我有一个很大的数据集,在它的简短版本中看起来像这样:
> df
Stimulus TimeDiff
S102 10332.4
S 66 1095.4
S103 2987.8
S 77 551.4
S112 3015.2
S 66 566.6
S114 5999.8
S 88 403.8
S104 4679.4
S 88 655.2
我想创建一个新列df $ Accuracy,我需要根据df $ Stimulus和df $ TimeDiff中的某些值(仅S 88,S 66,S 77)分配正确,错误的响应和未命中。例如,如果S 88之前是S114或S104,并且该行的df $ TimeDiff小于710,则在df $ Accuracy中分配“不正确”。所以数据集看起来像这样:
> df
Stimulus TimeDiff Accuracy
S102 10332.4 NA
S 66 1095.4 NA
S103 2987.8 NA
S 77 551.4 NA
S112 3015.2 NA
S 66 566.6 NA
S114 5999.8 NA
S 88 403.8 incorrect
S104 4679.4 NA
S 88 655.2 incorrect
最好的方法是什么?
答案 0 :(得分:1)
您可以使用ifelse
,
lag
和dplyr
功能
library(dplyr)
df$Accuracy <- with(df, ifelse(Stimulus %in% c('S88', 'S66', 'S77') &
lag(Stimulus) %in% c('S114', 'S104') &
TimeDiff < 710, 'incorrect', NA))
df
# Stimulus TimeDiff Accuracy
#1 S102 10332.4 <NA>
#2 S66 1095.4 <NA>
#3 S103 2987.8 <NA>
#4 S77 551.4 <NA>
#5 S112 3015.2 <NA>
#6 S66 566.6 <NA>
#7 S114 5999.8 <NA>
#8 S88 403.8 incorrect
#9 S104 4679.4 <NA>
#10 S88 655.2 incorrect
答案 1 :(得分:0)
我们可以使用data.table
方法,因为我们正在分配(:=
),所以它应该很有效。
library(data.table)
setDT(df)[Stimulus %chin% c("S 88", "S 66", "S 77") & shift(Stimulus) %chin%
c("S114", "S104") & TimeDiff < 710, Accuracy := "incorrect"]
df
# Stimulus TimeDiff Accuracy
# 1: S102 10332.4 NA
# 2: S 66 1095.4 NA
# 3: S103 2987.8 NA
# 4: S 77 551.4 NA
# 5: S112 3015.2 NA
# 6: S 66 566.6 NA
# 7: S114 5999.8 NA
# 8: S 88 403.8 incorrect
# 9: S104 4679.4 NA
#10: S 88 655.2 incorrect
df <- structure(list(Stimulus = c("S102", "S 66", "S103", "S 77", "S112",
"S 66", "S114", "S 88", "S104", "S 88"), TimeDiff = c(10332.4,
1095.4, 2987.8, 551.4, 3015.2, 566.6, 5999.8, 403.8, 4679.4,
655.2)), .Names = c("Stimulus", "TimeDiff"), class = "data.frame",
row.names = c(NA, -10L))