手动更改图例 ggplot2 的形状
Change manually the shape of a legend ggplot2
我一直在自定义以下情节中的主题、线条和颜色
library(dplyr)
library(ggplot2)
library(readr)
library(zoo)
cvper <- read.csv("https://cloud.minsa.gob.pe/s/Y8w3wHsEdYQSZRp/download", stringsAsFactors = FALSE)
nuevos_cvper <- cvper %>%
group_by(FECHA_RESULTADO) %>%
arrange(desc(FECHA_RESULTADO)) %>%
summarize (casos_x_dia= n()) %>%
mutate(media_movil = rollmean(casos_x_dia, k=7, fill = NA, align = "right"))
prueba_legend <- ggplot(nuevos_cvper) +
geom_line(aes (x = FECHA_RESULTADO, y = media_movil, color = "media_movil"), size = 1.5) +
geom_line(aes (x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia"), linetype = "dashed" ) +
geom_point (aes(x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia")) +
scale_colour_manual("", values = c("media_movil"="#CF3721", "casos_x_dia"="#31A9B8",
"casos_x_dia"="#31A9B8")) +
theme_bw () + theme(legend.position="bottom")
prueba_legend
它显示了一个带有短线的图例。我想将这些线更改为圆圈。我试过 scale_shape_manual，但它不起作用。有办法吗？
自较新的 ggplot2 版本之一（确保您通过 install.packages("ggplot2") 更新），参数 key_glyph= 可用于指定 draw_key 函数应该用于为给定的 geom 和美学绘制图例字形。 See here for some more information and examples of usage;但是，我将使用 mtcars 和 ggplot2 版本 3.3.2 通过以下示例进行演示：
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
geom_line(key_glyph = "point")
您可能和我一样注意到磅值对我来说有点小。这可以通过使用 override.aes= 通过为颜色美学指定的 guide_legend() 函数进行调整，使这些点更大一些。
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
geom_line(key_glyph = "point") +
guides(color=guide_legend(override.aes = list(size=3)))
也许您正在寻找这个：
library(ggplot2)
#Data
df2 <- structure(list(FECHA_RESULTADO = structure(c(18327, 18328, 18329,
18330, 18331, 18332, 18333, 18334, 18335, 18336, 18337, 18338,
18339, 18340, 18341, 18342, 18343, 18344, 18345, 18346, 18347,
18348, 18349, 18350, 18351, 18352, 18353, 18354, 18355, 18356,
18357, 18358, 18359, 18360, 18361, 18362, 18363, 18364, 18365,
18366, 18367, 18368, 18369, 18370, 18371, 18372, 18373, 18374,
18375, 18376, 18377, 18378, 18379, 18380, 18381, 18382, 18383,
18384, 18385, 18386, 18387, 18388, 18389, 18390, 18391, 18392,
18393, 18394, 18395, 18396, 18397, 18398, 18399, 18400, 18401,
18402, 18403, 18404, 18405, 18406, 18407, 18408, 18409, 18410,
18411, 18412, 18413, 18414, 18415, 18416, 18417, 18418, 18419,
18420, 18421, 18422, 18423, 18424, 18425, 18426, 18427, 18428,
18429, 18430, 18431, 18432, 18433, 18434, 18435, 18436, 18437,
18438, 18439, 18440, 18441, 18442, 18443, 18444, 18445, 18446,
18447, 18448, 18449, 18450, 18451, 18452, 18453, 18454, 18455,
18456, 18457, 18458, 18459, 18460, 18461, 18462, 18463, 18464,
18465, 18466, 18467, 18468, 18469, 18470, 18471, 18472, 18473,
18474, 18475, 18476, 18477, 18478, 18479, 18480, 18481, 18482,
18483, 18484, 18485, 18486, 18487, 18488), class = "Date"), casos_x_dia = c(1,
5, 2, 3, 1, 8, 8, 10, 19, 28, 20, 27, 56, 62, 56, 30, 39, 33,
64, 100, 52, 34, 136, 142, 130, 250, 117, 222, 292, 833, 444,
647, 1042, 1083, 817, 1038, 1404, 738, 1284, 1041, 1383, 1329,
1109, 1407, 1076, 2039, 2171, 2104, 2056, 2239, 2397, 1422, 3399,
3367, 4238, 3372, 2625, 3369, 1922, 3990, 3969, 3634, 3612, 3297,
2469, 936, 3601, 4348, 4441, 3739, 4304, 4125, 1785, 5130, 5198,
5290, 5514, 6100, 5399, 1792, 6968, 6919, 7371, 6425, 5745, 4613,
2262, 4242, 3774, 2614, 4029, 4944, 4764, 2637, 4743, 5310, 5726,
5069, 4661, 4500, 2441, 4363, 3376, 3915, 3436, 3447, 3526, 1446,
4335, 3768, 4109, 4154, 4331, 3526, 1598, 2729, 3748, 3648, 3349,
3862, 3518, 2299, 3783, 4035, 2598, 2495, 4913, 4246, 2380, 3114,
4194, 4432, 4535, 5141, 5066, 2228, 3756, 4815, 5972, 5474, 5960,
5626, 2950, 7071, 3017, 6721, 7248, 7601, 6697, 3194, 7818, 7754,
7508, 8442, 7407, 6759, 3491, 7679, 8473, 8560, 7590, 4805),
media_movil = c(NA, NA, NA, NA, NA, NA, 4, 5.28571428571429,
7.28571428571429, 11, 13.4285714285714, 17.1428571428571,
24, 31.7142857142857, 38.2857142857143, 39.8571428571429,
41.4285714285714, 43.2857142857143, 48.5714285714286, 54.8571428571429,
53.4285714285714, 50.2857142857143, 65.4285714285714, 80.1428571428571,
94, 120.571428571429, 123, 147.285714285714, 184.142857142857,
283.714285714286, 326.857142857143, 400.714285714286, 513.857142857143,
651.857142857143, 736.857142857143, 843.428571428571, 925,
967, 1058, 1057.85714285714, 1100.71428571429, 1173.85714285714,
1184, 1184.42857142857, 1232.71428571429, 1340.57142857143,
1502, 1605, 1708.85714285714, 1870.28571428571, 2011.71428571429,
2061.14285714286, 2255.42857142857, 2426.28571428571, 2731.14285714286,
2919.14285714286, 2974.28571428571, 3113.14285714286, 3184.57142857143,
3269, 3355, 3268.71428571429, 3303, 3399, 3270.42857142857,
3129.57142857143, 3074, 3128.14285714286, 3243.42857142857,
3261.57142857143, 3405.42857142857, 3642, 3763.28571428571,
3981.71428571429, 4103.14285714286, 4224.42857142857, 4478,
4734.57142857143, 4916.57142857143, 4917.57142857143, 5180.14285714286,
5426, 5723.28571428571, 5853.42857142857, 5802.71428571429,
5690.42857142857, 5757.57142857143, 5368.14285714286, 4918.85714285714,
4239.28571428571, 3897, 3782.57142857143, 3804.14285714286,
3857.71428571429, 3929.28571428571, 4148.71428571429, 4593.28571428571,
4741.85714285714, 4701.42857142857, 4663.71428571429, 4635.71428571429,
4581.42857142857, 4305.14285714286, 4046.42857142857, 3813.14285714286,
3639.71428571429, 3500.57142857143, 3358.42857142857, 3354.42857142857,
3410.42857142857, 3438.14285714286, 3540.71428571429, 3667,
3667, 3688.71428571429, 3459.28571428571, 3456.42857142857,
3390.57142857143, 3275.57142857143, 3208.57142857143, 3207.42857142857,
3307.57142857143, 3458.14285714286, 3499.14285714286, 3349.14285714286,
3227.14285714286, 3377.28571428571, 3481.28571428571, 3492.85714285714,
3397.28571428571, 3420, 3682, 3973.42857142857, 4006, 4123.14285714286,
4101.42857142857, 4193.14285714286, 4281.85714285714, 4501.85714285714,
4636, 4753, 4833, 4936.14285714286, 5409.71428571429, 5152.85714285714,
5259.85714285714, 5513.28571428571, 5747.71428571429, 5900.71428571429,
5935.57142857143, 6042.28571428571, 6719, 6831.42857142857,
7002, 6974.28571428571, 6983.14285714286, 7025.57142857143,
7005.71428571429, 7108.42857142857, 7258.71428571429, 7137,
6765.28571428571)), row.names = c(NA, -162L), class = "data.frame")
代码：
prueba_legend <- ggplot(df2) +
geom_line(aes (x = FECHA_RESULTADO, y = media_movil, color = "media_movil"),
size = 1.5,show.legend = F) +
geom_line(aes (x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia"),
linetype = "dashed",show.legend = F ) +
geom_point (aes(x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia")) +
scale_colour_manual("", values = c("media_movil"="#CF3721", "casos_x_dia"="#31A9B8",
"casos_x_dia"="#31A9B8")) +
theme_bw () + theme(legend.position="bottom")
prueba_legend
输出：
我一直在自定义以下情节中的主题、线条和颜色
library(dplyr)
library(ggplot2)
library(readr)
library(zoo)
cvper <- read.csv("https://cloud.minsa.gob.pe/s/Y8w3wHsEdYQSZRp/download", stringsAsFactors = FALSE)
nuevos_cvper <- cvper %>%
group_by(FECHA_RESULTADO) %>%
arrange(desc(FECHA_RESULTADO)) %>%
summarize (casos_x_dia= n()) %>%
mutate(media_movil = rollmean(casos_x_dia, k=7, fill = NA, align = "right"))
prueba_legend <- ggplot(nuevos_cvper) +
geom_line(aes (x = FECHA_RESULTADO, y = media_movil, color = "media_movil"), size = 1.5) +
geom_line(aes (x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia"), linetype = "dashed" ) +
geom_point (aes(x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia")) +
scale_colour_manual("", values = c("media_movil"="#CF3721", "casos_x_dia"="#31A9B8",
"casos_x_dia"="#31A9B8")) +
theme_bw () + theme(legend.position="bottom")
prueba_legend
它显示了一个带有短线的图例。我想将这些线更改为圆圈。我试过 scale_shape_manual，但它不起作用。有办法吗？
自较新的 ggplot2 版本之一（确保您通过 install.packages("ggplot2") 更新），参数 key_glyph= 可用于指定 draw_key 函数应该用于为给定的 geom 和美学绘制图例字形。 See here for some more information and examples of usage;但是，我将使用 mtcars 和 ggplot2 版本 3.3.2 通过以下示例进行演示：
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
geom_line(key_glyph = "point")
您可能和我一样注意到磅值对我来说有点小。这可以通过使用 override.aes= 通过为颜色美学指定的 guide_legend() 函数进行调整，使这些点更大一些。
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
geom_line(key_glyph = "point") +
guides(color=guide_legend(override.aes = list(size=3)))
也许您正在寻找这个：
library(ggplot2)
#Data
df2 <- structure(list(FECHA_RESULTADO = structure(c(18327, 18328, 18329,
18330, 18331, 18332, 18333, 18334, 18335, 18336, 18337, 18338,
18339, 18340, 18341, 18342, 18343, 18344, 18345, 18346, 18347,
18348, 18349, 18350, 18351, 18352, 18353, 18354, 18355, 18356,
18357, 18358, 18359, 18360, 18361, 18362, 18363, 18364, 18365,
18366, 18367, 18368, 18369, 18370, 18371, 18372, 18373, 18374,
18375, 18376, 18377, 18378, 18379, 18380, 18381, 18382, 18383,
18384, 18385, 18386, 18387, 18388, 18389, 18390, 18391, 18392,
18393, 18394, 18395, 18396, 18397, 18398, 18399, 18400, 18401,
18402, 18403, 18404, 18405, 18406, 18407, 18408, 18409, 18410,
18411, 18412, 18413, 18414, 18415, 18416, 18417, 18418, 18419,
18420, 18421, 18422, 18423, 18424, 18425, 18426, 18427, 18428,
18429, 18430, 18431, 18432, 18433, 18434, 18435, 18436, 18437,
18438, 18439, 18440, 18441, 18442, 18443, 18444, 18445, 18446,
18447, 18448, 18449, 18450, 18451, 18452, 18453, 18454, 18455,
18456, 18457, 18458, 18459, 18460, 18461, 18462, 18463, 18464,
18465, 18466, 18467, 18468, 18469, 18470, 18471, 18472, 18473,
18474, 18475, 18476, 18477, 18478, 18479, 18480, 18481, 18482,
18483, 18484, 18485, 18486, 18487, 18488), class = "Date"), casos_x_dia = c(1,
5, 2, 3, 1, 8, 8, 10, 19, 28, 20, 27, 56, 62, 56, 30, 39, 33,
64, 100, 52, 34, 136, 142, 130, 250, 117, 222, 292, 833, 444,
647, 1042, 1083, 817, 1038, 1404, 738, 1284, 1041, 1383, 1329,
1109, 1407, 1076, 2039, 2171, 2104, 2056, 2239, 2397, 1422, 3399,
3367, 4238, 3372, 2625, 3369, 1922, 3990, 3969, 3634, 3612, 3297,
2469, 936, 3601, 4348, 4441, 3739, 4304, 4125, 1785, 5130, 5198,
5290, 5514, 6100, 5399, 1792, 6968, 6919, 7371, 6425, 5745, 4613,
2262, 4242, 3774, 2614, 4029, 4944, 4764, 2637, 4743, 5310, 5726,
5069, 4661, 4500, 2441, 4363, 3376, 3915, 3436, 3447, 3526, 1446,
4335, 3768, 4109, 4154, 4331, 3526, 1598, 2729, 3748, 3648, 3349,
3862, 3518, 2299, 3783, 4035, 2598, 2495, 4913, 4246, 2380, 3114,
4194, 4432, 4535, 5141, 5066, 2228, 3756, 4815, 5972, 5474, 5960,
5626, 2950, 7071, 3017, 6721, 7248, 7601, 6697, 3194, 7818, 7754,
7508, 8442, 7407, 6759, 3491, 7679, 8473, 8560, 7590, 4805),
media_movil = c(NA, NA, NA, NA, NA, NA, 4, 5.28571428571429,
7.28571428571429, 11, 13.4285714285714, 17.1428571428571,
24, 31.7142857142857, 38.2857142857143, 39.8571428571429,
41.4285714285714, 43.2857142857143, 48.5714285714286, 54.8571428571429,
53.4285714285714, 50.2857142857143, 65.4285714285714, 80.1428571428571,
94, 120.571428571429, 123, 147.285714285714, 184.142857142857,
283.714285714286, 326.857142857143, 400.714285714286, 513.857142857143,
651.857142857143, 736.857142857143, 843.428571428571, 925,
967, 1058, 1057.85714285714, 1100.71428571429, 1173.85714285714,
1184, 1184.42857142857, 1232.71428571429, 1340.57142857143,
1502, 1605, 1708.85714285714, 1870.28571428571, 2011.71428571429,
2061.14285714286, 2255.42857142857, 2426.28571428571, 2731.14285714286,
2919.14285714286, 2974.28571428571, 3113.14285714286, 3184.57142857143,
3269, 3355, 3268.71428571429, 3303, 3399, 3270.42857142857,
3129.57142857143, 3074, 3128.14285714286, 3243.42857142857,
3261.57142857143, 3405.42857142857, 3642, 3763.28571428571,
3981.71428571429, 4103.14285714286, 4224.42857142857, 4478,
4734.57142857143, 4916.57142857143, 4917.57142857143, 5180.14285714286,
5426, 5723.28571428571, 5853.42857142857, 5802.71428571429,
5690.42857142857, 5757.57142857143, 5368.14285714286, 4918.85714285714,
4239.28571428571, 3897, 3782.57142857143, 3804.14285714286,
3857.71428571429, 3929.28571428571, 4148.71428571429, 4593.28571428571,
4741.85714285714, 4701.42857142857, 4663.71428571429, 4635.71428571429,
4581.42857142857, 4305.14285714286, 4046.42857142857, 3813.14285714286,
3639.71428571429, 3500.57142857143, 3358.42857142857, 3354.42857142857,
3410.42857142857, 3438.14285714286, 3540.71428571429, 3667,
3667, 3688.71428571429, 3459.28571428571, 3456.42857142857,
3390.57142857143, 3275.57142857143, 3208.57142857143, 3207.42857142857,
3307.57142857143, 3458.14285714286, 3499.14285714286, 3349.14285714286,
3227.14285714286, 3377.28571428571, 3481.28571428571, 3492.85714285714,
3397.28571428571, 3420, 3682, 3973.42857142857, 4006, 4123.14285714286,
4101.42857142857, 4193.14285714286, 4281.85714285714, 4501.85714285714,
4636, 4753, 4833, 4936.14285714286, 5409.71428571429, 5152.85714285714,
5259.85714285714, 5513.28571428571, 5747.71428571429, 5900.71428571429,
5935.57142857143, 6042.28571428571, 6719, 6831.42857142857,
7002, 6974.28571428571, 6983.14285714286, 7025.57142857143,
7005.71428571429, 7108.42857142857, 7258.71428571429, 7137,
6765.28571428571)), row.names = c(NA, -162L), class = "data.frame")
代码：
prueba_legend <- ggplot(df2) +
geom_line(aes (x = FECHA_RESULTADO, y = media_movil, color = "media_movil"),
size = 1.5,show.legend = F) +
geom_line(aes (x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia"),
linetype = "dashed",show.legend = F ) +
geom_point (aes(x = FECHA_RESULTADO, y = casos_x_dia, color = "casos_x_dia")) +
scale_colour_manual("", values = c("media_movil"="#CF3721", "casos_x_dia"="#31A9B8",
"casos_x_dia"="#31A9B8")) +
theme_bw () + theme(legend.position="bottom")
prueba_legend
输出：