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Merged
merged 4 commits into from
Oct 18, 2024
Merged

Viv3ckj/update graphs #42

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39102b5
Fix Rmd issue by switching from combined service to clinical service
viv3ckj Oct 15, 2024
d360dc2
Fix NAs for age band, and added new colours and gradients
viv3ckj Oct 17, 2024
2064d96
Update colours, grapha and code to work with new released output
viv3ckj Oct 18, 2024
7f1ba8d
Set standard figure size, remove unnecessary code, and tidy Rmd
viv3ckj Oct 18, 2024
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1 change: 1 addition & 0 deletions analysis/measures_definition_pf_codes_conditions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,6 +74,7 @@
when(age.is_null()).then("Missing"),
)


# IMD groupings for IMD breakdown
imd = addresses.for_patient_on(INTERVAL.start_date).imd_rounded
max_imd = 32844
Expand Down
4 changes: 1 addition & 3 deletions lib/functions/function_plot_measures.R
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Original file line number Diff line number Diff line change
Expand Up @@ -71,9 +71,7 @@ plot_measures <- function(
theme(
legend.position = legend_position,
plot.title = element_text(hjust = 0.5)
) +

scale_colour_brewer(palette = "Set1")
)

# Automatically change y scale depending selected value
if (rlang::as_label(enquo(select_value)) %in% c("numerator", "denominator")) {
Expand Down
62 changes: 33 additions & 29 deletions reports/pharmacy_first_report.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -34,11 +34,9 @@ if (Sys.getenv("OPENSAFELY_BACKEND") != "") {

# Define dictionaries with tidy names and mappings for measures
pf_measures_name_dict <- list(
# blood_pressure_service = "Blood Pressure Service",
# contraception_service = "Contraception Service",
consultation_service = "Consultation Service",
pharmacy_first_service = "Pharmacy First Service",
combined_service = "Pharmacy First Services",
combined_pf_service = "Pharmacy First Services",
acute_otitis_media = "Acute Otitis Media",
herpes_zoster = "Herpes Zoster",
acute_sinusitis = "Acute Sinusitis",
Expand All @@ -49,8 +47,6 @@ pf_measures_name_dict <- list(
)

pf_measures_name_mapping <- list(
# blood_pressure_service = "clinical_service",
# contraception_service = "clinical_service",
consultation_service = "clinical_service",
pharmacy_first_service = "clinical_service",
combined_pf_service = "pharmacy_first_services",
Expand Down Expand Up @@ -111,6 +107,13 @@ df_measures$region <- factor(

df_measures <- df_measures %>%
mutate(sex = factor(sex, levels = c("female", "male"), labels = c("Female", "Male")))

df_measures$age_band[is.na(df_measures$age_band)] <- "Missing"

gradient_palette <- c("#001F4D", "#0056B3", "#007BFF", "#66B3E2", "#A4D8E1", "grey")
region_palette <- c("red", "navy", "#018701", "#ffa600ca", "purple", "brown", "#f4a5b2", "cyan", "green", "grey")
ethnicity_palette <- c("#42db0188", "#0056B3", "#ff0000c2", "#a52a2a5a", "purple", "grey")
sex_palette <- c("red", "blue")
```

# Background
Expand All @@ -137,7 +140,7 @@ Links to the codelist for each analysis can be found beneath the relevant sectio

### Total population

```{r, message=FALSE, warning=FALSE}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "clinical_service") %>%
Expand All @@ -150,11 +153,11 @@ plot_measures(
select_interval_date = interval_end,
colour_var = NULL,
guide_nrow = 1,
facet_wrap = FALSE,
facet_var = NULL,
facet_wrap = TRUE,
facet_var = measure,
title = "Number of consultations for each clinical service per month",
y_label = "Number of codes for consultations",
)
)
```

### Breakdown by age
Expand All @@ -176,7 +179,8 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical service by age band per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = gradient_palette)

```

### Breakdown by sex
Expand All @@ -198,7 +202,7 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical service by sex per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = sex_palette)
```

### Breakdown by IMD
Expand All @@ -220,7 +224,7 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical service by IMD per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = gradient_palette)
```

### Breakdown by region
Expand All @@ -242,12 +246,12 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical service by region per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = region_palette)
```

### Clinical Services by ethnicity
### Breakdown by ethnicity

```{r, message=FALSE, warning=FALSE}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "pharmacy_first_services") %>%
Expand All @@ -264,7 +268,7 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical service by ethnicity per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = ethnicity_palette)
```

## Clinical Condition
Expand All @@ -274,7 +278,7 @@ Here we show the number of consultations for each of the Pharmacy First Clinical

### Total population

```{r, message=FALSE, warning=FALSE}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "clinical_condition") %>%
Expand All @@ -286,16 +290,16 @@ plot_measures(
select_value = numerator,
select_interval_date = interval_end,
guide_nrow = 1,
facet_wrap = FALSE,
facet_var = NULL,
facet_wrap = TRUE,
facet_var = measure,
title = "Number of consultations for each clinical condition per month",
y_label = "Number of codes for consultations",
)
```

### Breakdown by age

```{r, message=FALSE, warning=FALSE, fig.height=15, fig.width=8}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "clinical_condition") %>%
Expand All @@ -312,12 +316,12 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical condition by age band per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = gradient_palette)
```

### Breakdown by sex

```{r, message=FALSE, warning=FALSE, fig.height=15, fig.width=8}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "clinical_condition") %>%
Expand All @@ -334,12 +338,12 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical condition by sex per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = sex_palette)
```

### Breakdown by IMD

```{r, message=FALSE, warning=FALSE, fig.height=15, fig.width=8}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "clinical_condition") %>%
Expand All @@ -356,12 +360,12 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical condition by IMD per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = gradient_palette)
```

### Breakdown by region

```{r, message=FALSE, warning=FALSE, fig.height=15, fig.width=8}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "clinical_condition") %>%
Expand All @@ -378,12 +382,12 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical condition by region per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = region_palette)
```

### Clinical Conditions by ethnicity

```{r, message=FALSE, warning=FALSE, fig.height=15, fig.width=8}
```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=8}
# Select measures and breakdown
df_measures_selected <- df_measures %>%
filter(measure_desc == "clinical_condition") %>%
Expand All @@ -400,5 +404,5 @@ plot_measures(
facet_var = measure,
title = "Number of consultations for each clinical condition by ethnicity per month",
y_label = "Number of codes for consultations",
)
) + scale_color_manual(values = ethnicity_palette)
```