R Megre 数据框列和重新编码答案

【问题标题】：R Megre Data Frame Column and RecodeR Megre 数据框列和重新编码
【发布时间】：2021-04-12 06:36:53
【问题描述】：

我有 2 个如下所示的 R 数据框：

数据帧 1：

identifier	ef_posterior	position_no	classification
11111	0.260	1	yes
11111	0.0822	2	yes
11111	0.00797	3	yes
11111	0.04	4	no
11111	0.245	5	yes
11111	0.432	6	yes
11112	0.342	1	maybe
11112	0.453	2	yes
11112	0.0032	3	yes
11112	0.241	5	no
11112	0.0422	6	yes
11112	0.311	4	no

数据框 2：

study_identifier	%LVEF
11111	62
11112	76

我想将这两个数据框合并并重新排列成这样的：

Study_identifier 和 identifier 是同一个东西（只是不同的列名）。另外，我想重新编码分类，使yes = 0，no = 1，maybe = 2

identifier	pos_1	pos_1_class	pos_2	pos_2_class	pos_3	pos_3_class	pos_4	pos_4_class	pos_5	pos_5_class	pos_6	pos_6_class	%LVEF
11111	0.260	0	0.0822	0	0.00797	0	0.04	1	0.245	0	0.432	0	62
11112	0.342	2	0.453	0	0.0032	0	0.311	1	0.241	1	0.0422	0	76

df1 %>% mutate(position_no = paste0("position_", position_no)) %>%
  pivot_wider(id_cols = identifier, names_from = position_no, values_from = ef_posterior) %>%
  left_join(df2 %>% mutate(study_identifier = as.numeric(as.character(study_identifier))), by = c("identifier" = "study_identifier"))

这是我现在的代码，但我不知道在哪里放置分类列的代码

我该怎么做呢？任何帮助将不胜感激！

【问题讨论】：

标签： r dataframe merge

【解决方案1】：

您可以使用dplyr 和case_when 轻松重新编码：

df1 %>% mutate(
  classification = 
    case_when( classification == "yes" ~ 1,
               classification == "no" ~ 0,
               classification == "maybe" ~ 2)
)

我会通过以下方式解决：

library(tidyverse)
df1 <- data.frame(
  stringsAsFactors = FALSE,
  identifier = c(11111L,11111L,11111L,11111L,
                 11111L,11111L,11112L,11112L,11112L,11112L,11112L,
                 11112L),
  ef_posterior = c(0.26,0.0822,0.00797,0.04,
                   0.245,0.432,0.342,0.453,0.0032,0.241,0.0422,0.311),
  position_no = c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 5L, 6L, 4L),
  classification = c("yes","yes","yes","no",
                     "yes","yes","maybe","yes","yes","no","yes","no")
)

df2 <- data.frame(
  check.names = FALSE,
  study_identifier = c(11111L, 11112L),
  `%LVEF` = c(62L, 76L)
)

df1 %>% mutate(
  classification = 
    case_when( classification == "yes" ~ 1,
               classification == "no" ~ 0,
               classification == "maybe" ~ 2)
) %>% 
  pivot_wider(
    id_cols = c(identifier), names_from = c(position_no), values_from = c(classification,ef_posterior)) %>% 
left_join(df2, by = c("identifier" = "study_identifier"))
#> # A tibble: 2 x 14
#>   identifier classification_1 classification_2 classification_3 classification_4
#>        <int>            <dbl>            <dbl>            <dbl>            <dbl>
#> 1      11111                1                1                1                0
#> 2      11112                2                1                1                0
#> # … with 9 more variables: classification_5 <dbl>, classification_6 <dbl>,
#> #   ef_posterior_1 <dbl>, ef_posterior_2 <dbl>, ef_posterior_3 <dbl>,
#> #   ef_posterior_4 <dbl>, ef_posterior_5 <dbl>, ef_posterior_6 <dbl>,
#> #   `%LVEF` <int>

由 reprex 包 (v0.3.0) 于 2021-04-12 创建

【讨论】：