R/mod_04_experience_fct_helpers.R
In aassumpcao/ColombiaDashboard: Colombia Diaspora Survey Results
Defines functions
plot_q4_4
plot_q3_9
Documented in
plot_q3_9
plot_q4_4
#' helper function
#' @name plot_q3_9
#' @description function to prepare the variables of interest
#' @param country area of study
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q3_9 <- function(country = NULL){
# filter data for question q3_9
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(dplyr::matches('q3_9'), .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- data %>%
tidyr::pivot_longer(
-.data$q3_2, names_to = 'question', values_to = 'where'
) %>%
dplyr::mutate(where = stringr::str_split(.data$where, ',')) %>%
tidyr::unnest(.data$where) %>%
dplyr::mutate(
where = cut(
as.numeric(.data$where),
breaks = c(0, 5, 10, 20, 50),
include.lowest = TRUE)
) %>%
dplyr::mutate(question = dplyr::case_when(
.data$question == 'q3_9_1' ~ 1,
.data$question == 'q3_9_2' ~ 2,
.data$question == 'q3_9_3' ~ 3
)) %>%
dplyr::filter(!is.na(.data$where)) %>%
dplyr::count(.data$question, .data$where) %>%
dplyr::group_by(.data$question) %>%
dplyr::mutate(
percentage = round((.data$n / sum(.data$n)) * 100, 1),
labels = ifelse(.data$percentage > 5, paste0(.data$percentage, '%'), ''),
nsample = paste0('(n = ', sum(.data$n), ')')
) %>%
dplyr::ungroup()
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(where = ordered(.data$where))
levels <- c('In Colombia', 'In country of residence', 'In other countries')
# set plot data
p <- ggplot(
data_final,
aes(
x = .data$question,
y = .data$percentage,
fill = .data$where,
group = .data$where
)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$labels), position = position_stack(vjust = .5), size = 5
)+
guides(fill = guide_legend(title = 'Years of Experience:')) +
scale_y_reverse() +
scale_x_continuous(
breaks = 1:3,
labels = stringr::str_wrap(levels, width = 20),
sec.axis = dup_axis(labels = .data$nsample)
) +
# scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
scale_fill_brewer(direction = -1) +
labs(x = element_blank(), y = paste0('Unique Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
panel.border = element_blank(), axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(), text = element_text(size = 14),
legend.position = 'top'
)
# return result
return(p)
}
#' helper function
#' @name plot_q4_4
#' @description function to prepare the variables of interest
#' @param country what were they doing before leaving and currently
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q4_4 <- function(country = NULL){
# create StatStratum
StatStratum <- ggalluvial::StatStratum
# filter data
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(SOURCE = .data$q4_4, TARGET = .data$q5_6, .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# prepare data
data_prepared <- data %>%
dplyr::select(-.data$q3_2) %>%
dplyr::filter(!is.na(.data$SOURCE) & !is.na(.data$TARGET)) %>%
dplyr::filter_all(dplyr::all_vars(!stringr::str_detect(.data$., 'Other'))) %>%
dplyr::count(.data$SOURCE, .data$TARGET) %>%
dplyr::filter(.data$SOURCE != .data$TARGET)
# isolate the number of variables
samples <- sum(data_prepared$n)
# prepare data
data_final <- data_prepared %>%
dplyr::mutate(fill = as.character(dplyr::row_number())) %>%
dplyr::group_by(.data$SOURCE) %>%
dplyr::mutate(SOURCE_n = sum(as.integer(.data$n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(SOURCE = paste0(.data$SOURCE, ' [', .data$SOURCE_n, ']')) %>%
dplyr::group_by(.data$TARGET) %>%
dplyr::mutate(TARGET_n = sum(as.integer(.data$n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(TARGET = paste0(.data$TARGET, ' [', .data$TARGET_n, ']')) %>%
{ggalluvial::to_lodes_form(data.frame(.), key = 'where', axes = 1:2)}
# prepare data for plot
p <- ggplot(
data = data_final, aes(
x = .data$where,
stratum = .data$stratum,
alluvium = .data$alluvium,
y = as.integer(.data$n),
fill = .data$stratum,
label = .data$stratum
)
)
# create graph
p <- p +
scale_y_continuous() +
scale_x_discrete(expand = c(.2, .2)) +
ggalluvial::geom_flow(width = .1, color = 'white') +
ggalluvial::geom_stratum(alpha = .7, width = .1, color = 'white') +
ggrepel::geom_text_repel(
aes(
label = ifelse(
as.numeric(.data$where) == 1,
stringr::str_wrap(as.character(.data$stratum), width = 20),
NA
)
),
stat = StatStratum, size = 5, direction = 'y', nudge_x = -.1, hjust = 1,
segment.color = NA
) +
ggrepel::geom_text_repel(
aes(
label = ifelse(
as.numeric(.data$where) == 2,
stringr::str_wrap(as.character(.data$stratum), width = 20),
NA
)
),
stat = StatStratum, size = 5, direction = 'y', nudge_x = .1, hjust = 0,
segment.color = NA
) +
labs(y = element_blank(), x = paste0('Respondents: ', samples)) +
theme(
legend.position = 'none', panel.grid = element_blank(),
panel.background = element_blank(),
axis.ticks.y = element_blank(), axis.text.y = element_blank(),
axis.ticks.x = element_blank(), axis.text.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 14)
)
# return chart
return(p)
}
#' helper function
#' @name plot_q5_3
#' @description function to prepare the variables of interest
#' @param country plot time of residency
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_3 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_3')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- data %>%
DiasporaSurveyResults::count_unique('q5_3')
# extract labels
data_prepared <- DiasporaSurveyResults::extract_labels(data_prepared)$data
data_labels <- DiasporaSurveyResults::extract_labels(data_prepared)$labels
# reorder labels
data_labels <- data_labels[c(2,5,3,4,6,1)]
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(question = ordered(.data$question, levels = data_labels))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$abs_total, fill = .data$question)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$abs_total, y = .075*max(.data$abs_total), size = 12),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 10)}) +
labs(y = element_blank(), x = paste0('Respondents: ', samples)) +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_4
#' @description function to prepare the variables of interest
#' @param country why settle
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_4 <- function(country = NULL){
# filter data for question q4_4
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(.data$q5_4, .data$q5_5, .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# prepare data
data_prepared <- data %>%
dplyr::select(-.data$q3_2) %>%
dplyr::transmute(
Respondent = .data$q5_4, Parents = .data$q5_5, id = dplyr::row_number()
) %>%
tidyr::pivot_longer(!.data$id, names_to = 'group', values_to = 'values')
# compute sample sizes
sample1 <- data_prepared %>%
dplyr::filter(.data$group == 'Parents') %>%
tidyr::drop_na(.data$values) %>%
nrow()
sample2 <- data_prepared %>%
dplyr::filter(.data$group == 'Respondent') %>%
tidyr::drop_na(.data$values) %>%
nrow()
# prepare data
data_final <- data_prepared %>%
DiasporaSurveyResults::count_multiple('values', 'group') %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)')) %>%
dplyr::mutate(
perc_total = ifelse(
.data$group == 'Parents',
.data$abs_total / sample1,
.data$abs_total / sample2
),
perc_label = paste0(round(.data$perc_total, 2) * 100, '%')
) %>%
dplyr::mutate(
abs_label = ifelse(
.data$group == 'Parents', -1*.data$abs_label, .data$abs_label
),
group = factor(
.data$group,
levels = c('Parents', 'Respondent'),
labels = c(
paste0('Parents (n = ', sample1, ')'),
paste0('Respondent (n = ', sample2, ')')
)
)
) %>%
dplyr::group_by(.data$group) %>%
dplyr::arrange(.data$abs_label, .by_group = TRUE)
# extract labels
data_labels <- c(
'Better quality of life',
'Better employment opportunities',
'Family ties, friends or romantic partner',
'Better personal security',
'Better educational opportunities',
'More political stability',
'Cultural or language familiarity',
'Scholarship offer',
'Better business/investment opportunities',
'My parents or grandparents made the decision',
'I don\'t know'
)
# assign labels
data_final <- data_final %>%
dplyr::filter(.data$question %in% data_labels) %>%
dplyr::mutate(question = ordered(.data$question, levels = rev(data_labels)))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$abs_label, fill = .data$group)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(
label = ifelse(
.data$abs_label < 0, -1*.data$abs_label, .data$abs_label
),
y = ifelse(
.data$abs_label < 0,.75*min(.data$abs_label),.05*max(.data$abs_label)
)
),
size = 5,
alpha = 1
) +
scale_y_continuous(labels = function(x){ifelse(x < 0, -1*x, x)}) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
geom_hline(aes(yintercept = 0)) +
labs(x = element_blank(), y = element_blank()) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.title = element_blank(), legend.position = 'top',
axis.ticks.x = element_blank(), text = element_text(size = 14)
)
# return chart
return(p)
}
#' helper function
#' @name plot_q5_8
#' @description function to prepare the variables of interest
#' @param country what educational leve are their pursuing
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_8 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_8')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- DiasporaSurveyResults::count_unique(data, 'q5_8')
data_prepared <- data_prepared %>%
dplyr::mutate(
perc_total = (.data$abs_total / samples) * 100,
perc_label = paste0(round(.data$perc_total, 2), '%')
) %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)'))
# extract labels
data_prepared <- DiasporaSurveyResults::extract_labels(data_prepared)$data
data_labels <- c(
'High school degree',
'Technical associate degree',
'Bachelor\'s degree in college',
'Specialization degree',
'Master\'s degree',
'Professional degree (JD, MD)',
'Doctoral degree'
)
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(question = ordered(.data$question, levels = data_labels))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$perc_total, fill = .data$question)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$perc_label, y = .075*max(.data$perc_total), size = 14),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
labs(x = element_blank(), y = paste0('Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_9
#' @description function to prepare the variables of interest
#' @param country what area of study are they in?
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_9 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_9')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# compute sample size
samples <- nrow(data)
# prepare data for plot
data_prepared <- DiasporaSurveyResults::count_unique(data, 'q5_9')
data_prepared <- data_prepared %>%
dplyr::mutate(
labels = .data$question %>%
paste0(' [', round(.data$perc_total, 1), '%]') %>%
stringr::str_wrap(width = 15)
) %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)'))
# set plot data
p <- ggplot(
data_prepared,
aes(
area = .data$abs_label, fill = .data$perc_total, label = .data$labels
)
)
# create plot
p <- p +
treemapify::geom_treemap(start = 'topleft', alpha = .7) +
treemapify::geom_treemap_text(
start = 'topleft', place = 'center', size = 16
) +
labs(y = element_blank(), x = paste0('Unique Respondents: ', samples)) +
scale_fill_distiller(palette = 'Reds', direction = 1) +
theme(
legend.position = 'none',
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_12
#' @description function to prepare the variables of interest
#' @param country role in main job
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_11 <- function(country = NULL){
# filter data for question q4_4
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(.data$q122, .data$q5_11, .data$q3_2) %>%
tidyr::drop_na()
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# prepare data
data_prepared <- data %>%
dplyr::select(-.data$q3_2) %>%
dplyr::transmute(
`In Colombia` = .data$q122,
Currently = .data$q5_11,
id = dplyr::row_number()
) %>%
tidyr::pivot_longer(!.data$id, names_to = 'group', values_to = 'values')
# compute sample sizes
sample1 <- data_prepared %>%
dplyr::filter(.data$group == 'In Colombia') %>%
tidyr::drop_na(.data$values) %>%
nrow()
sample2 <- data_prepared %>%
dplyr::filter(.data$group == 'Currently') %>%
tidyr::drop_na(.data$values) %>%
nrow()
# prepare data
data_final <- data_prepared %>%
DiasporaSurveyResults::count_multiple('values', 'group') %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)')) %>%
dplyr::mutate(
perc_total = ifelse(
.data$group == 'In Colombia',
.data$abs_total / sample1,
.data$abs_total / sample2
),
perc_label = paste0(round(.data$perc_total, 2) * 100, '%')
) %>%
dplyr::mutate(
abs_label = ifelse(
.data$group == 'In Colombia', -1*.data$abs_label, .data$abs_label
),
group = factor(
.data$group,
levels = c('In Colombia', 'Currently'),
labels = c('In Colombia', 'Currently')
)
) %>%
dplyr::group_by(.data$group) %>%
dplyr::arrange(.data$abs_label, .by_group = TRUE)
# extract labels
data_labels <- c(
'Employee',
'Self-Employed or freelancer',
'Business Owner',
'Trainee or intern',
'Unpaid Employee'
)
# assign labels
data_final <- data_final %>%
dplyr::filter(.data$question %in% data_labels) %>%
dplyr::mutate(question = ordered(.data$question, levels = rev(data_labels)))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$abs_label, fill = .data$group)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(
label = ifelse(
.data$abs_label < 0, -1*.data$abs_label, .data$abs_label
),
y = ifelse(
.data$abs_label < 0,.13*min(.data$abs_label),.13*max(.data$abs_label)
)
),
alpha = 1, size = 5
) +
scale_y_continuous(labels = function(x){ifelse(x < 0, -1*x, x)}) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
geom_hline(aes(yintercept = 0)) +
labs(x = element_blank(), y = element_blank()) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.title = element_blank(), legend.position = 'top',
axis.ticks.x = element_blank(), text = element_text(size = 16)
)
# return chart
return(p)
}
#' helper function
#' @name plot_q5_12
#' @description function to prepare the variables of interest
#' @param country entrepreneurship background
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_12 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_12')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- DiasporaSurveyResults::count_multiple(data, 'q5_12')
data_prepared <- data_prepared %>%
dplyr::mutate(
question = stringr::str_remove(.data$question, ' \\(please specify\\)')
)
# extract labels
data_prepared <- DiasporaSurveyResults::extract_labels(data_prepared)$data
data_labels <- c(
'I have taught classes on entrepreneurship',
'I have taken classes on entrepreneurship',
'I used to run a business',
'I am currently running a business',
'I have a history of investing in startup businesses',
'I currently invest in startup businesses',
'Other background with entrepreneurship',
'I do not have a background with entrepreneurship'
)
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(question = ordered(.data$question, levels = data_labels))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$perc_total, fill = .data$question)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$perc_label, y = .075*max(.data$perc_total), size = 12),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
labs(x = element_blank(), y = paste0('Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_13
#' @description function to prepare the variables of interest
#' @param country other background
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_13 <- function(country = NULL){
# filter data
data <- DiasporaSurveyResults::survey_data %>%
dplyr::filter(!is.na(.data$q5_12_8_text) & .data$q5_12_8_text != 'NA') %>%
dplyr::select(background = .data$q5_12_8_text, .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# compute sample size
samples <- nrow(data)
# reorganize
data_prepared <- data %>%
dplyr::count(.data$background) %>%
dplyr::arrange(dplyr::desc(.data$n)) %>%
dplyr::filter(!stringr::str_detect(.data$background, '^(Other)$')) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(
background = ordered(.data$background, levels = rev(.data$background))
)
# prepare final data
p <- ggplot(
data_prepared,
aes(x = .data$background, y = .data$n, fill = .data$background)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$n, y = .075*max(.data$n), size = 12),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 30)}) +
labs(x = element_blank(), y = paste0('Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return plot
return(p)
}
aassumpcao/ColombiaDashboard documentation built on Dec. 18, 2021, 9:27 p.m.
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Defines functions plot_q4_4 plot_q3_9
Documented in plot_q3_9 plot_q4_4
#' helper function
#' @name plot_q3_9
#' @description function to prepare the variables of interest
#' @param country area of study
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q3_9 <- function(country = NULL){
# filter data for question q3_9
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(dplyr::matches('q3_9'), .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- data %>%
tidyr::pivot_longer(
-.data$q3_2, names_to = 'question', values_to = 'where'
) %>%
dplyr::mutate(where = stringr::str_split(.data$where, ',')) %>%
tidyr::unnest(.data$where) %>%
dplyr::mutate(
where = cut(
as.numeric(.data$where),
breaks = c(0, 5, 10, 20, 50),
include.lowest = TRUE)
) %>%
dplyr::mutate(question = dplyr::case_when(
.data$question == 'q3_9_1' ~ 1,
.data$question == 'q3_9_2' ~ 2,
.data$question == 'q3_9_3' ~ 3
)) %>%
dplyr::filter(!is.na(.data$where)) %>%
dplyr::count(.data$question, .data$where) %>%
dplyr::group_by(.data$question) %>%
dplyr::mutate(
percentage = round((.data$n / sum(.data$n)) * 100, 1),
labels = ifelse(.data$percentage > 5, paste0(.data$percentage, '%'), ''),
nsample = paste0('(n = ', sum(.data$n), ')')
) %>%
dplyr::ungroup()
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(where = ordered(.data$where))
levels <- c('In Colombia', 'In country of residence', 'In other countries')
# set plot data
p <- ggplot(
data_final,
aes(
x = .data$question,
y = .data$percentage,
fill = .data$where,
group = .data$where
)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$labels), position = position_stack(vjust = .5), size = 5
)+
guides(fill = guide_legend(title = 'Years of Experience:')) +
scale_y_reverse() +
scale_x_continuous(
breaks = 1:3,
labels = stringr::str_wrap(levels, width = 20),
sec.axis = dup_axis(labels = .data$nsample)
) +
# scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
scale_fill_brewer(direction = -1) +
labs(x = element_blank(), y = paste0('Unique Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
panel.border = element_blank(), axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(), text = element_text(size = 14),
legend.position = 'top'
)
# return result
return(p)
}
#' helper function
#' @name plot_q4_4
#' @description function to prepare the variables of interest
#' @param country what were they doing before leaving and currently
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q4_4 <- function(country = NULL){
# create StatStratum
StatStratum <- ggalluvial::StatStratum
# filter data
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(SOURCE = .data$q4_4, TARGET = .data$q5_6, .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# prepare data
data_prepared <- data %>%
dplyr::select(-.data$q3_2) %>%
dplyr::filter(!is.na(.data$SOURCE) & !is.na(.data$TARGET)) %>%
dplyr::filter_all(dplyr::all_vars(!stringr::str_detect(.data$., 'Other'))) %>%
dplyr::count(.data$SOURCE, .data$TARGET) %>%
dplyr::filter(.data$SOURCE != .data$TARGET)
# isolate the number of variables
samples <- sum(data_prepared$n)
# prepare data
data_final <- data_prepared %>%
dplyr::mutate(fill = as.character(dplyr::row_number())) %>%
dplyr::group_by(.data$SOURCE) %>%
dplyr::mutate(SOURCE_n = sum(as.integer(.data$n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(SOURCE = paste0(.data$SOURCE, ' [', .data$SOURCE_n, ']')) %>%
dplyr::group_by(.data$TARGET) %>%
dplyr::mutate(TARGET_n = sum(as.integer(.data$n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(TARGET = paste0(.data$TARGET, ' [', .data$TARGET_n, ']')) %>%
{ggalluvial::to_lodes_form(data.frame(.), key = 'where', axes = 1:2)}
# prepare data for plot
p <- ggplot(
data = data_final, aes(
x = .data$where,
stratum = .data$stratum,
alluvium = .data$alluvium,
y = as.integer(.data$n),
fill = .data$stratum,
label = .data$stratum
)
)
# create graph
p <- p +
scale_y_continuous() +
scale_x_discrete(expand = c(.2, .2)) +
ggalluvial::geom_flow(width = .1, color = 'white') +
ggalluvial::geom_stratum(alpha = .7, width = .1, color = 'white') +
ggrepel::geom_text_repel(
aes(
label = ifelse(
as.numeric(.data$where) == 1,
stringr::str_wrap(as.character(.data$stratum), width = 20),
NA
)
),
stat = StatStratum, size = 5, direction = 'y', nudge_x = -.1, hjust = 1,
segment.color = NA
) +
ggrepel::geom_text_repel(
aes(
label = ifelse(
as.numeric(.data$where) == 2,
stringr::str_wrap(as.character(.data$stratum), width = 20),
NA
)
),
stat = StatStratum, size = 5, direction = 'y', nudge_x = .1, hjust = 0,
segment.color = NA
) +
labs(y = element_blank(), x = paste0('Respondents: ', samples)) +
theme(
legend.position = 'none', panel.grid = element_blank(),
panel.background = element_blank(),
axis.ticks.y = element_blank(), axis.text.y = element_blank(),
axis.ticks.x = element_blank(), axis.text.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 14)
)
# return chart
return(p)
}
#' helper function
#' @name plot_q5_3
#' @description function to prepare the variables of interest
#' @param country plot time of residency
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_3 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_3')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- data %>%
DiasporaSurveyResults::count_unique('q5_3')
# extract labels
data_prepared <- DiasporaSurveyResults::extract_labels(data_prepared)$data
data_labels <- DiasporaSurveyResults::extract_labels(data_prepared)$labels
# reorder labels
data_labels <- data_labels[c(2,5,3,4,6,1)]
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(question = ordered(.data$question, levels = data_labels))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$abs_total, fill = .data$question)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$abs_total, y = .075*max(.data$abs_total), size = 12),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 10)}) +
labs(y = element_blank(), x = paste0('Respondents: ', samples)) +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_4
#' @description function to prepare the variables of interest
#' @param country why settle
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_4 <- function(country = NULL){
# filter data for question q4_4
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(.data$q5_4, .data$q5_5, .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# prepare data
data_prepared <- data %>%
dplyr::select(-.data$q3_2) %>%
dplyr::transmute(
Respondent = .data$q5_4, Parents = .data$q5_5, id = dplyr::row_number()
) %>%
tidyr::pivot_longer(!.data$id, names_to = 'group', values_to = 'values')
# compute sample sizes
sample1 <- data_prepared %>%
dplyr::filter(.data$group == 'Parents') %>%
tidyr::drop_na(.data$values) %>%
nrow()
sample2 <- data_prepared %>%
dplyr::filter(.data$group == 'Respondent') %>%
tidyr::drop_na(.data$values) %>%
nrow()
# prepare data
data_final <- data_prepared %>%
DiasporaSurveyResults::count_multiple('values', 'group') %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)')) %>%
dplyr::mutate(
perc_total = ifelse(
.data$group == 'Parents',
.data$abs_total / sample1,
.data$abs_total / sample2
),
perc_label = paste0(round(.data$perc_total, 2) * 100, '%')
) %>%
dplyr::mutate(
abs_label = ifelse(
.data$group == 'Parents', -1*.data$abs_label, .data$abs_label
),
group = factor(
.data$group,
levels = c('Parents', 'Respondent'),
labels = c(
paste0('Parents (n = ', sample1, ')'),
paste0('Respondent (n = ', sample2, ')')
)
)
) %>%
dplyr::group_by(.data$group) %>%
dplyr::arrange(.data$abs_label, .by_group = TRUE)
# extract labels
data_labels <- c(
'Better quality of life',
'Better employment opportunities',
'Family ties, friends or romantic partner',
'Better personal security',
'Better educational opportunities',
'More political stability',
'Cultural or language familiarity',
'Scholarship offer',
'Better business/investment opportunities',
'My parents or grandparents made the decision',
'I don\'t know'
)
# assign labels
data_final <- data_final %>%
dplyr::filter(.data$question %in% data_labels) %>%
dplyr::mutate(question = ordered(.data$question, levels = rev(data_labels)))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$abs_label, fill = .data$group)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(
label = ifelse(
.data$abs_label < 0, -1*.data$abs_label, .data$abs_label
),
y = ifelse(
.data$abs_label < 0,.75*min(.data$abs_label),.05*max(.data$abs_label)
)
),
size = 5,
alpha = 1
) +
scale_y_continuous(labels = function(x){ifelse(x < 0, -1*x, x)}) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
geom_hline(aes(yintercept = 0)) +
labs(x = element_blank(), y = element_blank()) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.title = element_blank(), legend.position = 'top',
axis.ticks.x = element_blank(), text = element_text(size = 14)
)
# return chart
return(p)
}
#' helper function
#' @name plot_q5_8
#' @description function to prepare the variables of interest
#' @param country what educational leve are their pursuing
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_8 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_8')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- DiasporaSurveyResults::count_unique(data, 'q5_8')
data_prepared <- data_prepared %>%
dplyr::mutate(
perc_total = (.data$abs_total / samples) * 100,
perc_label = paste0(round(.data$perc_total, 2), '%')
) %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)'))
# extract labels
data_prepared <- DiasporaSurveyResults::extract_labels(data_prepared)$data
data_labels <- c(
'High school degree',
'Technical associate degree',
'Bachelor\'s degree in college',
'Specialization degree',
'Master\'s degree',
'Professional degree (JD, MD)',
'Doctoral degree'
)
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(question = ordered(.data$question, levels = data_labels))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$perc_total, fill = .data$question)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$perc_label, y = .075*max(.data$perc_total), size = 14),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
labs(x = element_blank(), y = paste0('Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_9
#' @description function to prepare the variables of interest
#' @param country what area of study are they in?
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_9 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_9')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# compute sample size
samples <- nrow(data)
# prepare data for plot
data_prepared <- DiasporaSurveyResults::count_unique(data, 'q5_9')
data_prepared <- data_prepared %>%
dplyr::mutate(
labels = .data$question %>%
paste0(' [', round(.data$perc_total, 1), '%]') %>%
stringr::str_wrap(width = 15)
) %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)'))
# set plot data
p <- ggplot(
data_prepared,
aes(
area = .data$abs_label, fill = .data$perc_total, label = .data$labels
)
)
# create plot
p <- p +
treemapify::geom_treemap(start = 'topleft', alpha = .7) +
treemapify::geom_treemap_text(
start = 'topleft', place = 'center', size = 16
) +
labs(y = element_blank(), x = paste0('Unique Respondents: ', samples)) +
scale_fill_distiller(palette = 'Reds', direction = 1) +
theme(
legend.position = 'none',
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_12
#' @description function to prepare the variables of interest
#' @param country role in main job
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_11 <- function(country = NULL){
# filter data for question q4_4
data <- DiasporaSurveyResults::survey_data %>%
dplyr::select(.data$q122, .data$q5_11, .data$q3_2) %>%
tidyr::drop_na()
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# prepare data
data_prepared <- data %>%
dplyr::select(-.data$q3_2) %>%
dplyr::transmute(
`In Colombia` = .data$q122,
Currently = .data$q5_11,
id = dplyr::row_number()
) %>%
tidyr::pivot_longer(!.data$id, names_to = 'group', values_to = 'values')
# compute sample sizes
sample1 <- data_prepared %>%
dplyr::filter(.data$group == 'In Colombia') %>%
tidyr::drop_na(.data$values) %>%
nrow()
sample2 <- data_prepared %>%
dplyr::filter(.data$group == 'Currently') %>%
tidyr::drop_na(.data$values) %>%
nrow()
# prepare data
data_final <- data_prepared %>%
DiasporaSurveyResults::count_multiple('values', 'group') %>%
dplyr::filter(!stringr::str_detect(.data$question, '^(Other)')) %>%
dplyr::mutate(
perc_total = ifelse(
.data$group == 'In Colombia',
.data$abs_total / sample1,
.data$abs_total / sample2
),
perc_label = paste0(round(.data$perc_total, 2) * 100, '%')
) %>%
dplyr::mutate(
abs_label = ifelse(
.data$group == 'In Colombia', -1*.data$abs_label, .data$abs_label
),
group = factor(
.data$group,
levels = c('In Colombia', 'Currently'),
labels = c('In Colombia', 'Currently')
)
) %>%
dplyr::group_by(.data$group) %>%
dplyr::arrange(.data$abs_label, .by_group = TRUE)
# extract labels
data_labels <- c(
'Employee',
'Self-Employed or freelancer',
'Business Owner',
'Trainee or intern',
'Unpaid Employee'
)
# assign labels
data_final <- data_final %>%
dplyr::filter(.data$question %in% data_labels) %>%
dplyr::mutate(question = ordered(.data$question, levels = rev(data_labels)))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$abs_label, fill = .data$group)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(
label = ifelse(
.data$abs_label < 0, -1*.data$abs_label, .data$abs_label
),
y = ifelse(
.data$abs_label < 0,.13*min(.data$abs_label),.13*max(.data$abs_label)
)
),
alpha = 1, size = 5
) +
scale_y_continuous(labels = function(x){ifelse(x < 0, -1*x, x)}) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
geom_hline(aes(yintercept = 0)) +
labs(x = element_blank(), y = element_blank()) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.title = element_blank(), legend.position = 'top',
axis.ticks.x = element_blank(), text = element_text(size = 16)
)
# return chart
return(p)
}
#' helper function
#' @name plot_q5_12
#' @description function to prepare the variables of interest
#' @param country entrepreneurship background
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_12 <- function(country = NULL){
# filter data for question q2_3
data <- DiasporaSurveyResults::prepare_data('q5_12')
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# isolate the number of variables
samples <- nrow(data)
# prepare data for plot
data_prepared <- DiasporaSurveyResults::count_multiple(data, 'q5_12')
data_prepared <- data_prepared %>%
dplyr::mutate(
question = stringr::str_remove(.data$question, ' \\(please specify\\)')
)
# extract labels
data_prepared <- DiasporaSurveyResults::extract_labels(data_prepared)$data
data_labels <- c(
'I have taught classes on entrepreneurship',
'I have taken classes on entrepreneurship',
'I used to run a business',
'I am currently running a business',
'I have a history of investing in startup businesses',
'I currently invest in startup businesses',
'Other background with entrepreneurship',
'I do not have a background with entrepreneurship'
)
# assign labels
data_final <- data_prepared %>%
dplyr::mutate(question = ordered(.data$question, levels = data_labels))
# set plot data
p <- ggplot(
data_final,
aes(x = .data$question, y = .data$perc_total, fill = .data$question)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$perc_label, y = .075*max(.data$perc_total), size = 12),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 25)}) +
labs(x = element_blank(), y = paste0('Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return result
return(p)
}
#' helper function
#' @name plot_q5_13
#' @description function to prepare the variables of interest
#' @param country other background
#' @import ggplot2
#' @importFrom rlang .data .env
#' @export
plot_q5_13 <- function(country = NULL){
# filter data
data <- DiasporaSurveyResults::survey_data %>%
dplyr::filter(!is.na(.data$q5_12_8_text) & .data$q5_12_8_text != 'NA') %>%
dplyr::select(background = .data$q5_12_8_text, .data$q3_2)
# add country filter
if (!is.null(country)) {
if (country != 'All') {
data <- dplyr::filter(data, .data$q3_2 == country)
}
}
# compute sample size
samples <- nrow(data)
# reorganize
data_prepared <- data %>%
dplyr::count(.data$background) %>%
dplyr::arrange(dplyr::desc(.data$n)) %>%
dplyr::filter(!stringr::str_detect(.data$background, '^(Other)$')) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(
background = ordered(.data$background, levels = rev(.data$background))
)
# prepare final data
p <- ggplot(
data_prepared,
aes(x = .data$background, y = .data$n, fill = .data$background)
)
# create plot
p <- p +
geom_bar(stat = 'identity', alpha = .7) +
geom_text(
aes(label = .data$n, y = .075*max(.data$n), size = 12),
alpha = 1
) +
scale_x_discrete(labels = function(x){stringr::str_wrap(x, width = 30)}) +
labs(x = element_blank(), y = paste0('Respondents: ', samples)) +
coord_flip() +
theme_bw() +
theme(
panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(), panel.border = element_blank(),
legend.position = 'none', axis.ticks.x = element_blank(),
axis.title.x = element_text(margin = margin(10,0,0,0), hjust = 1),
text = element_text(size = 18)
)
# return plot
return(p)
}
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.