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library(zoo) # moving averages        
library(tidyverse) # all tidyverse packages
library(hrbrthemes) # themes for graphs
library(socviz) # %nin%
library(geofacet) # maps
library(usmap) # lat and long
library(socviz) # for %nin%
library(ggmap) # mapping

library(readxl)
obs <- read_excel("obs.xlsx")
DT::datatable(obs)

obs <- obs %>%
    dplyr::arrange(fecha) %>% 
    dplyr::mutate(promedio_2dias = zoo::rollmean(PM2.5, k = 2, fill = NA),
                  promedio_3dias = zoo::rollmean(PM2.5, k = 3, fill = NA),
                  promedio_5dias = zoo::rollmean(PM2.5, k = 5, fill = NA)) %>% 
  dplyr::ungroup()

obs %>% select(fecha,PM2.5, promedio_2dias, promedio_3dias, promedio_5dias )

## # A tibble: 1,826 × 5
##    fecha               PM2.5 promedio_2dias promedio_3dias promedio_5dias
##    <dttm>              <dbl>          <dbl>          <dbl>          <dbl>
##  1 2015-01-01 00:00:00  20.9           16.1           NA             NA  
##  2 2015-01-02 00:00:00  11.2           11.6           14.7           NA  
##  3 2015-01-03 00:00:00  11.9           11.3           11.3           13.3
##  4 2015-01-04 00:00:00  10.7           11.3           11.5           12.1
##  5 2015-01-05 00:00:00  12.0           13.3           12.4           12.1
##  6 2015-01-06 00:00:00  14.5           13.1           12.7           12.9
##  7 2015-01-07 00:00:00  11.6           13.7           14.0           13.5
##  8 2015-01-08 00:00:00  15.8           14.6           13.6           14.3
##  9 2015-01-09 00:00:00  13.3           14.8           15.1           13.5
## 10 2015-01-10 00:00:00  16.2           13.5           13.4           13.3
## # ℹ 1,816 more rows

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