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397 lines
14 KiB
Markdown
[#]: subject: "Data Visualisation in R: Graphs"
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[#]: via: "https://www.opensourceforu.com/2022/07/data-visualisation-in-r-graphs/"
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[#]: author: "Shakthi Kannan https://www.opensourceforu.com/author/shakthi-kannan/"
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[#]: collector: "lkxed"
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[#]: translator: " "
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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Data Visualisation in R: Graphs
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======
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In this tenth article in the R series, we will continue to explore data visualisation in R with the lattice and ggplot2 packages.
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![Data-Visualisation-in-R-Graphs-Featured-image][1]
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We will be using the R version 4.1.2 installed on Parabola GNU/Linux-libre (x86-64) for the example code snippets in this article.
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```
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$ R --version
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R version 4.1.2 (2021-11-01) -- “Bird Hippie”
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Copyright (C) 2021 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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```
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R is free software and comes with absolutely no warranty. You are welcome to redistribute it under the terms of the GNU General Public License versions 2 or 3. For more information about these matters, see https://www.gnu.org/licenses/.
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### Lattice
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#### Line chart
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Consider the consumer prices (annual per cent) inflation data for India between 1960 and 2022 available from the World Bank. You can use the years in the x-axis, and the inflation on the y-axis to produce a line chart using the xyplot function, as shown below:
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```
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> x<-c(1960:2020)
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> y<-c(1.77,1.69,3.63,2.94,13.35,9.47,10.80,13.06,3.23,-0.58,5.09,3.07,6.44,16.94,28.59,5.74,
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-7.63,8.30,2.52,6.27,11.34,13.11,7.89,11.86,8.31,5.55,8.72,8.80,9.38,7.07,8.97,13.87,11.78,6.32,10.24,10.22,8.97,7.16,13.23,4.66,4.00,3.77,4.29,3.80,3.76,4.24,5.79,6.37,8.34,10.88,11.98,8.85,9.31,11.06,6.64,4.90,4.94,3.32,3.94,3.72,6.62)
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> d <- data.frame(x,y)
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> xyplot(y~x, data=d, type=”l”, main=”Inflation, consumer prices (annual %)”)
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```
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The line chart is shown in Figure 1.
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![Figure 1: Line chart][2]
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The *xyplot* accepts the following arguments:
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| Argument | Description |
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| :- | :- |
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| data | A data frame containing values |
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| groups | A grouping variable in the data |
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| main | The title of the chart |
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| strip | A logical condition on whether to draw strips |
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| x | The primary numeric variable |
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| xlab | The label for x-axis |
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| xlim | A numeric vector that specifies left and right limits for x-axis |
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| ylab | The label for y-axis |
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| ylim | A numeric vector of length two that mentions lower and upper limits for y-axis |
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**The barchart function**
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The *bar chart* function produces a bar chart for the given data. In the following example, we specify a function to the axis argument to use the year on the x-axis.
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![Figure 2: Bar chart][3]
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```
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> barchart(y~x|x, data=d, horizontal=FALSE, axis=function(side, ...) { if (side==”bottom”) panel.axis(at=seq_along(d$x), label=d$x, outside=TRUE, rot=0, tck=0) else axis.default(side, ...)}, main=”Inflation, consumer prices (annual %)”)
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```
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The additional set of arguments available to the xyplot and barchart are listed below:
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| Argument | Description |
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| :- | :- |
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| box.ratio | Specifies the ratio of the width of rectangles in barchart |
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| panel | Plots x and y variables in each panel |
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| default.prepanel | A default function as a fallback to the prepanel function |
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| auto.key | Used to produce a suitable legend |
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| aspect | The physical aspect ratio of the panels |
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| axis | A function responsible for drawing the axis annotation |
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| horizontal | The orientation of the bar chart |
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| subscripts | A logical flag to pass a ‘subscripts’ vector to the panel function |
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| subset | A set of rows from the data is used in the plot |
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**Scatter plot**
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You can also display individual charts on a panel grid. For example, the all India consumer price index (rural/urban) data set up to November 2021 is available from https://data.gov.in/catalog/all-india-consumer-price-index-ruralurban-0 for the different states in India. We can read the data from the downloaded file using the read.csv function, as shown below:
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```
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> cpi <- read.csv(file=”CPI.csv”, sep=”,”)
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```
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```
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> head(cpi)
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Sector Year Name Andhra.Pradesh Arunachal.Pradesh Assam Bihar
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1 Rural 2011 January 104 NA 104 NA
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2 Urban 2011 January 103 NA 103 NA
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3 Rural+Urban 2011 January 103 NA 104 NA
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4 Rural 2011 February 107 NA 105 NA
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5 Urban 2011 February 106 NA 106 NA
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6 Rural+Urban 2011 February 105 NA 105 NA
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Chattisgarh Delhi Goa Gujarat Haryana Himachal.Pradesh Jharkhand Karnataka
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1 105 NA 103 104 104 104 105 104
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2 104 NA 103 104 104 103 104 104
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3 104 NA 103 104 104 103 105 104
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4 107 NA 105 106 106 05 107 106
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5 106 NA 105 107 107 105 107 108
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6 105 NA 104 105 106 104 106 106
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```
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The aggregate function can be used to obtain the values for the state of Andhra Pradesh as follows:
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```
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ap <- aggregate(x=cpi$Andhra.Pradesh, by=list(cpi$Year), FUN=sum)
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> head(ap)
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Group.1 x
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1 2011 3911.28
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2 2012 4255.40
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3 2013 4516.60
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4 2014 4673.60
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5 2015 4822.20
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6 2016 4921.50
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```
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A simple scatter plot can be displayed for the consumer price indexes using the following arguments to the xyplot function:
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```
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> xyplot(x~Group.1, ap, main=”Andhra Pradesh Consumer Price Index upto November 2021”, xlab=”Year”, ylab=”Consumer Price Index”)
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```
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The corresponding scatter plot illustration is shown in Figure 3.
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![Figure 3: Scatter plot][4]
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#### Panel grid
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You can also visualise the values per year (Group.1) using the xyplot:
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```
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> xyplot(x~Group.1|Group.1, ap, groups=Group.1, main=”Andhra Pradesh Consumer Price Index upto November 2021”, xlab=”Year”, ylab=”Consumer Price Index”, auto.key=TRUE)
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```
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The output chart produced by R is as shown in Figure 4.
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![Figure 4: Grouping chart][5]
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In addition to the above listed plotting functions, lattice provides the bwplot function for box-and-whisker plots, and the stripplot function for one-dimensional scatter plots.
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### ggplot2
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The ggplot2 R package implements a grammar of graphics that specifies how to plot data. You can install the package using the following command:
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```
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> install.packages(“ggplot2”)
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*** installing help indices
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*** copying figures
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** building package indices
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** installing vignettes
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** testing if installed package can be loaded from temporary location
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** testing if installed package can be loaded from final location
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** testing if installed package keeps a record of temporary installation path
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* DONE (ggplot2)
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```
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The library needs to be loaded into the R session before you can use its functions:
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```
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library(ggplot2)
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```
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#### Scatter plot
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The same consumer prices (annual per cent) inflation data for India can be plotted using the quick plot or qplot function from the ggplot2 package in R. For example:
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```
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> x<-c(1960:2020)
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> y<-c(1.77,1.69,3.63,2.94,13.35,9.47,10.80,13.06,3.23,-0.58,5.09,3.07,6.44,16.94,28.59,5.74,-7.63,8.30,2.52,6.27,11.34,13.11,7.89,11.86,8.31,5.55,8.72,8.80,9.38,7.07,8.97,13.87,11.78,6.32,10.24,10.22,8.97,7.16,13.23,4.66,4.00,3.77,4.29,3.80,3.76,4.24,5.79,6.37,8.34,10.88,11.98,8.85,9.31,11.06,6.64,4.90,4.94,3.32,3.94,3.72,6.62)
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> d <- data.frame(x,y)
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> qplot(x=x, y=y, data=d, xlab=”Year”, ylab=”Inflation”, main=”Inflation, consumer prices (annual %)”)
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```
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The simple scatter plot is shown in Figure 5.
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![Figure 5: Simple qplot][6]
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We can also store the results of the plot to a variable and ask R to provide a summary of the same, as shown below:
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```
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> ex1 <- qplot(x=x, y=y, data=d)
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> summary(ex1)
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data: x, y [61x2]
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mapping: x = ~x, y = ~y
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faceting: <ggproto object: Class FacetNull, Facet, gg>
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compute_layout: function
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draw_back: function
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draw_front: function
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draw_labels: function
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draw_panels: function
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finish_data: function
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init_scales: function
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map_data: function
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params: list
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setup_data: function
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setup_params: function
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shrink: TRUE
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train_scales: function
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vars: function
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super: <ggproto object: Class FacetNull, Facet, gg>
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-----------------------------------
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geom_point: na.rm = FALSE
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stat_identity: na.rm = FALSE
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position_identity
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```
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#### Line chart
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We can generate a line chart by specifying the geom attribute as ‘line’, as shown below:
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```
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> qplot(x=x, y=y, data=d, xlab=”Year”, ylab=”Inflation”, main=”Inflation, consumer prices (annual %)”, geom=”line”)
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```
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The corresponding line graph is shown in Figure 6.
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![Figure 6: qplot line graph][7]
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The ‘Bank Marketing Data Set’ for a Portuguese banking institution is available from the UCI machine learning repository available at https://archive.ics.uci.edu/ml/datasets/Bank+Marketing. The data can be used for public research use. There are four data sets available, and we will use the read.csv() function to import the data from a ‘bank.csv’ file into a data frame.
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```
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bank <- read.csv(file=”bank.csv”, sep=”;”)
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> bank[1:3,]
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age job marital education default balance housing loan contact day
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1 30 unemployed married primary no 1787 no no cellular 19
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2 33 services married secondary no 4789 yes yes cellular 11
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3 35 management single tertiary no 1350 yes no cellular 16
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month duration campaign pdays previous poutcome y
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1 oct 79 1 -1 0 unknown no
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2 may 220 1 339 4 failure no
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3 apr 185 1 330 1 failure no
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```
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### Bar chart
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The geometry argument can be specified as ‘bar’ to produce a bar chart, as indicated below:
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```
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> qplot(x=job, data=bank, geom=”bar”, weight=balance, ylab=”Balance”, xlab=”Category”)
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```
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The produced bar chart is shown in Figure 7.
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![Figure 7: Bar chart][8]
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We can also list a summary of the chart by storing the results of the plot to a variable, and invoking the summary function on the same. For example:
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```
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> barchart <- qplot(x=job, data=bank, geom=”bar”, weight=balance, ylab=”Balance”, xlab=”Category”)
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> summary (barchart)
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data: age, job, marital, education, default, balance, housing, loan,
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contact, day, month, duration, campaign, pdays, previous, poutcome, y
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[4521x17]
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mapping: x = ~job, weight = ~balance
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faceting: <ggproto object: Class FacetNull, Facet, gg>
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compute_layout: function
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draw_back: function
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draw_front: function
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draw_labels: function
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draw_panels: function
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finish_data: function
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init_scales: function
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map_data: function
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params: list
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setup_data: function
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setup_params: function
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shrink: TRUE
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train_scales: function
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vars: function
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super: <ggproto object: Class FacetNull, Facet, gg>
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-----------------------------------
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geom_bar: width = NULL, na.rm = FALSE, orientation = NA
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stat_count: width = NULL, na.rm = FALSE, orientation = NA
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position_stack
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```
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The qplot function accepts the following arguments:
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| Argument | Description |
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| :- | :- |
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| asp | The y/x aspect ratio |
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| data | Optional data frame that contains x and y |
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| geom | The geometry to use |
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| main | The title of the chart |
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| margin | Display margins |
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| position | The adjustments to specify the position |
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| x | X values |
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| xlab | The x-axis label |
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| xlim | The limits for the x-axis |
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| y | Y values |
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| ylab | The y-axis label |
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| ylim | The limits for the y-axis |
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#### ggplot
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The ggplot function can be used to create a new ggplot object for input data, and also specify aesthetic mappings for the same.
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For the bank.csv data, we can tabulate the job and marital status together using the with function as follows:
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```
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> with(bank, table(job, marital))
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marital
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job divorced married single
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admin. 69 266 143
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blue-collar 79 693 174
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entrepreneur 16 132 20
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housemaid 13 84 15
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management 119 557 293
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retired 43 176 11
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self-employed 15 127 41
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services 62 236 119
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student 0 10 74
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technician 89 411 268
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unemployed 22 75 31
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unknown 1 30 7
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```
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You can now plot the above categorical data using ggplot, as follows:
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```
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> ggplot(bank, aes(x = job, fill = marital)) + geom_bar()
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```
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The resultant graph is shown in Figure 8.
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![Figure 8: ggplot categorical graph][9]
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The age distribution can be plotted as a density using the geom_density function as follows:
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```
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> ggplot(bank, aes(x = age)) + geom_density()
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```
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The corresponding graph is shown in Figure 9.
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![Figure 9: ggplot density graph][10]
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A box plot for the age and marital status can be visualised using the following arguments to ggplot:
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```
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> ggplot(bank, aes(x = age, y = marital)) + geom_boxplot() + coord_flip()
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```
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The output graph is as shown in Figure 10.
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![Figure 10: ggplot boxplot graph][11]
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The ggplot function accepts the following arguments:
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| Argument | Description |
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| :- | :- |
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| data | The data frame for the plot |
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| mapping | The aesthetic mappings to be used in the plot |
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| environment | The globalenv() environment for the aesthetics |
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Do try and explore more functions and charts in the graphics packages available in R.
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/07/data-visualisation-in-r-graphs/
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作者：[Shakthi Kannan][a]
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选题：[lkxed][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/shakthi-kannan/
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[b]: https://github.com/lkxed
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[1]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Data-Visualisation-in-R-Graphs-Featured-image.jpg
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[2]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-1-Line-chart.jpg
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[3]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-2-Bar-chart.jpg
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[4]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-3-Scatter-plot.jpg
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[5]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-4-Grouping-chart.jpg
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[6]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-5-Simple-qplot.jpg
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[7]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-6-qplot-line-graph.jpg
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[8]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-7-Bar-chart.jpg
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[9]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-8-ggplot-categorical-graph.jpg
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[10]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-9-ggplot-density-graph.jpg
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[11]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-10-ggplot-boxplot-graph.jpg
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