How to impute missing values not at random? - r
My data consists of 202 cases, each stand for a single interview. The variables reflect the interviewers' and interviewees' behaviours during different parts of the interview: p1, g1, pA, gA. in some interviews, certain parts were not carried out. part p1 wasn't carried out in one interview. part g1 wasn't conducted in 46 cases. part pA wasn't conducted with 14 subjects and gA with 27.
Different variables are different facets of the same underlying concept or latent variable. for example, all four variables belonging to part pA - pAx1, pAx2, pAx3, pAx4 - are different measures of the interviewee's cooperativeness during part pA.
I would like to impute the missing values while accounting for the fact that there is a pattern for values to be missing, such that if a value is missing for a variable of part pA, e.g., pAx1, then, necessarily that the other values pertaining to part pA - pAx2, pAx3, pAx4 - are also missing.
Help would be much appreciated!
this is my data -
df <- structure(list(p1x1 = c(0.54, 0.77, 0.84, 0.84, 0.75, 0.35, 0.67,
0.23, 0.9, 0.81, 0.76, 0.85, 0.61, 0.8, 0.1, 0.81, 0.96, 0.68,
0.83, 0.8, 0.89, 0.85, 1, 0.83, 0.52, 0.74, 0.47, 0.51, 1, 0.83,
0.93, 0, 0.31, 0.95, 0, 0.39, 0.84, 0.62, 0.81, 0.58, 0.7, 0.54,
0.94, 0.76, 0.76, 0.14, 0.67, 0.65, 1, 0.69, 0.31, 0.43, 0.83,
0.79, 0.94, 0.84, 0.28, 0.76, 0.78, 0.91, 0.89, 0.63, 0.76, 0.34,
0.91, 1, 0.72, 0.89, 0.43, 0.85, 0.8, 0.45, 0.12, 0.19, 0.91,
0.74, 0.88, 0.62, 0.92, 0.72, 0.54, 0.59, 0.74, 0.8, 1, 0.66,
0.48, 0.7, 0.96, 0.87, 0.65, 0.61, 0.79, 0.8, 0.93, 0.83, 0.88,
0.76, 0.58, 0.79, 0.65, 0.88, 0.37, 0.74, 0.63, 0.64, 0.58, 0.86,
0.62, 0.57, 0.09, 0.61, 0.29, 0.9, 0.91, 0.73, 0.92, 0.9, 0.56,
0.89, 0.89, 0.62, 0.24, 0.65, 0.76, 0.69, 0.42, 0.8, 0.39, 0.58,
0.72, 0.73, 0.48, NA, 0.5, 0.72, 0.91, 0.58, 0.8, 0, 0.47, 0.5,
0.85, 0.93, 0.81, 0.89, 0.93, 0.55, 0.78, 0.72, 0.77, 0.44, 0.57,
0.78, 0.84, 0.83, 0.62, 0.3, 0.67, 0.96, 0.62, 0.73, 0.29, 0.76,
0.86, 0.7, 0.54, 0.28, 0.74, 0.67, 0.17, 0.05, 0.62, 0.76, 0.73,
1, 0.7, 0.92, 0.31, 1, 0.33, 0.59, 0.62, 0.78, 0.26, 0.76, 0.7,
0.81, 0.82, 0.81, 0.83, 0.3, 0.79, 0, 0.72, 0.67, 0.78, 0.11,
0.32, 0.39, 0.6, 0.7), p1x2 = c(0, 0.08, 0.32, 0.11, 0.12, 0,
0.17, 0.08, 0.38, 0.12, 0, 0.15, 0.25, 0.05, 0, 0.15, 0.13, 0.08,
0.08, 0.13, 0.06, 0.46, 0.21, 0.14, 0.19, 0.11, 0.24, 0.08, 0.36,
0.08, 0.29, 0, 0, 0.14, 0, 0.07, 0.16, 0.04, 0.33, 0.32, 0.22,
0.08, 0.29, 0.06, 0.43, 0.07, 0.06, 0.16, 0.18, 0.19, 0.08, 0.1,
0.17, 0.21, 0.06, 0.11, 0.06, 0.24, 0.22, 0.13, 0.21, 0.26, 0.1,
0, 0.23, 0.44, 0.21, 0.16, 0, 0.15, 0.4, 0.07, 0, 0, 0.31, 0.1,
0.38, 0.43, 0.16, 0.12, 0.12, 0.18, 0.3, 0.45, 0.33, 0.02, 0.19,
0.15, 0.15, 0.2, 0.02, 0.04, 0.21, 0.27, 0.07, 0.14, 0.06, 0.05,
0.37, 0.05, 0.35, 0.25, 0.21, 0.09, 0.08, 0.08, 0.06, 0.71, 0.04,
0.05, 0, 0.04, 0.32, 0.4, 0.55, 0.12, 0.08, 0, 0.19, 0.33, 0.11,
0.06, 0.02, 0.29, 0.12, 0.03, 0.04, 0.33, 0.27, 0.25, 0, 0, 0.19,
NA, 0.08, 0.32, 0.48, 0.08, 0.07, 0, 0.11, 0.17, 0.2, 0.33, 0.19,
0.22, 0.33, 0.09, 0.28, 0.28, 0, 0.44, 0.27, 0.17, 0.32, 0.06,
0.29, 0, 0.1, 0.25, 0.22, 0.45, 0, 0.09, 0.14, 0.33, 0, 0.24,
0.21, 0.06, 0, 0, 0.5, 0.52, 0.36, 0.4, 0.2, 0.33, 0.14, 0.12,
0.08, 0.17, 0.31, 0, 0, 0.16, 0.02, 0, 0.45, 0.19, 0, 0, 0.02,
0, 0.25, 0.43, 0.39, 0, 0.21, 0, 0.02, 0.25), p1x3 = c(0.46,
0.12, 0.21, 0.47, 0.29, 0.4, 0.33, 0.38, 0.21, 0.12, 0.41, 0.1,
0.29, 0.45, 0.9, 0.3, 0.22, 0.18, 0, 0.27, 0.17, 0.23, 0, 0.28,
0.19, 0.16, 0.59, 0.38, 0.07, 0.25, 0.36, 1, 0.75, 0.14, 1, 0.43,
0.21, 0.42, 0.1, 0.42, 0.39, 0.53, 0.06, 0.35, 0.33, 0.64, 0.28,
0.29, 0.24, 0.19, 0.69, 0.61, 0.08, 0.37, 0.06, 0.26, 0.56, 0.34,
0.48, 0.17, 0.25, 0.11, 0.14, 0.24, 0.14, 0.07, 0.28, 0.37, 0.46,
0.35, 0.6, 0.52, 0.81, 0.39, 0.07, 0.23, 0.08, 0.19, 0.08, 0.44,
0.73, 0.3, 0.11, 0.15, 0.25, 0.32, 0.24, 0.44, 0.07, 0.13, 0.22,
0.26, 0.29, 0.2, 0.29, 0.28, 0.06, 0.29, 0.42, 0.05, 0.6, 0.25,
0.68, 0.26, 0.42, 0.31, 0.36, 0.14, 0.29, 0.03, 0.5, 0.14, 0.54,
0.3, 0.05, 0.35, 0.38, 0.3, 0.06, 0.11, 0.3, 0.41, 0.44, 0.47,
0.18, 0.28, 0.67, 0, 0.45, 0.25, 0.28, 0.27, 0.24, NA, 0.42,
0.24, 0.48, 0.21, 0.2, 1, 0.79, 0.33, 0.1, 0.07, 0.19, 0.28,
0.13, 0.45, 0.17, 0.17, 0.08, 0.62, 0.2, 0.26, 0.12, 0.17, 0.29,
0.7, 0.33, 0.04, 0.38, 0.18, 0.71, 0.24, 0.21, 0.41, 0.31, 0.56,
0, 0.39, 0.83, 0.65, 0.62, 0, 0.32, 0, 0.4, 0.08, 0.43, 0.65,
0.25, 0.28, 0.31, 0.09, 0.71, 0.08, 0.09, 0.17, 0.09, 0.24, 0.33,
0.52, 0.21, 1, 0.28, 0, 0.22, 0.89, 0.32, 0.48, 0.53, 0.45),
p1x4 = c(0, 0.71, 0.78, 0.73, 0.73, 0.75, NA, 0, 0.78, 1,
0.8, 0.71, 0.88, 0.9, NA, 0.73, 1, 0.57, 0.83, 0.67, 0.67,
1, 1, 0.47, 0, 0.86, NA, 0.4, 0.88, 0.86, 1, NA, 0.33, 0.73,
0, 0.28, 0.89, 0.62, 0.45, 0.4, 0.75, 0.42, 0.8, 0.5, 0.67,
0.33, 0.54, 0.25, 0.9, 0.54, NA, 0.33, 0, 0.67, 0.82, 0.62,
NA, 0.62, 0.5, NA, 0.81, 0, 0.6, 0, 0.88, 0, 0.45, 0.8, 0,
0.89, NA, 0.47, NA, 0.3, 0.25, NA, 0, 0, 0.82, 0, 0.5, 0.53,
0.61, 0.58, 1, 0, 0.23, 0.53, 0.78, 0, 0.33, 0.57, 0.57,
0.89, 1, 0.6, 0.88, 0.9, 0.5, 0.56, 0.42, 0.75, NA, 0.71,
0, 0.59, NA, NA, 0.33, 0.4, 0.22, 0.33, 0.3, 0.86, 0.7, 0.78,
1, 0.92, 0, 0.89, 0.61, 0.6, 0.16, 0.4, 0.55, 0, 0.36, 0.6,
0, 0.43, 0.5, 0.42, 0.36, NA, 0.33, 0.8, 0.81, 0, 0.62, 0,
0.56, 0.6, 0, 0.88, 0.67, 0.83, 1, 0.36, 0, 0.4, 0, 0.29,
0.45, 0.82, 0.67, 0.8, 0.59, 0.17, 0.24, 0, 0, 0.69, 0.25,
0.56, 0.38, 0.64, NA, 0, 0.64, 0.75, NA, NA, 0.44, 0.65,
0.67, 1, 0.78, NA, 0.17, 0.9, 0, 0.53, 0.22, 1, 0, 0, 0.53,
0.56, 1, 0.77, 0, 0, 0, NA, 0.73, 0.33, 0.71, NA, 0, 0, 0.46,
0.78), p1y1 = c(0.42, 0.27, 0.63, 0.32, 0.46, 0.8, 0.5, 0.31,
0.59, 0.38, 0.24, 0.55, 0.71, 0.7, 0.8, 0.59, 0.35, 0.08,
0.33, 0.6, 0.22, 0.46, 0.43, 0.38, 0.33, 0.32, 0.41, 0.24,
0.43, 0.33, 0.64, 1, 0.44, 0.33, 0.5, 0.25, 0.53, 0.29, 0.33,
0.89, 0.26, 0.34, 0.59, 0.35, 0.48, 0.43, 0.44, 0.45, 0.53,
0.46, 0.69, 0.18, 0.54, 0.32, 0.41, 0.58, 0.17, 0.28, 0.26,
0.35, 0.43, 0.58, 0.33, 0.07, 0.27, 0.59, 0.59, 0.58, 0.14,
0.54, 1, 0.24, 0.35, 0.24, 0.29, 0.13, 0.88, 0.38, 0.48,
0.16, 0.35, 0.36, 0.41, 0.45, 1, 0.22, 0.33, 0.22, 0.15,
0.27, 0.02, 0.35, 0.57, 0.6, 0.5, 0.52, 0.41, 0.57, 0.42,
0.53, 0.35, 0.31, 0.58, 0.34, 0.37, 0.5, 0.44, 0.71, 0.46,
0.16, 0.32, 0.39, 0.43, 0.6, 0.86, 0.38, 0.33, 0.55, 0.5,
0.56, 0.19, 0.38, 0.13, 0.53, 0.65, 0.22, 0.46, 0.4, 0.42,
0.5, 0.32, 0.42, 0.33, 0, 0.5, 0.56, 0.26, 0.12, 0.47, 0.5,
0.53, 0, 0.55, 0.4, 0.29, 0.17, 0.33, 0.45, 0.72, 0.33, 0.77,
0.75, 0.6, 0.25, 0.48, 1, 0.33, 0.5, 0.59, 0.38, 0.22, 0.45,
0.35, 0.24, 0.57, 0.48, 0.31, 0.36, 0.32, 0.56, 0.46, 0.25,
0.25, 0.64, 0.91, 0.67, 0.5, 0.92, 0.17, 0.47, 0.83, 0.24,
0.23, 0.43, 0.32, 0.55, 0.14, 0.09, 0.73, 0.29, 0.39, 0.39,
0.32, 1.2, 0.39, 0.48, 0.39, 0.33, 0.74, 0.55, 0.29, 0.6),
g1y2 = c(0.46, 0.79, 0.83, 0.44, NA, 0.84, NA, NA, 1.44,
0.55, 0.86, 0.35, 0.63, 1.05, NA, 1.45, 0.67, 0.85, 0.45,
1.13, 0.42, 0.45, 0.6, 1.12, 1, 0.63, NA, NA, 0.68, 1.09,
1.28, NA, 1.17, 0.93, NA, 0.45, 0.5, 1.06, 0.51, 0.86, 1.09,
1.28, 0.83, 0.94, 1.1, NA, 0.95, NA, 1.1, 0.94, NA, 0.31,
1.33, 0.97, 0.57, 0.94, NA, NA, 0.79, NA, 1.02, 0.62, 1.11,
0.52, 0.97, 0.89, NA, 1, 0.46, 0.85, NA, 0.5, NA, 1.25, 0.75,
NA, 0.71, 1, 0.6, 0.51, 0.8, 0.86, 1.03, 0.8, 0.79, 0.6,
NA, 0.87, 0.57, 0.36, 0.64, 0.43, 0.88, 1.14, 0.76, NA, 0.71,
0.77, 0.7, 0, 0.94, 0.93, NA, 0.47, NA, 0.98, NA, NA, NA,
0.44, 1, 0.62, 0.7, 0.96, 0.94, 0.74, 0.65, 0.86, 1.5, 0.92,
NA, 1.11, 0.75, 1.09, 0.79, 0.6, 0.75, 0.71, NA, 0.62, 1.08,
0.58, 0.62, NA, 0.67, 1.11, 1.11, 0.32, 0.77, NA, 1.5, 0.47,
NA, 0.93, NA, 0.4, NA, 0.94, 1, 0.72, 0.85, 0.73, 0.79, 0.32,
0.81, 0.92, 0.93, NA, 1, 0.7, 0.88, 1, NA, 0.85, 1, 0.92,
0.67, NA, 0.68, 0.64, NA, NA, 0.67, 1, NA, 1.08, 1.21, NA,
NA, 1, NA, 0.72, 0.5, 0.95, 1, 0.79, 0.65, 0.72, 1.03, 0.86,
0.84, NA, 1.11, NA, 0.97, NA, 0.85, NA, NA, 1.22, 0.31, 0.81
), g1y3 = c(0.21, 0.05, 0.13, 0, NA, 0.18, NA, NA, 0.12,
0.1, 0.27, 0.08, 0.11, 0.35, NA, 0.36, 0.33, 0.03, 0.27,
0.13, 0.17, 0.05, 0.4, 0.06, 0.5, 0.07, NA, NA, 0.08, 0.18,
0.11, NA, 0.5, 0.13, NA, 0.27, 0.17, 0.06, 0.14, 0.29, 0.18,
0.05, 0.12, 0.19, 0.05, NA, 0.2, NA, 0.3, 0.28, NA, 0.38,
0.33, 0.12, 0.05, 0.29, NA, NA, 0.15, NA, 0.07, 0.12, 0.06,
0, 0.05, 0.09, NA, 0.09, 0, 0.15, NA, 0.12, NA, 0.12, 0.12,
NA, 0.06, 0.25, 0.08, 0, 0.06, 0.14, 0.09, 0.16, 0.07, 0.07,
NA, 0.1, 0.11, 0.36, 0.06, 0.29, 0.19, 0.14, 0.05, NA, 0.09,
0.04, 0.04, 0, 0.1, 0.21, NA, 0.07, NA, 0.14, NA, NA, NA,
0.08, 0, 0.23, 0.03, 0.15, 0.18, 0.04, 0.15, 0.1, 0.5, 0.08,
NA, 0.05, 0.5, 0.27, 0.03, 0.1, 0.09, 0.18, NA, 0.1, 0.15,
0.18, 0.23, NA, 0.1, 0.05, 0.33, 0.05, 0.31, NA, 0.08, 0,
NA, 0.31, NA, 0.2, NA, 0.18, 0.17, 0.11, 0.15, 0.04, 0.14,
0.09, 0.06, 0.08, 0.21, NA, 0.12, 0.04, 0.27, 0.14, NA, 0.07,
0.11, 0.12, 0, NA, 0.04, 0.18, NA, NA, 0.09, 0.17, NA, 0.08,
0.12, NA, NA, 0.15, NA, 0.13, 0.3, 0.09, 0.12, 0.09, 0.18,
0.1, 0.16, 0.29, 0.05, NA, 0.17, NA, 0.06, NA, 0.08, NA,
NA, 0.11, 0.2, 0.19), g1y4 = c(0, 0, 0, 0, NA, 0, NA, NA,
0, 0, 0, 0, 0, 0, NA, 0, 0, 0.17, 0, 0, 0, 0, 0, 0, 0, 0,
NA, NA, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, NA, NA, 0, NA, 0,
0, 0, 0.1, 0, 0, NA, 0, 0, 0, NA, 0, NA, 0, 0, NA, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0,
0, 0, 0, 0, 0, NA, 0, NA, 0, NA, NA, NA, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA,
0, 0.08, 0, 0, 0, NA, 0, 0, NA, 0, NA, 0, NA, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0,
0, NA, NA, 0, 0, NA, 0, 0, NA, NA, 0, NA, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, NA, 0, NA, 0, NA, 0, NA, NA, 0, 0, 0), g1y5 = c(0.21,
0.11, 0.13, 0.25, NA, 0, NA, NA, 0.12, 0.25, 0, 0.23, 0.37,
0.05, NA, 0, 0, 0.1, 0.18, 0.13, 0.33, 0.36, 0.1, 0.06, 0,
0.2, NA, NA, 0.16, 0, 0, NA, 0.17, 0, NA, 0.09, 0.2, 0.06,
0.3, 0.14, 0, 0, 0.12, 0.25, 0, NA, 0, NA, 0, 0.06, NA, 0.23,
0, 0, 0.3, 0, NA, NA, 0.06, NA, 0, 0.5, 0.03, 0.07, 0.28,
0.08, NA, 0.15, 0.15, 0, NA, 0.31, NA, 0, 0, NA, 0.37, 0,
0.2, 0.34, 0.1, 0, 0, 0, 0.21, 0.37, NA, 0.03, 0.18, 0.18,
0.24, 0.21, 0, 0, 0.05, NA, 0.13, 0.12, 0.32, 0, 0, 0, NA,
0.25, NA, 0, NA, NA, NA, 0.28, 0, 0.15, 0.22, 0, 0.12, 0.13,
0.15, 0, 0, 0, NA, 0, 0, 0, 0.24, 0.4, 0.06, 0.18, NA, 0.38,
0, 0.21, 0, NA, 0.29, 0.02, 0, 0.26, 0, NA, 0, 0.35, NA,
0, NA, 0.2, NA, 0, 0, 0, 0, 0.12, 0, 0.5, 0.1, 0.2, 0, NA,
0.08, 0.36, 0, 0, NA, 0.07, 0, 0.08, 0, NA, 0.28, 0.11, NA,
NA, 0.03, 0, NA, 0, 0, NA, NA, 0, NA, 0.06, 0.1, 0, 0, 0.27,
0.11, 0.17, 0.08, 0, 0.11, NA, 0, NA, 0, NA, 0.15, NA, NA,
0, 0.4, 0), g1y6 = c(0.68, 0.47, 0.43, 0.44, NA, 0.47, NA,
NA, 0.44, 0.65, 0.32, 0.77, 0.63, 0.7, NA, 0.45, 0.67, 0.24,
0.91, 0.47, 0.92, 0.77, 0.8, 0.21, 0.5, 0.6, NA, NA, 0.43,
0.18, 0.22, NA, 1, 0.13, NA, 0.73, 0.67, 0.31, 0.6, 0.43,
0.27, 0.26, 0.5, 0.75, 0.08, NA, 0.2, NA, 0.5, 0.44, NA,
0.85, 0.33, 0.34, 0.54, 0.29, NA, NA, 0.3, NA, 0.13, 0.75,
0.17, 0.57, 0.44, 0.28, NA, 0.5, 0.46, 0.38, NA, 0.69, NA,
0.25, 0.62, NA, 0.57, 0.25, 0.52, 0.54, 0.29, 0.14, 0.11,
0.32, 0.55, 0.53, NA, 0.27, 0.5, 0.91, 0.52, 0.86, 0.44,
0.14, 0.3, NA, 0.38, 0.31, 0.56, 1, 0.16, 0.29, NA, 0.6,
NA, 0.14, NA, NA, NA, 0.68, 0.29, 0.77, 0.46, 0.19, 0.47,
0.35, 0.8, 0.28, 0.5, 0.15, NA, 0.05, 0.5, 0.36, 0.47, 0.7,
0.31, 0.53, NA, 0.71, 0.31, 0.61, 0.69, NA, 0.62, 0.11, 0.33,
0.84, 0.43, NA, 0.17, 0.59, NA, 0.52, NA, 1, NA, 0.29, 0.25,
0.5, 0.31, 0.45, 0.36, 0.82, 0.52, 0.6, 0.25, NA, 0.48, 0.47,
0.39, 0.23, NA, 0.26, 0.11, 0.33, 0.67, NA, 0.44, 0.46, NA,
NA, 0.42, 0.17, NA, 0.17, 0.25, NA, NA, 0.23, NA, 0.32, 0.7,
0.32, 0.12, 0.45, 0.49, 0.45, 0.32, 0.43, 0.37, NA, 0.39,
NA, 0.11, NA, 0.35, NA, NA, 0.11, 0.8, 0.31), g1y7 = c(0.46,
0.42, 0.3, 0.44, NA, 0.29, NA, NA, 0.31, 0.55, 0.05, 0.69,
0.53, 0.35, NA, 0.09, 0.33, 0.21, 0.64, 0.33, 0.75, 0.73,
0.4, 0.15, 0, 0.53, NA, NA, 0.35, 0, 0.11, NA, 0.5, 0, NA,
0.45, 0.5, 0.25, 0.47, 0.14, 0.09, 0.21, 0.38, 0.56, 0.02,
NA, 0, NA, 0.2, 0.17, NA, 0.46, 0, 0.22, 0.49, 0, NA, NA,
0.15, NA, 0.07, 0.62, 0.11, 0.57, 0.38, 0.19, NA, 0.41, 0.46,
0.23, NA, 0.56, NA, 0.12, 0.5, NA, 0.51, 0, 0.44, 0.54, 0.22,
0, 0.03, 0.16, 0.48, 0.47, NA, 0.17, 0.39, 0.55, 0.45, 0.57,
0.25, 0, 0.24, NA, 0.29, 0.27, 0.52, 1, 0.06, 0.07, NA, 0.53,
NA, 0, NA, NA, NA, 0.6, 0.29, 0.54, 0.43, 0.04, 0.29, 0.3,
0.65, 0.17, 0, 0.08, NA, 0, 0, 0.09, 0.44, 0.6, 0.22, 0.35,
NA, 0.62, 0.15, 0.42, 0.46, NA, 0.52, 0.06, 0, 0.79, 0.11,
NA, 0.08, 0.59, NA, 0.21, NA, 0.8, NA, 0.12, 0.08, 0.39,
0.15, 0.41, 0.21, 0.73, 0.45, 0.52, 0.04, NA, 0.36, 0.43,
0.12, 0.09, NA, 0.2, 0, 0.21, 0.67, NA, 0.4, 0.29, NA, NA,
0.33, 0, NA, 0.08, 0.12, NA, NA, 0.08, NA, 0.19, 0.4, 0.23,
0, 0.36, 0.32, 0.34, 0.16, 0.14, 0.32, NA, 0.22, NA, 0.06,
NA, 0.27, NA, NA, 0, 0.6, 0.12), pAx1 = c(0.2, 0.56, 0.67,
NA, 0.7, 0.5, 1, NA, 1, NA, 1, 0.67, 0.67, 0.57, 0.85, 0.91,
0.82, 0.65, 1, 0.8, 0.67, 1, 0.67, 0.5, 0.64, 0.45, 0.8,
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0.5, 0.47, 0.52, 0.86, 1, 1, 0.5, 1, 0.14, 0.58, 0.7, 0.5,
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0.62, 1), gAy2 = c(NA, 0.4, 1.27, 0.25, 1.03, 1, NA, 0.6,
1.23, 0.69, 0.78, 0.81, 0, 1.07, NA, 1.11, 0.38, 0.59, 0.29,
NA, 0.33, 0.38, 0.2, NA, 0.5, 0.5, 0.67, 0.67, 1, NA, NA,
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1.25, 0.6, 0.79, NA, 0.52, 1.2, 0.84, 1, 0.46, 0.18, 0.62,
0.71, 0.4, 0.12, 0.2, 1.25, 1, NA, 0.92, 0.38, 0.58, 1.38,
1, 0.7, NA, 0.4, 0.69, 0.89, 0.36, 0.67, 0.87, 0.38, 1.08,
0.94, NA, 0.73, 0.29, 0.83, NA, 1, 0.47, 0.98, 0.11, 2),
gAy3 = c(NA, 0.2, 0, 0, 0.08, 1, NA, 0.2, 0, 0.15, 0.07,
0, 1, 0.1, NA, 0.22, 0, 0.18, 0.43, NA, 0.11, 0.15, 0.4,
NA, 0.75, 0.5, 0.5, 0.22, 1, NA, NA, 0.14, NA, 0.4, 0.33,
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0.3, 0.06, 0.14, 0, 0.12, 0.11, 0.03, 0.25, 0.5, 0.11, 1,
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1, 0.11, 0.08, 0.05, 0.21, 0.14, 0.09, 0.08, 0.1, 0.18, 0.3,
0.67, NA, 0, 0.11, NA, NA, 0.07, 0.38, NA, NA, 0.11, 0.33,
0.27, 0.5, 0, 0.05, 0, 0.12, 0.15, 1, 0.06, 0, NA, NA, 0,
0.6, 0, 0.05, 0.21, 0.2, 0.5, 0.18, 0.29, 1, 0, NA, 0.08,
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0.13, 0, 0.1, 0.5, 0.17, NA, 0.15, 0.15, 0.25, 0, 0.2, 0.04,
NA, 0, 0, 0, 0, 0, 0.33, 0.12, 0, 0.08, NA, 0.13, 0.14, 0.5,
NA, 1, 0.47, 0.1, 0, 1), gAy4 = c(NA, 0, 0, 0, 0, 0, NA,
0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 0, NA, 0,
0, 0, 0, 0, NA, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0,
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0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA,
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NA, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0.57, 0, 0, 0, 0, 0, 0, NA,
0, 0, 0, NA, 0, 0, 0, 0, 0), gAy5 = c(NA, 0.4, 0.18, 0.33,
0.08, 0, NA, 0, 0, 0.08, 0.15, 0, 0, 0.13, NA, 0, 0.19, 0,
0.14, NA, 0.44, 0.31, 0, NA, 0, 0, 0, 0.11, 0, NA, NA, 0.18,
NA, 0, 0, 0, NA, 0.2, 0.1, 0.32, 0.25, 0, 0.21, 0.27, 0,
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NA, 0.14, 0.11, NA, NA, 0.21, 0, NA, NA, 0.17, 0, 0.27, 0,
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0, 0, 0, 0, 0), gAy6 = c(NA, 0.8, 0.27, 0.67, 0.37, 1, NA,
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1, NA, 1, 0.85, 1, NA, 0.75, 0.5, 0.83, 0.78, 1, NA, NA,
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NA, 0.78, 0, 0.11, 0.5, 0.57, 0.64, 0.75, 0.24, 0.6, 0.88,
0.7, 0, 0, NA, 0.23, 0.54, 0.5, 0.5, 0.6, 0.33, NA, 0.8,
0.69, 0.14, 0.36, 0.57, 0.4, 0.62, 0.08, 0.27, NA, 0.47,
0.71, 0.33, NA, 0, 0.27, 0.15, 0.95, 0)), row.names = c(NA,
-202L), class = "data.frame")
If it makes sense to impute the values, then even if you do not have the 4 questions of a part, you can predict them using the relationship between variables and the similarities between observations.
To take into account the colinearities, you can use methods based on low rank,
see the package missMDA for instance with imputePCA or imputeMFA function, in addition you can have a look at the website
https://rmisstastic.netlify.com/
for information,
Best,
JJ
Related
What is Error: Aesthetics must be valid data columns Problematic aesthetic(s) in geom_rect and how to fix it?
I would like to make shade on certain date each year in time-series plot that is similar to this: Using geom_rect for time series shading in R However, I got error: Error: Aesthetics must be valid data columns. Problematic aesthetic(s): x = Date. Did you mistype the name of a data column or forget to add after_stat()? What is the meaning of that error? I'm sure the error come from geom_rect function, but I don't know how to fix it. Here is the example of my data: structure(list(Date = structure(c(4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 4036, 4037, 4038, 4039, 4040, 4041, 4042, 4043, 4044, 4045, 4046, 4047, 4048, 4049, 4050, 4051, 4052, 4053, 4054, 4055, 4056, 4057, 4058, 4059, 4060, 4061, 4062, 4063, 4064, 4065, 4066, 4067, 4068, 4069, 4070, 4071, 4072, 4073, 4074, 4075, 4076, 4077, 4078, 4079, 4080, 4081, 4082, 4083, 4084, 4085, 4086, 4087, 4088, 4089, 4090, 4091, 4092, 4093, 4094, 4095, 4096, 4097, 4098, 4099, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108, 4109, 4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120, 4121, 4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131, 4132, 4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 4141, 4142, 4143, 4144, 4145, 4146, 4147, 4148, 4149, 4150, 4151, 4152, 4153, 4154, 4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4167, 4168, 4169, 4170, 4171, 4172, 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4339, 4340, 4341, 4342, 4343, 4344, 4345, 4346, 4347, 4348, 4349, 4350, 4351, 4352, 4353, 4354, 4355, 4356, 4357, 4358, 4359, 4360, 4361, 4362, 4363, 4364, 4365, 4366, 4367, 4368, 4369, 4370, 4371, 4372, 4373, 4374, 4375, 4376, 4377, 4378, 4379, 4380, 4381, 4382, 4383, 4384, 4385, 4386, 4387, 4388, 4389, 4390, 4391, 4392, 4393, 4394, 4395, 4396, 4397, 4398, 4399, 4400, 4401, 4402, 4403, 4404, 4405, 4406, 4407, 4408, 4409, 4410, 4411, 4412, 4413, 4414, 4415, 4416, 4417, 4418, 4419, 4420, 4421, 4422, 4423, 4424, 4425, 4426, 4427, 4428, 4429, 4430, 4431, 4432, 4433, 4434, 4435, 4436, 4437, 4438, 4439, 4440, 4441, 4442, 4443, 4444, 4445, 4446, 4447, 4448, 4449, 4450, 4451, 4452, 4453, 4454, 4455, 4456, 4457, 4458, 4459, 4460, 4461, 4462, 4463, 4464, 4465, 4466, 4467, 4468, 4469, 4470, 4471, 4472, 4473, 4474, 4475, 4476, 4477, 4478, 4479, 4480, 4481, 4482, 4483, 4484, 4485, 4486, 4487, 4488, 4489, 4490, 4491, 4492, 4493, 4494, 4495, 4496, 4497, 4498, 4499, 4500, 4501, 4502, 4503, 4504, 4505, 4506, 4507, 4508, 4509, 4510, 4511, 4512, 4513, 4514, 4515, 4516, 4517, 4518, 4519, 4520, 4521, 4522, 4523, 4524, 4525, 4526, 4527, 4528, 4529, 4530, 4531, 4532, 4533, 4534, 4535, 4536, 4537, 4538, 4539, 4540, 4541, 4542, 4543, 4544, 4545, 4546, 4547, 4548, 4549, 4550, 4551, 4552, 4553, 4554, 4555, 4556, 4557, 4558, 4559, 4560, 4561, 4562, 4563, 4564, 4565, 4566, 4567, 4568, 4569, 4570, 4571, 4572, 4573, 4574, 4575, 4576, 4577, 4578, 4579, 4580, 4581, 4582, 4583, 4584, 4585, 4586, 4587, 4588, 4589, 4590, 4591, 4592, 4593, 4594, 4595, 4596, 4597, 4598, 4599, 4600, 4601, 4602, 4603, 4604, 4605, 4606, 4607, 4608, 4609, 4610, 4611, 4612, 4613, 4614, 4615, 4616, 4617, 4618, 4619, 4620, 4621, 4622, 4623, 4624, 4625, 4626, 4627, 4628, 4629, 4630, 4631, 4632, 4633, 4634, 4635, 4636, 4637, 4638, 4639, 4640, 4641, 4642, 4643, 4644, 4645, 4646, 4647, 4648, 4649, 4650, 4651, 4652, 4653, 4654, 4655, 4656, 4657, 4658, 4659, 4660, 4661, 4662, 4663, 4664, 4665, 4666, 4667, 4668, 4669, 4670, 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1.71, 1.71, 1.71, 1.71, 1.71, 1.71, 1.71, NA, NA, NA, NA, NA, NA, 1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59, 1.59, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, NA, NA, NA, NA, NA, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, NA, NA, NA, NA, NA, NA, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, NA, NA, NA, NA, NA, NA, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16, 0.67, 0.67, 0.67, 0.67, 0.67, 0.67, 0.67, 0.94, 0.94, 0.94, 0.94, 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2.93, 2.93, 2.93, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.57, 0.57, 0.57, 0.57, 0.57, 0.57, 0.57, 0.57, 0.57, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, NA, NA, NA, NA, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.51, 0.51, 0.51, 0.51, 0.51, 0.51, 0.51, 0.51, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.48, 2.48, 2.48, 2.48, 2.48, 2.48, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 1.35, 1.35, 1.35, 1.35, 1.35, 1.35, 1.35, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 1.84, 1.84, 1.84, 1.84, 1.84, 1.84, 1.84, 2.56, 2.56, 2.56, 2.56, 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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.47, 2.47, 2.47, 2.47, 2.47, 2.47, 2.47, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.72, 2.72, 2.72, 2.72, 2.72, 2.72, 2.72, 2.72, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, NA, NA, NA, NA, NA, NA, 2.26, 2.26, 2.26, 2.26, 2.26, 2.26, 2.26, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.63, 1.63, 1.63, 1.63, 1.63, 1.63, 1.63, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.48, 1.48, 1.48, 1.48, 1.48), V = c(0.847, 0.847, 0.847, 0.847, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 1.178, 1.178, 1.178, 1.178, 1.178, 1.178, 1.178, 1.178, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.351, 1.351, 1.351, 1.351, 1.351, 1.351, 1.351, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.633, 0.633, 0.633, 0.633, 0.633, 0.633, 0.633, 0.755, 0.755, 0.755, 0.755, 0.755, 0.755, 0.755, 0.268, 0.268, 0.268, 0.268, 0.268, 0.268, 0.268, 0.116, 0.116, 0.116, 0.116, 0.116, 0.116, 0.116, 0.145, 0.145, 0.145, 0.145, 0.145, 0.145, 0.145, 0.138, 0.138, 0.138, 0.138, 0.138, 0.138, 0.138, 0.392, 0.392, 0.392, 0.392, 0.392, 0.392, 0.392, 0.438, 0.438, 0.438, 0.438, 0.438, 0.438, 0.438, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.517, 0.517, 0.517, 0.517, 0.517, 0.517, 0.517, 0.517, 0.269, 0.269, 0.269, 0.269, 0.269, 0.269, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673, 0.673, 0.161, 0.161, 0.161, 0.161, 0.161, 0.161, 0.535, 0.535, 0.535, 0.535, 0.535, 0.535, 0.535, 0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.091, 0.091, 0.091, 0.091, 0.091, 0.091, 0.091, 0.091, 0.121, 0.121, 0.121, 0.121, 0.121, 0.121, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.108, 0.108, 0.108, 0.108, 0.108, 0.108, 0.108, 0.278, 0.278, 0.278, 0.278, 0.278, 0.278, 0.278, 0.162, 0.162, 0.162, 0.162, 0.162, 0.162, 0.162, 0.162, 0.162, NA, NA, NA, NA, NA, 0.064, 0.064, 0.064, 0.064, 0.064, 0.064, 0.064, 0.064, NA, NA, NA, NA, NA, NA, 0.062, 0.062, 0.062, 0.062, 0.062, 0.062, 0.062, 0.095, 0.095, 0.095, 0.095, 0.095, 0.095, 0.095, 0.031, 0.031, 0.031, 0.031, 0.031, 0.031, 0.031, 0.343, 0.343, 0.343, 0.343, 0.343, 0.343, 0.343, 0.767, 0.767, 0.767, 0.767, 0.767, 0.767, 0.767, 0.442, 0.442, 0.442, 0.442, 0.442, 0.442, 0.442, 1.085, 1.085, 1.085, 1.085, 1.085, 1.085, 1.085, 0.711, 0.711, 0.711, 0.711, 0.711, 0.711, 0.711, 1.036, 1.036, 1.036, 1.036, 1.036, 1.036, 1.036, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.132, 0.132, 0.132, 0.132, 0.132, 0.132, 0.132, 0.032, 0.032, 0.032, 0.032, 0.032, 0.032, 0.032, 0.032, 0.134, 0.134, 0.134, 0.134, 0.134, 0.134, 0.065, 0.065, 0.065, 0.065, 0.065, 0.065, 0.065, 0.201, 0.201, 0.201, 0.201, 0.201, 0.201, 0.201, 0.109, 0.109, 0.109, 0.109, 0.109, 0.109, 0.109, 1.189, 1.189, 1.189, 1.189, 1.189, 1.189, 1.189, 0.479, 0.479, 0.479, 0.479, 0.479, 0.479, 0.479, 0.565, 0.565, 0.565, 0.565, 0.565, 0.565, 0.565, 0.243, 0.243, 0.243, 0.243, 0.243, 0.243, 0.243, 0.142, 0.142, 0.142, 0.142, 0.142, 0.142, 0.142, 2.73, 2.73, 2.73, 2.73, 2.73, 2.73, 2.73, 1.848, 1.848, 1.848, 1.848, 1.848, 1.848, 1.848, 2.126, 2.126, 2.126, 2.126, 2.126, 2.126, 2.126, 2.59, 2.59, 2.59, 2.59, 2.59, 2.59, 2.59, 2.077, 2.077, 2.077, 2.077, 2.077, 2.077, 2.077, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 1.944, 1.944, 1.944, 1.944, 1.944, 1.944, 1.944, 1.944, 1.38, 1.38, 1.38, 1.38, 1.38, 1.38, 1.384, 1.384, 1.384, 1.384, 1.384, 1.384, 1.384, 0.807, 0.807, 0.807, 0.807, 0.807, 0.807, 0.807, 0.573, 0.573, 0.573, 0.573, 0.573, 0.573, 0.573, 0.573, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.598, 0.598, 0.598, 0.598, 0.598, 0.598, 0.598, 0.535, 0.535, 0.535, 0.535, 0.535, 0.535, 0.535, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.063, 0.063, 0.063, 0.063, 0.063, 0.063, 0.063, 0.063, 0.063, NA, NA, NA, NA, NA, NA, NA, 0.139, 0.139, 0.139, 0.139, 0.139, 0.139, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.015, 0.015, 0.015, 0.015, 0.015, 0.015, 0.015, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.024, 0.081, 0.081, 0.081, 0.081, 0.081, 0.081, 0.081, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.428, 0.428, 0.428, 0.428, 0.428, 0.428, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.044, 0.044, 0.044, 0.044, 0.044, 0.044, 0.044, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, 0.057, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.548, 0.548, 0.548, 0.548, 0.548, 0.548, 0.048, 0.048, 0.048, 0.048, 0.048, 0.048, 0.048, 0.019, 0.019, 0.019, 0.019, 0.019, 0.019, 0.019, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.105, 0.105, 0.105, 0.105, 0.105, 0.105, 0.105, 0.105, NA, NA, NA, NA, NA, NA, 0.261, 0.261, 0.261, 0.261, 0.261, 0.261, 0.261, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.176, 0.327, 0.327, 0.327, 0.327, 0.327, 0.327, 0.327, 0.254, 0.254, 0.254, 0.254, 0.254, 0.254, 0.254, 0.258, 0.258, 0.258, 0.258, 0.258 ), Zn = c(6.19, 6.19, 6.19, 6.19, 8.7, 8.7, 8.7, 8.7, 8.7, 8.7, 8.7, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 9.91, 9.91, 9.91, 9.91, 9.91, 9.91, 7.8, 7.8, 7.8, 7.8, 7.8, 7.8, 7.8, 11.89, 11.89, 11.89, 11.89, 11.89, 11.89, 11.89, 6.86, 6.86, 6.86, 6.86, 6.86, 6.86, 6.86, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.18, 2.18, 2.18, 2.18, 2.18, 2.18, 2.18, 2.18, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.09, 3.09, 3.09, 3.09, 3.09, 3.09, 3.09, 4.43, 4.43, 4.43, 4.43, 4.43, 4.43, 4.43, 2.28, 2.28, 2.28, 2.28, 2.28, 2.28, 2.28, 3.25, 3.25, 3.25, 3.25, 3.25, 3.25, 3.25, 3.25, 3.25, NA, NA, NA, NA, NA, 3.71, 3.71, 3.71, 3.71, 3.71, 3.71, 3.71, 3.71, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, 1.11, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.87, 0.87, 0.87, 0.87, 0.87, 0.87, 0.87, 0.79, 0.79, 0.79, 0.79, 0.79, 0.79, 0.79, 0.79, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.53, 1.53, 1.53, 1.53, 1.53, 1.53, 1.53, 1.53, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1.65, 1.65, 1.65, 1.65, 1.65, 1.65, 1.65, 1.65, NA, NA, NA, NA, NA, NA, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 2.86, 2.86, 2.86, 2.86, 2.86, 2.86, 2.86, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 3.06, 3.06, 3.06, 3.06, 3.06, 3.06, 3.06, 1.86, 1.86, 1.86, 1.86, 1.86, 1.86, 1.86, 2.41, 2.41, 2.41, 2.41, 2.41, 2.41, 2.41, 1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 2.88, 2.88, 2.88, 2.88, 2.88, 2.88, 2.88, 2.35, 2.35, 2.35, 2.35, 2.35, 2.35, 2.35, 2.35, 1.41, 1.41, 1.41, 1.41, 1.41, 1.41, 2.02, 2.02, 2.02, 2.02, 2.02, 2.02, 2.02, 3.31, 3.31, 3.31, 3.31, 3.31, 3.31, 3.31, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 7, 7, 7, 7, 7, 7, 7, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 9.07, 9.07, 9.07, 9.07, 9.07, 9.07, 9.07, 3.28, 3.28, 3.28, 3.28, 3.28, 3.28, 3.28, 1.55, 1.55, 1.55, 1.55, 1.55, 1.55, 1.55, 9.58, 9.58, 9.58, 9.58, 9.58, 9.58, 9.58, 7.54, 7.54, 7.54, 7.54, 7.54, 7.54, 7.54, 13.86, 13.86, 13.86, 13.86, 13.86, 13.86, 13.86, 14.27, 14.27, 14.27, 14.27, 14.27, 14.27, 14.27, 9.59, 9.59, 9.59, 9.59, 9.59, 9.59, 9.59, 7.08, 7.08, 7.08, 7.08, 7.08, 7.08, 7.08, 18.08, 18.08, 18.08, 18.08, 18.08, 18.08, 18.08, 18.08, 10.95, 10.95, 10.95, 10.95, 10.95, 10.95, 7.36, 7.36, 7.36, 7.36, 7.36, 7.36, 7.36, 6.18, 6.18, 6.18, 6.18, 6.18, 6.18, 6.18, 5.25, 5.25, 5.25, 5.25, 5.25, 5.25, 5.25, 5.25, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 6.39, 6.39, 6.39, 6.39, 6.39, 6.39, 6.39, 4.33, 4.33, 4.33, 4.33, 4.33, 4.33, 4.33, 2.92, 2.92, 2.92, 2.92, 2.92, 2.92, 2.92, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, NA, NA, NA, NA, NA, NA, NA, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, 5.21, NA, NA, NA, NA, NA, NA, 2.09, 2.09, 2.09, 2.09, 2.09, 2.09, 2.09, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03, 2.03, NA, NA, NA, NA, NA, NA, 2.63, 2.63, 2.63, 2.63, 2.63, 2.63, 2.63, 4.31, 4.31, 4.31, 4.31, 4.31, 4.31, 4.31, 1.77, 1.77, 1.77, 1.77, 1.77, 1.77, 1.77, 4.15, 4.15, 4.15, 4.15, 4.15, 4.15, 4.15, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 2.22, 5.24, 5.24, 5.24, 5.24, 5.24, 5.24, 5.24, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 7.47, 7.47, 7.47, 7.47, 7.47)), row.names = 167:896, class = "data.frame") Here is my code: library (ggplot2) library (dplyr) library (tidyr) shade <- df1 %>% transmute(year = year(Date)) %>% unique() %>% mutate( from = as.Date(paste0(year, "-02-21")), to = as.Date(paste0(year, "-04-30")) ) ggplot(df1, aes(x=Date)) + geom_rect(data = shade, aes(xmin = from, xmax = to, ymin = -Inf, ymax = Inf), color='grey', alpha=0.2) + geom_line( aes(y=V, color='V')) + geom_line( aes(y= Cu / coeff, color = 'Cu')) + geom_line( aes(y= Pb / coeff, color = 'Pb')) + geom_line( aes(y= / coeff, color = 'Zn')) + scale_y_continuous(name = "V", sec.axis = sec_axis(~.*coeff, name = "Cu, Pb, Zn"))+ theme_bw()+ theme(legend.position = c(0.2, 0.9),legend.direction="horizontal")+labs(color = NULL, fill = NULL) If you have any idea what happened and how to fix it, please let me know. Thank you. Best regards.
Try to use only ggplot(df1) and not putting aes() inside ggplot(), each geom_line read df1 and geom_rect read shade. ggplot(df1) + geom_line( aes(x=Date, y=V, color='V')) + geom_line( aes(x=Date, y= Cu, color = 'Cu')) + geom_line( aes(x=Date, y= Pb, color = 'Pb')) + geom_line( aes(x=Date, y= Zn, color = 'Zn')) + geom_rect(data = shade, aes(xmin = from, xmax = to, ymin = -Inf, ymax = Inf), color='grey', alpha=0.2) + scale_y_continuous(name = "V", sec.axis = sec_axis(~., name = "Cu, Pb, Zn"))+ theme_bw()+ theme(legend.position = c(0.2, 0.9),legend.direction="horizontal")+labs(color = NULL, fill = NULL) # or u can use ggplot() + geom_line(data = df1, aes(x=Date, y=V, color='V')) + geom_line(data = df1, aes(x=Date, y= Cu, color = 'Cu')) + geom_line(data = df1, aes(x=Date, y= Pb, color = 'Pb')) + geom_line(data = df1, aes(x=Date, y= Zn, color = 'Zn')) + geom_rect(data = shade, aes(xmin = from, xmax = to, ymin = -Inf, ymax = Inf), color='grey', alpha=0.2) + scale_y_continuous(name = "V", sec.axis = sec_axis(~., name = "Cu, Pb, Zn"))+ theme_bw()+ theme(legend.position = c(0.2, 0.9),legend.direction="horizontal")+labs(color = NULL, fill = NULL)
In my opinion the error you are getting is beacuse geom_rect() is trying to find columns (Date in this case) in previously declared aes(). I couldn't test my theory since there are some problems with your code (e.g. no coeff in df1 object).
Grouping by column in ggplot2
I am an old ggplot2 user, but I am getting a beating from this one. It must be something I am not noticing: I want a simple line plot across time (x-axis) vs covariances in the y-axis. My grouping is not being picked up by ggplot2. So please consider the MWE code: umxSim3 %>% mutate(group = case_when(x2y == 0.4 & aur == 0.8 & aury == 0.8 ~ "x2y0.4_aur0.8_aury0.8", x2y == 0.2 & aur == 0.6 & aury == 0.6 ~ "x2y0.2_aur0.6_aury0.6")) %>% drop_na() %>% ggplot(aes(temp, cova),color = as.factor(group)) + geom_line(stat = "summary") + geom_point(stat = "summary") + theme_bw(12) + theme(legend.position = c(0.8, 0.8), panel.border = element_rect(colour = "black"), legend.background = element_rect(linetype = 1, size = 0.2, colour = 1))+ scale_color_manual(values = cb_palette) for the data: structure(list(temp = c(15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 15L, 15L, 16L, 15L, 16L, 16L, 16L, 16L, 16L, 17L, 16L, 16L, 17L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 16L, 18L, 19L, 19L, 19L, 18L, 18L, 19L, 19L, 19L, 17L, 19L, 20L, 20L, 19L, 19L, 18L, 20L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 21L, 22L, 21L, 21L, 21L, 22L, 22L, 22L, 21L, 22L, 22L, 23L, 23L, 22L, 22L, 23L, 22L, 23L, 23L, 23L, 23L, 23L), immedx = c(0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02), immedy = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01), x2y = c(0, 0, 0.2, 0, 0.2, 0.4, 0, 0.2, 0.2, 0, 0.4, 0.4, 0, 0.4, 0.2, 0.4, 0, 0.2, 0, 0.2, 0.2, 0.2, 0, 0.4, 0.4, 0, 0, 0, 0.4, 0.2, 0.4, 0.2, 0.2, 0, 0.4, 0, 0, 0.2, 0.2, 0.4, 0.4, 0, 0, 0.4, 0.2, 0.2, 0.2, 0, 0.2, 0.4, 0, 0.4, 0, 0.4, 0.2, 0, 0, 0.4, 0.2, 0.4, 0.2, 0.4, 0.4, 0, 0.2, 0.4, 0, 0.2, 0.2, 0.4, 0.4, 0.4, 0, 0.2, 0, 0.2, 0.4, 0, 0, 0, 0.2, 0, 0.4, 0.4, 0.2, 0, 0.4, 0, 0.4, 0.2, 0.2, 0, 0.2, 0.2, 0.4, 0, 0.4, 0, 0.4, 0, 0.2, 0.4), aur = c(0.6, 0.6, 0.6, 0.8, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6, 0.8, 0.8, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.6, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.6), aury = c(0.8, 0.6, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8, 0.8, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.6, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.8, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.6, 0.6, 0.8, 0.6, 0.6, 0.8, 0.6, 0.8, 0.8, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8), cova = c(0.0767650449455536, 0.023479773134789, 0.079250541929235, 0.0497028270157154, 0.217607799079627, 0.168683335594918, 0.150794456586447, 0.27283026101063, 0.690314230152603, 0.0235259541982523, 0.702603056392517, 0.490115926773996, 0.0505076752505145, 1.93010433620619, 0.0798575573537466, 0.171573575141921, 0.0784006741340135, 0.28486748087407, 0.156814170306613, 0.0803151708822509, 0.754445007375547, 0.226801448778645, 0.079735730985492, 0.755994056890623, 0.525290423561159, 0.0511604604964653, 0.0235558887825711, 0.162030264083756, 0.559246976325596, 0.295696528479825, 0.173935711302792, 0.235063054293184, 0.818154209385252, 0.080823259527356, 0.80766448894273, 0.0235752474868944, 0.0516895405415864, 0.305415501682546, 0.0806599420516001, 0.59193655065581, 0.175863895489332, 0.166532200794381, 0.0235877421429864, 2.22373977593273, 0.242470710342392, 0.0809195781739381, 0.249100119500662, 0.0521181251083318, 0.881239650574519, 0.857515819530478, 0.0817077551411662, 0.177436314599814, 0.170404901664453, 2.54251804738705, 0.314120482284585, 0.0824262284334331, 0.0235957929692925, 0.62332889414795, 0.943529017271466, 2.88745078017984, 0.0811150355928273, 0.905481581223059, 3.25958506793805, 0.0524651556999694, 0.321903676726418, 0.178717602990971, 0.173727014344408, 0.255023388431499, 1.00487624869021, 0.653409404225594, 3.66000427224439, 0.951522116145514, 0.0236009729296943, 0.0812621413155391, 0.0830092693966824, 0.260308295568005, 0.179761001622845, 0.0527460575456124, 0.17657007247267, 0.0236043015726037, 0.328852247070274, 0.0834820393132251, 0.995620065250206, 0.682176435239891, 1.06515843598909, 0.0529733721858174, 0.180610243434384, 0.178998254606776, 4.08982898920573, 0.265017899614053, 0.335047641163975, 0.0236064382275949, 0.0814561191768848, 1.12427313098231, 0.709638986736915, 0.0838651574035433, 1.03777649778554, 0.0531572840695336, 0.181301171395821, 0.181068531268503, 0.269210391598863, 0.735814722588949 ), ncp = c(0.00000046267086872831, 0.0000000313011696562171, -2364.79037081984, -0.000000597414327785373, 351.394396380274, 151.990268357971, 0.00000743399141356349, 19.7703358054714, 2440.01464180378, -7240.58302952931, 70.9914281773526, 149.740843244857, -3309.66286159754, 16647.7277528127, -2582.00822814501, -1561.43493972577, -0.000000475047272630036, 19.6414650627339, 0.0000108720123535022, 315.029701597647, 135.427727860704, 22.4076952430914, 0.0000000233412720263004, -752.562054366463, 80.5575949394988, -0.00000442085001850501, -1179.58693431943, 0.000000114778231363744, -7327.08611100476, -1949.39119122034, -2388.61869873563, 22.1016658367153, 2824.56886877323, -0.0000042213941924274, -730.911226568125, -1099.31476127236, 0.00000000339059624820948, 2378.51963226983, -3114.53016945277, 3984.12834500313, 175.787574969581, 0.00000653910683467984, -0.000000110070686787367, 1181.21838523608, 21.8320325434906, -3186.31096406218, 345.549440360701, 13365.6075074154, 14.6078058980493, 2258.65831825051, -700.956671146072, -406.573908048507, 0.0000124756261357106, 1164.89840225065, 19.3309273536142, -0.00000834093225421384, -6412.21120826376, 69.809013458027, 1435.53987527536, 1438.76661482502, 28.9054297961484, 2385.37075061088, 27660.1530534023, 0.000000160784111358225, -3822.79946768601, -2315.2593892417, 0.00000594364973949268, 27.1325407072363, 3411.75060408833, 2138.85256412776, 30763.6924561956, 64.9265390225191, -2428.93211152896, -2914.7903949441, 0.00000096066651167348, 678.510061382112, -826.474972046213, 0.000000630076101515442, 0.00000610958522884175, -0.00000443263706983998, -3691.07725347468, 0.00000263373658526689, 12496.6406096131, 111.59293522677, 13.801709866937, 0.0000000575091689825058, -482.465671315367, 0.00000894447293831035, -944.987300472989, 26.6739735744122, -1686.73710901346, 0.000000106025254353881, -1838.70931961411, 1602.34840365657, -5004.48179096662, -0.0000282387336483225, 1476.99946196973, 0.000000139494659379125, -3032.83538644376, 0.000000328800524584949, -4550.10670159884, 2268.41146332065), df = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), proximal = c(0.02, 0.02, -0.039, 0.02, 0.02, 0.023, 0.02, 0.02, 0.02, -0.229, 0.02, 0.02, 0.096, 0.02, -0.241, 0.174, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.04, 0.02, 0.02, 0.022, 0.02, 0.129, -0.093, -0.216, 0.02, 0.02, 0.02, -0.061, 0.014, 0.02, -0.025, -0.715, 0.024, 0.02, 0.02, 0.02, 0.02, 0.02, 0.036, 0.02, -6.749, 0.02, 0.02, 0.012, -0.032, 0.02, 0.02, 0.02, 0.02, -0.093, 0.021, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, -0.27, -0.194, 0.02, 0.02, 0.02, 0.02, 0.02, 0.022, -0.071, -0.103, 0.02, 0.02, 0.121, 0.02, 0.02, 0.02, 0.083, 0.02, 0.02, -0.013, 0.02, 0.02, -0.401, 0.02, 0.115, 0.02, 0.084, 0.02, 0.041, 0.018, -0.099, 0.02, 0.02, 0.02, -0.089, 0.02, -0.056, -0.063), distal = c(0, 0, 0.209, 0, 0.2, 0.396, 0, 0.2, 0.2, 0.145, 0.4, 0.4, -0.09, 0.4, 0.341, 0.273, 0, 0.2, 0, 0.2, 0.2, 0.2, 0, 0.392, 0.4, 0, -0.019, 0, 0.503, 0.29, 0.54, 0.2, 0.2, 0, 0.474, 0, 0, 0.237, 0.621, 0.396, 0.4, 0, 0, 0.4, 0.2, 0.195, 0.2, 6.526, 0.2, 0.4, 0.005, 0.425, 0, 0.4, 0.2, 0, 0.143, 0.4, 0.2, 0.4, 0.2, 0.4, 0.4, 0, 0.512, 0.613, 0, 0.2, 0.2, 0.4, 0.4, 0.396, 0.085, 0.264, 0, 0.2, 0.309, 0, 0, 0, 0.173, 0, 0.4, 0.409, 0.2, 0, 0.708, 0, 0.307, 0.2, 0.131, 0, 0.205, 0.201, 0.594, 0, 0.4, 0, 0.519, 0, 0.238, 0.465), historical = c(0.02, 0.02, 0.02, 0.02, 0.02, 0.026, 0.02, 0.02, 0.02, 0.062, 0.02, 0.02, -0.15, 0.02, -0.225, -0.121, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.085, 0.02, 0.02, 0.037, 0.02, 0.124, -0.082, -0.202, 0.02, 0.02, 0.02, 0.07, 0.024, 0.02, -0.022, -0.69, 0.023, 0.02, 0.02, 0.02, 0.02, 0.02, 0.038, 0.02, 0.151, 0.02, 0.02, 0.082, -0.063, 0.02, 0.02, 0.02, 0.02, 0.159, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, -0.117, -0.033, 0.02, 0.02, 0.02, 0.02, 0.02, 0.025, -0.086, -0.044, 0.02, 0.022, -0.168, 0.02, 0.02, 0.02, 0.299, 0.02, 0.02, -0.015, 0.02, 0.02, -0.128, 0.02, -0.088, 0.02, 0.035, 0.02, 0.041, 0.019, -0.218, 0.02, 0.02, 0.02, -0.062, 0.02, -0.088, -0.057)), class = "data.frame", row.names = c(NA, -102L)) For this data I am getting: Whereas I expected it to be colored by two groups: 2y == 0.4 & aur == 0.8 & aury == 0.8 ~ "x2y0.4_aur0.8_aury0.8", x2y == 0.2 & aur == 0.6 & aury == 0.6 ~ "x2y0.2_aur0.6_aury0.6")
You need to move the color specification inside the aes call:
umxSim3 %>%
mutate(group = case_when(x2y == 0.4 & aur == 0.8 & aury == 0.8 ~ "x2y0.4_aur0.8_aury0.8",
x2y == 0.2 & aur == 0.6 & aury == 0.6 ~ "x2y0.2_aur0.6_aury0.6")) %>%
drop_na() %>%
ggplot(aes(temp, cova, color = as.factor(group))) +
geom_line(stat = "summary") +
geom_point(stat = "summary") +
theme_bw(12) +
theme(legend.position = c(0.8, 0.8),
panel.border = element_rect(colour = "black"),
legend.background = element_rect(linetype = 1, size = 0.2, colour = 1))+
scale_color_manual(values = c('red', 'blue'), name = 'Group')
Note you didn't provide the cb_palette object, so I have just set the colors to red and blue for this example.
ggplot: How to color/fill area between ROC curves and diagonal?
I have this ROC curve
Written with this code:
ggplot(a, aes(y = TPR, x = FPR, color = model)) +
geom_line() +
geom_segment(aes(y = 0, yend = 1, x = 0, xend = 1), color = "grey50")
I want to color the space between red and green curve, and the area between the green curve and the diagonal.
I tried to color the expected output manually in free hand (my apologies for the artistic skills)
I sought solutions using geom_area() but could not get it work.
How can I fill these area?
Here is my data sample. My apologies for many datapoints, but that was the only way I could reproduce "the full curves" reaching (0,0) and (1,1).
a <- structure(list(model = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), levels = c("Null model",
"SSA+", "SSA-"), class = "factor"), risk = c(1, 1, 1, 1, 1, 0.99,
0.99, 0.99, 0.98, 0.98, 0.97, 0.97, 0.97, 0.96, 0.95, 0.95, 0.94,
0.93, 0.92, 0.91, 0.91, 0.91, 0.91, 0.9, 0.89, 0.89, 0.88, 0.87,
0.87, 0.85, 0.85, 0.81, 0.81, 0.8, 0.78, 0.77, 0.76, 0.76, 0.76,
0.76, 0.75, 0.74, 0.72, 0.69, 0.69, 0.69, 0.67, 0.66, 0.65, 0.65,
0.64, 0.63, 0.63, 0.6, 0.59, 0.58, 0.58, 0.57, 0.57, 0.57, 0.53,
0.53, 0.52, 0.5, 0.46, 0.46, 0.46, 0.45, 0.44, 0.42, 0.41, 0.4,
0.4, 0.39, 0.38, 0.37, 0.35, 0.31, 0.29, 0.27, 0.27, 0.26, 0.24,
0.23, 0.2, 0.19, 0.19, 0.18, 0.18, 0.16, 0.15, 0.15, 0.11, 0.11,
0.09, 0.07, 0.06, 0.04, 0.93, 0.92, 0.92, 0.91, 0.91, 0.9, 0.9,
0.9, 0.9, 0.89, 0.86, 0.86, 0.86, 0.86, 0.86, 0.85, 0.85, 0.84,
0.83, 0.82, 0.81, 0.81, 0.81, 0.8, 0.79, 0.78, 0.78, 0.77, 0.77,
0.76, 0.75, 0.74, 0.74, 0.74, 0.73, 0.72, 0.71, 0.7, 0.66, 0.65,
0.65, 0.64, 0.63, 0.61, 0.6, 0.59, 0.56, 0.54, 0.52, 0.51, 0.51,
0.5, 0.47, 0.45, 0.45, 0.43, 0.42, 0.42, 0.38, 0.36, 0.34, 0.32,
0.32, 0.31, 0.3, 0.3, 0.29, 0.28, 0.27, 0.27, 0.26, 0.24, 0.23,
0.18, 0.16, 0.14, 0.13, 0.13, 0.12, 0.09), TPR = c(0.02, 0.03,
0.05, 0.07, 0.08, 0.1, 0.11, 0.13, 0.15, 0.16, 0.18, 0.2, 0.21,
0.23, 0.25, 0.26, 0.28, 0.3, 0.31, 0.33, 0.34, 0.34, 0.36, 0.38,
0.38, 0.39, 0.41, 0.43, 0.44, 0.44, 0.44, 0.46, 0.48, 0.49, 0.49,
0.51, 0.52, 0.54, 0.56, 0.57, 0.59, 0.61, 0.62, 0.62, 0.64, 0.66,
0.67, 0.69, 0.7, 0.72, 0.74, 0.74, 0.75, 0.75, 0.77, 0.77, 0.79,
0.8, 0.8, 0.82, 0.82, 0.82, 0.84, 0.84, 0.84, 0.85, 0.85, 0.87,
0.89, 0.9, 0.92, 0.92, 0.93, 0.93, 0.95, 0.95, 0.95, 0.97, 0.98,
0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0.03, 0.05, 0.07, 0.08, 0.1, 0.11, 0.11,
0.13, 0.15, 0.15, 0.16, 0.18, 0.21, 0.23, 0.25, 0.25, 0.26, 0.26,
0.28, 0.31, 0.33, 0.33, 0.33, 0.34, 0.38, 0.39, 0.43, 0.49, 0.51,
0.56, 0.59, 0.61, 0.62, 0.66, 0.69, 0.7, 0.7, 0.72, 0.72, 0.74,
0.75, 0.75, 0.77, 0.77, 0.79, 0.79, 0.79, 0.8, 0.82, 0.84, 0.84,
0.85, 0.87, 0.89, 0.89, 0.89, 0.89, 0.9, 0.92, 0.93, 0.93, 0.93,
0.93, 0.93, 0.93, 0.95, 0.98, 0.98, 0.98, 0.98, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1), FPR = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.03, 0.03, 0.03, 0.05, 0.05, 0.05, 0.05,
0.05, 0.08, 0.11, 0.11, 0.11, 0.11, 0.13, 0.13, 0.13, 0.16, 0.16,
0.16, 0.16, 0.16, 0.16, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18,
0.18, 0.21, 0.21, 0.24, 0.24, 0.26, 0.26, 0.26, 0.29, 0.29, 0.32,
0.34, 0.34, 0.37, 0.39, 0.39, 0.42, 0.42, 0.42, 0.42, 0.42, 0.45,
0.45, 0.47, 0.47, 0.5, 0.53, 0.53, 0.53, 0.55, 0.58, 0.61, 0.63,
0.66, 0.68, 0.71, 0.74, 0.76, 0.76, 0.79, 0.82, 0.84, 0.87, 0.89,
0.92, 0.95, 0.97, 1, 0, 0, 0, 0, 0, 0, 0.03, 0.03, 0.03, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.08, 0.08, 0.11, 0.11, 0.11, 0.11,
0.13, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.18, 0.18, 0.18, 0.18,
0.18, 0.18, 0.21, 0.24, 0.26, 0.26, 0.29, 0.29, 0.29, 0.32, 0.32,
0.34, 0.34, 0.37, 0.39, 0.39, 0.39, 0.39, 0.42, 0.42, 0.45, 0.45,
0.47, 0.5, 0.53, 0.53, 0.53, 0.53, 0.55, 0.58, 0.61, 0.63, 0.66,
0.66, 0.66, 0.71, 0.74, 0.76, 0.76, 0.79, 0.82, 0.84, 0.87, 0.89,
0.92, 0.95, 0.97, 1)), row.names = c(NA, -178L), class = c("data.table",
"data.frame"))
You can use geom_ribbon. The ymax will be TPR, and since the diagonal occurs at TPR = FPR, the ymin will be FPR.
ggplot(a, aes(y = TPR, x = FPR)) +
geom_ribbon(aes(ymin = FPR, ymax = TPR, fill = model)) +
geom_line(aes(group = model), color = "black") +
geom_segment(aes(y = 0, yend = 1, x = 0, xend = 1), color = "grey50") +
scale_fill_manual(values = c("#ba6329", "#5f7c37")) +
coord_equal() +
theme_light(base_size = 16)
How to perform rolling regression in R with this dataset?
Let's suppose I have the following dataframe made of up 219 rows. The dataset is not perfectly monthly for some structural reasons.
df = structure(list(X1 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X2 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X3 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X4 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X5 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X6 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X7 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86), X8 = c(0.67, -1.45, 0.01, -1.07, -0.8, 0.21, -0.27,
0.44, 1.09, 0.99, 0.62, -0.43, -0.29, -0.57, -1.1, 0.18, 0.26,
0.38, -2.38, 0.79, 0.11, 0.03, 1.02, 0.02, 0.33, 1.03, -0.41,
-1.46, -0.06, 1.95, -1.04, -0.95, 1.61, 0.46, -0.6, -1.42, -0.8,
0.92, 0.84, -1, 1.55, -0.86, 0.58, -0.35, 1.13, 0.39, -0.71,
-0.67, 1.47, -0.01, 0.09, -1.19, 0.22, -1.8, -0.59, 1.06, -1.05,
1.42, -1.91, 0.73, 0.75, 0.82, -0.69, -0.52, 1.1, -0.56, -0.52,
1.16, -0.35, -0.71, 0.92, -0.01, 0.89, -0.06, 0.87, 0.96, 0.97,
0.38, 0.95, -0.23, -0.43, -1.17, 0.65, -0.76, 2.12, -0.16, 2.21,
1.06, -0.35, 0.44, -0.46, 1.56, 1.66, -0.51, 1.08, -0.81, 0.71,
1.08, 0.79, -0.44, 0.92, -0.03, -0.15, -0.25, -0.48, 0.28, -0.86,
-1.07, -2.52, 0.15, -0.5, 1.13, 1.94, -0.35, -0.3, -0.12, -0.04,
2.48, -0.3, -0.28, -3.04, 0.68, 1.02, -1.07, 1.59, -0.11, -0.44,
1.27, 0.1, -0.1, 1.32, 0.08, 1.24, 1.46, 0.33, 1.55, -0.87, 1.26,
-0.56, 0.76, -0.51, -0.24, -0.94, 0.88, -0.08, -2.27, 1.09, 1.15,
-1.59, -0.65, 1.22, 0, 1.49, -2.03, 0.16, 0.21, 0.25, -2.21,
1.43, 0.67, -1.33, 0.06, -0.34, 0.15, 1.93, -0.94, 0.21, -0.97,
-0.95, -0.43, 1.86, 0.96, -0.32, 0.69, -0.54, 0.16, -0.04, -0.78,
1.39, -0.39, -0.52, -0.82, -0.51, -0.18, -0.38, -0.68, 0.44,
1.38, -0.27, 0.63, -0.56, 0.12, -1.02, 1.59, -1.03, -0.77, -0.17,
-0.89, 0.56, -0.22, 1.43, -0.55, 0.69, 0.82, -0.32, 0.55, -0.94,
0.31, 0.55, 1.11, -0.54, 0.58, -1.49, 2.33, -1.45, 1.05, 0.28,
1.68, 0.86)), row.names = c(NA, -219L), class = "data.frame")
Then, what I want to do is setting up a rolling regression in a time window that encompasses, say, 2 years (24 months). To do so, I run the following codes:
library(rollRegres)
library(zoo)
roll_model1 = roll_regres(X1 ~ ., df, 24L, do_compute = c("sigmas", "r.squareds"), do_downdates = TRUE)
roll_model2 = rollapply(df, width = 24, FUN = function(x) coef(lm(X1 ~ ., data = as.data.frame(x))), by.column = FALSE, align = "right")
In the first case, the model doesn't work. In the second case, I only get results for the intercept (and only coefficinets). Besides, I don't understand why there are 196 coefficient observations.
Can anyone help me run a rolling regression over 2 years window with this dataset?
Thanks!
All columns of df are the same
all(df == df[, 1])
## [1] TRUE
so it can perfectly predict X1 using X2 and the others are not needed so it gives NA.
Regarding the rollapply code it only gave coefficients because that is what you asked for coef(lm(...)) . Your function should return a vector of whatever it is you want to get out.
It does a regression for rows 1:24, rows 2:25, ... rows 196:219 so clearly there are 196 such sets so the result has 196 rows. If you specify fill=NA then it will pad it with NAs to give the same number of rows as df.
Note that rollapplyr is available which defaults to align = "right".
Here is a possible function that returns a variety of information:
library(broom)
stats <- function(x) {
fm <- lm(X1 ~., as.data.frame(x))
c(coef(fm), unlist(glance(fm)))
}
rollapplyr(df, width = 24, FUN = stats, by.column = FALSE)
Summary of Dataset using lapply
This is a novice question, however, I am finding it very difficult to understand how to use lapply correctly, especially when the ID used is not numeric.
There are possibly better methods to trying to find the summary I have in mind, but for now, I'm trying to use lapply. Essentially, I have a large df with 17 columns. Two of the column are ID and Date. Not all IDs have a recorded value in a given column name. What I am interested in is finding the total number of rows available for each column, and the number of unique IDs that exist for that column. I have a dput example that makes things clearer. For example, Var8 has only 6 rows of data available, as a result it has 6 unique IDs. Also, Var15 has 20 rows and 12 unique IDs. But I want to know this for all Var15. I can do this manually using
Var8=df[!(is.na(df$Var8)),]
length(df$ID)
length(unique(df$ID))
remove(Var8)
But trying to automate:
lapply(COL.NAMES, function(x){
temp=df[!(is.na(df$paste(x))),]
rows=length(temp$ID)
num_comp=length(unique(temp$ID))
return(rows)
return(num_comp)
remove(temp)
})
leaves me with an error: attempt to apply non-function.
COL.NAMES<-c("Var1","Var2","Var3","Var4","Var5","Var6","Var7","Var8","Var9","Var10","Var11","Var12","Var13","Var14","Var15")
structure(list(ID = structure(c(1L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 2L, 3L, 4L, 1L, 5L, 6L, 7L, 8L, 9L, 10L, 11L), .Label = c("Comp1",
"Comp10", "Comp11", "Comp12", "Comp2", "Comp3", "Comp4", "Comp5",
"Comp6", "Comp7", "Comp8", "Comp9"), class = "factor"), Date = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("0/1/2014", "0/1/2015"), class = "factor"),
Var1 = c(0.57, 0.34, 0.38, 0.93, 0.54, 0.17, 0.08, 0.28,
0.99, 1, 0.61, 0.73, 0.15, 0.09, 0.64, 0.3, 0.12, 0.79, 0.79,
0.15), Var2 = c(0.7, 0.77, 0.93, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.46, 0.26), Var3 = c(0.65,
0.7, 0.83, 0.7, 0.43, 0.81, 0.21, 0.44, 0.25, 0.77, 0.24,
0.29, 0.87, 0.42, 1, NA, NA, NA, NA, 0.79), Var4 = c(1, 0.7,
0.69, NA, NA, NA, NA, 0.2, 0.61, 0.89, 0.45, 0.02, 0.97,
0.33, 0.34, 0.81, 0.99, 0.35, 0.48, 0.33), Var5 = c(0.47,
0.95, 0.38, 0.69, 0.84, 0.21, 0.62, 0.59, 0.45, 0.63, 0.18,
0.49, NA, NA, NA, NA, 0.17, 0.15, 0.6, 0.44), Var6 = c(NA,
NA, NA, NA, 0.24, 0.07, 0.75, 0.24, 0.82, 0.14, 0.86, 0.63,
0.82, 0.92, 0.55, 0.22, 0.87, 0.69, 0.64, 0.73), Var7 = c(0.2,
0.11, 0.82, 0.31, 0.97, NA, NA, NA, NA, 0.83, 0.84, 0.81,
0.72, 0.36, 0.09, 0.15, 0.46, 0.79, 0.75, 0.39), Var8 = c(0.28,
0.55, NA, NA, NA, NA, 0.56, 0.89, 0.92, 0.46, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), Var9 = c(0.11, 0.36, 1, 0.44,
0.53, 0.6, 0.24, 0.56, 0.6, 0.55, 0.55, 0.05, 0.77, 0.9,
NA, NA, NA, NA, 0.4, 0.33), Var10 = c(0.74, 0.13, 0.09, 0.61,
NA, NA, NA, NA, 0.27, 0.71, 0.56, 0.3, 0.36, 0.44, 0.78,
0.9, 0.46, 0.49, 0.87, 0.36), Var11 = c(0.58, 0.99, 0.07,
0.83, 0.45, 0.07, 0.16, 0.43, 0.34, 0.31, 0.06, 0.67, 0.02,
0.52, 0.19, 0.49, 0.31, 0.02, 0.62, 0.21), Var12 = c(0.93,
0.26, 0.77, 0.8, 0.67, 0.83, 0.12, 0.39, 0.78, 0.75, 0.44,
NA, NA, NA, NA, 0.42, 0.49, 0.06, 0.8, 0.54), Var13 = c(0.44,
0.75, NA, NA, NA, NA, 0.58, 0.3, 0.47, 0.88, 0.36, 0.21,
0.87, 0.33, 0.12, 0.31, 0.95, 0.59, 0.18, 0.43), Var14 = c(0.55,
0.03, 0.37, 0.66, NA, 0.91, 0.78, 0.84, 0.96, 0.34, 0.25,
0.92, 0.71, 0.41, 0.23, 0.54, 0.8, 0.87, 0.3, 0.37), Var15 = c(0.71,
0.66, 0.01, 0.7, 0.4, 0.04, 0.3, 1, 0.59, 0.69, 0.88, 0.28,
0.44, 0.51, 0.2, 0.17, 0.6, 0.11, 0.85, 0.04)), .Names = c("ID",
"Date", "Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7",
"Var8", "Var9", "Var10", "Var11", "Var12", "Var13", "Var14",
"Var15"), class = "data.frame", row.names = c(NA, -20L))
I would advise getting yourself familiar with data wrangling using dplyr. The magrittr pipes %>% implemented will help you with understanding the usage of apply.
Here's how I would change your function:
library(dplyr)
tmp<-lapply(COL.NAMES, function(x) df[,c("ID", x)] %>% na.omit) # loop and extract 15 data.frames, each with 2 columns; remove rows with missing value
rows <- sapply(tmp, nrow)
num_comp <- lapply(tmp, '[[', "ID") %>% lapply(., unique) %>% sapply(., length) #extract only ID column from list of 15 data.frame; loop across each vector to retain unique values; count length of vector.
Another approach would be, df1 <- data.frame(n_rows = colSums(!is.na(df[,-(1:2)]), na.rm = TRUE), unique_IDs = sapply(df[,-2], function(i) length(unique(df$ID[!is.na(i)])))[-1]) head(df1) # n_rows unique_IDs #Var1 20 12 #Var2 5 5 #Var3 16 12 #Var4 16 12 #Var5 16 12 #Var6 16 12
I am not sure if I have understood correctly but this could be your solution .
x is your dataframe
try1 <- function(df){
temp <- sum(!is.na(df)) ## no of non na entries
temp2 <- length(unique(df)) # length unique entries `
temp <- list("x"=temp,"y"=temp2)
temp
}
> lapply(x,try1)
Here is a data.table soln
library(data.table)
dd <- as.data.table(x)
COL.NAMES<-c("Var1","Var2","Var3","Var4","Var5","Var6","Var7","Var8","Var9","Var10","Var11","Var12","Var13","Var14","Var15")
dd[,lapply(.SD, try1),.SDcols=COL.NAMES]
However, I didn't use lapply,this solution does work
find.uniques<- function(df){
for(i in 1:ncol(df)){
uniques<- data.frame()
uniques[i,1]<- length(!is.na(unique(df[,i])))
uniques[i,2]<- length(which(!is.na(unique(df[,i]))))
}
return(uniques)
}
Result is a data.frame with V1 as how many rows are available, V2 how many IDs there are for each column.
You can also return(as.data.frame(t(uniques))) to change the rows to columns to see what is available for each column.