From 7ed76b2e848cc80523b2bfa57ded40f1e1f12c13 Mon Sep 17 00:00:00 2001
From: elipousson
-attendance_msde_SY0919_long <- attendance_msde_SY0919 %>%
+attendance_msde_SY0919_long <- attendance_msde_SY0919 |>
tidyr::pivot_longer(
cols = c(5:15),
names_to = "variable",
@@ -137,7 +137,7 @@ Pivot attendance data to long form
value column and remove rows the value is unavailable for that year or
school.
-attendance_msde_SY0919_long <- attendance_msde_SY0919_long %>%
+attendance_msde_SY0919_long <- attendance_msde_SY0919_long |>
mutate(
variable_type = case_when(
stringr::str_detect(variable, "_pct") ~ "percent",
@@ -145,19 +145,19 @@ Pivot attendance data to long form
stringr::str_detect(variable, "_denom") ~ "denominator"
),
.before = value
- ) %>%
+ ) |>
filter(!is.na(value))
This is helpful when you want to compare percent values to one
another and exclude the count variables. The following examples shows
how to do this in comparing the overall attendance rate to the chronic
absentee rate.
-attendance_msde_SY0919_long %>%
+attendance_msde_SY0919_long |>
filter(
variable_type == "percent",
stringr::str_detect(variable, "attend_rate|chronic_absentee"),
school_year >= 2018
- ) %>%
+ ) |>
ggplot(aes(x = variable, y = value, color = grade_band)) +
geom_jitter(alpha = 0.6) +
scale_y_continuous(labels = scales::label_percent(scale = 1))
diff --git a/articles/attendance_data_files/figure-html/plot_percent-1.png b/articles/attendance_data_files/figure-html/plot_percent-1.png
index 8f0b92b093f0006067a47bec3bae6afbf5449e0f..692db35b00341d107e261cf5b7936045c960b96a 100644
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