Counting occurrences in R data frame with multiple filters-CodePudding

I have a table that looks like so:

Gender	Time	Payband
male	part time	£15,001-20000
male	full time	£25001-30000
female	full time	£35001-40000
male	part time	£35001-40000
female	part time	£35001-40000
female	full time	£25001-30000

And I need R code that makes 2 different dataframes that are filtered by 'Time' and give a count of the different genders in each payband. For example this table below would be filtered where time == part time:

Payband	Male	Female	Total
£15001-20000	1	0	1
£20001-25000	0	0	0
£25001-30000	0	0	0
£35001-40000	1	1	2

There would also be a dataframe where time == full time

I imagine it would be a case of using things such as group_by and summarize but I just can't wrap my head around how to do it. Any help is greatly appreciated and I hope I am explaining the problem properly.

CodePudding user response：

You can do

pay <- c("£15,001-20000", "£20001-25000", "£25001-30000", "£35001-40000")

with(subset(df, Time == 'full time'), t(table(Gender, factor(Payband, pay)))) |>
  as.data.frame() |>
  tidyr::pivot_wider(names_from = 'Gender', values_from = 'Freq') |>
  dplyr::rename(Payband = Var1)
#> # A tibble: 4 x 3
#> Payband       female  male
#> <fct>          <int> <int>
#> 1 £15,001-20000      0     0
#> 2 £20001-25000       0     0
#> 3 £25001-30000       1     1
#> 4 £35001-40000       1     0

with(subset(df, Time == 'part time'), t(table(Gender, factor(Payband, pay)))) |>
  as.data.frame() |>
  tidyr::pivot_wider(names_from = 'Gender', values_from = 'Freq') |>
  dplyr::rename(Payband = Var1)
#> # A tibble: 4 x 3
#>  Payband       female  male
#>  <fct>          <int> <int>
#> 1 £15,001-20000      0     1
#> 2 £20001-25000       0     0
#> 3 £25001-30000       0     0
#> 4 £35001-40000       1     1