README.md
In avallecam/cdcper: R functions for the CDC Peru
cdcper
El objetivo de cdcper es agilizar la creación de variables, gráficos e
importación bases de datos de relevancia para el Centro Nacional de
Epidemiología, Prevención y Control de Enfermedades (CDC
Perú).
Installation
if(!require("remotes")) install.packages("remotes")
remotes::install_github("avallecam/cdcper")
Main functionalities
General usefull functions
cdc_edades_peru: crea categorias de edades comunmente usadas.read_reunis_total: brinda la poblacion del año en curso con la
base de datos de REUNIS.read_reunis_edad: brinda la población estratificada por sexo y
etapas de vida con la base de datos de REUNIS.read_inei_poblacion: lee archivos de población del INEI.
Prioritization functions for Data Mining
cdc_pareto_lista: calcula porcentaje de aporte individual y aporte
acumulado de elementos en una lista a priorizar.cdc_carga_coalesce: permite unir (logical connector OR) los listas
priorizadas y generar una lista concenso.
Visualization functions
cdc_dotwhiskers_plot: genera un grafico punto-bigotes con la
estimación puntual del promedio e intervalo de confianza de una
variable continua por niveles de una variable categórica.
Data sets availability
denominadores_departamento_2000_2020_peru department level
population estimates 2000-2020edad_estandarizada_who estandardize factors to estandardize raw
rates by age
Example
This is a basic example which shows you how to solve a common problem:
library(cdcper)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#> Registered S3 method overwritten by 'cli':
#> method from
#> print.boxx spatstat.geom
## basic example code
example(cdc_dotwhiskers_plot)
#>
#> cdc_d_> # inspiration: https://twitter.com/robinson_es/status/1193204992120958976/photo/1
#> cdc_d_>
#> cdc_d_> library(tidyverse)
#>
#> cdc_d_> library(charlatan)
#>
#> cdc_d_> n_obs <- 33
#>
#> cdc_d_> set.seed(n_obs)
#>
#> cdc_d_> ch_data_wide <- tibble(
#> cdc_d_+ #values
#> cdc_d_+ values_01 = ch_integer(n = n_obs,min = 5,max = 10),
#> cdc_d_+ values_02 = ch_integer(n = n_obs,min = 5,max = 20),
#> cdc_d_+ values_03 = ch_integer(n = n_obs,min = 5,max = 30),
#> cdc_d_+ category = ch_integer(n = n_obs,min = 0,max = 1) %>% as.logical()) %>%
#> cdc_d_+ pivot_longer(cols = values_01:values_03,
#> cdc_d_+ names_to = "variable",
#> cdc_d_+ values_to = "value")
#>
#> cdc_d_> # ch_data_wide %>%
#> cdc_d_> # group_by(variable) %>%
#> cdc_d_> # skimr::skim_without_charts()
#> cdc_d_>
#> cdc_d_> cdcper::cdc_dotwhiskers_plot(data = ch_data_wide,
#> cdc_d_+ var_categorical = variable,
#> cdc_d_+ var_continuous = value) %>%
#> cdc_d_+ ggplot(aes(x = estimate,y = variable)) +
#> cdc_d_+ geom_errorbarh(aes(xmax = conf.high, xmin = conf.low)) +
#> cdc_d_+ geom_point()
#>
#> cdc_d_> cdcper::cdc_dotwhiskers_plot_2(data = ch_data_wide,
#> cdc_d_+ var_continuous = value,
#> cdc_d_+ category, variable)
#> # A tibble: 6 x 12
#> # Groups: category [2]
#> category variable total_n t_test estimate statistic p.value parameter
#> <lgl> <chr> <int> <list> <dbl> <dbl> <dbl> <dbl>
#> 1 FALSE values_01 18 <htest> 7.33 15.6 1.73e-11 17
#> 2 FALSE values_02 18 <htest> 10.2 11.2 3.06e- 9 17
#> 3 FALSE values_03 18 <htest> 17.8 10.1 1.41e- 8 17
#> 4 TRUE values_01 15 <htest> 7.67 19.2 1.81e-11 14
#> 5 TRUE values_02 15 <htest> 15.3 16.0 2.14e-10 14
#> 6 TRUE values_03 15 <htest> 17.8 9.86 1.11e- 7 14
#> # ... with 4 more variables: conf.low <dbl>, conf.high <dbl>, method <chr>,
#> # alternative <chr>
example(cdc_pareto_lista)
library(tidyverse)
library(charlatan)
n_obs <- 11
set.seed(n_obs)
ch_data_wide <- tibble(
#names
name = ch_currency(n = n_obs),
#values
category = ch_integer(n = n_obs,min = 0,max = 1) %>% as.logical(),
# category_02 = ch_integer(n = n_obs,min = 0,max = 1) %>% as.logical(),
value_01 = ch_beta(n = n_obs,shape1 = 2,shape2 = 8),
value_02 = ch_integer(n = n_obs,min = 0.7,max = 10)) %>%
pivot_longer(cols = value_01:value_02,
names_to = "variable",
values_to = "numeric") %>%
mutate(beta = ch_beta(n = n_obs*2,shape1 = 1,shape2 = 8))
cdcper::cdc_pareto_lista(data = ch_data_wide,
variable = numeric,
pareto_cut = 80) %>%
avallecam::print_inf()
#> # A tibble: 22 x 8
#> name category variable numeric beta pct_numeric cum_numeric cut_numeric
#> <chr> <lgl> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 DJF TRUE value_02 7.7 0.0979 18.2 18.2 dentro_del_~
#> 2 LSL FALSE value_02 7.7 0.00606 18.2 36.4 dentro_del_~
#> 3 TWD FALSE value_02 6.7 0.212 15.8 52.3 dentro_del_~
#> 4 HUF FALSE value_02 4.7 0.0828 11.1 63.4 dentro_del_~
#> 5 CUP FALSE value_02 3.7 0.0197 8.75 72.1 dentro_del_~
#> 6 VUV FALSE value_02 2.7 0.0159 6.39 78.5 dentro_del_~
#> 7 PKR FALSE value_02 2.7 0.0286 6.39 84.9 no
#> 8 ZWD TRUE value_02 1.7 0.000367 4.02 88.9 no
#> 9 RSD FALSE value_02 0.7 0.0792 1.66 90.6 no
#> 10 TOP TRUE value_02 0.7 0.0617 1.66 92.3 no
#> 11 ILS FALSE value_02 0.7 0.0280 1.66 93.9 no
#> 12 HUF FALSE value_01 0.468 0.0417 1.11 95.0 no
#> 13 ZWD TRUE value_01 0.394 0.00573 0.931 95.9 no
#> 14 DJF TRUE value_01 0.347 0.113 0.821 96.8 no
#> 15 LSL FALSE value_01 0.305 0.0188 0.722 97.5 no
#> 16 TWD FALSE value_01 0.221 0.295 0.522 98.0 no
#> 17 VUV FALSE value_01 0.189 0.0767 0.448 98.5 no
#> 18 CUP FALSE value_01 0.187 0.138 0.442 98.9 no
#> 19 RSD FALSE value_01 0.145 0.0252 0.342 99.2 no
#> 20 TOP TRUE value_01 0.116 0.0146 0.274 99.5 no
#> 21 PKR FALSE value_01 0.113 0.0146 0.268 99.8 no
#> 22 ILS FALSE value_01 0.0920 0.00812 0.218 100 no
cdcper::cdc_pareto_lista(data = ch_data_wide,
variable = numeric,
pareto_cut = 80) %>%
cdcper::cdc_pareto_plot(pct_ = pct_numeric,
cum_ = cum_numeric,
variable_value = numeric,
variable_label = name) #%>%
# plotly::ggplotly()
example(cdc_yearweek_to_date)
#>
#> cdc___> library(tidyverse)
#>
#> cdc___> library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
#>
#> cdc___> library(aweek)
#>
#> cdc___> data_ts <- tibble(date=seq(ymd('2012-04-07'),
#> cdc___+ ymd('2012-04-22'),
#> cdc___+ by = '5 day')) %>%
#> cdc___+ mutate(#value = rnorm(n(),mean = 5),
#> cdc___+ #using aweek
#> cdc___+ epiweek_d = date2week(date, week_start = "Sunday"),
#> cdc___+ epiweek_w = date2week(date, week_start = "Sunday", floor_day = TRUE),
#> cdc___+ #using lubridate
#> cdc___+ epiweek_n = epiweek(date),
#> cdc___+ day_of_week = wday(date,label = T,abbr = F),
#> cdc___+ month = month(date,label = F,abbr = F),
#> cdc___+ year = year(date)) %>%
#> cdc___+ print()
#> # A tibble: 4 x 7
#> date epiweek_d epiweek_w epiweek_n day_of_week month year
#> <date> <aweek> <aweek> <dbl> <ord> <dbl> <dbl>
#> 1 2012-04-07 2012-W14-7 2012-W14 14 sábado 4 2012
#> 2 2012-04-12 2012-W15-5 2012-W15 15 jueves 4 2012
#> 3 2012-04-17 2012-W16-3 2012-W16 16 martes 4 2012
#> 4 2012-04-22 2012-W17-1 2012-W17 17 domingo 4 2012
#>
#> cdc___> data_ts %>%
#> cdc___+ # use the function
#> cdc___+ cdc_yearweek_to_date(year_integer = year,
#> cdc___+ week_integer = epiweek_n)
#> # A tibble: 4 x 10
#> date epiweek_d epiweek_w epiweek_n day_of_week month year year_integer
#> <date> <aweek> <aweek> <dbl> <ord> <dbl> <dbl> <dbl>
#> 1 2012-04-07 2012-W14-7 2012-W14 14 sábado 4 2012 2012
#> 2 2012-04-12 2012-W15-5 2012-W15 15 jueves 4 2012 2012
#> 3 2012-04-17 2012-W16-3 2012-W16 16 martes 4 2012 2012
#> 4 2012-04-22 2012-W17-1 2012-W17 17 domingo 4 2012 2012
#> # ... with 2 more variables: week_integer <chr>, epi_date <date>
example(cdc_edades_peru)
#>
#> cdc_d_> library(tidyverse)
#>
#> cdc_d_> library(charlatan)
#>
#> cdc_d_> library(skimr)
#>
#> cdc_d_> library(rlang)
#>
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#>
#> %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
#> flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
#> splice
#>
#> cdc_d_> data_edad <- tibble(age=charlatan::ch_integer(n = 100,min = 2,max = 100))
#>
#> cdc_d_> data_edad %>% skimr::skim_without_charts()
#> -- Data Summary ------------------------
#> Values
#> Name Piped data
#> Number of rows 100
#> Number of columns 1
#> _______________________
#> Column type frequency:
#> numeric 1
#> ________________________
#> Group variables None
#>
#> -- Variable type: numeric ------------------------------------------------------
#> # A tibble: 1 x 10
#> skim_variable n_missing complete_rate mean sd p0 p25 p50 p75
#> * <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 age 0 1 50.8 27.2 2 28 50.5 73.2
#> p100
#> * <dbl>
#> 1 100
#>
#> cdc_d_> data_edad %>%
#> cdc_d_+ cdc_edades_peru(variable_edad = age) %>% glimpse()
#> Rows: 100
#> Columns: 11
#> $ age <dbl> 93, 22, 28, 68, 22, 23, 73, 31, 93, 38, 10, 33~
#> $ edad <dbl> 93, 22, 28, 68, 22, 23, 73, 31, 93, 38, 10, 33~
#> $ edad_etapas_de_vida_c <fct> 60a_mas, 18_29a, 18_29a, 60a_mas, 18_29a, 18_2~
#> $ edad_etapas_de_vida_t <fct> adulto_mayor, joven, joven, adulto_mayor, jove~
#> $ edad_grupo_x <chr> "75 años a más", "15 a 24 años", "25 a 34 años~
#> $ edad_quinquenal <ord> 90 a mas años, 20 a 24 años, 25 a 29 años, 65~
#> $ edad_decenios <ord> "[90,100)", "[20,30)", "[20,30)", "[60,70)", "~
#> $ edad_quinquenal_raw <ord> "[90,95)", "[20,25)", "[25,30)", "[65,70)", "[~
#> $ edad_etapas_de_vida_n <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10_14a~
#> $ edad_inei_grupos <chr> "65a_mas", "15_29a", "15_29a", "65a_mas", "15_~
#> $ edad_inei_grupos_labels <chr> "65+", "15-29", "15-29", "65+", "15-29", "15-2~
#>
#> cdc_d_> data_edad %>%
#> cdc_d_+ cdc_edades_peru(variable_edad = age) %>%
#> cdc_d_+ select(age,edad_quinquenal) %>%
#> cdc_d_+ group_by(edad_quinquenal) %>%
#> cdc_d_+ skimr::skim() %>%
#> cdc_d_+ select(edad_quinquenal,numeric.p0:numeric.p100)
#> # A tibble: 19 x 6
#> edad_quinquenal numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100
#> <ord> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 a 4 años 2 2.5 3 3 3
#> 2 5 a 9 años 6 6.5 7 8 9
#> 3 10 a 14 años 10 10 10 11 12
#> 4 15 a 19 años 16 16 16.5 17.5 19
#> 5 20 a 24 años 21 21.8 22 23 24
#> 6 25 a 29 años 25 25.2 27 28 28
#> 7 30 a 34 años 30 30 31 31 33
#> 8 35 a 39 años 35 36.5 38 38.5 39
#> 9 40 a 44 años 40 42 42 43 44
#> 10 45 a 49 años 45 45 46 48 48
#> 11 50 a 54 años 50 51 52 52 54
#> 12 55 a 59 años 55 55.8 57 58.2 59
#> 13 60 a 64 años 60 60.8 62 63.2 64
#> 14 65 a 69 años 65 66 68 68 69
#> 15 70 a 74 años 71 71 73 73 74
#> 16 75 a 79 años 75 76.2 77 77 79
#> 17 80 a 84 años 84 84 84 84 84
#> 18 85 a 89 años 85 86 87 88.2 89
#> 19 90 a mas años 90 92 93 97 100
example(mutate_ewcdf)
#>
#> mtt_wc> # source("https://raw.githubusercontent.com/NicolasWoloszko/stat_ecdf_weighted/master/stat_ecdf_weighted.R")
#> mtt_wc> # https://stat.ethz.ch/pipermail/r-help/2012-October/337288.html
#> mtt_wc> # https://stackoverflow.com/questions/32487457/r-ggplot-weighted-cdf
#> mtt_wc>
#> mtt_wc> library(tidyverse)
#>
#> mtt_wc> x <- rnorm(100)
#>
#> mtt_wc> w <- runif(100)
#>
#> mtt_wc> a <- ecdf(x = x)
#>
#> mtt_wc> b <- spatstat.geom::ewcdf(x = x,weights = w)
#>
#> mtt_wc> #plot(a)
#> mtt_wc> #plot(b)
#> mtt_wc>
#> mtt_wc> tibble(x=x,w=w) %>%
#> mtt_wc+ mutate_ewcdf(variable = x,weights = w) %>%
#> mtt_wc+ ggplot() +
#> mtt_wc+ geom_step(aes(x = x,y = ecdf_x),color="black") +
#> mtt_wc+ geom_step(aes(x = x,y = ewcdf_x),color="red")
example(gg_forecast)
#>
#> gg_frc> # from: ?sw_sweep
#> gg_frc>
#> gg_frc> library(forecast)
#>
#> gg_frc> library(sweep)
#>
#> gg_frc> library(dplyr)
#>
#> gg_frc> library(ggplot2)
#>
#> gg_frc> # ETS forecasts
#> gg_frc> a <- USAccDeaths %>%
#> gg_frc+ ets() %>%
#> gg_frc+ forecast(level = c(80, 95, 99)) %>%
#> gg_frc+ sw_sweep()
#>
#> gg_frc> # crear false observed values for predicted time units
#> gg_frc> b <- a %>%
#> gg_frc+ filter(key=="forecast") %>%
#> gg_frc+ mutate(key="observed") %>%
#> gg_frc+ mutate(value=mean(value)) %>%
#> gg_frc+ mutate(across(.cols = c(lo.80:hi.99),
#> gg_frc+ .fns = ~(.x=NA_real_)))
#>
#> gg_frc> # create plot
#> gg_frc> a %>%
#> gg_frc+ union_all(b) %>%
#> gg_frc+ # avallecam::print_inf()
#> gg_frc+ gg_forecast()
example(cdc_datatable_html)
#>
#> cdc_d_> library(tidyverse)
#>
#> cdc_d_> mtcars %>%
#> cdc_d_+ as_tibble() %>%
#> cdc_d_+ cdc_cut_integer(qsec,3) %>%
#> cdc_d_+ count(qsec_cut)
#> # A tibble: 3 x 2
#> qsec_cut n
#> <fct> <int>
#> 1 [14,17] 11
#> 2 (17,19] 10
#> 3 (19,23] 11
#>
#> cdc_d_> iris %>%
#> cdc_d_+ group_by(Species) %>%
#> cdc_d_+ summarise_all(.funs = mean) %>%
#> cdc_d_+ cdc_datatable_html()
#> This version of bslib is designed to work with rmarkdown version 2.7 or higher.
To-Do
( ) issue: no visible global function definition estas usando muchas
nombres de columna no declarados
avallecam/cdcper documentation built on Dec. 19, 2021, 5:46 a.m.
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cdcper
El objetivo de cdcper es agilizar la creación de variables, gráficos e
importación bases de datos de relevancia para el Centro Nacional de
Epidemiología, Prevención y Control de Enfermedades (CDC
Perú).
Installation
if(!require("remotes")) install.packages("remotes")
remotes::install_github("avallecam/cdcper")
Main functionalities
General usefull functions
cdc_edades_peru: crea categorias de edades comunmente usadas.read_reunis_total: brinda la poblacion del año en curso con la base de datos de REUNIS.read_reunis_edad: brinda la población estratificada por sexo y etapas de vida con la base de datos de REUNIS.read_inei_poblacion: lee archivos de población del INEI.
Prioritization functions for Data Mining
cdc_pareto_lista: calcula porcentaje de aporte individual y aporte acumulado de elementos en una lista a priorizar.cdc_carga_coalesce: permite unir (logical connector OR) los listas priorizadas y generar una lista concenso.
Visualization functions
cdc_dotwhiskers_plot: genera un grafico punto-bigotes con la estimación puntual del promedio e intervalo de confianza de una variable continua por niveles de una variable categórica.
Data sets availability
denominadores_departamento_2000_2020_perudepartment level population estimates 2000-2020edad_estandarizada_whoestandardize factors to estandardize raw rates by age
Example
This is a basic example which shows you how to solve a common problem:
library(cdcper)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#> Registered S3 method overwritten by 'cli':
#> method from
#> print.boxx spatstat.geom
## basic example code
example(cdc_dotwhiskers_plot)
#>
#> cdc_d_> # inspiration: https://twitter.com/robinson_es/status/1193204992120958976/photo/1
#> cdc_d_>
#> cdc_d_> library(tidyverse)
#>
#> cdc_d_> library(charlatan)
#>
#> cdc_d_> n_obs <- 33
#>
#> cdc_d_> set.seed(n_obs)
#>
#> cdc_d_> ch_data_wide <- tibble(
#> cdc_d_+ #values
#> cdc_d_+ values_01 = ch_integer(n = n_obs,min = 5,max = 10),
#> cdc_d_+ values_02 = ch_integer(n = n_obs,min = 5,max = 20),
#> cdc_d_+ values_03 = ch_integer(n = n_obs,min = 5,max = 30),
#> cdc_d_+ category = ch_integer(n = n_obs,min = 0,max = 1) %>% as.logical()) %>%
#> cdc_d_+ pivot_longer(cols = values_01:values_03,
#> cdc_d_+ names_to = "variable",
#> cdc_d_+ values_to = "value")
#>
#> cdc_d_> # ch_data_wide %>%
#> cdc_d_> # group_by(variable) %>%
#> cdc_d_> # skimr::skim_without_charts()
#> cdc_d_>
#> cdc_d_> cdcper::cdc_dotwhiskers_plot(data = ch_data_wide,
#> cdc_d_+ var_categorical = variable,
#> cdc_d_+ var_continuous = value) %>%
#> cdc_d_+ ggplot(aes(x = estimate,y = variable)) +
#> cdc_d_+ geom_errorbarh(aes(xmax = conf.high, xmin = conf.low)) +
#> cdc_d_+ geom_point()
#>
#> cdc_d_> cdcper::cdc_dotwhiskers_plot_2(data = ch_data_wide,
#> cdc_d_+ var_continuous = value,
#> cdc_d_+ category, variable)
#> # A tibble: 6 x 12
#> # Groups: category [2]
#> category variable total_n t_test estimate statistic p.value parameter
#> <lgl> <chr> <int> <list> <dbl> <dbl> <dbl> <dbl>
#> 1 FALSE values_01 18 <htest> 7.33 15.6 1.73e-11 17
#> 2 FALSE values_02 18 <htest> 10.2 11.2 3.06e- 9 17
#> 3 FALSE values_03 18 <htest> 17.8 10.1 1.41e- 8 17
#> 4 TRUE values_01 15 <htest> 7.67 19.2 1.81e-11 14
#> 5 TRUE values_02 15 <htest> 15.3 16.0 2.14e-10 14
#> 6 TRUE values_03 15 <htest> 17.8 9.86 1.11e- 7 14
#> # ... with 4 more variables: conf.low <dbl>, conf.high <dbl>, method <chr>,
#> # alternative <chr>
example(cdc_pareto_lista)
library(tidyverse)
library(charlatan)
n_obs <- 11
set.seed(n_obs)
ch_data_wide <- tibble(
#names
name = ch_currency(n = n_obs),
#values
category = ch_integer(n = n_obs,min = 0,max = 1) %>% as.logical(),
# category_02 = ch_integer(n = n_obs,min = 0,max = 1) %>% as.logical(),
value_01 = ch_beta(n = n_obs,shape1 = 2,shape2 = 8),
value_02 = ch_integer(n = n_obs,min = 0.7,max = 10)) %>%
pivot_longer(cols = value_01:value_02,
names_to = "variable",
values_to = "numeric") %>%
mutate(beta = ch_beta(n = n_obs*2,shape1 = 1,shape2 = 8))
cdcper::cdc_pareto_lista(data = ch_data_wide,
variable = numeric,
pareto_cut = 80) %>%
avallecam::print_inf()
#> # A tibble: 22 x 8
#> name category variable numeric beta pct_numeric cum_numeric cut_numeric
#> <chr> <lgl> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 DJF TRUE value_02 7.7 0.0979 18.2 18.2 dentro_del_~
#> 2 LSL FALSE value_02 7.7 0.00606 18.2 36.4 dentro_del_~
#> 3 TWD FALSE value_02 6.7 0.212 15.8 52.3 dentro_del_~
#> 4 HUF FALSE value_02 4.7 0.0828 11.1 63.4 dentro_del_~
#> 5 CUP FALSE value_02 3.7 0.0197 8.75 72.1 dentro_del_~
#> 6 VUV FALSE value_02 2.7 0.0159 6.39 78.5 dentro_del_~
#> 7 PKR FALSE value_02 2.7 0.0286 6.39 84.9 no
#> 8 ZWD TRUE value_02 1.7 0.000367 4.02 88.9 no
#> 9 RSD FALSE value_02 0.7 0.0792 1.66 90.6 no
#> 10 TOP TRUE value_02 0.7 0.0617 1.66 92.3 no
#> 11 ILS FALSE value_02 0.7 0.0280 1.66 93.9 no
#> 12 HUF FALSE value_01 0.468 0.0417 1.11 95.0 no
#> 13 ZWD TRUE value_01 0.394 0.00573 0.931 95.9 no
#> 14 DJF TRUE value_01 0.347 0.113 0.821 96.8 no
#> 15 LSL FALSE value_01 0.305 0.0188 0.722 97.5 no
#> 16 TWD FALSE value_01 0.221 0.295 0.522 98.0 no
#> 17 VUV FALSE value_01 0.189 0.0767 0.448 98.5 no
#> 18 CUP FALSE value_01 0.187 0.138 0.442 98.9 no
#> 19 RSD FALSE value_01 0.145 0.0252 0.342 99.2 no
#> 20 TOP TRUE value_01 0.116 0.0146 0.274 99.5 no
#> 21 PKR FALSE value_01 0.113 0.0146 0.268 99.8 no
#> 22 ILS FALSE value_01 0.0920 0.00812 0.218 100 no
cdcper::cdc_pareto_lista(data = ch_data_wide,
variable = numeric,
pareto_cut = 80) %>%
cdcper::cdc_pareto_plot(pct_ = pct_numeric,
cum_ = cum_numeric,
variable_value = numeric,
variable_label = name) #%>%
# plotly::ggplotly()
example(cdc_yearweek_to_date)
#>
#> cdc___> library(tidyverse)
#>
#> cdc___> library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
#>
#> cdc___> library(aweek)
#>
#> cdc___> data_ts <- tibble(date=seq(ymd('2012-04-07'),
#> cdc___+ ymd('2012-04-22'),
#> cdc___+ by = '5 day')) %>%
#> cdc___+ mutate(#value = rnorm(n(),mean = 5),
#> cdc___+ #using aweek
#> cdc___+ epiweek_d = date2week(date, week_start = "Sunday"),
#> cdc___+ epiweek_w = date2week(date, week_start = "Sunday", floor_day = TRUE),
#> cdc___+ #using lubridate
#> cdc___+ epiweek_n = epiweek(date),
#> cdc___+ day_of_week = wday(date,label = T,abbr = F),
#> cdc___+ month = month(date,label = F,abbr = F),
#> cdc___+ year = year(date)) %>%
#> cdc___+ print()
#> # A tibble: 4 x 7
#> date epiweek_d epiweek_w epiweek_n day_of_week month year
#> <date> <aweek> <aweek> <dbl> <ord> <dbl> <dbl>
#> 1 2012-04-07 2012-W14-7 2012-W14 14 sábado 4 2012
#> 2 2012-04-12 2012-W15-5 2012-W15 15 jueves 4 2012
#> 3 2012-04-17 2012-W16-3 2012-W16 16 martes 4 2012
#> 4 2012-04-22 2012-W17-1 2012-W17 17 domingo 4 2012
#>
#> cdc___> data_ts %>%
#> cdc___+ # use the function
#> cdc___+ cdc_yearweek_to_date(year_integer = year,
#> cdc___+ week_integer = epiweek_n)
#> # A tibble: 4 x 10
#> date epiweek_d epiweek_w epiweek_n day_of_week month year year_integer
#> <date> <aweek> <aweek> <dbl> <ord> <dbl> <dbl> <dbl>
#> 1 2012-04-07 2012-W14-7 2012-W14 14 sábado 4 2012 2012
#> 2 2012-04-12 2012-W15-5 2012-W15 15 jueves 4 2012 2012
#> 3 2012-04-17 2012-W16-3 2012-W16 16 martes 4 2012 2012
#> 4 2012-04-22 2012-W17-1 2012-W17 17 domingo 4 2012 2012
#> # ... with 2 more variables: week_integer <chr>, epi_date <date>
example(cdc_edades_peru)
#>
#> cdc_d_> library(tidyverse)
#>
#> cdc_d_> library(charlatan)
#>
#> cdc_d_> library(skimr)
#>
#> cdc_d_> library(rlang)
#>
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#>
#> %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
#> flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
#> splice
#>
#> cdc_d_> data_edad <- tibble(age=charlatan::ch_integer(n = 100,min = 2,max = 100))
#>
#> cdc_d_> data_edad %>% skimr::skim_without_charts()
#> -- Data Summary ------------------------
#> Values
#> Name Piped data
#> Number of rows 100
#> Number of columns 1
#> _______________________
#> Column type frequency:
#> numeric 1
#> ________________________
#> Group variables None
#>
#> -- Variable type: numeric ------------------------------------------------------
#> # A tibble: 1 x 10
#> skim_variable n_missing complete_rate mean sd p0 p25 p50 p75
#> * <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 age 0 1 50.8 27.2 2 28 50.5 73.2
#> p100
#> * <dbl>
#> 1 100
#>
#> cdc_d_> data_edad %>%
#> cdc_d_+ cdc_edades_peru(variable_edad = age) %>% glimpse()
#> Rows: 100
#> Columns: 11
#> $ age <dbl> 93, 22, 28, 68, 22, 23, 73, 31, 93, 38, 10, 33~
#> $ edad <dbl> 93, 22, 28, 68, 22, 23, 73, 31, 93, 38, 10, 33~
#> $ edad_etapas_de_vida_c <fct> 60a_mas, 18_29a, 18_29a, 60a_mas, 18_29a, 18_2~
#> $ edad_etapas_de_vida_t <fct> adulto_mayor, joven, joven, adulto_mayor, jove~
#> $ edad_grupo_x <chr> "75 años a más", "15 a 24 años", "25 a 34 años~
#> $ edad_quinquenal <ord> 90 a mas años, 20 a 24 años, 25 a 29 años, 65~
#> $ edad_decenios <ord> "[90,100)", "[20,30)", "[20,30)", "[60,70)", "~
#> $ edad_quinquenal_raw <ord> "[90,95)", "[20,25)", "[25,30)", "[65,70)", "[~
#> $ edad_etapas_de_vida_n <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10_14a~
#> $ edad_inei_grupos <chr> "65a_mas", "15_29a", "15_29a", "65a_mas", "15_~
#> $ edad_inei_grupos_labels <chr> "65+", "15-29", "15-29", "65+", "15-29", "15-2~
#>
#> cdc_d_> data_edad %>%
#> cdc_d_+ cdc_edades_peru(variable_edad = age) %>%
#> cdc_d_+ select(age,edad_quinquenal) %>%
#> cdc_d_+ group_by(edad_quinquenal) %>%
#> cdc_d_+ skimr::skim() %>%
#> cdc_d_+ select(edad_quinquenal,numeric.p0:numeric.p100)
#> # A tibble: 19 x 6
#> edad_quinquenal numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100
#> <ord> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 a 4 años 2 2.5 3 3 3
#> 2 5 a 9 años 6 6.5 7 8 9
#> 3 10 a 14 años 10 10 10 11 12
#> 4 15 a 19 años 16 16 16.5 17.5 19
#> 5 20 a 24 años 21 21.8 22 23 24
#> 6 25 a 29 años 25 25.2 27 28 28
#> 7 30 a 34 años 30 30 31 31 33
#> 8 35 a 39 años 35 36.5 38 38.5 39
#> 9 40 a 44 años 40 42 42 43 44
#> 10 45 a 49 años 45 45 46 48 48
#> 11 50 a 54 años 50 51 52 52 54
#> 12 55 a 59 años 55 55.8 57 58.2 59
#> 13 60 a 64 años 60 60.8 62 63.2 64
#> 14 65 a 69 años 65 66 68 68 69
#> 15 70 a 74 años 71 71 73 73 74
#> 16 75 a 79 años 75 76.2 77 77 79
#> 17 80 a 84 años 84 84 84 84 84
#> 18 85 a 89 años 85 86 87 88.2 89
#> 19 90 a mas años 90 92 93 97 100
example(mutate_ewcdf)
#>
#> mtt_wc> # source("https://raw.githubusercontent.com/NicolasWoloszko/stat_ecdf_weighted/master/stat_ecdf_weighted.R")
#> mtt_wc> # https://stat.ethz.ch/pipermail/r-help/2012-October/337288.html
#> mtt_wc> # https://stackoverflow.com/questions/32487457/r-ggplot-weighted-cdf
#> mtt_wc>
#> mtt_wc> library(tidyverse)
#>
#> mtt_wc> x <- rnorm(100)
#>
#> mtt_wc> w <- runif(100)
#>
#> mtt_wc> a <- ecdf(x = x)
#>
#> mtt_wc> b <- spatstat.geom::ewcdf(x = x,weights = w)
#>
#> mtt_wc> #plot(a)
#> mtt_wc> #plot(b)
#> mtt_wc>
#> mtt_wc> tibble(x=x,w=w) %>%
#> mtt_wc+ mutate_ewcdf(variable = x,weights = w) %>%
#> mtt_wc+ ggplot() +
#> mtt_wc+ geom_step(aes(x = x,y = ecdf_x),color="black") +
#> mtt_wc+ geom_step(aes(x = x,y = ewcdf_x),color="red")
example(gg_forecast)
#>
#> gg_frc> # from: ?sw_sweep
#> gg_frc>
#> gg_frc> library(forecast)
#>
#> gg_frc> library(sweep)
#>
#> gg_frc> library(dplyr)
#>
#> gg_frc> library(ggplot2)
#>
#> gg_frc> # ETS forecasts
#> gg_frc> a <- USAccDeaths %>%
#> gg_frc+ ets() %>%
#> gg_frc+ forecast(level = c(80, 95, 99)) %>%
#> gg_frc+ sw_sweep()
#>
#> gg_frc> # crear false observed values for predicted time units
#> gg_frc> b <- a %>%
#> gg_frc+ filter(key=="forecast") %>%
#> gg_frc+ mutate(key="observed") %>%
#> gg_frc+ mutate(value=mean(value)) %>%
#> gg_frc+ mutate(across(.cols = c(lo.80:hi.99),
#> gg_frc+ .fns = ~(.x=NA_real_)))
#>
#> gg_frc> # create plot
#> gg_frc> a %>%
#> gg_frc+ union_all(b) %>%
#> gg_frc+ # avallecam::print_inf()
#> gg_frc+ gg_forecast()
example(cdc_datatable_html)
#>
#> cdc_d_> library(tidyverse)
#>
#> cdc_d_> mtcars %>%
#> cdc_d_+ as_tibble() %>%
#> cdc_d_+ cdc_cut_integer(qsec,3) %>%
#> cdc_d_+ count(qsec_cut)
#> # A tibble: 3 x 2
#> qsec_cut n
#> <fct> <int>
#> 1 [14,17] 11
#> 2 (17,19] 10
#> 3 (19,23] 11
#>
#> cdc_d_> iris %>%
#> cdc_d_+ group_by(Species) %>%
#> cdc_d_+ summarise_all(.funs = mean) %>%
#> cdc_d_+ cdc_datatable_html()
#> This version of bslib is designed to work with rmarkdown version 2.7 or higher.
To-Do
( ) issue: no visible global function definition estas usando muchas nombres de columna no declarados
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