collect some examples of rowwise relevant (?) data

rowwise-examples.R

``` r
# from dplyr --------------------------------------------------------------

# https://dplyr.tidyverse.org/articles/rowwise.html#creating

library(tidyverse)

df <- tibble(
  x = 1:2, 
  y = 3:4, 
  z = 5:6
)

df
#> # A tibble: 2 × 3
#>       x     y     z
#>   <int> <int> <int>
#> 1     1     3     5
#> 2     2     4     6

# from experience ---------------------------------------------------------

# 
# - hospital admissions
# - multiple weeks
# - multiple regions
# 

# from tidyr --------------------------------------------------------------

# total number of constructions across regions per month
tidyr::construction #%>% write_csv("data-out/LOADME-2.csv")
#> # A tibble: 9 × 9
#>    Year Month  `1 unit` `2 to 4 units` `5 units or more` Northeast Midwest South
#>   <dbl> <chr>     <dbl> <lgl>                      <dbl>     <dbl>   <dbl> <dbl>
#> 1  2018 Janua…      859 NA                           348       114     169   596
#> 2  2018 Febru…      882 NA                           400       138     160   655
#> 3  2018 March       862 NA                           356       150     154   595
#> 4  2018 April       797 NA                           447       144     196   613
#> 5  2018 May         875 NA                           364        90     169   673
#> 6  2018 June        867 NA                           342        76     170   610
#> 7  2018 July        829 NA                           360       108     183   594
#> 8  2018 August      939 NA                           286        90     205   649
#> 9  2018 Septe…      835 NA                           304       117     175   560
#> # ℹ 1 more variable: West <dbl>

# total number of subjects interviewed per religion
tidyr::relig_income #%>% write_csv("data-out/LOADME-3.csv")
#> # A tibble: 18 × 11
#>    religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` `$75-100k`
#>    <chr>      <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>      <dbl>
#>  1 Agnostic      27        34        60        81        76       137        122
#>  2 Atheist       12        27        37        52        35        70         73
#>  3 Buddhist      27        21        30        34        33        58         62
#>  4 Catholic     418       617       732       670       638      1116        949
#>  5 Don’t k…      15        14        15        11        10        35         21
#>  6 Evangel…     575       869      1064       982       881      1486        949
#>  7 Hindu          1         9         7         9        11        34         47
#>  8 Histori…     228       244       236       238       197       223        131
#>  9 Jehovah…      20        27        24        24        21        30         15
#> 10 Jewish        19        19        25        25        30        95         69
#> 11 Mainlin…     289       495       619       655       651      1107        939
#> 12 Mormon        29        40        48        51        56       112         85
#> 13 Muslim         6         7         9        10         9        23         16
#> 14 Orthodox      13        17        23        32        32        47         38
#> 15 Other C…       9         7        11        13        13        14         18
#> 16 Other F…      20        33        40        46        49        63         46
#> 17 Other W…       5         2         3         4         2         7          3
#> 18 Unaffil…     217       299       374       365       341       528        407
#> # ℹ 3 more variables: `$100-150k` <dbl>, `>150k` <dbl>,
#> #   `Don't know/refused` <dbl>


# from gist ---------------------------------------------------------------

# source 
# https://gist.github.com/avallecam/d20c91360baa7aa12e90757cf7cd787a

library(tidyverse)
library(purrr)

data <- 
  tibble(
    hypertension = c(1,0,0,NA,0,NA,0),
    asthma = c(1,0,1,1,NA,NA,0),
    diabetes = c(1,0,1,0,NA,NA,1)
  ) %>% 
  mutate(id_patient = seq(1,nrow(.))) %>% 
  select(id_patient, everything())

# total number of comorbid illnesses present in per patient 
data #%>% write_csv("data-out/LOADME.csv")
#> # A tibble: 7 × 4
#>   id_patient hypertension asthma diabetes
#>        <int>        <dbl>  <dbl>    <dbl>
#> 1          1            1      1        1
#> 2          2            0      0        0
#> 3          3            0      1        1
#> 4          4           NA      1        0
#> 5          5            0     NA       NA
#> 6          6           NA     NA       NA
#> 7          7            0      0        1

# ref: https://www.nature.com/articles/s41591-022-02156-9

# researched --------------------------------------------------------------

# are symptoms also valid as multimorbidity?
# cough
# fever
# dyspnea

# useful nut not

# tidyr::billboard # number entries are rankings, not reproduction number
# tidyr::population # sum of values across years is usually not useful
# tidyr::world_bank_pop # sum of values across years is usually not useful
```

<sup>Created on 2023-10-04 with [reprex v2.0.2](https://reprex.tidyverse.org)</sup>