data-raw/dhakaPPI.R
In validmeasures/washdata: Urban Water and Sanitation Survey Dataset
################################################################################
#
# Bangladesh PPI look-up table
#
################################################################################
#
#
#
score <- c(0:100)
nl <- c(0.762, 0.762, 0.762, 0.762, 0.762, 0.706, 0.706, 0.706, 0.706, 0.706,
0.636, 0.636, 0.636, 0.636, 0.636, 0.464, 0.464, 0.464, 0.464, 0.464,
0.371, 0.371, 0.371, 0.371, 0.371, 0.266, 0.266, 0.266, 0.266, 0.266,
0.191, 0.191, 0.191, 0.191, 0.191, 0.150, 0.150, 0.150, 0.150, 0.150,
0.127, 0.127, 0.127, 0.127, 0.127, 0.066, 0.066, 0.066, 0.066, 0.066,
0.039, 0.039, 0.039, 0.039, 0.039, 0.015, 0.015, 0.015, 0.015, 0.015,
0.009, 0.009, 0.009, 0.009, 0.009, 0.004, 0.004, 0.004, 0.004, 0.004,
0.002, 0.002, 0.002, 0.002, 0.002, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
nu100 <- c(0.873, 0.873, 0.873, 0.873, 0.873, 0.846, 0.846, 0.846, 0.846, 0.846,
0.821, 0.821, 0.821, 0.821, 0.821, 0.680, 0.680, 0.680, 0.680, 0.680,
0.627, 0.627, 0.627, 0.627, 0.627, 0.504, 0.504, 0.504, 0.504, 0.504,
0.409, 0.409, 0.409, 0.409, 0.409, 0.360, 0.360, 0.360, 0.360, 0.360,
0.267, 0.267, 0.267, 0.267, 0.267, 0.196, 0.196, 0.196, 0.196, 0.196,
0.147, 0.147, 0.147, 0.147, 0.147, 0.071, 0.071, 0.071, 0.071, 0.071,
0.053, 0.053, 0.053, 0.053, 0.053, 0.044, 0.044, 0.044, 0.044, 0.044,
0.023, 0.023, 0.023, 0.023, 0.023, 0.012, 0.012, 0.012, 0.012, 0.012,
0.005, 0.005, 0.005, 0.005, 0.005, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
nu150 <- c(0.984, 0.984, 0.984, 0.984, 0.984, 0.977, 0.977, 0.977, 0.977, 0.977,
0.976, 0.976, 0.976, 0.976, 0.976, 0.962, 0.962, 0.962, 0.962, 0.962,
0.961, 0.961, 0.961, 0.961, 0.961, 0.887, 0.887, 0.887, 0.887, 0.887,
0.843, 0.843, 0.843, 0.843, 0.843, 0.808, 0.808, 0.808, 0.808, 0.808,
0.761, 0.761, 0.761, 0.761, 0.761, 0.658, 0.658, 0.658, 0.658, 0.658,
0.550, 0.550, 0.550, 0.550, 0.550, 0.426, 0.426, 0.426, 0.426, 0.426,
0.348, 0.348, 0.348, 0.348, 0.348, 0.286, 0.286, 0.286, 0.286, 0.286,
0.246, 0.246, 0.246, 0.246, 0.246, 0.214, 0.214, 0.214, 0.214, 0.214,
0.170, 0.170, 0.170, 0.170, 0.170, 0.083, 0.083, 0.083, 0.083, 0.083,
0.039, 0.039, 0.039, 0.039, 0.039, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
nu200 <- c(1.000, 1.000, 1.000, 1.000, 1.000, 0.995, 0.995, 0.995, 0.995, 0.995,
0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995,
0.995, 0.995, 0.995, 0.995, 0.995, 0.979, 0.979, 0.979, 0.979, 0.979,
0.960, 0.960, 0.960, 0.960, 0.960, 0.936, 0.936, 0.936, 0.936, 0.936,
0.919, 0.919, 0.919, 0.919, 0.919, 0.866, 0.866, 0.866, 0.866, 0.866,
0.813, 0.813, 0.813, 0.813, 0.813, 0.756, 0.756, 0.756, 0.756, 0.756,
0.649, 0.649, 0.649, 0.649, 0.649, 0.525, 0.525, 0.525, 0.525, 0.525,
0.510, 0.510, 0.510, 0.510, 0.510, 0.403, 0.403, 0.403, 0.403, 0.403,
0.320, 0.320, 0.320, 0.320, 0.320, 0.249, 0.249, 0.249, 0.249, 0.249,
0.099, 0.099, 0.099, 0.099, 0.099, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
extreme <- c(0.658, 0.658, 0.658, 0.658, 0.658, 0.656, 0.656, 0.656, 0.656, 0.656,
0.572, 0.572, 0.572, 0.572, 0.572, 0.425, 0.425, 0.425, 0.425, 0.425,
0.327, 0.327, 0.327, 0.327, 0.327, 0.229, 0.229, 0.229, 0.229, 0.229,
0.169, 0.169, 0.169, 0.169, 0.169, 0.138, 0.138, 0.138, 0.138, 0.138,
0.111, 0.111, 0.111, 0.111, 0.111, 0.054, 0.054, 0.054, 0.054, 0.054,
0.045, 0.045, 0.045, 0.045, 0.045, 0.018, 0.018, 0.018, 0.018, 0.018,
0.010, 0.010, 0.010, 0.010, 0.010, 0.001, 0.001, 0.001, 0.001, 0.001,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp125 <- c(0.979, 0.979, 0.979, 0.979, 0.979, 0.893, 0.893, 0.893, 0.893, 0.893,
0.888, 0.888, 0.888, 0.888, 0.888, 0.816, 0.816, 0.816, 0.816, 0.816,
0.780, 0.780, 0.780, 0.780, 0.780, 0.658, 0.658, 0.658, 0.658, 0.658,
0.570, 0.570, 0.570, 0.570, 0.570, 0.503, 0.503, 0.503, 0.503, 0.503,
0.408, 0.408, 0.408, 0.408, 0.408, 0.335, 0.335, 0.335, 0.335, 0.335,
0.242, 0.242, 0.242, 0.242, 0.242, 0.145, 0.145, 0.145, 0.145, 0.145,
0.109, 0.109, 0.109, 0.109, 0.109, 0.087, 0.087, 0.087, 0.087, 0.087,
0.056, 0.056, 0.056, 0.056, 0.056, 0.043, 0.043, 0.043, 0.043, 0.043,
0.027, 0.027, 0.027, 0.027, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp175 <- c(0.988, 0.988, 0.988, 0.988, 0.988, 0.982, 0.982, 0.982, 0.982, 0.982,
0.982, 0.982, 0.982, 0.982, 0.982, 0.969, 0.969, 0.969, 0.969, 0.969,
0.963, 0.963, 0.963, 0.963, 0.963, 0.916, 0.916, 0.916, 0.916, 0.916,
0.879, 0.879, 0.879, 0.879, 0.879, 0.836, 0.836, 0.836, 0.836, 0.836,
0.796, 0.796, 0.796, 0.796, 0.796, 0.688, 0.688, 0.688, 0.688, 0.688,
0.603, 0.603, 0.603, 0.603, 0.603, 0.504, 0.504, 0.504, 0.504, 0.504,
0.404, 0.404, 0.404, 0.404, 0.404, 0.322, 0.322, 0.322, 0.322, 0.322,
0.315, 0.315, 0.315, 0.315, 0.315, 0.258, 0.258, 0.258, 0.258, 0.258,
0.197, 0.197, 0.197, 0.197, 0.197, 0.107, 0.107, 0.107, 0.107, 0.107,
0.051, 0.051, 0.051, 0.051, 0.051, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp200 <- c(1.000, 1.000, 1.000, 1.000, 1.000, 0.987, 0.987, 0.987, 0.987, 0.987,
0.987, 0.987, 0.987, 0.987, 0.987, 0.986, 0.986, 0.986, 0.986, 0.986,
0.984, 0.984, 0.984, 0.984, 0.984, 0.953, 0.953, 0.953, 0.953, 0.953,
0.935, 0.935, 0.935, 0.935, 0.935, 0.907, 0.907, 0.907, 0.907, 0.907,
0.874, 0.874, 0.874, 0.874, 0.874, 0.796, 0.796, 0.796, 0.796, 0.796,
0.742, 0.742, 0.742, 0.742, 0.742, 0.652, 0.652, 0.652, 0.652, 0.652,
0.546, 0.546, 0.546, 0.546, 0.546, 0.445, 0.445, 0.445, 0.445, 0.445,
0.429, 0.429, 0.429, 0.429, 0.429, 0.340, 0.340, 0.340, 0.340, 0.340,
0.267, 0.267, 0.267, 0.267, 0.267, 0.146, 0.146, 0.146, 0.146, 0.146,
0.066, 0.066, 0.066, 0.066, 0.066, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp250 <- c(1.000, 1.000, 1.000, 1.000, 1.000, 0.997, 0.997, 0.997, 0.997, 0.997,
0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997,
0.997, 0.997, 0.997, 0.997, 0.997, 0.987, 0.987, 0.987, 0.987, 0.987,
0.982, 0.982, 0.982, 0.982, 0.982, 0.969, 0.969, 0.969, 0.969, 0.969,
0.949, 0.949, 0.949, 0.949, 0.949, 0.915, 0.915, 0.915, 0.915, 0.915,
0.879, 0.879, 0.879, 0.879, 0.879, 0.843, 0.843, 0.843, 0.843, 0.843,
0.732, 0.732, 0.732, 0.732, 0.732, 0.633, 0.633, 0.633, 0.633, 0.633,
0.604, 0.604, 0.604, 0.604, 0.604, 0.507, 0.507, 0.507, 0.507, 0.507,
0.409, 0.409, 0.409, 0.409, 0.409, 0.333, 0.333, 0.333, 0.333, 0.333,
0.123, 0.123, 0.123, 0.123, 0.123, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
#
#
#
ppiMatrixBGD <- data.frame(score, nl, nu100, nu150, nu200, extreme,
ppp125, ppp175, ppp200, ppp250)
#
#
#
write.csv(ppiMatrixBGD, file = "data-raw/ppiMatrixBGD.csv", row.names = FALSE)
#
#
#
devtools::use_data(ppiMatrixBGD, overwrite = TRUE)
#
# Clean-up
#
rm(nu100, nu150, nu200, extreme, ppp125, ppp175, ppp200, ppp250)
validmeasures/washdata documentation built on April 18, 2024, 8:16 a.m.
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################################################################################
#
# Bangladesh PPI look-up table
#
################################################################################
#
#
#
score <- c(0:100)
nl <- c(0.762, 0.762, 0.762, 0.762, 0.762, 0.706, 0.706, 0.706, 0.706, 0.706,
0.636, 0.636, 0.636, 0.636, 0.636, 0.464, 0.464, 0.464, 0.464, 0.464,
0.371, 0.371, 0.371, 0.371, 0.371, 0.266, 0.266, 0.266, 0.266, 0.266,
0.191, 0.191, 0.191, 0.191, 0.191, 0.150, 0.150, 0.150, 0.150, 0.150,
0.127, 0.127, 0.127, 0.127, 0.127, 0.066, 0.066, 0.066, 0.066, 0.066,
0.039, 0.039, 0.039, 0.039, 0.039, 0.015, 0.015, 0.015, 0.015, 0.015,
0.009, 0.009, 0.009, 0.009, 0.009, 0.004, 0.004, 0.004, 0.004, 0.004,
0.002, 0.002, 0.002, 0.002, 0.002, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
nu100 <- c(0.873, 0.873, 0.873, 0.873, 0.873, 0.846, 0.846, 0.846, 0.846, 0.846,
0.821, 0.821, 0.821, 0.821, 0.821, 0.680, 0.680, 0.680, 0.680, 0.680,
0.627, 0.627, 0.627, 0.627, 0.627, 0.504, 0.504, 0.504, 0.504, 0.504,
0.409, 0.409, 0.409, 0.409, 0.409, 0.360, 0.360, 0.360, 0.360, 0.360,
0.267, 0.267, 0.267, 0.267, 0.267, 0.196, 0.196, 0.196, 0.196, 0.196,
0.147, 0.147, 0.147, 0.147, 0.147, 0.071, 0.071, 0.071, 0.071, 0.071,
0.053, 0.053, 0.053, 0.053, 0.053, 0.044, 0.044, 0.044, 0.044, 0.044,
0.023, 0.023, 0.023, 0.023, 0.023, 0.012, 0.012, 0.012, 0.012, 0.012,
0.005, 0.005, 0.005, 0.005, 0.005, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
nu150 <- c(0.984, 0.984, 0.984, 0.984, 0.984, 0.977, 0.977, 0.977, 0.977, 0.977,
0.976, 0.976, 0.976, 0.976, 0.976, 0.962, 0.962, 0.962, 0.962, 0.962,
0.961, 0.961, 0.961, 0.961, 0.961, 0.887, 0.887, 0.887, 0.887, 0.887,
0.843, 0.843, 0.843, 0.843, 0.843, 0.808, 0.808, 0.808, 0.808, 0.808,
0.761, 0.761, 0.761, 0.761, 0.761, 0.658, 0.658, 0.658, 0.658, 0.658,
0.550, 0.550, 0.550, 0.550, 0.550, 0.426, 0.426, 0.426, 0.426, 0.426,
0.348, 0.348, 0.348, 0.348, 0.348, 0.286, 0.286, 0.286, 0.286, 0.286,
0.246, 0.246, 0.246, 0.246, 0.246, 0.214, 0.214, 0.214, 0.214, 0.214,
0.170, 0.170, 0.170, 0.170, 0.170, 0.083, 0.083, 0.083, 0.083, 0.083,
0.039, 0.039, 0.039, 0.039, 0.039, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
nu200 <- c(1.000, 1.000, 1.000, 1.000, 1.000, 0.995, 0.995, 0.995, 0.995, 0.995,
0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995,
0.995, 0.995, 0.995, 0.995, 0.995, 0.979, 0.979, 0.979, 0.979, 0.979,
0.960, 0.960, 0.960, 0.960, 0.960, 0.936, 0.936, 0.936, 0.936, 0.936,
0.919, 0.919, 0.919, 0.919, 0.919, 0.866, 0.866, 0.866, 0.866, 0.866,
0.813, 0.813, 0.813, 0.813, 0.813, 0.756, 0.756, 0.756, 0.756, 0.756,
0.649, 0.649, 0.649, 0.649, 0.649, 0.525, 0.525, 0.525, 0.525, 0.525,
0.510, 0.510, 0.510, 0.510, 0.510, 0.403, 0.403, 0.403, 0.403, 0.403,
0.320, 0.320, 0.320, 0.320, 0.320, 0.249, 0.249, 0.249, 0.249, 0.249,
0.099, 0.099, 0.099, 0.099, 0.099, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
extreme <- c(0.658, 0.658, 0.658, 0.658, 0.658, 0.656, 0.656, 0.656, 0.656, 0.656,
0.572, 0.572, 0.572, 0.572, 0.572, 0.425, 0.425, 0.425, 0.425, 0.425,
0.327, 0.327, 0.327, 0.327, 0.327, 0.229, 0.229, 0.229, 0.229, 0.229,
0.169, 0.169, 0.169, 0.169, 0.169, 0.138, 0.138, 0.138, 0.138, 0.138,
0.111, 0.111, 0.111, 0.111, 0.111, 0.054, 0.054, 0.054, 0.054, 0.054,
0.045, 0.045, 0.045, 0.045, 0.045, 0.018, 0.018, 0.018, 0.018, 0.018,
0.010, 0.010, 0.010, 0.010, 0.010, 0.001, 0.001, 0.001, 0.001, 0.001,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp125 <- c(0.979, 0.979, 0.979, 0.979, 0.979, 0.893, 0.893, 0.893, 0.893, 0.893,
0.888, 0.888, 0.888, 0.888, 0.888, 0.816, 0.816, 0.816, 0.816, 0.816,
0.780, 0.780, 0.780, 0.780, 0.780, 0.658, 0.658, 0.658, 0.658, 0.658,
0.570, 0.570, 0.570, 0.570, 0.570, 0.503, 0.503, 0.503, 0.503, 0.503,
0.408, 0.408, 0.408, 0.408, 0.408, 0.335, 0.335, 0.335, 0.335, 0.335,
0.242, 0.242, 0.242, 0.242, 0.242, 0.145, 0.145, 0.145, 0.145, 0.145,
0.109, 0.109, 0.109, 0.109, 0.109, 0.087, 0.087, 0.087, 0.087, 0.087,
0.056, 0.056, 0.056, 0.056, 0.056, 0.043, 0.043, 0.043, 0.043, 0.043,
0.027, 0.027, 0.027, 0.027, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp175 <- c(0.988, 0.988, 0.988, 0.988, 0.988, 0.982, 0.982, 0.982, 0.982, 0.982,
0.982, 0.982, 0.982, 0.982, 0.982, 0.969, 0.969, 0.969, 0.969, 0.969,
0.963, 0.963, 0.963, 0.963, 0.963, 0.916, 0.916, 0.916, 0.916, 0.916,
0.879, 0.879, 0.879, 0.879, 0.879, 0.836, 0.836, 0.836, 0.836, 0.836,
0.796, 0.796, 0.796, 0.796, 0.796, 0.688, 0.688, 0.688, 0.688, 0.688,
0.603, 0.603, 0.603, 0.603, 0.603, 0.504, 0.504, 0.504, 0.504, 0.504,
0.404, 0.404, 0.404, 0.404, 0.404, 0.322, 0.322, 0.322, 0.322, 0.322,
0.315, 0.315, 0.315, 0.315, 0.315, 0.258, 0.258, 0.258, 0.258, 0.258,
0.197, 0.197, 0.197, 0.197, 0.197, 0.107, 0.107, 0.107, 0.107, 0.107,
0.051, 0.051, 0.051, 0.051, 0.051, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp200 <- c(1.000, 1.000, 1.000, 1.000, 1.000, 0.987, 0.987, 0.987, 0.987, 0.987,
0.987, 0.987, 0.987, 0.987, 0.987, 0.986, 0.986, 0.986, 0.986, 0.986,
0.984, 0.984, 0.984, 0.984, 0.984, 0.953, 0.953, 0.953, 0.953, 0.953,
0.935, 0.935, 0.935, 0.935, 0.935, 0.907, 0.907, 0.907, 0.907, 0.907,
0.874, 0.874, 0.874, 0.874, 0.874, 0.796, 0.796, 0.796, 0.796, 0.796,
0.742, 0.742, 0.742, 0.742, 0.742, 0.652, 0.652, 0.652, 0.652, 0.652,
0.546, 0.546, 0.546, 0.546, 0.546, 0.445, 0.445, 0.445, 0.445, 0.445,
0.429, 0.429, 0.429, 0.429, 0.429, 0.340, 0.340, 0.340, 0.340, 0.340,
0.267, 0.267, 0.267, 0.267, 0.267, 0.146, 0.146, 0.146, 0.146, 0.146,
0.066, 0.066, 0.066, 0.066, 0.066, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
ppp250 <- c(1.000, 1.000, 1.000, 1.000, 1.000, 0.997, 0.997, 0.997, 0.997, 0.997,
0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997, 0.997,
0.997, 0.997, 0.997, 0.997, 0.997, 0.987, 0.987, 0.987, 0.987, 0.987,
0.982, 0.982, 0.982, 0.982, 0.982, 0.969, 0.969, 0.969, 0.969, 0.969,
0.949, 0.949, 0.949, 0.949, 0.949, 0.915, 0.915, 0.915, 0.915, 0.915,
0.879, 0.879, 0.879, 0.879, 0.879, 0.843, 0.843, 0.843, 0.843, 0.843,
0.732, 0.732, 0.732, 0.732, 0.732, 0.633, 0.633, 0.633, 0.633, 0.633,
0.604, 0.604, 0.604, 0.604, 0.604, 0.507, 0.507, 0.507, 0.507, 0.507,
0.409, 0.409, 0.409, 0.409, 0.409, 0.333, 0.333, 0.333, 0.333, 0.333,
0.123, 0.123, 0.123, 0.123, 0.123, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000)
#
#
#
ppiMatrixBGD <- data.frame(score, nl, nu100, nu150, nu200, extreme,
ppp125, ppp175, ppp200, ppp250)
#
#
#
write.csv(ppiMatrixBGD, file = "data-raw/ppiMatrixBGD.csv", row.names = FALSE)
#
#
#
devtools::use_data(ppiMatrixBGD, overwrite = TRUE)
#
# Clean-up
#
rm(nu100, nu150, nu200, extreme, ppp125, ppp175, ppp200, ppp250)
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