library(tidyverse)
## -- Attaching packages --------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.1 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts ------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(forecast)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
library(pastecs)
##
## Attaching package: 'pastecs'
## The following objects are masked from 'package:dplyr':
##
## first, last
## The following object is masked from 'package:tidyr':
##
## extract
contract_payments_all <- read.csv('Lambda_cleanpayments.csv')
View(contract_payments_all)
#rename columns
colnames(contract_payments_all) [1] <- "month"
#create separate dataframes for actual and expected
contract_payments_actual <- filter(contract_payments_all, e.or.a == "actual")
contract_payments_expected <- filter(contract_payments_all, e.or.a == "expected")
#create separate dataframes for new and cumulative
contract_payments_new <- filter(contract_payments_all, n.or.c == "new")
contract_payments_cumulative <- filter(contract_payments_all, n.or.c == "cumulative")
#descriptive stats
stat.desc(contract_payments_new)
## month year n.or.c e.or.a paying.contracts
## nbr.val NA 2.400000e+01 NA NA 17.0000000
## nbr.null NA 0.000000e+00 NA NA 0.0000000
## nbr.na NA 0.000000e+00 NA NA 7.0000000
## min NA 2.020000e+03 NA NA 3.0000000
## max NA 2.021000e+03 NA NA 41.0000000
## range NA 1.000000e+00 NA NA 38.0000000
## sum NA 4.848200e+04 NA NA 337.0000000
## median NA 2.020000e+03 NA NA 20.0000000
## mean NA 2.020083e+03 NA NA 19.8235294
## SE.mean NA 5.763034e-02 NA NA 2.4573349
## CI.mean NA 1.192174e-01 NA NA 5.2093173
## var NA 7.971014e-02 NA NA 102.6544118
## std.dev NA 2.823299e-01 NA NA 10.1318513
## coef.var NA 1.397615e-04 NA NA 0.5111023
stat.desc(contract_payments_cumulative)
## month year n.or.c e.or.a paying.contracts
## nbr.val NA 2.400000e+01 NA NA 17.0000000
## nbr.null NA 0.000000e+00 NA NA 0.0000000
## nbr.na NA 0.000000e+00 NA NA 7.0000000
## min NA 2.020000e+03 NA NA 3.0000000
## max NA 2.021000e+03 NA NA 266.0000000
## range NA 1.000000e+00 NA NA 263.0000000
## sum NA 4.848200e+04 NA NA 1641.0000000
## median NA 2.020000e+03 NA NA 71.0000000
## mean NA 2.020083e+03 NA NA 96.5294118
## SE.mean NA 5.763034e-02 NA NA 20.9230238
## CI.mean NA 1.192174e-01 NA NA 44.3548290
## var NA 7.971014e-02 NA NA 7442.1397059
## std.dev NA 2.823299e-01 NA NA 86.2678370
## coef.var NA 1.397615e-04 NA NA 0.8936948
#graph new
ggplot(contract_payments_new, aes(x=month, y=paying.contracts))+
geom_bar(stat = 'identity', aes(fill = month, colour = month))+
scale_fill_brewer(palette = "Paired")+
scale_x_discrete(name = "Month", limits=c("Feb", "Mar", "Apr", "May", "June", "July", "Aug", "Sept", "Oct", "Nov", "Dec", "Jan"))+
facet_grid(rows = vars(e.or.a))+
ggtitle("New Lambda Contracts")+
ylab("# of Paying Contracts")+
theme_bw()+
theme(axis.text = element_text(size = 12), axis.title = element_text(size = 14), plot.title = element_text(size = 18), legend.title = element_text(size = 14), plot.title.position = "panel")
## Warning: Removed 7 rows containing missing values (position_stack).
#graph cumulative
ggplot(contract_payments_cumulative, aes(x=month, y=paying.contracts))+
geom_bar(stat = 'identity', aes(fill = month, colour = month))+
scale_fill_brewer(palette = "Set3")+
scale_x_discrete(name = "Month", limits=c("Feb", "Mar", "Apr", "May", "June", "July", "Aug", "Sept", "Oct", "Nov", "Dec", "Jan"))+
facet_grid(rows = vars(e.or.a))+
ggtitle("Cumulative Lambda Contracts")+
ylab("# of Paying Contracts")+
theme_bw()+
theme(axis.text = element_text(size = 12), axis.title = element_text(size = 14), plot.title = element_text(size = 18), legend.title = element_text(size = 14), plot.title.position = "panel")
## Warning: Removed 7 rows containing missing values (position_stack).
