this comes from the data

provinces

Substantial geographic heterogeneity was observed in the ability to establish epidemiological links

Finally, a reminder that as usual summarise() returns a dataframe, which can be further used and modified using mutate() - this means that we should be able to add a variable about the proportion of female in each sub_prefecture using our newly created n_female ! Can you add a mutate() after your summarise() to make this happen ?

sub_pref_df <- df_linelist %>% summarise( .by = sub_prefecture, n_patients = n(), mean_age = mean(age), min_admission = min(date_admission, na.rm = TRUE), n_female = sum(sex == "f", na.rm = TRUE), n_hosp = sum(hospitalisation == "yes", na.rm = TRUE), mean_age_hosp = mean(age[hospitalisation == "yes"], na.rm = TRUE), mean_age_female = mean(age[sex == "f"], na.rm = TRUE), n_death_u6m = sum(outcome[age_group == "< 6 months"] == "dead", na.rm = TRUE) ) %>% mutate( prop_female = n_female / n_patients, prop_hosp = n_hosp / n_patients ) sub_pref_df

_linelist %>% summarise( .by = sub_prefecture, n_patients = n(), mean_age = mean(age), min_admission = min(date_admission, na.rm = TRUE), n_female = sum(sex == "f", na.rm = TRUE), n_hosp = sum(hospitalisation == "yes", na.rm = TRUE), mean_age_hosp = mean(age[hospitalisation == "yes"], na.rm = TRUE) ) %>% mutate( prop_female = n_female / n_patients, prop_hosp = n_hosp / n_patients )

also call the proportion of dead patients

Note. You can write either summarize() (US spelling) or summarise() (British spelling) in R.

Finally, a reminder that as usual summarise() returns a dataframe, which can be further used and modified using mutate() - this means that we should be able to add a variable about the proportion of female in each sub_prefecture using our newly created n_female ! Can you add a mutate() after your summarise() to make this happen ?

also call the proportion of dead patients

Can you try to use the syntax to calculate the mean age of female ?

organized and well docume

Rstudio project, installing packages

Foreshadowing. File paths actually work a bit differently in Rmarkdown files than they do in R scripts, but this is something we will talk about much later in the course. If you don’t know what RMarkdown is at the moment, don’t worry about it

Importing .xlsx files

Add the code to create an object called path_data_raw

OneDrive doesn’t play well with R as it will attempt to constantly synchronize certain project files in a way that can cause errors or memory problems.

The principles you learned in the Data Management module will apply here as well: we should do our best to ensure that our projects won’t just work today but can also be reused and shared in the future. While doing this is not always easy, there are several best practices that can help us, and one of the most important is to start with a good, organized code base.

OneDrive doesn’t play well with R as it will attempt to constantly synchronize certain project files in a way that can cause errors or memory problems.

Published December 18, 2024

Resources

If you want to learn about importing several sheets in one go, or several similar files from a folder, go to the satellite on multiple imports.

Importing .xlsx files

This type of function, providing an unified interface to other specific functions is known as a wrapper

Create a new section in your code called File Paths Add the code to create an object called path_data_raw that contains the path to your raw data folder using the function here(). We can now pass our new variable path_data_raw back into here() in order to create a full path to a specific data file.

The first is to use the base R function file.path(), which will accept a set of the relevant parts (folders) in your desired path and combine them into a file path using the syntax of your local operating system, whichever it is: file.path("data", "raw", "exemple_linelist.xlsx") [1] "data/raw/exemple_linelist.xlsx" Note that the path is relative, here to the current working directory While file.path() works fine

Foreshadowing. File paths actually work a bit differently in Rmarkdown files than they do in R scripts, but this is something we will talk about much later in the course. If you don’t know what RMarkdown is at the moment, don’t worry about it.