entityMap|blocks|key|aqb8j|text|创建数据|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4ovdc|library(data.table)
df+<-+fread("
Basket+++F_Type_1+++F_Qty_1+++F_P_1+++F_Type_2+++F_Qty_2+++F_P_2+
AAA+++++++Apple+++++10++++++++2.5+++++Banana+++++9+++++++++2
BBB+++++++Peach+++++5+++++++++6+++++++Melon++++++20++++++++5
")

df
#++++Basket+F_Type_1+F_Qty_1+F_P_1+F_Type_2+F_Qty_2+F_P_2
#+1:++++AAA++++Apple++++++10+++2.5+++Banana+++++++9+++++2
#+2:++++BBB++++Peach+++++++5+++6.0++++Melon++++++20+++++5|code-block|syntax|javascript|8s1b|对于从1到sum(grepl('F_P_',+names(df)))的数字，将F_Total_{number}设置为F_Qty_{number}*F_P_{number}|offset|length|style|CODE|9i8a8|for(i+in+seq(sum(grepl('F_P_',+names(df)))))
++df[,+paste0('F_Total_',+i)+:=+Reduce(`*`,+.SD)
+++++,+.SDcols+=+paste0(c('F_Qty_',+'F_P_'),+i)]

df
#++++Basket+F_Type_1+F_Qty_1+F_P_1+F_Type_2+F_Qty_2+F_P_2+F_Total_1+F_Total_2
#+1:++++AAA++++Apple++++++10+++2.5+++Banana+++++++9+++++2++++++++25++++++++18
#+2:++++BBB++++Peach+++++++5+++6.0++++Melon++++++20+++++5++++++++30+++++++100|ba81a|或使用df作为data.frame的base+R中|5alpu|for(i+in+seq(sum(grepl('F_P_',+names(df)))))
++df[paste0('F_Total_',+i)]+<-+Reduce(`*`,+df[paste0(c('F_Qty_',+'F_P_'),+i)])|42j7b^0|0|0|5|T|13|G|1M|R|0|0|3|2|7|A|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|U|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|V|9|@]|A|@]|B|$F|G]]|$2|H|4|I|6|7|8|W|9|@$J|X|K|Y|L|M]|$J|Z|K|10|L|M]|$J|11|K|12|L|M]]|A|@]|B|$]]|$2|N|4|O|6|E|8|13|9|@]|A|@]|B|$F|G]]|$2|P|4|Q|6|7|8|14|9|@$J|15|K|16|L|M]|$J|17|K|18|L|M]]|A|@]|B|$]]|$2|R|4|S|6|E|8|19|9|@]|A|@]|B|$F|G]]|$2|T|4|-4|6|7|8|1A|9|@]|A|@]|B|$]]]]

Create data

<pre><code>library(data.table)
df &lt;- fread("
Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 
AAA Apple 10 2.5 Banana 9 2
BBB Peach 5 6 Melon 20 5
")

df
# Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2
# 1: AAA Apple 10 2.5 Banana 9 2
# 2: BBB Peach 5 6.0 Melon 20 5
</code></pre>

For numbers from 1 to <code>sum(grepl('F_P_', names(df)))</code>, set <code>F_Total_{number}</code> as <code>F_Qty_{number}*F_P_{number}</code> 

<pre><code>for(i in seq(sum(grepl('F_P_', names(df)))))
 df[, paste0('F_Total_', i) := Reduce(`*`, .SD)
 , .SDcols = paste0(c('F_Qty_', 'F_P_'), i)]

df
# Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 F_Total_1 F_Total_2
# 1: AAA Apple 10 2.5 Banana 9 2 25 18
# 2: BBB Peach 5 6.0 Melon 20 5 30 100
</code></pre>

Or in base R with <code>df</code> as a <code>data.frame</code>

<pre><code>for(i in seq(sum(grepl('F_P_', names(df)))))
 df[paste0('F_Total_', i)] &lt;- Reduce(`*`, df[paste0(c('F_Qty_', 'F_P_'), i)])
</code></pre>

entityMap|blocks|key|2mfn8|text|在base+R中，您可以使用[[]]表单访问带有字符串的列，因此可以轻松地进行循环|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|keuq|df+<-+read.table(text+=+"Basket+++F_Type_1+++F_Qty_1+++F_P_1+++F_Type_2+++F_Qty_2+++F_P_2+
AAA+++++++Apple+++++10++++++++2.5+++++Banana+++++9+++++++++2
BBB+++++++Peach+++++5+++++++++6+++++++Melon++++++20++++++++5",header+=+T)

for(i+in+1:2)
{
++df[[paste0("F_Total_",i)]]+<-+as.numeric(df[[paste0("F_P_",i)]])*as.numeric(df[[paste0("F_Qty_",i)]])
}

++Basket+F_Type_1+F_Qty_1+F_P_1+F_Type_2+F_Qty_2+F_P_2+F_Total_1+F_Total_2
1++++AAA++++Apple++++++10+++2.5+++Banana+++++++9+++++2++++++++25++++++++18
2++++BBB++++Peach+++++++5+++6.0++++Melon++++++20+++++5++++++++30+++++++100|code-block|syntax|javascript|b03ne^0|E|4|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|M|9|@$A|N|B|O|C|D]]|E|@]|F|$]]|$2|G|4|H|6|I|8|P|9|@]|E|@]|F|$J|K]]|$2|L|4|-4|6|7|8|Q|9|@]|E|@]|F|$]]]]

In base R, you can use the <code>[[]]</code> form to access column with a string, so you can loop easily

<pre><code>df &lt;- read.table(text = "Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 
AAA Apple 10 2.5 Banana 9 2
BBB Peach 5 6 Melon 20 5",header = T)

for(i in 1:2)
{
 df[[paste0("F_Total_",i)]] &lt;- as.numeric(df[[paste0("F_P_",i)]])*as.numeric(df[[paste0("F_Qty_",i)]])
}

 Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 F_Total_1 F_Total_2
1 AAA Apple 10 2.5 Banana 9 2 25 18
2 BBB Peach 5 6.0 Melon 20 5 30 100
</code></pre>

entityMap|0|type|LINK|mutability|MUTABLE|data|url|http://stat.ethz.ch/R-manual/R-devel/library/base/html/mapply.html|blocks|key|aic0m|text|我们创建一个查找特定名称的函数，然后计算行式乘积。此函数的重量级函数是mapply函数。我们添加最后一步来重命名结果data.frame。|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|9ojpp|fun1+<-+function(data){
++qty_names+<-+names(data)[grepl(pattern+=+"Qty",+x+=+names(data))]

++p_names+<-+names(data)[grepl(pattern+=+"P",+x+=+names(data))]

++setNames(
++++data.frame(
++++++mapply(qty_names,+p_names,+
+++++++++++++FUN+=+function(n1,+n2)+apply(data[c(n1,n2)],+1,+prod))),
++++++paste0('F_Total_',+1:length(p_names)))


}

cbind(dat,+fun1(dat))

++Basket+F_Type_1+F_Qty_1+F_P_1+F_Type_2+F_Qty_2+F_P_2+F_Total_1+F_Total_2
1++++AAA++++Apple++++++10+++2.5+++Banana+++++++9+++++2++++++++25++++++++18
2++++BBB++++Peach+++++++5+++6.0++++Melon++++++20+++++5++++++++30+++++++100|code-block|syntax|javascript|d1n79^0|Z|6|1M|A|Z|6|0|0|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|S|G|@$H|T|I|U|J|K]|$H|V|I|W|J|K]]|L|@$H|X|I|Y|A|Z]]|6|$]]|$A|M|C|N|2|O|F|10|G|@]|L|@]|6|$P|Q]]|$A|R|C|-4|2|E|F|11|G|@]|L|@]|6|$]]]]

We create a function that finds the specific names, and then calculates the rowwise products. The heavy lifter of this function is the <a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/mapply.html" rel="nofollow noreferrer"><code>mapply</code></a> function. We add a final step to rename the resultant <code>data.frame</code>.

<pre><code>fun1 &lt;- function(data){
 qty_names &lt;- names(data)[grepl(pattern = "Qty", x = names(data))]

 p_names &lt;- names(data)[grepl(pattern = "P", x = names(data))]

 setNames(
 data.frame(
 mapply(qty_names, p_names, 
 FUN = function(n1, n2) apply(data[c(n1,n2)], 1, prod))),
 paste0('F_Total_', 1:length(p_names)))


}

cbind(dat, fun1(dat))

 Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 F_Total_1 F_Total_2
1 AAA Apple 10 2.5 Banana 9 2 25 18
2 BBB Peach 5 6.0 Melon 20 5 30 100
</code></pre>

entityMap|blocks|key|cia1j|text|下面是一种使用tidyverse函数重塑数据的方法。基本上，我们使用tidyr动词将您的数据重塑为更“整洁”的格式。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|cj919|#+library(dplyr);+library(tidyr)
dd+%25>%25+select(Basket,+contains("_Qty_"),+contains("_P_"))+%25>%25+
++gather("key",+"value",+-Basket)+%25>%25+
++separate(key,+c("F",+"Val",+"Grp"))+%25>%25+
++group_by(Basket,+Grp)+%25>%25+
++spread(Val,+value)+%25>%25+
++mutate(Total=P*Qty,+GrpN=paste0("Total_",+Grp))+%25>%25+
++ungroup()+%25>%25+
++select(Basket,+GrpN,+Total)+%25>%25+
++spread(GrpN+,Total)

#+++Basket+Total_1+Total_2
#+++<fct>++++<dbl>+++<dbl>
#+1+AAA+++++++++25++++++18
#+2+BBB+++++++++30+++++100|code-block|syntax|javascript|1hi13^0|7|9|Y|5|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|M|9|@$A|N|B|O|C|D]|$A|P|B|Q|C|D]]|E|@]|F|$]]|$2|G|4|H|6|I|8|R|9|@]|E|@]|F|$J|K]]|$2|L|4|-4|6|7|8|S|9|@]|E|@]|F|$]]]]

Here's a method using <code>tidyverse</code> functions for reshaping your data. Basically using <code>tidyr</code> verbs, we reshape your data into a more "tidy" format.

<pre><code># library(dplyr); library(tidyr)
dd %&gt;% select(Basket, contains("_Qty_"), contains("_P_")) %&gt;% 
 gather("key", "value", -Basket) %&gt;% 
 separate(key, c("F", "Val", "Grp")) %&gt;% 
 group_by(Basket, Grp) %&gt;% 
 spread(Val, value) %&gt;% 
 mutate(Total=P*Qty, GrpN=paste0("Total_", Grp)) %&gt;% 
 ungroup() %&gt;% 
 select(Basket, GrpN, Total) %&gt;% 
 spread(GrpN ,Total)

# Basket Total_1 Total_2
# &lt;fct&gt; &lt;dbl&gt; &lt;dbl&gt;
# 1 AAA 25 18
# 2 BBB 30 100
</code></pre>

entityMap|blocks|key|blqp9|text|建议:如果你能以长格式存储你的数据，这将使未来的维护变得更加清晰。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|frl4e|Basket+++++Item+++++Type++++Qty+Price++++Total
AAA+++++++++++1++++Apple+++++10+++2.5+++++++25
AAA+++++++++++2++++Banana+++++9+++++2+++++++18
BBB+++++++++++1++++Peach++++++5+++6.0+++++++30
BBB+++++++++++2++++Melon+++++20+++++5++++++100
BBB+++++++++++3++++Orange++++11+++2.7+++++29.7|code-block|syntax|javascript|fiiak|然后，当您确实需要上述格式的数据时，可以使用data.table包来转置数据。|offset|length|style|CODE|4ervd|library(data.table)
fruits+<-+data.frame("Basket"+=+c("AAA",+"AAA",+"BBB",+"BBB",+"BBB"),
+++++++++++++++++"Item"+=+c(1,2,1,2,3),
+++++++++++++++++"Type"+=+c("Apple",+"Banana",+"Peach",+"Melon",+"Orange"),
+++++++++++++++++"Qty"+=+c(10,+9,+5,+20,+11),
+++++++++++++++++"Price"+=+c(2.5,+2,+6.0,+5,+2.7)
)

fruits$Total+<-+fruits$Qty+*+fruits$Price
fruits.New+<-+data.table::dcast(setDT(fruits),
++++++++++++++++++++++++++++formula+=+Basket+~+Item,
++++++++++++++++++++++++++++value.var+=+c("Type",+"Price",+"Qty",+"Total"))|2etek|这样，您只需维护一个公式，而不必担心apply，同时还为您提供了可变列号的灵活性。|dc4ft|如果列名很重要，那么它应该相对容易修复。|9ft2t^0|0|0|M|A|0|0|I|5|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|U|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|V|9|@]|A|@]|B|$F|G]]|$2|H|4|I|6|7|8|W|9|@$J|X|K|Y|L|M]]|A|@]|B|$]]|$2|N|4|O|6|E|8|Z|9|@]|A|@]|B|$F|G]]|$2|P|4|Q|6|7|8|10|9|@$J|11|K|12|L|M]]|A|@]|B|$]]|$2|R|4|S|6|7|8|13|9|@]|A|@]|B|$]]|$2|T|4|-4|6|7|8|14|9|@]|A|@]|B|$]]]]

Suggestion: If you could store your data in the long format, it will make future maintenance much cleaner.

<pre><code>Basket Item Type Qty Price Total
AAA 1 Apple 10 2.5 25
AAA 2 Banana 9 2 18
BBB 1 Peach 5 6.0 30
BBB 2 Melon 20 5 100
BBB 3 Orange 11 2.7 29.7
</code></pre>

Then, when you really need the data in the above format, use the <code>data.table</code> package to transpose the data.

<pre><code>library(data.table)
fruits &lt;- data.frame("Basket" = c("AAA", "AAA", "BBB", "BBB", "BBB"),
 "Item" = c(1,2,1,2,3),
 "Type" = c("Apple", "Banana", "Peach", "Melon", "Orange"),
 "Qty" = c(10, 9, 5, 20, 11),
 "Price" = c(2.5, 2, 6.0, 5, 2.7)
)

fruits$Total &lt;- fruits$Qty * fruits$Price
fruits.New &lt;- data.table::dcast(setDT(fruits),
 formula = Basket ~ Item,
 value.var = c("Type", "Price", "Qty", "Total"))
</code></pre>

This way, you only have to maintain one formula and not to worry about <code>apply</code>, while giving you the flexibility to have variable column numbers too.

The column name should be relatively easy to fix if it's important.

entityMap|blocks|key|5upng|text|setNames(df[grepl("F_Qty",names(df))]*df[grepl("F_P",names(df))],paste0("F_Total_",1:2))

++F_Total_1+F_Total_2
1++++++++25++++++++18
2++++++++30+++++++100|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|ac1h5|unstyled^0|0^^$0|$]|1|@$2|3|4|5|6|7|8|G|9|@]|A|@]|B|$C|D]]|$2|E|4|-4|6|F|8|H|9|@]|A|@]|B|$]]]]

<pre><code>setNames(df[grepl("F_Qty",names(df))]*df[grepl("F_P",names(df))],paste0("F_Total_",1:2))

 F_Total_1 F_Total_2
1 25 18
2 30 100
</code></pre>

Suppose I have a dataframe that has multiple components and their properties listed out in multiple columns and I want to run multiple functions against the columns. My approach was to try and base it off the substring in each column header, but I haven't been able to figure out how to do that. Below is an example of the data frame.

<pre><code>Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 
AAA Apple 10 2.5 Banana 9 2
BBB Peach 5 6 Melon 20 5
</code></pre>

I essentially want to cbind two new columns to the end of this dataframe that multiplies Qty and P so you get two new columns at the end like below.

<pre><code>F_Total_1 F_Total_2
25 18
30 100
</code></pre>

The input is dynamic so on occasion it could be 2 fruits or 10 fruits in certain baskets. But I can figure that portion out, it's moreso trying to figure out how to multiply columns based on the Substrings '1' or '2'.

I appreciate all your help and any other approaches you may have!

Multiply Columns by Substrings in R

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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 假设我有一个dataframe，它有多个组件及其属性列在多个列中，并且我想对这些列运行多个函数。我的方法是尝试以每个列标题中的子字符串为基础，但我还不能想出如何做到这一点。下面是数据框的示例。 Basket   F_Type_1   F_Qty_1   F_P_1   F_Type_2   F_Qty_2   F_P...

问将列与R中的子字符串相乘
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