CODEWITHSUNDEEP
r
Stataq
Stataq

Reputation: 2307

how to build a summary table with different counts on col

I have a df that looks like this:

![enter image description here

It can be build using codes:

structure(list(ID = c(1, 2, 3, 4, 5), Pass = c(0, 1, 1, 1, 1), 
    Math = c(0, 0, 1, 1, 1), ELA = c(0, 1, 0, 1, 0), PE = c(0, 
    0, 1, 1, 1)), row.names = c(NA, -5L), class = c("tbl_df", 
"tbl", "data.frame"))

How can I get a summary table like this;

enter image description here

I thought I could do it by:

st_count <- Reduce(function(x,y) merge(x, y, all = TRUE),list(table(df$Math),
                                                               table(df$ELA),
                                                               table(df$PE)))

But it did not work as i want. I think i might complicate a simple question. any suggestion?

Upvotes: 0

Views: 50

Answers (3)

GKi
GKi

Reputation: 39737

You can use colSums:

colSums(df[3:5])
#Math  ELA   PE 
#   3    2    3

and to come to columns ether cbind

cbind(colSums(df[3:5]))
#     [,1]
#Math    3
#ELA     2
#PE      3

or stack

stack(colSums(df[3:5]))[2:1]
#   ind values
#1 Math      3
#2  ELA      2
#3   PE      3

Alternatively you can use aggregate

aggregate(values~ind, stack(df[3:5]), sum)
#   ind values
#1 Math      3
#2  ELA      2
#3   PE      3

or xtabs

xtabs(values~ind, stack(df[3:5]))
#ind
#Math  ELA   PE 
#   3    2    3

or sapply

sapply(df[3:5], sum)
#Math  ELA   PE 
#   3    2    3

or apply

apply(df[3:5], 2, sum)
#Math  ELA   PE 
#   3    2    3

...

The names can be set by using setNames or names you have created an object.

Upvotes: 1

akrun
akrun

Reputation: 887971

Using tidyverse

library(dplyr)
library(tidyr)
df1 %>%
   pivot_longer(cols = -ID) %>%
   group_by(Subject = name) %>%
   summarise(Count = sum(value))

Upvotes: 1

ThomasIsCoding
ThomasIsCoding

Reputation: 102910

Try stack + colSums

> setNames(rev(stack(colSums(df))),c("Subject","Student Count"))
  Subject Student Count
1    Math             4
2     ELA             2
3      PE             3

Upvotes: 1

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