library(smokingPrevalence)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(withr)
library(utils)
library(readxl)The data is released in wide form and the following imports this data as it appears in the spreadsheet form:
get_smoking_wide(clean = FALSE)
#> New names:
#> * `` -> ...2
#> * `` -> ...3
#> * `` -> ...4
#> * `` -> ...5
#> * `` -> ...6
#> * ...
#> # A tibble: 52 × 21
#> `All persons age… ...2 ...3 ...4 ...5 ...6 ...7 ...8 ...9 ...10 ...11
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr>
#> 1 <NA> Men <NA> <NA> <NA> <NA> <NA> NA Women <NA> <NA>
#> 2 <NA> 16-24 25-34 35-49 50-59 60 a… All … NA 16-24 25-34 35-49
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 4 Unweighted 1 <NA> <NA> <NA> <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 5 1974 2 3 47.3… 55.1… 55.2… 52.7… 44.5 51.3… NA 41.2… 46.8… 49
#> 6 1976 2 3 42.7… 48.2… 50.5 49.8… 39.7… 46.2… NA 40.2… 42.8… 45.2…
#> 7 1978 2 3 40.1… 48.3… 48 47.7… 38.2… 44.5 NA 38.2… 42.3… 42.7…
#> 8 1980 2 3 38.2… 47.2… 45.3… 46.7… 35.6… 42.3… NA 36.5 44 42.6…
#> 9 1982 2 3 35.7… 40.2… 40.2… 41.6… 32.7… 37.7… NA 35 36.7… 37.7…
#> 10 1984 2 3 35 40.2… 38.6… 39.3… 30.3… 36.2… NA 34.5 35.7… 35.7…
#> # … with 42 more rows, and 10 more variables: ...12 <chr>, ...13 <chr>,
#> # ...14 <chr>, ...15 <lgl>, ...16 <chr>, ...17 <chr>, ...18 <chr>,
#> # ...19 <chr>, ...20 <chr>, Percentages <chr>To tidy the wide data which combines the 2 row headers to 1 and removes blank rows and columns:
get_smoking_wide()
#> New names:
#> * `` -> ...2
#> * `` -> ...3
#> * `` -> ...4
#> * `` -> ...5
#> * `` -> ...6
#> * ...
#> New names:
#> * `` -> ...2
#> * `` -> ...3
#> * `` -> ...4
#> * `` -> ...5
#> * `` -> ...6
#> * ...
#> # A tibble: 33 × 19
#> header `Men 16-24` `Men 25-34` `Men 35-49` `Men 50-59` `Men 60 and over`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1974 47.4 55.1 55.3 52.8 44.5
#> 2 1976 42.8 48.2 50.5 49.9 39.8
#> 3 1978 40.1 48.4 48 47.7 38.3
#> 4 1980 38.3 47.3 45.4 46.7 35.6
#> 5 1982 35.8 40.2 40.2 41.6 32.8
#> 6 1984 35 40.3 38.6 39.4 30.3
#> 7 1986 36.1 36.9 37.4 35.2 28.5
#> 8 1988 33.2 36.9 37 32.5 25.8
#> 9 1990 33.2 36.3 34.3 27.8 24.1
#> 10 1992 34.8 34.3 31.9 28.2 21
#> # … with 23 more rows, and 13 more variables: Men All aged 16 and over <dbl>,
#> # Women 16-24 <dbl>, Women 25-34 <dbl>, Women 35-49 <dbl>, Women 50-59 <dbl>,
#> # Women 60 and over <dbl>, Women All aged 16 and over <dbl>,
#> # Allpersons 16-24 <dbl>, Allpersons 25-34 <dbl>, Allpersons 35-49 <dbl>,
#> # Allpersons 50-59 <dbl>, Allpersons 60 and over <dbl>,
#> # Allpersons All aged 16 and over <dbl>
#or explicitly
get_smoking_wide(clean = TRUE)
#> New names:
#> * `` -> ...2
#> * `` -> ...3
#> * `` -> ...4
#> * `` -> ...5
#> * `` -> ...6
#> * ...
#> New names:
#> * `` -> ...2
#> * `` -> ...3
#> * `` -> ...4
#> * `` -> ...5
#> * `` -> ...6
#> * ...
#> # A tibble: 33 × 19
#> header `Men 16-24` `Men 25-34` `Men 35-49` `Men 50-59` `Men 60 and over`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1974 47.4 55.1 55.3 52.8 44.5
#> 2 1976 42.8 48.2 50.5 49.9 39.8
#> 3 1978 40.1 48.4 48 47.7 38.3
#> 4 1980 38.3 47.3 45.4 46.7 35.6
#> 5 1982 35.8 40.2 40.2 41.6 32.8
#> 6 1984 35 40.3 38.6 39.4 30.3
#> 7 1986 36.1 36.9 37.4 35.2 28.5
#> 8 1988 33.2 36.9 37 32.5 25.8
#> 9 1990 33.2 36.3 34.3 27.8 24.1
#> 10 1992 34.8 34.3 31.9 28.2 21
#> # … with 23 more rows, and 13 more variables: Men All aged 16 and over <dbl>,
#> # Women 16-24 <dbl>, Women 25-34 <dbl>, Women 35-49 <dbl>, Women 50-59 <dbl>,
#> # Women 60 and over <dbl>, Women All aged 16 and over <dbl>,
#> # Allpersons 16-24 <dbl>, Allpersons 25-34 <dbl>, Allpersons 35-49 <dbl>,
#> # Allpersons 50-59 <dbl>, Allpersons 60 and over <dbl>,
#> # Allpersons All aged 16 and over <dbl>