library(tidyverse)
library(forecast)
df <- data.frame(val = runif(100),
key = c(rep('a', 50), key = rep('b', 50)))
df_acf <- df %>%
group_by(key) %>%
summarise(list_acf=list(acf(val, plot=FALSE))) %>%
mutate(acf_vals=purrr::map(list_acf, ~as.numeric(.x$acf))) %>%
select(-list_acf) %>%
unnest() %>%
group_by(key) %>%
mutate(lag=row_number() - 1)
df_ci <- df %>%
group_by(key) %>%
summarise(ci = qnorm((1 + 0.95)/2)/sqrt(n()))
ggplot(df_acf, aes(x=lag, y=acf_vals)) +
geom_bar(stat="identity", width=.05) +
geom_hline(yintercept = 0) +
geom_hline(data = df_ci, aes(yintercept = -ci), color="blue", linetype="dotted") +
geom_hline(data = df_ci, aes(yintercept = ci), color="blue", linetype="dotted") +
labs(x="Lag", y="ACF") +
facet_wrap(~key)
ggAcf
相同的方式计算置信区间。 - Adam Spannbauerlibrary(forecast)
df <- data.frame(val = runif(100),
key = c(rep('a', 50), key = rep('b', 50)))
a = subset(df, key == "a")
ap = ggAcf(a$val)
b = subset(df, key == "b")
bp = ggAcf(b$val)
library(grid)
grid.newpage()
pushViewport(viewport(layout=grid.layout(1,2)))
print(ap, vp=viewport(layout.pos.row = 1, layout.pos.col = 1))
print(bp, vp=viewport(layout.pos.row = 1, layout.pos.col = 2))
或者:
grid.newpage()
pushViewport(viewport(layout=grid.layout(1,2)))
print(ap, vp=viewport(layout.pos.row = 1, layout.pos.col = 1))
print(bp, vp=viewport(layout.pos.row = 1, layout.pos.col = 2))
Adam Spannbauer的回答非常好,输出结果与forecast::ggAcf
非常相似,可能只有点状置信区间线与ggAcf
生成的虚线不同(如果需要很容易修复)。
一个快速而简单的替代方法是使用ggfortify::autoplot
,并为不同的facet值使用列表,如下面的示例:
# Load ggfortify
require(ggfortify)
# Create sample data frame
df <- data.frame(val = runif(100),
key = c(rep('a', 50), key = rep('b', 50)))
# Create list with ACF objects for different key values
acf.key <- list()
for (i in 1:length(unique(df$key))) {
acf.key[[i]] <- acf(df$val[df$key==unique(df$key)[[i]]])
}
# Plot using ggfortify::autoplot
autoplot(acf.key, ncol=2)
很不幸,似乎无法像标准的ggplot
那样在绘图上方获取横跨标题的横幅,因此最终结果不如上面的回答那样精细。我还无法同时保留左侧图表的标签并删除右侧图表的y轴标签。