Example of Functions Part I

The goal of CME.assistant is to share reusable helper functions within the CME team. Also feel free to download, further revise and compile locally.

Functions for data engineering

Read the final aggregated file and output final IGME estimates in various forms

Choose output format among raw, long, wide_year and wide_ind, default to “long”
* The long-format dataset contains Official Name, ISO3Code, Indicator, Year, Quantile, Sex * The function will output all available indicators in the dataset * Optional: subset by country ISO (c_iso) or year (year_range)

get.CME.UI.data

# without supplying dir_file, by default returns the published IGME 2020 final
# results
dt_1 <- get.CME.UI.data(use_IGME_year = 2020)
#> Indicators:U5MR, IMR, NMR, Under.five.Deaths, Infant.Deaths, Neonatal.Deaths
head(dt_1)
#>    OfficialName ISO3Code Indicator Year Quantile   Sex Value
#> 1:  Afghanistan      AFG       IMR 1990    Lower Total 111.2
#> 2:  Afghanistan      AFG       IMR 1990   Median Total 120.4
#> 3:  Afghanistan      AFG       IMR 1990    Upper Total 130.9
#> 4:  Afghanistan      AFG       IMR 1991    Lower Total 108.2
#> 5:  Afghanistan      AFG       IMR 1991   Median Total 116.8
#> 6:  Afghanistan      AFG       IMR 1991    Upper Total 126.2

# can read final output file from Dropbox for any indicator
# loading Dropbox files requires access to corresponding files
dir_list <- load.final_dir()
dt_2 <- get.CME.UI.data(dir_file = dir_list$dir_female_2020)
head(dt_2)

Make CC profiles

dir_cqt_files <- file.path(CME.assistant::get.IGME.dir(2020),
                           "figData/cqt_backup")
list.files(dir_cqt_files)
dt_cqt <- get.dt.cqt(dir_cqt_files)
dt_cqt[, table(Shortind, Sex)]
# table to be added into the profile by country: 
dt1 <- get.table.by.iso(iso0 = "AFG")

Locate the latest master datasets directories

Automatically point to the latest file using the date in file names

input_dirs <- load.IGMEinput.dir()
dir_U5MR   <- get.dir_U5MR(input_dirs$dir_IGME_20)
dir_IMR    <- get.dir_IMR(input_dirs$dir_IGME_20)
dir_NMR    <- get.dir_NMR(y5 = TRUE) # either 5-year or not
dir_gender <- get.dir_gender(plotting = TRUE) # either dataset for plotting or modeling

Extra

get.match

# a helpful function to revise variable, you can provide a __new_list__ to define the labels
get.match(c("a","b", "c"), new_list = list("a" = "new label 1",
                                           "b" = "new label 2"))
#> [1] "new label 1" "new label 2" "c"

UNICEF_colors

str(UNICEF_colors)
#> List of 5
#>  $ UNICEF_blue    : chr "#1CABE2"
#>  $ cp_UNICEF_seq  : chr [1:8] "#002759" "#00377D" "#0058AB" "#0083CF" ...
#>  $ cp_UNICEF_div  : chr [1:10] "#002759" "#0058AB" "#1CABE2" "#69DBFF" ...
#>  $ cp_UNICEF_qua  : chr [1:10] "#0058AB" "#1CABE2" "#00833D" "#80BD41" ...
#>  $ cp_UNICEF_grays: chr [1:5] "#333333" "#6B6B6B" "#B3B3B3" "#D7D7D7" ...