Question

假设我有一个这样的数据框：

     X.                      Name             Type Total  HP Attack Defense Sp..Atk Sp..Def Speed
795 718          Zygarde50% Forme    Dragon/Ground   600 108    100     121      81      95    95
796 719                   Diancie       Rock/Fairy   600  50    100     150     100     150    50
797 719       DiancieMega Diancie       Rock/Fairy   700  50    160     110     160     110   110
798 720       HoopaHoopa Confined    Psychic/Ghost   600  80    110      60     150     130    70
799 720        HoopaHoopa Unbound     Psychic/Dark   680  80    160      60     170     130    80
800 721                 Volcanion       Fire/Water   600  80    110     120     130      90    70

如果我想计算平均值（总计，HP，攻击，防御等等），按类型Dragon，键入Ground，键入Rock，键入Fairy等...（而不是Dragon / Ground类型），Rock / Fairy），我该怎么办？属于任何两种类型的小宠物的统计数据将用于计算两者的平均统计数据。

我使用dplyr包中的函数编写了代码：

summaryStats_byType<- summarise(byType,
                            count = n(),
                            averageTotal = mean(Total, na.rm = T),
                            averageHP = mean(HP, na.rm = T),
                            averageDefense = mean(Defense, na.rm = T),
                            averageSpAtk = mean(Sp..Atk, na.rm = T),
                            averageSpDef = mean(Sp..Def, na.rm = T),
                            averageSpeed = mean(Speed, na.rm = T))

但显然它将“Dragon / Ground”视为一种类型而不是两种。

Answer 1

一种方法是将Type列分成长格式（我从cSplit选择splitstackshape来执行此操作）和group_by照常分割，即

library(splitstackshape)
library(dplyr)

df1 <- cSplit(df, 'Type', sep = '/', 'long')
df1 %>% 
  group_by(Type) %>% 
  summarise_each(funs(mean), -c(X., Name))

# A tibble: 9 × 8
#     Type Total    HP Attack Defense Sp..Atk Sp..Def Speed
#   <fctr> <dbl> <dbl>  <dbl>   <dbl>   <dbl>   <dbl> <dbl>
#1    Dark   680    80    160      60     170     130    80
#2  Dragon   600   108    100     121      81      95    95
#3   Fairy   650    50    130     130     130     130    80
#4    Fire   600    80    110     120     130      90    70
#5   Ghost   600    80    110      60     150     130    70
#6  Ground   600   108    100     121      81      95    95
#7 Psychic   640    80    135      60     160     130    75
#8    Rock   650    50    130     130     130     130    80
#9   Water   600    80    110     120     130      90    70

或者（如@DavidArenburg所述）我们也可以使用separate_rows中的tidyr作为管道的一部分，即

library(tidyr)
library(dplyr)
df %>% 
  separate_rows(Type) %>% 
  group_by(Type) %>% 
  summarise_each(funs(mean), -c(X., Name))

当然会产生相同的结果

数据

dput(df) structure(list(X. = c(718L, 719L, 719L, 720L, 720L, 721L), Name = structure(c(6L, 1L, 2L, 3L, 4L, 5L), .Label = c("Diancie", "DiancieMega_Diancie", "HoopaHoopa_Confined", "HoopaHoopa_Unbound", "Volcanion", "Zygarde50%_Forme" ), class = "factor"), Type = structure(c(1L, 5L, 5L, 4L, 3L, 2L), .Label = c("Dragon/Ground", "Fire/Water", "Psychic/Dark", "Psychic/Ghost", "Rock/Fairy"), class = "factor"), Total = c(600L, 600L, 700L, 600L, 680L, 600L), HP = c(108L, 50L, 50L, 80L, 80L, 80L), Attack = c(100L, 100L, 160L, 110L, 160L, 110L), Defense = c(121L, 150L, 110L, 60L, 60L, 120L), Sp..Atk = c(81L, 100L, 160L, 150L, 170L, 130L), Sp..Def = c(95L, 150L, 110L, 130L, 130L, 90L), Speed = c(95L, 50L, 110L, 70L, 80L, 70L)), .Names = c("X.", "Name", "Type", "Total", "HP", "Attack", "Defense", "Sp..Atk", "Sp..Def", "Speed" ), class = "data.frame", row.names = c("795", "796", "797", "798", "799", "800"))

计算R中连接字符串中的统计数据

1 个答案: