Workshop 3 - Burkina Faso dataset

Workshop 3 Assignment: Burkina Faso febrile diseases data

Abdominal pain distribution of patients per sex group

Work primarily done by group member 1, Giulia Rathmes

The dataset we chose provides information on symptoms of febrile diseases in Burkina faso.

burkina_faso <- read_csv(here("week_03/data/01_febrile_diseases_burkina_faso.csv"))

I chose to look at the difference in the distribution of abdominal pain between male and female patients.

The plot below shows the distribution of abdominal pain for each sex:

And the table below shows similar information:

freq_table <- tabyl(burkina_faso, `abd_pain`, sexe)
flex <- flextable(freq_table)
flex_with_caption <- set_caption(flex, caption = "Abdominal pain distribution of febrile disease patients")
flex_with_caption

For both sexes, most patients did not have abdominal pain. 219 female patients had abdominal pain, whereas 179 male patients had abdominal pain.

Areas of improvement

• The male category is coded as 2 and does not show the label “male”.
• The abdominal pain group labels are too horizontally compressed.
• I do not yet know how to change the variable names, so I left in the abd_pain name in the table and plot.
• The table should have the “yes” row first and then the “no” row, but I do not know how to change the two.

Work primarily done by group member 2, Christopher Chew

The dataset we chose provides data from an accuracy study comparing gold standard culture techniques with a haemocytometry based algorithm (IMS), procalcitonin (PCT) and C-reactive protein (CRP). Data were collected from approximately 900 patients aged between 3 months and 100 years presenting with an acute febrile illness.

col_diagnosis <- read_csv(here("week_03/data/01_febrile_diseases_burkina_faso.csv"))

I chose to look at the distribution of IMS diagnoses by sex.

The plot below shows the age distributions for each sex:

And the table below shows similar information:

freq_table <- tabyl(col_diagnosis, `ims_final_full_classification`, sexe)
flex <- flextable(freq_table)
flex_with_caption <- set_caption(flex, caption = "Distribution of IMS diagnoses by sex")
flex_with_caption

For both sexes, the diagnosis with the most number of people was Bacteria. In men, there were 119 patients with this diagnosis, and in women there were 169 .

Areas of improvement

• The age group labels are too small.
• Not sure how to change the variable name categories, so I left ‘2’ to represent ‘males’ in the table and plot.