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[英]A more efficient way to strip a character from column names in an R dataframe?
[英]Is there a more efficient way to handle facts which are duplicating in an R dataframe?
我有一個看起來像這樣的數據框:
ID <- c(1,1,1,2,2,2,2,3,3,3,3)
Fact <- c(233,233,233,50,50,50,50,15,15,15,15)
Overall_Category <- c("Purchaser","Purchaser","Purchaser","Car","Car","Car","Car","Car","Car","Car","Car")
Descriptor <- c("Country", "Gender", "Eyes", "Color", "Financed", "Type", "Transmission", "Color", "Financed", "Type", "Transmission")
Members <- c("America", "Male", "Brown", "Red", "Yes", "Sedan", "Manual", "Blue","No", "Van", "Automatic")
df <- data.frame(ID, Fact, Overall_Category, Descriptor, Members)
數據框維度的工作方式如下:
問題在於,根據應用於給定事實的維度數量,針對給定 ID 重復提交的單個事實。 我想要的是一種根據其 ID 僅顯示一次事實的方法,並將適用的維度存儲在該單個 ID 上。
我通過這樣做實現了它:
df1 <- pivot_wider(df,
id_cols = ID,
names_from = c(Overall_Category, Descriptor, Members),
names_prefix = "zzzz",
values_from = Fact,
names_sep = "-",
names_repair = "unique")
ColumnNames <- df1 %>% select(matches("zzzz")) %>% colnames()
df2 <- df1 %>% mutate(mean_sel = rowMeans(select(., ColumnNames), na.rm = T))
df3 <- df2 %>% mutate_at(ColumnNames, function(x) ifelse(!is.na(x), deparse(substitute(x)), NA))
df3 <- df3 %>% unite('Descriptor', ColumnNames, na.rm = T, sep = "_")
df3 <- df3 %>% mutate_at("Descriptor", str_replace_all, "zzzz", "")
但是由於pivot_wide,它似乎不能很好地擴展具有多個維度的事實,並且通常看起來不是一種非常有效的方法。
有一個更好的方法嗎?
我認為你想要帶有sep
和collapse
參數的簡單paste
library(dplyr, warn.conflicts = F)
df %>% group_by(ID, Fact) %>%
summarise(Descriptor = paste(paste(Overall_Category, Descriptor, Members, sep = '-'), collapse = '_'), .groups = 'drop')
# A tibble: 3 x 3
ID Fact Descriptor
<dbl> <dbl> <chr>
1 1 233 Purchaser-Country-America_Purchaser-Gender-Male_Purchaser-Eyes-Brown
2 2 50 Car-Color-Red_Car-Financed-Yes_Car-Type-Sedan_Car-Transmission-Manual
3 3 15 Car-Color-Blue_Car-Financed-No_Car-Type-Van_Car-Transmission-Automatic
您可以unite
的列和每個ID
它們組合在一起,並采取平均的Fact
值。
library(dplyr)
library(tidyr)
df %>%
unite(Descriptor, Overall_Category:Members, sep = '-', na.rm = TRUE) %>%
group_by(ID) %>%
summarise(Descriptor = paste0(Descriptor, collapse = '_'),
mean_sel = mean(Fact, na.rm = TRUE))
# ID Descriptor mean_sel
# <dbl> <chr> <dbl>
#1 1 Purchaser-Country-America_Purchaser-Gender-Male_Purchas… 233
#2 2 Car-Color-Red_Car-Financed-Yes_Car-Type-Sedan_Car-Trans… 50
#3 3 Car-Color-Blue_Car-Financed-No_Car-Type-Van_Car-Transmi… 15
str_c
一個選項
library(dplyr)
library(stringr)
df %>%
group_by(ID, Fact) %>%
summarise(Descriptor = str_c(Overall_Category, Descriptor, Members, sep= "-", collapse="_"), .groups = 'drop')
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