The ggplot2 package operates on R dataframes. This is because (for the most part) the tidyverse packages focus ondataframes, in one way or another. In fact, the name “tidyverse” comes from the concept of a “tidy” dataframe. A so-called “tidy” dataframe is a dataset where every ...
ggplot2 is the most popular plotting library in R, and it is part of the tidyverse library ecosystem. The library makes it easy to build great-looking and complex visualizations in an intuitive layer-building approach. You can learn how to make a histogram with basic R in a separate ...
:exclamation: This is a read-only mirror of the CRAN R package repository. ggplot2 — Create Elegant Data Visualisations Using the Grammar of Graphics. Homepage: https://ggplot2.tidyverse.org, https://github.com/tidyverse/ggplot2 Report bugs for this p
set_names(nm = names(.) %>% tolower()) %>% # 将数据框的列名字都改为小写的 filter(!is.na(crop_production)) # 筛选crop——production为空的 第五部分是ggplot2的主题部分,也就是用来控制ggplot2的布局、字体、背景等。 #Set theme font_family1 <- 'Times New Roman' #'Century Gothic'...
library(tidyverse) library(ggplot2) library(cowplot) library(dplyr) 图层 ggplot()图层:包括数据和映射,这个图层的作用想当于画好一个画布,告诉你坐标轴的数据。 geom_XXX() 图层:这个图层主要是告诉绘图的类型,是散点、密度、直方还是箱线等,拿geom_point()散点图来讲,关于点的颜色、透明度这些点本身的属性...
ggplot2is aRpackage dedicated to data visualization. It can greatly improve thequalityandaestheticsof your graphics, and will make you much moreefficientin creating them. It can sometimes be tricky to control the colors of a ggplot2 graph. This section describes the most common use cases, making...
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链接:https://ggplot2.tidyverse.org/reference/ 内容如下: Plot basics Layer: geoms Layer: stats Layer: position adjustment Layer: annotations Aesthetics Scales Guides: axes and legends Facetting Facetting: labels Coordinate systems Themes Programming with ggplot2 ...
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链接:https://ggplot2.tidyverse.org/reference/ 内容如下: Plot basics Layer: geoms Layer: stats Layer: position adjustment Layer: annotations Aesthetics Scales Guides: axes and legends Facetting Facetting: labels Coordinate systems Themes Programming with ggplot2 ...