r - GG 图图例堆叠所有形状/从图例中删除一项-6ren

r - GG 图图例堆叠所有形状/从图例中删除一项

转载作者：行者123 更新时间：2023-12-04 07:41:09

我的 ggplot 有问题，我想为某些值设置不同的形状。但是在图例中，所有东西都堆叠在一起，因此图例不再清晰。此外，我想从图例中删除日本或找到另一种方法将其设置为红色且形状=17。也许添加到过滤器 Japan=='FALSE'？我尝试过但没有成功...
这是我的数据示例:

    structure(list(Country = c("Albania", "Aruba", "Austria", "Barbados", 
"Bosnia and Herzegovina", "Canada", "China, Hong Kong SAR", "China, Macao SAR", 
"Croatia", "Curaçao", "Denmark", "Finland", "France", "Germany", 
"Iceland", "Italy", "Japan", "Latvia", "Lithuania", "Malta", 
"Mauritius", "Montenegro", "Netherlands", "New Zealand", "Poland", 
"Portugal", "Republic of Korea", "Serbia", "Singapore", "Slovenia", 
"Sri Lanka", "Taiwan", "Thailand", "Trinidad and Tobago", "United States of America"
), `Dependency Ratio 1990` = c(0.371731839842905, 0.42945960478559, 
0.698167620530499, 0.444513116903726, 0.511357742868368, 0.519783119456753, 
0.444426949479237, 0.30306654331295, 0.723691486939267, 0.424414908111054, 
0.68769508504734, 0.641530173960242, 0.690189226564259, 0.755969184286434, 
0.520917100019657, 0.763735128335739, 0.692461922514607, 0.728970209495916, 
0.655093765838824, 0.556158238426314, 0.308439455191019, 0.551893405455789, 
0.582543266573117, 0.548269437314668, 0.592240027149362, 0.744368260326749, 
0.33818760118961, 0.653157768845158, 0.294237762460344, 0.611402526341597, 
0.354595845574429, 0.391092962761626, 0.331304119150256, 0.35111793456609, 
0.562804979721953), `Average Age 1990` = c(40.3688042387203, 
42.5004114258846, 46.6904752788518, 42.5683625031078, 42.4530074518545, 
44.2409448871874, 42.0677766503007, 39.7173235436725, 46.2329924328207, 
42.2501753565583, 47.1375106133558, 46.3380103826365, 46.6915593676301, 
46.9202073747455, 44.129974503284, 47.1071528898825, 46.6077408054755, 
46.4664135824761, 45.460688263743, 44.9450928096016, 39.2332051727974, 
43.66848, 45.1863467813393, 44.5466909246095, 44.9318462263063, 
46.8407998745322, 39.6873706785703, 45.3128111624097, 39.2982502106955, 
45.1205082490539, 40.2124158913374, 40.9051762916043, 39.4534335710941, 
40.4173693037492, 45.0904477728946), ...4 = c(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), 
    `Dependency Ratio 2000` = c(0.457221087782508, 0.45940989018547, 
    0.742565467519652, 0.541204030550029, 0.597208883500012, 
    0.622668782217446, 0.473107405821069, 0.344264744501091, 
    0.780230513979289, 0.633789601501269, 0.790840886898238, 
    0.816743961984905, 0.766496601277572, 0.821136017787255, 
    0.572251283384235, 0.849390138872188, 0.927739183233871, 
    0.791488299481733, 0.704545225683664, 0.702609326498817, 
    0.35385418751795, 0.612564625368555, 0.69550083971213, 0.617845149047375, 
    0.611138887992547, 0.758176723785889, 0.399735388267277, 
    0.715467873467691, 0.383896159972764, 0.671137540638121, 
    0.407831309113246, 0.419443507121452, 0.374126385687095, 
    0.409593048372564, 0.615930392620661), `Average Age 2000` = c(42.9309383891972, 
    43.8674007980144, 47.739334648896, 45.3560289004102, 45.2990249348384, 
    46.3058678455289, 44.3575197674921, 42.5567755821042, 47.8706196243093, 
    46.6926342578517, 47.9056748231027, 48.2912968951969, 48.1201704908476, 
    48.7598382100637, 45.3475147626354, 48.7908038019529, 48.7739160208226, 
    47.837540150878, 46.9150297452015, 46.5384376276976, 41.1677637838199, 
    45.6830176554619, 46.9484767952653, 46.0162750047118, 45.6870193241911, 
    47.5083105450284, 41.9472090972845, 46.7580169116961, 42.3830413567395, 
    46.6274879755993, 41.6098644987726, 42.77485916275, 41.5907978667698, 
    41.9719792296039, 46.2781534087236), ...7 = c(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), `Dependency Ratio 2015` = c(0.769855001031037, 0.896573728647162, 
    0.952116351996821, 0.847635802309437, 0.907658891504387, 
    0.913339561634508, 0.877436210055064, 0.597279210234922, 
    1.04631340359464, 1.01174318826707, 0.977891738355926, 1.08472890326446, 
    1.01372962185931, 1.12997699958302, 0.77882885539859, 1.08347132170333, 
    1.20679093156161, 0.990147667477283, 0.994357961860711, 0.950194923573131, 
    0.666562280517562, 0.828817278052088, 0.989460150334804, 
    0.831814166715077, 0.847326002560978, 1.03326726133893, 0.749863481391909, 
    0.917215066264046, 0.671774028057953, 0.971441198307662, 
    0.611354032233621, 0.748057645284422, 0.665957813686028, 
    0.602213503073687, 0.855245238291093), `Average Age 2015` = c(46.3902100558352, 
    47.9818955923079, 49.878498965043, 48.4578763127188, 48.3870694416244, 
    48.7063314226308, 48.5131007402609, 44.1281261495054, 50.522561636728, 
    49.6455293947711, 49.9810732770387, 50.7119476819108, 50.6805196046482, 
    51.5236122201751, 47.3079677856577, 51.8838669025279, 53.2344169277342, 
    50.0674941000466, 49.9057070057583, 49.1748722211516, 45.3413745873924, 
    47.6617051653597, 49.9107746561504, 48.0022465682781, 48.1684244717051, 
    51.1526322354916, 47.160655712273, 49.0269050604693, 45.5481140676913, 
    50.0264456515826, 44.8882173741791, 47.0138589294768, 46.131374630996, 
    44.5204789350954, 48.0998439723386), `rgdpe 1990` = c(12005.7568359375, 
    2575.25561523438, 208007.234375, 4099.8515625, 6946.330078125, 
    915724.6875, 151044.28125, 9127.78125, 64448.71484375, NA, 
    144018.203125, 136194.359375, 1581529.625, 2204488.5, 8510.248046875, 
    1560881.5, 3552613.25, 44957.03515625, 53623.6875, 5354.54541015625, 
    11257.8095703125, 6702.1552734375, 427072.25, 76859.65625, 
    335254.875, 157535.140625, 565140.75, 113435.8046875, 64860.5703125, 
    42714.70703125, 55257.37109375, 430917.25, 308367.4375, 15085.6611328125, 
    9847675), `pop 1990` = c(3.286073, 0.062149, 7.723949, 0.260936, 
    4.463423, 27.541319, 5.727938, 0.343808, 4.776374, NA, 5.141115, 
    4.996222, 58.235697, 79.053984, 0.255043, 57.048236, 124.50524, 
    2.664439, 3.696035, 0.362015, 1.055868, 0.615002, 14.965448, 
    3.398172, 37.960193, 9.895364, 42.918419, 9.517675, 3.012966, 
    2.006405, 17.325773, 20.278946, 56.558186, 1.221116, 252.120309
    ), `emp 1990` = c(1.32407820224762, NA, 3.56034135818481, 
    0.105200000107288, 1.68987882137299, 13.2902002334595, 2.73075985908508, 
    0.16329999268055, 2.17813229560852, NA, 2.63417220115662, 
    2.47324681282043, 23.6595039367676, 39.5477294921875, 0.138074412941933, 
    22.8031978607178, 65.1040191650391, 1.25425291061401, 1.70560574531555, 
    0.132750615477562, 0.403737008571625, 0.174824863672256, 
    6.80782461166382, 1.52131986618042, 15.0829668045044, 4.46721506118774, 
    18.2060832977295, 4.61394643783569, 1.52955627441406, 1.1292530298233, 
    5.04270553588867, 8.64918994903564, 28.7045097351074, 0.374099999666214, 
    123.046020507812), ...5 = c(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), `rgdpe 2000` = c(15180.880859375, 
    4031.13427734375, 314579.6875, 6460.9755859375, 21482.595703125, 
    1276875.5, 256729.140625, 13199.31640625, 58379.2734375, 
    NA, 203091.515625, 186055.46875, 2135621.25, 3030253, 11653.5791015625, 
    2081385.625, 4696670.5, 26473.306640625, 42346.78125, 9706.8212890625, 
    18593.318359375, 4850.50634765625, 691869.625, 114914.1171875, 
    563679.1875, 259576.953125, 1150272, 60002.0703125, 166273.53125, 
    48606.53125, 90442.875, 789527.25, 504829.28125, 18303.63671875, 
    14110581), `pop 2000` = c(3.129243, 0.090853, 8.069276, 0.271515, 
    3.751176, 30.588383, 6.606327, 0.427782, 4.428075, NA, 5.341194, 
    5.187954, 60.874357, 81.400882, 0.280435, 56.692178, 127.524174, 
    2.384164, 3.501839, 0.393645, 1.185145, 0.613559, 15.926188, 
    3.858999, 38.556693, 10.297112, 47.379241, 7.516346, 4.028871, 
    1.987717, 18.777601, 22.18453, 62.952642, 1.267153, 281.710909
    ), `emp 2000` = c(0.962967455387115, 0.0419000014662743, 
    3.7599310874939, 0.12899999320507, 0.643303751945496, 14.952766418457, 
    3.20262169837952, 0.195299997925758, 1.67029082775116, NA, 
    2.75595617294312, 2.30501818656921, 25.6252250671387, 39.6031150817871, 
    0.1570855230093, 22.91796875, 65.9155044555664, 0.930018603801727, 
    1.40124833583832, 0.146938025951385, 0.464872002601624, 0.176752656698227, 
    8.20334815979004, 1.81842231750488, 14.4786930084229, 5.076171875, 
    21.4411239624023, 3.0847954750061, 2.08465480804443, 0.917375922203064, 
    6.30462980270386, 9.59665679931641, 31.47385597229, 0.503100037574768, 
    138.636108398438), ...9 = c(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), `rgdpe 2015` = c(32037.935546875, 
    3959.59252929688, 448794.71875, 4856.21044921875, 40599.22265625, 
    1659691.75, 411350.65625, 62493.578125, 99181.7265625, 4041.00463867188, 
    278112.53125, 237412.921875, 2772463.25, 3915258.25, 16865.345703125, 
    2296760.75, 5094436, 51517.390625, 87529.2890625, 17455.76171875, 
    25619.560546875, 10165.248046875, 872643.75, 174613.65625, 
    1069768.375, 314019.625, 1928056.875, 108470.875, 451476.4375, 
    68875.1796875, 242116.15625, 1125999, 1108115.875, 38140.46484375, 
    18905122), `pop 2015` = c(2.890513, 0.104341, 8.67866, 0.285324, 
    3.429361, 36.026676, 7.185996, 0.602085, 4.232874, 0.159847, 
    5.688695, 5.481122, 66.596315, 81.787411, 0.330243, 60.578494, 
    127.985133, 1.997674, 2.93188, 0.433559, 1.259456, 0.626956, 
    16.938499, 4.614532, 38.034079, 10.368351, 50.823093, 7.095383, 
    5.592152, 2.071199, 20.908027, 23.462914, 68.714511, 1.370328, 
    320.87831), `emp 2015` = c(0.926395297050476, 0.0467174984514713, 
    4.27823972702026, 0.128199994564056, 0.616872131824493, 18.3558368682861, 
    3.77715754508972, 0.396699994802475, 1.69973313808441, 0.0617999993264675, 
    2.83158588409424, 2.52453279495239, 27.3850765228271, 42.5355796813965, 
    0.181162342429161, 24.4446144104004, 66.9830322265625, 0.897909104824066, 
    1.35488307476044, 0.191138163208961, 0.563370883464813, 0.221699982881546, 
    8.80725860595703, 2.36527323722839, 15.8249969482422, 4.60829973220825, 
    26.079252243042, 2.56693267822266, 3.65548992156982, 0.950674414634705, 
    7.83100032806396, 11.1978015899658, 37.9529876708984, 0.623300015926361, 
    150.248474121094), GDP_per_capita_1990 = c(3653.52712369369, 
    41436.7989064084, 26930.1667288326, 15712.0963090566, 1556.27868524337, 
    33249.1224367286, 26369.74793547, 26549.0659030622, 13493.2303968973, 
    NA, 28013.0289100711, 27259.4691298745, 27157.3915394195, 
    27885.8621470614, 33367.895009371, 27360.7320654051, 28533.8452421762, 
    16872.9834521451, 14508.4360672991, 14790.949021881, 10662.1372845019, 
    10897.7780128154, 28537.2178634412, 22617.9417198423, 8831.74843183753, 
    15920.0955745539, 13167.790500391, 11918.4364550691, 21527.1497628915, 
    21289.1749329024, 3189.31634933402, 21249.4894951641, 5452.2158383934, 
    12353.995142814, 39059.4277750151), GDP_per_capita_2015 = c(11083.8233721402, 
    37948.5775418759, 51712.4439429589, 17019.9858729681, 11838.7135843237, 
    46068.41191788, 57243.3739526156, 103795.274961177, 23431.2966940429, 
    25280.4534252872, 48888.6346077615, 43314.6574506096, 41630.8807777127, 
    47871.1601471283, 51069.5024667442, 37913.7974278463, 39804.9045274657, 
    25788.6875561278, 29854.3218216639, 40261.5600616064, 20341.7670382094, 
    16213.6546214966, 51518.3635810942, 37839.9491541071, 28126.5749855544, 
    30286.3613509998, 37936.6300079375, 15287.5292285138, 80733.9352542635, 
    33253.7721809927, 11580.0575659291, 47990.5863355251, 16126.3735835943, 
    27833.0916713006, 58916.7962147395), change_log_GDP_per_cap_1990_2015 = c(1.10979365523243, 
    -0.0879373277889428, 0.652451349776046, 0.0799574118764497, 
    2.02907746023446, 0.326099133412088, 0.775094455358737, 1.36342588458029, 
    0.551884486717116, NA, 0.556875228192604, 0.46309013522626, 
    0.427192960710386, 0.540393411165555, 0.425593287986558, 
    0.32620624489414, 0.332899198982464, 0.424222198470275, 0.721599336011078, 
    1.00138172800831, 0.645977367508866, 0.39729484866726, 0.590729193215088, 
    0.514621927745218, 1.15836185302803, 0.643115306926695, 1.05814340225474, 
    0.248950929776939, 1.32184407992377, 0.44596948530371, 1.28947786000817, 
    0.814672002339478, 1.08443394063506, 0.812246156576203, 0.411041939497887
    ), change_dependency_ratio_1990_2015 = c(0.398123161188132, 
    0.467114123861573, 0.253948731466323, 0.403122685405711, 
    0.396301148636019, 0.393556442177755, 0.433009260575827, 
    0.294212666921972, 0.322621916655375, 0.587328280156014, 
    0.290196653308586, 0.44319872930422, 0.323540395295048, 0.374007815296586, 
    0.257911755378933, 0.319736193367588, 0.514329009047002, 
    0.261177457981367, 0.339264196021887, 0.394036685146817, 
    0.358122825326543, 0.2769238725963, 0.406916883761686, 0.283544729400408, 
    0.255085975411616, 0.288899001012185, 0.411675880202299, 
    0.264057297418888, 0.377536265597609, 0.360038671966065, 
    0.256758186659192, 0.356964682522796, 0.334653694535773, 
    0.251095568507597, 0.29244025856914)), class = "data.frame", row.names = c(NA, 
-35L))

这是我的代码:

#Adding OECD
#Exclude japan for fig
OECD <- c("Dem. People's Republic of Korea",'Mexico','Chile',
          'New Zealand','Czechia','Hungary','Slovakia','Denmark','Estonia',
          'Finland','Iceland','Ireland','Latvia','Lithuania','Norway',
          'Sweden','United Kingdom','Greece','Italy','Portugal','Slovenia',
          'Spain','Austria','Belgium','France','Germany','Luxembourg',
          'Netherlands','Switzerland','Australia','Canada','United States of America',
          'Poland','Turkey','Israel')

#Figure 2
library(tidyverse)
df  %>%
  mutate(OECD = factor(Country %in% OECD, labels = c("NonOECD","OECD"))) %>% mutate(Japan = factor(Country == 'Japan' , labels=c('FALSE','TRUE')))-> newdata

ggplot() +  
  geom_point(data = filter(newdata, OECD == 'NonOECD'),aes(x = change_dependency_ratio_1990_2015, y = change_log_GDP_per_cap_1990_2015, colour='NonOECD'),shape = 16, size=3) +
   geom_point(data = filter(newdata, Japan == 'TRUE'),aes(x = change_dependency_ratio_1990_2015, y = change_log_GDP_per_cap_1990_2015,colour='Japan'),shape = 17, size=4) +
  geom_point(data = filter(newdata, OECD == 'OECD'),aes(x = change_dependency_ratio_1990_2015, y = change_log_GDP_per_cap_1990_2015, colour='OECD' ),shape = 18, size=4) + 
  
  scale_color_manual(values = c(NonOECD = "cyan4", OECD = "orange",Japan='red'),
                     labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries")) + geom_abline(size=1, col='grey')+
  
  theme_classic()+ theme( panel.grid.major.y =element_line(color='grey', size=0.7),legend.title = element_blank(),
                          panel.grid.minor.y =element_blank(), 
                          legend.background = element_blank(), legend.box.background = element_rect(colour = "black"),
                          legend.spacing.y = unit(0, "mm"),legend.direction = 'horizontal',
                          legend.position = "bottom",aspect.ratio = 0.7, axis.text = element_text(colour = 1, size = 13),)

我得到的例子:

它应该是什么样子:

提前谢谢你!!

最佳答案

下面的代码生成一个与预期输出等效的图。
两个主要区别是:

"Japan"没有数据在问题中，所以我替换了 "Portugal" (我的国家)为此；

没有GDP数据，无论是否记录，所以我创建了一个带有随机统一数字的新列，runif .

情节实际上很简单，创建因子 OECD首先创建一个逻辑向量，然后使用 ifelse为特殊国家分配一个整数值，在本例中为 "Portugal"并将 2 添加到另一个逻辑向量的元素，得到 FALSE/TRUE + 2 == 0/1 + 2 .
为了不把情节和主题混在一起，我还创建了一个自定义主题，代码在最后。

library(tidyverse)

set.seed(2021)

df %>% 
  mutate(OECD = Location %in% OECD,
         OECD = ifelse(Location == "Portugal", 1L, OECD + 2L),
         OECD = factor(OECD, labels = c("Portugal", "NonOECD","OECD"))) %>% 
  mutate(GDP = runif(n(), -2, 2)) %>% 
  ggplot(aes(x = `Dependency Ratio`, y = GDP, color = OECD, shape = OECD, size = OECD)) +  
  geom_point() +
  scale_color_manual(
    values = c(NonOECD = "cyan4", OECD = "orange", Portugal = 'red'),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) + 
  scale_shape_manual(
    values = c(NonOECD = 16, OECD = 18, Portugal = 17),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) +
  scale_size_manual(
    values = c(NonOECD = 4, OECD = 3, Portugal = 4),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) +
  geom_abline(size = 1, col = 'grey') +
  theme_custom_Cas()

为了从图例中删除特殊国家，对数据进行子集化。下面的代码将输出 %>%管道到一个新的数据集并在图中使用它。

set.seed(2021)

df %>% 
  mutate(OECD = Location %in% OECD,
         OECD = ifelse(Location == "Portugal", 1L, OECD + 2L),
         OECD = factor(OECD, labels = c("Portugal", "NonOECD","OECD"))) %>% 
  mutate(GDP = runif(n(), -2, 2)) -> newdata

ggplot(newdata, aes(x = `Dependency Ratio`, y = GDP, color = OECD, shape = OECD, size = OECD)) +  
  geom_point(data = subset(newdata, OECD != "Portugal")) +
  # In the special country's layer the color, shape and size must be
  # outside aes() and show.legend = FALSE
  geom_point(
    data = subset(newdata, OECD == "Portugal"),
    color = "red", shape = 17, size = 4,
    show.legend = FALSE
  ) +
  scale_color_manual(
    values = c(NonOECD = "cyan4", OECD = "orange", Portugal = 'red'),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) + 
  scale_shape_manual(
    values = c(NonOECD = 16, OECD = 18, Portugal = 17),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) +
  scale_size_manual(
    values = c(NonOECD = 4, OECD = 3, Portugal = 4),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) +
  geom_abline(size = 1, col = 'grey') +
  theme_custom_Cas()

为了有填充颜色，必须改变点的形状。见 help("points")最右边的点，填充为灰色。这些形状允许边框( ggplot 美感 color )和填充颜色( ggplot 美感 fill )。

ggplot(newdata, aes(x = `Dependency Ratio`, y = GDP, fill = OECD, shape = OECD, size = OECD)) +  
  geom_point(data = subset(newdata, OECD != "Portugal")) +
  # In the special country's layer the color, shape and size must be
  # outside aes() and show.legend = FALSE
  geom_point(
    data = subset(newdata, OECD == "Portugal"),
    fill = "red", shape = 24, size = 4,
    show.legend = FALSE
  ) +
  scale_fill_manual(
    values = c(NonOECD = "cyan4", OECD = "orange", Portugal = 'red'),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) + 
  scale_shape_manual(
    values = c(NonOECD = 21, OECD = 23, Portugal = 24),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) +
  scale_size_manual(
    values = c(NonOECD = 4, OECD = 3, Portugal = 4),
    labels = c(NonOECD = "All Countries except OECD", OECD = "OECD countries", Portugal = "Portugal")
  ) +
  geom_abline(size = 1, col = 'grey') +
  theme_custom_Cas()

自定义主题代码。

theme_custom_Cas <- function(){ 
  theme_classic() %+replace%    #replace elements we want to change
    theme(
      panel.grid.major.y = element_line(color = 'grey', size = 0.7),
      legend.title = element_blank(),
      panel.grid.minor.y = element_blank(), 
      legend.background = element_blank(), 
      legend.box.background = element_rect(colour = "black"),
      legend.spacing.y = unit(0, "mm"),
      legend.direction = 'horizontal',
      legend.position = "bottom",
      aspect.ratio = 0.7, 
      axis.text = element_text(colour = 1, size = 13)    
    )
}

关于r - GG 图图例堆叠所有形状/从图例中删除一项，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/67459523/

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r - GG 图图例堆叠所有形状/从图例中删除一项