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month.R
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month.R
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#用的是原始数据这一页
a <- read.delim("clipborad",stringsAsFactors = T)
plot(a$当日剩余案件量, type = 'l')
plot(a$联络客户数, type = 'l')
plot(a$当日人均案件量, type = 'l')
plot(a$尝试呼叫次数, type = 'l')
plot(a$接通次数, type = 'l')
plot(a$可联客户数, type = 'l')
plot(a$有效联络客户数, type = 'l')
plot(a$队列人数, type = 'l')
connect_rate <- as.numeric(a$有效联络客户数)/as.numeric(a$联络客户数)
plot(connect_rate, type = 'l')
plot(density(connect_rate))
t.test(connect_rate)
get_rate <- as.numeric(a$接通次数)/as.numeric(a$尝试呼叫次数)
plot(get_rate, type ='l')
a$connect_rate <- connect_rate
a$get_rate <- get_rate
lm(a$connect_rate~.-get_rate,data = a[, -1])
after <- a[227:length(a$date),]
before <- a[109:226,]
library(corrplot)
library(smbinning)
corrplot(cor(a[,-1]))
smbinning.eda(a[,-1])
par(mfrow = c(2,1))
hist(before$联络客户数, col = 'lightblue', lwd = 1, breaks = c(100))
hist(after$联络客户数, col = 2, lwd = 1, breaks = c(100))
plot(density(before$联络客户数));plot(density(before$联络客户数))
par(mfrow = c(1,1))
t.test(before$联络客户数, after$联络客户数) #<<<联络客户并无不同
par(mfrow = c(2,1))
hist(before$connect_rate, col = 'lightblue', lwd = 1, breaks = c(100))
hist(after$connect_rate, col = 2, lwd = 1, breaks = c(100))
plot(density(before$connect_rate));plot(density(before$connect_rate))
par(mfrow = c(1,1))
t.test(before$connect_rate, after$connect_rate) #<<<connect_rate并无不同
#####<<<<<<<<<<<<<前后下降是一致的>>>>>>>>>>>>>>>#####
md_before <- lm(before$connect_rate~.-get_rate, data = before[, -1])
md_after <- lm(after$connect_rate~.-get_rate, data = after[, -1])
md_before$coefficients - md_after$coefficients
barplot((md_before$coefficients - md_after$coefficients)[2:8])
#<<<可联客户减少了
lm(before$联络客户数~before$可联客户数, data = before)
lm(after$联络客户数~after$可联客户数, data = after)
boxplot(before$可联客户数/before$联络客户数)
boxplot(after$可联客户数/after$联络客户数)
#<<<之后连线的客户中可联的少了
plot(a$可联客户数, type = 'l')
flask <- diff(a$可联客户数)/a$可联客户数[1:(length(a$可联客户数)-1)]
summary(flask)
plot(flask, ylim = c(0,1), type = 'l')
#===关注联络客户数和可联库户数===*
####<<<<可联客户的敏感性更高>>>>####
plot(a$联络客户数, type = 'l')
plot(a$可联客户数, type = 'l')
t.test(a$联络客户数, a$可联客户数)
plot(a$联络客户数/a$可联客户数)
#<<<1个可联客户需要的联络次数出现上升，6-8
library(ggplot2)
ggplot(data = a, aes(x = a$联络客户数, y = a$可联客户数)) +geom_point() + stat_smooth(method = 'lm')
ggplot(data = a, aes(x = a$联络客户数, y = a$可联客户数)) +geom_point() + stat_smooth(method = 'loess')
ggplot(data = before, aes(x = before$联络客户数, y = before$可联客户数)) +geom_point()
ggplot(data = after, aes(x = after$联络客户数, y = after$可联客户数)) +geom_point()
ggplot(data = a, aes(x = a$联络客户数, y = a$可联客户数)) +geom_point(size = a$get_rate*10, alpha = 0.7)
#弹性
p <- a$connect_rate
q <- a$联络客户数
(q[1] - q[311])/(p[1] - p[311])*(p[311]/q[311]) #2.158086
(q[311] - q[1])/q[1] #0.6672489
diff(q)/q[2:length(q)]
p <- a$connect_rate
q <- a$可联客户数
diff(q)/q[2:length(q)]
(q[1] - q[311])/(p[1] - p[311])*(p[311]/q[311]) #5.163335
#<<<对于connect_rate可联客户的弹性为5.16，联络客户为2.15
(q[311] - q[1])/q[1] #0.8275168
q <- after$联络客户数
(q[85] - q[1])/q[1]
q <- after$可联客户数
(q[85] - q[1])/q[1]
q <- before$联络客户数
(q[83] - q[1])/q[1]
q <- before$可联客户数
(q[83] - q[1])/q[1]
q <- a$联络客户数
pert_connect <- diff(q)/q[2:length(q)]
q <- a$可联客户数
pert_avail <- diff(q)/q[2:length(q)]
what <- data.frame(pert_connect = pert_connect, pert_avail = pert_avail)
matplot(what, ylim = c(0,1))
a$date <- as.Date(a$date)
a4 <- a[a$date < "2016-05-1",]
a5 <- a[a$date <"2016-06-1" & a$date > "2016-05-01",]
a6 <- a[a$date <"2016-07-1" & a$date > "2016-06-01",]
a7 <- a[a$date <"2016-08-1" & a$date > "2016-07-01",]
a8 <- a[a$date <"2016-09-1" & a$date > "2016-08-01",]
a9 <- a[a$date <"2016-10-1" & a$date > "2016-09-01",]
a10 <- a[a$date <"2016-11-1" & a$date > "2016-10-01",]
a11 <- a[a$date <"2016-12-1" & a$date > "2016-11-01",]
a12 <- a[a$date <"2017-01-1" & a$date > "2016-12-01",]
a4_17 <- a[a$date <"2017-05-1" & a$date > "2017-04-01",]
a5_17 <- a[a$date <"2017-06-1" & a$date > "2017-05-01",]
a6_17 <- a[a$date <"2017-07-1" & a$date > "2017-06-01",]
quick <- function(x){
i <- nrow(x)
q <- x$联络客户数
print((q[i] - q[1])/q[1])
q <- x$可联客户数
print((q[i] - q[1])/q[1])
print(rep("i",10))
}
quick(a4)
quick(a5)
quick(a6)
quick(a7)
quick(a8)
quick(a9)
quick(a10)
quick(a11)
quick(a12)
quick(a4_17)
quick(a5_17)
quick(a6_17)