Visualizing responses to an analytics strategy survey

Refreshing a style-forward graphic to improve understanding
R
#MakeoverMonday
bar-plot
Author

Mickey Rafa

Published

October 14, 2020

Figure 1: Original graphic depicting the proportion of respondents to the dataIQ Analytics Strategy Adoption Survey.
Figure 2: Bar plot visualizing the composition of respondents from the dataIQ Analytics Strategy Adoption Survey. The financial services sector is highlighted.

1. Load Packages & Setup

Show code
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,
    httr,
    readxl,
    sysfonts,
    ggtext,
    janitor,   #for clean_names()
    scales,
    glue,
    here
)

font_add_google("Montserrat")

2. Read in the Data

Show code
GET("https://query.data.world/s/vihrcc3hjn3umv2dy67iu2zxuyf2en", write_disk(tf <- tempfile(fileext = ".xlsx")))
Response [https://download.data.world/file_download/makeovermonday/2020w41-data-assets-and-data-culture/Book1.xlsx?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50Om1yYWZhMyIsImlzcyI6ImFnZW50Om1yYWZhMzo6ODg0MjQ2ZDItMTgzMy00NmZjLTk2YTMtZjQ2MWMzMDJjOTZiIiwiaWF0IjoxNjAyNTI1NjIyLCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiI4NjdiNWU5MjcyM2U4NWEyY2I1ZGFhMzc1NzY2YzkyODY5ODM5Y2VhIn0.RuS7i_y0cGnKRPP1uTVAHZvRhsHkwkg5ExCuT0IKWSw3YrEbh36F7jROtavuinjPgy-pZTixSYm54OMrF1VHkw]
  Date: 2024-12-12 19:09
  Status: 200
  Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
  Size: 9.19 kB
<ON DISK>  /var/folders/ck/dmgn8lbx6vl8sl89vlhr0sv00000gn/T//RtmpCqT2p5/file41623c6cafd.xlsx
Show code
df <- read_excel(tf) %>% 
  clean_names()

3. Examine the Data

Show code
df %>% 
  glimpse()
Rows: 16
Columns: 2
$ industry_sector     <chr> "Other", "Travel and leisure", "Logistics", "Telec…
$ respondents_percent <dbl> 10.9, 7.6, 2.2, 2.2, 13.0, 6.5, 8.7, 5.4, 3.3, 8.7…

4. Tidy the Data

Show code
financial_sector <- c('Financial services - other', 
                      'Financial services - insurance',
                      'Financial services - banking')

df <- df %>% 
  mutate(financial_sector = 
           ifelse(industry_sector %in% financial_sector,
                  'Financial', 'Other')) %>% 
  mutate(industry_sector_2 = ifelse(financial_sector == 'Financial', industry_sector, 'Other'))

industry_map <- df %>% 
  select(financial_sector, industry_sector_2)

5. Visualization Parameters

Show code
mm_week <- 41
mm_year <- 2020

my_theme <- theme(
  text = element_text(family = 'Montserrat', size=12, color='black'), 
  plot.title = element_textbox(),
  plot.subtitle = element_textbox(),
  axis.ticks = element_blank(), 
  axis.title = element_blank(),
  axis.text.x = element_blank(),
  axis.text.y = element_text(size=14, margin = margin(r=-10)),
  plot.background = element_rect(fill = 'white'),
  panel.background =  element_blank(), 
  legend.background = element_blank(), 
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  panel.border = element_blank(), 
  legend.title=element_blank(), 
  legend.position = 'none')

industry_cols <- c('Financial' = 'darkorange2',
                   'Other' = 'gray80')

6. Plot

Show code
respondents_plot <- df %>% 
  ggplot(.,
         aes(x=respondents_percent / 100,
               y=reorder(industry_sector, respondents_percent))) + 
  geom_col(aes(fill=financial_sector)) + 
  geom_text(aes(x=respondents_percent / 100 + .01,
                label=paste(respondents_percent, '%', sep="")),
            family='Montserrat', fontface='bold', size=7) + 
  annotate("text", label="Makeover Monday\nWeek 41 (2020)", x=.11, y=2, family="Montserrat", 
           size=7, lineheight=.9) + 
  labs(title='Responses to data and analytics strategy adoption survey by **dataIQ**',
       subtitle=glue("Percent of respondents by industry, highlighting the <span style='color:darkorange2'>**financial sector**")) + 
  scale_x_continuous(limits = c(0, .15),
                     labels = percent) + 
  scale_fill_manual(values = industry_cols) + 
  my_theme + 
  theme(plot.title = element_textbox_simple(size=rel(2), margin = margin(b=5)),
        plot.subtitle = element_textbox_simple(size=rel(1.35), margin = margin(b=5)),
        axis.text = element_text(size=rel(1.4)))

7. Save

Show code
# Save the plot as PNG
ggsave(
  filename = glue("mm_{mm_year}_{mm_week}.png"),
  plot = respondents_plot,
  width = 10, height = 6, units = "in", dpi = 320
)

# make thumbnail for page
magick::image_read(glue("mm_{mm_year}_{mm_week}.png")) %>%
  magick::image_resize(geometry = "400") %>%
  magick::image_write(glue("mm_{mm_year}_{mm_week}_thumbnail.png"))

8. Session Info

R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.6.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Denver
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.1      glue_1.8.0      scales_1.3.0    janitor_2.2.0  
 [5] ggtext_0.1.2    sysfonts_0.8.9  readxl_1.4.3    httr_1.4.7     
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] utf8_1.2.4        generics_0.1.3    xml2_1.3.6        stringi_1.8.4    
 [5] hms_1.1.3         digest_0.6.37     magrittr_2.0.3    evaluate_1.0.1   
 [9] grid_4.4.0        timechange_0.3.0  fastmap_1.2.0     rprojroot_2.0.4  
[13] cellranger_1.1.0  jsonlite_1.8.9    fansi_1.0.6       textshaping_0.4.0
[17] cli_3.6.3         rlang_1.1.4       commonmark_1.9.2  munsell_0.5.1    
[21] withr_3.0.1       yaml_2.3.10       tools_4.4.0       tzdb_0.4.0       
[25] colorspace_2.1-1  curl_5.2.3        vctrs_0.6.5       R6_2.5.1         
[29] magick_2.8.5      lifecycle_1.0.4   snakecase_0.11.1  htmlwidgets_1.6.4
[33] ragg_1.3.3        pkgconfig_2.0.3   pillar_1.9.0      gtable_0.3.5     
[37] Rcpp_1.0.13       systemfonts_1.1.0 xfun_0.48         tidyselect_1.2.1 
[41] rstudioapi_0.17.1 knitr_1.48        farver_2.1.2      htmltools_0.5.8.1
[45] labeling_0.4.3    rmarkdown_2.28    compiler_4.4.0    markdown_1.13    
[49] gridtext_0.1.5

9. Github Repository