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Or Or (Should be faster for big data set because avoiding calling Edit: Here's how Adding a marginally faster timing output: What type of visualization to use for what sort of problem? This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2. This is part 3 of a three part tutorial on ggplot2, an aesthetically pleasing (and very popular) graphics framework in R. This tutorial is primarily geared towards those having some basic knowledge of the R programming language and want to make complex and nice looking charts with R ggplot2.
Top 50 ggplot2 Visualizations - The Master ListAn effective chart is one that:
The list below sorts the visualizations based on its primary purpose. Primarily, there are 8 types of objectives you may construct plots. So, before you actually make the plot, try and figure what findings and relationships you would like to convey or examine through the visualization. Chances are it will fall under one (or sometimes more) of these 8 categories. 1. CorrelationThe following plots help to examine how well correlated two variables are. ScatterplotThe most frequently used plot for data analysis is undoubtedly the scatterplot. Whenever you want to understand the nature of relationship between two variables, invariably the first choice is the scatterplot. It can be drawn using 9. Additionally, 0 which draws a smoothing line (based on loess) by default, can be tweaked to draw the line of best fit by setting 1. Scatterplot With EncirclingWhen presenting the results, sometimes I would encirlce certain special group of points or region in the chart so as to draw the attention to those peculiar cases. This can be conveniently done using the 2 in 3 package. Within 2, set the 5 to a new dataframe that contains only the points (rows) or interest. Moreover, You can 6 the curve so as to pass just outside the points. The 7 and 8 (thickness) of the curve can be modified as well. See below example. Jitter PlotLet’s look at a new data to draw the scatterplot. This time, I will use the 9 dataset to plot city mileage (
1). What we have here is a scatterplot of city and highway mileage in 9 dataset. We have seen a similar scatterplot and this looks neat and gives a clear idea of how the city mileage (
But, this innocent looking plot is hiding something. Can you find out? The original data has 234 data points but the chart seems to display fewer points. What has happened? This is because there are many overlapping points appearing as a single dot. The fact that both 0 and 1 are integers in the source dataset made it all the more convenient to hide this detail. So just be extra careful the next time you make scatterplot with integers. So how to handle this? There are few options. We can make a jitter plot with 7. As the name suggests, the overlapping points are randomly jittered around its original position based on a threshold controlled by the 8 argument. 8, more the points are moved jittered from their original position. Counts ChartThe second option to overcome the problem of data points overlap is to use what is called a counts chart. Whereever there is more points overlap, the size of the circle gets bigger. Bubble plotWhile scatterplot lets you compare the relationship between 2 continuous variables, bubble chart serves well if you want to understand relationship within the underlying groups based on:
In simpler words, bubble charts are more suitable if you have 4-Dimensional data where two of them are numeric (X and Y) and one other categorical (color) and another numeric variable (size). The bubble chart clearly distinguishes the range of 0 between the manufacturers and how the slope of lines-of-best-fit varies, providing a better visual comparison between the groups. Animated Bubble chartAn animated bubble chart can be implemented using the 1 package. It is same as the bubble chart, but, you have to show how the values change over a fifth dimension (typically time). The key thing to do is to set the 2 to the desired column on which you want to animate. Rest of the procedure related to plot construction is the same. Once the plot is constructed, you can animate it using 3 by setting a chosen 4. Marginal Histogram / BoxplotIf you want to show the relationship as well as the distribution in the same chart, use the marginal histogram. It has a histogram of the X and Y variables at the margins of the scatterplot. This can be implemented using the 5 function from the ‘ 6’ package. Apart from a 7, you could choose to draw a marginal 8 or 9 plot by setting the respective 0 option. CorrelogramCorrelogram let’s you examine the corellation of multiple continuous variables present in the same dataframe. This is conveniently implemented using the 1 package. 2. DeviationCompare variation in values between small number of items (or categories) with respect to a fixed reference. Diverging barsDiverging Bars is a bar chart that can handle both negative and positive values. This can be implemented by a smart tweak with 2. But the usage of 2 can be quite confusing. Thats because, it can be used to make a bar chart as well as a histogram. Let me explain. By default, 2 has the 5 set to 6. That means, when you provide just a continuous X variable (and no Y variable), it tries to make a histogram out of the data. In order to make a bar chart create bars instead of histogram, you need to do two things.
In order to make sure you get diverging bars instead of just bars, make sure, your categorical variable has 2 categories that changes values at a certain threshold of the continuous variable. In below example, the 9 from mtcars dataset is normalised by computing the z score. Those vehicles with mpg above zero are marked green and those below are marked red. Diverging Lollipop ChartLollipop chart conveys the same information as bar chart and diverging bar. Except that it looks more modern. Instead of geom_bar, I use 6 and 7 to get the lollipops right. Let’s draw a lollipop using the same data I prepared in the previous example of diverging bars. 0 Diverging Dot PlotDot plot conveys similar information. The principles are same as what we saw in Diverging bars, except that only point are used. Below example uses the same data prepared in the . 1 Area ChartArea charts are typically used to visualize how a particular metric (such as % returns from a stock) performed compared to a certain baseline. Other types of %returns or %change data are also commonly used. The 8 implements this. 2 3. RankingUsed to compare the position or performance of multiple items with respect to each other. Actual values matters somewhat less than the ranking. Ordered Bar ChartOrdered Bar Chart is a Bar Chart that is ordered by the Y axis variable. Just sorting the dataframe by the variable of interest isn’t enough to order the bar chart. In order for the bar chart to retain the order of the rows, the X axis variable (i.e. the categories) has to be converted into a factor. Let’s plot the mean city mileage for each manufacturer from 9 dataset. First, aggregate the data and sort it before you draw the plot. Finally, the X variable is converted to a factor. Let’s see how that is done. 3 The X variable is now a 3, let’s plot. 4 Lollipop ChartLollipop charts conveys the same information as in bar charts. By reducing the thick bars into thin lines, it reduces the clutter and lays more emphasis on the value. It looks nice and modern. 5 Dot PlotDot plots are very similar to lollipops, but without the line and is flipped to horizontal position. It emphasizes more on the rank ordering of items with respect to actual values and how far apart are the entities with respect to each other. 6 Slope ChartSlope charts are an excellent way of comparing the positional placements between 2 points on time. At the moment, there is no builtin function to construct this. Following code serves as a pointer about how you may approach this. 7 Dumbbell PlotDumbbell charts are a great tool if you wish to: 1. Visualize relative positions (like growth and decline) between two points in time. 2. Compare distance between two categories. In order to get the correct ordering of the dumbbells, the Y variable should be a factor and the levels of the factor variable should be in the same order as it should appear in the plot. 8 4. DistributionWhen you have lots and lots of data points and want to study where and how the data points are distributed. HistogramBy default, if only one variable is supplied, the 2 tries to calculate the count. In order for it to behave like a bar chart, the 7 option has to be set and 8 and 9 values must be provided. Histogram on a continuous variableHistogram on a continuous variable can be accomplished using either 2 or 06. When using 06, you can control the number of bars using the 08 option. Else, you can set the range covered by each bin using 09. The value of 09 is on the same scale as the continuous variable on which histogram is built. Since, 11 gives facility to control both number of 08 as well as 09, it is the preferred option to create histogram on continuous variables. 9 Histogram on a categorical variableHistogram on a categorical variable would result in a frequency chart showing bars for each category. By adjusting 8, you can adjust the thickness of the bars. 0 Density plot1 Box PlotBox plot is an excellent tool to study the distribution. It can also show the distributions within multiple groups, along with the median, range and outliers if any. The dark line inside the box represents the median. The top of box is 75%ile and bottom of box is 25%ile. The end points of the lines (aka whiskers) is at a distance of 1.5*IQR, where IQR or Inter Quartile Range is the distance between 25th and 75th percentiles. The points outside the whiskers are marked as dots and are normally considered as extreme points. Setting 15 adjusts the width of the boxes to be proportional to the number of observation it contains. 2 3 Dot + Box PlotOn top of the information provided by a box plot, the dot plot can provide more clear information in the form of summary statistics by each group. The dots are staggered such that each dot represents one observation. So, in below chart, the number of dots for a given manufacturer will match the number of rows of that manufacturer in source data. 4 Tufte BoxplotTufte box plot, provided by 16 package is inspired by the works of Edward Tufte. Tufte’s Box plot is just a box plot made minimal and visually appealing. 5 Violin PlotA violin plot is similar to box plot but shows the density within groups. Not much info provided as in boxplots. It can be drawn using 17. 6 Population PyramidPopulation pyramids offer a unique way of visualizing how much population or what percentage of population fall under a certain category. The below pyramid is an excellent example of how many users are retained at each stage of a email marketing campaign funnel. 7 5. CompositionWaffle ChartWaffle charts is a nice way of showing the categorical composition of the total population. Though there is no direct function, it can be articulated by smartly maneuvering the ggplot2 using 18 function. The below template should help you create your own waffle. 8 Pie ChartPie chart, a classic way of showing the compositions is equivalent to the waffle chart in terms of the information conveyed. But is a slightly tricky to implement in ggplot2 using the 19. 9 0 TreemapTreemap is a nice way of displaying hierarchical data by using nested rectangles. The 20 package provides the necessary functions to convert the data in desired format (
22). In order to create a treemap, the data must be converted to desired format using 23. The important requirement is, your data must have one variable each that describes the 24 of the tiles, variable for 25 color, variable that has the tile’s 26 and finally the parent 27. Once the data formatting is done, just call 28 on the treemapified data. 1 Bar ChartBy default, 2 has the 5 set to 6. That means, when you provide just a continuous X variable (and no Y variable), it tries to make a histogram out of the data. In order to make a bar chart create bars instead of histogram, you need to do two things.
A bar chart can be drawn from a categorical column variable or from a separate frequency table. By adjusting 8, you can adjust the thickness of the bars. If your data source is a frequency table, that is, if you don’t want ggplot to compute the counts, you need to set the 7 inside the 2. 2 3 It can be computed directly from a column variable as well. In this case, only X is provided and 7 is not set. 4 6. ChangeTime Series Plot From a Time Series Object (44) The 45 package allows autoplot to automatically plot directly from a time series object ( 44). 5 Time Series Plot From a Data FrameUsing 47, a time series (or line chart) can be drawn from a 48 as well. The X axis breaks are generated by default. In below example, the breaks are formed once every 10 years. Default X Axis Labels6 Time Series Plot For a Monthly Time SeriesIf you want to set your own time intervals (breaks) in X axis, you need to set the breaks and labels using 49. 7 Time Series Plot For a Yearly Time Series8 Time Series Plot From Long Data Format: Multiple Time Series in Same Dataframe ColumnIn this example, I construct the ggplot from a long data format. That means, the column names and respective values of all the columns are stacked in just 2 variables ( 50 and 51 respectively). If you were to convert this data to wide format, it would look like the 52 dataset. In below example, the 53 is drawn for 51 column and the 55 is set to 50. This way, with just one call to 53, multiple colored lines are drawn, one each for each unique value in 50 column. The 49 changes the X axis breaks and labels, and 60 changes the color of the lines. 9 0 Time Series Plot From Wide Data Format: Data in Multiple Columns of DataframeAs noted in the of this tutorial, whenever your plot’s geom (like points, lines, bars, etc) changes the 25, 8, 63, 64 or 65 based on another column, a legend is automatically drawn. But if you are creating a time series (or even other types of plots) from a wide data format, you have to draw each line manually by calling 47 once for every line. So, a legend will not be drawn by default. However, having a legend would still be nice. This can be done using the 67 format of functions (like, 68 if only the color of your lines change). Using this function, you can give a legend title with the 69 argument, tell what color the legend should take with the 70 argument and also set the legend labels. Even though the below plot looks exactly like the previous one, the approach to construct this is different. You might wonder why I used this function in previous example for long data format as well. Note that, in previous example, it was used to change the color of the line only. Without 68, you would still have got a legend, but the lines would be of a different (default) color. But in current example, without 68, you wouldn’t even have a legend. Try it out! 1 Stacked Area ChartStacked area chart is just like a line chart, except that the region below the plot is all colored. This is typically used when:
This can be plotted using 73 which works very much like 53. But there is an important point to note. By default, each 8 starts from the bottom of Y axis (which is typically 0), but, if you want to show the contribution from individual components, you want the 73 to be stacked over the top of previous component, rather than the floor of the plot itself. So, you have to add all the bottom layers while setting the 9 of 73. In below example, I have set it as 79 for the topmost 8. However nice the plot looks, the caveat is that, it can easily become complicated and uninterprettable if there are too many components. 2 Calendar HeatmapWhen you want to see the variation, especially the highs and lows, of a metric like stock price, on an actual calendar itself, the calendar heat map is a great tool. It emphasizes the variation visually over time rather than the actual value itself. This can be implemented using the 81. But getting it in the right format has more to do with the data preparation rather than the plotting itself. 3 Slope ChartSlope chart is a great tool of you want to visualize change in value and ranking between categories. This is more suitable over a time series when there are very few time points. 4 Seasonal PlotIf you are working with a time series object of class 44 or 83, you can view the seasonal fluctuations through a seasonal plot drawn using 84. Below is an example using the native 85 and 86 time series. You can see the traffic increase in air passengers over the years along with the repetitive seasonal patterns in traffic. Whereas Nottingham does not show an increase in overal temperatures over the years, but they definitely follow a seasonal pattern. 5 7. GroupsHierarchical Dendrogram6 ClustersIt is possible to show the distinct clusters or groups using 2. If the dataset has multiple weak features, you can compute the principal components and draw a scatterplot using PC1 and PC2 as X and Y axis. The 2 can be used to encircle the desired groups. The only thing to note is the 5 argument to 90. You need to provide a subsetted dataframe that contains only the observations (rows) that belong to the group as the 5 argument. 7 8. SpatialThe 92 package provides facilities to interact with the google maps api and get the coordinates (latitude and longitude) of places you want to plot. The below example shows satellite, road and hybrid maps of the city of Chennai, encircling some of the places. I used the 93 function to get the coordinates of these places and 94 to get the maps. The type of map to fetch is determined by the value you set to the 95. You can also zoom into the map by setting the 96 argument. The default is 10 (suitable for large cities). Reduce this number (up to 3) if you want to zoom out. It can be zoomed in till 21, suitable for buildings. What does Top_n do in R?top_n: Select top (or bottom) n rows (by value) What is Topr an R package for viewing and annotating genetic association results?Results: topr provides a fast and elegant visual display of association results, along with the annotation of association peaks with their nearest gene. Association results from multiple analyses can be viewed simultaneously over the entire genome or in a more detailed regional view along with gene information. |
