Order/Sort/Rank a table - r

I have a table like this
table(mtcars$gear, mtcars$cyl)
I want to rank the rows by the ones with more observations in the 4 cylinder. E.g.
4 6 8
4 8 4 0
5 2 1 2
3 1 2 12
I have been playing with order/sort/rank without much success. How could I order tables output?


We can convert table to data.frame and then order by the column.
sort_col <- "4"
tab <- as.data.frame.matrix(table(mtcars$gear, mtcars$cyl))
tab[order(-tab[sort_col]), ]
# OR tab[order(tab[sort_col], decreasing = TRUE), ]
# 4 6 8
#4 8 4 0
#5 2 1 2
#3 1 2 12
If we don't want to convert it into data frame and want to maintain the table structure we can do
tab <- table(mtcars$gear, mtcars$cyl)
tab[order(-tab[,dimnames(tab)[[2]] == sort_col]),]
# 4 6 8
# 4 8 4 0
# 5 2 1 2
# 3 1 2 12


Could try this. Use sort for the relevant column, specifying decreasing=TRUE; take the names of the sorted rows and subset using those.
table(mtcars$gear, mtcars$cyl)[names(sort(table(mtcars$gear, mtcars$cyl)[,1], dec=T)), ]
4 6 8
4 8 4 0
5 2 1 2
3 1 2 12


In the same scope as Milan, but using the order() function, instead of looking for names() in a sort()-ed list.
The [,1] is to look at the first column when ordering.
table(mtcars$gear, mtcars$cyl)[order(table(mtcars$gear, mtcars$cyl)[,1], decreasing=T),]

Related

How to assign values in one column to other columns in wide data using R

There is a wide data set, a simple example is
df<-data.frame("id"=c(1:6),
"ax"=c(1,2,2,3,4,4),
"bx"=c(7,8,8,9,10,10),
"cx"=c(11,12,12,13,14,14))
I'm looking for a way to assign the values in "ax" to column "bx" and "cx". Here, imagine we have thousands of columns we intend to replace with "ax", so I want this to be done in an automated approach using R. The expected output look like
df<-data.frame("id"=c(1:6),
"ax"=c(1,2,2,3,4,4),
"bx"=c(1,2,2,3,4,4),
"cx"=c(1,2,2,3,4,4))
I've thought of, and tried using mutate_at and ends_with, but this has not work for me. For example, I tried
df %>%
mutate_at(vars(ends_with("x")), labels = "ax")
and this prints an error. Not sure what's wrong or what's to be added to get this working, so I would like to request your help on this. Thank you very much!

A simple way using base R would be :
change_cols <- grep('x$', names(df))
df[change_cols] <- df$ax
df
# id ax bx cx
#1 1 1 1 1
#2 2 2 2 2
#3 3 2 2 2
#4 4 3 3 3
#5 5 4 4 4
#6 6 4 4 4

I would suggest this tidyverse approach using across() to select the range of variables you want:
library(tidyverse)
#Data
df<-data.frame("id"=c(1:6),
"ax"=c(1,2,2,3,4,4),
"bx"=c(7,8,8,9,10,10),
"cx"=c(11,12,12,13,14,14))
#Mutate
df %>% mutate(across(c(bx:cx), ~ ax))
Output:
id ax bx cx
1 1 1 1 1
2 2 2 2 2
3 3 2 2 2
4 4 3 3 3
5 5 4 4 4
6 6 4 4 4

Another option with mutate_at()
df %>%
mutate_at(vars(matches("x$")), ~ax)
# id ax bx cx
# 1 1 1 1 1
# 2 2 2 2 2
# 3 3 2 2 2
# 4 4 3 3 3
# 5 5 4 4 4
# 6 6 4 4 4

Recoding specific column values using reference list

My dataframe looks like this
data = data.frame(ID=c(1,2,3,4,5,6,7,8,9,10),
Gender=c('Male','Female','Female','Female','Male','Female','Male','Male','Female','Female'))
And I have a reference list that looks like this -
ref=list(Male=1,Female=2)
I'd like to replace values in the Gender column using this reference list, without adding a new column to my dataframe.
Here's my attempt
do.call(dplyr::recode, c(list(data), ref))
Which gives me the following error -
no applicable method for 'recode' applied to an object of class
"data.frame"
Any inputs would be greatly appreciated

An option would be do a left_join after stacking the 'ref' list to a two column data.frame
library(dplyr)
left_join(data, stack(ref), by = c('Gender' = 'ind')) %>%
select(ID, Gender = values)
A base R approach would be
unname(unlist(ref)[as.character(data$Gender)])
#[1] 1 2 2 2 1 2 1 1 2 2

In base R:
data$Gender = sapply(data$Gender, function(x) ref[[x]])

You can use factor, i.e.
factor(data$Gender, levels = names(ref), labels = ref)
#[1] 1 2 2 2 1 2 1 1 2 2

You can unlist ref to give you a named vector of codes, and then index this with your data:
transform(data,Gender=unlist(ref)[as.character(Gender)])
ID Gender
1 1 1
2 2 2
3 3 2
4 4 2
5 5 1
6 6 2
7 7 1
8 8 1
9 9 2
10 10 2

Surprisingly, that one works as well:
data$Gender <- ref[as.character(data$Gender)]
#> data
# ID Gender
# 1 1 1
# 2 2 2
# 3 3 2
# 4 4 2
# 5 5 1
# 6 6 2
# 7 7 1
# 8 8 1
# 9 9 2
# 10 10 2

Merge multiple data frames with partially matching rows

I have data frames with lists of elements such as NAMES. There are different names in dataframes, but most of them match together. I'd like to combine all of them in one list in which I'd see whether some names are missing from any of df.
DATA sample for df1:
X x
1 1 rh_Structure/Focus_S
2 2 rh_Structure/Focus_C
3 3 lh_Structure/Focus_S
4 4 lh_Structure/Focus_C
5 5 RH_Type-Function-S
6 6 RH_REFERENT-S
and for df2
X x
1 1 rh_Structure/Focus_S
2 2 rh_Structure/Focus_C
3 3 lh_Structure/Focus_S
4 4 lh_Structure/Focus_C
5 5 UCZESTNIK
6 6 COACH
and expected result would be:
NAME. df1 df2
1 COACH NA 6
2 lh_Structure/Focus_C 4 4
3 lh_Structure/Focus_S 3 3
4 RH_REFERENT-S 6 NA
5 rh_Structure/Focus_C 2 2
6 rh_Structure/Focus_S 1 1
7 RH_Type-Function-S 5 NA
8 UCZESTNIK NA 5
I can do that with merge.data.frame(df1,df2,by = "x", all=T),
but the I can't do it with more df with similar structure. Any help would be appreciated.

It might be easier to work with this in a long form. Just rbind all the datasets below one another with a flag for which dataset they came from. Then it's relatively straightforward to get a tabulation of all the missing values (and as an added bonus, you can see if you have any duplicates in any of the source datasets):
dfs <- c("df1","df2")
dfall <- do.call(rbind, Map(cbind, mget(dfs), src=dfs))
table(dfall$x, dfall$src)
# df1 df2
# COACH 0 1
# lh_Structure/Focus_C 1 1
# lh_Structure/Focus_S 1 1
# RH_REFERENT-S 1 0
# rh_Structure/Focus_C 1 1
# rh_Structure/Focus_S 1 1
# RH_Type-Function-S 1 0
# UCZESTNIK 0 1

Select max or equal value from several columns in a data frame

I'm trying to select the column with the highest value for each row in a data.frame. So for instance, the data is set up as such.
> df <- data.frame(one = c(0:6), two = c(6:0))
> df
one two
1 0 6
2 1 5
3 2 4
4 3 3
5 4 2
6 5 1
7 6 0
Then I'd like to set another column based on those rows. The data frame would look like this.
> df
one two rank
1 0 6 2
2 1 5 2
3 2 4 2
4 3 3 3
5 4 2 1
6 5 1 1
7 6 0 1
I imagine there is some sort of way that I can use plyr or sapply here but it's eluding me at the moment.

There might be a more efficient solution, but
ranks <- apply(df, 1, which.max)
ranks[which(df[, 1] == df[, 2])] <- 3
edit: properly spaced!

Calculating the occurrences of numbers in the subsets of a data.frame

I have a data frame in R which is similar to the follows. Actually my real ’df’ dataframe is much bigger than this one here but I really do not want to confuse anybody so that is why I try to simplify things as much as possible.
So here’s the data frame.
id <-c(1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3)
a <-c(3,1,3,3,1,3,3,3,3,1,3,2,1,2,1,3,3,2,1,1,1,3,1,3,3,3,2,1,1,3)
b <-c(3,2,1,1,1,1,1,1,1,1,1,2,1,3,2,1,1,1,2,1,3,1,2,2,1,3,3,2,3,2)
c <-c(1,3,2,3,2,1,2,3,3,2,2,3,1,2,3,3,3,1,1,2,3,3,1,2,2,3,2,2,3,2)
d <-c(3,3,3,1,3,2,2,1,2,3,2,2,2,1,3,1,2,2,3,2,3,2,3,2,1,1,1,1,1,2)
e <-c(2,3,1,2,1,2,3,3,1,1,2,1,1,3,3,2,1,1,3,3,2,2,3,3,3,2,3,2,1,3)
df <-data.frame(id,a,b,c,d,e)
df
Basically what I would like to do is to get the occurrences of numbers for each column (a,b,c,d,e) and for each id group (1,2,3) (for this latter grouping see my column ’id’).
So, for column ’a’ and for id number ’1’ (for the latter see column ’id’) the code would be something like this:
as.numeric(table(df[1:10,2]))
##The results are:
[1] 3 7
Just to briefly explain my results: in column ’a’ (and regarding only those records which have number ’1’ in column ’id’) we can say that number '1' occured 3 times and number '3' occured 7 times.
Again, just to show you another example. For column ’a’ and for id number ’2’ (for the latter grouping see again column ’id’):
as.numeric(table(df[11:20,2]))
##After running the codes the results are:
[1] 4 3 3
Let me explain a little again: in column ’a’ and regarding only those observations which have number ’2’ in column ’id’) we can say that number '1' occured 4 times, number '2' occured 3 times and number '3' occured 3 times.
So this is what I would like to do. Calculating the occurrences of numbers for each custom-defined subsets (and then collecting these values into a data frame). I know it is not a difficult task but the PROBLEM is that I’m gonna have to change the input ’df’ dataframe on a regular basis and hence both the overall number of rows and columns might change over time…
What I have done so far is that I have separated the ’df’ dataframe by columns, like this:
for (z in (2:ncol(df))) assign(paste("df",z,sep="."),df[,z])
So df.2 will refer to df$a, df.3 will equal df$b, df.4 will equal df$c etc. But I’m really stuck now and I don’t know how to move forward…
Is there a proper, ”automatic” way to solve this problem?

How about -
> library(reshape)
> dftab <- table(melt(df,'id'))
> dftab
, , value = 1
variable
id a b c d e
1 3 8 2 2 4
2 4 6 3 2 4
3 4 2 1 5 1
, , value = 2
variable
id a b c d e
1 0 1 4 3 3
2 3 3 3 6 2
3 1 4 5 3 4
, , value = 3
variable
id a b c d e
1 7 1 4 5 3
2 3 1 4 2 4
3 5 4 4 2 5
So to get the number of '3's in column 'a' and group '1'
you could just do
> dftab[3,'a',1]
[1] 4

A combination of tapply and apply can create the data you want:
tapply(df$id,df$id,function(x) apply(df[id==x,-1],2,table))
However, when a grouping doesn't have all the elements in it, as in 1a, the result will be a list for that id group rather than a nice table (matrix).
$`1`
$`1`$a
1 3
3 7
$`1`$b
1 2 3
8 1 1
$`1`$c
1 2 3
2 4 4
$`1`$d
1 2 3
2 3 5
$`1`$e
1 2 3
4 3 3
$`2`
a b c d e
1 4 6 3 2 4
2 3 3 3 6 2
3 3 1 4 2 4
$`3`
a b c d e
1 4 2 1 5 1
2 1 4 5 3 4
3 5 4 4 2 5

I'm sure someone will have a more elegant solution than this, but you can cobble it together with a simple function and dlply from the plyr package.
ColTables <- function(df) {
counts <- list()
for(a in names(df)[names(df) != "id"]) {
counts[[a]] <- table(df[a])
}
return(counts)
}
results <- dlply(df, "id", ColTables)
This gets you back a list - the first "layer" of the list will be the id variable; the second the table results for each column for that id variable. For example:
> results[['2']]['a']
$a
1 2 3
4 3 3
For id variable = 2, column = a, per your above example.

A way to do it is using the aggregate function, but you have to add a column to your dataframe
> df$freq <- 0
> aggregate(freq~a+id,df,length)
a id freq
1 1 1 3
2 3 1 7
3 1 2 4
4 2 2 3
5 3 2 3
6 1 3 4
7 2 3 1
8 3 3 5
Of course you can write a function to do it, so it's easier to do it frequently, and you don't have to add a column to your actual data frame
> frequency <- function(df,groups) {
+ relevant <- df[,groups]
+ relevant$freq <- 0
+ aggregate(freq~.,relevant,length)
+ }
> frequency(df,c("b","id"))
b id freq
1 1 1 8
2 2 1 1
3 3 1 1
4 1 2 6
5 2 2 3
6 3 2 1
7 1 3 2
8 2 3 4
9 3 3 4

You didn't say how you'd like the data. The by function might give you the output you like.
by(df, df$id, function(x) lapply(x[,-1], table))

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