我收到错误消息"Error: Discrete value supplied to continuous scale"。我尝试了建议的解决方案，但没有帮助。在我的数据中，column 1是方法的名称。我想要在y-axis上使用方法名称，并在x-axis上使用月份名称，然后我将使用geom_tile使用所有方法的准确性得分来填充热图。
dput(results)
structure(list(V1 = c("Pers", "58.73", "68.58", "54.25", "47.69",
"42.98", "40.6", "37.47", "40.81", "51.37", "57.13", "63.08",
"75.75", "62.49", "54.1", "60.85", "47.78", "46.23", "35.7",
"39.96", "40.14", "50.89", "56", "62.29", "68.12"), V2 = c("Clear-sky Pers",
"46.68", "59.05", "37.28", "32.82", "28.89", "29.9", "26.58",
"22.87", "27.77", "49.75", "52.66", "63.74", "52.41", "42.38",
"45.54", "32.16", "32.83", "22.41", "31.01", "23.99", "28.45",
"48.3", "53.44", "57.96"), V3 = c("Bagged MARS", "39.82", "51.28",
"36.43", "32.51", "25.39", "27.93", "26.35", "23.27", "28.62",
"26.16", "36.28", "55.49", "45.14", "33.34", "41.7", "31.49",
"31.63", "21.88", "29.32", "23.47", "29.34", "30.59", "32.03",
"46.87"), V4 = c("Bagged MARS using gCV Pruning", "40.16", "51.16",
"36.4", "32.47", "25.45", "27.98", "26.41", "23.27", "28.59",
"26.33", "36.45", "55.47", "45.46", "33.29", "41.91", "31.5",
"31.64", "21.92", "29.35", "23.49", "29.32", "30.64", "32.05",
"46.95"), V5 = c("Bayesian Generalized Linear Model", "38.43",
"52.1", "36.74", "33.11", "24.98", "28.33", "25.9", "23.33",
"29.04", "26.58", "35.23", "54.92", "44.84", "33.2", "41.44",
"32.27", "31.6", "21.96", "28.94", "23.31", "29.32", "30.85",
"31.39", "45.57"), V6 = c("Bayesian Regularized Neural Networks",
"36.04", "50.2", "35.43", "32.39", "24.31", "27.84", "24.82",
"22.52", "26.97", "25.14", "33.29", "53.37", "42.94", "31.03",
"39.42", "30.7", "30.08", "21.32", "27.81", "22.36", "28.06",
"29.34", "30.35", "43.84"), V7 = c("Bayesian Ridge Regression",
"38.62", "51.54", "36.85", "32.74", "25.03", "28.01", "25.82",
"23.16", "28.72", "26.65", "35.53", "55.13", "44.61", "33.03",
"41.38", "31.74", "31.64", "21.65", "28.84", "23.28", "29.15",
"31.05", "31.53", "45.7"), V8 = c("Boosted Generalized Linear Model",
"43.54", "52.36", "39.77", "34.33", "27.37", "28.73", "26.55",
"24.38", "30.94", "30.72", "39.88", "57.57", "46.36", "37.41",
"45.41", "33.53", "32.73", "22.28", "28.77", "25.19", "31.15",
"33.76", "35.7", "48.53"), V9 = c("Boosted Linear Model", "110.08",
"78.7", "52.57", "39.61", "35.96", "35.48", "33.23", "33.92",
"37.37", "51.98", "99.36", "215.31", "117.81", "67.24", "60.16",
"40.15", "39.39", "36.3", "35.53", "32.66", "38.57", "57.07",
"93.16", "159.24"), V10 = c("Boosted Smoothing Spline", "43.97",
"51.77", "37.48", "33.33", "26.44", "28.51", "26.64", "23.95",
"28.91", "28.04", "40.9", "62.15", "48.88", "36.86", "43.89",
"32.58", "32.07", "22.97", "28.71", "24.32", "30.14", "32.08",
"36.55", "52.15"), V11 = c("Conditional Inference Tree1", "39.82",
"55.73", "38.41", "34.83", "27.2", "31.44", "29.94", "23.55",
"29.34", "30.24", "39.86", "67.82", "51.5", "36.04", "42.72",
"32.07", "34.13", "22.43", "30.4", "23.92", "31.59", "35.06",
"34.57", "50.32"), V12 = c("Cubist", "33.5", "51.07", "33.97",
"30.65", "23.59", "26.42", "25.03", "21.41", "27.43", "25.5",
"30.89", "50.48", "40.23", "30.64", "39.35", "29.92", "29.78",
"21.05", "27.52", "21.69", "27.85", "28.45", "30.92", "43.16"
), V13 = c("Elasticnet", "38.43", "52.09", "36.74", "33.11",
"24.98", "28.33", "25.9", "23.34", "29.04", "26.59", "35.24",
"54.92", "44.84", "33.2", "41.45", "32.27", "31.6", "21.96",
"28.93", "23.31", "29.32", "30.86", "31.4", "45.57"), V14 = c("eXtreme Gradient Boosting1",
"36.68", "52.85", "36.71", "32.11", "25.66", "28.42", "26.17",
"21.12", "27.94", "27.52", "33.97", "54.64", "44.5", "34.29",
"42.02", "32.23", "33.12", "21.84", "28", "22.19", "28.36", "30.83",
"32.79", "43.7"), V15 = c("eXtreme Gradient Boosting2", "37.68",
"51.46", "35.73", "30.87", "25.5", "28.02", "25.45", "22.34",
"28.06", "26.55", "35.49", "57.21", "46.39", "35.52", "41.72",
"31.16", "31.33", "21.14", "28.25", "22.51", "28.33", "30.09",
"33.12", "47.95"), V16 = c("Gaussian Process", "38.42", "52.09",
"36.74", "33.11", "24.97", "28.34", "25.89", "23.34", "29.03",
"26.59", "35.24", "54.92", "44.86", "33.2", "41.44", "32.27",
"31.61", "21.96", "28.93", "23.31", "29.32", "30.86", "31.4",
"45.57"), V17 = c("Gaussian Process with Polynomial Kernel",
"35.59", "49.85", "35.08", "31.03", "23.82", "27.04", "24.65",
"22.14", "26.8", "24.69", "32.66", "52.76", "42.84", "31.22",
"40.27", "29.82", "30.43", "21.23", "28.17", "21.69", "27.15",
"29.19", "29.37", "43.69"), V18 = c("Gaussian Process with Radial Basis Function Kernel",
"34.79", "49.54", "34.38", "31.47", "23.93", "26.79", "24.65",
"21.86", "27.02", "25.13", "32.68", "53.85", "42.52", "31.67",
"39.99", "30.79", "31.19", "21.94", "27.79", "21.96", "27.25",
"29.62", "29.63", "43.6"), V19 = c("Generalized Linear Model",
"38.43", "52.09", "36.74", "33.11", "24.98", "28.33", "25.9",
"23.34", "29.04", "26.58", "35.23", "54.92", "44.83", "33.2",
"41.44", "32.27", "31.6", "21.96", "28.94", "23.31", "29.32",
"30.85", "31.39", "45.57"), V20 = c("Generalized Linear Model with Stepwise Feature Selection",
"38.5", "51.69", "36.81", "32.97", "25.07", "28.17", "25.91",
"23.44", "29.08", "26.59", "35.36", "55.09", "44.93", "33.1",
"41.36", "32.14", "31.68", "21.91", "29", "23.41", "29.34", "30.97",
"31.56", "45.64"), V21 = c("glmnet", "38.51", "51.71", "36.74",
"32.94", "24.95", "28.17", "25.82", "23.25", "28.97", "26.62",
"35.43", "54.99", "44.71", "33.18", "41.42", "31.99", "31.55",
"21.8", "28.8", "23.31", "29.23", "30.92", "31.53", "45.65"),
V22 = c("Independent Component Regression", "64.48", "65.31",
"59.31", "47.77", "37.03", "37.93", "35.77", "32.59", "42.25",
"46.73", "63.67", "86.5", "68.29", "57.1", "63.21", "50.57",
"44.58", "28.25", "37.25", "35.92", "44.67", "51.94", "55.68",
"71.15"), V23 = c("k-Nearest Neighbors1", "53.75", "60.79",
"43.11", "37.76", "30.56", "31.38", "30.94", "26.4", "34.61",
"37.92", "50.99", "72.14", "56.45", "50.02", "49.67", "39.95",
"37.59", "24.82", "30.98", "27.38", "33.81", "41.64", "48.35",
"58.98"), V24 = c("k-Nearest Neighbors2", "53.66", "59.26",
"43.71", "37.13", "30.49", "31.28", "29.87", "25.62", "34.07",
"36.48", "50.61", "72.3", "55.43", "49.71", "50.18", "39",
"37.1", "24.92", "30.2", "27.55", "32.68", "40.49", "47.34",
"56.89"), V25 = c("L2 Regularized Support Vector Machine (dual) with Linear Kernel",
"38.87", "52.41", "36.85", "33.06", "25.07", "28.37", "25.66",
"23.36", "28.95", "26.93", "36.15", "55.55", "44.98", "33.42",
"41.68", "32.25", "31.76", "21.81", "28.85", "23.43", "29.42",
"31.17", "31.88", "46.16"), V26 = c("Least Angle Regression1",
"38.43", "52.09", "36.74", "33.11", "24.98", "28.33", "25.9",
"23.34", "29.04", "26.58", "35.23", "54.92", "44.83", "33.2",
"41.44", "32.27", "31.6", "21.96", "28.94", "23.31", "29.32",
"30.85", "31.39", "45.57"), V27 = c("Least Angle Regression2",
"38.54", "51.66", "36.75", "32.9", "24.97", "28.11", "25.81",
"23.24", "28.95", "26.63", "35.45", "55.02", "44.71", "33.17",
"41.42", "31.93", "31.55", "21.78", "28.8", "23.33", "29.23",
"30.91", "31.53", "45.65"), V28 = c("Linear Regression",
"38.43", "52.09", "36.74", "33.11", "24.98", "28.33", "25.9",
"23.34", "29.04", "26.58", "35.23", "54.92", "44.83", "33.2",
"41.44", "32.27", "31.6", "21.96", "28.94", "23.31", "29.32",
"30.85", "31.39", "45.57"), V29 = c("Linear Regression with Backwards Selection",
"45.75", "55.54", "41.96", "35.34", "29.44", "31.08", "28.69",
"25.84", "33.65", "30.81", "41.37", "59.3", "49.04", "38.46",
"47.73", "35.02", "34.48", "23.93", "31.67", "26.38", "33.15",
"35.01", "36.44", "52.08"), V30 = c("Linear Regression with Forward Selection",
"45.75", "55.54", "41.96", "35.34", "29.44", "31.08", "28.69",
"25.84", "33.65", "30.81", "41.37", "59.3", "49.04", "38.46",
"47.73", "35.02", "34.48", "23.93", "31.67", "26.38", "33.15",
"35.01", "36.44", "52.08"), V31 = c("Linear Regression with Stepwise Selection1",
"45.75", "55.54", "41.96", "35.34", "29.44", "31.08", "28.69",
"25.84", "33.65", "30.81", "41.37", "59.3", "49.04", "38.46",
"47.73", "35.02", "34.48", "23.93", "31.67", "26.38", "33.15",
"35.01", "36.44", "52.08"), V32 = c("Linear Regression with Stepwise Selection2",
"38.5", "51.69", "36.81", "32.97", "25.07", "28.17", "25.91",
"23.44", "29.08", "26.59", "35.36", "55.09", "44.93", "33.1",
"41.36", "32.14", "31.68", "21.91", "29", "23.41", "29.34",
"30.97", "31.56", "45.64"), V33 = c("Model Averaged Neural Network",
"35.6", "50.16", "34.29", "32", "23.78", "26.95", "24.27",
"21.88", "26.82", "24.81", "33.78", "55.53", "45.01", "31.83",
"40.06", "29.72", "30.83", "21.16", "27.19", "21.51", "27.06",
"29.64", "31.56", "46.37"), V34 = c("Monotone Multi-Layer Perceptron Neural Network",
"37.92", "52.03", "36.65", "33.14", "25.06", "28.29", "25.78",
"23.32", "29.12", "26.52", "34.95", "54.91", "45.1", "33.17",
"41.28", "32.07", "31.82", "21.68", "28.64", "23.21", "29.29",
"31.01", "31.56", "46.01"), V35 = c("Multi-Layer Perceptron1",
"35.31", "50.61", "33.84", "31.61", "23.45", "26.84", "25.01",
"21.85", "26.94", "25.11", "31.51", "51.96", "43.54", "31.39",
"39.99", "29.85", "30.68", "21.44", "27.65", "22.45", "27.57",
"28.98", "30.18", "43.1"), V36 = c("Multi-Layer Perceptron2",
"35.12", "50.64", "35.52", "33.06", "24.99", "27.72", "24.66",
"22.77", "27.88", "24.9", "32.6", "52.27", "43.67", "31.2",
"40.75", "30.76", "31.75", "21.52", "28.14", "22.29", "28.5",
"29.54", "29.28", "42.99"), V37 = c("Multi-Layer Perceptron, multiple layers",
"34.68", "51.31", "34.64", "32.11", "23.97", "27.32", "25.37",
"22.53", "28.16", "25.94", "32.22", "53.61", "42.42", "31.53",
"40.65", "31.05", "30.7", "21.49", "27.92", "22.87", "28.25",
"29.35", "30.83", "43.27"), V38 = c("Multi-Layer Perceptron, with multiple layers",
"35.61", "50.6", "33.6", "31.75", "23.08", "27.61", "25.32",
"22.33", "27.77", "25.28", "32.78", "53.73", "43.31", "31.95",
"40.73", "30.31", "30.22", "21.36", "27.79", "22.46", "28.16",
"29.55", "31.15", "44.56"), V39 = c("Multivariate Adaptive Regression Spline",
"41.68", "51.87", "37.06", "33.14", "26.23", "28.72", "26.66",
"23.88", "29.33", "27.44", "37.39", "55.9", "46.2", "33.84",
"43.5", "32.02", "32.38", "22.36", "29.85", "24.1", "29.72",
"31.12", "32.39", "48.54"), V40 = c("Multivariate Adaptive Regression Splines",
"41.42", "51.71", "36.81", "33", "26.06", "28.59", "26.65",
"24", "29.27", "27.24", "37.11", "55.89", "46.27", "33.95",
"43.38", "32.1", "32.32", "22.34", "29.79", "24.11", "29.67",
"31.13", "32.69", "47.84"), V41 = c("Negative Binomial Generalized Linear Model",
"42.81", "53.78", "37.91", "34.91", "27.94", "31.24", "29.2",
"26.38", "29.4", "27.99", "39.89", "61.41", "50.25", "36.51",
"41.83", "35.07", "34.05", "28.45", "33.86", "26.33", "30.55",
"33.29", "35.24", "52.61"), V42 = c("Neural Network", "36.54",
"50.38", "33.78", "31.81", "23.32", "26.9", "24.39", "21.96",
"26.82", "25", "34.25", "56.28", "45.03", "32.29", "40.4",
"29.68", "30.83", "21.53", "26.78", "21.95", "27.56", "30.16",
"31.93", "47.19"), V43 = c("Neural Networks with Feature Extraction",
"36.35", "50.06", "33.85", "32.87", "24.87", "27.84", "26.55",
"22.78", "27.77", "27.26", "34.9", "56.73", "45.47", "31.16",
"39.59", "30.23", "32.12", "21.46", "27.45", "22.29", "28.46",
"31.34", "32.86", "46.93"), V44 = c("Non-Convex Penalized Quantile Regression",
"37.43", "52.33", "37.1", "33.2", "25.48", "28.74", "26.56",
"23.77", "29.58", "26.53", "34.27", "54.56", "44.35", "32.63",
"41.49", "32.17", "32.18", "22.36", "29.55", "23.8", "29.82",
"30.94", "31.13", "45.12"), V45 = c("Non-Informative Model",
"116.8", "89.25", "70.83", "63.06", "57.13", "54.53", "52.35",
"55.06", "59.52", "74.86", "114.22", "217.39", "121.69",
"81.62", "76.36", "63.22", "60.7", "56.81", "56.55", "54.5",
"60.2", "73.41", "106.28", "160.14"), V46 = c("Non-Negative Least Squares",
"49.93", "55.84", "43.86", "37.28", "30.72", "30.73", "28.48",
"27.17", "34.78", "36.48", "46.07", "62.29", "51.04", "42.91",
"50.2", "37.64", "35.1", "24.09", "30.54", "28.43", "34.96",
"38.66", "41.77", "53.62"), V47 = c("partDSA", "83.16", "71.02",
"46.78", "45.63", "38.18", "38.31", "36.71", "36.6", "39.75",
"45.63", "70.28", "109.86", "77.89", "58", "57.51", "46.59",
"45.27", "37.45", "37.95", "35.83", "39.26", "48.09", "64.29",
"93.3"), V48 = c("Partial Least Squares1", "54.32", "59.04",
"44.77", "38.44", "29.87", "31.2", "27.05", "25.42", "32.47",
"35.41", "48.48", "68.73", "53.24", "45.09", "52.47", "36.36",
"35.39", "23.64", "29.63", "27.14", "33.29", "38.16", "41.31",
"57.75"), V49 = c("Penalized Linear Regression", "38.8",
"51.5", "36.85", "32.75", "25", "27.99", "25.77", "23.14",
"28.86", "26.74", "35.68", "55.19", "44.71", "33.23", "41.43",
"31.81", "31.51", "21.63", "28.73", "23.35", "29.15", "31.05",
"31.71", "45.76"), V50 = c("Principal Component Analysis",
"64.49", "65.31", "59.31", "47.77", "37.03", "37.93", "35.76",
"32.59", "42.25", "46.73", "63.67", "86.5", "68.29", "57.11",
"63.21", "50.57", "44.58", "28.25", "37.25", "35.92", "44.67",
"51.95", "55.68", "71.15"), V51 = c("Projection Pursuit Regression",
"34.91", "51.89", "35.77", "32.05", "24.03", "28.01", "25.69",
"22.3", "27.71", "25.21", "31.67", "53.37", "43.33", "30.54",
"40.49", "30.63", "29.74", "21.57", "28.22", "22.57", "27.85",
"29.51", "31.21", "43.45"), V52 = c("Quantile Random Forest",
"66.17", "72.68", "55.9", "48.02", "46.77", "46.3", "44.93",
"39.39", "45.11", "52.09", "64.06", "87.14", "76.56", "59.63",
"62.01", "49.56", "50.61", "42.47", "46.51", "42.12", "46.86",
"55.8", "60.88", "81.43"), V53 = c("Quantile Regression Neural Network",
"34.69", "52.56", "34.91", "32.67", "24.25", "27.15", "25.01",
"21.93", "27.1", "24.8", "31.22", "51.92", "42.94", "30.75",
"41.94", "29.29", "31.15", "21.29", "28.22", "22.59", "27.58",
"29.26", "29.66", "43.24"), V54 = c("Quantile Regression with LASSO penalty",
"37.43", "52.33", "37.1", "33.2", "25.48", "28.74", "26.56",
"23.77", "29.58", "26.53", "34.27", "54.56", "44.35", "32.63",
"41.49", "32.17", "32.18", "22.36", "29.55", "23.8", "29.82",
"30.94", "31.13", "45.12"), V55 = c("Random Forest1", "33.19",
"48.87", "34.21", "30.39", "23.81", "26.03", "24.46", "21.28",
"26.61", "25.54", "32.59", "52.32", "41.48", "31.5", "40.43",
"29.64", "30.39", "21.12", "26.81", "21.33", "27.66", "28.45",
"31.33", "41.45"), V56 = c("Random Forest by Randomization",
"32.62", "49.17", "34.55", "30.33", "23.75", "26.16", "24.8",
"21.11", "26.76", "25.76", "31.75", "51.01", "41.38", "30.85",
"39.93", "29.38", "30.04", "21.42", "27.2", "21.66", "27.56",
"28.25", "31.14", "41.01"), V57 = c("Relaxed Lasso", "38.35",
"53.28", "37.32", "34.07", "25.41", "29.35", "26.63", "24.04",
"29.51", "26.65", "34.69", "54.57", "45.78", "33.59", "41.95",
"33.59", "32.32", "22.92", "29.76", "23.78", "29.93", "31.12",
"30.89", "45.77"), V58 = c("Ridge Regression", "38.43", "52.09",
"36.74", "33.11", "24.98", "28.33", "25.9", "23.34", "29.04",
"26.59", "35.24", "54.92", "44.84", "33.2", "41.45", "32.27",
"31.6", "21.96", "28.93", "23.31", "29.32", "30.86", "31.4",
"45.57"), V59 = c("Self-Organizing Maps", "68.34", "67.63",
"56.9", "43.81", "35.01", "35.4", "34.82", "28.93", "39.39",
"52.58", "60.73", "97.42", "67.67", "59.62", "63.77", "46.68",
"42.6", "31.61", "38.82", "30.85", "40.32", "55.88", "54.07",
"75.36"), V60 = c("Sparse Partial Least Squares", "38.45",
"52.02", "36.76", "33.1", "24.99", "28.33", "25.9", "23.38",
"28.95", "26.59", "35.2", "54.93", "44.71", "33.1", "41.45",
"32.25", "31.61", "21.92", "28.91", "23.31", "29.27", "30.89",
"31.45", "45.54"), V61 = c("Stochastic Gradient Boosting",
"37.37", "50.46", "35.78", "31.25", "25.36", "27.88", "25.82",
"22.32", "28.03", "26.11", "34.56", "56.13", "44.63", "33.66",
"40.97", "31.02", "30.84", "21.53", "28.33", "22.32", "28.67",
"30.59", "33.43", "45.86"), V62 = c("Support Vector Machines with Linear Kernel",
"37.47", "52.19", "36.93", "33.15", "25.45", "28.63", "26.51",
"23.74", "29.36", "26.38", "34.23", "54.64", "44.38", "32.58",
"41.41", "32.23", "32.13", "22.21", "29.52", "23.74", "29.73",
"30.91", "30.95", "45.17"), V63 = c("Support Vector Machines with Polynomial Kernel",
"34.4", "50.5", "35.17", "31.49", "24.11", "27.06", "24.38",
"21.88", "26.39", "24.56", "31.42", "51.65", "41.79", "30.53",
"40.31", "29.6", "30.46", "20.81", "27.98", "21.54", "27.22",
"28.89", "29.2", "42.97"), V64 = c("Support Vector Machines with Radial Basis Function Kernel1",
"34.28", "50.18", "35.22", "31.37", "23.92", "27.09", "24.84",
"21.87", "26.76", "24.9", "31.87", "52.7", "41.91", "30.93",
"40.07", "30.12", "30.63", "20.58", "27.43", "21.89", "27.45",
"29.65", "29.46", "42.94"), V65 = c("Support Vector Machines with Radial Basis Function Kernel2",
"34.3", "50.18", "35.22", "31.37", "23.91", "27.1", "24.83",
"21.88", "26.75", "24.89", "31.9", "52.73", "41.92", "30.92",
"40.06", "30.09", "30.62", "20.58", "27.44", "21.88", "27.45",
"29.62", "29.45", "42.96"), V66 = c("Support Vector Machines with Radial Basis Function Kernel3",
"34.76", "50.36", "35.19", "31.44", "23.82", "26.97", "24.71",
"21.93", "26.69", "24.69", "32.06", "52.42", "42.01", "30.47",
"39.87", "29.67", "30.45", "20.58", "27.67", "21.6", "27.51",
"29.56", "29.34", "42.85"), V67 = c("The Bayesian lasso",
"38.4", "51.52", "36.85", "32.88", "25.04", "28.08", "25.93",
"23.27", "28.74", "26.61", "35.35", "54.93", "44.68", "33.05",
"41.33", "31.78", "31.69", "21.83", "28.88", "23.37", "29.14",
"30.97", "31.44", "45.66"), V68 = c("The lasso", "38.48",
"51.77", "36.73", "32.98", "24.95", "28.21", "25.85", "23.28",
"28.99", "26.6", "35.36", "54.95", "44.74", "33.17", "41.42",
"32.04", "31.58", "21.83", "28.83", "23.31", "29.24", "30.89",
"31.48", "45.6"), V69 = c("Tree Models from Genetic Algorithms",
"44.36", "59.09", "39.65", "37.51", "27.41", "28.46", "28.55",
"24.71", "28.34", "30.08", "39.06", "67.37", "51.24", "38.47",
"47.35", "32.36", "35.52", "21.08", "31.27", "27.15", "31.94",
"35.56", "36.52", "51.04"), V70 = c("Tree-Based Ensembles",
"59.16", "56.47", "40.37", "36.2", "29.12", "29.71", "28.92",
"25.56", "30.35", "34.5", "52.12", "82.58", "60.79", "46.2",
"48.32", "35.73", "35.17", "25.89", "28.99", "25.82", "31.53",
"37.76", "49.3", "67.16")), .Names = c("V1", "V2", "V3",
"V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13",
"V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22",
"V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31",
"V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40",
"V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49",
"V50", "V51", "V52", "V53", "V54", "V55", "V56", "V57", "V58",
"V59", "V60", "V61", "V62", "V63", "V64", "V65", "V66", "V67",
"V68", "V69", "V70"), row.names = c(NA, -25L), class = "data.frame")
results <- as.data.frame(t(results))
names(results) <- c("Name","m1","m2","m3","m4","m5","m6","m7","m8","m9","m10","m11","m12", "m13","m14","m15","m16","m17","m18","m19","m20","m21","m22","m23","m24")
library(ggplot2)
library(reshape2)
results.m <- melt(results,id.vars="Name")
p <- ggplot(results.m, aes(variable, Name)) +
geom_tile(aes(fill = value),colour = "white") +
scale_fill_gradient(low = "white", high = "steelblue")
p
答案 0 :(得分：0)
答案
results <- as.data.frame(fread("nRMSE-Monthly-1hAhead.csv", header = T, sep = ","))
results <- cbind(results, seq(1,24,1))
names(results)[dim(results)[2]] <- c("Month")
results.m <- melt(results,id.vars="Month")
p <- ggplot(results.m, aes(Month, variable)) +
geom_tile(aes(fill = value)) +
scale_fill_gradient(low = "red", high = "green")
p