R：PCA ggplot 错误 "arguments imply differing number of rows"

Question

我有一个数据集： https://docs.google.com/spreadsheets/d/1ZgyRQ2uTw-MjjkJgWCIiZ1vpnxKmF3o15a5awndttgo/edit?usp=sharing

我正在尝试应用 PCA 分析并根据此 post 中提供的图表获得图表：

https://stats.stackexchange.com/questions/61215/how-to-interpret-this-pca-biplot-coming-from-a-survey-of-what-areas-people-are-i

但是，错误似乎并没有消失：

 Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = 
 TRUE,  : 
 arguments imply differing number of rows: 0, 1006

以下是我无法找到错误源的代码。想对错误检测有一些帮助。有什么提示吗？目标是生成按 Happiness.in.life 级别分组的 PCA 图。我修改了原始代码以适合我的数据集。最初，group 由 Genders 确定，它有 2 个级别。我正在尝试做的是基于 Happiness.in.life 的 5 个级别构建一个图表。但是，我似乎不能使用旧代码...

谢谢！

library(magrittr)
library(dplyr)
library(tidyr)
df <- happiness_reduced %>% dplyr::select(Happiness.in.life:Internet.usage, Happiness.in.life)  
head(df)
vars_on_hap <- df %>% dplyr::select(-Happiness.in.life)
head(vars_on_hap) 
group<-df$Happiness.in.life

fit <- prcomp(vars_on_hap)
pcData <- data.frame(fit$x)
vPCs <- fit$rotation[, c("PC1", "PC2")] %>% as.data.frame()

multiple <- min( 
(max(pcData[,"PC1"]) - min(pcData[,"PC1"]))/(max(vPCs[,"PC1"])-
min(vPCs[,"PC1"])), 
(max(pcData[,"PC2"]) - min(pcData[,"PC2"]))/(max(vPCs[,"PC2"])-
 min(vPCs[,"PC2"])) 
)

ggplot(pcData, aes(x=PC1, y=PC2)) + 
geom_point(aes(colour=groups))   + 
coord_equal() + 
geom_text(data=vPCs, 
        aes(x = fit$rotation[, "PC1"]*multiple*0.82, 
            y = fit$rotation[,"PC2"]*multiple*0.82, 
            label=rownames(fit$rotation)), 
        size = 2, vjust=1, color="black") +
geom_segment(data=vPCs, 
           aes(x = 0, 
               y = 0,
               xend = fit$rotation[,"PC1"]*multiple*0.8, 
               yend = fit$rotation[,"PC2"]*multiple*0.8), 
           arrow = arrow(length = unit(.2, 'cm')), 
           color = "grey30")

Answer 1

以下是如何在 ggplot2 中绘制 PCA 结果的方法：

library(tidyverse)
library(ggrepel)

一个好主意（不是在所有情况下，例如，如果它们都在相同的单位）是在 PCA 之前缩放变量

hapiness %>% #this is the data from google drive. In the future try not top post such links on SO because they tend to be unusable after some time has passed
  select(-Happiness.in.life) %>%
  prcomp(center = TRUE, scale. = TRUE) -> fit

现在我们可以继续绘制拟合：

fit$x %>%  #coordinates of the points are in x element
  as.data.frame()%>% #convert matrix to data frame
  select(PC1, PC2) %>%  #select the first two PC
  bind_cols(hapiness = as.factor(hapiness$Happiness.in.life)) %>% #add the coloring variable
  ggplot() + 
  geom_point(aes(x = PC1, y = PC2, colour = hapiness)) + #plot points and color
  geom_segment(data = fit$rotation %>% #data we want plotted by geom_segment is in rotation element
           as.data.frame()%>%
           select(PC1, PC2) %>%
           rownames_to_column(), #get to row names so you can label after
           aes(x = 0, y = 0, xend = PC1 * 7,  yend = PC2* 7,  group = rowname), #I scaled the rotation by 7 so it fits in the plot nicely
               arrow = arrow(angle = 20, type = "closed", ends = "last",length = unit(0.2,"cm")), 
               color = "grey30") +
  geom_text_repel(data = fit$rotation %>%
                    as.data.frame()%>%
                    select(PC1, PC2) %>%
                    rownames_to_column(),
                  aes(x = PC1*7,
                      y = PC2*7,
                      label = rowname)) +
  coord_equal(ratio = fit$sdev[2]^2 / fit$sdev[1]^2) + #I like setting the ratio to the ratio of eigen values 
  xlab(paste("PC1", round(fit$sdev[1]^2/ sum(fit$sdev^2) *100, 2), "%")) +
  ylab(paste("PC2", round(fit$sdev[2]^2/ sum(fit$sdev^2) *100, 2), "%")) +
  theme_bw()

看看左边所有快乐的人（因为使用的颜色很难注意到，我建议使用 ggpubr 库中的调色板 jco）get_palette('jco', 5) 即 scale_color_manual(values = get_palette('jco', 5))

使用库 ggord 可以实现非常相似的情节：

library(ggord)

ggord(fit, grp_in = as.factor(hapiness$Happiness.in.life),
      size = 1, ellipse = F, ext = 1.2, vec_ext = 5)

主要区别在于 ggord 对轴使用等比例缩放。另外，我将旋转缩放了 5，而不是第一个图中的 7。

如您所见，我不喜欢很多中间数据帧。

R：PCA ggplot 错误 "arguments imply differing number of rows"

R: PCA ggplot Error "arguments imply differing number of rows"

r

ggplot2

pca

dimensionality-reduction