r - Calculated mean and sum values group by the first row

Question

I have a dataframe, I would like to calculate all the mean values of x and all the sum of y group by the first row of the dateframe.

The dateframe to be calculate

The following link is the result I want. The result expected

Here are the data.

dt=structure(list(year = structure(c(5L, 1L, 2L, 3L, 4L), .Label = c("1980", 
    "1981", "1982", "1985", "group"), class = "factor"), x1 = structure(c(4L, 
    1L, 3L, 2L, 1L), .Label = c("1", "2", "4", "A"), class = "factor"), 
        y1 = structure(c(4L, 1L, 3L, 2L, 2L), .Label = c("1", "3", 
        "5", "A"), class = "factor"), x2 = structure(c(5L, 1L, 4L, 
        3L, 2L), .Label = c("2", "4", "5", "6", "A"), class = "factor"), 
        y2 = structure(c(4L, 1L, 3L, 3L, 2L), .Label = c("3", "5", 
        "7", "A"), class = "factor"), x3 = structure(c(4L, 1L, 3L, 
        2L, 1L), .Label = c("4", "6", "8", "B"), class = "factor"), 
        y3 = structure(c(4L, 1L, 3L, 2L, 1L), .Label = c("3", "5", 
        "6", "B"), class = "factor"), x4 = structure(c(4L, 1L, 3L, 
        2L, 3L), .Label = c("2", "4", "5", "C"), class = "factor"), 
        y4 = structure(c(5L, 1L, 2L, 3L, 4L), .Label = c("3", "4", 
        "5", "6", "C"), class = "factor"), x5 = structure(c(5L, 2L, 
        1L, 3L, 4L), .Label = c("3", "4", "6", "7", "C"), class = "factor"), 
        y5 = structure(c(4L, 2L, 1L, 3L, 2L), .Label = c("2", "5", 
        "8", "C"), class = "factor")), class = "data.frame", row.names = c(NA, 
    -5L))

And result expected,

result_expected <- structure(list(year = c(1980L, 1981L, 1982L, 1985L), A_x_mean = c(1.5, 
5, 3.5, 2.5), A_y_sum = c(4L, 12L, 10L, 8L), B_x_mean = c(4L, 
8L, 6L, 4L), B_y_sum = c(3L, 6L, 5L, 3L), C_x_mean = 3:6, C_y_sum = c(8L, 
6L, 13L, 11L)), class = "data.frame", row.names = c(NA, -4L))

I have search key words in goole and stackoverflow, but no proper answers. My current thinking is to calculate unique group A,B,C in first row.

require(tidyverse)
group_variables <- dt%>%gather(key,value)%>%distinct(value)%>%arrange(value)

then get the row in group_variables by the for

for i in group_variables{......}

or can I change the structure of the dataframe by gathe and spread in tidyr,and by dplyr method, something just like following code,

dt_new%>% group_by (group)%>%
          summarise(mean=mean(x,na.rm=TRUE),
          sum=sum(x,na.rm=TURE))

StupidWolf · Accepted Answer

First we need to take out the first row having the group, make the data frame long, simplify x1,x2,x3 to x etc and put the groups back:

group_var = sapply(dt[1,-1],as.character)
mat <- 
dt[-1,] %>% pivot_longer(-year) %>% 
   mutate(value=as.numeric(as.character(value))) %>% 
   mutate(group=as.character(group_var[as.character(name)])) %>% 
   mutate(name=substr(name,1,1))

mat
# A tibble: 40 x 4
   year  name  value group
      
 1 1980  x         1 A    
 2 1980  y         1 A    
 3 1980  x         2 A    
 4 1980  y         3 A    
 5 1980  x         4 B    
 6 1980  y         3 B    
 7 1980  x         2 C    
 8 1980  y         3 C    
 9 1980  x         4 C    
10 1980  y         5 C

Now what's left is to group them according to year, name and group and do the respective function, so we define a function:

func = function(DF,func){
DF %>% 
   group_by(group,name,year) %>% 
   summarise_all(func) %>%
   mutate(label=paste(group,name,func,sep="_")) %>%
   ungroup %>%
   select(year,value,label) %>%
   pivot_wider(values_from=value,names_from=label)
}

And we apply it over two parts of the data:

cbind(func(mat %>% filter(name=="x"),"mean"),func(mat %>% filter(name=="y"),"sum"))

year A_x_mean B_x_mean C_x_mean year A_y_sum B_y_sum C_y_sum
1 1980      1.5        4        3 1980       4       3       8
2 1981      5.0        8        4 1981      12       6       6
3 1982      3.5        6        5 1982      10       5      13
4 1985      2.5        4        6 1985       8       3      11

r - Calculated mean and sum values group by the first row

Answers (2)

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