如何在R中對數據進行條件分組？
[英]How to do conditional grouping of data in R?
問題描述
這里我有按年份和型號的銷售數據：
df <- data.frame (model = c("A","B","C","D","E","A","B","C","D","E","A","B","C","D","E","A","B","C","D","E"),
Year = c(2017,2017,2017,2017,2017,2018,2018,2018,2018,2018,2019,2019,2019,2019,2019,2020,2020,2020,2020,2020),
sales = c(900,235,456,345,144,333,555,445,456,434,8911,4560,4567,4566,5555,224,14,15,170,1180))
model Year sales
1 A 2017 900
..................
17 B 2020 14
18 C 2020 15
19 D 2020 170
20 E 2020 1180
這里我添加份額和累積份額列，並應用以下條件語句：如果模型在2020 年的累積份額 > 90%，則歸類為“不顯着”。 因此，該條件僅適用於 2020 年，然后結果將分布在整個期間。 例如，如果我在 2020 年將 E、A 模型和其他歸類為不顯着，接下來我需要將 E 和 A 模型分開並將其他模型轉換為不顯着。
df2 <- df %>%
group_by(Year) %>% mutate(Share = 100 * sales/ sum(sales),
order = order(order(-Share))) %>% arrange(Year, order, by_group = TRUE) %>%
mutate(CumulativeShare= cumsum(Share)) %>%ungroup() %>%
mutate(threshold.90 = model %in% model[Year == max(Year) & CumulativeShare < 90]) %>%
mutate(model = ifelse(threshold.90, model, 'insignificant'))
model Year sales Share order CumulativeShare threshold.90
1 A 2017 900 43.2692308 1 43.26923 TRUE
2 insignificant 2017 456 21.9230769 2 65.19231 FALSE
3 insignificant 2017 345 16.5865385 3 81.77885 FALSE
4 insignificant 2017 235 11.2980769 4 93.07692 FALSE
5 E 2017 144 6.9230769 5 100.00000 TRUE
6 insignificant 2018 555 24.9662618 1 24.96626 FALSE
7 insignificant 2018 456 20.5128205 2 45.47908 FALSE
8 insignificant 2018 445 20.0179937 3 65.49708 FALSE
9 E 2018 434 19.5231669 4 85.02024 TRUE
10 A 2018 333 14.9797571 5 100.00000 TRUE
11 A 2019 8911 31.6452999 1 31.64530 TRUE
12 E 2019 5555 19.7272630 2 51.37256 TRUE
13 insignificant 2019 4567 16.2186157 3 67.59118 FALSE
14 insignificant 2019 4566 16.2150645 4 83.80624 FALSE
15 insignificant 2019 4560 16.1937569 5 100.00000 FALSE
16 E 2020 1180 73.6119775 1 73.61198 TRUE
17 A 2020 224 13.9737991 2 87.58578 TRUE
18 insignificant 2020 170 10.6051154 3 98.19089 FALSE
19 insignificant 2020 15 0.9357455 4 99.12664 FALSE
20 insignificant 2020 14 0.8733624 5 100.00000 FALSE
但是，如果單個模型在 2020 年的份額在邏輯上超過 90%，那么所有模型的累積份額都將超過 90%。 因此，它們都被歸類為“無關緊要”。 例如，如果我們將數據框中的最后一個值從 1180 更改為 20000，則輸出如下所示：
df <- data.frame (model = c("A","B","C","D","E","A","B","C","D","E","A","B","C","D","E","A","B","C","D","E"),
Year = c(2017,2017,2017,2017,2017,2018,2018,2018,2018,2018,2019,2019,2019,2019,2019,2020,2020,2020,2020,2020),
sales = c(900,235,456,345,144,333,555,445,456,434,8911,4560,4567,4566,5555,224,14,15,170,20000))
df2 <- df %>% ...:
model Year sales Share order CumulativeShare threshold.90
1 insignificant 2017 900 43.26923077 1 43.26923 FALSE
2 insignificant 2017 456 21.92307692 2 65.19231 FALSE
3 insignificant 2017 345 16.58653846 3 81.77885 FALSE
4 insignificant 2017 235 11.29807692 4 93.07692 FALSE
5 insignificant 2017 144 6.92307692 5 100.00000 FALSE
6 insignificant 2018 555 24.96626181 1 24.96626 FALSE
7 insignificant 2018 456 20.51282051 2 45.47908 FALSE
8 insignificant 2018 445 20.01799370 3 65.49708 FALSE
9 insignificant 2018 434 19.52316689 4 85.02024 FALSE
10 insignificant 2018 333 14.97975709 5 100.00000 FALSE
11 insignificant 2019 8911 31.64529990 1 31.64530 FALSE
12 insignificant 2019 5555 19.72726304 2 51.37256 FALSE
13 insignificant 2019 4567 16.21861572 3 67.59118 FALSE
14 insignificant 2019 4566 16.21506446 4 83.80624 FALSE
15 insignificant 2019 4560 16.19375688 5 100.00000 FALSE
16 insignificant 2020 20000 97.92880576 1 97.92881 FALSE
17 insignificant 2020 224 1.09680262 2 99.02561 FALSE
18 insignificant 2020 170 0.83239485 3 99.85800 FALSE
19 insignificant 2020 15 0.07344660 4 99.93145 FALSE
20 insignificant 2020 14 0.06855016 5 100.00000 FALSE
因此，我想避免這種特定情況並添加一個條件：
如果2020年單個模型的SHARE超過90%，則應單獨保留，所有其他模型應歸類為不顯着。
預期輸出：
model Year sales Share order CumulativeShare threshold.90
1 insignificant 2017 900 43.26923077 1 43.26923 FALSE
2 insignificant 2017 456 21.92307692 2 65.19231 FALSE
3 insignificant 2017 345 16.58653846 3 81.77885 FALSE
4 insignificant 2017 235 11.29807692 4 93.07692 FALSE
5 E 2017 144 6.92307692 5 100.00000 FALSE
6 insignificant 2018 555 24.96626181 1 24.96626 FALSE
7 insignificant 2018 456 20.51282051 2 45.47908 FALSE
8 insignificant 2018 445 20.01799370 3 65.49708 FALSE
9 E 2018 434 19.52316689 4 85.02024 FALSE
10 insignificant 2018 333 14.97975709 5 100.00000 FALSE
11 insignificant 2019 8911 31.64529990 1 31.64530 FALSE
12 E 2019 5555 19.72726304 2 51.37256 FALSE
13 insignificant 2019 4567 16.21861572 3 67.59118 FALSE
14 insignificant 2019 4566 16.21506446 4 83.80624 FALSE
15 insignificant 2019 4560 16.19375688 5 100.00000 FALSE
16 E 20000 97.92880576 1 97.92881 FALSE
17 insignificant 2020 224 1.09680262 2 99.02561 FALSE
18 insignificant 2020 170 0.83239485 3 99.85800 FALSE
19 insignificant 2020 15 0.07344660 4 99.93145 FALSE
20 insignificant 2020 14 0.06855016 5 100.00000 FALSE
2 個解決方案
解決方案1
0 已采納 2022-07-14 12:19:22
我想我會使用幾個臨時變量來幫助您在這里跟蹤。 本質上，您需要知道最后一年的第一名模型以及最后一年的累積值。 然后，任何滿足“最后一年少於 90 或最后一年首次進入”條件的模型都將被保留。
df %>%
group_by(Year) %>%
mutate(Share = 100 * sales/ sum(sales),
order = order(order(-Share))) %>%
arrange(Year, order, by_group = TRUE) %>%
mutate(CumulativeShare= cumsum(Share)) %>%
ungroup() %>%
mutate(finalyear = Year == max(Year),
finval = CumulativeShare[finalyear][match(model, model[finalyear])],
finlast = c(FALSE, diff(finalyear) == 1),
keep = finval <90 | finlast[finalyear][match(model, model[finalyear])],
model = ifelse(keep, model, 'insignificant')) %>%
select(-finalyear, -finval, -finlast, -keep)
使用您的第一個示例數據集，這看起來像
#> # A tibble: 20 x 6
#> model Year sales Share order CumulativeShare
#> <chr> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 A 2017 900 43.3 1 43.3
#> 2 insignificant 2017 456 21.9 2 65.2
#> 3 insignificant 2017 345 16.6 3 81.8
#> 4 insignificant 2017 235 11.3 4 93.1
#> 5 E 2017 144 6.92 5 100
#> 6 insignificant 2018 555 25.0 1 25.0
#> 7 insignificant 2018 456 20.5 2 45.5
#> 8 insignificant 2018 445 20.0 3 65.5
#> 9 E 2018 434 19.5 4 85.0
#> 10 A 2018 333 15.0 5 100
#> 11 A 2019 8911 31.6 1 31.6
#> 12 E 2019 5555 19.7 2 51.4
#> 13 insignificant 2019 4567 16.2 3 67.6
#> 14 insignificant 2019 4566 16.2 4 83.8
#> 15 insignificant 2019 4560 16.2 5 100
#> 16 E 2020 1180 73.6 1 73.6
#> 17 A 2020 224 14.0 2 87.6
#> 18 insignificant 2020 170 10.6 3 98.2
#> 19 insignificant 2020 15 0.936 4 99.1
#> 20 insignificant 2020 14 0.873 5 100
使用您的第二個數據集，它看起來像這樣：
#> # A tibble: 20 x 6
#> model Year sales Share order CumulativeShare
#> <chr> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 insignificant 2017 900 43.3 1 43.3
#> 2 insignificant 2017 456 21.9 2 65.2
#> 3 insignificant 2017 345 16.6 3 81.8
#> 4 insignificant 2017 235 11.3 4 93.1
#> 5 E 2017 144 6.92 5 100
#> 6 insignificant 2018 555 25.0 1 25.0
#> 7 insignificant 2018 456 20.5 2 45.5
#> 8 insignificant 2018 445 20.0 3 65.5
#> 9 E 2018 434 19.5 4 85.0
#> 10 insignificant 2018 333 15.0 5 100
#> 11 insignificant 2019 8911 31.6 1 31.6
#> 12 E 2019 5555 19.7 2 51.4
#> 13 insignificant 2019 4567 16.2 3 67.6
#> 14 insignificant 2019 4566 16.2 4 83.8
#> 15 insignificant 2019 4560 16.2 5 100
#> 16 E 2020 20000 97.9 1 97.9
#> 17 insignificant 2020 224 1.10 2 99.0
#> 18 insignificant 2020 170 0.832 3 99.9
#> 19 insignificant 2020 15 0.0734 4 99.9
#> 20 insignificant 2020 14 0.0686 5 100
由reprex 包於 2022-07-14 創建 (v2.0.1)
解決方案2
-2 2022-07-14 09:31:11
這應該可以解決問題：
df %>%
group_by(Year) %>%
mutate(Share = 100 * sales/ sum(sales),order = order(order(-Share))) %>% arrange(Year,order, by_group = TRUE) %>%
mutate(CumulativeShare= cumsum(Share)) %>% ungroup() %>%
#added this
mutate(test = ifelse(Year == max(Year) & Share > 90, model, NA)) %>%
mutate(threshold.90 = model %in% model[Year == max(Year) & CumulativeShare < 90] ) %>%
#and modified that
mutate(model = ifelse(threshold.90 | !is.na(test), model, 'insignificant'))
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