[英]R creating a comprehensive table of correlation between combinations of columns
這是我的數據集。 我在看棒球數據。
structure(list(INDEX = 1:6, TARGET_WINS = c(39L, 70L, 86L, 70L,
82L, 75L), TEAM_BATTING_H = c(1445L, 1339L, 1377L, 1387L, 1297L,
1279L), TEAM_BATTING_2B = c(194L, 219L, 232L, 209L, 186L, 200L
), TEAM_BATTING_3B = c(39L, 22L, 35L, 38L, 27L, 36L), TEAM_BATTING_HR = c(13L,
190L, 137L, 96L, 102L, 92L), TEAM_BATTING_BB = c(143L, 685L,
602L, 451L, 472L, 443L), TEAM_BATTING_SO = c(842L, 1075L, 917L,
922L, 920L, 973L), TEAM_BASERUN_SB = c(NA, 37L, 46L, 43L, 49L,
107L), TEAM_BASERUN_CS = c(NA, 28L, 27L, 30L, 39L, 59L), TEAM_BATTING_HBP = c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_
), TEAM_PITCHING_H = c(9364L, 1347L, 1377L, 1396L, 1297L, 1279L
), TEAM_PITCHING_HR = c(84L, 191L, 137L, 97L, 102L, 92L), TEAM_PITCHING_BB = c(927L,
689L, 602L, 454L, 472L, 443L), TEAM_PITCHING_SO = c(5456L, 1082L,
917L, 928L, 920L, 973L), TEAM_FIELDING_E = c(1011L, 193L, 175L,
164L, 138L, 123L), TEAM_FIELDING_DP = c(NA, 155L, 153L, 156L,
168L, 149L)), row.names = c(NA, 6L), class = "data.frame")
我正在嘗試創建一個多元線性回歸並決定要包含哪些預測變量。 問題是,我認為其中一些變量將真正相互關聯。 例如,其中一列是“擊球手的基本命中(任何類型的擊球)”,另一列是“擊球手的雙打”等等。 所以我認為如果一個球員得分雙倍,它會在多個不同的列中檢查+1。
我試圖弄清楚要包括哪些變量,我想到的一個策略是確定這些變量中的哪些彼此相關以及它們的相關性有多強。 也許我不會包括彼此之間真正密切相關的變量。 (對此有幫助嗎?)
我開始走這條路,一一查看皮爾遜相關性:
cor(moneyball_training_data$TEAM_BATTING_H, moneyball_training_data$TEAM_BATTING_2B)
cor(moneyball_training_data$TEAM_BATTING_H, moneyball_training_data$TEAM_BATTING_3B)
cor(moneyball_training_data$TEAM_BATTING_H, moneyball_training_data$TEAM_BATTING_HR)
但后來我看到所有這些變量之間有多少排列:這個 dataframe 中有 16 列,我想 select 任意兩個,16,/(2.(16 - 2)。)如果我的數學是正確的。 這將是 120 行代碼。 而且很容易糾結並忘記我已經完成了哪些......所以效率不高。
所以我最初的問題是:是否有任何有效的編碼方法來比較 dataframe 中變量之間的綜合相關性集?
然后我在 Stack Overflow 上發現了這篇很棒的帖子,我認為它回答了我的問題,但我仍然無法讓它發揮作用。
旁注 - 我還試圖找出哪些列具有 NA 值,以防這里的 NA 值有所不同。
any(is.na(moneyball_training_data$TARGET_WINS)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_H)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_2B)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_3B)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_HR)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_BB)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_SO)) # TRUE
any(is.na(moneyball_training_data$TEAM_BATTING_SB)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_CS)) # FALSE
any(is.na(moneyball_training_data$TEAM_BATTING_HBP)) # TRUE
any(is.na(moneyball_training_data$TEAM_PITCHING_H)) # FALSE
any(is.na(moneyball_training_data$TEAM_PITCHING_HR)) # FALSE
any(is.na(moneyball_training_data$TEAM_PITCHING_BB)) # FALSE
any(is.na(moneyball_training_data$TEAM_PITCHING_SO))# TRUE
any(is.na(moneyball_training_data$TEAM_FIELDING_E)) # FALSE
any(is.na(moneyball_training_data$TEAM_FIELDING_DP)) # TRUE
(旁注 - 是否有更有效的方法來執行此 an(is.na)) 代碼?)
為了繼續,我現在按照另一個 Stack Overflow 答案的方向,整潔的方法,我不完全理解,但給出答案的人似乎很聰明:
# function to use later (to filter out rows)
f = function(x,y) grepl(x,y)
f = Vectorize(f)
moneyball_training_data %>%
select(-INDEX) %>% # remove unnecessary columns
cor() %>% # get all correlations (even ones you don't care about)
data.frame() %>% # save result as a dataframe
mutate(v1 = row.names(.)) %>% # add row names as a column
gather(v2,cor, -v1) %>% # reshape data
filter(f(v1,v2) & v1 != v2)
但是結果怎么可能只是 3 x 3 dataframe? 我期望像下面的圖一樣,其中每個數字都是 x 和 y 的相關性,其中刪除了冗余的空白空間。
1 2 3 4 5 6 7
1 12 13 14 15 16 17
2 23 24 25 26 27
3 34 35 36 37
4 45 46 47
5 56 57
6 67
7
你期待這樣的矩陣嗎?
df <- structure(list(INDEX = 1:6, TARGET_WINS = c(39L, 70L, 86L, 70L,
82L, 75L), TEAM_BATTING_H = c(1445L, 1339L, 1377L, 1387L, 1297L,
1279L), TEAM_BATTING_2B = c(194L, 219L, 232L, 209L, 186L, 200L
), TEAM_BATTING_3B = c(39L, 22L, 35L, 38L, 27L, 36L), TEAM_BATTING_HR = c(13L,
190L, 137L, 96L, 102L, 92L), TEAM_BATTING_BB = c(143L, 685L,
602L, 451L, 472L, 443L), TEAM_BATTING_SO = c(842L, 1075L, 917L,
922L, 920L, 973L), TEAM_BASERUN_SB = c(NA, 37L, 46L, 43L, 49L,
107L), TEAM_BASERUN_CS = c(NA, 28L, 27L, 30L, 39L, 59L), TEAM_BATTING_HBP = c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_
), TEAM_PITCHING_H = c(9364L, 1347L, 1377L, 1396L, 1297L, 1279L
), TEAM_PITCHING_HR = c(84L, 191L, 137L, 97L, 102L, 92L), TEAM_PITCHING_BB = c(927L,
689L, 602L, 454L, 472L, 443L), TEAM_PITCHING_SO = c(5456L, 1082L,
917L, 928L, 920L, 973L), TEAM_FIELDING_E = c(1011L, 193L, 175L,
164L, 138L, 123L), TEAM_FIELDING_DP = c(NA, 155L, 153L, 156L,
168L, 149L)), row.names = c(NA, 6L), class = "data.frame")
# install.packages("corrr")
library(corrr)
df1 <- corrr::correlate(df, method = "pearson")
# 1. Output:
# A tibble: 17 x 18
term INDEX TARGET_WINS TEAM_BATTING_H TEAM_BATTING_2B TEAM_BATTING_3B TEAM_BATTING_HR TEAM_BATTING_BB
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 INDEX NA 0.642 -0.820 -0.291 0.0236 0.0826 0.205
2 TARG~ 0.642 NA -0.685 0.367 -0.373 0.673 0.788
3 TEAM~ -0.820 -0.685 NA 0.192 0.496 -0.449 -0.502
4 TEAM~ -0.291 0.367 0.192 NA -0.0789 0.640 0.653
5 TEAM~ 0.0236 -0.373 0.496 -0.0789 NA -0.752 -0.676
6 TEAM~ 0.0826 0.673 -0.449 0.640 -0.752 NA 0.984
7 TEAM~ 0.205 0.788 -0.502 0.653 -0.676 0.984 NA
8 TEAM~ 0.134 0.401 -0.560 0.377 -0.754 0.864 0.799
9 TEAM~ 0.790 -0.00267 -0.690 -0.356 0.413 -0.528 -0.541
10 TEAM~ 0.874 -0.0332 -0.834 -0.598 0.261 -0.578 -0.623
11 TEAM~ NA NA NA NA NA NA NA
12 TEAM~ -0.662 -0.923 0.733 -0.358 0.448 -0.771 -0.852
13 TEAM~ -0.352 0.308 -0.127 0.661 -0.767 0.891 0.809
14 TEAM~ -0.914 -0.793 0.736 0.0225 0.0863 -0.341 -0.464
15 TEAM~ -0.667 -0.930 0.719 -0.360 0.424 -0.757 -0.842
16 TEAM~ -0.707 -0.925 0.757 -0.314 0.418 -0.733 -0.820
17 TEAM~ 0.0666 0.265 -0.144 -0.583 -0.447 -0.123 -0.150
快速回答隱藏在這篇文章中的一個附帶問題:更有效的方法來查找其中包含 NA 值的列,而不是逐個查找
moneyball_training_data %>% summarise(across(, ~ any(is.na(.x))))
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.