使用嵌套类别使长数据变宽
Making long data wide with nested categories
我正在尝试将长数据转换为宽格式，但我有多个类别需要嵌套。我当前的数据如下所示：
YRTR sub_cou SUBJ PATH path_count pre_drop_count freq
20173 ACCT 2251 ACCT 1051 -> 2251 1 235 0.40%
20183 ACCT 2251 ACCT 1051 -> 2251 1 217 0.50%
20203 ACCT 2251 ACCT 1051 -> 2251 1 248 0.40%
20213 ACCT 2251 ACCT 1051 -> 2251 1 219 0.50%
20213 ACCT 2251 ACCT 1051 and 2251 -> NA 1 219 0.50%
20173 ACCT 2251 ACCT 1853 -> 2251 2 235 0.90%
20183 ACCT 2251 ACCT 2251 -> 1051 1 217 0.50%
20173 ACCT 2251 ACCT 2251 -> 2251 224 235 95.30%
20183 ACCT 2251 ACCT 2251 -> 2251 210 217 96.80%
20193 ACCT 2251 ACCT 2251 -> 2251 240 258 93%
20203 ACCT 2251 ACCT 2251 -> 2251 223 248 89.90%
20213 ACCT 2251 ACCT 2251 -> 2251 204 219 93.20%
20173 ACCT 2251 ACCT 2251 -> NA 11 235 4.70%
20183 ACCT 2251 ACCT 2251 -> NA 6 217 2.80%
20193 ACCT 2251 ACCT 2251 -> NA 18 258 7.00%
20203 ACCT 2251 ACCT 2251 -> NA 25 248 10.10%
20213 ACCT 2251 ACCT 2251 -> NA 14 219 6.40%
20173 ACCT 2251 ACCT NA -> 2251 17 235 7.20%
20183 ACCT 2251 ACCT NA -> 2251 23 217 10.60%
20193 ACCT 2251 ACCT NA -> 2251 29 258 11%
20203 ACCT 2251 ACCT NA -> 2251 37 248 14.90%
20213 ACCT 2251 ACCT NA -> 2251 40 219 18.30%
我正在尝试通过 YRTR 将其转换为宽格式，但也具有 path_count、pre_drop_count 和 freq 的值。所以理想情况下它看起来像这样：
20173 20183 20193 20203
sub_cou SUBJ PATH path_count pre_drop_count freq path_count pre_drop_count freq path_count pre_drop_count freq path_count pre_drop_count freq
ACCT 2251 ACCT 1853 -> 2251 2 235 0.90% NA NA NA NA NA NA NA NA NA
ACCT 2251 ACCT NA -> 2251 17 235 7.20% 23 217 10.60% 29 258 11% 37 248 14.90%
ACCT 2251 ACCT 2251 -> NA 11 235 4.70% 6 217 2.80% 18 258 7.00% 25 248 10.10%
ACCT 2251 ACCT 2251 -> 2251 224 235 95.30% 210 217 96.80% 240 258 93% 223 248 89.90%
ACCT 2251 ACCT 1051 -> 2251 1 235 0.40% 1 217 0.50% NA NA NA 1 248 0.40%
ACCT 2251 ACCT 2251 -> 1051 NA NA NA 1 217 0.50% NA NA NA NA NA NA
我试过使用 dcast，但它似乎只想将 YRTR 放在最前面。
编辑以添加 dput() 输出：
dput(path_agg2)
structure(list(YRTR = c(20173L, 20173L, 20173L, 20173L, 20173L,
20183L, 20183L, 20183L, 20183L, 20183L, 20193L, 20193L, 20193L,
20203L, 20203L, 20203L, 20203L, 20213L, 20213L, 20213L, 20213L,
20213L), sub_cou = c("ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251"), SUBJ = c("ACCT", "ACCT",
"ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT",
"ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT",
"ACCT", "ACCT", "ACCT", "ACCT"), PATH = c("1853 -> 2251", "NA -> 2251",
"2251 -> NA", "2251 -> 2251", "1051 -> 2251", "2251 -> NA", "2251 -> 1051",
"2251 -> 2251", "1051 -> 2251", "NA -> 2251", "NA -> 2251", "2251 -> 2251",
"2251 -> NA", "2251 -> 2251", "2251 -> NA", "NA -> 2251", "1051 -> 2251",
"2251 -> 2251", "1051 and 2251 -> NA", "1051 -> 2251", "2251 -> NA",
"NA -> 2251"), path_count = c(2L, 17L, 11L, 224L, 1L, 6L, 1L,
210L, 1L, 23L, 29L, 240L, 18L, 223L, 25L, 37L, 1L, 204L, 1L,
1L, 14L, 40L), pre_drop_count = c(235L, 235L, 235L, 235L, 235L,
217L, 217L, 217L, 217L, 217L, 258L, 258L, 258L, 248L, 248L, 248L,
248L, 219L, 219L, 219L, 219L, 219L), freq = c("0.9%", "7.2%",
"4.7%", "95.3%", "0.4%", "2.8%", "0.5%", "96.8%", "0.5%", "10.6%",
"11.2%", "93%", "7%", "89.9%", "10.1%", "14.9%", "0.4%", "93.2%",
"0.5%", "0.5%", "6.4%", "18.3%")), row.names = c(NA, -22L), class = "data.frame")
是否回答：
> path_agg2_wider <- path_agg2 %>% pivot_wider(
+ names_from = YRTR,
+ values_from = c(path_count, pre_drop_count, freq)
+ )
> path_agg2_wider <- path_agg2_wider[c(1:3,4,9,14,5,10,15,6,11,16,7,12,17,8,13,18)]
> path_agg2_wider
# A tibble: 7 x 18
sub_cou SUBJ PATH path_count_20173 pre_drop_count_~ freq_20173 path_count_20183 pre_drop_count_~ freq_20183 path_count_20193 pre_drop_count_~ freq_20193
<chr> <chr> <chr> <int> <int> <chr> <int> <int> <chr> <int> <int> <chr>
1 ACCT 2~ ACCT 1853~ 2 235 0.9% NA NA NA NA NA NA
2 ACCT 2~ ACCT NA -~ 17 235 7.2% 23 217 10.6% 29 258 11.2%
3 ACCT 2~ ACCT 2251~ 11 235 4.7% 6 217 2.8% 18 258 7%
4 ACCT 2~ ACCT 2251~ 224 235 95.3% 210 217 96.8% 240 258 93%
5 ACCT 2~ ACCT 1051~ 1 235 0.4% 1 217 0.5% NA NA NA
6 ACCT 2~ ACCT 2251~ NA NA NA 1 217 0.5% NA NA NA
7 ACCT 2~ ACCT 1051~ NA NA NA NA NA NA NA NA NA
# ... with 6 more variables: path_count_20203 <int>, pre_drop_count_20203 <int>, freq_20203 <chr>, path_count_20213 <int>, pre_drop_count_20213 <int>,
# freq_20213 <chr>
>
我正在尝试将长数据转换为宽格式，但我有多个类别需要嵌套。我当前的数据如下所示：
YRTR sub_cou SUBJ PATH path_count pre_drop_count freq
20173 ACCT 2251 ACCT 1051 -> 2251 1 235 0.40%
20183 ACCT 2251 ACCT 1051 -> 2251 1 217 0.50%
20203 ACCT 2251 ACCT 1051 -> 2251 1 248 0.40%
20213 ACCT 2251 ACCT 1051 -> 2251 1 219 0.50%
20213 ACCT 2251 ACCT 1051 and 2251 -> NA 1 219 0.50%
20173 ACCT 2251 ACCT 1853 -> 2251 2 235 0.90%
20183 ACCT 2251 ACCT 2251 -> 1051 1 217 0.50%
20173 ACCT 2251 ACCT 2251 -> 2251 224 235 95.30%
20183 ACCT 2251 ACCT 2251 -> 2251 210 217 96.80%
20193 ACCT 2251 ACCT 2251 -> 2251 240 258 93%
20203 ACCT 2251 ACCT 2251 -> 2251 223 248 89.90%
20213 ACCT 2251 ACCT 2251 -> 2251 204 219 93.20%
20173 ACCT 2251 ACCT 2251 -> NA 11 235 4.70%
20183 ACCT 2251 ACCT 2251 -> NA 6 217 2.80%
20193 ACCT 2251 ACCT 2251 -> NA 18 258 7.00%
20203 ACCT 2251 ACCT 2251 -> NA 25 248 10.10%
20213 ACCT 2251 ACCT 2251 -> NA 14 219 6.40%
20173 ACCT 2251 ACCT NA -> 2251 17 235 7.20%
20183 ACCT 2251 ACCT NA -> 2251 23 217 10.60%
20193 ACCT 2251 ACCT NA -> 2251 29 258 11%
20203 ACCT 2251 ACCT NA -> 2251 37 248 14.90%
20213 ACCT 2251 ACCT NA -> 2251 40 219 18.30%
我正在尝试通过 YRTR 将其转换为宽格式，但也具有 path_count、pre_drop_count 和 freq 的值。所以理想情况下它看起来像这样：
20173 20183 20193 20203
sub_cou SUBJ PATH path_count pre_drop_count freq path_count pre_drop_count freq path_count pre_drop_count freq path_count pre_drop_count freq
ACCT 2251 ACCT 1853 -> 2251 2 235 0.90% NA NA NA NA NA NA NA NA NA
ACCT 2251 ACCT NA -> 2251 17 235 7.20% 23 217 10.60% 29 258 11% 37 248 14.90%
ACCT 2251 ACCT 2251 -> NA 11 235 4.70% 6 217 2.80% 18 258 7.00% 25 248 10.10%
ACCT 2251 ACCT 2251 -> 2251 224 235 95.30% 210 217 96.80% 240 258 93% 223 248 89.90%
ACCT 2251 ACCT 1051 -> 2251 1 235 0.40% 1 217 0.50% NA NA NA 1 248 0.40%
ACCT 2251 ACCT 2251 -> 1051 NA NA NA 1 217 0.50% NA NA NA NA NA NA
我试过使用 dcast，但它似乎只想将 YRTR 放在最前面。
编辑以添加 dput() 输出：
dput(path_agg2)
structure(list(YRTR = c(20173L, 20173L, 20173L, 20173L, 20173L,
20183L, 20183L, 20183L, 20183L, 20183L, 20193L, 20193L, 20193L,
20203L, 20203L, 20203L, 20203L, 20213L, 20213L, 20213L, 20213L,
20213L), sub_cou = c("ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251", "ACCT 2251",
"ACCT 2251", "ACCT 2251", "ACCT 2251"), SUBJ = c("ACCT", "ACCT",
"ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT",
"ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT", "ACCT",
"ACCT", "ACCT", "ACCT", "ACCT"), PATH = c("1853 -> 2251", "NA -> 2251",
"2251 -> NA", "2251 -> 2251", "1051 -> 2251", "2251 -> NA", "2251 -> 1051",
"2251 -> 2251", "1051 -> 2251", "NA -> 2251", "NA -> 2251", "2251 -> 2251",
"2251 -> NA", "2251 -> 2251", "2251 -> NA", "NA -> 2251", "1051 -> 2251",
"2251 -> 2251", "1051 and 2251 -> NA", "1051 -> 2251", "2251 -> NA",
"NA -> 2251"), path_count = c(2L, 17L, 11L, 224L, 1L, 6L, 1L,
210L, 1L, 23L, 29L, 240L, 18L, 223L, 25L, 37L, 1L, 204L, 1L,
1L, 14L, 40L), pre_drop_count = c(235L, 235L, 235L, 235L, 235L,
217L, 217L, 217L, 217L, 217L, 258L, 258L, 258L, 248L, 248L, 248L,
248L, 219L, 219L, 219L, 219L, 219L), freq = c("0.9%", "7.2%",
"4.7%", "95.3%", "0.4%", "2.8%", "0.5%", "96.8%", "0.5%", "10.6%",
"11.2%", "93%", "7%", "89.9%", "10.1%", "14.9%", "0.4%", "93.2%",
"0.5%", "0.5%", "6.4%", "18.3%")), row.names = c(NA, -22L), class = "data.frame")
是否回答：
> path_agg2_wider <- path_agg2 %>% pivot_wider(
+ names_from = YRTR,
+ values_from = c(path_count, pre_drop_count, freq)
+ )
> path_agg2_wider <- path_agg2_wider[c(1:3,4,9,14,5,10,15,6,11,16,7,12,17,8,13,18)]
> path_agg2_wider
# A tibble: 7 x 18
sub_cou SUBJ PATH path_count_20173 pre_drop_count_~ freq_20173 path_count_20183 pre_drop_count_~ freq_20183 path_count_20193 pre_drop_count_~ freq_20193
<chr> <chr> <chr> <int> <int> <chr> <int> <int> <chr> <int> <int> <chr>
1 ACCT 2~ ACCT 1853~ 2 235 0.9% NA NA NA NA NA NA
2 ACCT 2~ ACCT NA -~ 17 235 7.2% 23 217 10.6% 29 258 11.2%
3 ACCT 2~ ACCT 2251~ 11 235 4.7% 6 217 2.8% 18 258 7%
4 ACCT 2~ ACCT 2251~ 224 235 95.3% 210 217 96.8% 240 258 93%
5 ACCT 2~ ACCT 1051~ 1 235 0.4% 1 217 0.5% NA NA NA
6 ACCT 2~ ACCT 2251~ NA NA NA 1 217 0.5% NA NA NA
7 ACCT 2~ ACCT 1051~ NA NA NA NA NA NA NA NA NA
# ... with 6 more variables: path_count_20203 <int>, pre_drop_count_20203 <int>, freq_20203 <chr>, path_count_20213 <int>, pre_drop_count_20213 <int>,
# freq_20213 <chr>
>