我想搜索一个表的所有活动记录，并在第二个表中查找它们各自的数据，并且如果没有找到则返回0的计数
这是我的例子。
SELECT employee.id, count(jobs.id)
FROM employee LEFT OUTER JOIN jobs ON employee.id = jobs.employee_id
where employee.state ='active' and jobs.startdatetime >= date(now())
group by employee.id
如果我运行w / o startdatetime范围，它将返回正确的行数 如果我添加startdatetime范围，那么所有没有工作的员工都会被删除
答案 0 :(得分：1)
正如已经评论过的那样，将条件## example
library("ggplot2")
library(grid)
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
example1 <- read.table(text="x height w wm color colorcode
A 74.87091 0 0.8477582 PT E6F598FF
B 75.7462 0.8477582 2.7575227 ES 51AAAEFF
C 154.96351 2.7575227 5.0641208 IT 9DD7A4FF
D 218.2934 5.0641208 5.582455 EL 3C93B8FF",header=TRUE)
example1$colorcode <- paste("#",example1$colorcode,sep="")
#example2 <- example1[-1,]
example3 <- read.table(text=" x height w wm color colorcode
E 23.31359 0 2.619406 BG B41947FF
F 28.60724 2.619406 3.282477 HU FEE08BFF
G 33.30486 3.282477 3.292582 CY DB474CFF
H 34.40072 3.292582 24.22946 PL FDCC7AFF
I 42.86401 24.22946 26.141007 RO 9E0142FF
C 45.83487 26.141007 47.13058 IT 9DD7A4FF
J 46.48877 47.13058 48.152381 SI FDB869FF
K 47.74536 48.152381 51.518293 CZ FEEC9FFF
L 50.12508 51.518293 278.176833 FR 66C2A5FF
M 72.17257 278.176833 284.513883 DK 4C65ACFF
N 73.16484 284.513883 285.837194 SK FBA15BFF
O 75.94599 285.837194 314.661756 AT CEEB9CFF
P 77.39849 314.661756 339.818609 BL 5E4FA2FF
D 77.87686 339.818609 340.366524 EL 3C93B8FF
Q 78.60072 340.366524 623.890373 DE 3A7CB7FF
R 80.39367 623.890373 624.871331 EE F7874FFF
S 83.13369 624.871331 626.13241 LV E75A47FF
T 87.46645 626.13241 628.725496 LT F46D43FF
U 89.35913 628.725496 629.62701 FI FAFDB7FF
V 94.46328 629.62701 766.334365 NL B6E1A1FF
W 106.02335 766.334365 871.746899 IR 81CCA4FF
X 107.62544 871.746899 881.874454 SE F0F9A7FF
B 112.60728 881.874454 892.735907 ES 51AAAEFF
Y 127.36419 892.735907 991.546621 UK FEF8B4FF
Z 342.85699 991.546621 997.847212 PT E6F598FF",header=TRUE)
example3$colorcode <- paste("#",example3$colorcode,sep="")
#example4 <- example3[-1,]
example1[,c("wm","w","height")] <- example1[,c("wm","w","height")] + 10
#example2[,c("wm","w","height")] <- example2[,c("wm","w","height")] + 10
example3[,c("wm","w","height")] <- example3[,c("wm","w","height")] + 10
#example4[,c("wm","w","height")] <- example4[,c("wm","w","height")] + 10
example.data <- list(example3,example1)
listplota <- list()
listplotb <- list()
AxisX_max <- 1100
AxisY_max <- 360
AxisX_min <- 0
AxisY_min <- 0
for(i in 1:2){
df.1 <- example.data[[i]]
p5 <- ggplot(df.1, aes(xmin = wm, xmax = w, ymin = 0, ymax = height, fill = x)) +
geom_rect(color="darkgrey")+ geom_text(aes(x = (wm+w)/2, y = 150 , label = x, size=16, angle=90)) +
theme_bw() + labs(x = "cummulated", y = "costs") +
theme(axis.ticks = element_blank(), legend.position = "none") +
#ylim(-150, 400) + xlim(-150, 1000) +
ylim(max(-150,AxisY_min), min(400,AxisY_max)) + xlim(AxisX_min, AxisX_max) +
scale_fill_manual(values=df.1$colorcode) +
ggtitle(paste("type ",i,sep="- ") ) + theme(plot.title = element_text(lineheight=.8, face="bold"))
listplota[[i]] <- p5
p55 <- ggplot(df.1, aes(xmin = wm, xmax = w, ymin = 0, ymax = height, fill = x)) +
geom_rect(color="darkgrey")+ geom_text(aes(x = (wm+w)/2, y = 150 , label = x, size=16, angle=90)) +
theme_bw() + labs(x = "cummulated", y = "costs") +
theme(axis.ticks = element_blank(), legend.position = "none") +
#ylim(-150, 400) + xlim(-150, 1000) +
ylim(max(-150,AxisY_min), min(400,AxisY_max)) + xlim(AxisX_min, AxisX_max) +
scale_fill_manual(values=df.1$colorcode) +
ggtitle(paste("type ",i,sep="- ") ) + theme(plot.title = element_text(lineheight=.8, face="bold"))
listplotb[[i]] <- p55
}
multiplot(plotlist=c(listplota,listplotb),cols=2)
移动到您的jobs.startdatetime >= date(now())子句作为连接条件而非
JOIN答案 1 :(得分：0)
只需使用
and (jobs.startdatetime >= date(now()) or jobs.startdatetime is null)
它会起作用