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Data visualization using R
- 1. VISUALIZING DATA IN R. Presentation by: Ummiya Mohammedi MSc-2Cs 1213163320
- 2. Data-Visualization tools and techniques offer executives and other knowledge workers new approaches to dramatically improve their ability to grasp information hiding in their data. Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software. It isn't just the attraction of the huge range of statistical analyses afforded by R that attracts data people to R. The language has also developed a rich ecosystem of charts, plots and visualizations over the years.
- 3. ggplot2 is a data visualization package for the statistical programming language R. Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of Graphics—a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. ggplot2 can serve as a replacement for the base graphics in R and contains a number of defaults for web and print display of common scales. Since 2005, ggplot2 has grown in use to become one of the most popular R packages. It is licensed under GNU GPL v2. ggplot2
- 4. Basic Visualization Histogram Bar / Line Chart Box plot Scatter plot Advanced Visualization Heat Map Mosaic Map Map Visualization 3D Graphs Correlogram
- 5. 1. Histogram Histogram is basically a plot that breaks the data into bins (or breaks) and shows frequency distribution of these bins. You can change the breaks also and see the effect it has data visualization in terms of understandability.
- 7. 2. Bar/ Line Chart Line Chart Below is the line chart showing the increase in air passengers over given time period. Line Charts are commonly preferred when we are to analyse a trend spread over a time period. Furthermore, line plot is also suitable to plots where we need to compare relative changes in quantities across some variable (like time). Below is the code: plot(AirPassengers,type="l") #Simple Line Plot
- 9. Bar Chart Bar Plots are suitable for showing comparison between cumulative totals across several groups. Stacked Plots are used for bar plots for various categories. Here’s the code:
- 11. 3. Box Plot ( including group-by option ) Box Plot shows 5 statistically significant numbers- the minimum, the 25th percentile, the median, the 75th percentile and the maximum. It is thus useful for visualizing the spread of the data is and deriving inferences accordingly. Here’s the basic code:
- 12. Ingest Data When reading data into R, we generally will use the read.table() or read.csv()function. This opens a file and returns the content of that file. In the above example we store the contents of the file in the variab le bugData. Notice that we use the <- operator in R instead of the = like in most other languages. There are certain parameters that we can pass in to table.read(). Among the most often used of these parameters are: sep, header, row.name, and col.name.
- 13. R provides the plot function that can be used to create time series charts. We can either pass in a complete data structure like in the example below (if it contains a plotting function), or we can pass in lists to serve as the x- and y- axes of the chart. ?plot
- 14. 1 plot(Nile, col="blue", bty="7", lwd=2, xlab="", ylab="", main="Flow of the River Nile")
- 15. R also provides a barplot() function to create bar charts. The barplot function accepts either a matrix or a vector value as the data structure.
- 16. barplot(as.matrix(USPersonalExpenditure), main="US Personal Expenditures")
- 17. R provides the hist() function to create histograms. The hist() function accepts a vector of values.
- 18. Usage ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame()) Arguments Data: Default dataset to use for plot. If not already a data.frame, will be converted to one by fortify. If not specified, must be suppled in each layer added to the plot. mapping Default list of aesthetic mappings to use for plot. If not specified, must be suppled in each layer added to the plot. environment If an variable defined in the aesthetic mapping is not found in the data, ggplot will look for it in this environment. It defaults to using the environment in which ggplot() is called.
- 19. ggplot() is used to construct the initial plot object, and is almost always followed by + to add component to the plot. There are three common ways to invoke ggplot: ggplot(df, aes(x, y, )) ggplot(df) ggplot() The first method is recommended if all layers use the same data and the same set of aesthetics, although this method can also be used to add a layer using data from another data frame. See the first example below. The second method specifies the default data frame to use for the plot, but no aesthetics are defined up front. This is useful when one data frame is used predominantly as layers are added, but the aesthetics may vary from one layer to another. The third method initializes a skeleton ggplot object which is fleshed out as layers are added. This method is useful when multiple data frames are used to produce different layers, as is often the case in complex graphics.