Is There a General Inverse of The Table() Function

Creating a table with individual trials from a frequency table in R (inverse of table function)

You may try this:

# create 'result' vector
# repeat 1s and 0s the number of times given in the respective 'count' column
result <- rep(rep(c(1, 0), nrow(df)), unlist(df[ , c("success.count", "fail.count")]))

# repeat each row in df the number of times given by the sum of 'count' columns
data.frame(df[rep(1:nrow(df), rowSums(df[ , c("success.count", "fail.count")]) ), c("factor.A", "factor.B")], result)

#     factor.A factor.B result
# 1          0        1      0
# 1.1        0        1      0
# 2          1        1      1
# 2.1        1        1      1
# 2.2        1        1      0

Is there a function to invert the number of occurrences of values in a data.table?

If we use the approach by the OP, then just replicate the rows by the reverse of 'N' and assign 'N' to NULL

initially[, .N, by = initially][rep(seq_len(.N), rev(N))][, N := NULL][]

General table into a quartely table

Change Date to date class, extract year from it and cast data into wide format.

library(dplyr)

df %>%
  mutate(Time = as.Date(Time),
         year = format(Time, '%Y')) %>%
  select(-Time, -Quarters) %>%
  tidyr::pivot_wider(names_from = Season, values_from = Qty, values_fn = sum)
  #OR in old tidyr
  #tidyr::pivot_wider(names_from = Season, values_from = Qty, 
  #                   values_fn = list(Qty = sum))

# A tibble: 3 x 5
#  year  Winter Spring Summer  Fall
#  <chr>  <dbl>  <dbl>  <dbl> <dbl>
#1 2017   4335.  1645.  4442. 4452.
#2 2018   6670.  8773. 10498. 9843.
#3 2019      0    559.   528.   NA

An alternative, instead of using values_fn we can sum it first and then cast to wide.

df %>%
  type.convert(as.is = TRUE) %>% 
  mutate(Time = as.Date(Time),
         year = format(Time, '%Y')) %>%
  select(-Time, -Quarters) %>%
  group_by(year, Season) %>%
  summarise(Qty = sum(Qty)) %>%
  tidyr::spread(Season, Qty)
  #OR
  #tidyr::pivot_wider(names_from = Season, values_from = Qty)

SQL: Reverse transpose a table

You could do this very simply with a UNION clause:

Select Scan_ID, 'A' as Region, A_Volume as volume
    union all
Select Scan_ID, 'B' as Region, B_Volume as volume
    union all
Select Scan_ID, 'C' as Region, C_Volume as volume

table() function in r - is there a better way with e.g., dplyr?

Another approach is to use tables::tabular() as follows.

textData <- "id Country                        Relationship_type
1 Algeria                                      2
2 Bulgaria                                     1
3 USA                                          2
4 Algeria                                      3
5 Germany                                      2
6 USA                                          1
7 Algeria                                      1
8 Bulgaria                                     3
9 USA                                          2
10 Algeria                                     2
11 Germany                                     1
12 USA                                         3"

df <- read.table(text=textData,header=TRUE)
library(tables)
tabular(Factor(Country) ~ Factor(Relationship_type),data=df)

...and the output:

          Relationship_type    
 Country  1                 2 3
 Algeria  1                 2 1
 Bulgaria 1                 0 1
 Germany  1                 1 0
 USA      1                 2 1

Still another approach is to recast the output from table() as a data frame, and pivot it wider with tidyr::pivot_wider().

# another approach: recast table output as data.frame
tableData <- data.frame(table(df$Country,df$Relationship_type))
library(dplyr)
library(tidyr)
tableData %>% 
     pivot_wider(id_cols = Var1,
                 names_from = Var2,
                 values_from = Freq)

...and the output:

> tableData %>% 
+      pivot_wider(id_cols = Var1,
+                  names_from = Var2,
+                  values_from = Freq)
# A tibble: 4 x 4
  Var1       `1`   `2`   `3`
  <fct>    <int> <int> <int>
1 Algeria      1     2     1
2 Bulgaria     1     0     1
3 Germany      1     1     0
4 USA          1     2     1

If we add a dplyr::rename() to the pipeline, we can rename the Var1 column to Country.

tableData %>% 
     pivot_wider(id_cols = Var1,
                 names_from = Var2,
                 values_from = Freq) %>%
     rename(Country = Var1)

As usual, there are many ways in R to accomplish this task. Depending on the reason why the desired output is a CSV file, there are a variety of approaches that could fit the requirements. If the ultimate goal is to create presentation quality tables, then it's worth a look at this summary of packages that create presentation quality tables: How gt fits with other packages that create display tables.

Find the inverse function in R

The package investr is able to apply an inverse regression.

How to check if a matrix has an inverse in the R language

@MAB has a good point. This uses solve(...) to decide if the matrix is invertible.

f <- function(m) class(try(solve(m),silent=T))=="matrix"
x <- matrix(rep(1,25),nc=5)          # singular
y <- matrix(1+1e-10*rnorm(25),nc=5)  # very nearly singular matrix
z <- 0.001*diag(1,5)                 # non-singular, but very smalll determinant
f(x)
# [1] FALSE
f(y)
# [1] TRUE
f(z)
# [1] TRUE