需求定义
每行: 每个客户-关联联系人的手机号 每列: 关联联系人编号 1,2,3
这个联系人编号有讲究吗？ 比如第一联系人、第二联系人
library(tidyverse)
## -- Attaching packages ---------------------------------------------- tidyverse 1.2.1 --
## √ ggplot2 3.1.0 √ purrr 0.2.5
## √ tibble 1.4.2 √ dplyr 0.7.8
## √ tidyr 0.8.2 √ stringr 1.3.1
## √ readr 1.2.1 √ forcats 0.3.0
## -- Conflicts ------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
df <- data.frame(id = sort(c(rep(letters[1:3], each = 2), 'a', 'a', 'b', 'd')),
name = LETTERS[1:10],
phone = 0:9)
df %>%
group_by(id) %>%
nest
## # A tibble: 4 x 2
## id data
## <fct> <list>
## 1 a <tibble [4 x 2]>
## 2 b <tibble [3 x 2]>
## 3 c <tibble [2 x 2]>
## 4 d <tibble [1 x 2]>
df <- data.frame(id = c('a', 'a', 'b'), phone = c(111, 222, 333), name = c('Bob', 'John', 'Tom')); df
## id phone name
## 1 a 111 Bob
## 2 a 222 John
## 3 b 333 Tom
df <- df %>% group_by(id) %>% mutate(number = 1:length(id))
df_1 <- df %>% filter(number == 1) %>% select(-number)
df_2 <- df %>% filter(number == 2) %>% select(-number)
df_1 %>% left_join(df_2, by = 'id')
## # A tibble: 2 x 5
## # Groups: id [?]
## id phone.x name.x phone.y name.y
## <fct> <dbl> <fct> <dbl> <fct>
## 1 a 111 Bob 222 John
## 2 b 333 Tom NA <NA>
df <- data.frame(id = c('a', 'a', 'b'), p = c(1, 2, 1), d = c('A', 'B', 'A')); df
## id p d
## 1 a 1 A
## 2 a 2 B
## 3 b 1 A
df2 <- data.frame(id = c('a', 'b'), p_1 = c(1, 1), p_2 = c(2, NA), d_1 = c('A', 'A'), d_2 = c('B', NA)); df2
## id p_1 p_2 d_1 d_2
## 1 a 1 2 A B
## 2 b 1 NA A <NA>
library(tidyverse)
df %>%
gather(key,value,-id) %>%
# 数据非唯一，因此需要加额外标签
mutate(key = str_c(key,value,sep = '_')) %>%
spread(key,value)
## Warning: attributes are not identical across measure variables;
## they will be dropped
## id d_A d_B p_1 p_2
## 1 a A B 1 2
## 2 b A <NA> 1 <NA>
# LETTERS 需要数字化，用如下表格替换即可
tibble(
upper = LETTERS
) %>%
mutate(num = row_number())
## # A tibble: 26 x 2
## upper num
## <chr> <int>
## 1 A 1
## 2 B 2
## 3 C 3
## 4 D 4
## 5 E 5
## 6 F 6
## 7 G 7
## 8 H 8
## 9 I 9
## 10 J 10
## # ... with 16 more rows