data-raw/visuals_prez.R
In USAID-OHA-SI/ingestMMD: Ingest Multi-Month Dispensing (MMD) Collection Tool Data
library(tidyverse)
library(ggplot2)
library(scales)
library(extrafont)
library(gridExtra)
df <- read_csv("out/MMD_National_Data_June_20190728.csv")
df_mer <- read_rds("~/ICPI/Data/MER_Structured_Datasets_OU_IM_FY17-19_20190621_v2_2.rds")
#colors
dblue <- "#4a66ac"
dblue50 <- "#90a2cf"
lblue <- "#629dd1"
lblue50 <- "#a0c4e3"
teal <- "#5aa2ae"
teal50 <- "#9bc7ce"
bgray <- "#7f8fa9"
#rename DRC
df <- mutate(df, operatingunit = ifelse(operatingunit == "Democratic Republic of the Congo", "DRC", operatingunit))
df_mer <- mutate(df_mer, operatingunit = ifelse(operatingunit == "Democratic Republic of the Congo", "DRC", operatingunit))
#how many OUs reported on eligible
ous <- df %>%
filter(regimen_mmd_eligible_share > 0) %>%
count(operatingunit, wt = regimen_mmd_eligible_share) %>%
pull(operatingunit)
df_tx <- df_mer %>%
filter(operatingunit %in% ous,
indicator %in% c("TX_CURR", "TX_PVLS"),
standardizeddisaggregate %in% c("Total Numerator", "Total Denominator"),
fiscal_year >= 2018) %>%
mutate(indicator = ifelse(numeratordenom == "D", paste0(indicator, "_D"), indicator)) %>%
group_by(operatingunit, indicator, fiscal_year) %>%
summarise(cumulative = sum(cumulative, na.rm = TRUE)) %>%
ungroup() %>%
spread(indicator, cumulative) %>%
group_by(operatingunit) %>%
mutate(`VL Coverage` = TX_PVLS_D / lag(TX_CURR),
`VL Suppression` = TX_PVLS / TX_PVLS_D) %>%
filter(fiscal_year == 2019) %>%
select(-fiscal_year)
df_comp <- df %>%
group_by(operatingunit) %>%
summarise_at(vars(regimen_count, regimen_mmd_eligible_count, regimen_mmd_enrolled_count),
sum, na.rm = TRUE) %>%
ungroup() %>%
filter(regimen_mmd_eligible_count > 0) %>%
left_join(df_tx, ., by = "operatingunit") %>%
#mutate(tx_curr_adj = ifelse(regimen_count > tx_curr_cum, regimen_count, tx_curr_cum)) %>%
summarise_at(vars(TX_CURR, regimen_count, regimen_mmd_eligible_count, regimen_mmd_enrolled_count),
sum, na.rm = TRUE) %>%
ungroup() %>%
rename(`TX_CURR [MER]` = TX_CURR,
`Tx regimens reported June` = regimen_count,
`MMD Eligible` = regimen_mmd_eligible_count,
`MMD Enrolled` = regimen_mmd_enrolled_count) %>%
gather(ind, val, -operatingunit) %>%
mutate(ind = factor(ind, c("TX_CURR [MER]", "Tx regimens reported June",
"MMD Eligible", "MMD Enrolled")))
ou_order <- df_comp %>%
spread(ind, val) %>%
mutate(mmd_share = `MMD Enrolled`/`TX_CURR [MER]`) %>%
arrange(mmd_share) %>%
pull(operatingunit)
df_comp %>%
mutate(operatingunit = factor(operatingunit, ou_order),
val = val/1000) %>%
ggplot(aes(ind, val, fill = ind)) +
geom_col(position = position_dodge()) +
scale_y_continuous(labels = comma) +
scale_fill_manual(values = c(bgray, lblue, teal, dblue)) +
labs(y = "patients (thousands)", x = "") +
#coord_flip() +
facet_wrap(. ~ operatingunit, scale = "free") +
theme_light() +
theme(legend.position = "top",
legend.text = element_text(size = 11, color = "#7f7f7f"),
legend.title = element_blank(),
text = element_text(family = "Gill Sans MT", size = 12),
#strip.text = element_text(size = 20),
axis.ticks = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_blank())
ggsave("out/MMD_cascade.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
df_ou_breakdown <- df %>%
group_by(operatingunit) %>%
summarise_at(vars(regimen_count, regimen_mmd_eligible_count, regimen_mmd_enrolled_count),
sum, na.rm = TRUE) %>%
ungroup() %>%
filter(regimen_mmd_eligible_count > 0) %>%
mutate(Enrolled = regimen_mmd_enrolled_count,
`Eligible, Not Enrolled` = regimen_mmd_eligible_count - regimen_mmd_enrolled_count,
`Not Eligible` = regimen_count - regimen_mmd_eligible_count) %>%
select(-ends_with("count")) %>%
gather(type, val, -operatingunit) %>%
mutate(type = factor(type, c("Enrolled", "Eligible, Not Enrolled", "Not Eligible"))) %>%
group_by(operatingunit) %>%
mutate(share = val /sum(val)) %>%
ungroup()
ou_order <- df_ou_breakdown %>%
select(-val) %>%
spread(type, share) %>%
arrange(Enrolled) %>%
pull(operatingunit)
df_ou_breakdown <- mutate(df_ou_breakdown, operatingunit = factor(operatingunit, ou_order))
df_ou_breakdown %>%
ggplot(aes(operatingunit, share, fill = type)) +
geom_col() +
geom_text(aes(label = percent(share)), hjust = -.2,
color = "#595959", family = "Gill Sans MT") +
scale_y_continuous(limits = c(0, 1.1), breaks = c(0, .25, .5, .75, 1),
label = percent) +
scale_fill_manual(values = c(dblue, teal, lblue)) +
labs(x = "", y = "") +
coord_flip() +
facet_wrap(. ~ type) +
theme_light() +
theme(legend.position = "none",
text = element_text(family = "Gill Sans MT", size = 14),
strip.text = element_text(size = 20),
axis.ticks = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_blank())
ggsave("out/MMD_enrollment.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
mmd_distro <- df %>%
mutate(mmdtarget_target = ifelse(mmdtarget_duration == "1 month" & operatingunit == "Ghana", 0, mmdtarget_target)) %>%
filter(!is.na(mmdtarget_target),
month == "2019-06-01") %>%
mutate(mmd = mmdtarget_duration!= "1 month")
ou_order <- mmd_distro %>%
filter(mmdtarget_duration == "1 month") %>%
select(operatingunit, mmdtarget_percent) %>%
arrange(desc(mmdtarget_percent)) %>%
distinct(operatingunit) %>%
pull(operatingunit)
mmd_distro %>%
mutate(mmdtarget_duration = str_remove(mmdtarget_duration, " mo.*"),
mmdtarget_duration = str_replace(mmdtarget_duration, "6", "6+"),
operatingunit = factor(operatingunit, ou_order),
mmdtarget_target = mmdtarget_target/1000) %>%
ggplot(aes(mmdtarget_duration, mmdtarget_target, fill = mmd)) +
geom_col() +
labs(y = "patients (thousands)", x = "month of Rx dispensed") +
scale_y_continuous(labels = comma) +
scale_fill_manual(values = c(lblue, dblue)) +
facet_wrap(. ~ operatingunit, scales = "free_y") +
theme_light() +
theme(legend.position = "none",
axis.title = element_text(color = "#595959"),
text = element_text(family = "Gill Sans MT", size = 13),
axis.text.x = element_text(color = "#595959"),
strip.text = element_text(size = 14),
axis.ticks = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank())
ggsave("out/MMD_distro.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
ou_sel <- df %>%
filter(category == "Plan",
month > "2019-07-01",
!is.na(mmdtarget_target)) %>%
distinct(operatingunit) %>%
pull()
plan <- df %>%
filter(category == "Plan",
operatingunit %in% ou_sel,
!is.na(mmdtarget_target)) %>%
mutate(mmd = ifelse(mmdtarget_duration!= "1 month", "MMD", "1 month")) %>%
group_by(operatingunit, month, mmd) %>%
summarise_at(vars(mmdtarget_target), sum, na.rm = TRUE) %>%
group_by(operatingunit, month) %>%
mutate(mmd_share = mmdtarget_target/sum(mmdtarget_target)) %>%
ungroup() %>%
mutate(label = case_when(month == max(month) ~ operatingunit))
# mutate(label = case_when(operatingunit == "Namibia" & month == min(month) ~ operatingunit,
# operatingunit != "Namibia" & month == max(month) ~ operatingunit))
plan %>%
filter(mmd == "MMD",
is.finite(mmd_share)) %>%
arrange(operatingunit, month) %>%
ggplot(aes(month, mmd_share, group = operatingunit, color = operatingunit)) +
geom_hline(yintercept = .8, linetype = "dotted", size = 1) +
geom_point(size = 4) +
geom_line(size = 1) +
geom_text(aes(label = label), vjust = -1, na.rm = TRUE, size = 5) +
labs(x = "", y = "share of patients on MMD") +
#expand_limits(y = 0) + #x = as.Date("2020-10-01")) +
expand_limits(y = 1.05) +
scale_y_continuous(labels = percent_format(1)) +
scale_color_manual(values = c(lblue, teal, lblue50, bgray, dblue, dblue50)) +
#scale_x_continuous(labels = date_format(monht,format = "%m%Y")) +
theme_light() +
theme(legend.position = "none",
axis.title.y = element_text(color = "#595959", size = 13),
text = element_text(family = "Gill Sans MT", size = 14),
axis.ticks = element_blank())
ggsave("out/MMD_trend.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
# df_vl <- mmd_distro %>%
# filter(!(operatingunit == "Kenya" & is.na(mmdtarget_notes))) %>% #remove kenya peds
# group_by(operatingunit, mmd) %>%
# summarise_at(vars(mmdtarget_target, mmdtarget_percent), sum, na.rm = TRUE) %>%
# ungroup() %>%
# filter(mmd == TRUE) %>%
# left_join(df_tx, by = "operatingunit") %>%
# filter(!is.na(TX_CURR)) %>%
# select(operatingunit, `MMD Share` = mmdtarget_percent, `VL Coverage`, `VL Suppression`) %>%
# gather(ind, val, -operatingunit) %>%
# mutate(operatingunit = factor(operatingunit, ou_order),
# lab_type = case_when(operatingunit == "Ghana" ~ ind))
#
# df_vl %>%
# ggplot(aes(val, operatingunit, color = ind)) +
# geom_point(size = 14,
# #ifelse(df_vl$ind == "MMD_Share", 16, 14),
# na.rm = TRUE) +
# geom_text(aes(label = lab_type), vjust = -2.3,
# family = "Gill Sans MT", na.rm = TRUE) +
# geom_text(aes(label = percent(val, 1)), color = ifelse(df_vl$ind == "MMD Share", "black", "white"),
# family = "Gill Sans MT",
# check_overlap = TRUE, na.rm = TRUE) +
# expand_limits(x = c(0, 1.05), y = 9) +
# scale_x_continuous(labels = percent) +
# scale_color_manual(values = c(teal, lblue, dblue)) +
# labs(x = "", y = "") +
# theme_light() +
# theme(legend.position = "none",
# text = element_text(family = "Gill Sans MT", size = 14),
# panel.border = element_blank(),
# # panel.grid.major.x = element_blank(),
# # panel.grid.minor.x = element_blank(),
# panel.grid.major.y = element_line(color = "gray", size = 1),
# axis.text.x = element_blank(),
# axis.ticks.y = element_blank())
#
# ggsave("out/MMD_vl.png", dpi = 300,
# units = c("in"), width = 8.6, height = 4)
#
# df_vl2 <- mmd_distro %>%
# filter(!(operatingunit == "Kenya" & is.na(mmdtarget_notes))) %>% #remove kenya peds
# group_by(operatingunit, mmd) %>%
# summarise_at(vars(mmdtarget_target, mmdtarget_percent), sum, na.rm = TRUE) %>%
# ungroup() %>%
# filter(mmd == TRUE) %>%
# left_join(df_tx, by = "operatingunit") %>%
# filter(!is.na(TX_CURR)) %>%
# select(operatingunit, `MMD Share` = mmdtarget_percent, `VL Coverage`, `VL Suppression`) %>%
# mutate(`VL Suppression` = `VL Coverage` * `VL Suppression`
# #`MMD Share` = `VL Suppression` * `MMD Share`
# ) %>%
# arrange(`VL Coverage`) %>%
# mutate(operatingunit = as_factor(operatingunit)) %>%
# gather(ind, val, -operatingunit) %>%
# mutate(lab_type = case_when(operatingunit == "Kenya" ~ ind))
#
# df_vl2 %>%
# ggplot(aes(val, operatingunit, color = ind)) +
# geom_point(size = 14,
# #ifelse(df_vl$ind == "MMD_Share", 16, 14),
# na.rm = TRUE) +
# geom_text(aes(label = lab_type), vjust = -2.3,
# hjust = ifelse(df_vl2$lab_type == "VL Coverage", .1, .5),
# family = "Gill Sans MT", na.rm = TRUE) +
# geom_text(aes(label = percent(val, 1)), color = ifelse(df_vl$ind == "MMD Share", "black", "white"),
# family = "Gill Sans MT",
# check_overlap = TRUE, na.rm = TRUE) +
# expand_limits(x = c(0, 1), y = 9) +
# scale_x_continuous(labels = percent) +
# scale_color_manual(values = c(teal50, lblue, dblue)) +
# labs(x = "", y = "") +
# theme_light() +
# theme(legend.position = "none",
# text = element_text(family = "Gill Sans MT", size = 14),
# panel.border = element_blank(),
# # panel.grid.major.x = element_blank(),
# # panel.grid.minor.x = element_blank(),
# panel.grid.major.y = element_line(color = "gray", size = 1),
# axis.text.x = element_blank(),
# axis.ticks.y = element_blank())
#
# ggsave("out/MMD_vl2.png", dpi = 300,
# units = c("in"), width = 8.6, height = 4)
df_vl2 <- mmd_distro %>%
filter(!(operatingunit == "Kenya" & is.na(mmdtarget_notes))) %>% #remove kenya peds
group_by(operatingunit, mmd) %>%
summarise_at(vars(mmdtarget_target, mmdtarget_percent), sum, na.rm = TRUE) %>%
ungroup() %>%
filter(mmd == TRUE) %>%
left_join(df_tx, by = "operatingunit") %>%
filter(!is.na(TX_CURR)) %>%
select(operatingunit, `MMD Share` = mmdtarget_percent, `VL Coverage`, `Documented VL Suppression` = `VL Suppression`) %>%
mutate(`Documented VL Suppression` = `VL Coverage` * `Documented VL Suppression`) %>%
arrange(`VL Coverage`) %>%
mutate(operatingunit = as_factor(operatingunit)) %>%
gather(ind, val, -operatingunit) %>%
mutate(lab_type = case_when(operatingunit == "Kenya" ~ ind))
df_vl3 <- filter(df_vl2, ind != "MMD Share")
v1 <- df_vl3 %>%
ggplot(aes(val, operatingunit, color = ind)) +
geom_point(size = 11,
#ifelse(df_vl$ind == "MMD_Share", 16, 14),
na.rm = TRUE) +
geom_text(aes(label = lab_type), vjust = -2.3,
hjust = ifelse(df_vl3$lab_type == "VL Coverage", .1, 1),
family = "Gill Sans MT", na.rm = TRUE) +
geom_text(aes(label = percent(val, 1)), hjust = ifelse(df_vl3$ind == "Documented VL Suppression", 1.8, .5),
color = ifelse(df_vl3$ind == "Documented VL Suppression", dblue, "white"),
family = "Gill Sans MT",
check_overlap = TRUE, na.rm = TRUE) +
expand_limits(x = c(0, 1.05), y = 13.5) +
scale_x_continuous(labels = percent) +
scale_color_manual(values = c(dblue, lblue)) +
labs(x = "", y = "") +
theme_light() +
theme(legend.position = "none",
text = element_text(family = "Gill Sans MT", size = 14),
panel.border = element_blank(),
# panel.grid.major.x = element_blank(),
# panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(color = "gray", size = 1),
axis.text.x = element_blank(),
axis.ticks = element_blank())
df_vl4 <-filter(df_vl2, ind == "MMD Share")
v2 <- df_vl4 %>%
ggplot(aes(val, operatingunit, color = ind)) +
geom_point(size = 11,
#ifelse(df_vl$ind == "MMD_Share", 16, 14),
na.rm = TRUE) +
geom_text(aes(label = lab_type), vjust = -2.3,
hjust = ifelse(df_vl4$lab_type == "VL Coverage", .1, .5),
family = "Gill Sans MT", na.rm = TRUE) +
geom_text(aes(label = percent(val, 1)), color = "black",
family = "Gill Sans MT",
check_overlap = TRUE, na.rm = TRUE) +
expand_limits(x = c(0, 1.05), y = 13.5) +
scale_x_continuous(labels = percent) +
scale_color_manual(values = c(teal50)) +
labs(x = "", y = "") +
theme_light() +
theme(legend.position = "none",
text = element_text(family = "Gill Sans MT", size = 14),
panel.border = element_blank(),
# panel.grid.major.x = element_blank(),
# panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(color = "gray", size = 1),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_text(color = "white"))
grid.arrange(v1, v2, ncol = 2) %>%
ggsave("out/MMD_vl6.png", ., dpi = 300,
units = c("in"), width = 8.6, height = 4)
USAID-OHA-SI/ingestMMD documentation built on July 31, 2019, 7:36 a.m.
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library(tidyverse)
library(ggplot2)
library(scales)
library(extrafont)
library(gridExtra)
df <- read_csv("out/MMD_National_Data_June_20190728.csv")
df_mer <- read_rds("~/ICPI/Data/MER_Structured_Datasets_OU_IM_FY17-19_20190621_v2_2.rds")
#colors
dblue <- "#4a66ac"
dblue50 <- "#90a2cf"
lblue <- "#629dd1"
lblue50 <- "#a0c4e3"
teal <- "#5aa2ae"
teal50 <- "#9bc7ce"
bgray <- "#7f8fa9"
#rename DRC
df <- mutate(df, operatingunit = ifelse(operatingunit == "Democratic Republic of the Congo", "DRC", operatingunit))
df_mer <- mutate(df_mer, operatingunit = ifelse(operatingunit == "Democratic Republic of the Congo", "DRC", operatingunit))
#how many OUs reported on eligible
ous <- df %>%
filter(regimen_mmd_eligible_share > 0) %>%
count(operatingunit, wt = regimen_mmd_eligible_share) %>%
pull(operatingunit)
df_tx <- df_mer %>%
filter(operatingunit %in% ous,
indicator %in% c("TX_CURR", "TX_PVLS"),
standardizeddisaggregate %in% c("Total Numerator", "Total Denominator"),
fiscal_year >= 2018) %>%
mutate(indicator = ifelse(numeratordenom == "D", paste0(indicator, "_D"), indicator)) %>%
group_by(operatingunit, indicator, fiscal_year) %>%
summarise(cumulative = sum(cumulative, na.rm = TRUE)) %>%
ungroup() %>%
spread(indicator, cumulative) %>%
group_by(operatingunit) %>%
mutate(`VL Coverage` = TX_PVLS_D / lag(TX_CURR),
`VL Suppression` = TX_PVLS / TX_PVLS_D) %>%
filter(fiscal_year == 2019) %>%
select(-fiscal_year)
df_comp <- df %>%
group_by(operatingunit) %>%
summarise_at(vars(regimen_count, regimen_mmd_eligible_count, regimen_mmd_enrolled_count),
sum, na.rm = TRUE) %>%
ungroup() %>%
filter(regimen_mmd_eligible_count > 0) %>%
left_join(df_tx, ., by = "operatingunit") %>%
#mutate(tx_curr_adj = ifelse(regimen_count > tx_curr_cum, regimen_count, tx_curr_cum)) %>%
summarise_at(vars(TX_CURR, regimen_count, regimen_mmd_eligible_count, regimen_mmd_enrolled_count),
sum, na.rm = TRUE) %>%
ungroup() %>%
rename(`TX_CURR [MER]` = TX_CURR,
`Tx regimens reported June` = regimen_count,
`MMD Eligible` = regimen_mmd_eligible_count,
`MMD Enrolled` = regimen_mmd_enrolled_count) %>%
gather(ind, val, -operatingunit) %>%
mutate(ind = factor(ind, c("TX_CURR [MER]", "Tx regimens reported June",
"MMD Eligible", "MMD Enrolled")))
ou_order <- df_comp %>%
spread(ind, val) %>%
mutate(mmd_share = `MMD Enrolled`/`TX_CURR [MER]`) %>%
arrange(mmd_share) %>%
pull(operatingunit)
df_comp %>%
mutate(operatingunit = factor(operatingunit, ou_order),
val = val/1000) %>%
ggplot(aes(ind, val, fill = ind)) +
geom_col(position = position_dodge()) +
scale_y_continuous(labels = comma) +
scale_fill_manual(values = c(bgray, lblue, teal, dblue)) +
labs(y = "patients (thousands)", x = "") +
#coord_flip() +
facet_wrap(. ~ operatingunit, scale = "free") +
theme_light() +
theme(legend.position = "top",
legend.text = element_text(size = 11, color = "#7f7f7f"),
legend.title = element_blank(),
text = element_text(family = "Gill Sans MT", size = 12),
#strip.text = element_text(size = 20),
axis.ticks = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_blank())
ggsave("out/MMD_cascade.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
df_ou_breakdown <- df %>%
group_by(operatingunit) %>%
summarise_at(vars(regimen_count, regimen_mmd_eligible_count, regimen_mmd_enrolled_count),
sum, na.rm = TRUE) %>%
ungroup() %>%
filter(regimen_mmd_eligible_count > 0) %>%
mutate(Enrolled = regimen_mmd_enrolled_count,
`Eligible, Not Enrolled` = regimen_mmd_eligible_count - regimen_mmd_enrolled_count,
`Not Eligible` = regimen_count - regimen_mmd_eligible_count) %>%
select(-ends_with("count")) %>%
gather(type, val, -operatingunit) %>%
mutate(type = factor(type, c("Enrolled", "Eligible, Not Enrolled", "Not Eligible"))) %>%
group_by(operatingunit) %>%
mutate(share = val /sum(val)) %>%
ungroup()
ou_order <- df_ou_breakdown %>%
select(-val) %>%
spread(type, share) %>%
arrange(Enrolled) %>%
pull(operatingunit)
df_ou_breakdown <- mutate(df_ou_breakdown, operatingunit = factor(operatingunit, ou_order))
df_ou_breakdown %>%
ggplot(aes(operatingunit, share, fill = type)) +
geom_col() +
geom_text(aes(label = percent(share)), hjust = -.2,
color = "#595959", family = "Gill Sans MT") +
scale_y_continuous(limits = c(0, 1.1), breaks = c(0, .25, .5, .75, 1),
label = percent) +
scale_fill_manual(values = c(dblue, teal, lblue)) +
labs(x = "", y = "") +
coord_flip() +
facet_wrap(. ~ type) +
theme_light() +
theme(legend.position = "none",
text = element_text(family = "Gill Sans MT", size = 14),
strip.text = element_text(size = 20),
axis.ticks = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_blank())
ggsave("out/MMD_enrollment.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
mmd_distro <- df %>%
mutate(mmdtarget_target = ifelse(mmdtarget_duration == "1 month" & operatingunit == "Ghana", 0, mmdtarget_target)) %>%
filter(!is.na(mmdtarget_target),
month == "2019-06-01") %>%
mutate(mmd = mmdtarget_duration!= "1 month")
ou_order <- mmd_distro %>%
filter(mmdtarget_duration == "1 month") %>%
select(operatingunit, mmdtarget_percent) %>%
arrange(desc(mmdtarget_percent)) %>%
distinct(operatingunit) %>%
pull(operatingunit)
mmd_distro %>%
mutate(mmdtarget_duration = str_remove(mmdtarget_duration, " mo.*"),
mmdtarget_duration = str_replace(mmdtarget_duration, "6", "6+"),
operatingunit = factor(operatingunit, ou_order),
mmdtarget_target = mmdtarget_target/1000) %>%
ggplot(aes(mmdtarget_duration, mmdtarget_target, fill = mmd)) +
geom_col() +
labs(y = "patients (thousands)", x = "month of Rx dispensed") +
scale_y_continuous(labels = comma) +
scale_fill_manual(values = c(lblue, dblue)) +
facet_wrap(. ~ operatingunit, scales = "free_y") +
theme_light() +
theme(legend.position = "none",
axis.title = element_text(color = "#595959"),
text = element_text(family = "Gill Sans MT", size = 13),
axis.text.x = element_text(color = "#595959"),
strip.text = element_text(size = 14),
axis.ticks = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank())
ggsave("out/MMD_distro.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
ou_sel <- df %>%
filter(category == "Plan",
month > "2019-07-01",
!is.na(mmdtarget_target)) %>%
distinct(operatingunit) %>%
pull()
plan <- df %>%
filter(category == "Plan",
operatingunit %in% ou_sel,
!is.na(mmdtarget_target)) %>%
mutate(mmd = ifelse(mmdtarget_duration!= "1 month", "MMD", "1 month")) %>%
group_by(operatingunit, month, mmd) %>%
summarise_at(vars(mmdtarget_target), sum, na.rm = TRUE) %>%
group_by(operatingunit, month) %>%
mutate(mmd_share = mmdtarget_target/sum(mmdtarget_target)) %>%
ungroup() %>%
mutate(label = case_when(month == max(month) ~ operatingunit))
# mutate(label = case_when(operatingunit == "Namibia" & month == min(month) ~ operatingunit,
# operatingunit != "Namibia" & month == max(month) ~ operatingunit))
plan %>%
filter(mmd == "MMD",
is.finite(mmd_share)) %>%
arrange(operatingunit, month) %>%
ggplot(aes(month, mmd_share, group = operatingunit, color = operatingunit)) +
geom_hline(yintercept = .8, linetype = "dotted", size = 1) +
geom_point(size = 4) +
geom_line(size = 1) +
geom_text(aes(label = label), vjust = -1, na.rm = TRUE, size = 5) +
labs(x = "", y = "share of patients on MMD") +
#expand_limits(y = 0) + #x = as.Date("2020-10-01")) +
expand_limits(y = 1.05) +
scale_y_continuous(labels = percent_format(1)) +
scale_color_manual(values = c(lblue, teal, lblue50, bgray, dblue, dblue50)) +
#scale_x_continuous(labels = date_format(monht,format = "%m%Y")) +
theme_light() +
theme(legend.position = "none",
axis.title.y = element_text(color = "#595959", size = 13),
text = element_text(family = "Gill Sans MT", size = 14),
axis.ticks = element_blank())
ggsave("out/MMD_trend.png", dpi = 300,
units = c("in"), width = 8.6, height = 4)
# df_vl <- mmd_distro %>%
# filter(!(operatingunit == "Kenya" & is.na(mmdtarget_notes))) %>% #remove kenya peds
# group_by(operatingunit, mmd) %>%
# summarise_at(vars(mmdtarget_target, mmdtarget_percent), sum, na.rm = TRUE) %>%
# ungroup() %>%
# filter(mmd == TRUE) %>%
# left_join(df_tx, by = "operatingunit") %>%
# filter(!is.na(TX_CURR)) %>%
# select(operatingunit, `MMD Share` = mmdtarget_percent, `VL Coverage`, `VL Suppression`) %>%
# gather(ind, val, -operatingunit) %>%
# mutate(operatingunit = factor(operatingunit, ou_order),
# lab_type = case_when(operatingunit == "Ghana" ~ ind))
#
# df_vl %>%
# ggplot(aes(val, operatingunit, color = ind)) +
# geom_point(size = 14,
# #ifelse(df_vl$ind == "MMD_Share", 16, 14),
# na.rm = TRUE) +
# geom_text(aes(label = lab_type), vjust = -2.3,
# family = "Gill Sans MT", na.rm = TRUE) +
# geom_text(aes(label = percent(val, 1)), color = ifelse(df_vl$ind == "MMD Share", "black", "white"),
# family = "Gill Sans MT",
# check_overlap = TRUE, na.rm = TRUE) +
# expand_limits(x = c(0, 1.05), y = 9) +
# scale_x_continuous(labels = percent) +
# scale_color_manual(values = c(teal, lblue, dblue)) +
# labs(x = "", y = "") +
# theme_light() +
# theme(legend.position = "none",
# text = element_text(family = "Gill Sans MT", size = 14),
# panel.border = element_blank(),
# # panel.grid.major.x = element_blank(),
# # panel.grid.minor.x = element_blank(),
# panel.grid.major.y = element_line(color = "gray", size = 1),
# axis.text.x = element_blank(),
# axis.ticks.y = element_blank())
#
# ggsave("out/MMD_vl.png", dpi = 300,
# units = c("in"), width = 8.6, height = 4)
#
# df_vl2 <- mmd_distro %>%
# filter(!(operatingunit == "Kenya" & is.na(mmdtarget_notes))) %>% #remove kenya peds
# group_by(operatingunit, mmd) %>%
# summarise_at(vars(mmdtarget_target, mmdtarget_percent), sum, na.rm = TRUE) %>%
# ungroup() %>%
# filter(mmd == TRUE) %>%
# left_join(df_tx, by = "operatingunit") %>%
# filter(!is.na(TX_CURR)) %>%
# select(operatingunit, `MMD Share` = mmdtarget_percent, `VL Coverage`, `VL Suppression`) %>%
# mutate(`VL Suppression` = `VL Coverage` * `VL Suppression`
# #`MMD Share` = `VL Suppression` * `MMD Share`
# ) %>%
# arrange(`VL Coverage`) %>%
# mutate(operatingunit = as_factor(operatingunit)) %>%
# gather(ind, val, -operatingunit) %>%
# mutate(lab_type = case_when(operatingunit == "Kenya" ~ ind))
#
# df_vl2 %>%
# ggplot(aes(val, operatingunit, color = ind)) +
# geom_point(size = 14,
# #ifelse(df_vl$ind == "MMD_Share", 16, 14),
# na.rm = TRUE) +
# geom_text(aes(label = lab_type), vjust = -2.3,
# hjust = ifelse(df_vl2$lab_type == "VL Coverage", .1, .5),
# family = "Gill Sans MT", na.rm = TRUE) +
# geom_text(aes(label = percent(val, 1)), color = ifelse(df_vl$ind == "MMD Share", "black", "white"),
# family = "Gill Sans MT",
# check_overlap = TRUE, na.rm = TRUE) +
# expand_limits(x = c(0, 1), y = 9) +
# scale_x_continuous(labels = percent) +
# scale_color_manual(values = c(teal50, lblue, dblue)) +
# labs(x = "", y = "") +
# theme_light() +
# theme(legend.position = "none",
# text = element_text(family = "Gill Sans MT", size = 14),
# panel.border = element_blank(),
# # panel.grid.major.x = element_blank(),
# # panel.grid.minor.x = element_blank(),
# panel.grid.major.y = element_line(color = "gray", size = 1),
# axis.text.x = element_blank(),
# axis.ticks.y = element_blank())
#
# ggsave("out/MMD_vl2.png", dpi = 300,
# units = c("in"), width = 8.6, height = 4)
df_vl2 <- mmd_distro %>%
filter(!(operatingunit == "Kenya" & is.na(mmdtarget_notes))) %>% #remove kenya peds
group_by(operatingunit, mmd) %>%
summarise_at(vars(mmdtarget_target, mmdtarget_percent), sum, na.rm = TRUE) %>%
ungroup() %>%
filter(mmd == TRUE) %>%
left_join(df_tx, by = "operatingunit") %>%
filter(!is.na(TX_CURR)) %>%
select(operatingunit, `MMD Share` = mmdtarget_percent, `VL Coverage`, `Documented VL Suppression` = `VL Suppression`) %>%
mutate(`Documented VL Suppression` = `VL Coverage` * `Documented VL Suppression`) %>%
arrange(`VL Coverage`) %>%
mutate(operatingunit = as_factor(operatingunit)) %>%
gather(ind, val, -operatingunit) %>%
mutate(lab_type = case_when(operatingunit == "Kenya" ~ ind))
df_vl3 <- filter(df_vl2, ind != "MMD Share")
v1 <- df_vl3 %>%
ggplot(aes(val, operatingunit, color = ind)) +
geom_point(size = 11,
#ifelse(df_vl$ind == "MMD_Share", 16, 14),
na.rm = TRUE) +
geom_text(aes(label = lab_type), vjust = -2.3,
hjust = ifelse(df_vl3$lab_type == "VL Coverage", .1, 1),
family = "Gill Sans MT", na.rm = TRUE) +
geom_text(aes(label = percent(val, 1)), hjust = ifelse(df_vl3$ind == "Documented VL Suppression", 1.8, .5),
color = ifelse(df_vl3$ind == "Documented VL Suppression", dblue, "white"),
family = "Gill Sans MT",
check_overlap = TRUE, na.rm = TRUE) +
expand_limits(x = c(0, 1.05), y = 13.5) +
scale_x_continuous(labels = percent) +
scale_color_manual(values = c(dblue, lblue)) +
labs(x = "", y = "") +
theme_light() +
theme(legend.position = "none",
text = element_text(family = "Gill Sans MT", size = 14),
panel.border = element_blank(),
# panel.grid.major.x = element_blank(),
# panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(color = "gray", size = 1),
axis.text.x = element_blank(),
axis.ticks = element_blank())
df_vl4 <-filter(df_vl2, ind == "MMD Share")
v2 <- df_vl4 %>%
ggplot(aes(val, operatingunit, color = ind)) +
geom_point(size = 11,
#ifelse(df_vl$ind == "MMD_Share", 16, 14),
na.rm = TRUE) +
geom_text(aes(label = lab_type), vjust = -2.3,
hjust = ifelse(df_vl4$lab_type == "VL Coverage", .1, .5),
family = "Gill Sans MT", na.rm = TRUE) +
geom_text(aes(label = percent(val, 1)), color = "black",
family = "Gill Sans MT",
check_overlap = TRUE, na.rm = TRUE) +
expand_limits(x = c(0, 1.05), y = 13.5) +
scale_x_continuous(labels = percent) +
scale_color_manual(values = c(teal50)) +
labs(x = "", y = "") +
theme_light() +
theme(legend.position = "none",
text = element_text(family = "Gill Sans MT", size = 14),
panel.border = element_blank(),
# panel.grid.major.x = element_blank(),
# panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(color = "gray", size = 1),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_text(color = "white"))
grid.arrange(v1, v2, ncol = 2) %>%
ggsave("out/MMD_vl6.png", ., dpi = 300,
units = c("in"), width = 8.6, height = 4)
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