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.
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