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当我在 R ggplot2 中绘制缺失的日期时,我如何调整 X 轴的比例并将中断设为整数

[英]How I can adjust the scale of axis X with breaks as.integer when I graph missing dates in R ggplot2

How I can adjust the scales axis X in breaks as.integer when I have a lot of data graphing missing dates.当我有大量数据绘制缺失日期时,如何将刻度轴 X 调整为整数。

The code that I am using is the next (@Stefan Helped me):我正在使用的代码是下一个(@Stefan 帮助我):

#SET OF DATA
df <- read.table(text="
    Fecha - T - Tmin - Tmax
    2015-07-01 - 11,16 - 7,3 - 17
    2015-07-02 - 11,49 - 8 - 17,1
    2015-07-03 - 11,2 - 8,8 - 15,8
    2015-07-04 - 11,20 - 8,6 - 16
    2015-07-05 - 11,23 - 8,9 - 15,7
    2015-07-06 - 10,40 - 7,7 - 15,4
    2015-07-07 - 10,10 - 8,1 - 14,8
    2015-07-08 - 10,04 - 7,3 - 15,4
    2018-01-01 - 11,08 - 4,9 - 17,8
    2018-01-02 - 11,40 - 4,2 - 16,3
    2018-01-03 - 9,000 - 5,5 - 13,5
    2018-01-04 - 8,584 - 6 - 12,8
    2018-01-05 - 8,679 - 7,3 - 11,9
    2018-01-06 - 8,75 - 6,8 - 13
    2018-01-07 - 9,33 - 6,4 - 15,2
    2018-01-08 - 9,63 - 6,3 - 13,9
", header = TRUE, dec = ",")

INITIAL CODE初始代码

mmp1 <- df[,!grepl("^X", names(df))]
mmp1$Fecha <- as.Date(mmp1$Fecha)

library(ggplot2)
library(scales)
library(dplyr)
library(tibble)

mmp2 <- mmp1 %>% 
  mutate(
    year_fecha = as.character(lubridate::year(Fecha)),
    Fecha2 = format(Fecha, "%d-%m"),
    Fecha2 = forcats::fct_reorder(Fecha2, Fecha)) %>% 
  arrange(Fecha) %>% 
  rowid_to_column(var = "Fecha3")

# Put the theme code aside
polish <- theme(text = element_text(size=11)) +
  theme(axis.text.x=element_text(angle=45, hjust=1))+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(panel.background = element_rect(fill = 'white', colour = 'white', size = 1.2, linetype = 7))+
  theme(text=element_text(family="arial", face="bold", size=12))+
  theme(axis.title.y = element_text(face="bold", family = "arial", vjust=1.5, colour="black", hjust = 0.5, size=rel(1.2)))+
  theme(axis.title.x = element_text(face="bold", family = "arial", vjust=0.5, colour="black", size=rel(1.2)))+
  theme(axis.text.x = element_text(family= "sans",face = "plain", colour="black", size=rel(1.1)))+
  theme(axis.text.y = element_text(family= "sans",face = "plain", colour="black", size=rel(1.1)))+
  theme(axis.line = element_line(size = 1, colour = "black"))+
  theme(legend.title = element_text(colour="black", size=12, face="bold", family = "arial"))+
  theme(legend.key = element_rect(fill = "white"))

# Simple and prefered solution: Facet by e.g. by year
w1 <- ggplot(data = mmp2) +
  geom_line(mapping = aes(x = Fecha, y = Tmin, colour="Min"), size=0.71) +
  geom_line(mapping = aes(x = Fecha, y = T, colour="P"), size=0.71) +
  geom_line(mapping = aes(x = Fecha, y = Tmax, colour="Max"), size=0.71) +
  scale_x_date(date_breaks = "1 day", date_labels = "%d-%m", expand = (c(0.001,0.008)))+
  scale_y_continuous(breaks=seq(-4, 28, 2), limits = c(1,18), expand=c(0,0)) +
  scale_colour_manual(name="Leyenda",
                      values=c(Min="green", P="#56B4E9", Max="Red")) +
  ylab("Temperatura (C)")+
  xlab("Tiempo") +
  guides(colour=guide_legend(order = 2),
         shape=guide_legend(order = 2)) +
  facet_wrap(~year_fecha, scales = "free_x") +
  polish

w1

The first result is:第一个结果是:

第一个结果是

# Hacky solutions with some manual labelling
labs <- select(mmp2, Fecha3, Fecha2) %>% 
  tibble::deframe()

date_lab <- function(x) {
  labs[as.character(x)]
}

# Draw the data as one continuous line
w2 <- ggplot(data = mmp2) +
  geom_line(mapping = aes(x = Fecha3, y = Tmin, colour="Min"), size=0.71) +
  geom_line(mapping = aes(x = Fecha3, y = T, colour="P"), size=0.71) +
  geom_line(mapping = aes(x = Fecha3, y = Tmax, colour="Max"), size=0.71) +
  scale_x_continuous(breaks = as.integer(names(labs)), labels = date_lab, expand = (c(0.001,0.008))) +
  scale_y_continuous(breaks=seq(-4, 28, 2), limits = c(1,18), expand=c(0,0)) +
  scale_colour_manual(name="Leyenda",
                      values=c(Min="green", P="#56B4E9", Max="Red")) +
  ylab("Temperatura (C)")+
  xlab("Tiempo") +
  guides(colour=guide_legend(order = 2),
         shape=guide_legend(order = 2)) +
  polish
w2

Second result is:第二个结果是:

第二个结果

Using the same code but graphing a lot of data I have this problem:使用相同的代码但绘制大量数据我有这个问题:

包含大量数据的图表

How I can adjust this axix X?我如何调整这个 axix X? Thank you.谢谢你。

First question was to remove the "gaps" in your data.第一个问题是删除数据中的“空白”。 As I said, the simplest solution would be facetting by eg year.正如我所说,最简单的解决方案是按年份分面。 This would allow you to work with a date scale.这将允许您使用日期刻度。

Your second problem is related to the overplotting of labels.您的第二个问题与标签的过度绘制有关。 This kind of overplotting naturally arises with dates when trying to plot single days.当试图绘制单日时,这种过度绘制自然会随着日期出现。 With a lot of days, say one year or more it is simply not possible to label all days.有很多天,比如一年或更长时间,根本不可能标记所有天。 The solution therefore is to restrict the days to plot.因此,解决方案是限制绘图的天数。 When working with a date scale this can be easily achieved via some helper function (eg breaks = breaks_width("1 week") ).当使用日期刻度时,这可以通过一些辅助函数轻松实现(例如, breaks = breaks_width("1 week") )。

To mimic this behaviour for the hacky solution I added this code:为了模仿 hacky 解决方案的这种行为,我添加了以下代码:

breaks <- mmp2 %>% 
  # Plots first, 8th, 15th, ... of day of a month
  mutate(days_to_plot = lubridate::day(Fecha) %in% c(1, 8, 15, 22, 29)) %>% 
  filter(days_to_plot) %>% 
  pull(Fecha3)

This is not a perfect solution but it reduces the lables to plot considerably.这不是一个完美的解决方案,但它大大减少了要绘制的标签。

So try this with your large dataset:所以用你的大数据集试试这个:

library(ggplot2)
library(scales)
library(dplyr)
library(tibble)
library(lubridate)

mmp2 <- mmp1 %>% 
  mutate(
    year_fecha = as.character(lubridate::year(Fecha)),
    Fecha2 = format(Fecha, "%d-%m"),
    Fecha2 = forcats::fct_reorder(Fecha2, Fecha)) %>% 
  arrange(Fecha) %>% 
  rowid_to_column(var = "Fecha3")

# Put the seem code aside
polish <- theme(text = element_text(size=11)) +
  theme(axis.text.x=element_text(angle=45, hjust=1))+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(panel.background = element_rect(fill = 'white', colour = 'white', size = 1.2, linetype = 7))+
  theme(text=element_text(family="sans", face="bold", size=12))+
  theme(axis.title.y = element_text(face="bold", family = "sans", vjust=1.5, colour="black", hjust = 0.5, size=rel(1.2)))+
  theme(axis.title.x = element_text(face="bold", family = "sans", vjust=0.5, colour="black", size=rel(1.2)))+
  theme(axis.text.x = element_text(family= "sans",face = "plain", colour="black", size=rel(1.1)))+
  theme(axis.text.y = element_text(family= "sans",face = "plain", colour="black", size=rel(1.1)))+
  theme(axis.line = element_line(size = 1, colour = "black"))+
  theme(legend.title = element_text(colour="black", size=12, face="bold", family = "arial"))+
  theme(legend.key = element_rect(fill = "white"))

# Draw the data as one continuous line
# Hacky solutions with some manual labelling
labs <- select(mmp2, Fecha3, Fecha2) %>% 
  tibble::deframe()

date_lab <- function(x) {
  labs[as.character(x)]
}

# Which detas/days should be shown on x-axis
breaks <- mmp2 %>% 
  # Plots first, 8th, 15th, ... of day of a month
  mutate(days_to_plot = day(Fecha) %in% c(1, 8, 15, 22, 29)) %>% 
  filter(days_to_plot) %>% 
  pull(Fecha3)

w4 <- ggplot(data = mmp2) +
  geom_line(mapping = aes(x = Fecha3, y = Tmin, colour="Min"), size=0.71) +
  geom_line(mapping = aes(x = Fecha3, y = T, colour="P"), size=0.71) +
  geom_line(mapping = aes(x = Fecha3, y = Tmax, colour="Max"), size=0.71) +
  scale_x_continuous(breaks = breaks, labels = date_lab, expand = (c(0.001,0.008))) +
  scale_y_continuous(breaks=seq(-4, 28, 2), limits = c(1,18), expand=c(0,0)) +
  scale_colour_manual(name="Leyenda",
                      values=c(Min="green", P="#56B4E9", Max="Red")) +
  ylab("Temperatura (C)")+
  xlab("Tiempo") +
  guides(colour=guide_legend(order = 2),
         shape=guide_legend(order = 2)) +
  polish
w4

Created on 2020-03-29 by the reprex package (v0.3.0)reprex 包(v0.3.0) 于 2020 年 3 月 29 日创建

If you still have problems with overplotting, then increas the width between lables or try the new guide_axis function introduced in ggplot 3.3.0, eg如果你仍然有guide_axis问题,那么增加标签之间的宽度或尝试ggplot 3.3.0中引入的新guide_axis函数,例如

scale_x_continuous(breaks = breaks, labels = date_lab, expand = (c(0.001,0.008)), guide = guide_axis(n.dodge = 2))

will split the lables on two rows.将标签分成两行。

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