.3 Variable Plot In ROptionDescriptionArgumentsmfrowFill by rowsNumber of rows and columnsmfcolFill by columnsNumber of rows and columns
mfrow combines plots filled by rows i.e it takes two arguments, the number of rows and number of columns and then starts filling the plots by row. Below is the syntax for mfrow.
Let us begin by combining 4 plots in 2 rows and 2 columns:Libraries, Code & Data
All the data sets used in this post can be found here and code can be downloaded from here.Case Study 1
Let us begin by combining 4 plots in 2 rows and 2 columns. The plots will be filled by rows as we are using the mfrow function:Case Study 2
Combine 2 plots in 1 row and 2 columns.Case Study 3
Combine 2 plots in 2 rows and 1 column.Case Study 4
Combine 3 plots in 1 row and 3 columns.Case Study 5
Combine 3 plots in 3 rows and 1 column.mfcol
mfcol combines plots filled by columns i.e it takes two arguments, the number of rows and number of columns and then starts filling the plots by columns. Below is the syntax for mfrow:
Let us begin by combining 4 plots in 2 rows and 2 columns:Case Study 6
Combine 3 plots in 3 rows and 1 column.Special Cases
What happens if we specify lesser or more number of graphs? In the next two examples, we will specify lesser or more number of graphs than we ask the par() function to combine. Let us see what happens in such instances:
Case 1: Lesser number of graphs specifiedWe will specify that 4 plots need to be combined in 2 rows and 2 columns but provide only 3 graphs.
Case 2: Extra graph specifiedWe will specify that 4 plots need to be combined in 2 rows and 2 columns but specify 6 graphs instead of 4.Case Study 8Layout
At the core of the layout() function is a matrix. We communicate the structure in which the plots must be combined using a matrix. As such, the layout function is more flexible compared to the par() function.OptionDescriptionValuematrixmatrix specifying location of plantsmatrixwidthswidth of columnsvectorheightsheight of rowsvector
Let us begin by combining 4 plots in a 2 row/2 column structure. We do this by creating a layout using the matrix function.Case Study 1
Combine 4 plots in 2 rows/2 columns filled by rows.Case Study 2
Combine 4 plots in 2 rows/2 columns filled by columns
To fill the plots by column, we specify byrow = FALSE in the matrix.Case Study 3
Combine 3 plots in 2 rows/2 columns filled by rows
The magic of the layout() function begins here. We want to combine 3 plots and the first plot should occupy both the columns in row 1 and the next 2 plots should be in row 2. If you look at the matrix below, 1 is specified twice and since the matrix is filled by row, it will occupy both the columns in the first row. Similarly the first plot will occupy the entire first row. It will be crystal clear when you see the plot.Case Study 4
Combine 3 plots in 2 rows/2 columns filled by rows
The plots must be filled by rows and the third plot must occupy both the columns of the second row while the other two plots will be placed in the first row. The matrix would look like this:Case Study 5Plot 3 Lines In R
Combine 3 plots in 2 rows/2 columns filled by columns
The plots must be filled by columns and the first plot must occupy both the rows of the first column while the other two plots will be placed in the second column in two rows. The matrix would look like this:Case Study 6
Combine 3 plots in 2 rows/2 columns filled by columns
The plots must be filled by columns and the first plot must occupy both the rows of the second column while the other two plots will be placed in the first column in two rows. The matrix would look like this:Widths
In all the layouts created so far, we have kept the size of the rows and columns equal. What if you want to modify the width and height of the columns and rows? The widths and heights arguments in the layout() function address the above mentioned issue. Let us check them out one by one: The widths argument is used for specifying the width of the columns. Based on the number of columns in the layout, you can specify the width of each column. Let us look at some examples.Case Study 7
Width of the 2nd column is twice the width of the 1st columnCase Study 8
Width of the 2nd column is twice that of the first and last columnHeights
The heights arguments is used to modify the height of the rows and based on the number of rows specified in the layout, we can specify the height of each row.Case Study 93 Plots In R V
Height of the 2nd row is twice that of the first rowPutting it all together…
Before we end this section, let us combine plots using both the widths and heights option
Contents
We look at some of the ways R can display informationgraphically. This is a basic introduction to some of the basicplotting commands. It is assumed that you know how to enter data orread data files which is covered in the first chapter, and it isassumed that you are familiar with the different data types.
In each of the topics that follow it is assumed that two differentdata sets, w1.dat and trees91.csvhave been read and defined using the same variables as in the firstchapter. Both of these data sets come from the study discussed on theweb site given in the first chapter. We assume that they are readusing “read.csv” into variables w1 and tree:
A strip chart is the most basic type of plot available. It plots thedata in order along a line with each data point represented as abox. Here we provide examples using the w1 data frame mentioned atthe top of this page, and the one column of the data is w1$vals.
To create a strip chart of this data use the stripchart command:
As you can see this is about as bare bones as you can get. There is notitle nor axes labels. It only shows how the data looks if you were toput it all along one line and mark out a box at each point. If youwould prefer to see which points are repeated you can specify thatrepeated points be stacked:
A variation on this is to have the boxes moved up and down so thatthere is more separation between them:
If you do not want the boxes plotting in the horizontal direction youcan plot them in the vertical direction:
Since you should always annotate your plots there are many differentways to add titles and labels. One way is within the stripchartcommand itself:
If you have a plot already and want to add a title, you can use thetitle command:
Note that this simply adds the title and labels and will write overthe top of any titles or labels you already have.
A histogram is very common plot. It plots the frequencies that dataappears within certain ranges. Here we provide examples using the w1data frame mentioned at the top of this page, and the one column ofdata is w1$vals.
To plot a histogram of the data use the “hist” command:
As you can see R will automatically calculate the intervals touse. There are many options to determine how to break up theintervals. Here we look at just one way, varying the domain size andnumber of breaks. If you would like to know more about the otheroptions check out the help page:
You can specify the number of breaks to use using the breaksoption. Here we look at the histogram for various numbers of breaks:
You can also vary the size of the domain using the xlim option. Thisoption takes a vector with two entries in it, the left value and theright value:
The options for adding titles and labels are exactly the same as forstrip charts. You should always annotate your plots and there are manydifferent ways to add titles and labels. One way is within the histcommand itself:
If you have a plot already and want to change or add a title, you can use the title command:
Note that this simply adds the title and labels and will write overthe top of any titles or labels you already have.
It is not uncommon to add other kinds of plots to a histogram. Forexample, one of the options to the stripchart command is to add it toa plot that has already been drawn. For example, you might want tohave a histogram with the strip chart drawn across the top. Theaddition of the strip chart might give you a better idea of thedensity of the data:
A boxplot provides a graphical view of the median, quartiles, maximum,and minimum of a data set. Here we provide examples using twodifferent data sets. The first is the w1 data frame mentioned at thetop of this page, and the one column of data is w1$vals. The secondis the tree data frame from the trees91.csvdata file which is also mentioned at the top of the page.
House of rising sun youtube. We first use the w1 data set and look at the boxplot of this data set:
Again, this is a very plain graph, and the title and labels can bespecified in exactly the same way as in the stripchart and histcommands:
Note that the default orientation is to plot the boxplotvertically. Because of this we used the ylab option to specify theaxis label. There are a large number of options for this command. Tosee more of the options see the help page:
As an example you can specify that the boxplot be plotted horizontallyby specifying the horizontal option:
The option to plot the box plot horizontally can be put to good use todisplay a box plot on the same image as a histogram. You need tospecify the add option, specify where to put the box plot using the atoption, and turn off the addition of axes using the axes option:
If you are feeling really crazy you can take a histogram and add a box plot and a strip chart:
Some people shell out good money to have this much fun.
For the second part on boxplots we will look at the second data frame,“tree,” which comes from the trees91.csv file.To reiterate the discussion at the top of this page and the discussionin the data types chapter, we need to specify which columns arefactors:
We can look at the boxplot of just the data for the stem biomass:
That plot does not tell the whole story. It is for all of the trees,but the trees were grown in different kinds of environments. Theboxplot command can be used to plot a separate box plot for eachlevel. In this case the data is held in “tree$STBM,” and the differentlevels are stored as factors in “tree$C.” The command to createdifferent boxplots is the following:
Note that for the level called “2” there are four outliers which areplotted as little circles. There are many options to annotate yourplot including different labels for each level. Please use thehelp(boxplot) command for more information.
A scatter plot provides a graphical view of the relationship betweentwo sets of numbers. Here we provide examples using the tree dataframe from the trees91.csvdata file which is mentioned at the top of the page. In particular welook at the relationship between the stem biomass (“tree$STBM”) andthe leaf biomass (“tree$LFBM”).
The command to plot each pair of points as an x-coordinate and a y-coorindate is “plot:”
It appears that there is a strong positive association between thebiomass in the stems of a tree and the leaves of the tree. It appearsto be a linear relationship. In fact, the corelation between these twosets of observations is quite high:
Getting back to the plot, you should always annotate your graphs. Thetitle and labels can be specified in exactly the same way as with theother plotting commands:
The final type of plot that we look at is the normal quantileplot. This plot is used to determine if your data is close to beingnormally distributed. You cannot be sure that the data is normallydistributed, but you can rule out if it is not normallydistributed. Here we provide examples using the w1 data framementioned at the top of this page, and the one column of data isw1$vals.
The command to generate a normal quantile plot is qqnorm. You can giveit one argument, the univariate data set of interest:
You can annotate the plot in exactly the same way as all of the otherplotting commands given here:
After you creat the normal quantile plot you can also add thetheoretical line that the data should fall on if they were normallydistributed:3 Plots In R H
In this example you should see that the data is not quite normallydistributed. There are a few outliers, and it does not match up at thetails of the distribution.
Register here: http://gg.gg/uy9z2
https://diarynote.indered.space
*3 Variable Plot In R
*Plot 3 Lines In R
*3 Plots In R V
*3 Plots In R H10 min read
Most of figures and plots that I find on research papers are 2-dimensional (i.e., x-axis y-axis), but sometimes, I prefer to visualize three valiables simultaneously and to know how they are related to each other. For this purpose, I found a -new to me- package named scatterplot3d. As usual, I will use the NHANES data. The plot function is used to draw points (markers) in a diagram. The function takes parameters for specifying points in the diagram. Parameter 1 specifies points on the x-axis. Parameter 2 specifies points on the y-axis. At its simplest, you can use the plot function to plot.2017/09/09Introduction
This is the tenth post in the series Data Visualization With R. In the previous post, we learnt how to add text annotations to plots. In this post, we will learn how to combine multiple plots. Often, it is useful to have multiple plots in the same frame as it allows us to get a comprehensive view of a particular variable or compare among different variables. The Graphics package offers two methods to combine multiple plots. par() can be used to set graphical parameters regarding plot layout using the mfcol and mfrow arguments. layout() serves the same purpose but offers more flexibility by allowing us to modify the height and width of rows and columns.
A boxplot provides a graphical view of the median, quartiles, maximum, and minimum of a data set. Here we provide examples using two different data sets. R par function. We can put multiple graphs in a single plot by setting some graphical parameters with the help of par function. R programming has a lot of graphical parameters which control the way our graphs are displayed. The par function helps us in setting or inquiring about these parameters. For example, you can look at all the. Combine 3 plots in 2 rows/2 columns filled by rows The magic of the layout function begins here. We want to combine 3 plots and the first plot should occupy both the columns in row 1 and the next 2 plots should be in row 2.
par() allows us to customize the graphical parameters(title, axis, font, color, size) for a particular session. For combining multiple plots, we can use the graphical parameters mfrow and mfcol. These two parameters create a matrix of plots filled by rows and columns respectively. Let us combine plots using both the above parameters.3 Variable Plot In ROptionDescriptionArgumentsmfrowFill by rowsNumber of rows and columnsmfcolFill by columnsNumber of rows and columns
mfrow combines plots filled by rows i.e it takes two arguments, the number of rows and number of columns and then starts filling the plots by row. Below is the syntax for mfrow.
Let us begin by combining 4 plots in 2 rows and 2 columns:Libraries, Code & Data
All the data sets used in this post can be found here and code can be downloaded from here.Case Study 1
Let us begin by combining 4 plots in 2 rows and 2 columns. The plots will be filled by rows as we are using the mfrow function:Case Study 2
Combine 2 plots in 1 row and 2 columns.Case Study 3
Combine 2 plots in 2 rows and 1 column.Case Study 4
Combine 3 plots in 1 row and 3 columns.Case Study 5
Combine 3 plots in 3 rows and 1 column.mfcol
mfcol combines plots filled by columns i.e it takes two arguments, the number of rows and number of columns and then starts filling the plots by columns. Below is the syntax for mfrow:
Let us begin by combining 4 plots in 2 rows and 2 columns:Case Study 6
Combine 3 plots in 3 rows and 1 column.Special Cases
What happens if we specify lesser or more number of graphs? In the next two examples, we will specify lesser or more number of graphs than we ask the par() function to combine. Let us see what happens in such instances:
Case 1: Lesser number of graphs specifiedWe will specify that 4 plots need to be combined in 2 rows and 2 columns but provide only 3 graphs.
Case 2: Extra graph specifiedWe will specify that 4 plots need to be combined in 2 rows and 2 columns but specify 6 graphs instead of 4.Case Study 8Layout
At the core of the layout() function is a matrix. We communicate the structure in which the plots must be combined using a matrix. As such, the layout function is more flexible compared to the par() function.OptionDescriptionValuematrixmatrix specifying location of plantsmatrixwidthswidth of columnsvectorheightsheight of rowsvector
Let us begin by combining 4 plots in a 2 row/2 column structure. We do this by creating a layout using the matrix function.Case Study 1
Combine 4 plots in 2 rows/2 columns filled by rows.Case Study 2
Combine 4 plots in 2 rows/2 columns filled by columns
To fill the plots by column, we specify byrow = FALSE in the matrix.Case Study 3
Combine 3 plots in 2 rows/2 columns filled by rows
The magic of the layout() function begins here. We want to combine 3 plots and the first plot should occupy both the columns in row 1 and the next 2 plots should be in row 2. If you look at the matrix below, 1 is specified twice and since the matrix is filled by row, it will occupy both the columns in the first row. Similarly the first plot will occupy the entire first row. It will be crystal clear when you see the plot.Case Study 4
Combine 3 plots in 2 rows/2 columns filled by rows
The plots must be filled by rows and the third plot must occupy both the columns of the second row while the other two plots will be placed in the first row. The matrix would look like this:Case Study 5Plot 3 Lines In R
Combine 3 plots in 2 rows/2 columns filled by columns
The plots must be filled by columns and the first plot must occupy both the rows of the first column while the other two plots will be placed in the second column in two rows. The matrix would look like this:Case Study 6
Combine 3 plots in 2 rows/2 columns filled by columns
The plots must be filled by columns and the first plot must occupy both the rows of the second column while the other two plots will be placed in the first column in two rows. The matrix would look like this:Widths
In all the layouts created so far, we have kept the size of the rows and columns equal. What if you want to modify the width and height of the columns and rows? The widths and heights arguments in the layout() function address the above mentioned issue. Let us check them out one by one: The widths argument is used for specifying the width of the columns. Based on the number of columns in the layout, you can specify the width of each column. Let us look at some examples.Case Study 7
Width of the 2nd column is twice the width of the 1st columnCase Study 8
Width of the 2nd column is twice that of the first and last columnHeights
The heights arguments is used to modify the height of the rows and based on the number of rows specified in the layout, we can specify the height of each row.Case Study 93 Plots In R V
Height of the 2nd row is twice that of the first rowPutting it all together…
Before we end this section, let us combine plots using both the widths and heights option
Contents
We look at some of the ways R can display informationgraphically. This is a basic introduction to some of the basicplotting commands. It is assumed that you know how to enter data orread data files which is covered in the first chapter, and it isassumed that you are familiar with the different data types.
In each of the topics that follow it is assumed that two differentdata sets, w1.dat and trees91.csvhave been read and defined using the same variables as in the firstchapter. Both of these data sets come from the study discussed on theweb site given in the first chapter. We assume that they are readusing “read.csv” into variables w1 and tree:
A strip chart is the most basic type of plot available. It plots thedata in order along a line with each data point represented as abox. Here we provide examples using the w1 data frame mentioned atthe top of this page, and the one column of the data is w1$vals.
To create a strip chart of this data use the stripchart command:
As you can see this is about as bare bones as you can get. There is notitle nor axes labels. It only shows how the data looks if you were toput it all along one line and mark out a box at each point. If youwould prefer to see which points are repeated you can specify thatrepeated points be stacked:
A variation on this is to have the boxes moved up and down so thatthere is more separation between them:
If you do not want the boxes plotting in the horizontal direction youcan plot them in the vertical direction:
Since you should always annotate your plots there are many differentways to add titles and labels. One way is within the stripchartcommand itself:
If you have a plot already and want to add a title, you can use thetitle command:
Note that this simply adds the title and labels and will write overthe top of any titles or labels you already have.
A histogram is very common plot. It plots the frequencies that dataappears within certain ranges. Here we provide examples using the w1data frame mentioned at the top of this page, and the one column ofdata is w1$vals.
To plot a histogram of the data use the “hist” command:
As you can see R will automatically calculate the intervals touse. There are many options to determine how to break up theintervals. Here we look at just one way, varying the domain size andnumber of breaks. If you would like to know more about the otheroptions check out the help page:
You can specify the number of breaks to use using the breaksoption. Here we look at the histogram for various numbers of breaks:
You can also vary the size of the domain using the xlim option. Thisoption takes a vector with two entries in it, the left value and theright value:
The options for adding titles and labels are exactly the same as forstrip charts. You should always annotate your plots and there are manydifferent ways to add titles and labels. One way is within the histcommand itself:
If you have a plot already and want to change or add a title, you can use the title command:
Note that this simply adds the title and labels and will write overthe top of any titles or labels you already have.
It is not uncommon to add other kinds of plots to a histogram. Forexample, one of the options to the stripchart command is to add it toa plot that has already been drawn. For example, you might want tohave a histogram with the strip chart drawn across the top. Theaddition of the strip chart might give you a better idea of thedensity of the data:
A boxplot provides a graphical view of the median, quartiles, maximum,and minimum of a data set. Here we provide examples using twodifferent data sets. The first is the w1 data frame mentioned at thetop of this page, and the one column of data is w1$vals. The secondis the tree data frame from the trees91.csvdata file which is also mentioned at the top of the page.
House of rising sun youtube. We first use the w1 data set and look at the boxplot of this data set:
Again, this is a very plain graph, and the title and labels can bespecified in exactly the same way as in the stripchart and histcommands:
Note that the default orientation is to plot the boxplotvertically. Because of this we used the ylab option to specify theaxis label. There are a large number of options for this command. Tosee more of the options see the help page:
As an example you can specify that the boxplot be plotted horizontallyby specifying the horizontal option:
The option to plot the box plot horizontally can be put to good use todisplay a box plot on the same image as a histogram. You need tospecify the add option, specify where to put the box plot using the atoption, and turn off the addition of axes using the axes option:
If you are feeling really crazy you can take a histogram and add a box plot and a strip chart:
Some people shell out good money to have this much fun.
For the second part on boxplots we will look at the second data frame,“tree,” which comes from the trees91.csv file.To reiterate the discussion at the top of this page and the discussionin the data types chapter, we need to specify which columns arefactors:
We can look at the boxplot of just the data for the stem biomass:
That plot does not tell the whole story. It is for all of the trees,but the trees were grown in different kinds of environments. Theboxplot command can be used to plot a separate box plot for eachlevel. In this case the data is held in “tree$STBM,” and the differentlevels are stored as factors in “tree$C.” The command to createdifferent boxplots is the following:
Note that for the level called “2” there are four outliers which areplotted as little circles. There are many options to annotate yourplot including different labels for each level. Please use thehelp(boxplot) command for more information.
A scatter plot provides a graphical view of the relationship betweentwo sets of numbers. Here we provide examples using the tree dataframe from the trees91.csvdata file which is mentioned at the top of the page. In particular welook at the relationship between the stem biomass (“tree$STBM”) andthe leaf biomass (“tree$LFBM”).
The command to plot each pair of points as an x-coordinate and a y-coorindate is “plot:”
It appears that there is a strong positive association between thebiomass in the stems of a tree and the leaves of the tree. It appearsto be a linear relationship. In fact, the corelation between these twosets of observations is quite high:
Getting back to the plot, you should always annotate your graphs. Thetitle and labels can be specified in exactly the same way as with theother plotting commands:
The final type of plot that we look at is the normal quantileplot. This plot is used to determine if your data is close to beingnormally distributed. You cannot be sure that the data is normallydistributed, but you can rule out if it is not normallydistributed. Here we provide examples using the w1 data framementioned at the top of this page, and the one column of data isw1$vals.
The command to generate a normal quantile plot is qqnorm. You can giveit one argument, the univariate data set of interest:
You can annotate the plot in exactly the same way as all of the otherplotting commands given here:
After you creat the normal quantile plot you can also add thetheoretical line that the data should fall on if they were normallydistributed:3 Plots In R H
In this example you should see that the data is not quite normallydistributed. There are a few outliers, and it does not match up at thetails of the distribution.
Register here: http://gg.gg/uy9z2
https://diarynote.indered.space
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