hw10
張瑜君
2019年5月6日
02_sales_analysis_code_checkpoint_4.R
1.Load libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.5.3## -- Attaching packages ------------------------------------------------------ tidyverse 1.2.1 --## √ ggplot2 3.1.0 √ purrr 0.3.1
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## √ readr 1.3.1 √ forcats 0.4.0## Warning: package 'ggplot2' was built under R version 3.5.3## Warning: package 'readr' was built under R version 3.5.3## Warning: package 'forcats' was built under R version 3.5.3## -- Conflicts --------------------------------------------------------- tidyverse_conflicts() --
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## as.Date, as.Date.numeric##
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## first, last##
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## legend## Loading required package: quantmod## Warning: package 'quantmod' was built under R version 3.5.3## Loading required package: TTR## Warning: package 'TTR' was built under R version 3.5.3## Version 0.4-0 included new data defaults. See ?getSymbols.library(readxl)
library(writexl)## Warning: package 'writexl' was built under R version 3.5.32.Importing Files
bikes_tbl <- read_excel("bikes.xlsx")
bikeshops_tbl <- read_excel("bikeshops.xlsx")
orderlines_tbl <- read_excel("orderlines.xlsx")## New names:
## * `` -> `..1`3.Examining Data
bikes_tbl## # A tibble: 97 x 4
## bike.id model description price
## <dbl> <chr> <chr> <dbl>
## 1 1 Supersix Evo Black Inc. Road - Elite Road - Carbon 12790
## 2 2 Supersix Evo Hi-Mod Team Road - Elite Road - Carbon 10660
## 3 3 Supersix Evo Hi-Mod Dura Ace 1 Road - Elite Road - Carbon 7990
## 4 4 Supersix Evo Hi-Mod Dura Ace 2 Road - Elite Road - Carbon 5330
## 5 5 Supersix Evo Hi-Mod Utegra Road - Elite Road - Carbon 4260
## 6 6 Supersix Evo Red Road - Elite Road - Carbon 3940
## 7 7 Supersix Evo Ultegra 3 Road - Elite Road - Carbon 3200
## 8 8 Supersix Evo Ultegra 4 Road - Elite Road - Carbon 2660
## 9 9 Supersix Evo 105 Road - Elite Road - Carbon 2240
## 10 10 Supersix Evo Tiagra Road - Elite Road - Carbon 1840
## # ... with 87 more rowsglimpse(bikes_tbl)## Observations: 97
## Variables: 4
## $ bike.id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,...
## $ model <chr> "Supersix Evo Black Inc.", "Supersix Evo Hi-Mod Te...
## $ description <chr> "Road - Elite Road - Carbon", "Road - Elite Road -...
## $ price <dbl> 12790, 10660, 7990, 5330, 4260, 3940, 3200, 2660, ...bikeshops_tbl## # A tibble: 30 x 3
## bikeshop.id bikeshop.name location
## <dbl> <chr> <chr>
## 1 1 Pittsburgh Mountain Machines Pittsburgh, PA
## 2 2 Ithaca Mountain Climbers Ithaca, NY
## 3 3 Columbus Race Equipment Columbus, OH
## 4 4 Detroit Cycles Detroit, MI
## 5 5 Cincinnati Speed Cincinnati, OH
## 6 6 Louisville Race Equipment Louisville, KY
## 7 7 Nashville Cruisers Nashville, TN
## 8 8 Denver Bike Shop Denver, CO
## 9 9 Minneapolis Bike Shop Minneapolis, MN
## 10 10 Kansas City 29ers Kansas City, KS
## # ... with 20 more rowsorderlines_tbl## # A tibble: 15,644 x 7
## ..1 order.id order.line order.date customer.id product.id
## <chr> <dbl> <dbl> <dttm> <dbl> <dbl>
## 1 1 1 1 2011-01-07 00:00:00 2 48
## 2 2 1 2 2011-01-07 00:00:00 2 52
## 3 3 2 1 2011-01-10 00:00:00 10 76
## 4 4 2 2 2011-01-10 00:00:00 10 52
## 5 5 3 1 2011-01-10 00:00:00 6 2
## 6 6 3 2 2011-01-10 00:00:00 6 50
## 7 7 3 3 2011-01-10 00:00:00 6 1
## 8 8 3 4 2011-01-10 00:00:00 6 4
## 9 9 3 5 2011-01-10 00:00:00 6 34
## 10 10 4 1 2011-01-11 00:00:00 22 26
## # ... with 15,634 more rows, and 1 more variable: quantity <dbl>4.Joining Data
left_join(orderlines_tbl, bikes_tbl, by = c("product.id"="bike.id"))## # A tibble: 15,644 x 10
## ..1 order.id order.line order.date customer.id product.id
## <chr> <dbl> <dbl> <dttm> <dbl> <dbl>
## 1 1 1 1 2011-01-07 00:00:00 2 48
## 2 2 1 2 2011-01-07 00:00:00 2 52
## 3 3 2 1 2011-01-10 00:00:00 10 76
## 4 4 2 2 2011-01-10 00:00:00 10 52
## 5 5 3 1 2011-01-10 00:00:00 6 2
## 6 6 3 2 2011-01-10 00:00:00 6 50
## 7 7 3 3 2011-01-10 00:00:00 6 1
## 8 8 3 4 2011-01-10 00:00:00 6 4
## 9 9 3 5 2011-01-10 00:00:00 6 34
## 10 10 4 1 2011-01-11 00:00:00 22 26
## # ... with 15,634 more rows, and 4 more variables: quantity <dbl>,
## # model <chr>, description <chr>, price <dbl>orderlines_bikes_tbl <- orderlines_tbl %>% left_join(bikes_tbl, by = c("product.id"="bike.id"))
orderlines_tbl %>% left_join(bikes_tbl, by = c("product.id"="bike.id")) %>%
left_join(bikeshops_tbl, by = c("customer.id" = "bikeshop.id"))## # A tibble: 15,644 x 12
## ..1 order.id order.line order.date customer.id product.id
## <chr> <dbl> <dbl> <dttm> <dbl> <dbl>
## 1 1 1 1 2011-01-07 00:00:00 2 48
## 2 2 1 2 2011-01-07 00:00:00 2 52
## 3 3 2 1 2011-01-10 00:00:00 10 76
## 4 4 2 2 2011-01-10 00:00:00 10 52
## 5 5 3 1 2011-01-10 00:00:00 6 2
## 6 6 3 2 2011-01-10 00:00:00 6 50
## 7 7 3 3 2011-01-10 00:00:00 6 1
## 8 8 3 4 2011-01-10 00:00:00 6 4
## 9 9 3 5 2011-01-10 00:00:00 6 34
## 10 10 4 1 2011-01-11 00:00:00 22 26
## # ... with 15,634 more rows, and 6 more variables: quantity <dbl>,
## # model <chr>, description <chr>, price <dbl>, bikeshop.name <chr>,
## # location <chr>bikes_orderlines_joined_tbl<-orderlines_tbl %>% left_join(bikes_tbl, by = c("product.id" = "bike.id")) %>%
left_join(bikeshops_tbl, by = c("customer.id" = "bikeshop.id"))
bikes_orderlines_joined_tbl## # A tibble: 15,644 x 12
## ..1 order.id order.line order.date customer.id product.id
## <chr> <dbl> <dbl> <dttm> <dbl> <dbl>
## 1 1 1 1 2011-01-07 00:00:00 2 48
## 2 2 1 2 2011-01-07 00:00:00 2 52
## 3 3 2 1 2011-01-10 00:00:00 10 76
## 4 4 2 2 2011-01-10 00:00:00 10 52
## 5 5 3 1 2011-01-10 00:00:00 6 2
## 6 6 3 2 2011-01-10 00:00:00 6 50
## 7 7 3 3 2011-01-10 00:00:00 6 1
## 8 8 3 4 2011-01-10 00:00:00 6 4
## 9 9 3 5 2011-01-10 00:00:00 6 34
## 10 10 4 1 2011-01-11 00:00:00 22 26
## # ... with 15,634 more rows, and 6 more variables: quantity <dbl>,
## # model <chr>, description <chr>, price <dbl>, bikeshop.name <chr>,
## # location <chr>bikes_orderlines_joined_tbl %>% glimpse()## Observations: 15,644
## Variables: 12
## $ `..1` <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10...
## $ order.id <dbl> 1, 1, 2, 2, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 6, 6, ...
## $ order.line <dbl> 1, 2, 1, 2, 1, 2, 3, 4, 5, 1, 1, 2, 3, 4, 1, 2, ...
## $ order.date <dttm> 2011-01-07, 2011-01-07, 2011-01-10, 2011-01-10,...
## $ customer.id <dbl> 2, 2, 10, 10, 6, 6, 6, 6, 6, 22, 8, 8, 8, 8, 16,...
## $ product.id <dbl> 48, 52, 76, 52, 2, 50, 1, 4, 34, 26, 96, 66, 35,...
## $ quantity <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, ...
## $ model <chr> "Jekyll Carbon 2", "Trigger Carbon 2", "Beast of...
## $ description <chr> "Mountain - Over Mountain - Carbon", "Mountain -...
## $ price <dbl> 6070, 5970, 2770, 5970, 10660, 3200, 12790, 5330...
## $ bikeshop.name <chr> "Ithaca Mountain Climbers", "Ithaca Mountain Cli...
## $ location <chr> "Ithaca, NY", "Ithaca, NY", "Kansas City, KS", "...5.Wrangling Data
共有order_date、order_id、order_line、quantity、price、total_price、model、category_1、category_2、frame_material、bikeshop_name、city、state。
bikes_orderlines_wrangled_tbl <- bikes_orderlines_joined_tbl %>%
separate(description,
into = c("category.1", "category.2", "frame.material"),
sep = " - ",
remove = TRUE) %>%
separate(location,
into = c("city", "state"),
sep = ", ",
remove = FALSE) %>%
mutate(total.price = price * quantity) %>%
select(-1, -location) %>%
select(-ends_with(".id")) %>%
bind_cols(bikes_orderlines_joined_tbl %>% select(order.id)) %>%
select(contains("date"), contains("id"), contains("order"),
quantity, price, total.price,
everything()) %>%
rename(order_date = order.date) %>%
set_names(names(.) %>% str_replace_all("\\.", "_"))
bikes_orderlines_wrangled_tbl %>% glimpse()## Observations: 15,644
## Variables: 13
## $ order_date <dttm> 2011-01-07, 2011-01-07, 2011-01-10, 2011-01-10...
## $ order_id <dbl> 1, 1, 2, 2, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 6, 6,...
## $ order_line <dbl> 1, 2, 1, 2, 1, 2, 3, 4, 5, 1, 1, 2, 3, 4, 1, 2,...
## $ quantity <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,...
## $ price <dbl> 6070, 5970, 2770, 5970, 10660, 3200, 12790, 533...
## $ total_price <dbl> 6070, 5970, 2770, 5970, 10660, 3200, 12790, 533...
## $ model <chr> "Jekyll Carbon 2", "Trigger Carbon 2", "Beast o...
## $ category_1 <chr> "Mountain", "Mountain", "Mountain", "Mountain",...
## $ category_2 <chr> "Over Mountain", "Over Mountain", "Trail", "Ove...
## $ frame_material <chr> "Carbon", "Carbon", "Aluminum", "Carbon", "Carb...
## $ bikeshop_name <chr> "Ithaca Mountain Climbers", "Ithaca Mountain Cl...
## $ city <chr> "Ithaca", "Ithaca", "Kansas City", "Kansas City...
## $ state <chr> "NY", "NY", "KS", "KS", "KY", "KY", "KY", "KY",...6.Business Insights
6-1.Sales by Year
Step1 - Manipulate
sales_by_year_tbl <- bikes_orderlines_wrangled_tbl %>%
select(order_date, total_price) %>%
mutate(year = year(order_date)) %>%
group_by(year) %>%
summarize(sales = sum(total_price)) %>%
ungroup() %>%
mutate(sales_text = scales::dollar(sales))
sales_by_year_tbl## # A tibble: 5 x 3
## year sales sales_text
## <dbl> <dbl> <chr>
## 1 2011 11292885 $11,292,885
## 2 2012 12163075 $12,163,075
## 3 2013 16480775 $16,480,775
## 4 2014 13924085 $13,924,085
## 5 2015 17171510 $17,171,510Step2 - Visualize
sales_by_year_tbl %>%
ggplot(aes(x = year, y = sales)) +
geom_col(fill = "#2c3e50") +
geom_label(aes(label = sales_text)) +
geom_smooth(method = "lm", se = FALSE) +
theme_tq() +
scale_y_continuous(labels = scales::dollar) +
labs(
title = "Revenue by Year",
subtitle = "Upward trend",
x = "",
y = "Revenue")
6-2.Sales by Year and Category 2
Step1 - Manipulate
sales_by_year_cat_2_tbl <- bikes_orderlines_wrangled_tbl %>%
select(order_date, total_price, category_2) %>%
mutate(year = year(order_date)) %>%
group_by(year, category_2) %>%
summarise(sales = sum(total_price)) %>%
ungroup() %>%
mutate(sales_text = scales::dollar(sales))
sales_by_year_cat_2_tbl## # A tibble: 45 x 4
## year category_2 sales sales_text
## <dbl> <chr> <dbl> <chr>
## 1 2011 Cross Country Race 2917250 $2,917,250
## 2 2011 Cyclocross 378980 $378,980
## 3 2011 Elite Road 2493315 $2,493,315
## 4 2011 Endurance Road 1606230 $1,606,230
## 5 2011 Fat Bike 221600 $221,600
## 6 2011 Over Mountain 1328510 $1,328,510
## 7 2011 Sport 309290 $309,290
## 8 2011 Trail 1446560 $1,446,560
## 9 2011 Triathalon 591150 $591,150
## 10 2012 Cross Country Race 3360800 $3,360,800
## # ... with 35 more rowsStep2 - Visualize
p = sales_by_year_cat_2_tbl %>%
ggplot(aes(x = year, y = sales, fill = category_2)) +
geom_col() +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap(~ category_2, ncol = 3, scales = "free_y") +
theme_tq() +
scale_fill_tq() +
scale_y_continuous(labels = scales::dollar) +
labs(
title = "Revenue by Year and Category 2",
subtitle = "Each product category has an upward trend",
x = "",
y = "Revenue",
fill = "Product Secondary Category")
p
轉換成PDF，並儲存成pdf檔及png檔。
pdf("ggplot.pdf")
print(p)
dev.off()## png
## 2ggsave("myplot.pdf")## Saving 7 x 5 in imageggsave("myplot.png")## Saving 7 x 5 in imageggsave("myplot.png" , plot = p)## Saving 7 x 5 in image7.Writing Files
1.fs設定一個新的目錄。
2.利用write_xlsx將檔案儲存為xlsx檔。
3.利用write_csv將檔案儲存為csv檔。
4.利用write_rds將檔案儲存為rds檔。
fs::dir_create("data_wrangled_student")
bikes_orderlines_wrangled_tbl %>%
write_xlsx("data_wrangled_student/bike_orderlines.xlsx")
bikes_orderlines_wrangled_tbl %>%
write_csv("data_wrangled_student/bike_orderlines.csv")
bikes_orderlines_wrangled_tbl %>%
write_rds("data_wrangled_student/bike_orderlines.rds")台灣上市公司(不含TDR)之日收盤調整後股價
讀取「台灣上市公司(不含TDR)之日收盤調整後股價」，把第二欄-簡稱刪除。
並將證券代碼那一欄設定為id，年月日設定為date，收盤價設定為price。
day.price = read.table("D:/gittest/01/0506/price_2010_2018_day.txt")
day.price <- day.price[,-2]
colnames(day.price) <- c("id", "", "", "date", "price")
head(day.price)## id date price
## 1 證券代碼 上市別 TSE產業別 年月日 收盤價(元)
## 2 1101 TSE 01 20100104 19.70
## 3 1102 TSE 01 20100104 20.24
## 4 1103 TSE 01 20100104 13.91
## 5 1104 TSE 01 20100104 11.32
## 6 1108 TSE 01 20100104 6.82利用dcast，將長資料轉換為寬資料型態。
library(data.table)## Warning: package 'data.table' was built under R version 3.5.3##
## Attaching package: 'data.table'## The following objects are masked from 'package:xts':
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## first, last## The following objects are masked from 'package:lubridate':
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## hour, isoweek, mday, minute, month, quarter, second, wday,
## week, yday, year## The following objects are masked from 'package:dplyr':
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## between, first, last## The following object is masked from 'package:purrr':
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## transposedayprice.reorder = dcast(day.price,date~id)## Using 'price' as value column. Use 'value.var' to overridedim(dayprice.reorder)## [1] 2224 930head(dayprice.reorder)## date 1101 1102 1103 1104 1108 1109 1110 1201 1203 1210 1213
## 1 20100104 19.70 20.24 13.91 11.32 6.82 8.45 10.55 39.77 17.22 15.16 9.23
## 2 20100105 20.39 20.68 13.83 11.13 6.77 8.49 10.64 39.81 17.19 15.18 8.94
## 3 20100106 20.82 20.83 13.60 11.22 6.76 8.57 10.78 39.81 17.29 15.44 9.09
## 4 20100107 20.42 20.59 13.38 10.95 6.88 8.61 10.74 39.72 17.09 15.18 8.89
## 5 20100108 20.33 20.71 13.57 11.01 6.95 8.61 10.83 39.35 17.16 15.31 8.92
## 6 20100111 19.96 20.56 13.91 11.35 7.01 8.65 11.55 38.61 17.32 15.79 9.06
## 1215 1216 1217 1218 1219 1220 1225 1227 1229 1231 1232 1233
## 1 9.07 20.01 12.72 10.81 9.58 7.24 12.63 12.70 7.67 7.24 22.36 23.64
## 2 9.16 19.72 12.67 11.23 9.55 7.09 12.63 12.60 7.69 7.19 22.33 24.03
## 3 9.34 20.03 12.77 11.27 9.62 7.12 12.83 12.81 7.79 7.21 22.53 24.19
## 4 9.22 19.54 12.53 11.35 9.55 6.99 12.86 12.58 7.64 7.19 22.53 24.36
## 5 9.31 19.54 12.58 11.31 9.69 7.06 12.83 12.52 7.81 7.21 22.62 23.96
## 6 9.92 19.62 12.82 11.48 10.34 7.38 13.71 12.45 7.96 7.40 22.91 23.90
## 1234 1235 1236 1256 1262 1301 1303 1304 1305 1307 1308 1309
## 1 16.05 20.07 10.37 <NA> <NA> 43.84 39.24 9.79 7.75 17.56 9.80 11.25
## 2 16.31 20.22 10.31 <NA> <NA> 44.37 40.46 9.79 7.75 17.53 9.71 11.25
## 3 16.22 20.04 10.55 <NA> <NA> 45.81 42.92 10.43 8.28 17.60 10.20 11.86
## 4 16.09 20.22 10.37 <NA> <NA> 45.81 43.26 10.08 8.03 17.34 10.13 11.41
## 5 16.39 20.16 10.34 <NA> <NA> 46.20 43.32 10.46 8.41 17.34 10.82 11.82
## 6 16.56 20.25 10.46 <NA> <NA> 46.66 44.28 10.67 8.57 17.24 10.97 11.73
## 1310 1312 1313 1314 1315 1316 1319 1321 1323 1324 1325 1326
## 1 11.26 11.39 10.04 8.62 17.04 11.20 40.70 25.25 10.89 16.36 20.27 44.73
## 2 11.43 11.50 9.84 8.55 16.82 10.96 39.09 26.20 10.58 15.49 20.08 45.18
## 3 12.20 12.29 10.04 8.96 17.11 10.73 39.51 25.85 11.29 16.54 20.27 48.24
## 4 12.44 12.49 10.38 8.96 17.11 10.76 39.37 24.90 10.84 16.50 20.19 47.85
## 5 12.56 12.77 10.64 9.10 17.44 10.95 40.21 25.35 10.84 16.65 20.08 46.74
## 6 12.60 12.63 10.49 9.37 17.47 11.00 39.51 26.00 10.84 16.14 20.08 47.39
## 1337 1338 1339 1340 1341 1402 1409 1410 1413 1414 1416 1417 1418
## 1 <NA> <NA> <NA> <NA> <NA> 24.09 8.32 15.35 9.16 4.64 14.39 8.24 38.85
## 2 <NA> <NA> <NA> <NA> <NA> 24.42 8.08 14.30 9.17 4.58 14.01 8.20 38.15
## 3 <NA> <NA> <NA> <NA> <NA> 25.24 8.15 14.26 9.22 4.60 13.83 8.39 38.57
## 4 <NA> <NA> <NA> <NA> <NA> 24.82 8.46 14.34 9.21 4.57 13.76 8.42 37.72
## 5 <NA> <NA> <NA> <NA> <NA> 25.24 8.39 14.65 9.26 4.67 14.14 8.56 37.58
## 6 <NA> <NA> <NA> <NA> <NA> 25.27 8.08 14.73 9.89 4.82 13.95 8.45 40.12
## 1419 1423 1432 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443
## 1 30.71 8.11 4.03 17.20 4.80 13.71 20.59 1.60 17.45 9.88 12.75 7.22 6.82
## 2 31.55 8.08 4.15 16.89 4.80 13.59 21.17 1.60 17.25 9.74 12.78 7.52 6.91
## 3 31.97 8.36 4.43 17.87 4.77 13.64 21.03 1.50 17.12 9.99 12.84 7.49 6.99
## 4 31.39 8.31 4.69 17.57 4.85 13.68 21.79 1.40 17.12 10.07 12.60 7.54 6.97
## 5 31.76 8.41 4.72 17.33 5.00 13.80 21.66 1.49 18.32 9.99 12.75 7.56 7.08
## 6 32.02 8.99 5.02 17.50 5.00 13.90 21.53 1.59 19.58 10.14 13.64 8.08 7.57
## 1444 1445 1446 1447 1449 1451 1452 1453 1454 1455 1456 1457 1459 1460
## 1 5.23 5.91 8.26 10.11 7.41 19.90 5.21 6.37 5.23 10.34 7.55 6.27 6.86 5.68
## 2 5.01 5.82 8.27 9.83 7.46 19.90 5.18 6.38 5.33 10.08 8.07 5.99 6.78 5.64
## 3 5.10 5.88 8.37 10.18 7.77 19.95 5.23 6.44 5.21 10.23 8.41 6.06 6.91 5.70
## 4 5.11 5.71 8.41 10.14 7.66 19.78 5.29 6.48 5.21 10.23 8.18 5.89 6.93 5.67
## 5 5.17 5.77 8.47 10.59 7.80 19.90 5.38 6.43 5.20 10.53 7.95 5.96 7.01 5.76
## 6 5.22 5.78 8.67 11.07 8.19 19.90 5.43 6.88 5.42 11.25 8.12 6.24 7.11 5.86
## 1463 1464 1465 1466 1467 1468 1470 1471 1472 1473 1474 1475 1476
## 1 8.04 4.39 5.48 5.57 7.44 11.20 8.37 49.87 14.31 22.36 5.01 55.41 12.59
## 2 8.60 4.39 5.38 5.55 7.44 11.20 8.28 50.60 14.08 22.17 4.97 55.68 12.24
## 3 8.46 4.46 5.43 5.56 7.47 11.20 8.13 51.33 13.90 22.17 5.00 55.14 12.27
## 4 8.00 4.36 5.38 5.52 7.32 11.20 8.22 50.07 13.87 22.07 4.97 54.59 12.19
## 5 7.83 4.42 5.38 5.54 7.39 11.20 8.27 50.93 14.46 22.04 5.00 56.23 12.21
## 6 7.62 4.62 5.38 5.78 7.60 11.20 8.29 50.67 14.46 22.36 5.11 57.32 12.19
## 1477 1503 1504 1506 1507 1512 1513 1514 1515 1516 1517 1519
## 1 34.21 30.56 10.07 19.06 16.87 9.45 12.96 10.85 11.07 93.89 7.73 22.72
## 2 34.27 31.41 10.18 18.99 16.90 9.27 12.78 10.46 10.95 95.82 7.63 22.30
## 3 34.55 30.89 10.40 19.19 17.20 9.36 12.84 10.36 11.06 95.05 7.80 22.34
## 4 33.98 30.34 10.25 19.12 16.74 9.40 12.47 10.18 10.90 93.50 7.87 22.34
## 5 33.98 30.38 10.29 18.99 16.64 9.31 12.59 10.29 11.57 96.59 7.80 22.16
## 6 34.21 30.34 10.32 18.92 16.50 9.80 12.63 10.11 11.46 99.30 7.91 22.27
## 1521 1522 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533
## 1 28.49 18.39 13.21 41.05 7.45 25.79 7.84 51.90 24.06 12.28 21.21 23.94
## 2 27.70 18.12 12.85 40.00 7.14 25.50 7.60 50.44 24.52 12.12 20.59 24.07
## 3 27.43 18.19 12.75 39.16 7.10 26.15 7.70 53.90 24.84 11.99 20.74 25.75
## 4 26.79 18.26 13.40 37.68 7.08 25.72 7.50 51.44 25.10 12.12 21.14 26.01
## 5 26.61 18.29 13.40 38.46 7.03 26.26 8.00 52.44 25.26 11.99 21.25 25.84
## 6 26.94 18.39 13.35 38.25 7.23 25.58 8.54 52.08 24.74 11.86 21.32 25.06
## 1535 1536 1537 1538 1539 1540 1541 1558 1560 1568 1582 1583
## 1 28.88 11.65 24.49 108.38 7.12 18.95 16.02 40.75 25.33 <NA> 52.62 18.84
## 2 30.43 11.25 24.11 106.05 7.10 18.67 16.05 40.05 25.59 <NA> 51.02 18.62
## 3 30.34 11.25 24.23 112.17 7.18 18.78 15.98 40.05 25.72 <NA> 50.76 18.80
## 4 30.24 11.13 24.49 109.40 7.09 18.73 15.95 40.05 25.92 <NA> 48.77 18.84
## 5 29.92 11.13 24.40 109.69 7.12 18.84 16.43 40.35 26.77 <NA> 48.77 19.65
## 6 30.30 11.57 24.40 108.67 7.12 18.84 16.05 39.85 27.94 <NA> 49.29 19.65
## 1587 1589 1590 1592 1598 1603 1604 1605 1608 1609 1611 1612 1614
## 1 <NA> <NA> <NA> <NA> <NA> 15.60 6.31 10.92 8.92 7.32 18.93 9.64 28.12
## 2 <NA> <NA> <NA> <NA> <NA> 15.10 6.18 10.66 8.81 7.20 18.66 9.85 29.16
## 3 <NA> <NA> <NA> <NA> <NA> 15.21 6.21 10.88 8.88 7.27 18.81 9.78 29.12
## 4 <NA> <NA> <NA> <NA> <NA> 15.10 6.16 10.92 8.81 7.24 18.27 9.20 29.50
## 5 <NA> <NA> <NA> <NA> <NA> 15.21 6.29 11.01 8.84 7.41 18.00 9.37 29.25
## 6 <NA> <NA> <NA> <NA> <NA> 15.54 6.30 11.22 9.45 7.69 18.04 9.54 29.67
## 1615 1616 1617 1618 1626 1701 1702 1707 1708 1709 1710 1711 1712
## 1 7.55 7.47 6.16 16.77 <NA> 18.45 22.30 34.65 19.19 7.05 15.65 18.82 8.89
## 2 7.63 7.21 6.19 17.18 <NA> 18.25 22.60 33.61 19.31 6.83 15.78 18.85 8.86
## 3 7.60 7.37 6.14 17.14 <NA> 18.41 22.76 33.25 20.65 7.30 16.86 18.96 9.26
## 4 7.52 7.30 6.19 16.96 <NA> 17.95 22.45 33.54 20.62 7.03 17.32 19.08 9.02
## 5 7.72 7.40 6.30 16.99 <NA> 18.22 22.53 32.78 21.10 7.22 18.34 18.96 9.26
## 6 8.23 7.91 6.74 17.26 <NA> 18.41 22.98 33.07 21.21 7.18 18.09 18.50 9.23
## 1713 1714 1717 1718 1720 1721 1722 1723 1724 1725 1726 1727
## 1 9.61 10.13 16.21 6.19 18.17 12.54 80.92 55.96 8.94 11.14 29.01 11.61
## 2 9.45 10.13 15.99 6.38 18.20 12.59 86.25 55.37 8.80 11.11 29.01 11.24
## 3 9.76 10.19 16.11 6.83 18.36 13.43 86.25 55.50 9.40 11.23 29.07 12.03
## 4 9.57 9.88 15.94 7.30 18.36 12.96 86.60 55.57 8.98 11.26 29.10 11.51
## 5 9.73 10.11 16.21 7.81 18.59 13.85 85.54 55.70 9.54 11.46 29.36 12.29
## 6 10.39 10.13 16.28 8.09 18.43 14.78 86.25 55.96 9.54 11.43 29.17 13.13
## 1730 1731 1732 1733 1734 1735 1736 1737 1760 1762 1773 1776
## 1 10.39 12.95 16.13 38.88 22.36 12.83 26.65 19.10 <NA> <NA> 18.57 <NA>
## 2 10.24 12.88 16.04 38.27 22.06 12.12 26.22 19.24 <NA> <NA> 17.93 <NA>
## 3 10.27 13.21 16.40 38.27 22.06 12.19 25.80 19.70 <NA> <NA> 17.98 <NA>
## 4 10.09 13.06 16.04 38.33 21.92 11.35 24.51 19.19 <NA> <NA> 17.42 <NA>
## 5 10.18 13.06 16.09 38.45 22.39 11.55 25.18 19.19 <NA> <NA> 17.88 <NA>
## 6 10.18 13.06 16.18 38.70 22.39 11.79 24.62 19.33 <NA> <NA> 17.88 <NA>
## 1783 1786 1789 1802 1805 1806 1808 1809 1810 1817 1902 1903 1904
## 1 <NA> <NA> <NA> 20.33 13.20 16.76 8.13 17.43 9.44 <NA> 8.94 81.00 8.39
## 2 <NA> <NA> <NA> 20.73 13.20 16.60 7.96 16.80 9.32 <NA> 8.82 80.20 8.33
## 3 <NA> <NA> <NA> 22.17 13.20 16.72 7.99 16.80 9.40 <NA> 8.98 79.90 8.46
## 4 <NA> <NA> <NA> 21.66 12.93 16.35 8.13 16.17 9.15 <NA> 9.03 79.90 8.36
## 5 <NA> <NA> <NA> 22.37 12.87 16.43 8.69 16.17 9.28 <NA> 9.40 78.90 8.59
## 6 <NA> <NA> <NA> 23.03 13.35 17.01 8.78 16.33 9.36 <NA> 9.32 79.30 8.49
## 1905 1906 1907 1909 2002 2006 2007 2008 2009 2010 2012 2013 2014
## 1 11.90 6.56 9.70 6.64 22.10 22.03 7.66 7.29 10.80 8.51 8.14 18.05 20.01
## 2 11.59 6.39 9.53 6.64 22.13 21.62 7.75 7.29 10.60 8.20 8.00 18.05 19.28
## 3 11.86 6.83 9.70 6.97 22.19 21.78 7.88 7.40 10.68 8.22 8.06 18.05 19.56
## 4 12.09 6.69 9.53 6.79 22.03 21.87 7.74 7.50 10.96 8.11 8.23 18.05 19.28
## 5 12.32 6.69 9.67 6.91 22.58 22.19 8.28 8.03 11.16 8.37 8.47 18.58 19.84
## 6 12.20 6.80 9.70 6.94 22.88 23.34 8.85 8.59 11.92 8.57 8.72 19.86 20.29
## 2015 2017 2020 2022 2023 2024 2025 2027 2028 2029 2030 2031
## 1 32.97 11.03 16.72 13.31 8.45 4.78 5.57 12.44 19.08 16.16 14.67 22.55
## 2 32.43 10.49 16.26 13.14 8.19 4.80 5.57 12.15 18.50 16.09 14.22 22.07
## 3 32.55 10.54 16.32 14.04 8.32 4.97 5.95 12.07 18.67 16.19 14.26 22.10
## 4 32.73 10.39 15.96 15.01 8.13 4.91 6.27 11.88 18.58 16.02 14.26 21.68
## 5 33.97 10.73 16.02 16.04 8.68 5.26 6.68 12.22 19.17 16.26 14.67 23.17
## 6 35.28 11.27 16.20 17.16 9.03 5.62 7.14 12.81 20.50 16.63 15.05 24.79
## 2032 2033 2034 2038 2049 2059 2062 2069 2101 2102 2103 2104
## 1 16.80 18.47 14.89 16.34 28.54 135.01 22.16 <NA> 28.60 18.72 20.14 16.36
## 2 16.01 17.92 14.57 16.38 27.60 132.34 21.26 <NA> 28.94 18.39 19.96 16.38
## 3 16.25 18.08 14.57 16.42 27.60 133.49 21.46 <NA> 28.94 18.19 20.17 16.52
## 4 16.56 17.60 14.54 16.46 28.15 132.34 21.46 <NA> 29.37 18.29 20.04 16.26
## 5 17.70 17.76 14.81 16.66 29.37 131.96 21.46 <NA> 30.85 18.35 20.62 16.40
## 6 18.93 18.20 15.64 17.82 30.23 128.91 21.46 <NA> 32.99 18.79 20.49 16.60
## 2105 2106 2107 2108 2109 2114 2115 2201 2204 2206 2207 2208
## 1 27.09 17.30 17.19 11.00 19.13 16.36 <NA> 32.52 14.87 11.17 54.61 49.43
## 2 26.94 17.21 17.75 10.75 19.19 16.31 <NA> 32.73 15.01 11.09 54.90 49.51
## 3 26.94 17.35 18.13 10.82 19.02 16.04 <NA> 33.44 15.11 11.17 54.76 49.11
## 4 26.30 17.10 19.18 10.52 18.80 15.89 <NA> 32.77 15.17 11.17 54.90 48.64
## 5 26.33 17.17 19.51 10.63 19.08 15.94 <NA> 32.85 15.01 11.13 55.19 48.88
## 6 26.37 17.10 19.24 10.59 19.02 16.01 <NA> 32.56 14.91 11.13 54.90 50.39
## 2227 2228 2231 2236 2239 2243 2301 2302 2303 2305 2308 2312 2313
## 1 43.74 <NA> 23.99 <NA> <NA> <NA> 26.33 6.21 11.64 5.01 71.15 8.43 8.47
## 2 43.44 <NA> 23.58 <NA> <NA> <NA> 26.50 5.96 11.78 5.01 70.65 8.61 8.08
## 3 43.09 <NA> 23.45 <NA> <NA> <NA> 26.67 6.04 12.60 5.36 70.37 8.61 8.12
## 4 42.91 <NA> 22.97 <NA> <NA> <NA> 26.39 5.83 12.60 5.73 69.09 8.54 8.16
## 5 42.73 <NA> 22.80 <NA> <NA> <NA> 26.39 5.81 12.46 5.38 69.02 8.54 8.05
## 6 42.20 <NA> 23.24 <NA> <NA> <NA> 26.56 5.84 12.74 5.24 69.94 8.42 7.89
## 2314 2316 2317 2321 2323 2324 2327 2328 2329 2330 2331 2332
## 1 47.92 9.82 75.74 27.70 10.04 25.36 31.39 46.59 17.50 47.28 9.26 22.77
## 2 47.03 9.70 76.24 26.90 9.81 25.24 31.39 45.85 16.91 46.98 8.96 23.11
## 3 49.27 9.86 75.74 26.60 9.85 25.47 32.32 46.75 16.69 47.28 8.99 23.35
## 4 48.82 9.74 74.99 26.70 9.83 26.05 31.52 45.19 16.76 46.77 8.69 23.04
## 5 48.22 9.74 75.24 26.80 9.91 26.28 31.25 45.77 17.06 46.62 8.93 23.11
## 6 48.22 9.82 74.24 28.60 9.88 26.63 31.39 45.60 16.69 46.98 8.93 23.18
## 2337 2338 2340 2342 2344 2345 2347 2348 2349 2351 2352 2353
## 1 26.98 9.96 21.50 152.08 8.39 8.45 38.54 63.37 17.96 30.71 15.64 78.90
## 2 26.68 9.82 20.94 152.91 8.56 8.39 38.04 67.80 17.28 30.15 15.07 79.86
## 3 26.83 9.93 20.76 154.57 8.44 8.34 38.54 72.50 17.31 29.80 15.15 79.86
## 4 26.46 9.71 19.89 150.00 8.32 8.31 38.76 77.45 17.31 28.56 14.77 77.46
## 5 26.31 9.53 20.10 146.26 8.20 8.23 38.76 82.67 17.53 29.01 15.04 78.82
## 6 26.08 9.50 20.34 146.26 8.08 8.18 38.87 88.40 17.65 29.04 14.92 78.26
## 2354 2355 2356 2357 2358 2359 2360 2362 2363 2364 2365 2367
## 1 70.64 18.58 10.35 139.17 29.89 12.72 49.27 36.90 25.17 14.27 24.12 9.90
## 2 70.64 18.32 10.38 138.95 30.23 12.44 47.82 36.83 24.24 14.19 24.05 9.64
## 3 70.64 18.17 10.44 145.49 29.89 12.48 48.03 37.40 24.44 14.30 23.94 9.68
## 4 69.48 17.38 10.30 147.29 29.15 12.28 46.79 37.25 24.18 14.63 23.31 9.42
## 5 69.48 17.44 10.41 148.87 28.82 12.32 47.13 37.11 24.18 14.33 23.42 9.42
## 6 69.19 17.60 10.82 148.42 29.48 12.48 46.58 37.18 24.34 14.19 23.53 9.42
## 2368 2369 2371 2373 2374 2375 2376 2377 2379 2380 2382 2383
## 1 13.38 10.76 18.84 30.79 44.50 13.95 15.82 12.48 56.20 15.12 41.67 14.16
## 2 12.99 10.73 18.75 30.45 44.21 13.56 15.52 12.13 54.70 15.16 41.37 14.02
## 3 12.94 10.53 18.70 30.41 44.14 13.70 15.70 12.39 54.52 15.12 41.98 14.08
## 4 12.99 10.39 18.58 30.45 43.42 13.56 15.52 12.24 54.04 14.98 41.61 13.75
## 5 13.08 10.26 18.51 30.50 43.78 13.33 15.70 12.27 54.40 14.84 41.43 13.96
## 6 13.08 10.19 18.46 30.83 45.57 13.61 15.85 12.51 56.80 14.74 41.92 14.31
## 2385 2387 2388 2390 2392 2393 2395 2397 2399 2401 2402 2404
## 1 38.97 37.73 39.10 12.97 47.83 71.67 39.94 25.33 16.38 26.22 17.51 15.87
## 2 38.21 37.07 41.80 12.34 46.54 70.15 40.05 24.47 16.05 24.84 16.98 15.76
## 3 38.21 36.87 42.40 12.34 46.54 69.54 39.72 24.35 16.11 24.99 17.06 15.15
## 4 38.31 35.35 39.60 12.04 46.41 67.40 39.83 24.32 15.85 24.03 16.45 15.24
## 5 39.17 35.81 40.70 12.19 46.47 66.49 39.72 24.12 15.72 24.57 16.33 14.95
## 6 41.90 36.31 41.60 12.04 46.67 67.71 39.50 23.89 15.91 24.19 16.20 15.24
## 2405 2406 2408 2409 2412 2413 2414 2415 2417 2419 2420 2421
## 1 28.54 27.02 249.65 30.69 49.11 15.29 11.50 9.95 31.72 15.17 13.50 16.58
## 2 27.43 26.05 257.13 30.73 49.19 14.60 10.75 9.36 31.50 14.66 12.87 15.82
## 3 27.51 26.13 265.01 32.31 49.36 15.62 11.03 9.50 31.86 14.50 12.87 16.37
## 4 27.51 24.38 258.31 31.66 48.36 14.97 10.54 9.36 31.24 14.06 12.16 15.88
## 5 27.78 25.00 252.01 32.08 48.12 15.52 10.46 9.30 31.28 13.93 12.45 16.05
## 6 26.80 24.34 250.04 32.00 48.45 15.06 10.51 9.36 31.61 13.96 12.34 16.00
## 2423 2424 2425 2426 2427 2428 2429 2430 2431 2433 2434 2436
## 1 14.19 21.73 36.74 33.36 9.38 38.97 43.73 30.43 20.08 12.41 47.39 30.59
## 2 14.02 20.53 34.19 32.35 8.94 37.95 40.80 31.36 20.40 11.56 46.35 29.07
## 3 13.72 21.93 33.20 32.08 9.07 38.59 38.09 30.98 20.00 11.80 46.04 29.22
## 4 13.29 22.00 33.91 31.06 8.65 37.50 36.06 30.33 18.78 11.11 46.04 29.61
## 5 13.29 20.66 31.64 31.06 8.59 37.76 38.32 30.27 18.90 11.26 45.20 29.41
## 6 13.12 21.33 32.77 30.96 8.76 37.76 40.80 30.60 18.47 10.99 45.20 29.46
## 2438 2439 2440 2441 2442 2443 2444 2448 2449 2450 2451 2453
## 1 13.84 34.45 12.70 17.44 16.60 19.61 22.57 99.14 9.43 29.85 66.84 11.95
## 2 13.02 33.16 12.23 17.01 16.18 18.81 21.84 99.56 9.28 29.44 63.50 11.46
## 3 12.61 32.53 12.61 17.39 16.21 18.81 22.06 100.41 9.43 29.50 63.50 11.39
## 4 13.43 32.53 11.81 17.39 15.63 18.71 21.18 95.77 9.43 29.72 62.94 10.89
## 5 12.92 32.02 11.85 17.28 15.90 18.59 20.95 97.03 9.40 29.54 62.94 10.61
## 6 13.74 31.55 11.95 17.28 16.02 18.71 21.03 96.19 9.43 29.54 61.55 10.86
## 2454 2455 2456 2457 2458 2459 2460 2461 2462 2464 2465 2466
## 1 382.99 44.08 25.63 26.58 51.50 15.43 21.07 19.98 27.15 17.59 33.04 31.04
## 2 383.67 42.94 24.85 25.69 48.52 15.20 20.84 19.59 29.02 17.31 31.05 30.85
## 3 388.40 43.05 24.80 25.69 48.77 15.36 20.96 19.36 28.71 17.17 33.18 31.04
## 4 378.94 40.10 24.58 25.69 46.81 14.85 20.25 18.62 28.09 16.78 33.61 31.23
## 5 371.51 39.74 24.07 25.78 48.52 15.04 20.56 18.23 28.82 17.00 35.89 32.08
## 6 376.92 42.48 23.85 26.58 48.26 14.98 20.56 18.54 28.82 16.89 38.31 32.74
## 2467 2468 2471 2472 2474 2475 2476 2477 2478 2480 2481 2482
## 1 13.14 10.98 10.57 11.41 68.01 11.07 20.15 9.74 42.29 19.26 21.80 26.71
## 2 12.49 10.76 10.11 10.72 66.55 10.54 19.54 9.91 39.35 18.55 21.43 26.01
## 3 12.59 10.76 10.22 11.45 64.02 10.82 19.72 10.58 37.74 18.61 22.47 26.15
## 4 11.75 10.55 9.82 11.24 63.25 10.54 18.79 9.94 38.15 17.70 22.51 25.30
## 5 12.12 10.55 9.50 11.48 63.40 10.69 18.79 9.91 36.58 17.67 22.34 24.74
## 6 12.25 10.62 9.53 11.20 64.09 10.54 18.75 9.64 35.09 17.39 22.38 25.30
## 2483 2484 2485 2486 2488 2489 2491 2492 2493 2495 2496 2497
## 1 10.81 11.81 39.45 42.28 10.52 17.02 22.67 19.76 10.45 38.15 137.16 20.51
## 2 10.65 11.55 39.26 42.35 10.08 16.54 22.99 18.58 10.38 37.68 146.56 20.25
## 3 10.49 11.47 39.58 42.28 10.11 16.54 21.97 18.69 10.35 37.41 156.65 20.42
## 4 10.12 11.14 38.67 40.33 9.74 16.10 20.54 18.94 10.60 36.31 145.86 20.17
## 5 10.37 11.02 38.61 40.72 9.80 16.27 20.54 19.10 10.45 36.11 135.77 20.09
## 6 10.21 11.10 38.80 40.48 9.93 16.10 19.76 18.74 10.23 35.70 145.17 20.00
## 2498 2499 2501 2504 2505 2506 2509 2511 2514 2515 2516 2520
## 1 267.73 50.11 9.70 12.44 13.75 7.81 13.16 7.28 13.37 7.21 5.57 17.99
## 2 272.49 49.25 9.77 12.69 14.69 7.76 12.88 7.37 13.29 7.20 5.50 18.99
## 3 273.59 49.48 9.95 12.65 15.24 7.81 12.91 7.43 13.37 7.22 5.51 19.68
## 4 262.23 47.22 9.59 12.20 15.76 7.81 12.91 7.23 13.13 7.17 5.45 19.49
## 5 268.46 46.91 10.23 12.69 16.86 7.83 12.96 7.30 13.37 7.47 5.56 19.81
## 6 270.29 46.52 10.30 12.69 18.00 8.00 13.16 7.39 13.86 7.39 5.63 20.19
## 2524 2527 2528 2530 2534 2535 2536 2537 2538 2539 2540 2542 2543
## 1 22.83 12.46 10.89 7.69 8.19 10.65 26.15 7.62 8.23 7.79 8.50 15.34 5.96
## 2 22.94 12.16 10.40 7.59 8.39 10.52 25.61 7.69 8.25 7.38 9.08 15.34 5.92
## 3 22.90 12.16 11.06 7.52 8.36 10.62 25.47 7.75 8.59 7.33 9.08 15.65 5.93
## 4 23.27 12.49 10.35 7.89 8.39 10.65 25.08 7.97 8.42 7.24 9.09 15.89 5.92
## 5 23.00 13.35 10.18 8.16 8.97 11.39 25.42 8.40 8.63 7.24 9.06 16.17 6.18
## 6 24.59 14.28 10.18 8.23 9.58 11.54 26.10 8.96 9.23 7.35 9.67 16.11 6.22
## 2545 2546 2547 2548 2597 2601 2603 2605 2606 2607 2608 2609
## 1 35.14 12.83 17.05 38.92 <NA> 41.50 14.12 26.09 40.00 23.59 17.38 21.54
## 2 36.31 12.83 17.68 40.27 <NA> 43.60 14.62 27.39 42.78 23.75 17.90 21.98
## 3 35.99 12.74 17.80 39.59 <NA> 44.19 14.39 27.49 42.97 23.35 17.72 22.07
## 4 35.03 12.59 17.68 39.01 <NA> 43.60 14.47 26.77 41.55 23.14 17.53 22.07
## 5 35.30 12.83 17.74 39.46 <NA> 43.85 15.24 26.96 42.10 24.12 17.42 22.42
## 6 34.44 12.69 17.56 39.37 <NA> 43.77 15.59 26.83 42.35 24.77 17.49 23.12
## 2610 2611 2612 2613 2614 2615 2616 2617 2618 2630 2633 2634 2636
## 1 10.20 10.88 71.38 14.70 15.72 11.62 9.10 32.80 9.68 <NA> <NA> <NA> 9.08
## 2 10.52 10.92 73.56 14.93 15.79 11.69 9.21 34.22 9.89 <NA> <NA> <NA> 9.12
## 3 10.43 10.92 73.23 14.70 16.14 11.69 9.43 34.50 9.82 <NA> <NA> <NA> 9.46
## 4 10.20 10.77 72.11 15.70 15.58 11.55 9.32 33.23 9.65 <NA> <NA> <NA> 9.10
## 5 10.29 10.88 72.83 15.89 15.75 11.72 9.35 33.65 9.89 <NA> <NA> <NA> 9.72
## 6 10.29 11.00 72.59 15.66 16.85 11.89 9.66 33.44 9.86 <NA> <NA> <NA> 10.24
## 2637 2642 2701 2702 2704 2705 2706 2707 2712 2722 2723 2727 2731
## 1 <NA> <NA> 9.38 20.67 33.04 18.52 14.23 173.69 <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> 9.29 20.55 33.16 18.42 14.36 173.27 <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> 9.42 20.73 33.04 18.61 14.33 171.15 <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> 9.32 20.29 32.27 18.24 14.65 173.27 <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> 9.54 20.42 32.70 18.24 14.65 171.79 <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> 10.19 20.61 33.38 19.26 14.80 173.69 <NA> <NA> <NA> <NA> <NA>
## 2739 2748 2801 2809 2812 2816 2820 2823 2832 2834 2836 2838 2841 2845
## 1 <NA> <NA> 8.20 6.92 4.93 33.80 4.67 9.53 14.62 5.32 7.28 5.17 8.31 6.51
## 2 <NA> <NA> 8.20 6.88 5.09 32.97 4.68 9.74 15.34 5.36 7.76 5.26 8.28 6.62
## 3 <NA> <NA> 8.31 6.91 5.21 33.14 4.71 9.82 15.24 5.41 8.28 5.24 8.25 6.68
## 4 <NA> <NA> 8.28 6.97 5.20 33.47 4.92 9.51 15.05 5.38 8.18 5.33 8.22 6.49
## 5 <NA> <NA> 8.36 7.01 5.23 33.63 4.92 9.74 15.13 5.43 8.32 5.34 8.43 6.49
## 6 <NA> <NA> 8.45 7.05 5.20 32.86 4.91 9.66 15.13 5.43 8.42 5.33 9.00 6.49
## 2849 2850 2851 2852 2855 2867 2880 2881 2882 2883 2884 2885 2886
## 1 6.69 10.08 8.06 8.14 13.62 <NA> 9.58 23.77 35.77 6.53 5.59 15.22 11.25
## 2 6.76 10.25 8.00 8.10 13.53 <NA> 9.65 23.92 35.71 6.58 5.57 15.03 11.37
## 3 6.72 10.22 8.03 8.14 13.65 <NA> 9.82 24.68 36.31 6.59 5.65 15.35 11.49
## 4 6.97 10.19 8.03 8.24 13.34 <NA> 9.82 24.29 35.95 6.58 5.71 15.09 11.37
## 5 6.91 10.53 8.06 8.27 13.62 <NA> 9.80 24.41 36.31 6.66 5.80 15.26 11.46
## 6 6.79 10.45 7.97 8.27 13.62 <NA> 9.75 24.26 36.01 6.86 5.76 15.29 11.46
## 2887 2888 2889 2890 2891 2892 2897 2901 2903 2904 2905 2906 2908
## 1 6.06 10.60 5.66 6.58 8.74 9.33 <NA> 22.23 21.73 6.41 8.64 6.51 11.62
## 2 6.23 10.64 5.63 6.56 8.74 9.40 <NA> 22.23 22.11 6.36 8.74 6.39 11.39
## 3 6.30 11.16 5.69 6.63 9.01 9.52 <NA> 22.32 23.66 6.38 8.97 6.61 11.22
## 4 6.18 10.80 5.85 6.50 8.92 9.38 <NA> 22.15 22.44 6.19 8.71 6.45 11.12
## 5 6.23 11.12 5.99 6.53 8.96 9.42 <NA> 22.94 22.62 6.60 8.84 6.51 11.06
## 6 6.39 11.16 5.96 6.56 8.92 9.33 <NA> 22.50 22.74 6.80 8.92 6.42 11.09
## 2910 2911 2912 2913 2915 2923 2929 2936 2939 3002 3003 3004 3005
## 1 20.41 25.60 55.81 16.87 58.98 <NA> <NA> <NA> <NA> 17.46 20.43 6.85 18.91
## 2 20.29 25.71 56.03 17.44 58.67 <NA> <NA> <NA> <NA> 17.07 19.88 6.85 18.85
## 3 20.41 26.43 56.10 17.15 58.67 <NA> <NA> <NA> <NA> 17.11 20.43 6.69 18.70
## 4 20.88 26.83 56.32 17.36 57.43 <NA> <NA> <NA> <NA> 17.01 19.84 6.69 17.99
## 5 20.92 26.87 56.54 17.56 57.64 <NA> <NA> <NA> <NA> 16.82 20.09 6.46 17.80
## 6 20.83 27.16 56.62 17.52 57.33 <NA> <NA> <NA> <NA> 16.91 20.09 6.44 17.87
## 3006 3008 3010 3011 3013 3014 3015 3016 3017 3018 3019 3021
## 1 39.43 358.45 26.02 14.20 9.88 51.27 27.15 34.82 23.62 15.02 72.77 12.20
## 2 38.56 351.99 25.72 13.56 9.57 50.19 26.35 33.75 22.84 14.50 72.09 12.03
## 3 38.63 351.13 26.46 13.56 9.84 50.26 26.25 34.26 22.96 14.50 71.32 12.03
## 4 37.25 345.53 25.99 12.93 9.93 48.47 25.51 33.20 22.99 14.15 69.97 11.47
## 5 38.05 342.51 26.10 12.88 9.79 49.40 25.79 33.20 22.84 14.89 70.45 11.12
## 6 37.62 343.80 26.34 12.93 9.84 50.05 25.67 33.20 23.71 14.50 70.26 10.91
## 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033
## 1 17.49 13.34 14.38 17.12 45.26 24.00 11.47 15.30 17.73 36.01 9.87 9.38
## 2 17.07 13.68 13.76 16.87 42.31 23.01 11.28 14.82 17.97 35.79 9.62 9.16
## 3 17.07 13.65 13.76 17.03 42.40 23.75 11.67 15.04 18.11 35.57 9.74 9.33
## 4 16.71 13.11 12.94 16.26 40.28 22.10 11.30 14.43 17.49 33.37 9.62 9.11
## 5 16.65 13.19 12.99 16.09 40.46 21.44 11.47 14.43 17.25 32.71 9.77 9.07
## 6 16.49 13.11 12.94 16.05 41.77 21.28 11.50 14.40 18.11 32.31 9.81 9.03
## 3034 3035 3036 3037 3038 3040 3041 3042 3043 3044 3045 3046
## 1 62.66 61.78 11.30 34.21 11.88 11.56 51.95 33.72 24.15 63.96 41.91 67.84
## 2 59.69 60.26 11.22 33.79 11.67 11.00 50.25 32.80 23.53 63.06 41.98 65.94
## 3 60.28 61.51 11.24 33.64 11.41 10.88 50.18 32.22 23.53 61.55 41.78 65.47
## 4 61.47 59.54 10.73 33.11 11.45 10.62 47.99 30.96 22.92 62.15 40.51 64.04
## 5 61.47 60.26 10.86 33.08 11.15 10.39 48.19 32.00 22.52 62.15 40.51 63.57
## 6 62.95 59.63 10.96 33.11 11.15 10.50 49.01 32.11 22.96 66.37 41.04 63.81
## 3047 3048 3049 3050 3051 3052 3054 3055 3056 3057 3058 3059
## 1 18.79 9.66 45.39 12.57 4.35 8.79 27.62 18.03 10.28 22.44 16.30 52.49
## 2 18.79 9.23 42.55 12.27 4.65 8.65 27.34 17.74 10.02 21.46 16.00 51.01
## 3 19.02 8.94 41.13 12.15 4.96 8.94 26.78 17.81 10.19 21.38 15.84 51.84
## 4 17.72 8.58 39.15 12.36 5.30 8.65 24.90 17.44 9.96 20.79 15.62 51.01
## 5 17.52 9.18 41.13 13.20 5.60 8.67 25.00 17.26 9.70 20.98 15.43 50.85
## 6 17.28 9.01 39.86 14.12 5.99 8.32 25.47 16.89 9.77 20.95 15.81 50.27
## 3060 3062 3090 3094 3130 3149 3164 3167 3189 3209 3229 3231
## 1 34.19 37.12 11.15 31.84 52.12 <NA> 59.47 <NA> 60.27 7.66 15.56 25.95
## 2 33.58 36.29 10.88 30.85 52.12 <NA> 57.00 <NA> 58.55 7.47 15.25 26.25
## 3 33.35 36.41 10.73 30.75 51.82 <NA> 54.19 <NA> 58.48 7.47 15.02 27.68
## 4 31.74 35.47 10.61 29.86 50.33 <NA> 54.77 <NA> 56.05 7.30 14.35 27.47
## 5 33.35 35.43 10.52 30.45 50.98 <NA> 54.03 <NA> 56.41 7.37 14.62 27.56
## 6 33.80 35.81 10.59 30.26 51.22 <NA> 53.53 <NA> 58.20 7.45 14.35 27.64
## 3257 3266 3296 3305 3308 3311 3312 3321 3338 3346 3356 3376
## 1 <NA> 16.65 29.57 37.31 24.60 73.08 21.96 <NA> <NA> <NA> 53.41 102.03
## 2 <NA> 16.02 29.08 36.55 24.60 72.77 21.50 <NA> <NA> <NA> 51.90 102.03
## 3 <NA> 16.14 28.88 36.18 23.94 71.84 21.62 <NA> <NA> <NA> 52.47 100.72
## 4 <NA> 15.59 28.39 35.31 23.94 68.74 20.80 <NA> <NA> <NA> 51.15 99.42
## 5 <NA> 15.83 28.39 36.55 23.37 67.81 20.46 <NA> <NA> <NA> 51.34 100.40
## 6 <NA> 15.52 28.39 36.12 23.37 70.29 21.09 <NA> <NA> <NA> 50.96 100.40
## 3380 3383 3406 3413 3416 3419 3432 3437 3443 3450 3454 3481
## 1 18.32 31.27 51.33 <NA> 33.19 17.82 <NA> <NA> 125.61 17.60 21.95 42.51
## 2 18.19 31.09 50.21 <NA> 32.45 17.41 <NA> <NA> 122.55 16.51 21.12 42.59
## 3 18.44 33.23 49.75 <NA> 32.45 17.18 <NA> <NA> 122.93 16.73 21.12 44.43
## 4 18.44 33.27 49.38 <NA> 31.99 16.59 <NA> <NA> 122.93 16.46 20.43 43.68
## 5 18.13 33.79 48.54 <NA> 31.57 17.09 <NA> <NA> 121.78 17.60 20.63 45.36
## 6 18.22 33.72 48.45 <NA> 32.03 17.00 <NA> <NA> 122.17 18.80 20.41 46.78
## 3494 3501 3504 3515 3518 3528 3530 3532 3533 3535 3536 3545
## 1 25.03 33.38 227.56 77.09 63.08 <NA> <NA> 145.94 106.10 26.23 48.49 74.38
## 2 24.64 32.60 213.74 75.02 61.86 <NA> <NA> 145.94 104.21 25.07 46.40 71.07
## 3 25.38 33.90 213.74 75.02 61.38 <NA> <NA> 146.61 99.79 25.21 46.21 71.56
## 4 24.95 33.64 204.80 71.66 60.84 <NA> <NA> 149.81 99.79 26.97 43.56 68.54
## 5 24.84 33.79 216.99 73.73 62.33 <NA> <NA> 149.64 103.89 26.42 42.43 68.06
## 6 24.49 33.22 217.81 72.44 62.13 <NA> <NA> 147.28 104.21 26.46 43.18 68.15
## 3550 3557 3563 3576 3579 3583 3588 3591 3593 3596 3605 3607
## 1 <NA> 86.41 <NA> 59.27 67.89 <NA> 118.66 <NA> 67.70 48.63 69.06 52.79
## 2 <NA> 84.08 <NA> 58.13 65.77 <NA> 119.85 <NA> 66.79 46.95 66.36 51.35
## 3 <NA> 84.51 <NA> 59.81 65.34 <NA> 118.37 <NA> 66.56 46.95 66.36 50.72
## 4 <NA> 81.97 <NA> 58.13 62.80 <NA> 115.41 <NA> 66.64 45.61 65.28 47.95
## 5 <NA> 83.24 <NA> 56.60 64.07 <NA> 116.29 <NA> 65.95 45.39 65.01 47.89
## 6 <NA> 88.95 <NA> 55.45 62.12 <NA> 115.41 <NA> 65.35 45.83 65.28 47.43
## 3617 3622 3645 3653 3661 3665 3669 3673 3679 3682 3686 3694 3698
## 1 84.73 294.28 <NA> 101.70 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 81.87 302.28 <NA> 98.89 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 77.09 301.86 <NA> 99.45 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 71.99 296.81 <NA> 94.96 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 73.58 296.81 <NA> 94.11 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 71.99 299.76 <NA> 91.30 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3701 3702 3703 3704 3705 3706 3708 3711 3712 4104 4106 4108 4119
## 1 26.96 26.73 <NA> <NA> <NA> <NA> <NA> <NA> <NA> 49.20 22.51 42.08 37.00
## 2 28.81 26.44 <NA> <NA> <NA> <NA> <NA> <NA> <NA> 48.13 22.35 41.64 37.10
## 3 30.79 26.39 <NA> <NA> <NA> <NA> <NA> <NA> <NA> 47.88 22.35 41.54 37.10
## 4 32.89 26.19 <NA> <NA> <NA> <NA> <NA> <NA> <NA> 45.17 21.95 41.06 36.03
## 5 35.16 26.00 <NA> <NA> <NA> <NA> <NA> <NA> <NA> 44.92 22.25 40.86 36.54
## 6 35.34 26.29 <NA> <NA> <NA> <NA> <NA> <NA> <NA> 45.36 22.31 40.91 36.18
## 4133 4137 4141 4142 4144 4148 4155 4164 4190 4306 4414 4426 4438 4526
## 1 84.46 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.98 8.01 4.42 <NA> 17.82
## 2 82.55 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.91 8.01 4.44 <NA> 18.18
## 3 78.75 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 11.03 8.08 4.46 <NA> 17.92
## 4 73.80 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 11.16 8.05 4.42 <NA> 17.72
## 5 72.28 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 11.32 8.08 4.58 <NA> 17.72
## 6 77.23 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 11.23 8.17 4.65 <NA> 17.72
## 4532 4536 4540 4545 4551 4552 4555 4557 4560 4562 4566 4720 4722 4725
## 1 6.71 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.72 9.71 28.36
## 2 6.59 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.53 9.60 27.95
## 3 6.68 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.40 9.71 28.81
## 4 6.56 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.24 9.39 27.66
## 5 6.65 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.37 9.42 27.58
## 6 6.79 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 10.46 9.39 27.33
## 4737 4739 4746 4755 4763 4764 4766 4807 4904 4906 4912 4915 4916 4919
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 24.13 38.17 <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 24.07 38.50 <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 24.07 38.37 <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 23.59 37.12 <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 23.75 36.86 <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 24.23 36.79 <NA> <NA> <NA> <NA>
## 4927 4930 4934 4935 4938 4942 4943 4952 4956 4958 4960 4961 4968 4976
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4977 4989 4994 4999 5007 5203 5215 5225 5234 5243 5258 5259 5264 5269
## 1 <NA> <NA> <NA> <NA> 14.17 85.08 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> 14.07 85.08 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> 14.07 86.67 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> 14.20 85.72 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> 14.25 87.30 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> 15.10 90.78 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5284 5285 5288 5305 5388 5434 5469 5471 5484 5515 5519 5521
## 1 <NA> <NA> <NA> 21.60 17.20 22.00 21.11 46.85 22.70 10.17 5.88 9.74
## 2 <NA> <NA> <NA> 21.05 16.50 21.47 21.05 46.03 22.35 9.91 5.88 10.14
## 3 <NA> <NA> <NA> 20.86 16.22 21.62 21.01 46.47 22.61 9.96 5.88 10.02
## 4 <NA> <NA> <NA> 20.82 15.81 21.22 20.34 45.09 22.10 9.66 5.82 9.74
## 5 <NA> <NA> <NA> 20.45 16.09 21.17 20.21 44.10 21.67 9.96 5.97 10.38
## 6 <NA> <NA> <NA> 20.45 16.19 21.88 20.31 44.21 23.17 10.12 6.38 10.75
## 5522 5525 5531 5533 5534 5538 5607 5608 5706 5871 5876 5880 5906
## 1 35.32 10.29 14.77 12.84 30.72 <NA> 34.19 44.92 25.31 <NA> <NA> <NA> <NA>
## 2 36.07 10.42 15.13 12.70 31.87 <NA> 36.56 45.61 25.51 <NA> <NA> <NA> <NA>
## 3 36.02 10.42 14.79 12.72 31.74 <NA> 36.17 45.76 25.55 <NA> <NA> <NA> <NA>
## 4 35.46 10.32 14.68 12.51 31.56 <NA> 35.48 44.68 24.97 <NA> <NA> <NA> <NA>
## 5 36.58 10.59 14.83 12.80 31.82 <NA> 37.40 44.92 25.04 <NA> <NA> <NA> <NA>
## 6 35.60 11.32 14.66 12.87 31.03 <NA> 37.75 45.22 25.28 <NA> <NA> <NA> <NA>
## 5907 6005 6024 6108 6112 6115 6116 6117 6120 6128 6131 6133
## 1 <NA> 12.54 15.85 25.07 9.46 29.75 13.66 17.33 34.23 26.28 17.91 12.24
## 2 <NA> 12.57 15.61 24.26 9.11 29.99 13.34 18.53 33.11 25.84 17.62 12.03
## 3 <NA> 12.76 15.53 24.23 9.13 30.28 13.48 19.81 32.96 25.40 17.71 11.90
## 4 <NA> 12.38 15.44 23.60 8.82 29.37 13.02 21.18 31.60 24.82 17.30 11.77
## 5 <NA> 12.48 15.36 23.60 8.75 29.56 13.02 20.15 31.53 24.87 17.05 11.56
## 6 <NA> 12.41 15.24 24.32 8.53 29.85 13.04 20.02 31.19 24.67 17.05 11.52
## 6136 6139 6141 6142 6152 6153 6155 6164 6165 6166 6168 6172
## 1 13.85 24.77 19.36 12.58 24.68 18.95 22.72 19.55 25.58 14.15 25.22 17.84
## 2 13.26 23.77 18.49 12.68 24.05 18.48 21.61 19.00 25.11 14.10 24.46 19.08
## 3 13.64 23.38 18.59 12.71 24.14 18.88 21.79 18.94 25.30 14.30 25.72 20.41
## 4 13.49 23.74 18.17 12.40 23.33 17.93 21.14 18.23 24.00 14.08 24.25 21.82
## 5 13.37 23.20 18.38 12.23 23.36 17.86 20.99 18.29 24.00 14.17 23.71 20.32
## 6 13.26 23.56 18.31 12.27 23.45 17.79 21.05 18.52 24.56 14.12 23.71 18.91
## 6176 6177 6183 6184 6189 6191 6192 6196 6197 6201 6202 6205
## 1 23.70 6.34 13.38 20.82 11.84 16.46 35.74 12.38 72.33 15.09 24.28 21.02
## 2 23.88 6.10 13.45 20.91 11.51 15.86 35.05 11.90 71.41 14.99 23.44 20.26
## 3 23.98 6.14 13.48 21.07 11.36 15.96 34.99 11.80 76.40 16.03 23.52 20.45
## 4 23.02 6.13 13.25 20.93 11.08 15.30 34.76 11.06 80.32 15.20 22.39 20.80
## 5 23.32 6.29 13.58 20.95 11.32 15.70 34.93 10.94 84.93 15.17 22.93 20.32
## 6 23.27 6.21 13.68 21.00 11.41 15.70 34.47 11.22 90.70 15.30 23.04 20.36
## 6206 6209 6213 6214 6215 6216 6224 6225 6226 6230 6235 6239
## 1 33.91 20.86 16.48 28.20 23.95 13.39 35.73 101.74 19.42 35.15 19.99 65.98
## 2 33.75 19.89 16.43 27.28 23.51 14.33 34.53 95.11 18.61 33.62 19.54 65.37
## 3 33.75 19.93 16.48 26.84 23.29 13.95 34.53 94.20 18.50 32.83 19.63 64.77
## 4 33.44 18.67 16.09 26.02 22.85 13.37 32.99 91.91 17.91 32.21 19.19 65.98
## 5 33.44 19.09 15.98 26.29 22.70 13.54 33.90 90.31 17.58 32.34 19.28 65.98
## 6 33.44 18.84 16.25 26.46 22.66 13.49 33.67 90.77 17.69 32.70 19.10 65.98
## 6243 6251 6257 6269 6271 6277 6278 6281 6282 6283 6285 6288
## 1 63.16 30.46 11.08 26.49 68.15 38.59 38.41 30.11 17.13 29.36 27.61 <NA>
## 2 61.76 29.54 10.76 26.09 67.52 37.85 38.04 29.90 16.60 28.45 27.66 <NA>
## 3 63.16 29.98 10.81 25.84 71.98 37.96 37.86 29.58 16.49 28.28 27.35 <NA>
## 4 61.94 28.98 10.73 25.05 73.89 36.94 36.84 30.04 15.84 27.53 26.53 <NA>
## 5 62.37 29.42 10.89 25.35 71.34 37.45 36.56 30.04 16.54 27.70 26.22 <NA>
## 6 61.76 29.90 10.81 26.59 76.12 37.51 37.26 30.35 16.31 27.95 25.82 <NA>
## 6289 6405 6409 6412 6414 6415 6416 6431 6442 6443 6449 6451 6452 6456
## 1 16.79 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 16.25 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 16.32 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 15.85 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 16.19 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 15.99 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6464 6477 6504 6505 6525 6531 6533 6541 6552 6558 6573 6579 6581 6582
## 1 <NA> <NA> <NA> 60.51 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> 60.95 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> 62.27 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> 62.78 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> 62.93 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> 63.52 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6591 6605 6625 6641 6655 6666 6668 6670 6671 6674 8011 8016 8021 8028
## 1 <NA> 60.61 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 39.44 24.07 <NA>
## 2 <NA> 60.90 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 39.56 23.17 <NA>
## 3 <NA> 60.82 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 39.26 23.55 <NA>
## 4 <NA> 61.11 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 39.26 23.38 <NA>
## 5 <NA> 61.11 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 38.82 23.17 <NA>
## 6 <NA> 60.68 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 39.07 22.96 <NA>
## 8033 8039 8046 8070 8072 8081 8101 8103 8105 8110 8112
## 1 17.87 32.48 97.03 104.77 63.96 96.63 29.78 22.57 20.66 12.99 15.58
## 2 17.33 31.05 102.94 101.00 62.20 92.89 28.83 22.23 19.93 12.95 14.64
## 3 17.55 31.16 105.70 102.26 65.13 92.63 28.64 21.83 20.03 13.83 14.72
## 4 17.11 31.11 103.73 103.51 63.67 90.76 28.74 21.10 19.69 13.31 14.05
## 5 17.00 30.44 107.28 103.51 62.79 91.83 28.64 21.91 19.78 13.87 14.21
## 6 17.33 30.61 111.23 102.26 62.49 92.36 29.02 22.12 19.93 14.03 14.43
## 8114 8131 8150 8163 8201 8210 8213 8215 8222 8249 8261 8271 8341
## 1 27.85 27.26 <NA> 33.77 30.48 14.25 38.50 <NA> <NA> 14.06 50.69 <NA> <NA>
## 2 27.66 27.90 <NA> 33.11 29.73 13.57 36.61 <NA> <NA> 13.61 48.55 <NA> <NA>
## 3 28.24 29.83 <NA> 33.18 29.73 13.75 37.30 <NA> <NA> 13.61 48.92 <NA> <NA>
## 4 27.66 30.13 <NA> 32.35 28.90 13.16 35.88 <NA> <NA> 13.07 47.69 <NA> <NA>
## 5 26.95 30.07 <NA> 33.57 29.21 13.18 35.88 <NA> <NA> 13.10 49.28 <NA> <NA>
## 6 26.76 30.07 <NA> 33.11 29.33 13.44 35.93 <NA> <NA> 13.24 48.37 <NA> <NA>
## 8367 8374 8404 8411 8422 8427 8429 8442 8443 8454 8462 8463 8464 8466
## 1 <NA> 20.69 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> 20.54 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> 20.63 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> 20.13 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> 20.26 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> 20.26 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8467 8473 8478 8480 8481 8482 8488 8497 8499 8926 8940 8996 9802 9902
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 9.45 19.92 35.17 <NA> 12.04
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 9.48 19.92 33.30 <NA> 12.43
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 9.51 20.01 34.23 <NA> 12.23
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 9.43 19.76 32.42 <NA> 12.53
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 9.40 19.76 32.42 <NA> 12.33
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 9.48 20.50 32.92 <NA> 12.43
## 9904 9905 9906 9907 9908 9910 9911 9912 9914 9917 9918 9919
## 1 16.59 13.82 2.79 9.16 11.41 14.74 12.37 5.68 32.01 35.19 17.34 17.74
## 2 17.11 13.73 2.69 9.13 11.45 14.74 12.18 5.69 31.82 34.68 17.34 18.06
## 3 17.02 13.60 2.67 9.24 11.55 14.82 12.18 5.69 31.89 34.55 17.34 18.06
## 4 16.88 13.30 2.67 9.02 11.61 14.79 12.18 5.89 32.01 34.68 17.48 17.86
## 5 17.08 13.36 2.49 9.16 11.78 14.84 12.26 5.76 32.01 35.00 17.58 17.82
## 6 17.08 13.48 2.36 9.34 11.78 14.72 12.55 6.16 31.64 35.06 17.64 18.02
## 9921 9924 9925 9926 9927 9928 9929 9930 9931 9933 9934 9935
## 1 66.85 11.74 14.19 14.11 24.41 31.55 7.36 23.52 11.44 20.21 19.76 17.51
## 2 66.03 11.54 14.03 14.11 24.04 31.38 7.88 23.69 11.44 20.21 19.53 17.74
## 3 66.77 11.60 14.31 14.13 24.76 31.22 8.09 23.80 11.32 20.21 19.69 17.44
## 4 66.77 11.45 14.28 13.97 24.84 30.57 7.72 23.58 10.94 20.12 19.85 16.24
## 5 66.03 11.65 14.22 14.16 24.89 30.65 7.57 23.58 11.44 21.23 19.85 15.94
## 6 66.48 11.57 14.00 14.21 25.07 30.89 7.91 23.52 11.25 21.02 19.92 15.56
## 9937 9938 9939 9940 9941 9942 9943 9944 9945 9946 9955 9958
## 1 22.98 17.42 34.02 18.23 23.29 20.65 21.39 21.21 35.00 6.55 54.95 9.12
## 2 22.98 17.42 33.90 18.55 22.84 20.74 20.78 20.77 35.04 6.52 54.26 8.73
## 3 22.71 17.51 34.79 18.26 22.84 20.78 20.88 20.85 35.44 6.45 55.04 8.94
## 4 22.59 17.22 35.24 17.74 22.40 20.65 20.45 21.17 34.83 6.45 54.09 8.84
## 5 22.32 17.22 34.98 17.74 22.59 20.53 20.35 21.45 34.79 6.43 54.00 8.98
## 6 22.38 17.09 34.41 18.63 22.65 20.53 20.48 21.29 35.20 6.62 54.43 9.27
## 證券代碼
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>將dayprice.reorder存檔為rds檔。
write_rds(dayprice.reorder, "dayprice.reorder.rds")