d3 newb正在尝试别的东西： 我想在条形图中添加日期标签，只要日期与上一个栏中的日期不同。
bar.append("text")
.attr("visibility", function(d,i){
if(d.Datum == data[i-1].Datum) return "hidden"})
.attr("x", padding)
.attr("y", barHeight-2)
.attr("fill", "Gainsboro")
.text(function(d){ return d.Datum})
所以我想我会在文字标签上添加一个可见性。但是我无法从数据对象访问上一个日期......对于像我这样不是新手的人来说这可能很容易...
数据示例（在CSV导入之前）
Datum,Name,Kategorie,Betrag
01/10/15,,Lohn,1586.7
02/10/15,,lunch,-4.55
答案 0 :(得分：1)
因此，假设在d3解析文本文件后，您将获得以下数据：
var data = [
{
Datum: "01/10/15",
Name: "",
Kategorie: "Lohn",
Betrag: 1586.7
},{
...
}
];
我预先处理数据以包含bool关于它是否是该日期的第一个实例：
data.forEach(function(d,i){
d.isFirstInstanceOfDate = (i === 0 || d.Dataum !== data[i-1].Datum);
});
然后假设bar是g个元素的选择（已经包含rect），我会过滤它们，只在text上附加 bar
.filter(function(d){
return d.isFirstInstanceOfDate
})
.append('text')
...
第一个例子：
head(data)
timestamp day month year.x hour minute doy.x rn_1_1_1 ppfd_1_1_1
1 2013-07-06 00:00:00 6 7 2013 0 0 187.000 -84.37381 0.754
2 2013-07-06 00:30:00 6 7 2013 0 30 187.020 -84.07990 0.808
3 2013-07-06 01:00:00 6 7 2013 1 0 187.041 -82.19991 0.808
4 2013-07-06 01:30:00 6 7 2013 1 30 187.062 -81.12341 0.831
5 2013-07-06 02:00:00 6 7 2013 2 0 187.083 -79.57474 0.708
6 2013-07-06 02:30:00 6 7 2013 2 30 187.104 -77.72460 0.639
ppfdr_1_1_1 p_rain_1_1_1 swc_1_1_1 swc_2_1_1 swc_3_1_1 air_pressure air_pressure.1
1 0.624 0 0.07230304 0.09577876 0.134602791 101212.4165 1012.124165
2 0.587 0 0.07233134 0.09569421 0.134479816 101181.8094 1011.818094
3 0.713 0 0.07242914 0.09566160 0.134203719 101166.0948 1011.660948
4 0.72 0 0.07252077 0.09563419 0.134149141 101144.6151 1011.446151
5 0.564 0 0.07261925 0.09560297 0.134095791 101144.8662 1011.448662
6 0.706 0 0.07271843 0.09557789 0.134037119 101144.5084 1011.445084
u_rot v_rot w_rot wind_speed u. h_scr_qc01_man
1 5.546047919 1.42E-14 4.76E-16 5.546047919 0.426515403 -28.07603618
2 5.122724997 6.94E-15 -8.00E-16 5.122724997 0.408213459 -34.39110979
3 5.248639421 4.56E-15 7.28E-17 5.248639421 0.393959075 -33.29033501
4 4.845257286 2.81E-14 -1.33E-17 4.845257286 0.365475898 -32.62427147
5 4.486426895 1.39E-14 -4.43E-16 4.486426895 0.335905384 -33.80219189
6 4.109603841 7.08E-15 -9.76E-16 4.109603841 0.312610588 -35.77289349
fco2_scr_qc01_man le_scr_qc01_man fco2_scr_qc0 fco2_scr_qc0_man date year.y time
1 -0.306504951 NA NA NA 06-jul-13 2013 0:00
2 -0.206266524 NA -0.206266524 -0.206266524 06-jul-13 2013 0:30
3 -0.268508139 NA -0.268508139 -0.268508139 06-jul-13 2013 1:00
4 -0.203804516 0.426531598 -0.203804516 -0.203804516 06-jul-13 2013 1:30
5 -0.217438742 -0.358248118 -0.217438742 -0.217438742 06-jul-13 2013 2:00
6 -0.193778528 2.571063044 -0.193778528 -0.193778528 06-jul-13 2013 2:30
doy_ent doy.y doy_cum doy_cum_ent mes nrecord bat panel_temp vwc_0.1
1 187 187.0000 187.0000 187 7 24 12.57 22.93 0.06284828
2 187 187.0208 187.0208 187 7 25 12.56 22.85 0.06267169
3 187 187.0417 187.0417 187 7 26 12.55 22.58 0.06261738
4 187 187.0625 187.0625 187 7 27 12.54 22.3 0.06247716
5 187 187.0833 187.0833 187 7 28 12.53 22.01 0.06249525
6 187 187.1042 187.1042 187 7 29 12.52 21.82 0.06236862
vwc_0.5 vwc_1.5 temp_0.1 temp_0.5 temp_1.5 tempsd_0.1 tempsd_0.5 tempsd_1.5
1 0.07569027 0.1007845 30.9 28.96 25.14 0.372 0.961 0.767
2 0.07569027 0.1007743 30.8 28.85 24.99 0.181 1.361 1.087
3 0.07568554 0.1008558 30.53 28.8 25.03 0.98 1.476 0.351
4 0.07559577 0.1008507 30.52 29.09 25.11 0.186 0.229 0.556
5 0.07559577 0.1007743 30.11 29.09 24.87 1.331 0.191 0.954
6 0.07556271 0.1007285 30.15 29.33 25.04 1.447 1.078 0.2
pair pair_avg CO2_0.1 CO2_0.5 CO2_1.5 DCO2_0.1 DCO2_0.5
1 101.2124 101.2118 1161.592832 3275.1134 4888.231603 -24.67422109 34.88538221
2 101.1818 101.2131 1168.144925 3338.24016 4941.418642 6.55209301 63.12675931
3 101.1661 101.2090 1201.049131 3435.235974 5012.525851 32.90420541 96.9958144
4 101.1446 101.2007 1268.613941 3556.723878 5092.96558 67.56481067 121.4879035
5 101.1449 101.1906 1364.315214 3680.188043 5164.795759 95.7012722 123.464165
6 101.1445 101.1805 1472.975286 3808.988677 5236.40855 108.6600723 128.8006346
DCO2_1.5
1 31.30293041
2 53.18703947
3 71.10720845
4 80.43972916
5 71.83017884
6 71.61279156
## Daily avg - OPTION 1
data$timestamp <- as.POSIXct(data$timestamp, format = "%d/%m/%Y %H:%M",tz ="GMT")
> dates <- format(data$timestamp,"%Y/%m/%d",tz = "GMT")
> datadates <- cbind(data,dates)
> dailydata_avg <- aggregate(. ~ dates, datadates, FUN=mean, na.rm=TRUE, na.action = "na.pass")
head(dailydata_avg)
dates timestamp day month year.x hour minute doy.x rn_1_1_1 ppfd_1_1_1
1 2013/07/06 1373111100 6 7 2013 11.5 15 187.489 159.7788 3580.562
2 2013/07/07 1373197500 7 7 2013 11.5 15 188.489 154.0925 3506.688
3 2013/07/08 1373283900 8 7 2013 11.5 15 189.489 152.5259 3460.667
4 2013/07/09 1373370300 9 7 2013 11.5 15 190.489 131.1619 2965.250
5 2013/07/10 1373456700 10 7 2013 11.5 15 191.489 136.7853 3171.958
6 2013/07/11 1373543100 11 7 2013 11.5 15 192.489 145.2757 3282.167
ppfdr_1_1_1 p_rain_1_1_1 swc_1_1_1 swc_2_1_1 swc_3_1_1 air_pressure air_pressure.1
1 2552.396 1.0000 0.07095847 0.09606378 18341.81 25940.167 25940.167
2 2532.542 1.0000 0.06994341 0.09502167 18065.98 24891.000 24891.000
3 2523.562 1.0000 0.06860553 0.09379282 17777.02 23107.271 23107.271
4 2336.000 1.0000 0.06717054 0.09268716 17526.50 19309.500 19309.500
5 2607.229 1.0625 0.06620048 0.09166904 17275.56 8385.646 8385.646
6 2484.521 1.0000 0.06562964 0.09083684 17028.94 3535.438 3535.438
u_rot v_rot w_rot wind_speed u. h_scr_qc01_man fco2_scr_qc01_man
1 32167.83 2215.875 2041.354 32167.83 28531.44 18197.75 15365.65
2 30878.27 1911.312 1939.917 30878.27 26929.62 17605.52 14955.56
3 26052.96 2261.417 2116.458 26052.96 23305.83 19167.98 18399.33
4 17284.04 1987.438 2139.083 17284.04 17704.35 20349.92 18137.65
5 12028.06 2053.812 1960.417 12028.06 15670.00 21997.83 21120.19
6 15607.50 1997.417 1907.646 15607.50 15384.56 18000.94 18810.62
le_scr_qc01_man fco2_scr_qc0 fco2_scr_qc0_man date year.y time doy_ent doy.y
1 17409.67 13032.10 13027.90 137 2013 44.5 187 187.4896
2 15524.38 12077.17 12072.92 163 2013 44.5 188 188.4896
3 16407.71 14775.94 14770.56 189 2013 44.5 189 189.4896
4 16788.04 15024.79 15019.02 215 2013 44.5 190 190.4896
5 17955.58 17737.25 17730.75 241 2013 44.5 191 191.4896
6 14610.02 16605.48 16599.33 267 2013 44.5 192 192.4896
doy_cum doy_cum_ent mes nrecord bat panel_temp vwc_0.1 vwc_0.5 vwc_1.5
1 187.4896 187.5 7 28966.375 111.5208 1836.250 4638.833 4594.396 37.35417
2 188.4896 188.5 7 20801.417 111.7292 1900.812 4656.875 4392.979 26.68750
3 189.4896 189.5 7 4394.500 110.6042 1934.792 4675.604 4238.229 65.20833
4 190.4896 190.5 7 9467.708 104.0000 2090.896 4776.521 4178.729 54.12500
5 191.4896 191.5 7 14796.375 109.7500 2145.875 4907.292 4161.312 108.39583
6 192.4896 192.5 7 20127.958 109.3125 1934.375 4876.021 4123.458 143.10417
temp_0.1 temp_0.5 temp_1.5 tempsd_0.1 tempsd_0.5 tempsd_1.5 pair pair_avg CO2_0.1
1 2018.438 1565.812 797.8750 470.8125 474.3958 508.8333 101.1268 101.1323 10400.27
2 1998.438 1574.000 783.1875 478.3333 460.4583 566.0208 101.0764 101.0789 11292.75
3 1994.833 1568.104 780.2083 463.8125 453.1667 488.5625 100.9967 101.0036 13288.25
4 2042.625 1564.875 780.1667 465.0000 599.2708 437.6042 100.8520 100.8665 16156.60
5 2114.708 1576.729 780.5000 471.5833 406.5417 484.6875 100.4828 100.5169 18656.50
6 2124.604 1591.125 781.8125 516.7500 530.3333 510.7500 100.3025 100.2947 14586.60
CO2_0.5 CO2_1.5 DCO2_0.1 DCO2_0.5 DCO2_1.5
1 26360.38 34371.31 19795.81 20637.94 27123.92
2 26939.60 34558.17 18838.38 20464.56 20452.58
3 27603.06 34608.31 17413.15 19998.02 22754.85
4 28572.69 34678.38 19294.62 21894.92 18379.62
5 28983.29 34644.15 20251.17 20409.58 22077.40
6 28236.12 34736.67 17031.02 18852.04 19684.69`
## Daily avg - OPTION 2
data$timestamp <- as.POSIXct(data$timestamp, format = "%d/%m/%Y %H:%M",tz ="GMT")
datatime <- data$timestamp
dailydata_avg <- aggregate( data,
by = list('DATES'= format(datatime,'%Y%m%d' )),
FUN = mean, na.rm=T)
I obtain this console message:
1: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
2: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
3: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
head(dailydata_avg)
DATES timestamp day month year.x hour minute doy.x rn_1_1_1 ppfd_1_1_1
1 20130706 2013-07-06 13:45:00 6 7 2013 11.5 15 187.489 159.7788 NA
2 20130707 2013-07-07 13:45:00 7 7 2013 11.5 15 188.489 154.0925 NA
3 20130708 2013-07-08 13:45:00 8 7 2013 11.5 15 189.489 152.5259 NA
4 20130709 2013-07-09 13:45:00 9 7 2013 11.5 15 190.489 131.1619 NA
5 20130710 2013-07-10 13:45:00 10 7 2013 11.5 15 191.489 136.7853 NA
6 20130711 2013-07-11 13:45:00 11 7 2013 11.5 15 192.489 145.2757 NA
ppfdr_1_1_1 p_rain_1_1_1 swc_1_1_1 swc_2_1_1 swc_3_1_1 air_pressure air_pressure.1
1 NA NA 0.07095847 0.09606378 NA NA NA
2 NA NA 0.06994341 0.09502167 NA NA NA
3 NA NA 0.06860553 0.09379282 NA NA NA
4 NA NA 0.06717054 0.09268716 NA NA NA
5 NA NA 0.06620048 0.09166904 NA NA NA
6 NA NA 0.06562964 0.09083684 NA NA NA
u_rot v_rot w_rot wind_speed u. h_scr_qc01_man fco2_scr_qc01_man le_scr_qc01_man
1 NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA
fco2_scr_qc0 fco2_scr_qc0_man date year.y time doy_ent doy.y doy_cum doy_cum_ent
1 NA NA NA 2013 NA 187 187.4896 187.4896 187.5
2 NA NA NA 2013 NA 188 188.4896 188.4896 188.5
3 NA NA NA 2013 NA 189 189.4896 189.4896 189.5
4 NA NA NA 2013 NA 190 190.4896 190.4896 190.5
5 NA NA NA 2013 NA 191 191.4896 191.4896 191.5
6 NA NA NA 2013 NA 192 192.4896 192.4896 192.5
mes nrecord bat panel_temp vwc_0.1 vwc_0.5 vwc_1.5 temp_0.1 temp_0.5 temp_1.5
1 7 NA NA NA NA NA NA NA NA NA
2 7 NA NA NA NA NA NA NA NA NA
3 7 NA NA NA NA NA NA NA NA NA
4 7 NA NA NA NA NA NA NA NA NA
5 7 NA NA NA NA NA NA NA NA NA
6 7 NA NA NA NA NA NA NA NA NA
tempsd_0.1 tempsd_0.5 tempsd_1.5 pair pair_avg CO2_0.1 CO2_0.5 CO2_1.5 DCO2_0.1
1 NA NA NA 101.1268 101.1323 NA NA NA NA
2 NA NA NA 101.0764 101.0789 NA NA NA NA
3 NA NA NA 100.9967 101.0036 NA NA NA NA
4 NA NA NA 100.8520 100.8665 NA NA NA NA
5 NA NA NA 100.4828 100.5169 NA NA NA NA
6 NA NA NA 100.3025 100.2947 NA NA NA NA
DCO2_0.5 DCO2_1.5
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
答案 1 :(得分：0)
索引从0开始。第一次出错时从中减去1。你需要检查我是否＆gt; 0这样你就不会超出界限。
例如：
.style('visibility', function (d, i) {
if (i > 0) {
if (d.datetime === data[i - 1].datetime) {
return 'hidden';
}
}
return 'visible';
});
以下是两个样本小提琴：