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utilities.R
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# author: Ryan Yost
# date: 2020 - 11 - 22
## ----Helper functions----
####### returns country of study info
# TODO checking logic
getCountryInfo <- function(iso3, longOrWide){
comment <- " This chunck will pull data from a local excel book and display the country's attributes "
# TODO put in logic in case the countryCode is put in wrong
## - - - - - - - - - - - - - - - SET UP
# country code for country to get data for
countryCode <- iso3
# use this data to reference iso3 codes
countryClass_list <- read.xlsx(file = COUNTRY_CLASS_PATH, sheetIndex = 1, stringsAsFactors = FALSE )
countries_for_chart <- countryClass_list %>%
filter(WEO_ISO_3 %in% countryCode)
# WEO Code being a number gets it deleted by melt soon so changing it to a char
countries_for_chart$WEO_Code <- as.character(countries_for_chart$WEO_Code)
# removing columns with NA
CoSdata <- countries_for_chart %>%
select_if(~ !any(is.na(.)))
######## - - - - - - - - - - - - - - - - -FINAL OUPUT LOGIC
if(longOrWide == "long"){
# it would be better for viewing to have the data long
CoSmelt <- CoSdata %>%
melt(id.vars="Value") %>%
subset(select = c("variable","value"))
colnames(CoSmelt) <- c("Attribute", "Value")
return(CoSmelt)
} else if (longOrWide == "wide") {
return(CoSdata)
} else {
print("Please use long or wide as correct answers for longOrWide")
}
}
##### returns countries based on one filter
# TODO checking logic
# TODO: put in another file
getCompInfo <- function(Codetype, CodeValue, longOrWide, path){
comment <- " if oneOrMany == one then we are only pulling info about one country,
if it == many, then we are pulling data for a group,
if we are pulling for a group we need to know how we define a group, manyCodetype,
and then what group in that type we want manyCodeValue"
# use this data to reference iso3 codes
countryClass_list <- read.xlsx(file = path, sheetIndex = 1, stringsAsFactors = FALSE )
CoSdata <- countryClass_list %>%
filter(countryClass_list[Codetype] == CodeValue)
######## - - - - - - - - - - - - - - - - -FINAL OUPUT LOGIC
if(longOrWide == "long"){
# it would be better for viewing to have the data long
CoSmelt <- CoSdata %>%
melt(id.vars="Value") %>%
subset(select = c("variable","value"))
colnames(CoSmelt) <- c("Attribute", "Value")
return(CoSmelt)
} else if (longOrWide == "wide") {
return(CoSdata)
} else {
print("Please use long or wide as correct answers for longOrWide")
}
} # end of entire function
####### ---- returns countries based on 2 filters, do not use not supported
getTwoCompInfo <- function(Codetype, CodeValue, Codetype2, CodeValue2, longOrWide){
comment <- " Codetype = type of classification, column header
CodeValue = what we are filtering for,
this function allows for two levels of filtering"
# use this data to reference iso3 codes
countryClass_list <- read.xlsx(file = COUNTRY_CLASS_PATH, sheetIndex = 1, stringsAsFactors = FALSE )
CoSdata <- countryClass_list %>%
filter(countryClass_list[Codetype] == CodeValue)
CoSdata <- CoSdata %>%
filter(CoSdata[Codetype2] == CodeValue2)
######## - - - - - - - - - - - - - - - - -FINAL OUPUT LOGIC
if(longOrWide == "long"){
# it would be better for viewing to have the data long
CoSmelt <- CoSdata %>%
melt(id.vars="Value") %>%
subset(select = c("variable","value"))
colnames(CoSmelt) <- c("Attribute", "Value")
return(CoSmelt)
} else if (longOrWide == "wide") {
return(CoSdata)
} else {
print("Please use long or wide as correct answers for longOrWide")
}
} # end of entire function
## ----groupings table, echo=FALSE, fig.align="center"---------------------
cos_groupingTable <- function(){
comment <- " This chunck will pull data from a local excel book and display the country's attributes "
# TODO put in logic in case the countryCode is put in wrong
## - - - - - - - - - - - - - - - SET UP
# country code for country to get data for
countryCode <- COS_ISO3
# use this data to reference iso3 codes
countryClass_list <- read.xlsx(file = COUNTRY_CLASS_PATH, sheetIndex = 1, stringsAsFactors = FALSE )
countries_for_chart <- countryClass_list %>%
filter(WEO_ISO_3 == countryCode)
# WEO Code being a number gets it deleted by melt soon so changing it to a char
countries_for_chart$WEO_Code <- as.character(countries_for_chart$WEO_Code)
# removing columns with NA
CoSdata <- countries_for_chart %>%
select_if(~ !any(is.na(.)))
# it would be better for viewing to have the data long
CoSmelt <- CoSdata %>%
melt(id.vars="WEO_ISO_3") %>%
subset(select = c("variable","value"))
colnames(CoSmelt) <- c("Attribute", "Value")
kable(CoSmelt)
}
## ----01_scatter_WEO_WoRLD_TaxGDP_to_GDPPC, echo=FALSE, fig.align="center", message=FALSE, warning=FALSE----
make_chart_1_v2 <- function(COS_WEO3, COMP_DF, COMP_VALUE, MAX_DATE, DATA){
# take in raw data and format to use for ggplot chart
chart_data <- DATA %>%
filter(COUNTRY %in% COMP_DF$WEO_Code,
variable %in% c("NGDPRPC", "GGRT_GDP"),
# most recent year
dates == MAX_DATE) %>%
select(COUNTRY, value, variable) %>%
pivot_wider(names_from = variable, values_from = value) %>%
drop_na()
########################## CHART
# if no error we just return a blank string
errorMess = ""
# we need to create a set of points for the countries of comparison
# check if there is any data
if(dim(chart_data)[1]==0){
compPoint <- NULL
errorMess = "no data"
} else{
# scatterplots for group
compPoint <- geom_point(data = chart_data,
aes(x = log(NGDPRPC),
y = GGRT_GDP/100,
size = 0.15))
}
# handle if no data for cos
if(dim(chart_data %>% filter(COUNTRY == COS_WEO3))[1]==0){
cosPoint <- NULL
errorMess = "no data for COS"
} else{
# scatterplot for country of study in red
cosPoint <- geom_point(data = (chart_data %>% filter(COUNTRY == COS_WEO3)),
aes(x = log(NGDPRPC),
y = GGRT_GDP/100,
size = 0.15), color="red")
}
ggplot(chart_data, aes(x = log(NGDPRPC), y = GGRT_GDP/100)) +
compPoint +
cosPoint +
geom_text(label=chart_data$COUNTRY, label.size = 1, nudge_x = 2, nudge_y=.01) +
labs(
title = paste("Tax to GDP by GDP per Capita for", MAX_DATE),
subtitle = paste("Grouping:", gsub("_", " ", COMP_VALUE)),
y = "Tax to GDP",
x = "Log Real GDP per Capita USD",
caption = "Source: Random Data") +
theme_chart_01() +
scale_x_continuous(labels = scales::number_format(accuracy = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
expand_limits(y = 0)
}
chart_1 <- function(COS_WEO3, COMP_DF, COMP_VALUE, MAX_DATE, DATA){
tryCatch(
expr = make_chart_1_v2(COS_WEO3, COMP_DF, COMP_VALUE, MAX_DATE, DATA),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 4,
label = "Failed to make Tax-GDP scatterplot chart") +
theme_void()
}
)
}
## ----weo charts----
make_weoLine <- function(weo, weoCode, title, subtitle, COS_NAME, COS_WEO3, COMP_CODE, COMP_VALUE, COMP_DF){
chart_data <- weo %>%
filter(WEO_Code == COS_WEO3,
dates > MIN_DATE,
dates < MAX_DATE,
variable == weoCode) %>%
select(dates, variable, value, Value)
# ex country of study
compData <- weo %>%
filter(WEO_Code %in% COMP_DF$WEO_Code,
WEO_Code != COS_WEO3,
dates > MIN_DATE,
dates < MAX_DATE,
variable == weoCode) %>%
group_by(dates, variable) %>%
summarize(value = mean(value, na.rm = TRUE)) %>%
mutate(Value = COMP_VALUE)
mergedData <- bind_rows(chart_data, compData)
line <- mergedData %>%
ggplot(aes(x = dates, y = value, color = Value)) +
# line 1, Country of Study
geom_line(size = 1) +
# Title and caption
labs(title = paste(title),
subtitle = paste(subtitle),
y = "",
x = "",
caption = "Source: Random Data") +
# set y axis height
expand_limits(y = 0) +
scale_x_continuous(breaks = seq(min(mergedData$dates), max(mergedData$dates), by = 2)) +
# styling
scale_color_manual(values = c("#1B4F72","#2874A6"))+
#theme_piotr_edited()
ISORA_staffing() #+
scale_y_continuous(labels = scales::percent_format(accuracy = 1))
line
}
weoLine <- function(weo, weoCode, title, subtitle, COS_NAME, COS_WEO3, COMP_CODE, COMP_VALUE, COMP_DF){
tryCatch(
expr = make_weoLine(weo, weoCode, title, subtitle, COS_NAME, COS_WEO3, COMP_CODE, COMP_VALUE, COMP_DF),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 8,
label = paste("Failed to make", title, "chart")) +
theme_void()
}
)
}
make_weoBarStacked <- function(weo, COS_NAME, COS_WEO3){
indicators <- c("GGRTIgdp",
"GGRTGSgdp",
"GGRTTgdp",
"GGRTOEgdp")
chart_data <- weo %>%
filter(WEO_Code == COS_WEO3,
dates > MIN_DATE,
dates < MAX_DATE,
variable %in% indicators) %>%
select(dates, variable, value, Value) %>%
# https://stackoverflow.com/questions/35610437/using-dplyr-to-conditionally-replace-values-in-a-column
mutate(variable = case_when(variable == 'GGRTIgdp' ~ 'Income, Profits, Capital Gains',
variable == 'GGRTGSgdp' ~ 'Goods and Services',
variable == 'GGRTTgdp' ~ 'International Trade',
variable == 'GGRTOEgdp' ~ 'Other',
TRUE ~ 'Error'))
blues <- c("#1B4F72","#2874A6","#3498DB", "#85C1E9")#,"#AED6F1", "#5DADE2")#,"#D6EAF8")
chart <- ggplot(chart_data, aes(x = dates, y = value, fill = variable)) +
geom_bar(position="fill", stat="identity") +
# Title and caption
labs(title = paste(COS_NAME, "Taxes By Tax Type"),
#subtitle = paste(subtitle),
y = "",
x = "",
caption = "Source: Random Data") +
scale_fill_manual(values = blues) +
expand_limits(y = 0) +
scale_x_continuous(breaks = seq(min(chart_data$dates), max(chart_data$dates), by = 2)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
ISORA_staffing() +guides(fill=guide_legend(nrow=2,byrow=TRUE))
chart
}
weoBarStacked <- function(weo, COS_NAME, COS_WEO3){
tryCatch(
expr = make_weoBarStacked(weo, COS_NAME, COS_WEO3),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 3,
label = paste("Failed to make WEO Tax-GDP breakdown chart")) +
theme_void()
}
)
}
make_weoBarStacked_GGR <- function(weo, COS_NAME, COS_WEO3){
indicators <- c("GGRT_GDP",
"GGRSgdp",
"GGRGgdp",
"GGROgdp")
chart_data <- weo %>%
filter(WEO_Code == COS_WEO3,
dates > MIN_DATE,
dates < MAX_DATE,
variable %in% indicators) %>%
select(dates, variable, value, Value) %>%
# https://stackoverflow.com/questions/35610437/using-dplyr-to-conditionally-replace-values-in-a-column
mutate(variable = case_when(variable == 'GGRT_GDP' ~ 'Taxes',
variable == 'GGRSgdp' ~ 'Social Contributions',
variable == 'GGRGgdp' ~ 'Grants',
variable == 'GGROgdp' ~ 'Other',
TRUE ~ 'Error'))
blues <- c("#1B4F72","#2874A6","#3498DB", "#85C1E9")#,"#AED6F1", "#5DADE2")#,"#D6EAF8")
chart <- ggplot(chart_data, aes(x = dates, y = value, fill = variable)) +
geom_bar(position="fill", stat="identity") +
# Title and caption
labs(title = paste(COS_NAME, "General Government Revenue Breakdown"),
#subtitle = paste(subtitle),
y = "",
x = "",
caption = "Source: Random Data") +
scale_fill_manual(values = blues) +
expand_limits(y = 0) +
scale_x_continuous(breaks = seq(min(chart_data$dates), max(chart_data$dates), by = 2)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
ISORA_staffing() +guides(fill=guide_legend(nrow=2,byrow=TRUE))
chart
}
weoBarStacked_GGR <- function(weo, COS_NAME, COS_WEO3){
tryCatch(
expr = make_weoBarStacked_GGR(weo, COS_NAME, COS_WEO3),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 3,
label = paste("Failed to make WEO Tax-GDP breakdown chart")) +
theme_void()
}
)
}
## ----fad tp ----
make_tpChart <- function(rates, code, title, subtitle, citation, COS_NAME, COS_WEO3, COMP_CODE, COMP_VALUE, COMP_DF){
chart_data <- rates %>%
filter(WEO_Code == COS_WEO3,
dates > MIN_DATE,
dates < MAX_DATE,
variable == code) %>%
select(dates, variable, value, Value)
compData <- rates %>%
filter(WEO_Code %in% COMP_DF$WEO_Code,
WEO_Code != COS_WEO3,
dates > MIN_DATE,
dates < MAX_DATE,
variable == code) %>%
group_by(dates, variable) %>%
summarize(value = mean(value, na.rm = TRUE)) %>%
mutate(Value = COMP_VALUE)
mergedData <- bind_rows(chart_data, compData)
line <- mergedData %>%
ggplot(aes(x = dates, y = value, color = Value)) +
# line 1, Country of Study
geom_line(size = 1) +
# Title and caption
labs(title = paste(title),
subtitle = paste(subtitle),
y = "",
x = "",
caption = citation) +
# set y axis height
expand_limits(y = 0) +
scale_x_continuous(breaks = seq(min(mergedData$dates), max(mergedData$dates), by = 2)) +
# styling
scale_color_manual(values = c("#1B4F72","#2874A6"))+
#theme_piotr_edited()
ISORA_staffing()
line
}
tpChart <- function(rates, code, title, subtitle, citation, COS_NAME, COS_WEO3, COMP_CODE, COMP_VALUE, COMP_DF){
tryCatch(
expr = make_tpChart(rates, code, title, subtitle, citation, COS_NAME, COS_WEO3, COMP_CODE, COMP_VALUE, COMP_DF),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 8,
label = paste("Failed to make", title, "chart")) +
theme_void()
}
)
}
make_tpBarChart <- function(world, COS_NAME, COS_WEO3){
indicators <- c("Corporate Income Tax Revenue as a % of GDP.A",
"Goods and Services Tax Revenue as a % of GDP.A",
"Individual Income Tax Revenue as a % of GDP.A",
"Property Tax Revenue as a % of GDP.A",
"Taxes on Payroll and Workforce Revenue as a % of GDP.A",
"Trade Tax Revenue as a % of GDP.A")
chart_data <- world %>%
filter(WEO_Code == COS_WEO3,
dates > MIN_DATE,
dates < MAX_DATE,
variable %in% indicators) %>%
select(dates, variable, value, Value) %>%
# https://stackoverflow.com/questions/35610437/using-dplyr-to-conditionally-replace-values-in-a-column
mutate(variable = case_when(variable == 'Corporate Income Tax Revenue as a % of GDP.A' ~ 'Corporate Income Tax',
variable == 'Goods and Services Tax Revenue as a % of GDP.A' ~ 'Goods and Services Tax',
variable == 'Individual Income Tax Revenue as a % of GDP.A' ~ 'Individual Income Tax',
variable == 'Property Tax Revenue as a % of GDP.A' ~ 'Property Tax',
variable == 'Taxes on Payroll and Workforce Revenue as a % of GDP.A' ~ 'Payroll Tax',
variable == 'Trade Tax Revenue as a % of GDP.A' ~ 'Trade Tax',
TRUE ~ 'Error'))
blues <- c("#1B4F72","#2874A6","#3498DB", "#85C1E9","#AED6F1", "#5DADE2")#,"#D6EAF8")
TPchart <- ggplot(chart_data, aes(x = dates, y = value, fill = variable)) +
geom_bar(position="fill", stat="identity") +
# Title and caption
labs(title = paste(COS_NAME, "Taxes By Tax Type"),
#subtitle = paste(subtitle),
y = "",
x = "",
caption = "Source: Random Data") +
scale_fill_manual(values = blues) +
expand_limits(y = 0) +
scale_x_continuous(breaks = seq(min(chart_data$dates), max(chart_data$dates), by = 2)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
ISORA_staffing() + guides(fill=guide_legend(nrow=2,byrow=TRUE))
TPchart
}
tpBarChart <- function(world, COS_NAME, COS_WEO3){
tryCatch(
expr = make_tpBarChart(world, COS_NAME, COS_WEO3),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 3,
label = paste("Failed to make Tax Policy Tax-GDP breakdown chart")) +
theme_void()
}
)
}
## ----17_ISORA_Staffing, echo=FALSE, fig.align="center", message=FALSE, warning=FALSE----
functionalStaffing_v2 <- function(ISORA, COS_WEO3, COMP_DF, COS_NAME){
tryCatch(
expr = make_functionalStaffing_v2(ISORA, COS_WEO3, COMP_DF, COS_NAME),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 8,
label = "Failed to make ISORA functional staffing chart") +
theme_void()
}
)
}
make_functionalStaffing_v2 <- function(ISORA, COS_WEO3, COMP_DF, COS_NAME) {
chart_data <- ISORA %>%
# filter for data needed for chart
#filter(dataset == "ISORA") %>%
mutate(indicatorCode = as.character(indicatorCode))%>%
filter(indicatorCode %in% c("94030_207", "95230_207", "95240_207","94020_207")) %>%
# remove values that are missing, noted with a D
filter(value != 'D') %>%
# now we can cast to numeric
mutate(value = as.double(value))%>%
# filter by country of study
filter(CountryCode == COS_WEO3) %>%
# filter by latest year
filter(year == max(year)) %>%
# clean up names to chart
mutate(
Staff_Functional_Group = ifelse(indicatorCode == "94030_207", "Debt Collection",
ifelse(indicatorCode == "95230_207", "Taxpayer Services",
ifelse(indicatorCode == "95240_207", "Other",
ifelse(indicatorCode == "94020_207", "Audit and Verification", "Error"))))) %>%
select(Staff_Functional_Group, year, value, country) %>%
mutate(country = COS_NAME)
maxYear = unique(chart_data$year)
chart_data <- chart_data %>% select(-year)
chart_data_comp <- ISORA %>%
# filter for data needed for chart
#filter(dataset == "ISORA") %>%
mutate(indicator = as.character(indicatorCode))%>%
filter(indicatorCode %in% c("94030_207", "95230_207", "95240_207","94020_207")) %>%
# remove values that are missing, noted with a D
filter(value != 'D') %>%
# now we can cast to numeric
mutate(value = as.double(value))%>%
# remove any groups that do not sum to 100
group_by(CountryCode, year)%>%
mutate(group_sum_check = sum(value)) %>%
filter(between(group_sum_check, 99.5, 100.5)) %>%
ungroup() %>%
filter(CountryCode %in% COMP_DF$WEO_Code)%>%
filter(year == max(maxYear)) %>%
# clean up names to chart
mutate(
Staff_Functional_Group = ifelse(indicatorCode == "94030_207", "Debt Collection",
ifelse(indicatorCode == "95230_207", "Taxpayer Services",
ifelse(indicatorCode == "95240_207", "Other",
ifelse(indicatorCode == "94020_207", "Audit and Verification", "Error"))))) %>%
group_by(Staff_Functional_Group) %>%
summarize(value = mean(value)) %>%
mutate(country = COMP_VALUE)
c <- rbind(chart_data, chart_data_comp)
plot <- c %>%
ggplot(aes(x = reorder(Staff_Functional_Group, value), y = value/100, fill = country)) +
geom_bar(position = "dodge", stat = "identity") +
ggtitle(paste(COS_NAME, "Staffing by Functional Groups")) +
labs(subtitle = paste("percent of full time employees,", maxYear),
y = " ",
x = " ",
caption = "Source: Random Data") +
ISORA_staffing() +
#blues <- c("#1B4F72","#2874A6","#3498DB", "#85C1E9","#5DADE2","#AED6F1","#D6EAF8")
scale_fill_manual(values = c("#1B4F72","#2874A6")) +
coord_flip()+ scale_y_continuous(labels = scales::percent_format(accuracy = 1))
plot
}
genderStaffing <- function(ISORA, COS_ISO3, COMP_DF, COS_NAME){
tryCatch(
expr = make_genderStaffing(ISORA, COS_WEO3, COMP_DF, COS_NAME),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 8,
label = "Failed to make ISORA gender staffing chart") +
theme_void()
}
)
}
make_genderStaffing <- function(ISORA, COS_ISO3, COMP_DF, COS_NAME) {
chart_data <- ISORA %>%
select(country, CountryCode, IndicatorName, indicatorCode, Attribute, isora_surveyYear, year, value) %>%
# filter for data needed for chart
#filter(dataset == "ISORA") %>%
mutate(indicator = as.character(indicatorCode))%>%
# trim dataset to needed countries and indicators
filter(
indicator %in% c("84070_75", "84060_75", "95120_75"),
# remove countries not in comparable group
CountryCode %in% COS_WEO3,
# remove values that are missing, noted with a D
value != 'D',
# odd isora stuff to remove, data is is value attribute
Attribute != 'Observation',
# filter by latest year
year == max(year)
) %>%
# cast to numeric to ensure it plays nice
mutate(value = as.double(value)) %>%
# for each country in a year calculate total staff
group_by(CountryCode, year) %>%
mutate(totalStaff = sum(value)) %>%
ungroup() %>%
# for each country, indicator, and year calculate percent of total staff
group_by(CountryCode, indicatorCode, year) %>%
mutate(percentStaff = value / totalStaff,
label = ifelse(indicatorCode == "84070_75", "Female Staff",
ifelse(indicatorCode == "84060_75", "Male Staff",
ifelse(indicatorCode == "95120_75", "Other Staff", "Error"))),
count = n()) %>%
ungroup()%>%
# group by gender and calculate averages
group_by(label) %>%
mutate(averagePercent = mean(percentStaff)) %>%
select(label, averagePercent, year, count) %>%
unique() %>%
# include country/group label
mutate(country = COS_NAME)
chart_dataCOMP <- ISORA %>%
select(country, CountryCode, IndicatorName, indicatorCode, Attribute, isora_surveyYear, year, value) %>%
# filter for data needed for chart
#filter(dataset == "ISORA") %>%
mutate(indicator = as.character(indicatorCode))%>%
# trim dataset to needed countries and indicators
filter(
indicator %in% c("84070_75", "84060_75", "95120_75"),
# remove countries not in comparable group
CountryCode %in% COMP_DF$WEO_Code,
# remove values that are missing, noted with a D
value != 'D',
# odd isora stuff to remove, data is is value attribute
Attribute != 'Observation',
# filter by latest year for the country of study so it matches
year == max(chart_data$year)
) %>%
# cast to numeric to ensure it plays nice
mutate(value = as.double(value)) %>%
# for each country in a year calculate total staff
group_by(CountryCode, year) %>%
mutate(totalStaff = sum(value)) %>%
ungroup() %>%
# for each country, indicator, and year calculate percent of total staff
group_by(CountryCode, indicatorCode, year) %>%
mutate(percentStaff = value / totalStaff,
label = ifelse(indicatorCode == "84070_75", "Female Staff",
ifelse(indicatorCode == "84060_75", "Male Staff",
ifelse(indicatorCode == "95120_75", "Other Staff", "Error")))) %>%
ungroup()%>%
mutate(count = n())%>%
# group by gender and calculate averages
group_by(label) %>%
mutate(averagePercent = mean(percentStaff)) %>%
select(label, averagePercent, year, count) %>%
unique() %>%
# include country/group label
mutate(country = COMP_VALUE)
# combine group and country of study to chart
c <- rbind(chart_data, chart_dataCOMP)
plot <- c %>%
ggplot(aes(x = reorder(label, averagePercent), y = averagePercent, fill = country)) +
geom_bar(position = "dodge", stat = "identity") +
ggtitle(paste(COS_NAME, "Staffing by Gender")) +
labs(subtitle = paste("percent of full time employees,", unique(c$year)),
y = " ",
x = " ",
caption = "Source: Random Data") +
ISORA_staffing() +
#blues <- c("#1B4F72","#2874A6","#3498DB", "#85C1E9","#5DADE2","#AED6F1","#D6EAF8")
scale_fill_manual(values = c("#1B4F72","#2874A6")) +
coord_flip() + scale_y_continuous(labels = scales::percent_format(accuracy = 1))
plot
}
genderStaffingExec <- function(ISORA, COS_ISO3, COMP_DF, COS_NAME){
tryCatch(
expr = make_genderStaffingExec(ISORA, COS_WEO3, COMP_DF, COS_NAME),
error = function(e){
ggplot() +
annotate("text",
x = 1,
y = 1,
size = 8,
label = "Failed to make ISORA gender executive staffing chart") +
theme_void()
}
)
}
make_genderStaffingExec <- function(ISORA, COS_ISO3, COMP_DF, COS_NAME) {
chart_data <- ISORA %>%
select(country, CountryCode, IndicatorName, indicatorCode, Attribute, isora_surveyYear, year, value) %>%
# filter for data needed for chart
#filter(dataset == "ISORA") %>%
mutate(indicator = as.character(indicatorCode))%>%
# trim dataset to needed countries and indicators
filter(
indicator %in% c("84070_215", "84060_215", "95120_215"),
# remove countries not in comparable group
CountryCode %in% COS_WEO3,
# remove values that are missing, noted with a D
value != 'D',
# odd isora stuff to remove, data is is value attribute
Attribute != 'Observation',
# filter by latest year
year == max(year)
) %>%
# cast to numeric to ensure it plays nice
mutate(value = as.double(value)) %>%
# for each country in a year calculate total staff
group_by(CountryCode, year) %>%
mutate(totalStaff = sum(value)) %>%
ungroup() %>%
# for each country, indicator, and year calculate percent of total staff
group_by(CountryCode, indicatorCode, year) %>%
mutate(percentStaff = value / totalStaff,
label = ifelse(indicatorCode == "84070_215", "Female Executives",
ifelse(indicatorCode == "84060_215", "Male Executives",
ifelse(indicatorCode == "95120_215", "Other Executives", "Error"))),
count = n()) %>%
ungroup()%>%
# group by gender and calculate averages
group_by(label) %>%
mutate(averagePercent = mean(percentStaff)) %>%
select(label, averagePercent, year, count) %>%
unique() %>%
# include country/group label
mutate(country = COS_NAME)
chart_dataCOMP <- ISORA %>%
select(country, CountryCode, IndicatorName, indicatorCode, Attribute, isora_surveyYear, year, value) %>%
# filter for data needed for chart
#filter(dataset == "ISORA") %>%
mutate(indicator = as.character(indicatorCode))%>%
# trim dataset to needed countries and indicators
filter(
indicator %in% c("84070_215", "84060_215", "95120_215"),
# remove countries not in comparable group
CountryCode %in% COMP_DF$WEO_Code,
# remove values that are missing, noted with a D
value != 'D',
# odd isora stuff to remove, data is is value attribute
Attribute != 'Observation',
# filter by latest year for the country of study so it matches
year == max(chart_data$year)
) %>%
# cast to numeric to ensure it plays nice
mutate(value = as.double(value)) %>%
# for each country in a year calculate total staff
group_by(CountryCode, year) %>%
mutate(totalStaff = sum(value)) %>%
ungroup() %>%