From 9ef6180b73cb12c9431d1fbe60c3dfdf8918aff2 Mon Sep 17 00:00:00 2001 From: lkxed Date: Fri, 8 Jul 2022 17:59:54 +0800 Subject: [PATCH] =?UTF-8?q?[=E6=89=8B=E5=8A=A8=E9=80=89=E9=A2=98][tech]:?= =?UTF-8?q?=2020220708=20Data=20Visualisation=20in=20R-=20Graphs.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...0220708 Data Visualisation in R- Graphs.md | 396 ++++++++++++++++++ 1 file changed, 396 insertions(+) create mode 100644 sources/tech/20220708 Data Visualisation in R- Graphs.md diff --git a/sources/tech/20220708 Data Visualisation in R- Graphs.md b/sources/tech/20220708 Data Visualisation in R- Graphs.md new file mode 100644 index 0000000000..18e61bb333 --- /dev/null +++ b/sources/tech/20220708 Data Visualisation in R- Graphs.md @@ -0,0 +1,396 @@ +[#]: subject: "Data Visualisation in R: Graphs" +[#]: via: "https://www.opensourceforu.com/2022/07/data-visualisation-in-r-graphs/" +[#]: author: "Shakthi Kannan https://www.opensourceforu.com/author/shakthi-kannan/" +[#]: collector: "lkxed" +[#]: translator: " " +[#]: reviewer: " " +[#]: publisher: " " +[#]: url: " " + +Data Visualisation in R: Graphs +====== +In this tenth article in the R series, we will continue to explore data visualisation in R with the lattice and ggplot2 packages. + +![Data-Visualisation-in-R-Graphs-Featured-image][1] + +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. + +``` +$ R --version +R version 4.1.2 (2021-11-01) -- “Bird Hippie” +Copyright (C) 2021 The R Foundation for Statistical Computing +Platform: x86_64-pc-linux-gnu (64-bit) +``` + +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/. + +### Lattice + +#### Line chart + +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: + +``` +> x<-c(1960:2020) + +> 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) + +> d <- data.frame(x,y) + +> xyplot(y~x, data=d, type=”l”, main=”Inflation, consumer prices (annual %)”) +``` + +The line chart is shown in Figure 1. + +![Figure 1: Line chart][2] + +The *xyplot* accepts the following arguments: + +| Argument | Description | +| :- | :- | +| data | A data frame containing values | +| groups | A grouping variable in the data | +| main | The title of the chart | +| strip | A logical condition on whether to draw strips | +| x | The primary numeric variable | +| xlab | The label for x-axis | +| xlim | A numeric vector that specifies left and right limits for x-axis | +| ylab | The label for y-axis | +| ylim | A numeric vector of length two that mentions lower and upper limits for y-axis | + +**The barchart function** + +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. + +![Figure 2: Bar chart][3] + +``` +> 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 %)”) +``` + +The additional set of arguments available to the xyplot and barchart are listed below: + +| Argument | Description | +| :- | :- | +| box.ratio | Specifies the ratio of the width of rectangles in barchart | +| panel | Plots x and y variables in each panel | +| default.prepanel | A default function as a fallback to the prepanel function | +| auto.key | Used to produce a suitable legend | +| aspect | The physical aspect ratio of the panels | +| axis | A function responsible for drawing the axis annotation | +| horizontal | The orientation of the bar chart | +| subscripts | A logical flag to pass a ‘subscripts’ vector to the panel function | +| subset | A set of rows from the data is used in the plot | + +**Scatter plot** + +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: + +``` +> cpi <- read.csv(file=”CPI.csv”, sep=”,”) +``` + +``` +> head(cpi) +Sector Year Name Andhra.Pradesh Arunachal.Pradesh Assam Bihar +1 Rural 2011 January 104 NA 104 NA +2 Urban 2011 January 103 NA 103 NA +3 Rural+Urban 2011 January 103 NA 104 NA +4 Rural 2011 February 107 NA 105 NA +5 Urban 2011 February 106 NA 106 NA +6 Rural+Urban 2011 February 105 NA 105 NA +Chattisgarh Delhi Goa Gujarat Haryana Himachal.Pradesh Jharkhand Karnataka +1 105 NA 103 104 104 104 105 104 +2 104 NA 103 104 104 103 104 104 +3 104 NA 103 104 104 103 105 104 +4 107 NA 105 106 106 05 107 106 +5 106 NA 105 107 107 105 107 108 +6 105 NA 104 105 106 104 106 106 +``` + +The aggregate function can be used to obtain the values for the state of Andhra Pradesh as follows: + +``` +ap <- aggregate(x=cpi$Andhra.Pradesh, by=list(cpi$Year), FUN=sum) + +> head(ap) +Group.1 x +1 2011 3911.28 +2 2012 4255.40 +3 2013 4516.60 +4 2014 4673.60 +5 2015 4822.20 +6 2016 4921.50 +``` + +A simple scatter plot can be displayed for the consumer price indexes using the following arguments to the xyplot function: + +``` +> xyplot(x~Group.1, ap, main=”Andhra Pradesh Consumer Price Index upto November 2021”, xlab=”Year”, ylab=”Consumer Price Index”) +``` + +The corresponding scatter plot illustration is shown in Figure 3. + +![Figure 3: Scatter plot][4] + +#### Panel grid + +You can also visualise the values per year (Group.1) using the xyplot: + +``` +> 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) +``` + +The output chart produced by R is as shown in Figure 4. + +![Figure 4: Grouping chart][5] + +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. + +### ggplot2 + +The ggplot2 R package implements a grammar of graphics that specifies how to plot data. You can install the package using the following command: + +``` +> install.packages(“ggplot2”) + +*** installing help indices +*** copying figures +** building package indices +** installing vignettes +** testing if installed package can be loaded from temporary location +** testing if installed package can be loaded from final location +** testing if installed package keeps a record of temporary installation path +* DONE (ggplot2) +``` + +The library needs to be loaded into the R session before you can use its functions: + +``` +library(ggplot2) +``` + +#### Scatter plot + +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: + +``` +> x<-c(1960:2020) +> 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) +> d <- data.frame(x,y) +> qplot(x=x, y=y, data=d, xlab=”Year”, ylab=”Inflation”, main=”Inflation, consumer prices (annual %)”) +``` + +The simple scatter plot is shown in Figure 5. + +![Figure 5: Simple qplot][6] + +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: + +``` +> ex1 <- qplot(x=x, y=y, data=d) +> summary(ex1) +data: x, y [61x2] +mapping: x = ~x, y = ~y +faceting: +compute_layout: function +draw_back: function +draw_front: function +draw_labels: function +draw_panels: function +finish_data: function +init_scales: function +map_data: function +params: list +setup_data: function +setup_params: function +shrink: TRUE +train_scales: function +vars: function +super: +----------------------------------- +geom_point: na.rm = FALSE +stat_identity: na.rm = FALSE +position_identity +``` + +#### Line chart + +We can generate a line chart by specifying the geom attribute as ‘line’, as shown below: + +``` +> qplot(x=x, y=y, data=d, xlab=”Year”, ylab=”Inflation”, main=”Inflation, consumer prices (annual %)”, geom=”line”) +``` + +The corresponding line graph is shown in Figure 6. + +![Figure 6: qplot line graph][7] + +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. + +``` +bank <- read.csv(file=”bank.csv”, sep=”;”) + +> bank[1:3,] +age job marital education default balance housing loan contact day +1 30 unemployed married primary no 1787 no no cellular 19 +2 33 services married secondary no 4789 yes yes cellular 11 +3 35 management single tertiary no 1350 yes no cellular 16 +month duration campaign pdays previous poutcome y +1 oct 79 1 -1 0 unknown no +2 may 220 1 339 4 failure no +3 apr 185 1 330 1 failure no +``` + +### Bar chart + +The geometry argument can be specified as ‘bar’ to produce a bar chart, as indicated below: + +``` +> qplot(x=job, data=bank, geom=”bar”, weight=balance, ylab=”Balance”, xlab=”Category”) +``` + +The produced bar chart is shown in Figure 7. + +![Figure 7: Bar chart][8] + +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: + +``` +> barchart <- qplot(x=job, data=bank, geom=”bar”, weight=balance, ylab=”Balance”, xlab=”Category”) + +> summary (barchart) +data: age, job, marital, education, default, balance, housing, loan, +contact, day, month, duration, campaign, pdays, previous, poutcome, y +[4521x17] +mapping: x = ~job, weight = ~balance +faceting: +compute_layout: function +draw_back: function +draw_front: function +draw_labels: function +draw_panels: function +finish_data: function +init_scales: function +map_data: function +params: list +setup_data: function +setup_params: function +shrink: TRUE +train_scales: function +vars: function +super: +----------------------------------- +geom_bar: width = NULL, na.rm = FALSE, orientation = NA +stat_count: width = NULL, na.rm = FALSE, orientation = NA +position_stack +``` + +The qplot function accepts the following arguments: + +| Argument | Description | +| :- | :- | +| asp | The y/x aspect ratio | +| data | Optional data frame that contains x and y | +| geom | The geometry to use | +| main | The title of the chart | +| margin | Display margins | +| position | The adjustments to specify the position | +| x | X values | +| xlab | The x-axis label | +| xlim | The limits for the x-axis | +| y | Y values | +| ylab | The y-axis label | +| ylim | The limits for the y-axis | + +#### ggplot + +The ggplot function can be used to create a new ggplot object for input data, and also specify aesthetic mappings for the same. + +For the bank.csv data, we can tabulate the job and marital status together using the with function as follows: + +``` +> with(bank, table(job, marital)) +marital + +job divorced married single +admin. 69 266 143 +blue-collar 79 693 174 +entrepreneur 16 132 20 +housemaid 13 84 15 +management 119 557 293 +retired 43 176 11 +self-employed 15 127 41 +services 62 236 119 +student 0 10 74 +technician 89 411 268 +unemployed 22 75 31 +unknown 1 30 7 +``` + +You can now plot the above categorical data using ggplot, as follows: + +``` +> ggplot(bank, aes(x = job, fill = marital)) + geom_bar() +``` + +The resultant graph is shown in Figure 8. + +![Figure 8: ggplot categorical graph][9] + +The age distribution can be plotted as a density using the geom_density function as follows: + +``` +> ggplot(bank, aes(x = age)) + geom_density() +``` + +The corresponding graph is shown in Figure 9. + +![Figure 9: ggplot density graph][10] + +A box plot for the age and marital status can be visualised using the following arguments to ggplot: + +``` +> ggplot(bank, aes(x = age, y = marital)) + geom_boxplot() + coord_flip() +``` + +The output graph is as shown in Figure 10. + +![Figure 10: ggplot boxplot graph][11] + +The ggplot function accepts the following arguments: + +| Argument | Description | +| :- | :- | +| data | The data frame for the plot | +| mapping | The aesthetic mappings to be used in the plot | +| environment | The globalenv() environment for the aesthetics | + +Do try and explore more functions and charts in the graphics packages available in R. + +-------------------------------------------------------------------------------- + +via: https://www.opensourceforu.com/2022/07/data-visualisation-in-r-graphs/ + +作者：[Shakthi Kannan][a] +选题：[lkxed][b] +译者：[译者ID](https://github.com/译者ID) +校对：[校对者ID](https://github.com/校对者ID) + +本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 + +[a]: https://www.opensourceforu.com/author/shakthi-kannan/ +[b]: https://github.com/lkxed +[1]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Data-Visualisation-in-R-Graphs-Featured-image.jpg +[2]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-1-Line-chart.jpg +[3]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-2-Bar-chart.jpg +[4]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-3-Scatter-plot.jpg +[5]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-4-Grouping-chart.jpg +[6]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-5-Simple-qplot.jpg +[7]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-6-qplot-line-graph.jpg +[8]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-7-Bar-chart.jpg +[9]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-8-ggplot-categorical-graph.jpg +[10]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-9-ggplot-density-graph.jpg +[11]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-10-ggplot-boxplot-graph.jpg