-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathDay2_Exploratory_Data_Analysis.R
160 lines (135 loc) · 6.39 KB
/
Day2_Exploratory_Data_Analysis.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
##---------------- 設定環境 ----------------
#setwd(dir) #設定working directory的存放位置
# MAC : setwd("/Users/rladiestaipei/R_DragonBall/")
# Windows :
setwd("C://Users/lee/Desktop/R_DragonBall/")
#安裝套件(僅需執行一次)
#install.packages(c("tidyverse", "ploty", "zoo", "lubridate", "rmarkdown","data.table", "DT", "kableExtra"), dependencies = TRUE)
#load packages
library(tidyverse)
library(ggplot2)
library(dplyr)
library(magrittr)
library(corrplot)
library(gridExtra)
library(plotly)
options(dplyr.print_max=1e9)
##------------------------------------------------
## Part 1 : load data and split
##------------------------------------------------
# 讀取資料
train0 <- read.csv("train.csv", stringsAsFactors = FALSE)
test0 <- read.csv("test.csv", stringsAsFactors = FALSE)
# 分割 numeric and character 欄位
num_features <- names(which(sapply(train0, is.numeric)))
cat_features <- names(which(sapply(train0, is.character)))
train_numeric <- train0[, names(train0) %in% num_features]
train_categoric <- train0[, names(train0) %in% cat_features]
print(num_features)
print(cat_features)
##------------------------------------------------
## Part 2 : 檢視 missing value
##------------------------------------------------
# 先看num_features的36個欄位
# MSSubClass(住宅類型)、OverallQual(材料與完成度評比)、OverallCond(綜合狀況評比) 很明確屬於factor，先轉換。
train0$OverallCond <- as.factor(train0$OverallCond)
train0$OverallQual <- as.factor(train0$OverallQual)
train0$MSSubClass <- as.factor(train0$MSSubClass)
# 查看每個欄位的缺失值和比例
missing_values <- sapply(train0, function(x) sum(is.na(x)))
null_count <- data.frame(Count = missing_values, Proportion = missing_values/nrow(train0))
null_count_gteZero <- null_count[null_count$Count > 0, ]
null_count_gteZero[order(-null_count_gteZero$Count),]
# 刪除所有出現NA的欄位
train_non_null <- train0 %>%
select(-c(rownames(null_count_gteZero), OverallCond, OverallQual, MSSubClass))
##------------------------------------------------
## Part 3 : Various plots with SalePrice
##------------------------------------------------
# 先剔除出現NA的欄位，而且是數字的欄位
match_num_features <- paste(num_features, collapse = "|")
train_non_null_df <- select(train_non_null, matches(match_num_features))
theme_set(theme_bw()) # pre-set the bw theme.
# 篩選SF(面積)的欄位
train_SF <- select(train_non_null, matches("SF|SalePrice"))
# 各種面積與SalePrice的關係
train_SF %>%
# keep(is.numeric) %>%
gather(-SalePrice, key = "var", value = "value") %>%
ggplot(aes(x = value, y = SalePrice)) + # Plot the values
facet_wrap(~ var, scales = "free") + # In separate panels
geom_point()
# 篩選SF(面積)的欄位
train_Time <- select(train_non_null, matches("Yr|Year|Mo|year|yr|SalePrice"))
train_Time %>%
gather(-SalePrice, key = "var", value = "value") %>%
ggplot(aes(x = value, y = SalePrice)) + # Plot the values
facet_wrap(~ var, scales = "free") + # In separate panels
geom_point()
# 篩選Area(區域空間)的欄位
train_Area <- select(train_non_null, matches("Area|SalePrice"))
train_Area %>%
gather(-SalePrice, key = "var", value = "value") %>%
ggplot(aes(x = value, y = SalePrice)) + # Plot the values
facet_wrap(~ var, scales = "free") + # In separate panels
geom_point()
# 先剔除出現NA的欄位，而且是類別的欄位
match_cat_features <- paste(cat_features, collapse = "|")
train_non_null_df <- select(train_non_null, matches(match_num_features))
# 篩選房屋外觀的欄位
train_outside <- select(train_non_null, matches("Roof|MSSubClass|LotShape|Exterior|SalePrice"))
# 各種房屋外觀與SalePrice的關係
train_outside %>%
gather(-SalePrice, key = "var", value = "value") %>%
ggplot(aes(x = value, y = SalePrice)) + # Plot the values
facet_wrap(~ var, scales = "free") + # In separate panels
geom_boxplot(na.rm = T) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# 篩選房屋內部的欄位
train_inside <- select(train_non_null, matches("BldgType|Utilities|House|Bsmt|TotRmsAbvGrd|Fireplace|SalePrice"))
train_inside %>%
gather(-SalePrice, key = "var", value = "value") %>%
ggplot(aes(x = value, y = SalePrice)) + # Plot the values
facet_wrap(~ var, scales = "free") + # In separate panels
geom_boxplot(na.rm = T) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# 篩選Area(區域空間)的欄位
train_other <- select(train_non_null, matches("Electrical|Neighborhood|Street|Garage|MSZoning|SalePrice"))
train_other %>%
gather(-SalePrice, key = "var", value = "value") %>%
ggplot(aes(x = value, y = SalePrice)) + # Plot the values
facet_wrap(~ var, scales = "free") + # In separate panels
geom_boxplot(na.rm = T) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
correlations <- cor(train_non_null_df, use = "complete.obs")
cor_bar <- data.frame("cor" = correlations[,"SalePrice"])
cor_bar$item <- row.names(cor_bar)
cor_bar <- cor_bar[order(-cor_bar$cor),][-1,]
cor_bar$item <- factor(cor_bar$item, levels=cor_bar$item)
ggplot(cor_bar) +
geom_bar(stat='identity', aes(x = item, y = cor), width=.5) +
labs(title= "Correlations Bars") +
coord_flip()
##------------------------------------------------
## Part 5 : 檢視 SalePrice 分布
##------------------------------------------------
ggplot(data = train0[!is.na(train0$SalePrice),], aes(x=SalePrice)) +
geom_histogram(fill="blue", binwidth = 10000) +
scale_x_continuous(breaks= seq(0, 800000, by=100000))
summary(train0[!is.na(train0$SalePrice),]$SalePrice)
train0$SalePrice <- log(train0$SalePrice)
ggplot(train0, aes(x=SalePrice)) +
geom_histogram(fill="blue", binwidth = .05)
summary(train0[!is.na(train0$SalePrice),]$SalePrice)
##------------------------------------------------
## 本日小挑戰
##------------------------------------------------
#請挑選 training data 中你覺得其他重要的欄位，和房價有什麼關係。
# 篩選Fundation(地基類型), Functional(房屋損毀狀況), Bedroom, Bath, Kitchen的欄位
train_other <- select(train_non_null, matches("Foundation|Functional|Bedroom|Bath|Kitchen|SalePrice"))
train_other %>%
gather(-SalePrice, key = "var", value = "value") %>%
ggplot(aes(x = value, y = SalePrice)) + # Plot the values
facet_wrap(~ var, scales = "free") + # In separate panels
geom_boxplot(na.rm = T) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))