R 语言绘制数据：图表篇
R 语言 图表
![](/data/attachment/album/202207/15/155129rsfee22secwyii8w.jpg)


R 语言有非常多的绘图和数据可视化的包，比如 `graphics`、`lattice`、`ggplot2` 等。这是 R 语言系列的第 9 篇文章，我们会介绍 R 中用来绘图的各种函数。


本文使用的 R 是 4.1.2 版本，运行环境为 Parabola GNU/Linux-libre (x86-64)。



```
$ 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 是自由软件，没有任何担保责任。只要遵守 GNU 通用公共许可证的版本 2 或者版本 3，你就可以对它进行（修改和）再分发。详情见 <https://www.gnu.org/licenses/>。


### 折线图


我们以印度全境消费者物价指数（CPI -- 乡村/城市）数据集为研究对象，它可以从 <https://data.gov.in/catalog/all-india-consumer-price-index-ruralurban-0> 下载。选择“截止到 2021 年 11 月” 的版本，用 `read.csv` 函数读取下载好的文件，如下所示：



```
> 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 105 106 106 105 107 106
5 106 NA 105 107 107 105 107 108
6 105 NA 104 105 106 104 106 106
...

```

以 Punjab 州为例，对每年各月份的 CPI 值求和，然后用 `plot` 函数画一张折线图：



```
> punjab <- aggregate(x=cpi$Punjab, by=list(cpi$Year), FUN=sum)

> head(punjab)
Group.1 x
1 2011 3881.76
2 2012 4183.30
3 2013 4368.40
4 2014 4455.50
5 2015 4584.30
6 2016 4715.80

> plot(punjab$Group.1, punjab$x, type="l", main="Punjab Consumer Price Index upto November 2021", xlab="Year", ylab="Consumer Price Index")

```

`plot` 函数可以传入如下参数：




| 参数 | 描述 |
| --- | --- |
| `x` | 向量类型，用于绘制 x 轴的数据 |
| `y` | 向量或列表类型，用于绘制 y 轴的数据 |
| `type` | 设置绘图类型：`p` 画点；`l` 画线；`o` 同时画点和线，且相互重叠；`s` 画阶梯线；`h` 画铅垂线 |
| `xlim` | x 轴范围 |
| `ylim` | y 轴范围 |
| `main` | 标题 |
| `sub` | 副标题 |
| `xlab` | x 轴标题 |
| `ylab` | y 轴标题 |
| `axes` | 逻辑型，是否绘制坐标轴 |


结果如图 1。


![Figure 1: Line chart](/data/attachment/album/202207/15/155305d7rukd28pnpzugap.jpg)


### 自相关图


自相关图能在时序分析中展示一个变量是否具有自相关性，可以用 R 中的 `acf` 函数绘制。`acf` 函数可以设置三种自相关类型：`correlation`、`covariance` 或 `partial`。图 2 是 Punjab 州 CPI 值的自相关图，x 表示 CPI。



```
acf(punjab$x,main='x')

```

![Figure 2: ACF chart](/data/attachment/album/202207/15/155305zwqz9b1ason4oih9.jpg)


`acf` 函数可以传入以下参数：




| 参数 | 描述 |
| --- | --- |
| `x` | 一个单变量或多变量的时序对象，或者一个数值向量或数值矩阵 |
| `lag.max` | 最大滞后阶数 |
| `type` | 字符型，设置所计算的自相关类型：`correlation`、`covariance` 或 `partial` |
| `plot` | 逻辑性，若 `TRUE` 则绘制图像，若 `FALSE` 则打印传入数据的描述信息 |
| `i` | 一组要保留的时差滞后 |
| `j` | 一组要保留的名称或数字 |


### 柱状图


R 中画柱状图的函数是 `barplot`。下面的代码用来画 Punjab 州 CPI 的柱状图，如图3：



```
> barplot(punjab$x, main="Punjab Consumer Price Index", sub="Upto November 2021", xlab="Year", ylab="Consumer Price Index", col="navy")

```

![Figure 3: Line chart of Punjab's CPI](/data/attachment/album/202207/15/155305c5moccrnximxndch.jpg)


`barplot` 函数的使用方法非常灵活，可以传入以下参数：




| 参数 | 描述 |
| --- | --- |
| `height` | 数值向量或数值矩阵，包含用于绘图的数据 |
| `width` | 数值向量，用于设置柱宽 |
| `space` | 柱间距 |
| `beside` | 逻辑型，若 `FALSE` 则绘制堆积柱状图，若 `TRUE` 则绘制并列柱状图 |
| `density` | 数值型，设置阴影线的填充密度（条数/英寸），默认为 `NULL`，即不填充阴影线 |
| `angle` | 数值型，填充线条的角度，默认为 45 |
| `border` | 柱形边缘的颜色 |
| `main` | 标题 |
| `sub` | 副标题 |
| `xlab` | x 轴标题 |
| `ylab` | y 轴标题 |
| `xlim` | x 轴范围 |
| `ylim` | y 轴范围 |
| `axes` | 逻辑型，是否绘制坐标轴 |


用 `help` 命令可以查看 `barplot` 函数的详细信息：



```
> help(barplot)

barplot                package:graphics                R Documentation

Bar Plots

Description:

     Creates a bar plot with vertical or horizontal bars.

Usage:

     barplot(height, ...)

     ## Default S3 method:
     barplot(height, width = 1, space = NULL,
             names.arg = NULL, legend.text = NULL, beside = FALSE,
             horiz = FALSE, density = NULL, angle = 45,
             col = NULL, border = par("fg"),
             main = NULL, sub = NULL, xlab = NULL, ylab = NULL,
             xlim = NULL, ylim = NULL, xpd = TRUE, log = "",
             axes = TRUE, axisnames = TRUE,
             cex.axis = par("cex.axis"), cex.names = par("cex.axis"),
             inside = TRUE, plot = TRUE, axis.lty = 0, offset = 0,
             add = FALSE, ann = !add && par("ann"), args.legend = NULL, ...)

     ## S3 method for class 'formula'
     barplot(formula, data, subset, na.action,
             horiz = FALSE, xlab = NULL, ylab = NULL, ...)

```

### 饼图


绘制饼图时要多加注意，因为饼图不一定能展示出各扇形间的区别。（LCTT 译注：根据统计学家和一些心理学家的调查结果，这种以比例展示数据的统计图形 [实际上是很糟糕的可视化方式](https://bookdown.org/xiangyun/msg/gallery.html#sec:pie)，因此，R 关于饼图的帮助文件中清楚地说明了并不推荐使用饼图，而是使用条形图或点图作为替代。） 用 `subset` 函数获得 Gujarat 州在 2021 年 1 月 Rural、Urban、Rurual+Urban 的 CPI 值：



```
> jan2021 <- subset(cpi, Name=="January" & Year=="2021")

> jan2021$Gujarat
[1] 153.9 151.2 149.1

> names <- c('Rural', 'Urban', 'Rural+Urban')

```

使用 `pie` 函数为 Gujarat 州的 CPI 值生成饼图，如下所示：



```
> pie(jan2021$Gujarat, names, main="Gujarat CPI Rural and Urban Pie Chart")

```

![Figure 4: Pie chart](/data/attachment/album/202207/15/155306q88xuqdkcium8eu8.jpg)


`pie` 函数可以传入以下参数：




| 参数 | 描述 |
| --- | --- |
| `x | 元素大于 0 的数值向量 |
| `label` | 字符向量，用于设置每个扇形的标签 |
| `radius` | 饼图的半径 |
| `clockwise` | 逻辑型，若 `TRUE` 则顺时针绘图，若 `FALSE` 则逆时针绘图 |
| `density` | 数值型，设置阴影线的填充密度（条数/英寸），默认为 `NULL`，即不填充阴影线 |
| `angle` | 数值型，填充线条的角度，默认为 45 |
| `col` | 数值向量，用于设置颜色 |
| `lty` | 每个扇形的线条类型 |
| `main` | 标题 |


### 箱线图


（LCTT 译注：箱线图主要是 [从四分位数的角度出发](https://bookdown.org/xiangyun/msg/gallery.html#sec:boxplot) 描述数据的分布，它通过最大值（Q4）、上四分位数（Q3）、中位数（Q2）、下四分位数（Q1） 和最小值（Q0）五处位置来获取一维数据的分布概况。我们知道，这五处位置之间依次包含了四段数据，每段中数据量均为总数据量的 1/4。通过每一段数据占据的长度，我们可以大致推断出数据的集中或离散趋势。长度越短，说明数据在该区间上越密集，反之则稀疏。）


箱线图能够用“<ruby> 须线 <rt>  whisker </rt></ruby>” 展示一个变量的<ruby> 四分位距 <rt>  Interquartile Range </rt></ruby>（简称 IQR=Q3-Q1）。用上下四分位数分别加/减内四分位距，再乘以一个人为设定的倍数 `range`（见下面的参数列表），得到 `range * c(Q1-IQR, Q3+IQR)`，超过这个范围的数据点就被视作离群点，在图中直接以点的形式表示出来。


`boxplot` 函数可以传入以下参数：




| 参数 | 描述 |
| --- | --- |
| `data` | 数据框或列表，用于参数类型为公式的情况 |
| `x` | 数值向量或者列表，若为列表则对列表中每一个子对象依次作出箱线图 |
| `width` | 设置箱子的宽度 |
| `outline` | 逻辑型，设置是否绘制离群点 |
| `names` | 设置每个箱子的标签 |
| `border` | 设置每个箱子的边缘的颜色 |
| `range` | 延伸倍数，设置箱线图末端（须）延伸到什么位置 |
| `plot` | 逻辑型，设置是否生成图像，若 TRUE 则生成图像，若 FALSE 则打印传入数据的描述信息 |
| `horizontal` | 逻辑型，设置箱线图是否水平放置 |


用 `boxplot` 函数绘制部分州的箱线图：



```
> names <- c ('Andaman and Nicobar', 'Lakshadweep', 'Delhi', 'Goa', 'Gujarat', 'Bihar')
> boxplot(cpi$Andaman.and.Nicobar, cpi$Lakshadweep, cpi$Delhi, cpi$Goa, cpi$Gujarat, cpi$Bihar, names=names)

```

![Figure 5: Box plot](/data/attachment/album/202207/15/155306vgc2inwjju6induk.jpg)


### QQ 图


<ruby> QQ 图 <rt>  Quantile-Quantile plot </rt></ruby>可以用来对比两个数据集，也可以用来检查数据是否服从某种理论分布。`qqnorm` 函数能绘制正态分布 QQ 图，可以检验数据是否服从正态分布，用下面的代码绘制 Punjab 州 CPI 数据的 QQ 图：



```
> qqnorm(punjab$x)

```

![Figure 6: Q-Q plot](/data/attachment/album/202207/15/155306mr8da74db9j999ra.jpg)


`qqline` 函数可以向正态分布 QQ 图上添加理论分布曲线，它可以传入以下参数：




| 参数 | 描述 |
| --- | --- |
| `x` | 第一个数据样本 |
| `y` | 第二个数据样本 |
| `datax` | 逻辑型，设置是否以 x 轴表示理论曲线的值，默认为 `FALSE` |
| `probs` | 长度为 2 的数值向量，代表概率 |
| `xlab` | x 轴标题 |
| `ylab` | y 轴标题 |
| `qtype` | `[1,9]` 内的整数，设置分位计算类型，详情见 `help(quantile)` 的类型小节 |


### 等高图


等高图可以描述三维数据，在 R 中对应的函数是 `contour`，这个函数也可以用来向已有的图表添加等高线。等高图常与其他图表一起使用。我们用 `contour` 对 R 中的 `volcano` 数据集（奥克兰的火山地形信息）绘制等高图，代码如下：



```
> contour(volcano)

```

![Figure 7: Volcano](/data/attachment/album/202207/15/155307izljw1w1wwm40kdf.jpg)


`contour` 函数的常用参数如下：




| 参数 | 描述 |
| --- | --- |
| `x,y` | z 中数值对应的点在平面上的位置 |
| `z` | 数值向量 |
| `nlevels` | 设置等高线的条数，调整等高线的疏密 |
| `labels` | 等高线上的标记字符串，默认是高度的数值 |
| `xlim` | 设置 x 轴的范围 |
| `ylim` | 设置 y 轴的范围 |
| `zlim` | 设置 z 轴的范围 |
| `axes` | 设置是否绘制坐标轴 |
| `col` | 设置等高线的颜色 |
| `lty` | 设置线条的类型 |
| `lwd` | 设置线条的粗细 |
| `vfont` | 设置标签字体 |


等高线之间的区域可以用颜色填充，每种颜色表示一个高度范围，如下所示：



```
> filled.contour(volcano, asp = 1)
# asp 为图形纵横比，即 y 轴上的 1 单位长度和 x 轴上 1 单位长度的比率

```

填充结果见图 8。


![Figure 8: Filled volcano](/data/attachment/album/202207/15/155307wdncrqrwrcrwgh3p.jpg)


掌握上述内容后，你可以尝试 R 语言 `graphics` 包中的其他函数和图表（LCTT 译注：用 `help(package=graphics)` 可以查看 graphics 包提供的函数列表）。




---


via: <https://www.opensourceforu.com/2022/05/plotting-data-in-r-graphs/>


作者：[Shakthi Kannan](https://www.opensourceforu.com/author/shakthi-kannan/) 选题：[lkxed](https://github.com/lkxed) 译者：[tanloong](https://github.com/tanloong) 校对：[wxy](https://github.com/wxy)


本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出


OK
*R has a number of packages for plotting graphs and data visualisation, such as graphics, lattice, and ggplot2. In this ninth article in the R series, we shall explore the various functions to plot data in R.*

We will be using R version 4.1.2 installed on Parabola GNU/Linux-libre (x86-64) for the code snippets.

$ 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/.*

**Plot**

Consider the all-India consumer price index (CPI – rural/urban) data set up to November 2021 available at *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 105 106 106 105 107 106 5 106 NA 105 107 107 105 107 108 6 105 NA 104 105 106 104 106 106 ...

Let us aggregate the CPI values per year for the state of Punjab, and plot a line chart using the plot function, as follows:

> punjab <- aggregate(x=cpi$Punjab, by=list(cpi$Year), FUN=sum) > head(punjab) Group.1 x 1 2011 3881.76 2 2012 4183.30 3 2013 4368.40 4 2014 4455.50 5 2015 4584.30 6 2016 4715.80 > plot(punjab$Group.1, punjab$x, type=”l”, main=”Punjab Consumer Price Index upto November 2021”, xlab=”Year”, ylab=”Consumer Price Index”)

The following arguments are supported by the plot function:

Argument |
Description |

x | A vector for the x-axis |

y | The vector or list in the y-axis |

type | ‘p’ for points, ‘l’ for lines, ‘o’ for overplotted plots and lines, ‘s’ for stair steps, ‘h’ for histogram |

xlim | The x limits of the plot |

ylim | The y limits of the plot |

main | The title of the plot |

sub | The subtitle of the plot |

xlab | The label for the x-axis |

ylab | The label for the y-axis |

axes | Logical value to draw the axes |


The line chart is shown in Figure 1.

![Line chart](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-1-Line-chart-590x392.jpg)


![Line chart](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-1-Line-chart-590x392.jpg)

The autocorrelation plot can be used to obtain correlation statistics for time series analysis, and the same can be generated using the acf function in R. You can specify the following autocorrelation types: *correlation, covariance*, or partial. Figure 2 shows the ACF chart that represents the CPI values (‘x’ in the chart) for the state of Punjab.

![ACF chart](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-2-ACF-chart-590x541.jpg)


![ACF chart](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-2-ACF-chart-590x541.jpg)

The function* acf* accepts the following arguments:

Argument |
Description |

x | A univariate or multivariate object or vector or matrix |

lag.max | The maximum lag to calculate the acf |

type | Supported values ‘correlation’, ‘covariance’, ‘partial’ |

plot | The acf is plotted if this value is TRUE |

i | A set of time difference lags to retain |

j | A collection of names or numbers to retain |


**Bar chart**

The barplot function is used to draw a bar chart. The chart for Punjab’s CPI can be generated as follows, and is shown in Figure 3:

![Line chart of Punjab’s CPI](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-3-Line-chart-of-Punjabs-CPI-590x468.jpg)


![Line chart of Punjab’s CPI](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-3-Line-chart-of-Punjabs-CPI-590x468.jpg)

> barplot(punjab$x, main=”Punjab Consumer Price Index”, sub=”Upto November 2021”, xlab=”Year”, ylab=”Consumer Price Index”, col=”navy”)

The function is quite flexible and supports the following arguments:

Argument |
Description |

height | A numeric vector or matrix that contains the values |

width | A numeric vector that specifies the widths of the bars |

space | The amount of space between bars |

beside | A logical value to specify if the bars should be stacked or next to each other |

density | A numerical value that specifies the density of the shading lines |

angle | The angle used to shade the lines |

border | The colour of the border |

main | The title of the chart |

sub | The sub-title of the chart |

xlab | The label for the x-axis |

ylab | The label for the y-axis |

xlim | The limits for the x-axis |

ylim | The limits for the y-axis |

axes | A value that specifies whether the axes should be drawn |


You can get more details on the barplot function using the help command, as shown below:

> help(barplot) acf package:stats R Documentation Auto- and Cross- Covariance and -Correlation Function Estimation Description: The function ‘acf’ computes (and by default plots) estimates of the autocovariance or autocorrelation function. Function ‘pacf’ is the function used for the partial autocorrelations. Function ‘ccf’ computes the cross-correlation or cross-covariance of two univariate series. Usage: acf(x, lag.max = NULL, type = c(“correlation”, “covariance”, “partial”), plot = TRUE, na.action = na.fail, demean = TRUE, ...) pacf(x, lag.max, plot, na.action, ...) ## Default S3 method: pacf(x, lag.max = NULL, plot = TRUE, na.action = na.fail, ...) ccf(x, y, lag.max = NULL, type = c(“correlation”, “covariance”), plot = TRUE, na.action = na.fail, ...) ## S3 method for class ‘acf’ x[i, j]

**Pie chart**

Pie charts need to be used wisely, as they may not actually show relative differences among the slices. We can generate the Rural, Urban, and Rural+Urban values for the month of January 2021 for Gujarat as follows, using the subset function:

> jan2021 <- subset(cpi, Name==”January” & Year==”2021”) > jan2021$Gujarat [1] 153.9 151.2 149.1 > names <- c(‘Rural’, ‘Urban’, ‘Rural+Urban’)

![Pie chart](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-4-Pie-chart-590x454.jpg)


![Pie chart](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-4-Pie-chart-590x454.jpg)

The pie function can be used to generate the actual pie chart for the state of Gujarat, as shown below:

> pie(jan2021$Gujarat, names, main=”Gujarat CPI Rural and Urban Pie Chart”)

The following arguments are supported by the pie function:

Argument |
Description |

x | Positive numeric values to be plotted |

label | A vector of character strings for the labels |

radius | The size of the pie |

clockwise | A value to indicate if the pie should be drawn clockwise or counter-clockwise |

density | A value for the density of shading lines per inch |

angle | The angle that specifies the slope of the shading lines in degrees |

col | A numeric vector of colours to be used |

lty | The line type for each slice |

main | The title of the chart |


**Boxplot**

A boxplot shows the interquartile range between the 25th and 75th percentile using two ‘whiskers’ for the distribution of a variable. The values outside the range are plotted separately. The boxplot functions take the following arguments:

Argument |
Description |

data | A data frame or list that is defined |

x | A vector that contains the values to plot |

width | The width of the boxes to be plotted |

outline | A logical value indicating whether to draw the outliers |

names | The names of the labels for each box plot |

border | The colour to use for the outline of each box plot |

range | A maximum numerical amount the whiskers should extend from the boxes |

plot | The boxes are plotted if this value is TRUE |

horizontal | A logical value to indicate if the boxes should be drawn horizontally |


The boxplot for a few states from the CPI data is shown below:

> names <- c (‘Andaman and Nicobar’, ‘Lakshadweep’, ‘Delhi’, ‘Goa’, ‘Gujarat’, ‘Bihar’) > boxplot(cpi$Andaman.and.Nicobar, cpi$Lakshadweep, cpi$Delhi, cpi$Goa, cpi$Gujarat, cpi$Bihar, names=names)

![Box plot](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-5-ox-plot-590x482.jpg)


![Box plot](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-5-ox-plot-590x482.jpg)

![Q-Q plot](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-6-Q-Q-plot-590x529.jpg)


![Q-Q plot](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-6-Q-Q-plot-590x529.jpg)

**Q-Q plot**

The Quantile-Quantile (Q-Q) plot is a way to compare two data sets. You can also compare a data set with a theoretical distribution. The qqnorm function is a generic function, and we can view the Q-Q plot for the Punjab CPI data as shown below:

> qqnorm(punjab$x)

![Volcano](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-7-Volcano-590x451.jpg)


![Volcano](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-7-Volcano-590x451.jpg)

The* qqline* function adds a theoretical line to a normal, quantile-quantile plot. The following arguments are accepted by these functions:

Argument |
Description |

x | The first data sample |

y | The second data sample |

datax | A logical value indicating if values should be on the x-axis |

probs | A numerical vector representing probabilities |

xlab | The label for x-axis |

ylab | The label for y-axis |

qtype | The type of quantile computation |


**Contour plot**

The contour function is useful for plotting three-dimensional data. You can generate a new contour plot, or add contour lines to an existing chart. These are commonly used along with image charts. The volcano data set in R provides information on the Maunga Whau (Mt Eden) volcanic field, and the same can be visualised with the contour function as follows:

> contour(volcano)

The contour function accepts the following arguments:

Argument |
Description |

x,y | The location of the grid for z |

z | A numeric vector to be plotted |

nlevels | The number of contour levels |

labels | A vector of labels for the contour lines |

xlim | The x limits for the plot |

ylim | The y limits for the plot |

zlim | The z limits for the plot |

axes | A value to indicate to print the axes |

col | The colour for the contour lines |

lty | The line type to draw |

lwd | Width for the lines |

vfont | The font for the labels |


The areas between the contour lines can be filled using a solid colour to indicate the levels, as shown below:

> filled.contour(volcano, asp = 1)

The same volcano data set with the filled.contour colours is illustrated in Figure 8.

![Filled volcano](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-8-Filled-volcano-590x451.jpg)


![Filled volcano](https://www.opensourceforu.com/wp-content/uploads/2022/04/Figure-8-Filled-volcano-590x451.jpg)

You are encouraged to explore the other functions and charts in the graphics package in R.