【R言語】欠損値を数えてグラフ化

欠損値を数えてグラフ化する

データセットはBoston housingを使用する。
（参考）
https://www.kaggle.com/erikbruin/house-prices-lasso-xgboost-and-a-detailed-eda

rm(list = ls())

library(tidyverse)
library(data.table)
library(scales)
library(VIM)
library(corrr)
library(corrplot)
library(caret)
library(gridExtra)
library(scales)
library(Rmisc)
library(ggrepel)
library(randomForest)
library(psych)
library(xgboost)

path <- "C:/Users/XXXXXXX/"
filename <- "train.csv"
filename2 <- "test.csv"
df_train <- fread(str_c(path,filename), header=T)
df_test <- fread(str_c(path,filename2), header=T)

test_label <- df_test$Id

#train testを統合するためIDを消す
df_train$Id　<- NULL
df_test$Id <- NULL

all <- dplyr::bind_rows(df_train,df_test)

# any() usage：
is_na <- sapply(all, function(y) any(is.na(y)))
# count na
na_count <- sapply(all, function(y) sum(is.na(y)))

as.data.frame(na_count) %>% 
  rownames_to_column() %>% 
  arrange(desc(na_count)) %>% 
  mutate(na_prop = round(na_count / nrow(all)*100,1)) ->Na_summary

Na_summary %>% 
  dplyr::filter(na_count >1 ) %>% 
  ggplot(aes(x=reorder(factor(rowname),na_count),y=na_count))+
  geom_bar(stat="identity",fill="lightblue")+
  geom_text(aes(label=na_count))+
  labs(x="col name")+theme_bw()+
  scale_y_continuous(expand = c(0, 0),limits = c(0,3000))+
  coord_flip()

Nacount