Guten 標簽社區 :)
我目前正在研究一些公司的 ESGscore。ESG 分數在 0 到 100 之間變化。
我想將 ESGscore 分為 4 個部分：
0 - 25 --> poor --> 4
>25 - 50 --> medium --> 3
>50 - 75 --> good --> 2
75 - 100 --> excellent --> 1
問題dummy.code在于它正在重新排列 ESGscore。例如，AIR PRODUCTS & CHEMICALS INC的 ESGscore總是“優秀”，但輸出顯示它只是中等。
這就是代碼的樣子：
Datensatz_final_so$ESG.Kategorien <- ifelse(Datensatz_final_so$ESGscore <= 25, "4",
ifelse(Datensatz_final_so$ESGscore > 25 & Datensatz_final_so$ESGscore <= 50, "3",
ifelse(Datensatz_final_so$ESGscore > 50 & Datensatz_final_so$ESGscore <= 75, "2",
ifelse(Datensatz_final_so > 75, "1", 0))))``
# Create ESGscore dummy #
Dummy.ESG <- dummy.code(Datensatz_final_so$ESG.Kategorien)
colnames(Dummy.ESG) <- c("poor", "medium", "good", "excellent")
# Connect data and dummy #
Datensatz_final <- cbind(Datensatz_final, Dummy.ESG)
你知道怎么解決嗎？
一種方法是重新排列colnames到
colnames(Dummy.ESG) <- c("good", "excellent", "poor", "medium")
但它正在產生問題，即 R 在分析中選擇了媒介作為參考。
先感謝您！:)
資料示例：
structure(list(Company = c("AIR PRODUCTS & CHEMICALS INC", "AIR PRODUCTS & CHEMICALS INC",
"AIR PRODUCTS & CHEMICALS INC", "AIR PRODUCTS & CHEMICALS INC",
"AIR PRODUCTS & CHEMICALS INC", "AIR PRODUCTS & CHEMICALS INC",
"AIR PRODUCTS & CHEMICALS INC", "HESS CORP", "HESS CORP", "HESS CORP",
"HESS CORP", "HESS CORP", "HESS CORP", "HESS CORP", "APACHE CORP",
"APACHE CORP", "APACHE CORP", "APACHE CORP", "APACHE CORP", "APACHE CORP",
"APACHE CORP", "AVERY DENNISON CORP", "AVERY DENNISON CORP",
"AVERY DENNISON CORP", "AVERY DENNISON CORP", "AVERY DENNISON CORP",
"AVERY DENNISON CORP", "AVERY DENNISON CORP", "BALL CORP", "BALL CORP",
"BALL CORP", "BALL CORP", "BALL CORP", "BALL CORP", "BALL CORP",
"CHEVRON CORP", "CHEVRON CORP", "CHEVRON CORP", "CHEVRON CORP",
"CHEVRON CORP", "CHEVRON CORP", "CHEVRON CORP", "ECOLAB INC",
"ECOLAB INC", "ECOLAB INC", "ECOLAB INC", "ECOLAB INC", "ECOLAB INC",
"ECOLAB INC", "EXXON MOBIL CORP", "EXXON MOBIL CORP", "EXXON MOBIL CORP",
"EXXON MOBIL CORP", "EXXON MOBIL CORP", "EXXON MOBIL CORP", "EXXON MOBIL CORP",
"FMC CORP", "FMC CORP", "FMC CORP", "FMC CORP", "FMC CORP", "FMC CORP",
"FMC CORP", "HALLIBURTON CO", "HALLIBURTON CO", "HALLIBURTON CO",
"HALLIBURTON CO", "HALLIBURTON CO", "HALLIBURTON CO", "HALLIBURTON CO",
"HELMERICH & PAYNE", "HELMERICH & PAYNE", "HELMERICH & PAYNE",
"HELMERICH & PAYNE", "HELMERICH & PAYNE", "HELMERICH & PAYNE",
"HELMERICH & PAYNE"), Year = c(2011, 2012, 2013, 2014, 2015,
2016, 2017, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2011, 2012, 2013, 2014, 2015, 2016,
2017, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2011, 2012, 2013,
2014, 2015, 2016, 2017, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2011, 2012, 2013, 2014,
2015, 2016, 2017, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2011,
2012, 2013, 2014, 2015, 2016, 2017), gvkey = c(1209, 1209, 1209,
1209, 1209, 1209, 1209, 1380, 1380, 1380, 1380, 1380, 1380, 1380,
1678, 1678, 1678, 1678, 1678, 1678, 1678, 1913, 1913, 1913, 1913,
1913, 1913, 1913, 1988, 1988, 1988, 1988, 1988, 1988, 1988, 2991,
2991, 2991, 2991, 2991, 2991, 2991, 4213, 4213, 4213, 4213, 4213,
4213, 4213, 4503, 4503, 4503, 4503, 4503, 4503, 4503, 4510, 4510,
4510, 4510, 4510, 4510, 4510, 5439, 5439, 5439, 5439, 5439, 5439,
5439, 5581, 5581, 5581, 5581, 5581, 5581, 5581), ESGscore = c(84.2750015258789,
81.9225006103516, 77.4024963378906, 80.1125030517578, 78.6449966430664,
76.3775024414062, 79.2699966430664, 69.4899978637695, 65.8300018310547,
64.4300003051758, 74.3000030517578, 75.7600021362305, 71.4599990844727,
74.6900024414062, 55.8300018310547, 56.0900001525879, 57.5, 60.75,
60.8800010681152, 67.379997253418, 71.9899978637695, 82.9000015258789,
77.3899993896484, 76.9300003051758, 78.7399978637695, 76.2283325195312,
74.2125015258789, 68.3600006103516, 64.4100036621094, 65.6600036621094,
63.75, 67.7300033569336, 67.5699996948242, 74.4300003051758,
68.5699996948242, 86.5100021362305, 84.3099975585938, 82.6600036621094,
82.3399963378906, 88.4100036621094, 90.0800018310547, 92.25,
74.6999969482422, 72.3600006103516, 68.3899993896484, 67.9300003051758,
65.629997253418, 74.9000015258789, 74.8600006103516, 81.6999969482422,
79.370002746582, 79.0899963378906, 75.25, 81.9499969482422, 81.0199966430664,
88.3399963378906, 59.8199996948242, 55.6500015258789, 52.2999992370605,
51.8499984741211, 56.9199981689453, 66.620002746582, 65.3300018310547,
85.9800033569336, 83.9499969482422, 85.1100006103516, 67.4300003051758,
76.4400024414062, 69.9199981689453, 78.4599990844727, 19.0599994659424,
17.5200004577637, 18.1200008392334, 23.5025005340576, 35.5349998474121,
36.7350006103516, 41.1725006103516)), row.names = c(NA, -77L), class = c("tbl_df",
"tbl", "data.frame"))
uj5u.com熱心網友回復：
讓我們將您的資料作為df：
df<- structure(
list(
Company = c(
"AIR PRODUCTS & CHEMICALS INC",
...
...
),
row.names = c(NA,-77L),
class = c("tbl_df",
"tbl", "data.frame")
)
讓我們來分類并構建一個小資料框，然后有點 dplyr
ESG <- c("poor", "medium", "good", "excellent")
da <- data.frame(ESGColumn = 1:4,FlatESG = ESG)
df <- df |> dplyr::mutate(ESGColumn = floor(ESGscore/25) 1) |>
dplyr::left_join(da, by="ESGColumn") |>
dplyr::select(-"ESGColumn")
head(df)
# A tibble: 6 × 5
Company Year gvkey ESGscore FlatESG
<chr> <dbl> <dbl> <dbl> <chr>
1 AIR PRODUCTS & CHEMICALS INC 2011 1209 84.3 excellent
2 AIR PRODUCTS & CHEMICALS INC 2012 1209 81.9 excellent
3 AIR PRODUCTS & CHEMICALS INC 2013 1209 77.4 excellent
4 AIR PRODUCTS & CHEMICALS INC 2014 1209 80.1 excellent
5 AIR PRODUCTS & CHEMICALS INC 2015 1209 78.6 excellent
6 AIR PRODUCTS & CHEMICALS INC 2016 1209 76.4 excellent
格熱戈日
uj5u.com熱心網友回復：
您的示例資料不包括名為的變數，ESG.Kategorien但它確實包括ESGscore. 以下應該給你你想要的：
Datensatz_final_so$Dummy <- cut(Datensatz_final_so$ESGscore, breaks=c(0, 25, 50, 75, 100), labels=c("poor", "medium", "good", "excellent"))
table(Datensatz_final_so$Dummy)
#
# poor medium good excellent
# 4 3 38 32
levels(Datensatz_final_so$Dummy)
# [1] "poor" "medium" "good" "excellent"
請注意，您最初的分類將 75 分為優秀和優秀。
轉載請註明出處，本文鏈接：https://www.uj5u.com/shujuku/418384.html
標籤：