循环浏览R中的列 数据

如何解决循环浏览R中的列 数据

我正在尝试计算每列中有多少个“ S”作为从1到10行的“下游”，然后作为从15到25行的“上游”。

然后我要将输出保存在文本文件中。 好吧，我设法解决了一个例子。不幸的是，我在循环保存列时也遇到了问题。在这种情况下，列数为5，但可以根据文件而有所不同。

#data frame
S <- data.frame(scale = c(0,1,0),aa = c('A','C','D','E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y'))

    #input  (example)
   V1  V2  V3  V4  V5 
1  C   D   E    R   N  
2  C   A   M    K   P
3  V   T   Q    Q   E 
4  A   T   S    S   S
5  C   D   E    R   N  
6  C   A   M    K   P
7  V   T   Q    Q   E 
8  A   T   S    S   S
9  R   V   D    S   A
10 W   R   H    I   C
11 S   N   I    P   T
12 Q   A   S    D   E
13 C   D   E    R   N  
14 C   A   M    K   P
15 V   T   Q    Q   E 
16 A   T   S    S   S
17 C   D   E    R   N  
18 C   A   M    K   P
19 V   T   Q    Q   E 
20 A   T   S    S   S
21 R   V   D    S   A
22 W   R   H    I   C
23 S   N   I    P   T
24 G   A   D    S   S 
25 N   T   T    S   A




# matching the data from two data frames
  df11 <- df_trial %>%
  pivot_longer(cols = everything(),values_to = 'aa') %>%
  mutate(aa = replace(aa,aa == '-','')) %>%
  left_join(S,by = 'aa') %>%
  arrange(name) %>%  
  group_by(name) %>% 
  mutate(row = row_number())  
  view(df11)

  values_for_all <- df11 %>%
  pivot_wider(names_from = name,values_from = c(scale,aa)) %>% 
  select(-row) 
  view(values_for_all)
 #class(values_for_all)

#problem与循环遍历以下列：!!!!!!!!!!!!!!!!!

 #sum values from positions 1 to 10 and then from  15 to 25
    downstream <- sum(values_for_all$scale_V1[1:11])
    #view(downstream)
    upstream <- sum(values_for_all$scale_V1[15:25])
    #view(upstream)

res <- cbind(downstream,upstream) 
res_trial<- as.data.frame(t(res))
view(res_trial)
#class(res_trial)
#converting a matrix to the data frame
res_final <- as.data.frame(t(res_trial))
view(res_final)
#class(res_final)

#saving to a text file
write.table(res_final,"~/Desktop/R_work/test.txt",sep="\t",row.names=FALSE)

#expected outcome (example):

downstream   upstream
   2            0
   0            0

谢谢您的帮助！

解决方法

在对行进行子集设置后，我们可以使用colSums

colSums(df_trial[1:10,] == 'S')
# V1 V2 V3 V4 V5 
# 0  0  2  3  2 
colSums(df_trial[15:25,] == 'S')
# V1 V2 V3 V4 V5 
#1  0  2  5  3 

---

或与dplyr

library(dplyr)
df_trial %>%
       summarise(across(everything(),~ c(sum(.[1:10] == 'S'),sum(.[15:25] == 'S')))) %>%
       mutate(categ = c('upstream','downstream'),.before = 1)
#       categ V1 V2 V3 V4 V5
#1   upstream  0  0  2  3  2
#2 downstream  1  0  2  5  3

数据

df_trial <- structure(list(V1 = c("C","C","V","A","R","W","S","Q","G","N"),V2 = c("D","T","D","N","T"),V3 = c("E","M","E","H","I",V4 = c("R","K","P","S"),V5 = c("N","A")),class = "data.frame",row.names = c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25"))

,

这项工作：

> S_count <- data.frame(downstream = sum(sapply(S[1:10,],function(x) sum(grepl('S',x)))),+                       upstream = sum(sapply(S[15:25,stringsAsFactors = F)
> S_count
  downstream upstream
1          7       11
> 

以矩阵形式：

> S_count_vec <- rbind(downstream = sapply(S[1:10,x))),+                       upstream = sapply(S[15:25,x))))
> S_count_vec
           V1 V2 V3 V4 V5
downstream  0  0  2  3  2
upstream    1  0  2  5  3
> 

使用的数据：

> dput(S)
structure(list(V1 = c("C",row.names = c(NA,-25L
),class = c("tbl_df","tbl","data.frame"))