該賞金到期in 4天。這個問題的答案有資格獲得 50聲望獎勵。 Emman想引起更多人對這個問題的關注：
我正在為該問題尋找快速有效的解決方案。雖然@JonSpring 的答案適用于示例資料，但在應用我的真實資料時會導致錯誤。具體來說，tidyr::count()導致超越的錯誤.Machine$integer.max == 2147483647。所以我會很高興采用不同的策略來規避這個問題。
我正在嘗試按組匯總資料集，以便為每個組的值是否出現在資料未分組的最頻繁值中設定虛擬列。
例如，讓我們flights從nycflights13.
library(dplyr, warn.conflicts = FALSE)
library(nycflights13)
my_flights_raw <-
flights %>%
select(carrier, month, dest)
my_flights_raw
#> # A tibble: 336,776 x 3
#> carrier month dest
#> <chr> <int> <chr>
#> 1 UA 1 IAH
#> 2 UA 1 IAH
#> 3 AA 1 MIA
#> 4 B6 1 BQN
#> 5 DL 1 ATL
#> 6 UA 1 ORD
#> 7 B6 1 FLL
#> 8 EV 1 IAD
#> 9 B6 1 MCO
#> 10 AA 1 ORD
#> # ... with 336,766 more rows
我的最終目標：我有興趣了解每carrier一個month：它是否飛往最受歡迎的目的地。我通過每個月前 5 名最頻繁的值定義“最受歡迎”dest，然后與所有月份的前 5 名相交。
第 1 步
我首先按月進行簡單聚合：
my_flights_agg <-
my_flights_raw %>%
count(month, dest, name = "n_obs") %>%
arrange(month, desc(n_obs))
my_flights_agg
#> # A tibble: 1,113 x 3
#> month dest n_obs
#> <int> <chr> <int>
#> 1 1 ATL 1396
#> 2 1 ORD 1269
#> 3 1 BOS 1245
#> 4 1 MCO 1175
#> 5 1 FLL 1161
#> 6 1 LAX 1159
#> 7 1 CLT 1058
#> 8 1 MIA 981
#> 9 1 SFO 889
#> 10 1 DCA 865
#> # ... with 1,103 more rows
第 2 步
現在我將削減資料以僅保留每月最受歡迎的前 5 名。
my_flights_top_5_by_month <-
my_flights_agg %>%
group_by(month) %>%
slice_max(order_by = n_obs, n = 5)
my_flights_top_5_by_month
#> # A tibble: 60 x 3
#> # Groups: month [12]
#> month dest n_obs
#> <int> <chr> <int>
#> 1 1 ATL 1396
#> 2 1 ORD 1269
#> 3 1 BOS 1245
#> 4 1 MCO 1175
#> 5 1 FLL 1161
#> 6 2 ATL 1267
#> 7 2 ORD 1197
#> 8 2 BOS 1182
#> 9 2 MCO 1110
#> 10 2 FLL 1073
#> # ... with 50 more rows
第 3 步
現在只需獲取以下unique()內容my_flights_top_5_by_month$dest：
my_flights_top_dest_across_months <- unique(my_flights_top_5_by_month$dest)
## [1] "ATL" "ORD" "BOS" "MCO" "FLL" "LAX" "SFO" "CLT"
這是我的問題：給定my_flights_top_dest_across_months，我如何總結my_flights_raw為不同的carrier& month，這樣折疊原則是carrier& 的每個組合是否month對 中的每個dest值都有缺陷my_flights_top_dest_across_months？
期望的輸出
## carrier month ATL ORD BOS MCO FLL LAX SFO CLT
## <chr> <int> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
## 1 9E 1 TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 2 9E 2 TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 3 9E 3 TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 4 9E 4 FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 5 9E 5 TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 6 9E 6 FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 7 9E 7 FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 8 9E 8 FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 9 9E 9 FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## 10 9E 10 FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## # ... with 175 more rows
我目前有以下代碼效率低下。它適用于示例flights資料，但在應用于大型資料集（具有數百萬行和組）時需要永遠。知道如何更有效地完成上述任務嗎？
# too slow :(
my_flights_raw %>%
group_by(carrier, month) %>%
summarise(destinations_vec = list(unique(dest))) %>%
add_column(top_dest = list(my_flights_top_dest_across_month)) %>%
mutate(are_top_dest_included = purrr::map2(.x = destinations_vec, .y = top_dest, .f = ~ .y %in% .x ), .keep = "unused") %>%
mutate(across(are_top_dest_included, ~purrr::map(.x = ., .f = ~setNames(object = .x, nm = my_flights_top_dest_across_month)) )) %>%
tidyr::unnest_wider(are_top_dest_included)
uj5u.com熱心網友回復：
很可能data.table在這里使用庫會更快。我不會爭論。但是我已經掌握dplyr并希望使用這個特定庫中的函式提供一個非常酷的解決方案。
首先我們準備兩個小輔助函式。我們稍后會看到它們是如何作業的。
library(nycflights13)
library(tidyverse)
ftopDest = function(data, ntop){
data %>%
group_by(dest) %>%
summarise(ndest = n()) %>%
arrange(desc(ndest)) %>%
pull(dest) %>% .[1:ntop]
}
carrierToTopDest = function(data, topDest){
data %>% mutate(carrierToToDest = dest %in% topDest)
}
現在你只需要一個簡單的突變！
df = flights %>% nest_by(year, month) %>% #Step 1
mutate(topDest = list(ftopDest(data, 5)), #Step 2
data = list(carrierToTopDest(data, topDest))) #Step 3
但讓我逐步描述這里發生的事情。
在第一步中，讓我們將資料嵌套到一個tibble名為data.
步驟 1 后的輸出
# A tibble: 12 x 3
# Rowwise: year, month
year month data
<int> <int> <list<tibble[,17]>>
1 2013 1 [27,004 x 17]
2 2013 2 [24,951 x 17]
3 2013 3 [28,834 x 17]
4 2013 4 [28,330 x 17]
5 2013 5 [28,796 x 17]
6 2013 6 [28,243 x 17]
7 2013 7 [29,425 x 17]
8 2013 8 [29,327 x 17]
9 2013 9 [27,574 x 17]
10 2013 10 [28,889 x 17]
11 2013 11 [27,268 x 17]
12 2013 12 [28,135 x 17]
在第 2 步中，我們添加最受歡迎的航班目的地。
步驟 2 后的輸出
# A tibble: 12 x 4
# Rowwise: year, month
year month data topDest
<int> <int> <list<tibble[,17]>> <list>
1 2013 1 [27,004 x 17] <chr [5]>
2 2013 2 [24,951 x 17] <chr [5]>
3 2013 3 [28,834 x 17] <chr [5]>
4 2013 4 [28,330 x 17] <chr [5]>
5 2013 5 [28,796 x 17] <chr [5]>
6 2013 6 [28,243 x 17] <chr [5]>
7 2013 7 [29,425 x 17] <chr [5]>
8 2013 8 [29,327 x 17] <chr [5]>
9 2013 9 [27,574 x 17] <chr [5]>
10 2013 10 [28,889 x 17] <chr [5]>
11 2013 11 [27,268 x 17] <chr [5]>
12 2013 12 [28,135 x 17] <chr [5]>
In the last step, we add the carrierToToDest variable to the data variable, which determines whether the flight was going to one of the ntop places from the given month.
Output after step 3
# A tibble: 12 x 4
# Rowwise: year, month
year month data topDest
<int> <int> <list> <list>
1 2013 1 <tibble [27,004 x 18]> <chr [5]>
2 2013 2 <tibble [24,951 x 18]> <chr [5]>
3 2013 3 <tibble [28,834 x 18]> <chr [5]>
4 2013 4 <tibble [28,330 x 18]> <chr [5]>
5 2013 5 <tibble [28,796 x 18]> <chr [5]>
6 2013 6 <tibble [28,243 x 18]> <chr [5]>
7 2013 7 <tibble [29,425 x 18]> <chr [5]>
8 2013 8 <tibble [29,327 x 18]> <chr [5]>
9 2013 9 <tibble [27,574 x 18]> <chr [5]>
10 2013 10 <tibble [28,889 x 18]> <chr [5]>
11 2013 11 <tibble [27,268 x 18]> <chr [5]>
12 2013 12 <tibble [28,135 x 18]> <chr [5]>
How now we can see the most popular places. Let's do this:
df %>% mutate(topDest = paste(topDest, collapse = " "))
output
# A tibble: 12 x 4
# Rowwise: year, month
year month data topDest
<int> <int> <list> <chr>
1 2013 1 <tibble [27,004 x 18]> ATL ORD BOS MCO FLL
2 2013 2 <tibble [24,951 x 18]> ATL ORD BOS MCO FLL
3 2013 3 <tibble [28,834 x 18]> ATL ORD BOS MCO FLL
4 2013 4 <tibble [28,330 x 18]> ATL ORD LAX BOS MCO
5 2013 5 <tibble [28,796 x 18]> ORD ATL LAX BOS SFO
6 2013 6 <tibble [28,243 x 18]> ORD ATL LAX BOS SFO
7 2013 7 <tibble [29,425 x 18]> ORD ATL LAX BOS CLT
8 2013 8 <tibble [29,327 x 18]> ORD ATL LAX BOS SFO
9 2013 9 <tibble [27,574 x 18]> ORD LAX ATL BOS CLT
10 2013 10 <tibble [28,889 x 18]> ORD ATL LAX BOS CLT
11 2013 11 <tibble [27,268 x 18]> ATL ORD LAX BOS CLT
12 2013 12 <tibble [28,135 x 18]> ATL LAX MCO ORD CLT
Can we see flights to these destinations? Of course, it's not difficult.
df %>% select(-topDest) %>%
unnest(data) %>%
filter(carrierToToDest) %>%
select(year, month, flight, carrier, dest)
Output
# A tibble: 80,941 x 5
# Groups: year, month [12]
year month flight carrier dest
<int> <int> <int> <chr> <chr>
1 2013 1 461 DL ATL
2 2013 1 1696 UA ORD
3 2013 1 507 B6 FLL
4 2013 1 79 B6 MCO
5 2013 1 301 AA ORD
6 2013 1 1806 B6 BOS
7 2013 1 371 B6 FLL
8 2013 1 4650 MQ ATL
9 2013 1 1743 DL ATL
10 2013 1 3768 MQ ORD
# ... with 80,931 more rows
This is my recipe. Very simple and transparent in my opinion. I would be extremely obligated if you would try it on your data and let me know with efficiency.
Small update
I just noticed that I wanted to group not only after year (although you don't mention it, it must be so), month, but also by the carrier variable. So let's add it as another grouping variable.
df = flights %>% nest_by(year, month, carrier) %>%
mutate(topDest = list(ftopDest(data, 5)),
data = list(carrierToTopDest(data, topDest)))
output
# A tibble: 185 x 5
# Rowwise: year, month, carrier
year month carrier data topDest
<int> <int> <chr> <list> <list>
1 2013 1 9E <tibble [1,573 x 17]> <chr [5]>
2 2013 1 AA <tibble [2,794 x 17]> <chr [5]>
3 2013 1 AS <tibble [62 x 17]> <chr [5]>
4 2013 1 B6 <tibble [4,427 x 17]> <chr [5]>
5 2013 1 DL <tibble [3,690 x 17]> <chr [5]>
6 2013 1 EV <tibble [4,171 x 17]> <chr [5]>
7 2013 1 F9 <tibble [59 x 17]> <chr [5]>
8 2013 1 FL <tibble [328 x 17]> <chr [5]>
9 2013 1 HA <tibble [31 x 17]> <chr [5]>
10 2013 1 MQ <tibble [2,271 x 17]> <chr [5]>
# ... with 175 more rows
Now let's get acquainted with the new top 5 directions.
df %>% mutate(topDest = paste(topDest, collapse = " "))
output
# A tibble: 185 x 5
# Rowwise: year, month, carrier
year month carrier data topDest
<int> <int> <chr> <list> <chr>
1 2013 1 9E <tibble [1,573 x 17]> BOS PHL CVG MSP ORD
2 2013 1 AA <tibble [2,794 x 17]> DFW MIA ORD LAX BOS
3 2013 1 AS <tibble [62 x 17]> SEA NA NA NA NA
4 2013 1 B6 <tibble [4,427 x 17]> FLL MCO BOS PBI SJU
5 2013 1 DL <tibble [3,690 x 17]> ATL DTW MCO FLL MIA
6 2013 1 EV <tibble [4,171 x 17]> IAD DTW DCA RDU CVG
7 2013 1 F9 <tibble [59 x 17]> DEN NA NA NA NA
8 2013 1 FL <tibble [328 x 17]> ATL CAK MKE NA NA
9 2013 1 HA <tibble [31 x 17]> HNL NA NA NA NA
10 2013 1 MQ <tibble [2,271 x 17]> RDU CMH ORD BNA ATL
# ... with 175 more rows
總結一下，我想補充一點，表格對我來說非常清楚。我可以看到最受歡迎的df%>% mutate (topDest = paste (topDest, collapse =" "))方向。我可以過濾所有飛往最受歡迎目的地的航班，df%>% select (-topDest)%>% unnest (data)%>% filter (carrierToToDest)%>% select (year, month, flight, carrier, dest)并進行任何其他轉換。我認為在 100 多個變數上更廣泛地呈現相同的資訊對任何分析都很方便。
但是，如果您確實需要更廣泛的形式，請告訴我。我們會這樣做。
uj5u.com熱心網友回復：
這是你想要的嗎？據我所知，它與您的輸出匹配，但有更多行，因為它包括所有運營商的所有月份；carrier“OO”只有 5 個月內的航班，您的版本僅在摘要中顯示這 5 個月。
使用提供的資料（336k 行），這與您的函式花費的時間相似，但處理更大的資料時速度更快。當我在設定后對 100 倍大的資料運行這些時my_flights_raw <- my_flights_raw %>% tidyr::uncount(100)，使其達到 33M 行，下面的代碼快了大約 40%。
鑒于您要處理的群體數量眾多，我預計這種情況data.table會真正發揮出更好的性能。
library(tidyverse)
my_flights_raw %>%
count(carrier, month, dest) %>%
complete(carrier, month, dest) %>%
filter(dest %in% my_flights_top_dest_across_months) %>%
mutate(n = if_else(!is.na(n), TRUE, FALSE)) %>%
pivot_wider(names_from = dest, values_from = n)
uj5u.com熱心網友回復：
鑒于my_flights_top_5_by_month和my_flights_raw，我們可以嘗試以下data.table方法
library(data.table)
dcast(
setDT(my_flights_top_5_by_month)[
setDT(my_flights_raw),
on = .(month, dest)
][, n_obs := !is.na(n_obs)],
carrier month ~ dest,
fun.aggregate = function(x) sum(x) > 0,
value.var = "n_obs"
)
這使
carrier month ABQ ACK ALB ANC ATL AUS AVL BDL BGR BHM
1: 9E 1 FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
2: 9E 2 FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
3: 9E 3 FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
4: 9E 4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: 9E 5 FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
---
181: YV 8 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: YV 9 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: YV 10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: YV 11 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: YV 12 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
BNA BOS BQN BTV BUF BUR BWI BZN CAE CAK CHO CHS
1: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
CLE CLT CMH CRW CVG DAY DCA DEN DFW DSM DTW EGE
1: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
EYW FLL GRR GSO GSP HDN HNL HOU IAD IAH ILM IND
1: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
JAC JAX LAS LAX LEX LGA LGB MCI MCO MDW MEM MHT
1: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
MIA MKE MSN MSP MSY MTJ MVY MYR OAK OKC OMA ORD
1: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
2: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
3: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
4: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
5: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
ORF PBI PDX PHL PHX PIT PSE PSP PVD PWM RDU RIC
1: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
ROC RSW SAN SAT SAV SBN SDF SEA SFO SJC SJU SLC
1: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
SMF SNA SRQ STL STT SYR TPA TUL TVC TYS XNA
1: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
---
181: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
182: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
183: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
184: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
185: FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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