重塑数据帧形状并在R中连接列

我试图将数据帧的格式从宽改为长。此时，数据帧如下所示：

structure(list(study_site = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 
5L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 1L, 2L, 3L, 1L, 4L, 1L, 
4L, 3L, 3L, 3L, 1L, 3L, 5L, 4L, 4L, 4L, 3L, 3L, 5L, 5L, 4L, 4L, 
4L, 1L, 4L, 3L, 5L, 5L, 5L, 1L, 3L, 5L, 3L, 3L, 3L, 5L, 5L, 3L, 
4L, 2L), .Label = c("N", "no_nest", "O", "S", "W"), class = "factor"), 
    coords.N = structure(c(54L, 54L, 40L, 40L, 40L, 40L, 39L, 
    67L, 67L, 55L, 55L, 64L, 64L, 64L, 78L, 81L, 47L, 80L, 83L, 
    60L, 46L, 46L, 76L, 88L, 88L, 88L, 84L, 84L, 30L, 58L, 58L, 
    58L, 25L, 25L, 19L, 19L, 42L, 42L, 42L, 29L, 45L, 90L, 91L, 
    91L, 91L, 91L, 89L, 89L, 87L, 87L, 87L, 56L, 56L, 61L, 35L, 
    36L), .Label = c("40.40463", "48.40168", "48.40178", "48.40215", 
    "48.40235", "48.40309", "48.40390", "48.40393", "48.40396", 
    "48.40405", "48.40410", "48.40411", "48.40415", "48.40416", 
    "48.40424", "48.40425", "48.40430", "48.40435", "48.40436 ", 
    "48.40438", "48.40443", "48.40450", "48.40451", "48.40454", 
    "48.40455", "48.40459", "48.40460", "48.40461", "48.40466", 
    "48.40466 ", "48.40467", "48.40469", "48.40471", "48.40477", 
    "48.40479 ", "48.40481", "48.40482", "48.40483", "48.40488 ", 
    "48.40491", "48.40493", "48.40504 ", "48.40508", "48.40513", 
    "48.40515", "48.40519 ", "48.40522 ", "48.40523", "48.40525", 
    "48.40526", "48.40529", "48.40532", "48.40537", "48.40537 ", 
    "48.40538 ", "48.40543 ", "48.40549", "48.40549 ", "48.40557", 
    "48.40557 ", "48.40558", "48.40565", "48.40571", "48.40575", 
    "48.40580", "48.40584", "48.40586 ", "48.40591", "48.40596", 
    "48.40598", "48.40599", "48.40611", "48.40612", "48.40617", 
    "48.40626", "48.40632 ", "48.40633", "48.40635 ", "48.40636", 
    "48.40637", "48.40638 ", "48.40639", "48.40639 ", "48.40641 ", 
    "48.40652", "48.40655", "48.40656 ", "48.40657 ", "48.40687 ", 
    "48.40690 ", "48.40703", "48.40718", "48.40719", "48.40726", 
    "48.40742", "48.40748", "NO_DATA"), class = "factor"), coords.E = structure(c(67L, 
    67L, 49L, 49L, 49L, 49L, 27L, 67L, 67L, 70L, 70L, 68L, 68L, 
    68L, 87L, 94L, 68L, 83L, 90L, 73L, 52L, 52L, 2L, 95L, 95L, 
    95L, 93L, 93L, 32L, 69L, 69L, 69L, 55L, 55L, 24L, 24L, 29L, 
    29L, 29L, 30L, 48L, 85L, 1L, 1L, 1L, 1L, 78L, 78L, 79L, 79L, 
    79L, 64L, 64L, 63L, 66L, 45L), .Label = c(" 015.82024", " 015.82164", 
    "015.80237", "015.80263", "015.80309", "015.80341", "015.80369", 
    "015.80388", "015.80394", "015.80399", "015.80406", "015.80435", 
    "015.80436", "015.80466", "015.80512", "015.80517", "015.80548", 
    "015.80551", "015.80572", "015.80583", "015.80609", "015.80636", 
    "015.80659", "015.80703", "015.80723", "015.80779", "015.80795", 
    "015.80803", "015.80821", "015.80843", "015.80871", "015.80875", 
    "015.80888", "015.80897", "015.80901", "015.80903", "015.80905", 
    "015.80906", "015.80908", "015.80909", "015.80921", "015.80923", 
    "015.80929", "015.80939", "015.80993", "015.81007", "015.81018", 
    "015.81087", "015.81113", "015.81132", "015.81151", "015.81180", 
    "015.81241", "015.81273", "015.81305", "015.81406", "015.81422", 
    "015.81522", "015.81526", "015.81543", "015.81546", "015.81564", 
    "015.81628", "015.81632", "015.81678", "015.81682", "015.81700", 
    "015.81703", "015.81735", "015.81739", "015.81770", "015.81783", 
    "015.81784", "015.81800", "015.81849", "015.81992", "015.82012", 
    "015.82029", "015.82039", "015.82083", "015.82099", "015.82126", 
    "015.82180", "015.82230", "015.82232", "015.82255", "015.82265", 
    "015.82290", "015.82303", "015.82304", "015.82346", "015.82362", 
    "015.82376", "015.82398", "015.82451", "015.82500", "015.82519", 
    "015.82555", "015.82579", "015.82634", "NO_DATA"), class = "factor"), 
    study_ID = c(120L, 120L, 1L, 1L, 1L, 1L, 9L, 39L, 39L, 109L, 
    109L, 110L, 110L, 110L, 45L, 58L, 121L, 96L, 97L, 40L, 43L, 
    43L, 47L, 57L, 57L, 57L, 114L, 114L, 67L, 71L, 71L, 71L, 
    83L, 83L, 4L, 4L, 10L, 10L, 10L, 106L, 108L, 46L, 115L, 115L, 
    115L, 115L, 116L, 116L, 117L, 117L, 117L, 70L, 70L, 119L, 
    95L, 3L), species = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L), .Label = c("barn swallow", "no_nest"), class = "factor"), 
    first_visit = c(1L, 2L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 5L, 0L, 
    1L, 0L, 2L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 
    1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 4L, 
    1L, 8L, 0L, 0L, 1L, 2L, 1L, 5L, 0L, 0L, 1L, 0L, 1L, 1L, 0L
    ), second_visit = c(1L, 2L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 5L, 
    0L, 1L, 0L, 2L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
    0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 4L, 0L, 0L, 
    4L, 1L, 0L, 8L, 0L, 1L, 2L, 1L, 0L, 5L, 0L, 1L, 0L, 0L, 1L, 
    0L), third_visit = c(0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 
    0L, 5L, 0L, 1L, 2L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 2L, 0L, 1L, 0L, 2L, 0L, 0L, 6L, 
    1L, 4L, 1L, 0L, 0L, 8L, 1L, 2L, 1L, 0L, 0L, 5L, 1L, 1L, 0L, 
    0L, 0L), used_1st_visit = c(0L, 2L, 1L, 0L, 0L, 0L, 1L, 0L, 
    1L, 2L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
    0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 3L, 0L, 
    0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L), used_2nd_visit = c(0L, 2L, 1L, 0L, 0L, 0L, 1L, 
    0L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 
    1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 
    4L, 0L, 0L, 0L, 1L, 0L, 5L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L), used_3rd_visit = c(0L, 0L, 1L, 0L, 0L, 0L, 
    1L, 0L, 1L, 0L, 4L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 
    1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 2L, 0L, 1L, 0L, 2L, 
    0L, 0L, 6L, 1L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 2L, 
    0L, 1L, 0L, 0L, 0L), nest_condition = structure(c(3L, 5L, 
    5L, 2L, 5L, 5L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 2L, 5L, 3L, 2L, 
    4L, 5L, 5L, 5L, 5L, 3L, 2L, 5L, 5L, 2L, 2L, 5L, 1L, 5L, 5L, 
    5L, 5L, 5L, 5L, 3L, 5L, 5L, 5L, 2L, 3L, 5L, 5L, 5L, 2L, 5L, 
    3L, 5L, 5L, 5L, 2L, 5L, 3L, 5L, 4L), .Label = c(" ready ", 
    "damaged", "in_progress", "no_nest", "ready"), class = "factor"), 
    nesting_site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 
    1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L), .Label = c("inside", "no_nest", "outside"), class = "factor"), 
    distance = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 3L, 4L, 3L, 6L, 4L, 4L, 2L, 2L, 4L, 2L, 
    2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    3L, 2L, 4L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    4L, 6L), .Label = c("1", "2", "3", "4", "no_data", "no_nest"
    ), class = "factor"), material = structure(c(5L, 5L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 5L, 5L, 1L, 1L, 2L), .Label = c("fine", "fine plaster", 
    "medium fine plaster", "no_data", "rough", "rough plaster", 
    "smooth plaster", "under construction", "wood"), class = "factor"), 
    housetype = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 
    3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 
    3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 5L, 5L, 5L, 
    4L, 4L, 5L, 3L, 3L, 3L, 3L, 5L, 5L, 3L, 3L, 3L, 4L, 4L, 5L, 
    5L, 4L), .Label = c("auto repair shop", "barn ", "hall", 
    "residence", "stable"), class = "factor"), usage_house = structure(c(5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L), .Label = c("auto_repair", 
    "barn", "inhabited", "under construction", "used"), class = "factor"), 
    age = c(1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L)), row.names = c(1L, 
2L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
19L, 21L, 22L, 23L, 24L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 
35L, 36L, 37L, 38L, 39L, 40L, 41L, 89L, 90L, 91L, 92L, 93L, 94L, 
95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 
106L, 107L, 108L, 109L, 111L), class = "data.frame")

用过第1…第2。。。这意味着鸟类在第一次、第二次、第三次、第三次、第三次和第三次，。。。控制

我希望数据框中的每一行始终表示已使用/未使用的嵌套以及未使用的嵌套：

     ID species `1st_visit` `2nd_visit` `3rd_visit` used_1st_visit used_2nd_visit used_3rd_visit
  <dbl> <chr>         <dbl>       <dbl>       <dbl>          <dbl>          <dbl>          <dbl>
1   120 barn s~           1           1           0              0              0              0
2   120 barn s~           1           1           0              1              1              0
3   120 barn s~           1           1           0              1              1              0
4    39 barn s~           1           1           1              1              1              1
5     8 barn s~           1           1           1              1              0              0
6     8 barn s~           1           1           1              0              0              0

ID物种'1st\u visit``2nd\u visit``3rd\u visit`used\u 1st\u visit used\u 2nd\u visit used\u 3rd\u visit
1120谷仓s~11000
2120谷仓-1110
3120谷仓~1110
4 39个谷仓s~1
5 8个谷仓s~10 0
6 8谷仓s~1 10 0 0 0

不幸的是，我不知道如何连接列以获得最终数据帧。

---

有人有想法吗？

我不完全确定您的要求，但这就是我理解的：在长数据帧中

• 如果所有访问（coulmns used_first_vision、used_secu_vision等）均为0，则将它们合并到一行，并将其标记为0
• 如果任何访问不是0，则保留与非零访问相同的行数，并将它们标记为1

这是我的dplyr解决方案（它不是很漂亮，但很有效）：

#创建数据
dat%#生成长数据帧
选择（-c（任意））%>%#去掉名称列
分组依据（访问、嵌套条件）%>%#分组数据
mutate（used=ifelse（all（used==0）和row_number（）==1，10，used））%>%#如果整个组为0，则标记一行以供以后筛选
过滤器（已使用>0）%>%#过滤器
变异（used=ifelse（used=10,0,1））#更改为正确的数字

---

让我知道，如果这不是你要找的

对于未来的问题：如果您提供一点数据，例如使用

dput（）

，则获得问题答案的可能性要高得多。图片不是很有用，一个合适的迷你数据集只会让事情变得更简单！谢谢你的建议。我不知道该怎么做。我希望没有，这会好一点。感谢您的努力，但仍然无法直接在R中输入数据。请尝试

dput（您的_数据）

并将结果直接复制到您的问题中。请看：我也不确定我的解决方案是否就是您所寻找的——我的代码是否正常工作？如果您对它满意，您也可以通过勾选旁边的勾号来接受答案。是的，您的代码正常工作，谢谢。我现在正试图将它应用于我的整个数据帧。请发布真实数据而不是图像谢谢我已经尝试发布真实数据

# create data
dat <- data.frame("visits" = c("first", "first", "second", "second", "third", "third"), "study_id" = rep(120, 6), "used_first_visit" = c(0, 2, 0, 2, 0, 2), "used_sec_visit" = c(0, 2, 0, 2, 0, 2), "used_thrd_visit" = rep(0, 6), "nest_cond" = c("damaged", "ready", "damaged", "ready", "damaged", "ready"))

# make long data frame and filter values
dat_long <- dat %>%
  pivot_longer(c(3:5),names_to = "whatever", values_to = "used")  %>% # make long data frame
  select(-c(whatever)) %>% # get rid of name column
  group_by(visits, nest_cond) %>% # group data
  mutate(used = ifelse(all(used == 0) & row_number() == 1, 10, used)) %>% # if the whole group is 0, mark one row for later filtering
  filter(used > 0 ) %>% # filter
  mutate(used = ifelse(used == 10, 0, 1)) # change to correct numbers