英文:
`R`/`ggplot2`: Strangeness when combining individual `geom_histogram` layers
问题
I'm striving to replicate aspects of this nice demonstration using R/ggplot2.
For the first point (plotting different bin counts) I have managed nicely, but when replicating that approach for the second point (different maxima), it strangely fails and I can't figure out why.
Here's what I do for demonstrating the lack of robustness as pointed out in 2. of the above post:
library(dplyr)library(ggplot2)library(magrittr)requireNamespace("kmed")# Define the new maxima to be usedmaxima <- c(197, 202, 213, 224)# Replicate the UCI heart data setm_df <- bind_rows(replicate(length(maxima), kmed::heart, simplify = FALSE)) %>%dplyr::mutate(sm = rep(maxima, each = nrow(kmed::heart)),fsm = paste("Maximum Edited to:", rep(maxima, each = nrow(kmed::heart))))# Modify the maximam_df[which(m_df$thalach == max(m_df$thalach)), "thalach"] <- maxima# Generate individual geom_histogram layers, forcing an identical number of binsm_lp_hist <- plyr::llply(maxima, function(b) {geom_histogram(data = m_df %>% filter(sm == b),mapping = aes(x = thalach),bins = 20)})# Combine the layersm_p_hist <- Reduce("+", m_lp_hist, init = ggplot2::ggplot())m_p_hist +ggplot2::facet_wrap(. ~ fsm, scales = "free_y") +ggplot2::theme_bw() +ggplot2::labs(x = "Maximum Heart Rate Achieved", y = "Count")
When evaluating the resulting plot, the histograms are very similar (!), while stepping through the individual layers generated using ggplot() + m_p_list[[1]] etc. nicely shows the difference between the layers and thus the effect I intend to demonstrate (lack of robustness in histograms). What is going on here!?
Thanks for any pointers.
英文:
I'm striving to replicate aspects of this nice demonstration using R/ggplot2.
For the first point (plotting different bin counts) I have managed nicely, but when replicating that approach for the second point (different maxima), it strangely fails and I can't figure out why.
Here's what I do for demonstrating the lack of robustness as pointed out in 2. of above post:
library(dplyr)library(ggplot2)library(magrittr)requireNamespace("kmed")# Define the new maxima to be usedmaxima <- c(197, 202, 213, 224)# Replicate the UCI heart data setm_df <- bind_rows(replicate(length(maxima), kmed::heart, simplify = FALSE)) %>%dplyr::mutate(sm = rep(maxima, each = nrow(kmed::heart)),fsm = paste("Maximum Edited to:", rep(maxima, each = nrow(kmed::heart))))# Modify the maximam_df[which(m_df$thalach == max(m_df$thalach)), "thalach"] <- maxima# Generate individual geom_histogram layers, forcing identical number of binsm_lp_hist <- plyr::llply(maxima, function(b) {geom_histogram(data = m_df %>% filter(sm == b),mapping = aes(x = thalach),bins = 20)})# Combine the layersm_p_hist <- Reduce("+", m_lp_hist, init = ggplot2::ggplot())m_p_hist +ggplot2::facet_wrap(. ~ fsm, scales = "free_y") +ggplot2::theme_bw() +ggplot2::labs(x = "Maximum Heart Rate Achieved", y = "Count")
When evaluating the resulting plot, the histograms are very similar (!), while stepping through the individual layers generated using ggplot() + m_p_list[[1]] etc. nicely shows the difference between the layers and thus the effect I intend to demonstrate (lack of robustness in histograms). What is going on here!?
Thanks for any pointers
答案1
得分: 1
Here is the translated content:
你距离（我认为的）你想要的很接近。请尝试使用以下代码：```Rm_p_hist +ggplot2::facet_wrap(. ~ fsm, scales = "free") + # 替代 free_yggplot2::theme_bw() +ggplot2::labs(x = "达到的最大心率", y = "计数")
解释
你原始的代码将所有4个直方图层添加到单个 ggplot 对象中，然后再将它们分为4个单独的图表面板，而不允许 x 轴刻度在不同图表面板之间变化。因此，x 轴范围/限制是根据所有4个图层的组合数据计算的，每个直方图的20个箱子也是从相同的范围计算的。
通过将 scales = "free_y" 更改为 scales = "free"，我们允许 x 轴刻度变化，并且每个直方图的20个箱子是根据不同范围的值计算的。
顺便说一下，我不太清楚为什么你要通过 facet_wrap 创建这个图。我会选择创建4个单独的 ggplot 对象，然后将它们拼接成一个单一的图表进行演示，这将完全避免上述问题。例如：
lapply(m_lp_hist,function(p) ggplot() +p +labs(title = p$data$fsm[[1]]) +theme_bw()) %>%cowplot::plot_grid(plotlist = ., nrow = 2, ncol = 2)
Please note that the code and content have been translated, and I have excluded the parts you mentioned not to translate.<details><summary>英文:</summary>You are very close to (what I think) you want. Try this instead:
m_p_hist +
ggplot2::facet_wrap(. ~ fsm, scales = "free") + # instead of free_y
ggplot2::theme_bw() +
ggplot2::labs(x = "Maximum Heart Rate Achieved", y = "Count")
[![result][1]][1]## ExplanationYour original code adds all 4 histogram layers into a single ggplot object before faceting them out into 4 separate plot panels again, **without allowing x-axis scales to vary across facets.** As a result, the x axis range / limits are calculated from the combined data of all 4 layers, and the 20 bins for each histogram are calculated from the same range.By changing `scales = "free_y"` to `scales = "free"`, we allow x-axis scales to vary, and the 20 bins for each histogram are calculated from a different range of values.By the way, it's not very clear to me why you'd want to create this via `facet_wrap`. I'd have gone with making 4 separate ggplot objects & stitching them together into a single chart afterwards for presentation purpose, which would have avoided the above issue all together. E.g.:
lapply(m_lp_hist,
function(p) ggplot() +
p +
labs(title = p$data$fsm[1]) +
theme_bw()) %>%
cowplot::plot_grid(plotlist = ., nrow = 2, ncol = 2)
[1]: https://i.stack.imgur.com/iuWld.png</details>
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