-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathCode_air_meteor_byRegion.R
320 lines (305 loc) · 13.2 KB
/
Code_air_meteor_byRegion.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
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
library(tidyverse)
library(fpp3)
library(vroom) # for csv/txt files
library(readxl) # for xlsx files
library(fs) # for catch the path of files
library(stringr)
memory.limit(50000) # 메모리 늘리기
# 1. loading data -----------------------------------------------------------
## 2005-2014년: PM2.5 존재 X
clean <- function(f){
map_df(f, read_excel) %>%
mutate_at(vars(SO2:PM10), as.numeric) %>%
mutate(
SO2 = ifelse(SO2 < 0, NA, SO2),
CO = ifelse(CO < 0, NA, CO),
O3 = ifelse(O3 < 0, NA, O3),
NO2 = ifelse(NO2 < 0, NA, NO2),
PM10 = ifelse(PM10 < 0, NA, PM10)
) %>%
filter(!is.na(SO2) | !is.na(CO) | !is.na(O3) | !is.na(NO2) | !is.na(PM10))
}
f_05 <- dir_ls(path = "./Before wrangling/airpollution/2005/")
f_06 <- dir_ls(path = "./Before wrangling/airpollution/2006/")
f_07 <- dir_ls(path = "./Before wrangling/airpollution/2007/")
f_08 <- dir_ls(path = "./Before wrangling/airpollution/2008/")
f_09 <- dir_ls(path = "./Before wrangling/airpollution/2009/")
f_10 <- dir_ls(path = "./Before wrangling/airpollution/2010/")
f_11 <- dir_ls(path = "./Before wrangling/airpollution/2011/")
f_12 <- dir_ls(path = "./Before wrangling/airpollution/2012/")
f_13 <- dir_ls(path = "./Before wrangling/airpollution/2013/")
f_14 <- dir_ls(path = "./Before wrangling/airpollution/2014/")
air_2005 <- clean(f_05)
air_2006 <- clean(f_06)
air_2007 <- clean(f_07)
air_2008 <- clean(f_08)
air_2009 <- clean(f_09)
air_2010 <- clean(f_10)
air_2011 <- clean(f_11)
air_2012 <- clean(f_12)
air_2013 <- clean(f_13)
clean_csv <- function(f){
map_df(f, ~read.csv(.x, header = T, fileEncoding = "euc-kr")) %>%
mutate_at(vars(SO2:PM10), as.numeric) %>%
mutate(
SO2 = ifelse(SO2 < 0, NA, SO2),
CO = ifelse(CO < 0, NA, CO),
O3 = ifelse(O3 < 0, NA, O3),
NO2 = ifelse(NO2 < 0, NA, NO2),
PM10 = ifelse(PM10 < 0, NA, PM10)
) %>%
filter(!is.na(SO2) | !is.na(CO) | !is.na(O3) | !is.na(NO2) | !is.na(PM10)) %>%
as_tibble
}
air_2014 <- clean_csv(f_14)
# (1) 2015-2020년: PM2.5까지 존재
f_15 <- dir_ls(path = "../Before wrangling/airpollution/2015/")
f_16 <- dir_ls(path = "../Before wrangling/airpollution/2016/")
f_17 <- dir_ls(path = "../Before wrangling/airpollution/2017/")
f_18 <- dir_ls(path = "../Before wrangling/airpollution/2018/")
f_19 <- dir_ls(path = "../Before wrangling/airpollution/2019/")
f_20 <- dir_ls(path = "../Before wrangling/airpollution/2020/")
clean_csv2 <- function(f){
vroom(f) %>%
mutate_at(vars(SO2:PM25), as.numeric) %>%
mutate(
SO2 = ifelse(SO2 < 0, NA, SO2),
CO = ifelse(CO < 0, NA, CO),
O3 = ifelse(O3 < 0, NA, O3),
NO2 = ifelse(NO2 < 0, NA, NO2),
PM10 = ifelse(PM10 < 0, NA, PM10),
PM25 = ifelse(PM25 < 0, NA, PM25)
) %>%
filter(!is.na(SO2) | !is.na(CO) | !is.na(O3) | !is.na(NO2) | !is.na(PM10), !is.na(PM25))
}
air_2015 <- clean_csv2(f_15)
air_2016 <- clean_csv2(f_16)
air_2017 <- clean(f_17)
air_2018 <- clean(f_18)
air_2019 <- clean(f_19)
air_2020 <- map_df(f_20, ~read_excel(.x, col_types = "text")) %>%
# 일부파일은 측정소코드를 character로 읽어서 오류. 따라서, 모든 열 character로 읽도록 통일
mutate_at(vars(SO2:PM25), as.numeric) %>%
mutate(
SO2 = ifelse(SO2 < 0, NA, SO2),
CO = ifelse(CO < 0, NA, CO),
O3 = ifelse(O3 < 0, NA, O3),
NO2 = ifelse(NO2 < 0, NA, NO2),
PM10 = ifelse(PM10 < 0, NA, PM10),
PM25 = ifelse(PM25 < 0, NA, PM25)
) %>%
filter(!is.na(SO2) | !is.na(CO) | !is.na(O3) | !is.na(NO2) | !is.na(PM10), !is.na(PM25))
# 2. wrangling data -------------------------------------------------------
glimpse(air_2005) # 2005~2013까지 PM2.5 열 추가.
glimpse(air_2006)
glimpse(air_2007)
glimpse(air_2008)
glimpse(air_2009)
glimpse(air_2010)
glimpse(air_2011)
glimpse(air_2012)
glimpse(air_2013)
glimpse(air_2014)
glimpse(air_2015)
glimpse(air_2016)
glimpse(air_2017) # 열 "망" 제거
glimpse(air_2018) # 열 "망" 제거
glimpse(air_2019) # 열 "망" 제거
glimpse(air_2020) # 열 "망" 제거
# air_2018_duplicate <- air_2018 %>%
# select(-망) %>%
# mutate(측정일시 = ymd_h(측정일시)) %>%
# select(-주소, -측정소코드) %>%
# unite(지역_측정소명, c(지역, 측정소명)) %>%
# group_by(지역_측정소명, 측정일시) %>%
# summarise(n = n()) %>%
# filter(n > 1)
# duplicate_region <- air_2018_duplicate %>%
# select(지역_측정소명) %>%
# unique %>%
# pull
# air_2018_duplicate %>%
# filter(지역_측정소명 == duplicate_region[[1]]) %>%
# pull(측정일시) %>%
# range
# air2_2018 <- air_2018 %>%
# select(-망) %>%
# mutate(측정일시 = ymd_h(측정일시)) %>%
# select(-주소, -측정소코드) %>%
# unite(지역_측정소명, c(지역, 측정소명))
# air2_2018 %>%
# filter(
# 지역_측정소명 == duplicate_region[[1]],
# 측정일시 >= ymd_h("2018-10-01 01"),
# 측정일시 <= ymd_h("2019-01-01 00")) %>%
# view() # 경남 거제시_저구리 중복행 측정값 모두 동일함. 삭제해도 하나는 삭제해도 됨.
# air2_2018 %>%
# filter(
# 지역_측정소명 == duplicate_region[[1]],
# 측정일시 >= ymd_h("2018-10-01 01"),
# 측정일시 <= ymd_h("2019-01-01 00")) %>%
# distinct(지역_측정소명, 측정일시, .keep_all = TRUE)
# air2_2018 %>% # 중복행 제거한 것
# distinct(지역_측정소명, 측정일시, .keep_all = TRUE)
make_tsbl_daily <- function(tb){
tb %>%
mutate(측정일시 = ymd_h(측정일시)) %>%
select(-주소, -측정소코드) %>%
unite(지역_측정소명, c(지역, 측정소명)) %>%
# (지역_측정소명, 측정일시) 조합에 중복있을 경우 제거.
# air_2018의 경남 거제시_저구리 지역에서 확인해본 결과, 중복된 경우 측정값 동일했음
distinct(지역_측정소명, 측정일시, .keep_all = TRUE) %>%
as_tsibble(key = 지역_측정소명, index = 측정일시) %>%
group_by_key() %>%
index_by(date = ~ as_date(.)) %>% # hourly data to daily data
summarise(
mean_SO2 = mean(SO2, na.rm = TRUE),
mean_CO = mean(CO, na.rm = TRUE),
mean_O3 = mean(O3, na.rm = TRUE),
mean_NO2 = mean(NO2, na.rm = TRUE),
mean_PM10 = mean(PM10, na.rm = TRUE)
) %>%
as_tibble()
}
make_tsbl_daily2 <- function(tb){ # 2015년부터 PM2.5 존재
tb %>%
mutate(측정일시 = ymd_h(측정일시)) %>%
select(-주소, -측정소코드) %>%
unite(지역_측정소명, c(지역, 측정소명)) %>%
# (지역_측정소명, 측정일시) 조합에 중복있을 경우 제거.
# air_2018의 경남 거제시_저구리 지역에서 확인해본 결과, 중복된 경우 측정값 동일했음
distinct(지역_측정소명, 측정일시, .keep_all = TRUE) %>%
as_tsibble(key = 지역_측정소명, index = 측정일시) %>%
group_by_key() %>%
index_by(date = ~ as_date(.)) %>% # hourly data to daily data
summarise(
mean_SO2 = mean(SO2, na.rm = TRUE),
mean_CO = mean(CO, na.rm = TRUE),
mean_O3 = mean(O3, na.rm = TRUE),
mean_NO2 = mean(NO2, na.rm = TRUE),
mean_PM10 = mean(PM10, na.rm = TRUE),
mean_PM25 = mean(PM25, na.rm = TRUE)
) %>%
as_tibble()
}
daily_2005 <- make_tsbl_daily(air_2005)
daily_2006 <- make_tsbl_daily(air_2006)
daily_2007 <- make_tsbl_daily(air_2007)
daily_2008 <- make_tsbl_daily(air_2008)
daily_2009 <- make_tsbl_daily(air_2009)
daily_2010 <- make_tsbl_daily(air_2010)
daily_2011 <- make_tsbl_daily(air_2011)
daily_2012 <- make_tsbl_daily(air_2012)
daily_2013 <- make_tsbl_daily(air_2013)
daily_2014 <- make_tsbl_daily(air_2014)
daily_2015 <- make_tsbl_daily2(air_2015)
daily_2016 <- make_tsbl_daily2(air_2016)
daily_2017 <- make_tsbl_daily2(air_2017 %>% select(-망))
daily_2018 <- make_tsbl_daily2(air_2018 %>% select(-망))
daily_2019 <- make_tsbl_daily2(air_2019 %>% select(-망))
daily_2020 <- make_tsbl_daily2(air_2020 %>% select(-망))
# 3. load and wrangling rds files -----------------------------------------
daily_air <- bind_rows(daily_2005, daily_2006, daily_2007, daily_2008, daily_2009, daily_2010,
daily_2011, daily_2012, daily_2013, daily_2014, daily_2015, daily_2016,
daily_2017, daily_2018, daily_2019, daily_2020, .id = "groups") %>%
separate(지역_측정소명, c("지역", "측정소명"), sep = "_") %>%
mutate(mean_SO2 = ifelse(is.nan(mean_SO2), NA, mean_SO2),
mean_CO = ifelse(is.nan(mean_CO), NA, mean_CO),
mean_O3 = ifelse(is.nan(mean_O3), NA, mean_O3),
mean_NO2 = ifelse(is.nan(mean_NO2), NA, mean_NO2),
mean_PM10 = ifelse(is.nan(mean_PM10), NA, mean_PM10),
mean_PM25 = ifelse(is.nan(mean_PM25), NA, mean_PM25))
glimpse(daily_air)
summary(daily_air)
### 12월 31일 24시가 1월 1일로 처리되서 발생하는 문제로 확인됨
daily_air %>%
filter(groups == 11)
daily_air %>%
filter(year(date) > 2014, 지역 == "강원 강릉시")
daily_air %>%
filter(지역 == "강원 강릉시") %>%
group_by(date) %>%
summarise(n = n()) %>%
filter(n > 1)
ymd_h("2015123124")
### 어떻게 해결할까?
### 2016/2017/2018/2019/2020의 12월 31일 24시. 1월 1일로 처리. 그럼 1월1일끼리 평균
daily_air2 <- daily_air %>%
group_by(지역, date) %>% # 지역별
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE))) %>% # 같은 (지역, date)가 있으면 평균 내기.
ungroup() %>%
mutate(SO2_Avg = ifelse(is.nan(mean_SO2), NA, mean_SO2),
CO_Avg = ifelse(is.nan(mean_CO), NA, mean_CO),
O3_Avg = ifelse(is.nan(mean_O3), NA, mean_O3),
NO2_Avg = ifelse(is.nan(mean_NO2), NA, mean_NO2),
PM10_Avg = ifelse(is.nan(mean_PM10), NA, mean_PM10),
PM25_Avg = ifelse(is.nan(mean_PM25), NA, mean_PM25)) %>%
select(지역, date, SO2_Avg:PM25_Avg) %>%
filter(date < ymd("2021-01-01"))
### 한번 더 겹치는 것 없나 검토.
daily_air2 %>%
filter(year(date) > 2014, 지역 == "강원 강릉시")
daily_air2 %>%
group_by(지역, date) %>%
summarise(n = n()) %>%
filter(n > 1)
## 지역별
# write_rds(daily_air2, "./after wrangling/airpollution_byregion.rds")
# write_csv(daily_air2, "./after wrangling/airpollution_byregion.csv")
# read_rds("./after wrangling/airpollution_byregion.rds")
## 전국 평균
daily <- daily_air2 %>%
group_by(date) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE))) %>%
mutate(SO2_Avg = ifelse(is.nan(SO2_Avg), NA, SO2_Avg),
CO_Avg = ifelse(is.nan(CO_Avg), NA, CO_Avg),
O3_Avg = ifelse(is.nan(O3_Avg), NA, O3_Avg),
NO2_Avg = ifelse(is.nan(NO2_Avg), NA, NO2_Avg),
PM10_Avg = ifelse(is.nan(PM10_Avg), NA, PM10_Avg),
PM25_Avg = ifelse(is.nan(PM25_Avg), NA, PM25_Avg))
# write_rds(daily, "./after wrangling/airpollution_mean.rds")
# write_csv(daily, "./after wrangling/airpollution_mean.csv")
# read_rds("./after wrangling/airpollution_mean.rds")
## 전국평균 대기오염원 + 평균기온, 일교차, 강수량, 습도
daily_temp <- read.csv("./before wrangling/meteorological factors/National level/기온_20050101_20201231.csv",
skip = 7, header = T, fileEncoding = "euc-kr") %>%
tibble %>%
mutate(일교차 = 최고기온...-최저기온...,
날짜 = ymd(날짜)) %>%
select(-지점, -최저기온..., -최고기온...) %>%
rename(date = 날짜, 평균기온 = 평균기온...)
summary(daily_temp)
daily_rain <- read.csv("./before wrangling/meteorological factors/National level/강수량_20050101_20201231.csv",
skip = 7, header = T, fileEncoding = "euc-kr") %>%
tibble %>%
mutate(날짜 = ymd(날짜)) %>%
select(-지점) %>%
rename(date = 날짜, 강수량 = 강수량.mm.)
summary(daily_rain)
daily_humid <- read.csv("./before wrangling/meteorological factors/National level/습도_20050101_20201231.csv",
skip = 14, header = T, fileEncoding = "euc-kr") %>%
tibble %>%
mutate(일시 = ymd(일시)) %>%
arrange(일시) %>%
rename(date = 일시, 평균습도 = 평균습도..rh.) %>%
select(date, 평균습도)
summary(daily_humid)
daily_wind <- read.csv("./before wrangling/meteorological factors/National level/풍속_20050101_20201231.csv",
skip = 14, header = T, fileEncoding = "euc-kr") %>%
drop_na() %>%
tibble %>%
mutate(일시 = ymd(일시)) %>%
arrange(일시) %>%
rename(date = 일시, 평균풍속 = 평균풍속.m.s.) %>%
select(date, 평균풍속)
daily_all <- daily %>%
left_join(daily_temp) %>%
left_join(daily_rain) %>%
left_join(daily_humid) %>%
rename(Temperature_Avg = 평균기온,
Temperature_range = 일교차,
Precipitation = 강수량,
Humidity_Avg = 평균습도)
summary(daily_all)
write_rds(daily_all, "./after wrangling/airpollution_meteor_mean.rds")
write_csv(daily_all, "./after wrangling/airpollution_meteor_mean.csv")