R/Contract Obligation by Agency.R
In MSTRoth/PSC-MB: Repeated Functions for PSC Visualizations: Market Briefings
Defines functions
bar_primeob_by_agency
bar_primeob_by_agency_scaling
bar_primeob_by_agency_choosing
Documented in
bar_primeob_by_agency
bar_primeob_by_agency_choosing
bar_primeob_by_agency_scaling
#' Creating a basic, unscaled bar chart of Prime Contract Obligations by Agency
#'
#' @param company_name Name of the company, used in filename and chart title
#' @param FY Fiscal year to filter out, commonly the current fiscal year due to incomplete data
#' @param n_agencies The number of agencies to include in the view; defaults to 6
#' @param scale Dollar scale; default to 1000000
#' @param scale_text Dollar scale as text; defaults to Millions
#' @param FY_range Fiscal Year range included, for example "FY14-FY17"
#' @param num_size Size of heading text for agency names, default to 3. Note that the fewer
#' the agencies used, the larger the text should be
#' @param h Height of saved chart; defaults to 6
#' @param w Width of saved chart; defaults to 11. Note that more agencies requires a greater width
#'
#' @return A bar chart
#'
#' @details The returned chart will determine whether this function is sufficient or a different,
#' more scaled chart is needed
#'
#' @examples bar_primeob_by_agency(company_name = "Leidos", FY = 2018, n_agencies = 5,
#' scale = 1000000000, scale_text = "Billions", FY_range = "FY14-FY17",
#' num_size = 3, h = 7, w = 13)
bar_primeob_by_agency <- function(company_name,
FY = 1,
n_agencies = 6,
scale = 1000000,
scale_text = "Millions",
FY_range,
num_size = 3,
h = 6,
w = 11){
#Location for saving charts
setwd("X:/1 Marielle Folder/Data For R/Created Charts/2018/Contract Obligations by Agency")
data <- read_csv(paste("X:/1 Marielle Folder/Data For R/Contract Obligation by Agency/Company Profiles/", company_name,
" Company Profile.csv", sep = ""))
###Get top n agencyies by obligation
top_n_agencies <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
group_by(funding_agency) %>%
dplyr::summarize(grand_total_transaction_value = sum(transaction_value)) %>%
arrange(desc(grand_total_transaction_value)) %>%
top_n(n_agencies)
top_n_agencies <- top_n_agencies$funding_agency
###Process Data to get total transaction value by year
data.agency.year <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% top_n_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year$fiscal_year = as.character(data.agency.year$fiscal_year)
data.agency.year$facet = factor(data.agency.year$funding_agency, levels = c(top_n_agencies))
###Create Barplot and Save as JPG
plot <- plot.one(data.agency.year, "funding_agency", num_size, scale_text, company_name, FY_range)
return(plot)
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot,
width = w, height = h, units = "in")
}
#' Creating a bar chart of Prime Contract Obligations by Agency that is scaled into a Top
#' Range and Bottom Range (two scalings)
#'
#' @param company_name Name of the company, used in filename and chart title
#' @param FY Fiscal year to filter out, commonly the current fiscal year due to incomplete data
#' @param n_agencies The number of agencies to include in the view; defaults to 6
#' @param scale Dollar scale; default to 1000000
#' @param scale_text Dollar scale as text; defaults to Millions
#' @param FY_range Fiscal Year range included, for example "FY14-FY17"
#' @param top_range agencies to be included in the first scaling
#' @param bottom_range agencies to be included in the second scaling
#' @param optional_third_range if scaling data by three ranges, use this field; otherwise defaults to NULL
#' @param grid_division respective sizes of ranges to arrange the final grid. This generally is
#' the number of agencies in the top_range and the number in the bottom_range written as a vector.
#' @param num_size Size of heading text for agency names, default to 3. Note that the fewer
#' the agencies used, the larger the text should be
#' @param h Height of saved chart; defaults to 6
#' @param w Width of saved chart; defaults to 11. Note that more agencies requires a greater width
#'
#' @return A bar chart, with two differently scales charts of the data
#'
#' @details Unless already known, use the bar_primeob_by_agency() function to determine the Top Range
#' and Bottom Range agencies.
#'
#'
#' @examples bar_primeob_by_agency _scaling(company_name = "Leidos", FY = 2018,
#' n_agencies = 5, scale = 1000000000,
#' scale_text = "Billions", FY_range = "FY14-FY17",
#' top_range = c(1:2,4), bottom_range = c(3,5),
#' grid_division = c(3,2), num_size = 3,
#' h = 7, w = 13)
#'
#'
bar_primeob_by_agency_scaling <- function(company_name,
FY = 1,
n_agencies = 6,
scale = 1000000,
scale_text = "Millions",
FY_range,
top_range,
bottom_range,
optional_third_range = NULL,
grid_division,
num_size = 4,
h = 6,
w = 11){
#Location for saving charts
setwd("X:/1 Marielle Folder/Data For R/Created Charts/2018/Contract Obligations by Agency")
data <- read_csv(paste("X:/1 Marielle Folder/Data For R/Contract Obligation by Agency/Company Profiles/", company_name,
" Company Profile.csv", sep = ""))
###Get top n agencyies by obligation
top_n_agencies <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
group_by(funding_agency) %>%
dplyr::summarize(grand_total_transaction_value = sum(transaction_value)) %>%
arrange(desc(grand_total_transaction_value)) %>%
top_n(n_agencies)
if(is.null(optional_third_range)){
##Seperate Out different scales
top_n_agencies <- top_n_agencies$funding_agency
top_agencies <- top_n_agencies[top_range]
bottom_agencies <- top_n_agencies[bottom_range]
###Process Data to get total transaction value by year - for top and bottom agencies
data.agency.year.top <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% top_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.top$fiscal_year = as.character(data.agency.year.top$fiscal_year)
data.agency.year.top$facet = factor(data.agency.year.top$funding_agency, levels = c(top_agencies))
data.agency.year.bottom <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% bottom_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.bottom$fiscal_year = as.character(data.agency.year.bottom$fiscal_year)
data.agency.year.bottom$facet = factor(data.agency.year.bottom$funding_agency, levels = c(bottom_agencies))
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year.top, "funding_agency", num_size, scale_text)
plot2 <- plot.last(data.agency.year.bottom, "funding_agency", num_size)
plot.all<-grid.arrange(plot1, plot2, nrow = 1, widths = grid_division,
top = textGrob(paste(company_name, " Contract Obligations by Agency ", FY_range, sep = ""), gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot.all,
width = w, height = h, units = "in")
data.agency.year.all <- rbind(data.agency.year.top, data.agency.year.bottom)
}
else{
##Seperate Out different scales
top_n_agencies <- top_n_agencies$funding_agency
top_agencies <- top_n_agencies[top_range]
middle_agencies <- top_n_agencies[optional_third_range]
bottom_agencies <- top_n_agencies[bottom_range]
###Process Data to get total transaction value by year - for top and bottom agencies
data.agency.year.top <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% top_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.top$fiscal_year = as.character(data.agency.year.top$fiscal_year)
data.agency.year.top$facet = factor(data.agency.year.top$funding_agency, levels = c(top_agencies))
data.agency.year.bottom <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% bottom_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.bottom$fiscal_year = as.character(data.agency.year.bottom$fiscal_year)
data.agency.year.bottom$facet = factor(data.agency.year.bottom$funding_agency, levels = c(bottom_agencies))
data.agency.year.middle <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% middle_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.middle$fiscal_year = as.character(data.agency.year.middle$fiscal_year)
data.agency.year.middle$facet = factor(data.agency.year.middle$funding_agency, levels = c(middle_agencies))
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year.top, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year.middle, "funding_agency", num_size)
plot3 <- plot.last(data.agency.year.bottom, "funding_agency", num_size)
plot.all<-grid.arrange(plot1, plot2, plot3, nrow = 1, widths = grid_division,
top = textGrob(paste(company_name, " Contract Obligations by Agency ", FY_range, sep = ""), gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot.all,
width = w, height = h, units = "in")
data.agency.year.all <- rbind(data.agency.year.top, data.agency.year.middle, data.agency.year.bottom)
}
return(plot.all)
data.agency.year.all
}
#' Creating a bar chart of Prime Contract Obligations by Agency looking at an agency
#' (or sub-agency) chosen by name to display
#'
#' @param company_name Name of the company, used in filename and chart title
#' @param funding_agency_type1 Which field or subfield the agency is listed under
#' @param funding_agency_name1 The specific agency to display
#' @param funding_agency_type2 optional second agency type; defaults to null
#' @param funding_agency_name2 optional second agency name; defaults to null
#' @param funding_agency_type3 optional third agency type; defaults to null
#' @param funding_agency_name3 optional third agency type; defaults to null
#' @param funding_agency_type4 optional fourth agency type; defaults to null
#' @param funding_agency_name4 optional fourth agency name; defaults to null
#' @param funding_agency_type5 optional fifth agency type; defaults to null
#' @param funding_agency_name5 optional fifth agency name; defaults to null
#' @param FY Fiscal year to filter out, commonly the current fiscal year due to incomplete data
#' @param scale Dollar scale; default to 1000000
#' @param scale_text Dollar scale as text; defaults to Millions
#' @param num_size Size of heading text for agency names, default to 3. Note that the fewer
#' the agencies used, the larger the text should be
#' @param h Height of saved chart; defaults to 6
#' @param w Width of saved chart; defaults to 11. Note that more agencies requires a greater width
#'
#' @return A bar chart, with two differently scales charts of the data
#'
#' @details Unless already known, use the bar_primeob_by_agency() function to determine the Top Range
#' and Bottom Range agencies.
#'
#'
#' @examples bar_primeob_by_agency_choosing(company_name = "LMI Consulting",
#' funding_agency_type1 = "Funding Bureau",
#' funding_agency_name1 = "Customs and Border Protection
#' (CBP)", FY = 2018, scale = 1000000,
#' scale_text = "Millions", FY_range = "FY14-FY17",
#' num_size = 3, h = 6, w = 11)
#'
#' @export
bar_primeob_by_agency_choosing <- function(company_name,
funding_agency_type1,
funding_agency_name1,
funding_agency_type2 = NULL,
funding_agency_name2 = NULL,
funding_agency_type3 = NULL,
funding_agency_name3 = NULL,
funding_agency_type4 = NULL,
funding_agency_name4 = NULL,
funding_agency_type5 = NULL,
funding_agency_name5 = NULL,
FY = 1,
scale = 1000000,
scale_text = "Millions",
FY_range,
num_size = 3,
h = 6,
w = 11){
data <- read_csv(paste("X:/1 Marielle Folder/Data For R/Contract Obligation by Agency/Company Profiles/", company_name,
" Company Profile.csv", sep = ""))
###Process Data to get total transaction value by year
if(is.null(funding_agency_name2)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
###Create Barplot and Save as JPG
plot.all <- plot.one(data.agency.year1, "funding_agency", num_size, scale_text, funding_agency_name1, FY_range)
data.agency.year.all <- data.agency.year1
}
else{
if(is.null(funding_agency_name3)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
legend <- gtable_filter(ggplot_gtable(ggplot_build(plot1)), "guide-box")
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
legend, ncol = 3,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2)
}
else{
if(is.null(funding_agency_name4)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
data.agency.year3 <- process.data.get.sum(data, funding_agency_name3, funding_agency_type3, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
plot3 <- plot.middle(data.agency.year3, "funding_agency", num_size)
legend <- gtable_filter(ggplot_gtable(ggplot_build(plot1)), "guide-box")
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
plot3 + theme(legend.position='hidden'),
legend, ncol = 4,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2, data.agency.year3)
}
else{
if(is.null(funding_agency_name5)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
data.agency.year3 <- process.data.get.sum(data, funding_agency_name3, funding_agency_type3, FY, scale)
data.agency.year4 <- process.data.get.sum(data, funding_agency_name4, funding_agency_type4, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
plot3 <- plot.middle(data.agency.year3, "funding_agency", num_size)
plot4 <- plot.middle(data.agency.year4, "funding_agency", num_size)
legend <- gtable_filter(ggplot_gtable(ggplot_build(plot1)), "guide-box")
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
plot3 + theme(legend.position='hidden'),
plot4 + theme(legend.position='hidden'),
legend, ncol = 5,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2, data.agency.year3, data.agency.year4)
}
else{
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
data.agency.year3 <- process.data.get.sum(data, funding_agency_name3, funding_agency_type3, FY, scale)
data.agency.year4 <- process.data.get.sum(data, funding_agency_name4, funding_agency_type4, FY, scale)
data.agency.year5 <- process.data.get.sum(data, funding_agency_name5, funding_agency_type5, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
plot3 <- plot.middle(data.agency.year3, "funding_agency", num_size)
plot4 <- plot.middle(data.agency.year4, "funding_agency", num_size)
plot5 <- plot.middle(data.agency.year5, "funding_agency", num_size)
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
plot3 + theme(legend.position='hidden'),
plot4 + theme(legend.position='hidden'),
plot5 + theme(legend.position='hidden'),
legend, ncol = 6,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2, data.agency.year3,
data.agency.year4, data.agency.year5)
}
}
}
}
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot.all,
width = w, height = h, units = "in")
plot.all
data.agency.year.all
}
MSTRoth/PSC-MB documentation built on May 26, 2019, 2:36 a.m.
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Defines functions bar_primeob_by_agency bar_primeob_by_agency_scaling bar_primeob_by_agency_choosing
Documented in bar_primeob_by_agency bar_primeob_by_agency_choosing bar_primeob_by_agency_scaling
#' Creating a basic, unscaled bar chart of Prime Contract Obligations by Agency
#'
#' @param company_name Name of the company, used in filename and chart title
#' @param FY Fiscal year to filter out, commonly the current fiscal year due to incomplete data
#' @param n_agencies The number of agencies to include in the view; defaults to 6
#' @param scale Dollar scale; default to 1000000
#' @param scale_text Dollar scale as text; defaults to Millions
#' @param FY_range Fiscal Year range included, for example "FY14-FY17"
#' @param num_size Size of heading text for agency names, default to 3. Note that the fewer
#' the agencies used, the larger the text should be
#' @param h Height of saved chart; defaults to 6
#' @param w Width of saved chart; defaults to 11. Note that more agencies requires a greater width
#'
#' @return A bar chart
#'
#' @details The returned chart will determine whether this function is sufficient or a different,
#' more scaled chart is needed
#'
#' @examples bar_primeob_by_agency(company_name = "Leidos", FY = 2018, n_agencies = 5,
#' scale = 1000000000, scale_text = "Billions", FY_range = "FY14-FY17",
#' num_size = 3, h = 7, w = 13)
bar_primeob_by_agency <- function(company_name,
FY = 1,
n_agencies = 6,
scale = 1000000,
scale_text = "Millions",
FY_range,
num_size = 3,
h = 6,
w = 11){
#Location for saving charts
setwd("X:/1 Marielle Folder/Data For R/Created Charts/2018/Contract Obligations by Agency")
data <- read_csv(paste("X:/1 Marielle Folder/Data For R/Contract Obligation by Agency/Company Profiles/", company_name,
" Company Profile.csv", sep = ""))
###Get top n agencyies by obligation
top_n_agencies <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
group_by(funding_agency) %>%
dplyr::summarize(grand_total_transaction_value = sum(transaction_value)) %>%
arrange(desc(grand_total_transaction_value)) %>%
top_n(n_agencies)
top_n_agencies <- top_n_agencies$funding_agency
###Process Data to get total transaction value by year
data.agency.year <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% top_n_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year$fiscal_year = as.character(data.agency.year$fiscal_year)
data.agency.year$facet = factor(data.agency.year$funding_agency, levels = c(top_n_agencies))
###Create Barplot and Save as JPG
plot <- plot.one(data.agency.year, "funding_agency", num_size, scale_text, company_name, FY_range)
return(plot)
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot,
width = w, height = h, units = "in")
}
#' Creating a bar chart of Prime Contract Obligations by Agency that is scaled into a Top
#' Range and Bottom Range (two scalings)
#'
#' @param company_name Name of the company, used in filename and chart title
#' @param FY Fiscal year to filter out, commonly the current fiscal year due to incomplete data
#' @param n_agencies The number of agencies to include in the view; defaults to 6
#' @param scale Dollar scale; default to 1000000
#' @param scale_text Dollar scale as text; defaults to Millions
#' @param FY_range Fiscal Year range included, for example "FY14-FY17"
#' @param top_range agencies to be included in the first scaling
#' @param bottom_range agencies to be included in the second scaling
#' @param optional_third_range if scaling data by three ranges, use this field; otherwise defaults to NULL
#' @param grid_division respective sizes of ranges to arrange the final grid. This generally is
#' the number of agencies in the top_range and the number in the bottom_range written as a vector.
#' @param num_size Size of heading text for agency names, default to 3. Note that the fewer
#' the agencies used, the larger the text should be
#' @param h Height of saved chart; defaults to 6
#' @param w Width of saved chart; defaults to 11. Note that more agencies requires a greater width
#'
#' @return A bar chart, with two differently scales charts of the data
#'
#' @details Unless already known, use the bar_primeob_by_agency() function to determine the Top Range
#' and Bottom Range agencies.
#'
#'
#' @examples bar_primeob_by_agency _scaling(company_name = "Leidos", FY = 2018,
#' n_agencies = 5, scale = 1000000000,
#' scale_text = "Billions", FY_range = "FY14-FY17",
#' top_range = c(1:2,4), bottom_range = c(3,5),
#' grid_division = c(3,2), num_size = 3,
#' h = 7, w = 13)
#'
#'
bar_primeob_by_agency_scaling <- function(company_name,
FY = 1,
n_agencies = 6,
scale = 1000000,
scale_text = "Millions",
FY_range,
top_range,
bottom_range,
optional_third_range = NULL,
grid_division,
num_size = 4,
h = 6,
w = 11){
#Location for saving charts
setwd("X:/1 Marielle Folder/Data For R/Created Charts/2018/Contract Obligations by Agency")
data <- read_csv(paste("X:/1 Marielle Folder/Data For R/Contract Obligation by Agency/Company Profiles/", company_name,
" Company Profile.csv", sep = ""))
###Get top n agencyies by obligation
top_n_agencies <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
group_by(funding_agency) %>%
dplyr::summarize(grand_total_transaction_value = sum(transaction_value)) %>%
arrange(desc(grand_total_transaction_value)) %>%
top_n(n_agencies)
if(is.null(optional_third_range)){
##Seperate Out different scales
top_n_agencies <- top_n_agencies$funding_agency
top_agencies <- top_n_agencies[top_range]
bottom_agencies <- top_n_agencies[bottom_range]
###Process Data to get total transaction value by year - for top and bottom agencies
data.agency.year.top <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% top_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.top$fiscal_year = as.character(data.agency.year.top$fiscal_year)
data.agency.year.top$facet = factor(data.agency.year.top$funding_agency, levels = c(top_agencies))
data.agency.year.bottom <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% bottom_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.bottom$fiscal_year = as.character(data.agency.year.bottom$fiscal_year)
data.agency.year.bottom$facet = factor(data.agency.year.bottom$funding_agency, levels = c(bottom_agencies))
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year.top, "funding_agency", num_size, scale_text)
plot2 <- plot.last(data.agency.year.bottom, "funding_agency", num_size)
plot.all<-grid.arrange(plot1, plot2, nrow = 1, widths = grid_division,
top = textGrob(paste(company_name, " Contract Obligations by Agency ", FY_range, sep = ""), gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot.all,
width = w, height = h, units = "in")
data.agency.year.all <- rbind(data.agency.year.top, data.agency.year.bottom)
}
else{
##Seperate Out different scales
top_n_agencies <- top_n_agencies$funding_agency
top_agencies <- top_n_agencies[top_range]
middle_agencies <- top_n_agencies[optional_third_range]
bottom_agencies <- top_n_agencies[bottom_range]
###Process Data to get total transaction value by year - for top and bottom agencies
data.agency.year.top <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% top_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.top$fiscal_year = as.character(data.agency.year.top$fiscal_year)
data.agency.year.top$facet = factor(data.agency.year.top$funding_agency, levels = c(top_agencies))
data.agency.year.bottom <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% bottom_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.bottom$fiscal_year = as.character(data.agency.year.bottom$fiscal_year)
data.agency.year.bottom$facet = factor(data.agency.year.bottom$funding_agency, levels = c(bottom_agencies))
data.agency.year.middle <- data %>%
select("Fiscal Year", "Funding Agency", "Transaction Value") %>%
dplyr::rename(fiscal_year = "Fiscal Year",
funding_agency = "Funding Agency",
transaction_value = "Transaction Value") %>%
filter(fiscal_year != FY) %>%
filter(funding_agency %in% middle_agencies) %>%
dplyr::group_by(funding_agency, fiscal_year) %>%
dplyr::summarize(total_transaction_value = (sum(transaction_value)/scale))
data.agency.year.middle$fiscal_year = as.character(data.agency.year.middle$fiscal_year)
data.agency.year.middle$facet = factor(data.agency.year.middle$funding_agency, levels = c(middle_agencies))
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year.top, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year.middle, "funding_agency", num_size)
plot3 <- plot.last(data.agency.year.bottom, "funding_agency", num_size)
plot.all<-grid.arrange(plot1, plot2, plot3, nrow = 1, widths = grid_division,
top = textGrob(paste(company_name, " Contract Obligations by Agency ", FY_range, sep = ""), gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot.all,
width = w, height = h, units = "in")
data.agency.year.all <- rbind(data.agency.year.top, data.agency.year.middle, data.agency.year.bottom)
}
return(plot.all)
data.agency.year.all
}
#' Creating a bar chart of Prime Contract Obligations by Agency looking at an agency
#' (or sub-agency) chosen by name to display
#'
#' @param company_name Name of the company, used in filename and chart title
#' @param funding_agency_type1 Which field or subfield the agency is listed under
#' @param funding_agency_name1 The specific agency to display
#' @param funding_agency_type2 optional second agency type; defaults to null
#' @param funding_agency_name2 optional second agency name; defaults to null
#' @param funding_agency_type3 optional third agency type; defaults to null
#' @param funding_agency_name3 optional third agency type; defaults to null
#' @param funding_agency_type4 optional fourth agency type; defaults to null
#' @param funding_agency_name4 optional fourth agency name; defaults to null
#' @param funding_agency_type5 optional fifth agency type; defaults to null
#' @param funding_agency_name5 optional fifth agency name; defaults to null
#' @param FY Fiscal year to filter out, commonly the current fiscal year due to incomplete data
#' @param scale Dollar scale; default to 1000000
#' @param scale_text Dollar scale as text; defaults to Millions
#' @param num_size Size of heading text for agency names, default to 3. Note that the fewer
#' the agencies used, the larger the text should be
#' @param h Height of saved chart; defaults to 6
#' @param w Width of saved chart; defaults to 11. Note that more agencies requires a greater width
#'
#' @return A bar chart, with two differently scales charts of the data
#'
#' @details Unless already known, use the bar_primeob_by_agency() function to determine the Top Range
#' and Bottom Range agencies.
#'
#'
#' @examples bar_primeob_by_agency_choosing(company_name = "LMI Consulting",
#' funding_agency_type1 = "Funding Bureau",
#' funding_agency_name1 = "Customs and Border Protection
#' (CBP)", FY = 2018, scale = 1000000,
#' scale_text = "Millions", FY_range = "FY14-FY17",
#' num_size = 3, h = 6, w = 11)
#'
#' @export
bar_primeob_by_agency_choosing <- function(company_name,
funding_agency_type1,
funding_agency_name1,
funding_agency_type2 = NULL,
funding_agency_name2 = NULL,
funding_agency_type3 = NULL,
funding_agency_name3 = NULL,
funding_agency_type4 = NULL,
funding_agency_name4 = NULL,
funding_agency_type5 = NULL,
funding_agency_name5 = NULL,
FY = 1,
scale = 1000000,
scale_text = "Millions",
FY_range,
num_size = 3,
h = 6,
w = 11){
data <- read_csv(paste("X:/1 Marielle Folder/Data For R/Contract Obligation by Agency/Company Profiles/", company_name,
" Company Profile.csv", sep = ""))
###Process Data to get total transaction value by year
if(is.null(funding_agency_name2)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
###Create Barplot and Save as JPG
plot.all <- plot.one(data.agency.year1, "funding_agency", num_size, scale_text, funding_agency_name1, FY_range)
data.agency.year.all <- data.agency.year1
}
else{
if(is.null(funding_agency_name3)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
legend <- gtable_filter(ggplot_gtable(ggplot_build(plot1)), "guide-box")
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
legend, ncol = 3,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2)
}
else{
if(is.null(funding_agency_name4)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
data.agency.year3 <- process.data.get.sum(data, funding_agency_name3, funding_agency_type3, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
plot3 <- plot.middle(data.agency.year3, "funding_agency", num_size)
legend <- gtable_filter(ggplot_gtable(ggplot_build(plot1)), "guide-box")
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
plot3 + theme(legend.position='hidden'),
legend, ncol = 4,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2, data.agency.year3)
}
else{
if(is.null(funding_agency_name5)){
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
data.agency.year3 <- process.data.get.sum(data, funding_agency_name3, funding_agency_type3, FY, scale)
data.agency.year4 <- process.data.get.sum(data, funding_agency_name4, funding_agency_type4, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
plot3 <- plot.middle(data.agency.year3, "funding_agency", num_size)
plot4 <- plot.middle(data.agency.year4, "funding_agency", num_size)
legend <- gtable_filter(ggplot_gtable(ggplot_build(plot1)), "guide-box")
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
plot3 + theme(legend.position='hidden'),
plot4 + theme(legend.position='hidden'),
legend, ncol = 5,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2, data.agency.year3, data.agency.year4)
}
else{
data.agency.year1 <- process.data.get.sum(data, funding_agency_name1, funding_agency_type1, FY, scale)
data.agency.year2 <- process.data.get.sum(data, funding_agency_name2, funding_agency_type2, FY, scale)
data.agency.year3 <- process.data.get.sum(data, funding_agency_name3, funding_agency_type3, FY, scale)
data.agency.year4 <- process.data.get.sum(data, funding_agency_name4, funding_agency_type4, FY, scale)
data.agency.year5 <- process.data.get.sum(data, funding_agency_name5, funding_agency_type5, FY, scale)
###Create Barplot and Save as JPG
plot1 <- plot.first(data.agency.year1, "funding_agency", num_size, scale_text)
plot2 <- plot.middle(data.agency.year2, "funding_agency", num_size)
plot3 <- plot.middle(data.agency.year3, "funding_agency", num_size)
plot4 <- plot.middle(data.agency.year4, "funding_agency", num_size)
plot5 <- plot.middle(data.agency.year5, "funding_agency", num_size)
plot.all<-grid.arrange(plot1 + theme(legend.position='hidden'),
plot2 + theme(legend.position='hidden'),
plot3 + theme(legend.position='hidden'),
plot4 + theme(legend.position='hidden'),
plot5 + theme(legend.position='hidden'),
legend, ncol = 6,
top = textGrob(paste(company_name, "Contract Obligations by Agency ", FY_range, sep = ""),
gp = gpar(fontsize = 24)), bottom = "Fiscal Year")
data.agency.year.all <- rbind(data.agency.year1, data.agency.year2, data.agency.year3,
data.agency.year4, data.agency.year5)
}
}
}
}
ggsave(paste(company_name, " Contract Obligations by Agency.jpg", sep = ""), plot.all,
width = w, height = h, units = "in")
plot.all
data.agency.year.all
}
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