Script for processing MARS instrument

Author

Alberto González and Carlos Granell

Published

December 26, 2022

Abstract

R Script to generate Figure 5 of the paper A Fully Instrumentalised Geolocation- And Smartphone-Based Exposure Therapy for Anxiety Disorders: SyMptOMS-ET

Code

library(tidyverse)
library(magick)
library(webshot2)
library(gt)
library(here)
library(viridis)
library(hrbrthemes)
library(patchwork)

Code

code_file <- here::here("mars-data", "codes.csv")
data_file <- here::here("mars-data", "responses.csv")

codes <- readr::read_csv2(code_file, col_names = TRUE,
                          cols(code = col_character(),
                               category = col_character(),
                               short_name = col_character(),
                               long_name = col_character()))

mars <- read_csv2(data_file, col_names = TRUE,
                  cols(.default = col_integer(),
                       PART_ID = col_character()))

Code

mars %>%
  rowwise() %>% 
  mutate(A = mean(c(A1,A2,A3,A4,A5)),
         B = mean(c(B6,B7,B8,B9)),
         C = mean(c(C10,C11,C12)),
         D = mean(c(D13,D14,D15,D16)),
         F = mean(c(F1,F2,F3,F4,F5,F6))) -> mars

mars %>%
  pivot_longer(c('A1', 'A2', 'A3', 'A4', 'A5', 'A', 
                 'B6', 'B7', 'B8', 'B9', 'B',
                 'C10', 'C11', 'C12', 'C',
                 'D13', 'D14', 'D15', 'D16', 'D',
                 'E1','E2', 'E3', 'E4',
                 'F1','F2','F3','F4','F5','F6','F'), names_to = "code", values_to = "value") %>%
  mutate(code = factor(code),
         value = as.double(round(value, 2))) %>%
  select(code, value) %>%
  arrange(code) -> mars_long

dplyr::inner_join(mars_long, codes, by="code") %>%
  mutate(type = ifelse(short_name == "Average", "mean", "item")) %>%
  select(-long_name) -> mars_tidy

mars_tidy %>%
  filter(category=='Engagement') %>%
  group_by(code) %>%
  summarise(five = list(fivenum(value))) %>%
  tidyr::unnest(cols = c(five)) -> mars_fivenum_A


mars_tidy %>%
  filter(category=='Functionality') %>%
  group_by(code) %>%
  summarise(five = list(fivenum(value))) %>%
  tidyr::unnest(cols = c(five)) -> mars_fivenum_B

mars_tidy %>%
  filter(category=='Aesthetics') %>%
  group_by(code) %>%
  summarise(five = list(fivenum(value))) %>%
  tidyr::unnest(cols = c(five)) -> mars_fivenum_C


mars_tidy %>%
  filter(category=='Information') %>%
  group_by(code) %>%
  summarise(five = list(fivenum(value))) %>%
  tidyr::unnest(cols = c(five)) -> mars_fivenum_D

mars_tidy %>%
  filter(category=='Subjective quality') %>%
  group_by(code) %>%
  summarise(five = list(fivenum(value))) %>%
  tidyr::unnest(cols = c(five)) -> mars_fivenum_E

mars_tidy %>%
  filter(category=='App-specific') %>%
  group_by(code) %>%
  summarise(five = list(fivenum(value))) %>%
  tidyr::unnest(cols = c(five)) -> mars_fivenum_F


# table with mean and sd together
mars_tidy %>%
  group_by(code) %>%
  summarise(mean = round(mean(value), 2),
              sd = round(sd(value),2)) -> mars_stat

Boxplots for each MARS section

Code

pal <- viridis(6)
colorA = pal[1] 
colorB = pal[2] 
colorC = pal[3] 
colorD = pal[4] 
colorE = pal[5] 
colorF = pal[6] 

plot_width = 16 #cm
plot_height = 10 #cm
plot_dpi = 600

plot_title_size = 13
plot_subtitle_size = 12

Code

lbl_A <- c("A1", "A2", "A3", "A4", "A5", "A")
cat <- "Engagement"

mars_tidy %>%
  filter(category == cat) %>%
  mutate(code = forcats::fct_relevel(code, lbl_A)) %>%
  ggplot(aes(x=code, y=value)) +
  geom_boxplot(aes(alpha=type),
               width=0.5,
               color=colorA,
               fill=colorA) +
  scale_alpha_discrete(range = c(0.2, 0.5)) +
  stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) +  # display mean as black circle
  geom_dotplot(binaxis = "y", stackdir = "center", fill=colorA, color=colorA, alpha=0.8, dotsize = 0.6) + # display every data point
  geom_text(data = mars_fivenum_A,
            aes(x = code, y = five, label = sprintf("%.2f", five)),
            nudge_x = 0.45, size=3, alpha=1) +
  labs(x=NULL, y=NULL, title=paste("Section A -", cat, "scores"),
       subtitle="(A1) Entertainment, (A2) Interest, (A3) Customization, (A4) Interactivity, \n(A5) Target group, (A) Average Engagement score") +
  ylim(3,5) +
  theme_ipsum() +
  theme(
    legend.position = "none",
    plot.title = element_text(size=plot_title_size),
    plot.subtitle = element_text(size=plot_subtitle_size)) -> plotA

plotA

Code

ggsave(file = here::here("figs", "A.svg"),
      width = plot_width, height = plot_height, units="cm", dpi = plot_dpi)

Code

lbl_B <- c("B6", "B7", "B8", "B9", "B")
cat <- "Functionality"


mars_tidy %>%
  filter(category == cat) %>%
  mutate(code = forcats::fct_relevel(code, lbl_B)) %>%
  ggplot(aes(x=code, y=value)) +
  geom_boxplot(aes(alpha=type), 
               width=0.5, 
               color=colorB, 
               fill=colorB) + 
  scale_alpha_discrete(range = c(0.2, 0.5)) +
  stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) +  # display mean as black circle
  geom_dotplot(binaxis = "y", stackdir = "center", fill=colorB, color=colorB, alpha=0.8, dotsize = 0.6) + # display every data point on the boxplot
  geom_text(data = mars_fivenum_B, 
            aes(x = code, y = five, label = sprintf("%.2f", five)), 
            nudge_x = 0.45, size=3) +
  labs(x=NULL, y=NULL, title=paste("Section B -", cat, "scores"), 
       subtitle="(B6) Performance, (B7) Ease of use, (B8) Navigation, \n(B9) Gestural design, (B) Average Functionality score") +
  ylim(3,5) +
  theme_ipsum() +
  theme(
    legend.position = "none", 
    plot.title = element_text(size=plot_title_size),
    plot.subtitle = element_text(size=plot_subtitle_size)) -> plotB

plotB

Code

ggsave(file = here::here("figs", "B.svg"),
      width = plot_width, height = plot_height, units="cm", dpi = plot_dpi)

Code

lbl_C <- c("C10", "C11", "C12", "C")
cat <- "Aesthetics"

mars_tidy %>%
  filter(category == cat) %>%
  mutate(code = forcats::fct_relevel(code, lbl_C)) %>%
  ggplot(aes(x=code, y=value)) +
  geom_boxplot(aes(alpha=type), 
           width=0.5, 
           color=colorC, 
           fill=colorC) + 
  scale_alpha_discrete(range = c(0.2, 0.5)) +
  stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) +  # display mean as black circle
  geom_dotplot(binaxis = "y", stackdir = "center", fill=colorC, color=colorC, alpha=0.8, dotsize = 0.6) + # display every data point
  geom_text(data = mars_fivenum_C, 
            aes(x = code, y = five, label = sprintf("%.2f", five)), 
            nudge_x = 0.45, size=3) +
  labs(x=NULL, y=NULL, title=paste("Section C -", cat, "scores"), 
       subtitle="(C10) Layout, (C11) Graphics, (C12) Visual appeal,\n(C) Average Aesthetics score") +
  ylim(3,5) +
  theme_ipsum() +
  theme(
    legend.position = "none", 
    plot.title = element_text(size=plot_title_size),
    plot.subtitle = element_text(size=plot_subtitle_size)) -> plotC

plotC

Code

ggsave(file = here::here("figs", "C.svg"),
      width = plot_width, height = plot_height, units="cm", dpi = plot_dpi)

Code

lbl_D <- c("D13", "D14", "D15", "D16", "D")
cat <- "Information"

mars_tidy %>%
  filter(category == cat) %>%
  mutate(code = forcats::fct_relevel(code, lbl_D)) %>%
  ggplot(aes(x=code, y=value)) +
  geom_boxplot(aes(alpha=type), 
           width=0.5, 
           color=colorD, 
           fill=colorD) + 
  scale_alpha_discrete(range = c(0.2, 0.5)) +
  stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) +  # display mean as black circle
  geom_dotplot(binaxis = "y", stackdir = "center", fill=colorD, color=colorD, alpha=0.8, dotsize = 0.6) + # display every data point
  geom_text(data = mars_fivenum_D, 
            aes(x = code, y = five, label = sprintf("%.2f", five)), 
            nudge_x = 0.45, size=3) +
  labs(x=NULL, y=NULL, title=paste("Section D -", cat, "scores"), 
       subtitle="(D13) Quality of information, (D14) Quantity of information, (D15) Visual\ninformation, (D16) Credibility, (D) Average Information score") + 
  ylim(3,5) +
  theme_ipsum() +
  theme(
    legend.position = "none", 
    plot.title = element_text(size=plot_title_size),
    plot.subtitle = element_text(size=plot_subtitle_size)) -> plotD

plotD

Code

ggsave(file = here::here("figs", "D.svg"),
      width = plot_width, height = plot_height, units="cm", dpi = plot_dpi)

Code

lbl_E <- c("E1", "E2", "E3", "E4")
cat <- "Subjective quality"

mars_tidy %>%
  filter(category == cat) %>%
  mutate(code = forcats::fct_relevel(code, lbl_E)) %>%
  ggplot(aes(x=code, y=value)) +
  geom_boxplot(alpha=0.2, 
           width=0.5, 
           color=colorE, 
           fill=colorE) + 
  stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) +  # display mean as black circle
  geom_dotplot(binaxis = "y", stackdir = "center", fill=colorE, color=colorE, alpha=0.8, dotsize = 0.6) + # display every data point
  geom_text(data = mars_fivenum_E, 
            aes(x = code, y = five, label = sprintf("%.2f", five)), 
            nudge_x = 0.45, size=3) +
  labs(x=NULL, y=NULL, title="Section E - Subjective Quality scores", 
       subtitle="(E1) Likelihood to recommend, (E2) Expected usage times (from patient\nperspective) in next 12 months, (E3) Will you pay (from patient perspective)\nfor its use?, (E4) Star rating") + 
  ylim(2,5) +
  theme_ipsum() +
  theme(
    legend.position = "none", 
    plot.title = element_text(size=plot_title_size),
    plot.subtitle = element_text(size=plot_subtitle_size)) -> plotE

plotE

Code

ggsave(file = here::here("figs", "E.svg"),
      width = plot_width, height = plot_height, units="cm", dpi = plot_dpi)

Code

lbl_F <- c("F1", "F2", "F3", "F4", "F5", "F6", "F")
cat <- "App-specific"

mars_tidy %>%
  filter(category == cat) %>%
  mutate(code = forcats::fct_relevel(code, lbl_F)) %>%
  ggplot(aes(x=code, y=value)) +
  geom_boxplot(aes(alpha=type), 
           width=0.5, 
           color=colorF, 
           fill=colorF) + 
  scale_alpha_discrete(range = c(0.2, 0.5)) +
  stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) +  # display mean as black circle
  geom_dotplot(binaxis = "y", stackdir = "center", fill=colorF, color=colorF, alpha=0.8, dotsize = 0.6) + # display every data point
  geom_text(data = mars_fivenum_F, 
            aes(x = code, y = five, label = sprintf("%.2f", five)), 
            nudge_x = 0.45, size=3) +
  labs(x=NULL, y=NULL, title=paste("Section F -", cat, "scores to perceived anxiety in places"), 
       subtitle="(F1) Increase awareness of the importance to increase tolerance, (F2)\nIncrease knowledge on tolerance, (F3) Change attitudes towards improving\ntolerance, (F4) Increase motivation to address tolerance, (F5) Encourage\nfurther help-seeking, (F6) Behaviour change, (F) Average App-specific score") + 
  ylim(3,5) +
  theme_ipsum() +
  theme(
    legend.position = "none", 
    plot.title = element_text(size=plot_title_size),
    plot.subtitle = element_text(size=plot_subtitle_size)) -> plotF

plotF

Code

ggsave(file = here::here("figs", "F.svg"),
      width = plot_width, height = plot_height, units="cm", dpi = plot_dpi)

Average table for each MARS section

Code

as_ggplot <- function(x, filename) {
  path_gt_table_image <- here::here("figs")
  gt_table_image <- gt::gtsave(x, path = path_gt_table_image, filename = filename, expand=5)

  # save image in ggplot -------------------------------------------------------
  table_img <-
    magick::image_read(here::here("figs", filename)) %>%
    magick::image_ggplot(interpolate = TRUE)

  table_img
}

Code

rowname <- dplyr::tibble(stat = c("mean", "sd"))
cell_size = "small"

mars_stat %>%
  filter(str_starts(code, c("A"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_A)) %>%
  arrange(code) %>%
  select(code, mean) %>%
  pivot_wider(names_from = code, values_from = c("mean")) -> meanA
  
mars_stat %>%
  filter(str_starts(code, c("A"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_A)) %>%
  arrange(code) %>%
  select(code, sd) %>%
  pivot_wider(names_from = code, values_from = c("sd")) -> sdA
  
bind_rows(meanA, sdA) %>%
  bind_cols(rowname) %>%
  gt(rowname_col = "stat") %>%
  opt_table_lines("none") %>%
  cols_width(
    everything() ~ px(60)) %>%

  cols_align(
    align = "center",
    columns = everything()
  ) %>%

  tab_options(
    data_row.padding = px(1),     #Reduce the height of rows
    table.align = "left",
    table.width = px(250)) %>%

  # Apply style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
       cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below
       cell_text(weight = "bold")) #Make text bold)
  ) %>%

  tab_style(
    style = cell_text(size = cell_size),
    locations = cells_body(columns = everything())
  ) %>%
  as_ggplot(filename="tbl_A.png") -> tbl_A

Code

mars_stat %>%
  filter(str_starts(code, c("B"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_B)) %>%
  arrange(code) %>%
  select(code, mean) %>%
  pivot_wider(names_from = code, values_from = c("mean")) -> meanB

mars_stat %>%
  filter(str_starts(code, c("B"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_B)) %>%
  arrange(code) %>%
  select(code, sd) %>%
  pivot_wider(names_from = code, values_from = c("sd")) -> sdB

bind_rows(meanB, sdB) %>%
  bind_cols(rowname) %>%
  gt(rowname_col = "stat") %>%
  # gt() %>%
  # cols_hide(columns = c(stat)) %>%
  opt_table_lines("none") %>%
  cols_width(
    everything() ~ px(60)) %>%
  cols_align(
    align = "center",
    columns = everything()
  ) %>%

  tab_options(
    data_row.padding = px(1),     #Reduce the height of rows
    table.align = "left",
    table.width = px(250)) %>%

  # Apply style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
       cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below
       cell_text(weight = "bold")) #Make text bold)
  ) %>%
  tab_style(
    style = cell_text(size = cell_size),
    locations = cells_body(columns = everything())
  ) %>%
  as_ggplot(filename="tbl_B.png") -> tbl_B

Code

mars_stat %>%
  filter(str_starts(code, c("C"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_C)) %>%
  arrange(code) %>%
  select(code, mean) %>%
  pivot_wider(names_from = code, values_from = c("mean")) -> meanC
  
mars_stat %>%
  filter(str_starts(code, c("C"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_C)) %>%
  arrange(code) %>%
  select(code, sd) %>%
  pivot_wider(names_from = code, values_from = c("sd")) -> sdC
  
bind_rows(meanC, sdC) %>%
  bind_cols(rowname) %>%
  gt(rowname_col = "stat") %>%
  opt_table_lines("none") %>%
  cols_width(
    everything() ~ px(60)) %>%

  cols_align(
    align = "center",
    columns = everything()
  ) %>%

  tab_options(
    data_row.padding = px(1),     #Reduce the height of rows
    table.align = "left",
    table.width = px(250)) %>%

  # Apply style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
       cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below
       cell_text(weight = "bold")) #Make text bold)
  ) %>%
  tab_style(
    style = cell_text(size = cell_size),
    locations = cells_body(columns = everything())
  ) %>%
  as_ggplot(filename="tbl_C.png") -> tbl_C

Code

mars_stat %>%
  filter(str_starts(code, c("D"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_D)) %>%
  arrange(code) %>%
  select(code, mean) %>%
  pivot_wider(names_from = code, values_from = c("mean")) -> meanD
  
mars_stat %>%
  filter(str_starts(code, c("D"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_D)) %>%
  arrange(code) %>%
  select(code, sd) %>%
  pivot_wider(names_from = code, values_from = c("sd")) -> sdD
  
bind_rows(meanD, sdD) %>%
  bind_cols(rowname) %>%
  gt(rowname_col = "stat") %>%
  # gt() %>%
  # cols_hide(columns = c(stat)) %>%
  opt_table_lines("none") %>%
  cols_width(
    everything() ~ px(60)) %>%
  cols_align(
    align = "center",
    columns = everything()
  ) %>%

  tab_options(
    data_row.padding = px(1),     #Reduce the height of rows
    table.align = "left",
    table.width = px(250)) %>%

  # Apply style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
       cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below
       cell_text(weight = "bold")) #Make text bold)
  ) %>%
  tab_style(
    style = cell_text(size = cell_size),
    locations = cells_body(columns = everything())
  ) %>%
  as_ggplot(filename="tbl_D.png") -> tbl_D

Code

mars_stat %>%
  filter(str_starts(code, c("E"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_E)) %>%
  arrange(code) %>%
  select(code, mean) %>%
  pivot_wider(names_from = code, values_from = c("mean")) -> meanE
  
mars_stat %>%
  filter(str_starts(code, c("E"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_E)) %>%
  arrange(code) %>%
  select(code, sd) %>%
  pivot_wider(names_from = code, values_from = c("sd")) -> sdE
  
bind_rows(meanE, sdE) %>%
  bind_cols(rowname) %>%
  gt(rowname_col = "stat") %>%
  opt_table_lines("none") %>%
  cols_width(
    everything() ~ px(60)) %>%
  cols_align(
    align = "center",
    columns = everything()
  ) %>%

  tab_options(
    data_row.padding = px(1),     #Reduce the height of rows
    table.align = "left",
    table.width = px(250)) %>%

  # Apply style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
       cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below
       cell_text(weight = "bold")) #Make text bold)
  ) %>%
  tab_style(
    style = cell_text(size = cell_size),
    locations = cells_body(columns = everything())
  ) %>%
  as_ggplot(filename="tbl_E.png") -> tbl_E

Code

mars_stat %>%
  filter(str_starts(code, c("F"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_F)) %>%
  arrange(code) %>%
  select(code, mean) %>%
  pivot_wider(names_from = code, values_from = c("mean")) -> meanF
  
mars_stat %>%
  filter(str_starts(code, c("F"))) %>%
  mutate(code = forcats::fct_relevel(code, lbl_F)) %>%
  arrange(code) %>%
  select(code, sd) %>%
  pivot_wider(names_from = code, values_from = c("sd")) -> sdF
  
bind_rows(meanF, sdF) %>%
  bind_cols(rowname) %>%
  gt(rowname_col = "stat") %>%
  # gt() %>%
  # cols_hide(columns = c(stat)) %>%
  opt_table_lines("none") %>%
  cols_width(
    everything() ~ px(60)) %>%
  cols_align(
    align = "center",
    columns = everything()
  ) %>%

  tab_options(
    data_row.padding = px(1),     #Reduce the height of rows
    table.align = "left",
    table.width = px(7)) %>%

  # Apply style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
       cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below
       cell_text(weight = "bold")) #Make text bold)
  ) %>%
  tab_style(
    style = cell_text(size = cell_size),
    locations = cells_body(columns = everything())
  ) %>%
  as_ggplot(filename="tbl_F.png") -> tbl_F

Figure 1 (Figure 5 in the paper) shows the composite of the boxplots for each MARS section along with average tables

Code

(plotA + plotB) / (tbl_A + tbl_B) / (plotC + plotD) /  (tbl_C + tbl_D) / (plotE + plotF) / (tbl_E + tbl_F) +
  plot_layout(widths = c(1,1))

Figure 1: Results obtained from the answers to each of the six sections of the MARS instrument. All items are rated using a Likert scale from 1 to 5, with two exceptions: (E2) Expected usage times in the next 12 months, with possible answers: 1 (none), 2 (1-2 times), 3 (3-10 times), 4 (10-50 times) and 5 (> 50 times); and (E3) Will to pay for its use, with possible answers: 1 (No), 3(Maybe) and 5 (Yes)

Code

ggsave(file = here::here("figs", "MARS_WDP.svg"),
       width = 15, height = 20, dpi = plot_dpi)

--- title: "Script for processing MARS instrument" author: "Alberto González and Carlos Granell" date: 2022-12-26 abstract: "R Script to generate `Figure 5` of the paper *A Fully Instrumentalised Geolocation- And Smartphone-Based Exposure Therapy for Anxiety Disorders: SyMptOMS-ET*" format: html: code-fold: true code-tools: true --- ```{r} #| label: libraries #| message: false library(tidyverse) library(magick) library(webshot2) library(gt) library(here) library(viridis) library(hrbrthemes) library(patchwork) ``` ```{r} #| label: read-data #| message: false code_file <- here::here("mars-data", "codes.csv") data_file <- here::here("mars-data", "responses.csv") codes <- readr::read_csv2(code_file, col_names = TRUE, cols(code = col_character(), category = col_character(), short_name = col_character(), long_name = col_character())) mars <- read_csv2(data_file, col_names = TRUE, cols(.default = col_integer(), PART_ID = col_character())) ``` ```{r} #| label: data-preparation mars %>% rowwise() %>% mutate(A = mean(c(A1,A2,A3,A4,A5)), B = mean(c(B6,B7,B8,B9)), C = mean(c(C10,C11,C12)), D = mean(c(D13,D14,D15,D16)), F = mean(c(F1,F2,F3,F4,F5,F6))) -> mars mars %>% pivot_longer(c('A1', 'A2', 'A3', 'A4', 'A5', 'A', 'B6', 'B7', 'B8', 'B9', 'B', 'C10', 'C11', 'C12', 'C', 'D13', 'D14', 'D15', 'D16', 'D', 'E1','E2', 'E3', 'E4', 'F1','F2','F3','F4','F5','F6','F'), names_to = "code", values_to = "value") %>% mutate(code = factor(code), value = as.double(round(value, 2))) %>% select(code, value) %>% arrange(code) -> mars_long dplyr::inner_join(mars_long, codes, by="code") %>% mutate(type = ifelse(short_name == "Average", "mean", "item")) %>% select(-long_name) -> mars_tidy mars_tidy %>% filter(category=='Engagement') %>% group_by(code) %>% summarise(five = list(fivenum(value))) %>% tidyr::unnest(cols = c(five)) -> mars_fivenum_A mars_tidy %>% filter(category=='Functionality') %>% group_by(code) %>% summarise(five = list(fivenum(value))) %>% tidyr::unnest(cols = c(five)) -> mars_fivenum_B mars_tidy %>% filter(category=='Aesthetics') %>% group_by(code) %>% summarise(five = list(fivenum(value))) %>% tidyr::unnest(cols = c(five)) -> mars_fivenum_C mars_tidy %>% filter(category=='Information') %>% group_by(code) %>% summarise(five = list(fivenum(value))) %>% tidyr::unnest(cols = c(five)) -> mars_fivenum_D mars_tidy %>% filter(category=='Subjective quality') %>% group_by(code) %>% summarise(five = list(fivenum(value))) %>% tidyr::unnest(cols = c(five)) -> mars_fivenum_E mars_tidy %>% filter(category=='App-specific') %>% group_by(code) %>% summarise(five = list(fivenum(value))) %>% tidyr::unnest(cols = c(five)) -> mars_fivenum_F # table with mean and sd together mars_tidy %>% group_by(code) %>% summarise(mean = round(mean(value), 2), sd = round(sd(value),2)) -> mars_stat ``` ## Boxplots for each MARS section ```{r} #| label: globals-plots pal <- viridis(6) colorA = pal[1] colorB = pal[2] colorC = pal[3] colorD = pal[4] colorE = pal[5] colorF = pal[6] plot_width = 16 #cm plot_height = 10 #cm plot_dpi = 600 plot_title_size = 13 plot_subtitle_size = 12 ``` ```{r} #| label: boxplot-A #| warning: false lbl_A <- c("A1", "A2", "A3", "A4", "A5", "A") cat <- "Engagement" mars_tidy %>% filter(category == cat) %>% mutate(code = forcats::fct_relevel(code, lbl_A)) %>% ggplot(aes(x=code, y=value)) + geom_boxplot(aes(alpha=type), width=0.5, color=colorA, fill=colorA) + scale_alpha_discrete(range = c(0.2, 0.5)) + stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) + # display mean as black circle geom_dotplot(binaxis = "y", stackdir = "center", fill=colorA, color=colorA, alpha=0.8, dotsize = 0.6) + # display every data point geom_text(data = mars_fivenum_A, aes(x = code, y = five, label = sprintf("%.2f", five)), nudge_x = 0.45, size=3, alpha=1) + labs(x=NULL, y=NULL, title=paste("Section A -", cat, "scores"), subtitle="(A1) Entertainment, (A2) Interest, (A3) Customization, (A4) Interactivity, \n(A5) Target group, (A) Average Engagement score") + ylim(3,5) + theme_ipsum() + theme( legend.position = "none", plot.title = element_text(size=plot_title_size), plot.subtitle = element_text(size=plot_subtitle_size)) -> plotA plotA ggsave(file = here::here("figs", "A.svg"), width = plot_width, height = plot_height, units="cm", dpi = plot_dpi) ``` ```{r} #| label: boxplot-B #| warning: false lbl_B <- c("B6", "B7", "B8", "B9", "B") cat <- "Functionality" mars_tidy %>% filter(category == cat) %>% mutate(code = forcats::fct_relevel(code, lbl_B)) %>% ggplot(aes(x=code, y=value)) + geom_boxplot(aes(alpha=type), width=0.5, color=colorB, fill=colorB) + scale_alpha_discrete(range = c(0.2, 0.5)) + stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) + # display mean as black circle geom_dotplot(binaxis = "y", stackdir = "center", fill=colorB, color=colorB, alpha=0.8, dotsize = 0.6) + # display every data point on the boxplot geom_text(data = mars_fivenum_B, aes(x = code, y = five, label = sprintf("%.2f", five)), nudge_x = 0.45, size=3) + labs(x=NULL, y=NULL, title=paste("Section B -", cat, "scores"), subtitle="(B6) Performance, (B7) Ease of use, (B8) Navigation, \n(B9) Gestural design, (B) Average Functionality score") + ylim(3,5) + theme_ipsum() + theme( legend.position = "none", plot.title = element_text(size=plot_title_size), plot.subtitle = element_text(size=plot_subtitle_size)) -> plotB plotB ggsave(file = here::here("figs", "B.svg"), width = plot_width, height = plot_height, units="cm", dpi = plot_dpi) ``` ```{r} #| label: boxplot-C #| warning: false lbl_C <- c("C10", "C11", "C12", "C") cat <- "Aesthetics" mars_tidy %>% filter(category == cat) %>% mutate(code = forcats::fct_relevel(code, lbl_C)) %>% ggplot(aes(x=code, y=value)) + geom_boxplot(aes(alpha=type), width=0.5, color=colorC, fill=colorC) + scale_alpha_discrete(range = c(0.2, 0.5)) + stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) + # display mean as black circle geom_dotplot(binaxis = "y", stackdir = "center", fill=colorC, color=colorC, alpha=0.8, dotsize = 0.6) + # display every data point geom_text(data = mars_fivenum_C, aes(x = code, y = five, label = sprintf("%.2f", five)), nudge_x = 0.45, size=3) + labs(x=NULL, y=NULL, title=paste("Section C -", cat, "scores"), subtitle="(C10) Layout, (C11) Graphics, (C12) Visual appeal,\n(C) Average Aesthetics score") + ylim(3,5) + theme_ipsum() + theme( legend.position = "none", plot.title = element_text(size=plot_title_size), plot.subtitle = element_text(size=plot_subtitle_size)) -> plotC plotC ggsave(file = here::here("figs", "C.svg"), width = plot_width, height = plot_height, units="cm", dpi = plot_dpi) ``` ```{r} #| label: boxplot-D #| warning: false lbl_D <- c("D13", "D14", "D15", "D16", "D") cat <- "Information" mars_tidy %>% filter(category == cat) %>% mutate(code = forcats::fct_relevel(code, lbl_D)) %>% ggplot(aes(x=code, y=value)) + geom_boxplot(aes(alpha=type), width=0.5, color=colorD, fill=colorD) + scale_alpha_discrete(range = c(0.2, 0.5)) + stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) + # display mean as black circle geom_dotplot(binaxis = "y", stackdir = "center", fill=colorD, color=colorD, alpha=0.8, dotsize = 0.6) + # display every data point geom_text(data = mars_fivenum_D, aes(x = code, y = five, label = sprintf("%.2f", five)), nudge_x = 0.45, size=3) + labs(x=NULL, y=NULL, title=paste("Section D -", cat, "scores"), subtitle="(D13) Quality of information, (D14) Quantity of information, (D15) Visual\ninformation, (D16) Credibility, (D) Average Information score") + ylim(3,5) + theme_ipsum() + theme( legend.position = "none", plot.title = element_text(size=plot_title_size), plot.subtitle = element_text(size=plot_subtitle_size)) -> plotD plotD ggsave(file = here::here("figs", "D.svg"), width = plot_width, height = plot_height, units="cm", dpi = plot_dpi) ``` ```{r} #| label: boxplot-E #| warning: false lbl_E <- c("E1", "E2", "E3", "E4") cat <- "Subjective quality" mars_tidy %>% filter(category == cat) %>% mutate(code = forcats::fct_relevel(code, lbl_E)) %>% ggplot(aes(x=code, y=value)) + geom_boxplot(alpha=0.2, width=0.5, color=colorE, fill=colorE) + stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) + # display mean as black circle geom_dotplot(binaxis = "y", stackdir = "center", fill=colorE, color=colorE, alpha=0.8, dotsize = 0.6) + # display every data point geom_text(data = mars_fivenum_E, aes(x = code, y = five, label = sprintf("%.2f", five)), nudge_x = 0.45, size=3) + labs(x=NULL, y=NULL, title="Section E - Subjective Quality scores", subtitle="(E1) Likelihood to recommend, (E2) Expected usage times (from patient\nperspective) in next 12 months, (E3) Will you pay (from patient perspective)\nfor its use?, (E4) Star rating") + ylim(2,5) + theme_ipsum() + theme( legend.position = "none", plot.title = element_text(size=plot_title_size), plot.subtitle = element_text(size=plot_subtitle_size)) -> plotE plotE ggsave(file = here::here("figs", "E.svg"), width = plot_width, height = plot_height, units="cm", dpi = plot_dpi) ``` ```{r} #| label: boxplot-F #| warning: false lbl_F <- c("F1", "F2", "F3", "F4", "F5", "F6", "F") cat <- "App-specific" mars_tidy %>% filter(category == cat) %>% mutate(code = forcats::fct_relevel(code, lbl_F)) %>% ggplot(aes(x=code, y=value)) + geom_boxplot(aes(alpha=type), width=0.5, color=colorF, fill=colorF) + scale_alpha_discrete(range = c(0.2, 0.5)) + stat_summary(fun=mean, geom="point", shape=20, size=4, color="black", alpha=1) + # display mean as black circle geom_dotplot(binaxis = "y", stackdir = "center", fill=colorF, color=colorF, alpha=0.8, dotsize = 0.6) + # display every data point geom_text(data = mars_fivenum_F, aes(x = code, y = five, label = sprintf("%.2f", five)), nudge_x = 0.45, size=3) + labs(x=NULL, y=NULL, title=paste("Section F -", cat, "scores to perceived anxiety in places"), subtitle="(F1) Increase awareness of the importance to increase tolerance, (F2)\nIncrease knowledge on tolerance, (F3) Change attitudes towards improving\ntolerance, (F4) Increase motivation to address tolerance, (F5) Encourage\nfurther help-seeking, (F6) Behaviour change, (F) Average App-specific score") + ylim(3,5) + theme_ipsum() + theme( legend.position = "none", plot.title = element_text(size=plot_title_size), plot.subtitle = element_text(size=plot_subtitle_size)) -> plotF plotF ggsave(file = here::here("figs", "F.svg"), width = plot_width, height = plot_height, units="cm", dpi = plot_dpi) ``` ## Average table for each MARS section ```{r} #| label: save-table-as-plot as_ggplot <- function(x, filename) { path_gt_table_image <- here::here("figs") gt_table_image <- gt::gtsave(x, path = path_gt_table_image, filename = filename, expand=5) # save image in ggplot ------------------------------------------------------- table_img <- magick::image_read(here::here("figs", filename)) %>% magick::image_ggplot(interpolate = TRUE) table_img } ``` ```{r} #| label: table-A rowname <- dplyr::tibble(stat = c("mean", "sd")) cell_size = "small" mars_stat %>% filter(str_starts(code, c("A"))) %>% mutate(code = forcats::fct_relevel(code, lbl_A)) %>% arrange(code) %>% select(code, mean) %>% pivot_wider(names_from = code, values_from = c("mean")) -> meanA mars_stat %>% filter(str_starts(code, c("A"))) %>% mutate(code = forcats::fct_relevel(code, lbl_A)) %>% arrange(code) %>% select(code, sd) %>% pivot_wider(names_from = code, values_from = c("sd")) -> sdA bind_rows(meanA, sdA) %>% bind_cols(rowname) %>% gt(rowname_col = "stat") %>% opt_table_lines("none") %>% cols_width( everything() ~ px(60)) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( data_row.padding = px(1), #Reduce the height of rows table.align = "left", table.width = px(250)) %>% # Apply style to all column headers tab_style( locations = cells_column_labels(columns = everything()), style = list( cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below cell_text(weight = "bold")) #Make text bold) ) %>% tab_style( style = cell_text(size = cell_size), locations = cells_body(columns = everything()) ) %>% as_ggplot(filename="tbl_A.png") -> tbl_A ``` ```{r} #| label: table-B mars_stat %>% filter(str_starts(code, c("B"))) %>% mutate(code = forcats::fct_relevel(code, lbl_B)) %>% arrange(code) %>% select(code, mean) %>% pivot_wider(names_from = code, values_from = c("mean")) -> meanB mars_stat %>% filter(str_starts(code, c("B"))) %>% mutate(code = forcats::fct_relevel(code, lbl_B)) %>% arrange(code) %>% select(code, sd) %>% pivot_wider(names_from = code, values_from = c("sd")) -> sdB bind_rows(meanB, sdB) %>% bind_cols(rowname) %>% gt(rowname_col = "stat") %>% # gt() %>% # cols_hide(columns = c(stat)) %>% opt_table_lines("none") %>% cols_width( everything() ~ px(60)) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( data_row.padding = px(1), #Reduce the height of rows table.align = "left", table.width = px(250)) %>% # Apply style to all column headers tab_style( locations = cells_column_labels(columns = everything()), style = list( cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below cell_text(weight = "bold")) #Make text bold) ) %>% tab_style( style = cell_text(size = cell_size), locations = cells_body(columns = everything()) ) %>% as_ggplot(filename="tbl_B.png") -> tbl_B ``` ```{r} #| label: table-C mars_stat %>% filter(str_starts(code, c("C"))) %>% mutate(code = forcats::fct_relevel(code, lbl_C)) %>% arrange(code) %>% select(code, mean) %>% pivot_wider(names_from = code, values_from = c("mean")) -> meanC mars_stat %>% filter(str_starts(code, c("C"))) %>% mutate(code = forcats::fct_relevel(code, lbl_C)) %>% arrange(code) %>% select(code, sd) %>% pivot_wider(names_from = code, values_from = c("sd")) -> sdC bind_rows(meanC, sdC) %>% bind_cols(rowname) %>% gt(rowname_col = "stat") %>% opt_table_lines("none") %>% cols_width( everything() ~ px(60)) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( data_row.padding = px(1), #Reduce the height of rows table.align = "left", table.width = px(250)) %>% # Apply style to all column headers tab_style( locations = cells_column_labels(columns = everything()), style = list( cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below cell_text(weight = "bold")) #Make text bold) ) %>% tab_style( style = cell_text(size = cell_size), locations = cells_body(columns = everything()) ) %>% as_ggplot(filename="tbl_C.png") -> tbl_C ``` ```{r} #| label: table-D mars_stat %>% filter(str_starts(code, c("D"))) %>% mutate(code = forcats::fct_relevel(code, lbl_D)) %>% arrange(code) %>% select(code, mean) %>% pivot_wider(names_from = code, values_from = c("mean")) -> meanD mars_stat %>% filter(str_starts(code, c("D"))) %>% mutate(code = forcats::fct_relevel(code, lbl_D)) %>% arrange(code) %>% select(code, sd) %>% pivot_wider(names_from = code, values_from = c("sd")) -> sdD bind_rows(meanD, sdD) %>% bind_cols(rowname) %>% gt(rowname_col = "stat") %>% # gt() %>% # cols_hide(columns = c(stat)) %>% opt_table_lines("none") %>% cols_width( everything() ~ px(60)) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( data_row.padding = px(1), #Reduce the height of rows table.align = "left", table.width = px(250)) %>% # Apply style to all column headers tab_style( locations = cells_column_labels(columns = everything()), style = list( cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below cell_text(weight = "bold")) #Make text bold) ) %>% tab_style( style = cell_text(size = cell_size), locations = cells_body(columns = everything()) ) %>% as_ggplot(filename="tbl_D.png") -> tbl_D ``` ```{r} #| label: table-E mars_stat %>% filter(str_starts(code, c("E"))) %>% mutate(code = forcats::fct_relevel(code, lbl_E)) %>% arrange(code) %>% select(code, mean) %>% pivot_wider(names_from = code, values_from = c("mean")) -> meanE mars_stat %>% filter(str_starts(code, c("E"))) %>% mutate(code = forcats::fct_relevel(code, lbl_E)) %>% arrange(code) %>% select(code, sd) %>% pivot_wider(names_from = code, values_from = c("sd")) -> sdE bind_rows(meanE, sdE) %>% bind_cols(rowname) %>% gt(rowname_col = "stat") %>% opt_table_lines("none") %>% cols_width( everything() ~ px(60)) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( data_row.padding = px(1), #Reduce the height of rows table.align = "left", table.width = px(250)) %>% # Apply style to all column headers tab_style( locations = cells_column_labels(columns = everything()), style = list( cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below cell_text(weight = "bold")) #Make text bold) ) %>% tab_style( style = cell_text(size = cell_size), locations = cells_body(columns = everything()) ) %>% as_ggplot(filename="tbl_E.png") -> tbl_E ``` ```{r} #| label: table-F mars_stat %>% filter(str_starts(code, c("F"))) %>% mutate(code = forcats::fct_relevel(code, lbl_F)) %>% arrange(code) %>% select(code, mean) %>% pivot_wider(names_from = code, values_from = c("mean")) -> meanF mars_stat %>% filter(str_starts(code, c("F"))) %>% mutate(code = forcats::fct_relevel(code, lbl_F)) %>% arrange(code) %>% select(code, sd) %>% pivot_wider(names_from = code, values_from = c("sd")) -> sdF bind_rows(meanF, sdF) %>% bind_cols(rowname) %>% gt(rowname_col = "stat") %>% # gt() %>% # cols_hide(columns = c(stat)) %>% opt_table_lines("none") %>% cols_width( everything() ~ px(60)) %>% cols_align( align = "center", columns = everything() ) %>% tab_options( data_row.padding = px(1), #Reduce the height of rows table.align = "left", table.width = px(7)) %>% # Apply style to all column headers tab_style( locations = cells_column_labels(columns = everything()), style = list( cell_borders(sides = "bottom", weight = px(1)), #Give a thick border below cell_text(weight = "bold")) #Make text bold) ) %>% tab_style( style = cell_text(size = cell_size), locations = cells_body(columns = everything()) ) %>% as_ggplot(filename="tbl_F.png") -> tbl_F ``` @fig-5 (`Figure 5` in the paper) shows the composite of the boxplots for each MARS section along with average tables ```{r} #| label: fig-5 #| warning: false #| fig-cap: "Results obtained from the answers to each of the six sections of the MARS instrument. All items are rated using a Likert scale from 1 to 5, with two exceptions: (E2) Expected usage times in the next 12 months, with possible answers: 1 (none), 2 (1-2 times), 3 (3-10 times), 4 (10-50 times) and 5 (> 50 times); and (E3) Will to pay for its use, with possible answers: 1 (No), 3(Maybe) and 5 (Yes)" #| fig-width: 11 #| fig-asp: 1.4 #| out-width: 120% #| fig-align: center (plotA + plotB) / (tbl_A + tbl_B) / (plotC + plotD) / (tbl_C + tbl_D) / (plotE + plotF) / (tbl_E + tbl_F) + plot_layout(widths = c(1,1)) ``` ```{r} #| label: fig-5-save #| warning: false ggsave(file = here::here("figs", "MARS_WDP.svg"), width = 15, height = 20, dpi = plot_dpi) ```