pipster

The goal of pipster is to make use of {wbpip} functions easily.

Installation

You can install the development version of pipster from GitHub with:

# install.packages("devtools")
devtools::install_github("PIP-Technical-Team/pipster")

library(pipster)
library(collapse)
#> Warning: package 'collapse' was built under R version 4.3.2
#> collapse 2.0.7, see ?`collapse-package` or ?`collapse-documentation`
#> 
#> Attaching package: 'collapse'
#> The following object is masked from 'package:stats':
#> 
#>     D
## basic example code

Identify type of data

Group Data

# W: Weights, share of population, sum up to 100
# X: welfare vector with mean welfare by decile
# P:Cumulative share of population
# L: Cumulative share of welfare
# R: share of welfare, sum up to 1.

W = c(0.92, 2.47, 5.11, 7.9, 9.69, 15.24, 13.64, 16.99, 10, 9.78, 3.96, 1.81, 2.49)
X = c(24.84, 35.8, 45.36, 55.1, 64.92, 77.08, 91.75, 110.64, 134.9, 167.76, 215.48, 261.66, 384.97)
P = c(0.0092, 0.0339, 0.085, 0.164, 0.2609, 0.4133, 0.5497, 0.7196, 0.8196, 0.9174, 0.957, 0.9751, 1)
L = c(0.00208, 0.01013, 0.03122, 0.07083, 0.12808, 0.23498, 0.34887, 0.51994, 0.6427, 0.79201, 0.86966, 0.91277, 1)

R = (W * X) / sum(W * X)

# type 1 ------
## up to 1 ---------
identify_pip_type(welfare = L,
                    weight = P)
#> [1] "gd_1"

## up to 100 ---------
identify_pip_type(welfare = L*100,
                    weight = P)
#> [1] "gd_1"

# type 2 -----------
## up to 1 -----------
identify_pip_type(welfare = R,
                    weight = W/100)
#> ! vectors not sorted
#> [1] "gd_2"

## up to 100 ---------
identify_pip_type(welfare = R*100,
                   weight = W)
#> ! vectors not sorted
#> [1] "gd_2"

# type 5 -----------
identify_pip_type(welfare = X,
                    weight = W/100)
#> [1] "gd_5"

# type 3 -----------
identify_pip_type(welfare = X,
                   weight  = P)
#> [1] "gd_3"

Microdata

# l: length
# Y: welfare
# Q: population or weights
# I: imputation ID

l <- 300
Y <- sample(1000, l,replace = TRUE)
Q <- sample(35, l,replace = TRUE)
I <- sample(1:5, l,replace = TRUE)

identify_pip_type(welfare = Y,
                  weight  = Q)
#> ! vectors not sorted
#> [1] "md"
identify_pip_type(welfare = Y,
                  weight  = Q,
                  imputation_id = I)
#> ! vectors not sorted
#> [1] "id"

I2 <- rep(1, l)
identify_pip_type(welfare = Y,
                  weight  = Q,
                  imputation_id = I2)
#> ! vectors not sorted
#> [1] "md"

Convert to PIP format

Group Data

Convert Group Data Type-2 to Group Data Type-1 . Notice that the whole dataframe is parsed to the function because we need the whole dataframe back. It is not enough with parsing just the welfare and weight vetors.

pip_gd |> 
  fselect(R,W)
#>              R     W
#> 1  0.002079692  0.92
#> 2  0.008047104  2.47
#> 3  0.021093739  5.11
#> 4  0.039613054  7.90
#> 5  0.057248211  9.69
#> 6  0.106902117 15.24
#> 7  0.113888553 13.64
#> 8  0.171066582 16.99
#> 9  0.122764156 10.00
#> 10 0.149309315  9.78
#> 11 0.077653634  3.96
#> 12 0.043099829  1.81
#> 13 0.087234016  2.49

gd <- as_pip(dt = pip_gd,
             welfare_var = "R",
             weight_var = "W",
             pip_type = "gd_2")
#> i columns "welfare" and "W" have been rescaled to range (0,1]
gd |> 
  fselect(R,W)
#>               R      W
#>  1: 0.002079692 0.0092
#>  2: 0.010126796 0.0339
#>  3: 0.031220536 0.0850
#>  4: 0.070833589 0.1640
#>  5: 0.128081800 0.2609
#>  6: 0.234983917 0.4133
#>  7: 0.348872469 0.5497
#>  8: 0.519939051 0.7196
#>  9: 0.642703207 0.8196
#> 10: 0.792012522 0.9174
#> 11: 0.869666156 0.9570
#> 12: 0.912765984 0.9751
#> 13: 1.000000000 1.0000

class(gd)
#> [1] "pipgd"      "data.table" "data.frame"

Micro Data

md <-  as_pip(dt = pip_md,
              welfare_var = "welfare",
              weight_var = "weight") 
#> ! vectors not sorted
#> > PIP type identified: "md"
waldo::compare(md, roworderv(pip_md, "welfare"))
#> `class(old)`: "pipmd"  "data.table" "data.frame"
#> `class(new)`: "tbl_df" "tbl"        "data.frame"