即使缺少数据点，如何以特定顺序（月年）显示宽表？
[英]How to display wide table with specific order (month year) even when data points are missing?
问题描述
> df_1
# A tibble: 47 x 3
# Groups: therapy_class [9]
therapy_class Year_month count
<ord> <yearmon> <int>
1 ALK Inhibitors Dec 2019 16
2 ALK Inhibitors Jan 2020 14
3 ALK Inhibitors Feb 2020 14
4 ALK Inhibitors Mar 2020 22
5 ALK Inhibitors Apr 2020 13
6 ALK Inhibitors May 2020 17
7 Anti-VEGF-based therapies Dec 2019 33
8 Anti-VEGF-based therapies Jan 2020 35
9 Anti-VEGF-based therapies Feb 2020 36
10 Anti-VEGF-based therapies Mar 2020 20
# … with 37 more rows
A tibble: 10 x 7
therapy_class `Dec 2019` `Jan 2020` `Feb 2020` `Mar 2020` `Apr 2020` `May 2020`
<ord> <chr> <chr> <chr> <chr> <chr> <chr>
1 ALK Inhibitors "16 <br>[2.7%]" "14 <br>[2.0%]" "14 <br>[2.2%]" "22 <br>[3.3%]" "13 <br>[2.1%]" "17 <br>[3.4%]"
2 Anti-VEGF-based therapies "33 <br>[5.6%]" "35 <br>[4.9%]" "36 <br>[5.7%]" "20 <br>[3.0%]" "21 <br>[3.4%]" "20 <br>[4.0%]"
3 EGFR TKIs "52 <br>[8.8%]" "57 <br>[8.0%]" "60 <br>[9.5%]" "52 <br>[7.8%]" "56 <br>[9.2%]" "49 <br>[9.8%]"
4 EGFR-antibody based therapies "" "" "" "" "" ""
5 Non-platinum-based chemotherapy… "1 <br>[0.2%]" "4 <br>[0.6%]" "4 <br>[0.6%]" "" "1 <br>[0.2%]" ""
6 IO-based therapies "308 <br>[52.0%]" "385 <br>[54.0%]" "330 <br>[52.3%]" "379 <br>[56.7%]" "345 <br>[56.4%]" "265 <br>[52.9%]"
7 Platinum-based chemotherapy com… "123 <br>[20.8%]" "147 <br>[20.6%]" "128 <br>[20.3%]" "134 <br>[20.1%]" "120 <br>[19.6%]" "107 <br>[21.4%]"
8 Single agent chemotherapies "29 <br>[4.9%]" "33 <br>[4.6%]" "17 <br>[2.7%]" "28 <br>[4.2%]" "25 <br>[4.1%]" "22 <br>[4.4%]"
9 Other "30 <br>[5.1%]" "38 <br>[5.3%]" "42 <br>[6.7%]" "33 <br>[4.9%]" "31 <br>[5.1%]" "21 <br>[4.2%]"
10 <strong>Total</strong> "<strong>592</str… "<strong>713</str… "<strong>631</st… "<strong>668</st… "<strong>612</st… "<strong>501</st…
> df_2
# A tibble: 46 x 3
# Groups: therapy_class [9]
therapy_class Year_month count
<ord> <yearmon> <int>
1 ALK Inhibitors Dec 2019 16
2 ALK Inhibitors Feb 2020 14
3 ALK Inhibitors Mar 2020 22
4 ALK Inhibitors Apr 2020 13
5 ALK Inhibitors May 2020 17
6 Anti-VEGF-based therapies Dec 2019 33
7 Anti-VEGF-based therapies Jan 2020 35
8 Anti-VEGF-based therapies Feb 2020 36
9 Anti-VEGF-based therapies Mar 2020 20
10 Anti-VEGF-based therapies Apr 2020 21
# … with 36 more rows
> t2
# A tibble: 10 x 7
therapy_class `Dec 2019` `Feb 2020` `Mar 2020` `Apr 2020` `May 2020` `Jan 2020`
<ord> <chr> <chr> <chr> <chr> <chr> <chr>
1 ALK Inhibitors "16 <br>[2.7%]" "14 <br>[2.2%]" "22 <br>[3.3%]" "13 <br>[2.1%]" "17 <br>[3.4%]" ""
2 Anti-VEGF-based therapies "33 <br>[5.6%]" "36 <br>[5.7%]" "20 <br>[3.0%]" "21 <br>[3.4%]" "20 <br>[4.0%]" "35 <br>[5.0%]"
3 EGFR TKIs "52 <br>[8.8%]" "60 <br>[9.5%]" "52 <br>[7.8%]" "56 <br>[9.2%]" "49 <br>[9.8%]" "57 <br>[8.2%]"
4 EGFR-antibody based therapies "" "" "" "" "" ""
5 Non-platinum-based chemotherapy… "1 <br>[0.2%]" "4 <br>[0.6%]" "" "1 <br>[0.2%]" "" "4 <br>[0.6%]"
6 IO-based therapies "308 <br>[52.0%]" "330 <br>[52.3%]" "379 <br>[56.7%]" "345 <br>[56.4%]" "265 <br>[52.9%]" "385 <br>[55.1%]"
7 Platinum-based chemotherapy com… "123 <br>[20.8%]" "128 <br>[20.3%]" "134 <br>[20.1%]" "120 <br>[19.6%]" "107 <br>[21.4%]" "147 <br>[21.0%]"
8 Single agent chemotherapies "29 <br>[4.9%]" "17 <br>[2.7%]" "28 <br>[4.2%]" "25 <br>[4.1%]" "22 <br>[4.4%]" "33 <br>[4.7%]"
9 Other "30 <br>[5.1%]" "42 <br>[6.7%]" "33 <br>[4.9%]" "31 <br>[5.1%]" "21 <br>[4.2%]" "38 <br>[5.4%]"
10 <strong>Total</strong> "<strong>592</str… "<strong>631</str… "<strong>668</st… "<strong>612</st… "<strong>501</st… "<strong>699</st…
>
I am trying to create a wide table with counts and percentages from long table.
我正在尝试从长表中创建一个包含计数和百分比的宽表。 The columns are 'Month Year' which needs to be in order.列是“月年”，需要按顺序排列。 My issue is when there are rows missing for certain 'Month Year' for the first group (ALK Inhibitors) then the order of the column is disrupted.我的问题是，当第一组（ALK 抑制剂）的某个“月年”缺少行时，列的顺序就会被打乱。 The missing 'Month Year' is place at the end.缺少的“月年”放在最后。 Also the long table is not a fixed table.长桌也不是固定桌。 It is generated from function where user gets to choose the month year range.它是由用户可以选择月份年份范围的功能生成的。 So Year_month column could have any range.所以 Year_month 列可以有任何范围。
In this example I used Dec 2019 to May 2020 6 month range.在这个例子中，我使用了 2019 年 12 月到 2020 年 5 月的 6 个月范围。 "df_1" has all 6 month so the resulting wide table is as expected. “df_1”有全部 6 个月，因此生成的宽表符合预期。 "df_2" has Jan 2020 missing for ALK Inhibitors. “df_2”缺少 2020 年 1 月的 ALK 抑制剂。 So the resulting table has 'Jan 2020' at the end.因此，结果表的末尾是“Jan 2020”。
This is the code I am using the generate the wide table:这是我用来生成宽表的代码：
df_2 %>%
pivot_wider(names_from = Year_month, values_from = count) %>%
ungroup() %>%
mutate_at(.vars = vars(contains("20")), list(
~ ifelse(is.na(.), "", paste(., sprintf("<br>[%1.1f%%]", 100 * (. / sum(., na.rm = TRUE)))))
))
Here is the sample data df_2这是示例数据 df_2
structure(list(therapy_class = structure(c(1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("ALK Inhibitors",
"Anti-VEGF-based therapies", "EGFR TKIs", "EGFR-antibody based therapies",
"Non-platinum-based chemotherapy combinations", "IO-based therapies",
"Platinum-based chemotherapy combinations", "Single agent chemotherapies",
"Other"), class = c("ordered", "factor")), Year_month = structure(c(2019.91666666667,
2020.08333333333, 2020.16666666667, 2020.25, 2020.33333333333,
2019.91666666667, 2020, 2020.08333333333, 2020.16666666667, 2020.25,
2020.33333333333, 2019.91666666667, 2020, 2020.08333333333, 2020.16666666667,
2020.25, 2020.33333333333, NA, 2019.91666666667, 2020, 2020.08333333333,
2020.25, 2019.91666666667, 2020, 2020.08333333333, 2020.16666666667,
2020.25, 2020.33333333333, 2019.91666666667, 2020, 2020.08333333333,
2020.16666666667, 2020.25, 2020.33333333333, 2019.91666666667,
2020, 2020.08333333333, 2020.16666666667, 2020.25, 2020.33333333333,
2019.91666666667, 2020, 2020.08333333333, 2020.16666666667, 2020.25,
2020.33333333333), class = "yearmon"), count = c(16L, 14L, 22L,
13L, 17L, 33L, 35L, 36L, 20L, 21L, 20L, 52L, 57L, 60L, 52L, 56L,
49L, NA, 1L, 4L, 4L, 1L, 308L, 385L, 330L, 379L, 345L, 265L,
123L, 147L, 128L, 134L, 120L, 107L, 29L, 33L, 17L, 28L, 25L,
22L, 30L, 38L, 42L, 33L, 31L, 21L)), row.names = c(NA, -46L), groups = structure(list(
therapy_class = structure(1:9, .Label = c("ALK Inhibitors",
"Anti-VEGF-based therapies", "EGFR TKIs", "EGFR-antibody based therapies",
"Non-platinum-based chemotherapy combinations", "IO-based therapies",
"Platinum-based chemotherapy combinations", "Single agent chemotherapies",
"Other"), class = c("ordered", "factor")), .rows = structure(list(
1:5, 6:11, 12:17, 18L, 19:22, 23:28, 29:34, 35:40, 41:46), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -9L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
1 个解决方案
解决方案1
1 已采纳 2021-10-15 20:15:59
An option would be to create the missing year months with complete before doing the pivot_wider .一个选项是在执行pivot_wider之前用complete创建缺失的年份月份。 With pivot_wider , the default order is based on the unique value appearance in the order使用pivot_wider ，默认顺序基于订单中的唯一值外观
library(dplyr)
library(tidyr)
library(zoo)
df_2 %>%
ungroup %>%
mutate(Year_month = as.Date(Year_month)) %>%
complete(therapy_class, Year_month = seq(from = min(Year_month,
na.rm = TRUE), to = max(Year_month, na.rm = TRUE),
by = '1 month')) %>%
mutate(Year_month = as.yearmon(Year_month)) %>%
pivot_wider(names_from = Year_month, values_from = count) %>%
ungroup() %>%
mutate_at(.vars = vars(contains("20")),
list(
~ ifelse(is.na(.), "", paste(., sprintf("<br>[%1.1f%%]",
100 * (. / sum(., na.rm = TRUE)))))
))
-output -输出
# A tibble: 9 × 8
therapy_class `Dec 2019` `Jan 2020` `Feb 2020` `Mar 2020` `Apr 2020` `May 2020` `NA`
<ord> <chr> <chr> <chr> <chr> <chr> <chr> <int>
1 ALK Inhibitors "16 <br>[2.7%]" "" "14 <br>[2.2%]" "22 <br>[3.3%]" "13 <br>[2.1%… "17 <br>[3.4… NA
2 Anti-VEGF-based therapies "33 <br>[5.6%]" "35 <br>[5.0%]" "36 <br>[5.7%]" "20 <br>[3.0%]" "21 <br>[3.4%… "20 <br>[4.0… NA
3 EGFR TKIs "52 <br>[8.8%]" "57 <br>[8.2%]" "60 <br>[9.5%]" "52 <br>[7.8%]" "56 <br>[9.2%… "49 <br>[9.8… NA
4 EGFR-antibody based therapies "" "" "" "" "" "" NA
5 Non-platinum-based chemotherapy combinations "1 <br>[0.2%]" "4 <br>[0.6%]" "4 <br>[0.6%]" "" "1 <br>[0.2%]" "" NA
6 IO-based therapies "308 <br>[52.0%]" "385 <br>[55.1%]" "330 <br>[52.3%]" "379 <br>[56.7%]" "345 <br>[56.… "265 <br>[52… NA
7 Platinum-based chemotherapy combinations "123 <br>[20.8%]" "147 <br>[21.0%]" "128 <br>[20.3%]" "134 <br>[20.1%]" "120 <br>[19.… "107 <br>[21… NA
8 Single agent chemotherapies "29 <br>[4.9%]" "33 <br>[4.7%]" "17 <br>[2.7%]" "28 <br>[4.2%]" "25 <br>[4.1%… "22 <br>[4.4… NA
9 Other "30 <br>[5.1%]" "38 <br>[5.4%]" "42 <br>[6.7%]" "33 <br>[4.9%]" "31 <br>[5.1%… "21 <br>[4.2… NA
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