在 R 中，如何按行使用 argmax 函数并计算变量数？
In R, how can I use argmax function by row and count the variable number?
我有原始数据集。以下是原始数据样本：
sentiment
pos neu neg likes_count
1 1 0 0 5
2 0.2 0.3 0.5 6
3 0.3 0.3 0.4 6
4 0 0 1 3
5 0.2 0.7 0.1 1
在这个情绪原始数据中，"pos"指的是评论中正面的概率，"neu"指的是中立的概率，"neg"指的是负面的概率。 "likes_count" 是Facebook 评论的点赞数。我想在 pos、neu 和 neg 中选择概率最高的。并知道哪种情绪获得最高 likes_count。
例如pos : 0.6, neu : 0.2, neg : 0.2为正面评价。
我想要的输出如下：
Total_likes_count
Pos 5
Neu 1
Neg 15
你能帮我做这个吗？？
dput原始数据格式：
structure(list(likes_count = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 3L, 1L, 2L, 2L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
2L, 5L, 2L, 0L, 1L, 0L), neg = c(0, 0.41, 0, 0, 0,
0.19, 0, 1, 0, 0.52, 0, 0.11, 0.02, 0.05, 0.02, 0, 0, 0.01, 0.01,
0, 0, 0.97, 0, 0.01, 0.24, 0.34, 0.94, 0.44, 0.15, 0.01), neu = c(0,
0.1, 0, 0, 0, 0, 0.13, 0, 0.98, 0.32, 0, 0.08, 0.02, 0.04, 0.07,
0, 0, 0.98, 0.07, 0, 0, 0.03, 0.02, 0.21, 0.48, 0.62, 0.01, 0.2,
0.85, 0.67), pos = c(1, 0.48, 1, 1, 1, 0.81, 0.86,
0, 0.02, 0.16, 1, 0.81, 0.96, 0.91, 0.91, 1, 1, 0.01, 0.92, 1,
1, 0, 0.98, 0.78, 0.28, 0.04, 0.05, 0.36, 0, 0.32)), na.action = structure(c(`7` = 7L,
`11` = 11L, `38` = 38L, `53` = 53L, `88` = 88L, `101` = 101L,
`106` = 106L, `138` = 138L, `139` = 139L, `155` = 155L, `165` = 165L,
`176` = 176L, `178` = 178L, `179` = 179L, `199` = 199L, `200` = 200L,
`201` = 201L, `208` = 208L, `209` = 209L, `250` = 250L, `281` = 281L,
`293` = 293L, `299` = 299L, `316` = 316L, `321` = 321L, `322` = 322L,
`328` = 328L, `332` = 332L, `333` = 333L, `334` = 334L, `335` = 335L,
`336` = 336L, `342` = 342L, `347` = 347L, `352` = 352L, `354` = 354L,
`355` = 355L, `395` = 395L, `398` = 398L, `400` = 400L, `411` = 411L,
`420` = 420L, `449` = 449L, `454` = 454L, `456` = 456L, `457` = 457L,
`464` = 464L, `471` = 471L, `491` = 491L, `495` = 495L, `502` = 502L,
`503` = 503L, `504` = 504L, `506` = 506L, `526` = 526L, `536` = 536L,
`541` = 541L, `542` = 542L, `546` = 546L, `556` = 556L, `558` = 558L,
`563` = 563L, `579` = 579L, `581` = 581L, `582` = 582L, `584` = 584L,
`602` = 602L, `603` = 603L, `604` = 604L, `606` = 606L, `614` = 614L,
`617` = 617L, `619` = 619L, `620` = 620L, `621` = 621L, `622` = 622L,
`623` = 623L, `625` = 625L, `626` = 626L, `629` = 629L, `630` = 630L,
`631` = 631L, `632` = 632L, `633` = 633L, `636` = 636L, `637` = 637L,
`638` = 638L, `639` = 639L, `640` = 640L, `643` = 643L, `645` = 645L,
`646` = 646L, `647` = 647L, `648` = 648L, `650` = 650L, `652` = 652L,
`653` = 653L, `655` = 655L, `656` = 656L, `658` = 658L, `661` = 661L,
`665` = 665L, `666` = 666L, `667` = 667L, `669` = 669L, `671` = 671L,
`673` = 673L, `674` = 674L, `679` = 679L, `680` = 680L, `682` = 682L,
`683` = 683L, `684` = 684L, `685` = 685L, `686` = 686L, `687` = 687L,
`689` = 689L, `692` = 692L, `694` = 694L, `696` = 696L, `697` = 697L,
`699` = 699L, `700` = 700L, `701` = 701L, `702` = 702L, `703` = 703L,
`704` = 704L, `705` = 705L, `707` = 707L, `708` = 708L, `712` = 712L,
`713` = 713L, `714` = 714L, `717` = 717L, `718` = 718L, `719` = 719L,
`720` = 720L, `721` = 721L, `722` = 722L, `723` = 723L, `724` = 724L,
`725` = 725L, `726` = 726L, `727` = 727L, `728` = 728L, `730` = 730L,
`738` = 738L, `750` = 750L, `753` = 753L, `754` = 754L, `761` = 761L,
`766` = 766L, `767` = 767L, `769` = 769L, `771` = 771L, `775` = 775L,
`786` = 786L, `808` = 808L, `810` = 810L, `812` = 812L, `814` = 814L,
`817` = 817L, `820` = 820L, `841` = 841L, `862` = 862L, `864` = 864L,
`865` = 865L, `866` = 866L, `867` = 867L, `874` = 874L, `877` = 877L,
`878` = 878L, `881` = 881L, `882` = 882L, `890` = 890L, `891` = 891L,
`913` = 913L, `934` = 934L, `938` = 938L, `951` = 951L, `961` = 961L,
`962` = 962L, `967` = 967L, `971` = 971L, `972` = 972L, `981` = 981L,
`983` = 983L, `986` = 986L, `988` = 988L, `1000` = 1000L, `1014` = 1014L
), class = "omit"), row.names = c(NA, -30L), class = "data.frame")
您可以使用max.col 来查看哪一列具有最大值。
data.table
library(data.table)
data.table(likes = df$likes_count, maxcol = names(df)[-1][max.col(df[-1])]
)[, .(likes = sum(likes)), maxcol]
# maxcol likes
# 1: pos 12
# 2: neg 3
# 3: neu 8
dplyr
library(dplyr)
tibble(likes = df$likes_count, maxcol = names(df)[-1][max.col(df[-1])]) %>%
group_by(maxcol) %>%
summarise(likes = sum(likes))
# # A tibble: 3 x 2
# maxcol likes
# <chr> <int>
# 1 neg 3
# 2 neu 8
# 3 pos 12
我们可以使用
table(names(df1)[-1][max.col(df1[-1], 'first')])
如果要得到'likes_count'的sum一个base R选项是
rowsum(df1$likes_count, group = names(df1)[-1][max.col(df1[-1], 'first')])
# [,1]
#neg 3
#neu 8
#pos 12
或使用 pivot_longer 转换为长格式并获得加权计数
library(dplyr)
library(tidyr)
df1 %>%
mutate(rn = row_number()) %>%
pivot_longer(cols = neg:pos) %>% group_by(rn) %>%
slice(which.max(value)) %>%
ungroup %>%
count(name, wt = likes_count)
# A tibble: 3 x 2
# name n
# <chr> <int>
#1 neg 3
#2 neu 8
#3 pos 12
我有原始数据集。以下是原始数据样本：
sentiment
pos neu neg likes_count
1 1 0 0 5
2 0.2 0.3 0.5 6
3 0.3 0.3 0.4 6
4 0 0 1 3
5 0.2 0.7 0.1 1
在这个情绪原始数据中，"pos"指的是评论中正面的概率，"neu"指的是中立的概率，"neg"指的是负面的概率。 "likes_count" 是Facebook 评论的点赞数。我想在 pos、neu 和 neg 中选择概率最高的。并知道哪种情绪获得最高 likes_count。 例如pos : 0.6, neu : 0.2, neg : 0.2为正面评价。
我想要的输出如下：
Total_likes_count
Pos 5
Neu 1
Neg 15
你能帮我做这个吗？？
dput原始数据格式：
structure(list(likes_count = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 3L, 1L, 2L, 2L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
2L, 5L, 2L, 0L, 1L, 0L), neg = c(0, 0.41, 0, 0, 0,
0.19, 0, 1, 0, 0.52, 0, 0.11, 0.02, 0.05, 0.02, 0, 0, 0.01, 0.01,
0, 0, 0.97, 0, 0.01, 0.24, 0.34, 0.94, 0.44, 0.15, 0.01), neu = c(0,
0.1, 0, 0, 0, 0, 0.13, 0, 0.98, 0.32, 0, 0.08, 0.02, 0.04, 0.07,
0, 0, 0.98, 0.07, 0, 0, 0.03, 0.02, 0.21, 0.48, 0.62, 0.01, 0.2,
0.85, 0.67), pos = c(1, 0.48, 1, 1, 1, 0.81, 0.86,
0, 0.02, 0.16, 1, 0.81, 0.96, 0.91, 0.91, 1, 1, 0.01, 0.92, 1,
1, 0, 0.98, 0.78, 0.28, 0.04, 0.05, 0.36, 0, 0.32)), na.action = structure(c(`7` = 7L,
`11` = 11L, `38` = 38L, `53` = 53L, `88` = 88L, `101` = 101L,
`106` = 106L, `138` = 138L, `139` = 139L, `155` = 155L, `165` = 165L,
`176` = 176L, `178` = 178L, `179` = 179L, `199` = 199L, `200` = 200L,
`201` = 201L, `208` = 208L, `209` = 209L, `250` = 250L, `281` = 281L,
`293` = 293L, `299` = 299L, `316` = 316L, `321` = 321L, `322` = 322L,
`328` = 328L, `332` = 332L, `333` = 333L, `334` = 334L, `335` = 335L,
`336` = 336L, `342` = 342L, `347` = 347L, `352` = 352L, `354` = 354L,
`355` = 355L, `395` = 395L, `398` = 398L, `400` = 400L, `411` = 411L,
`420` = 420L, `449` = 449L, `454` = 454L, `456` = 456L, `457` = 457L,
`464` = 464L, `471` = 471L, `491` = 491L, `495` = 495L, `502` = 502L,
`503` = 503L, `504` = 504L, `506` = 506L, `526` = 526L, `536` = 536L,
`541` = 541L, `542` = 542L, `546` = 546L, `556` = 556L, `558` = 558L,
`563` = 563L, `579` = 579L, `581` = 581L, `582` = 582L, `584` = 584L,
`602` = 602L, `603` = 603L, `604` = 604L, `606` = 606L, `614` = 614L,
`617` = 617L, `619` = 619L, `620` = 620L, `621` = 621L, `622` = 622L,
`623` = 623L, `625` = 625L, `626` = 626L, `629` = 629L, `630` = 630L,
`631` = 631L, `632` = 632L, `633` = 633L, `636` = 636L, `637` = 637L,
`638` = 638L, `639` = 639L, `640` = 640L, `643` = 643L, `645` = 645L,
`646` = 646L, `647` = 647L, `648` = 648L, `650` = 650L, `652` = 652L,
`653` = 653L, `655` = 655L, `656` = 656L, `658` = 658L, `661` = 661L,
`665` = 665L, `666` = 666L, `667` = 667L, `669` = 669L, `671` = 671L,
`673` = 673L, `674` = 674L, `679` = 679L, `680` = 680L, `682` = 682L,
`683` = 683L, `684` = 684L, `685` = 685L, `686` = 686L, `687` = 687L,
`689` = 689L, `692` = 692L, `694` = 694L, `696` = 696L, `697` = 697L,
`699` = 699L, `700` = 700L, `701` = 701L, `702` = 702L, `703` = 703L,
`704` = 704L, `705` = 705L, `707` = 707L, `708` = 708L, `712` = 712L,
`713` = 713L, `714` = 714L, `717` = 717L, `718` = 718L, `719` = 719L,
`720` = 720L, `721` = 721L, `722` = 722L, `723` = 723L, `724` = 724L,
`725` = 725L, `726` = 726L, `727` = 727L, `728` = 728L, `730` = 730L,
`738` = 738L, `750` = 750L, `753` = 753L, `754` = 754L, `761` = 761L,
`766` = 766L, `767` = 767L, `769` = 769L, `771` = 771L, `775` = 775L,
`786` = 786L, `808` = 808L, `810` = 810L, `812` = 812L, `814` = 814L,
`817` = 817L, `820` = 820L, `841` = 841L, `862` = 862L, `864` = 864L,
`865` = 865L, `866` = 866L, `867` = 867L, `874` = 874L, `877` = 877L,
`878` = 878L, `881` = 881L, `882` = 882L, `890` = 890L, `891` = 891L,
`913` = 913L, `934` = 934L, `938` = 938L, `951` = 951L, `961` = 961L,
`962` = 962L, `967` = 967L, `971` = 971L, `972` = 972L, `981` = 981L,
`983` = 983L, `986` = 986L, `988` = 988L, `1000` = 1000L, `1014` = 1014L
), class = "omit"), row.names = c(NA, -30L), class = "data.frame")
您可以使用max.col 来查看哪一列具有最大值。
data.table
library(data.table)
data.table(likes = df$likes_count, maxcol = names(df)[-1][max.col(df[-1])]
)[, .(likes = sum(likes)), maxcol]
# maxcol likes
# 1: pos 12
# 2: neg 3
# 3: neu 8
dplyr
library(dplyr)
tibble(likes = df$likes_count, maxcol = names(df)[-1][max.col(df[-1])]) %>%
group_by(maxcol) %>%
summarise(likes = sum(likes))
# # A tibble: 3 x 2
# maxcol likes
# <chr> <int>
# 1 neg 3
# 2 neu 8
# 3 pos 12
我们可以使用
table(names(df1)[-1][max.col(df1[-1], 'first')])
如果要得到'likes_count'的sum一个base R选项是
rowsum(df1$likes_count, group = names(df1)[-1][max.col(df1[-1], 'first')])
# [,1]
#neg 3
#neu 8
#pos 12
或使用 pivot_longer 转换为长格式并获得加权计数
library(dplyr)
library(tidyr)
df1 %>%
mutate(rn = row_number()) %>%
pivot_longer(cols = neg:pos) %>% group_by(rn) %>%
slice(which.max(value)) %>%
ungroup %>%
count(name, wt = likes_count)
# A tibble: 3 x 2
# name n
# <chr> <int>
#1 neg 3
#2 neu 8
#3 pos 12