给定了两个每月的时间序列数据示例，来自。
我将需要创建一个包含3个子图的绘图：原始值的plot1，对于一个月的变化，plot2以及一年一度变化的plot3。
我可以使用下面的代码绘制图，但是代码太冗余了。所以我的问题是如何以简洁的方式实现这一目标？谢谢。
library(xlsx)
library(ggplot2)
library(reshape)
library(dplyr)
library(tidyverse)
library(lubridate)
library(cowplot)
library(patchwork)
df <- read.xlsx('./sample_data.xlsx', 'Sheet1')
colnames(df)
# df
cols <- c('food_index', 'energy_index')
df <- df %>% mutate(date=as.Date(date)) %>%
mutate(across(-contains('date'), as.numeric)) %>%
mutate(date= floor_date(date, 'month')) %>%
group_by(date) %>%
summarise_at(vars(cols), funs(mean(., na.rm=TRUE))) %>%
mutate(across(cols, list(yoy = ~(. - lag(., 12))/lag(., 12)))*100) %>%
mutate(across(cols, list(mom = ~(. - lag(., 1))/lag(., 1)))*100) %>%
filter(date >= '2018-01-01' & date <= '2021-12-31') %>%
as.data.frame()
df1 <- df %>%
select(!grep('mom|yoy', names(df)))
df1_long <- melt(df1, id.vars = 'date')
plot1 <- ggplot(df1_long[!is.na(df1_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: $'
)
# MoM changes
df2 <- df %>%
select(grep('date|mom', names(df)))
df2_long <- melt(df2, id.vars = 'date')
plot2 <- ggplot(df2_long[!is.na(df2_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: %'
)
# YoY changes
df3 <- df %>%
select(grep('date|yoy', names(df)))
df3_long <- melt(df3, id.vars = 'date')
plot3 <- ggplot(df3_long[!is.na(df3_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: %'
)
plot <- plot1 + plot2 + plot3 + plot_layout(ncol=1)
# plot <- plot_grid(plot1, plot2, plot3, labels = c('Value', 'MoM', 'YoY'), label_size = 12)
plot
out：
预期结果将与以下图相似（上图将显示原始数据，中间图将显示妈妈更改数据，而下图将显示yoy更改数据）：
参考：
https://waterdata.usgs.gov/blog/beyond-basic-plotting/
http://www.sthda.com/english/english/articles/24-gggpubr -PABLICATION-READY-plots/81-Ggplot2-Easy-way-to-mix-multiple-graphs-on-the-same-page/
Given two monthly time series data sample from this link.
I will need to create one plot containing 3 subplots: plot1 for the original values, plot2 for month over month changes, and plot3 for year over year changes.
I'm able to draw the plot with code below, but the code is too redundant. So my question is how could achieve that in a concise way? Thanks.
library(xlsx)
library(ggplot2)
library(reshape)
library(dplyr)
library(tidyverse)
library(lubridate)
library(cowplot)
library(patchwork)
df <- read.xlsx('./sample_data.xlsx', 'Sheet1')
colnames(df)
# df
cols <- c('food_index', 'energy_index')
df <- df %>% mutate(date=as.Date(date)) %>%
mutate(across(-contains('date'), as.numeric)) %>%
mutate(date= floor_date(date, 'month')) %>%
group_by(date) %>%
summarise_at(vars(cols), funs(mean(., na.rm=TRUE))) %>%
mutate(across(cols, list(yoy = ~(. - lag(., 12))/lag(., 12)))*100) %>%
mutate(across(cols, list(mom = ~(. - lag(., 1))/lag(., 1)))*100) %>%
filter(date >= '2018-01-01' & date <= '2021-12-31') %>%
as.data.frame()
df1 <- df %>%
select(!grep('mom|yoy', names(df)))
df1_long <- melt(df1, id.vars = 'date')
plot1 <- ggplot(df1_long[!is.na(df1_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit:
Out:
The expected result will be similar to the plot below (the upper plot will display the original data, the middle plot will display the mom changes data, and the lower plot will display the yoy changes data):
References:
https://waterdata.usgs.gov/blog/beyond-basic-plotting/
http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/
Side-by-side plots with ggplot2
)
# MoM changes
df2 <- df %>%
select(grep('date|mom', names(df)))
df2_long <- melt(df2, id.vars = 'date')
plot2 <- ggplot(df2_long[!is.na(df2_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: %'
)
# YoY changes
df3 <- df %>%
select(grep('date|yoy', names(df)))
df3_long <- melt(df3, id.vars = 'date')
plot3 <- ggplot(df3_long[!is.na(df3_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: %'
)
plot <- plot1 + plot2 + plot3 + plot_layout(ncol=1)
# plot <- plot_grid(plot1, plot2, plot3, labels = c('Value', 'MoM', 'YoY'), label_size = 12)
plot
Out:
The expected result will be similar to the plot below (the upper plot will display the original data, the middle plot will display the mom changes data, and the lower plot will display the yoy changes data):
References:
https://waterdata.usgs.gov/blog/beyond-basic-plotting/
http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/
Side-by-side plots with ggplot2
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评论（2）
也许这就是您要寻找的？通过使用绘图功能和EG
purrr :: map2将数据重新调整到正确的形状，您可以在不复制代码的情况下实现所需的结果。使用一些伪造的随机示例数据模仿您的真实数据：
>（到目前为止）有点棘手。在这种情况下，我要做的就是使用一个方面的“ hack”。为此，我稍微调整了该功能以进行字幕参数，然后切换到
purrr :: pmap：Maybe this is what you are looking for? By reshaping your data to the right shape, using a plot function and e.g.
purrr::map2you could achieve your desired result without duplicating your code like so.Using some fake random example data to mimic your true data:
DATA
EDIT Adding subtitles to each plot when using
patchworkis (as of the moment) a bit tricky. What I would do in this case would be to use a faceting "hack". To this end I slightly adjusted the function to take a subtitle argument and switched topurrr::pmap:目标输出是用刻面完成的，而不是将图缝合在一起。如果您愿意，您也可以执行此操作，但是它需要以不同的方式重塑数据。您采用哪种方法确实是一种品味问题。
The target output is done with facets rather than stitching plots together. You could do this too if you like, but it requires reshaping your data in a different way. Which approach you take is really a matter of taste.
Reproducible data taken from link in question