我的数据框看起来像这样：
df <- as.data.frame(matrix(c("True Organic", "True Organic", NA, NA, NA, 0,
"True Organic", "True Organic", NA, NA, NA, 0,
"Organic Search (SEO)", "Induced Organic", NA, NA, NA, 0,
"Display", NA, NA, NA, NA, 0,
"Social Ads (Act)", "Induced Organic", "Induced Organic", NA, NA, 1,
"Referral", "Social Ads (Act)", NA, NA, NA, 0,
"Special Emails", "Induced Organic", NA, NA, NA, 1,
"Daily Email", "Daily Email", "Daily Email", NA, NA, 0), nrow = 8,
ncol = 6, byrow = TRUE, dimnames = list(NULL, c("Node_1", "Node_2",
"Node_3", "Node_4",
"Node_5", "conversion"))), ,
stringsAsFactors = FALSE)
df
Node_1 Node_2 Node_3 Node_4 Node_5 conversion
1 True Organic True Organic <NA> <NA> <NA> 0
2 True Organic True Organic <NA> <NA> <NA> 0
3 Organic Search (SEO) Induced Organic <NA> <NA> <NA> 0
4 Display <NA> <NA> <NA> <NA> 0
5 Social Ads (Act) Induced Organic Induced Organic <NA> <NA> 1
6 Referral Social Ads (Act) <NA> <NA> <NA> 0
7 Special Emails Induced Organic <NA> <NA> <NA> 1
8 Daily Email Daily Email Daily Email <NA> <NA> 0
对于每一行，如果该行的转换列等于0，我想用字符串“Null”替换所有NAs，或者如果转换列等于1，则替换为“Conversion”。
我的最终输出应该如下所示。
df_desired <- as.data.frame(matrix(c("True Organic", "True Organic", "Null", "Null", "Null", 0,
"True Organic", "True Organic", "Null", "Null", "Null", 0,
"Organic Search (SEO)", "Induced Organic", "Null", "Null", "Null", 0,
"Display", "Null", "Null", "Null", "Null", 0,
"Social Ads (Act)", "Induced Organic", "Induced Organic", "Conversion", "Conversion", 1,
"Referral", "Social Ads (Act)","Null", "Null", "Null", 0,
"Special Emails", "Induced Organic", "Converison", "Conversion", "Conversion", 1,
"Daily Email", "Daily Email", "Daily Email", "Null", "Null", 0), nrow = 8,
ncol = 6, byrow = TRUE, dimnames = list(NULL, c("Node_1", "Node_2",
"Node_3", "Node_4",
"Node_5", "conversion"))), ,
stringsAsFactors = FALSE)
df_desired
Node_1 Node_2 Node_3 Node_4 Node_5 conversion
1 True Organic True Organic Null Null Null 0
2 True Organic True Organic Null Null Null 0
3 Organic Search (SEO) Induced Organic Null Null Null 0
4 Display Null Null Null Null 0
5 Social Ads (Act) Induced Organic Induced Organic Conversion Conversion 1
6 Referral Social Ads (Act) Null Null Null 0
7 Special Emails Induced Organic Converison Conversion Conversion 1
8 Daily Email Daily Email Daily Email Null Null 0
我可以使用嵌套的for循环来完成此操作。
for (i in 1:nrow(df)){
for (j in 1:ncol(df)){
df[i,j] <- ifelse(((is.na(df[i,j])) & df[i,]$conversion == "1"), "Conversion", df[i,j])
for (j in 1:ncol(df)){
df[i,j] <- ifelse(((is.na(df[i,j])) & df[i,]$conversion == "0"), "Null", df[i,j])
}
}
}
不幸的是，这不能很好地扩展。必须有更好的方法来做到这一点。任何建议将不胜感激。提前致谢！
答案 0 :(得分：3)
让我们写一个小函数来做一个向量（输入两个向量）：
foo = function(x, conversion) {
x = ifelse(!is.na(x), x, ifelse(conversion == 1, "Conversion", "Null"))
}
然后用一个简单的循环来对除conversion以外的所有列进行操作：
for (col in setdiff(names(df), "conversion")) {
df[[col]] = foo(df[[col]], df$conversion)
}
df
# Node_1 Node_2 Node_3 Node_4 Node_5 conversion
# 1 True Organic True Organic Null Null Null 0
# 2 True Organic True Organic Null Null Null 0
# 3 Organic Search (SEO) Induced Organic Null Null Null 0
# 4 Display Null Null Null Null 0
# 5 Social Ads (Act) Induced Organic Induced Organic Conversion Conversion 1
# 6 Referral Social Ads (Act) Null Null Null 0
# 7 Special Emails Induced Organic Conversion Conversion Conversion 1
# 8 Daily Email Daily Email Daily Email Null Null 0
这是一个稍微优化的版本。这可能会在1M +行上节省几秒钟。
foo_x = function(x, conversion) {
x_na = is.na(x)
conversion_1 = conversion == 1
x[x_na & conversion_1] = "Conversion"
x[x_na & !conversion_1] = "Null"
return(x)
}
答案 1 :(得分：2)
使用dplyr的另一种选择：
library(dplyr)
df <- df %>%
mutate_all(funs(case_when(
is.na(.) & conversion == 0 ~ "Null",
is.na(.) & conversion == 1 ~ "conversion",
TRUE ~ .
)))
答案 2 :(得分：1)
使用一个索引分配执行此操作，依赖每个row值的NA索引来对df$conversion列进行分组：
df[is.na(df)] <- c("Null","Conversion")[as.numeric(df$conversion)+1][row(df)[is.na(df)]]
# Node_1 Node_2 Node_3 Node_4 Node_5 conversion
#1 True Organic True Organic Null Null Null 0
#2 True Organic True Organic Null Null Null 0
#3 Organic Search (SEO) Induced Organic Null Null Null 0
#4 Display Null Null Null Null 0
#5 Social Ads (Act) Induced Organic Induced Organic Conversion Conversion 1
#6 Referral Social Ads (Act) Null Null Null 0
#7 Special Emails Induced Organic Conversion Conversion Conversion 1
#8 Daily Email Daily Email Daily Email Null Null 0
执行速度应该快。这是在4.2秒内处理的1.5M行和115列。
df <- df[sample(1:8,1.5e6,replace=TRUE),c(sample(1:5,115,replace=TRUE),6)]
dim(df)
#[1] 1500000 116
system.time({
df[is.na(df)] <- c("Null","Conversion")[as.numeric(df$conversion)+1][row(df)[is.na(df)]]
})
# user system elapsed
# 2.59 1.61 4.20