特定字符串的条件出现和使用 R 相对制作一个新的数据框

Question

我有一个包含 4 列和许多行的大数据框（示例已附上）。

#what I have
Arm <- c("5prime","3prime","5prime","CoMature","3prime","5prime","3prime","3prime")
Family <- c("LET-7","LET-7","LET-7","MIR-10","MIR-103","MIR-124","MIR-124","MIR-124")
Sequence <- c("ATCGGCA","ATGCTAC","ATCGGCA","ATCGTTT","TGAGGAG","TGATCAG","AATTCAG","AATTCAG")
Star_seq <- c("TTCAGGT","TATACTG","TTCAGGT","GAGATCA","CAAAAGC","CACATGC","AATATGC","AATATGC")
my_data_frame <- data.frame(Arm,Family,Sequence,Star_seq)

我想做的基本上是为 Family 列中的每个 i 计算 Arm 列中 '5prime'、'3prime' 或 'CoMature' 出现的次数。然后对于最常见的（'5prime'、'3prime' 或 'CoMature'）取第三和第四列。总而言之，我需要一个最终文件，显示 Family 列中每个 i 的最频繁臂（在第一行）以及它们在第三和第四列中的相对序列。

#what I want as output
five_prime_counts <- c("2","0","0","1")
three_prime_counts <- c("1","0","1","2")
CoMature_counts <- c("0","1","0","0")
Arm_new <- c("5prime","CoMature","3prime","3prime")
Family_new <- c("LET-7","MIR-10","MIR-103","MIR-124")
Sequence_new <- c("ATCGGCA","ATCGTTT","TGAGGAG","AATTCAG")
Star_seq_new <- c("TTCAGGT","GAGATCA","CAAAAGC","AATATGC")
my_data_frame_new <- data.frame(five_prime_counts,three_prime_counts,CoMature_counts,Arm_new,Family_new,Sequence_new,Star_seq_new)

Answer 1

我们可以为每个Family和Arm添加一个计数变量，得到对应的Sequence、Star_seq和Arm的最大计数值每个 Family 并以宽格式获取数据。

library(dplyr)

my_data_frame %>%
  add_count(Family, Arm) %>%
  group_by(Family) %>%
  mutate(Sequence = Sequence[which.max(n)], 
         Star_seq =  Star_seq[which.max(n)], 
         Arm_new = Arm[which.max(n)]) %>%
  distinct() %>%
  tidyr::pivot_wider(names_from = Arm, values_from = n, values_fill = list(n = 0))

#  Family  Sequence Star_seq Arm_new  `5prime` `3prime` CoMature
#  <fct>   <fct>    <fct>    <fct>       <int>    <int>    <int>
#1 LET-7   ATCGGCA  TTCAGGT  5prime          2        1        0
#2 MIR-10  ATCGTTT  GAGATCA  CoMature        0        0        1
#3 MIR-103 TGAGGAG  CAAAAGC  3prime          0        1        0
#4 MIR-124 AATTCAG  AATATGC  3prime          1        2        0

特定字符串的条件出现和使用 R 相对制作一个新的数据框

Conditional occurrences of specific strings and relatively making a new data frame using R

r

count

conditional-statements

dataframe

find-occurrences