library(dplyr)
##
## Присоединяю пакет: 'dplyr'
## Следующие объекты скрыты от 'package:stats':
##
## filter, lag
## Следующие объекты скрыты от 'package:base':
##
## intersect, setdiff, setequal, union
library(corrplot)
## corrplot 0.92 loaded
library(ggplot2)
library(tidyr)
library(viridis)
## Загрузка требуемого пакета: viridisLite
library(readxl)
library(circlize)
## ========================================
## circlize version 0.4.15
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================
library(waterfalls)
dataforR <- read_excel("dataforR.xlsx")
JD <- dataforR %>% filter(Company == "JD") %>% filter(Indicators %in% c("Asset Turnover", "Equity multiplier",
"Tax Burden", "Interest Burden", "Return on Sales"))
JD1 <- as.data.frame(t(JD[,-c(1,8)]))[-1,]
colnames(JD1) <- c("Asset Turnover", "Equity multiplier",
"Tax Burden", "Interest Burden", "Return on Sales")
JD1
## Asset Turnover Equity multiplier Tax Burden Interest Burden
## 2014 2.4900000 1.9800000 0.8610824 1.0000000
## 2015 2.39071865 2.22452183 1.50111928 0.96826134
## 2016 2.118774945 3.415115859 5.427662957 0.293240093
## 2017 2.103957565 3.679443216 -0.009958071 -2.285029940
## 2018 2.349931336 3.043098044 1.659360190 0.644520809
## Return on Sales
## 2014 -0.0504513
## 2015 -0.03562859
## 2016 -0.008246131
## 2017 -0.002304516
## 2018 -0.005668586
JD1$`Asset Turnover` <- as.numeric(JD1$`Asset Turnover`)
JD1$`Equity multiplier` <- as.numeric(JD1$`Equity multiplier`)
JD1$`Tax Burden` <- as.numeric(JD1$`Tax Burden`)
JD1$`Interest Burden` <- as.numeric(JD1$`Interest Burden`)
JD1$`Return on Sales` <- as.numeric(JD1$`Return on Sales`)
JD1$ROE <- JD1$`Asset Turnover`*JD1$`Equity multiplier`*JD1$`Tax Burden`*JD1$`Interest Burden`*JD1$`Return on Sales`
corrplot(as.matrix(cor(JD1)))
JD2 <- as.data.frame(cor(JD1))
JD2$Indicator1 <- rownames(JD2)
JD3 <- JD2 %>% pivot_longer(cols = "Asset Turnover":"ROE", names_to = "Indicator2", values_to = "corr")
ggplot(JD3, aes(Indicator1,Indicator2, fill = corr)) +
geom_tile(aes(fill = corr)) +
scale_fill_viridis() +
geom_text(aes(label = paste(round(corr,2)))) +
theme(axis.text.x= element_text(angle=90)) + xlab("") + ylab("") + theme_minimal()
corrplot(as.matrix(cor(JD1)), type = "upper", diag = FALSE, addCoef.col = TRUE, tl.col = "black",
method="color")
#для JD отследим динамику компонентов Asset Turnover, Equity multiplier, Interest Burden и Return on sales
dataforR
## # A tibble: 36 × 8
## Company Indicators `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 JD Net Profit Margin -4.34e-2 -5.18e-2 -1.31e-2 -5.24e-5 -6.06e-3 NA
## 2 JD Net Income -5.00e+3 -9.39e+3 -3.41e+3 -1.9 e+1 -2.80e+3 NA
## 3 JD Sales 1.15e+5 1.81e+5 2.60e+5 3.62e+5 4.62e+5 NA
## 4 JD Asset Turnover 2.49e+0 2.39e+0 2.12e+0 2.10e+0 2.35e+0 NA
## 5 JD Sales 1.15e+5 1.81e+5 2.60e+5 3.62e+5 4.62e+5 NA
## 6 JD Average Total As… 4.62e+4 7.58e+4 1.23e+5 1.72e+5 1.97e+5 NA
## 7 JD Equity multiplier 1.98e+0 2.22e+0 3.42e+0 3.68e+0 3.04e+0 NA
## 8 JD Average Total As… 4.62e+4 7.58e+4 1.23e+5 1.72e+5 1.97e+5 NA
## 9 JD Average Sharehol… 2.33e+4 3.41e+4 3.59e+4 4.68e+4 6.46e+4 NA
## 10 JD Tax Burden 8.61e-1 1.50e+0 5.43e+0 -9.96e-3 1.66e+0 NA
## # … with 26 more rows
JD4 <- t(unique((dataforR %>% filter(Company == "JD"))[c(5,6,8,9,14,15,17,18),-c(1,8)]))
View(JD4)
colnames(JD4) <- c("Sales", "Average Total Assets", "Average Shareholder's Equity", "Earnings before tax", "Operating profit")
JD5 <- as.data.frame(JD4[-1,])
JD5$Sales <- as.numeric(JD5$Sales)
JD5$`Average Total Assets` <- as.numeric(JD5$`Average Total Assets`)
JD5$`Average Shareholder's Equity` <- as.numeric(JD5$`Average Shareholder's Equity`)
JD5$`Earnings before tax` <- as.numeric(JD5$`Earnings before tax`)
JD5$`Operating profit` <- as.numeric(JD5$`Operating profit`)
JD5$ROE <- JD1$ROE
corrplot(as.matrix(cor(JD5)), type = "upper", diag = FALSE, addCoef.col = TRUE, tl.col = "black",
method="color")
JD5$years <- rownames(JD5)
JD6 <- JD5 %>% pivot_longer(cols = "Sales":"Operating profit", values_to = "Values", names_to = "Indicators")
ggplot(data = JD6, aes(x = years, y = Values, fill = Indicators)) + geom_col(position = "dodge") + facet_wrap(~Indicators, scales = "free") + theme_classic() + geom_label(aes(label = round(Values, 0)), size = 3) + theme(legend.position = "none") + scale_fill_manual(values = c("#c4d0d9", "#7a6e6c", "#c3beba","#f0e9dd", "#ecede3")) + ylab("") + xlab("") + scale_y_continuous(labels=scales::comma)
ggplot(data = JD6, aes(x = years, y = ROE, group = 1)) + geom_col(fill = "darkblue") + theme_classic() + geom_label(aes(label = round(ROE,3))) + scale_y_continuous(labels = scales::percent)
#Анализ Alibaba
Alibaba <- dataforR %>% filter(Company == "Alibaba") %>% filter(Indicators %in% c("Asset Turnover", "Equity multiplier",
"Tax Burden", "Interest Burden", "Return on Sales"))
Alibaba1 <- as.data.frame(t(Alibaba[,-c(1,3)]))[-1,]
colnames(Alibaba1) <- c("Asset Turnover", "Equity multiplier",
"Tax Burden", "Interest Burden", "Return on Sales")
Alibaba1
## Asset Turnover Equity multiplier Tax Burden Interest Burden
## 2015 0.4200000 1.8500000 1.7777778 0.5913119
## 2016 0.3263288 2.4790692 -3.0791724 -0.7955467
## 2017 0.3633190 1.5178516 1.0438019 0.8218916
## 2018 0.4089528 1.6029336 1.5820088 0.5600456
## 2019 0.4480371 1.5947427 6.1823085 0.2273492
## Return on Sales
## 2015 0.3035930
## 2016 0.2877312
## 2017 0.3036210
## 2018 0.2769613
## 2019 0.1514791
Alibaba1$`Asset Turnover` <- as.numeric(Alibaba1$`Asset Turnover`)
Alibaba1$`Equity multiplier` <- as.numeric(Alibaba1$`Equity multiplier`)
Alibaba1$`Tax Burden` <- as.numeric(Alibaba1$`Tax Burden`)
Alibaba1$`Interest Burden` <- as.numeric(Alibaba1$`Interest Burden`)
Alibaba1$`Return on Sales` <- as.numeric(Alibaba1$`Return on Sales`)
Alibaba1$ROE <- Alibaba1$`Asset Turnover`*Alibaba1$`Equity multiplier`*Alibaba1$`Tax Burden`*Alibaba1$`Interest Burden`*Alibaba1$`Return on Sales`
corrplot(as.matrix(cor(JD1)))
Alibaba2 <- as.data.frame(cor(Alibaba1))
Alibaba2$Indicator1 <- rownames(Alibaba2)
Alibaba3 <- Alibaba2 %>% pivot_longer(cols = "Asset Turnover":"ROE", names_to = "Indicator2", values_to = "corr")
ggplot(Alibaba3, aes(Indicator1,Indicator2, fill = corr)) +
geom_tile(aes(fill = corr)) +
scale_fill_viridis() +
geom_text(aes(label = paste(round(corr,2)))) +
theme(axis.text.x= element_text(angle=90)) + xlab("") + ylab("") + theme_minimal()
corrplot(as.matrix(cor(Alibaba1)), type = "upper", diag = FALSE, addCoef.col = TRUE, tl.col = "black",
method="color")
#для Alibaba отследим динамику компонентов Asset Turnover, Equity multiplier, Interest Burden и Tax Burden
dataforR
## # A tibble: 36 × 8
## Company Indicators `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 JD Net Profit Margin -4.34e-2 -5.18e-2 -1.31e-2 -5.24e-5 -6.06e-3 NA
## 2 JD Net Income -5.00e+3 -9.39e+3 -3.41e+3 -1.9 e+1 -2.80e+3 NA
## 3 JD Sales 1.15e+5 1.81e+5 2.60e+5 3.62e+5 4.62e+5 NA
## 4 JD Asset Turnover 2.49e+0 2.39e+0 2.12e+0 2.10e+0 2.35e+0 NA
## 5 JD Sales 1.15e+5 1.81e+5 2.60e+5 3.62e+5 4.62e+5 NA
## 6 JD Average Total As… 4.62e+4 7.58e+4 1.23e+5 1.72e+5 1.97e+5 NA
## 7 JD Equity multiplier 1.98e+0 2.22e+0 3.42e+0 3.68e+0 3.04e+0 NA
## 8 JD Average Total As… 4.62e+4 7.58e+4 1.23e+5 1.72e+5 1.97e+5 NA
## 9 JD Average Sharehol… 2.33e+4 3.41e+4 3.59e+4 4.68e+4 6.46e+4 NA
## 10 JD Tax Burden 8.61e-1 1.50e+0 5.43e+0 -9.96e-3 1.66e+0 NA
## # … with 26 more rows
Alibaba4 <- t(unique((dataforR %>% filter(Company == "Alibaba"))[c(5,6,8,9,14,15,11,12),-c(1,3)]))
colnames(Alibaba4) <- c("Sales", "Average Total Assets", "Average Shareholder's Equity", "Earnings before tax", "Operating profit", "Net Income")
Alibaba5 <- as.data.frame(Alibaba4[-1,])
Alibaba5$Sales <- as.numeric(Alibaba5$Sales)
Alibaba5$`Average Total Assets` <- as.numeric(Alibaba5$`Average Total Assets`)
Alibaba5$`Average Shareholder's Equity` <- as.numeric(Alibaba5$`Average Shareholder's Equity`)
Alibaba5$`Earnings before tax` <- as.numeric(Alibaba5$`Earnings before tax`)
Alibaba5$`Operating profit` <- as.numeric(Alibaba5$`Operating profit`)
Alibaba5$`Net Income` <- as.numeric(Alibaba5$`Net Income`)
Alibaba5$ROE <- Alibaba1$ROE
corrplot(as.matrix(cor(Alibaba5)), type = "upper", diag = FALSE, addCoef.col = TRUE, tl.col = "black",
method="color")
Alibaba5$years <- rownames(Alibaba5)
Alibaba6 <- Alibaba5 %>% pivot_longer(cols = "Sales":"Net Income", values_to = "Values", names_to = "Indicators")
ggplot(data = Alibaba6, aes(x = years, y = Values, fill = Indicators)) + geom_col(position = "dodge") + facet_wrap(~Indicators, scales = "free") + theme_classic() + geom_label(aes(label = round(Values, 0)), size = 3) + theme(legend.position = "none") + scale_fill_manual(values = c("#c4d0d9", "#7a6e6c", "#c3beba","#f0e9dd", "#ecede3", "#9d9c9a")) + ylab("") + xlab("") + scale_y_continuous(labels=scales::comma)
ggplot(data = Alibaba6, aes(x = years, y = ROE, group = 1)) + geom_col(fill = "darkblue") + theme_classic() + geom_label(aes(label = round(ROE,3))) + scale_y_continuous(labels = scales::percent)
dataforR1 <- read_excel("dataforR1.xlsx")
Alibaba7 <- (dataforR1 %>% filter(Company == "Alibaba") %>% select(-Company, -"2014", -"Structure2"))[-c(2:8),] %>% pivot_longer(cols = "2015":"2019", names_to = "Year", values_to = "Revenue")
chordDiagram(Alibaba7)
Alibaba8 <- (dataforR1 %>% filter(Company == "Alibaba") %>% filter(Structure1 == "Core commerce") %>% select(-Company, -"2014", -"Structure1"))[-c(1),] %>% pivot_longer(cols = "2015":"2019", names_to = "Year", values_to = "Revenue")
Alibaba9 <- (dataforR1 %>% filter(Company == "Alibaba") %>% select(-Company, -"2014"))[-c(1),] %>% pivot_longer(cols = "2015":"2019", names_to = "Year", values_to = "Revenue") %>% filter(Year == 2019)
ggplot(data = Alibaba8, aes(x = Year, y = Revenue, fill = Structure2)) + geom_col() + theme_minimal() + scale_y_continuous(labels = scales::comma) + xlab("") + ylab("") + theme(legend.position = "bottom") + scale_fill_viridis_d()
ggplot(data = Alibaba8, aes(x = Year, y = Revenue, fill = Structure2)) + geom_col(position = "fill") + theme_minimal() + scale_y_continuous(labels = scales::comma) + xlab("") + ylab("") + theme(legend.position = "bottom") + scale_fill_viridis_d()
#график водопада для 2019
waterfall(values = Alibaba9$Revenue, labels = Alibaba9$Structure2) + theme_minimal() + ylab("") + xlab("")
Alibaba9$group_id <- NULL
Alibaba9$group_id <- ifelse(Alibaba9$Structure1 == "Core commerce", "1",
ifelse(Alibaba9$Structure1 == "Cloud computing", "2",
ifelse(Alibaba9$Structure1 == "Digital media and entertainment", "3", "4")))
Alibaba10 <- as.data.frame(Alibaba9)
Alibaba10$startBar <- rep(0, 10)
Alibaba10$endBar <- rep(0, 10)
i <- NULL
for (i in 1:9) {
Alibaba10$endBar[i] <- Alibaba10$Revenue[i] + Alibaba10$startBar[i]
Alibaba10$startBar[i+1] <- Alibaba10$endBar[i]
Alibaba10$endBar[10] <- Alibaba10$startBar[10]+Alibaba10$Revenue[10]
}
Alibaba10
## Structure1 Structure2 Year
## 1 Core commerce China commerce retail 2019
## 2 Core commerce China commerce wholesale 2019
## 3 Core commerce International commerce retail 2019
## 4 Core commerce International commerce wholesale 2019
## 5 Core commerce Cainiao logistics services 2019
## 6 Core commerce Local consumer services 2019
## 7 Core commerce Other 2019
## 8 Cloud computing Cloud computing 2019
## 9 Digital media and entertainment Digital media and entertainment 2019
## 10 Innovation initiatives and others Innovation initiatives and others 2019
## Revenue group_id startBar endBar
## 1 247615 1 0 247615
## 2 9988 1 247615 257603
## 3 19558 1 257603 277161
## 4 8167 1 277161 285328
## 5 14885 1 285328 300213
## 6 18058 1 300213 318271
## 7 5129 1 318271 323400
## 8 24702 2 323400 348102
## 9 24077 3 348102 372179
## 10 4665 4 372179 376844
Alibaba10$endBar <- Alibaba10$startBar +Alibaba10$Revenue
Alibaba10$totalByX <- c(rep(323400, 7), 348102, 372179, 376844)
Alibaba10$group_id <- as.numeric(Alibaba10$group_id )
ggplot(Alibaba10, aes(fill = Structure2)) +
# Waterfall Chart
geom_rect(aes(x = Structure1,
xmin = group_id - 0.25, # control bar gap width
xmax = group_id + 0.25,
ymin = endBar,
ymax = startBar)) + scale_x_discrete(labels=c("Core commerce" = "Cloud computing", "Cloud computing" = "Core commerce")) + scale_fill_viridis_d() + scale_y_continuous(labels = scales::comma) + xlab("") + theme_classic() + theme(legend.position = "bottom")
## Warning in geom_rect(aes(x = Structure1, xmin = group_id - 0.25, xmax = group_id
## + : Ignoring unknown aesthetics: x
#JD
JD8 <- (dataforR1 %>% filter(Company == "JD") %>% select(-Company, -"2019", -"Structure2")) %>% pivot_longer(cols = "2014":"2018", names_to = "Year", values_to = "Revenue")
chordDiagram(JD8)
ggplot(data = JD8, aes(x = Year, y = Revenue, fill = Structure1)) + geom_col() + theme_minimal() + scale_y_continuous(labels = scales::comma) + xlab("") + ylab("") + theme(legend.position = "bottom") + scale_fill_viridis_d(option="G")
ggplot(data = JD8, aes(x = Year, y = Revenue, fill = Structure1)) + geom_col(position = "fill") + theme_minimal() + scale_y_continuous(labels = scales::comma) + xlab("") + ylab("") + theme(legend.position = "bottom") + scale_fill_viridis_d(option="G")
