From 50dc8a1ba396ec450b93ffcd4d4b2a362db91c76 Mon Sep 17 00:00:00 2001 From: Samantha Toet Date: Fri, 6 Dec 2024 15:49:51 -0500 Subject: [PATCH] add district-level summaries --- absenteeism.Rmd | 131 +++++++++++++++++++++++++++++------------------ absenteeism.html | 126 +++++++++++++++++++++++++++------------------ 2 files changed, 157 insertions(+), 100 deletions(-) diff --git a/absenteeism.Rmd b/absenteeism.Rmd index 3018f0f..6acb480 100644 --- a/absenteeism.Rmd +++ b/absenteeism.Rmd @@ -32,13 +32,92 @@ Based on a 180-day school year, that means approximately 18 days per year or 2 t ---------------------------------------------------- -## Charlottesville City Schools +## Charlottesville & Albemarle Public Schools -```{r cville, out.width="100%"} +```{r absenteeism} # Data downloaded in the absenteeism.R script chronic_absenteeism <- read_csv("/Users/sct2td/Desktop/absenteeism/chronic_absenteeism.csv") +# Set Community Lab School / Murray as high school +chronic_absenteeism <- chronic_absenteeism %>% + mutate(school_level = case_when( + school == "Murray School" ~ "High", + TRUE ~ school_level + )) + +all <- chronic_absenteeism %>% + filter(subgroup == "All Students") %>% + group_by(division, school_year, school_level) %>% + summarise(percent = mean(percent)) + +ggplot(all, aes(x = school_year, y = percent, colour = school_level, group = school_level)) + + geom_point() + + geom_path(size = 1) + + facet_wrap(~division) + + scale_y_continuous(labels = function(x) paste0(x, "%")) + + scale_color_manual(values = ec_colors, breaks = c("Elementary", "Middle", "High")) + + labs(y = "Rate of Chronic Absenteeism", + x = "School Year") + + theme_bw() + + theme(axis.text.x = element_text(angle = 25, vjust = .7)) + +``` + +## Student Demographics + +```{r demographics, out.width="100%"} + +all_average <- chronic_absenteeism %>% + filter(subgroup == "All Students") %>% + group_by(school_year, division) %>% + summarise(percent = mean(percent)) + +demo_means <- chronic_absenteeism %>% + filter(subgroup != "All Students") %>% + group_by(division, subgroup, school_year) %>% + summarise(percent = mean(percent)) + +demo_labels <- demo_means %>% + filter(school_year == "2023-2024") %>% + mutate(subgroup = case_when( + subgroup == "Economically Disadvantaged" ~ "Economically\nDisadvantaged", + subgroup == "Students with Disabilities" ~ "Students with\nDisabilities", + TRUE ~ subgroup)) + +avg_labels <- all_average %>% + filter(school_year == "2023-2024") + +avg_labels$label <- c("District Average", "District Average") + +ggplot(demo_means, aes(x = school_year, y = percent, group = subgroup, color = subgroup)) + + geom_point(data = all_average, aes(x = school_year, y = percent), + inherit.aes = FALSE, color = "darkgrey") + + geom_smooth(data = all_average, aes(x = school_year, y = percent, group = "average"), + color = "darkgrey", size = 2, se=FALSE) + + geom_point(size = 1) + + geom_path(size = 1) + + geom_label(data = demo_labels, aes(label = subgroup), hjust = -.1, size = 2.5) + + geom_label(data = avg_labels, aes(x = school_year, y = percent, label = label, group = "average"), + hjust = -.1, size = 2.5, color = "darkgrey") + + facet_wrap(~division) + + scale_y_continuous(labels = function(x) paste0(x, "%")) + + labs(title = "Student Demographics", + x = "School Year", + y = "Rate of Chronic Absenteeism") + + theme_bw() + + theme(axis.text.x = element_text(angle = 25, vjust = .7), legend.position = "none") + + scale_x_discrete(expand = expansion(add = c(0.5, 2.5))) + +``` + +------------------------------------------------------------------------ + + +## Charlottesville City Schools + +```{r cville, out.width="100%"} + ccs <- chronic_absenteeism %>% filter(subgroup == "All Students", division == "CCS") @@ -144,54 +223,6 @@ ggplot(acs_elem, aes(x = school_year, y = percent, group = school_short, color = ------------------------------------------------------------------------ -## Student Demographics - -```{r demographics, out.width="100%"} - -all_average <- chronic_absenteeism %>% - filter(subgroup == "All Students") %>% - group_by(school_year, division) %>% - summarise(percent = mean(percent)) - -demo_means <- chronic_absenteeism %>% - filter(subgroup != "All Students") %>% - group_by(division, subgroup, school_year) %>% - summarise(percent = mean(percent)) - -demo_labels <- demo_means %>% - filter(school_year == "2023-2024") %>% - mutate(subgroup = case_when( - subgroup == "Economically Disadvantaged" ~ "Economically\nDisadvantaged", - subgroup == "Students with Disabilities" ~ "Students with\nDisabilities", - TRUE ~ subgroup)) - -avg_labels <- all_average %>% - filter(school_year == "2023-2024") - -avg_labels$label <- c("District Average", "District Average") - -ggplot(demo_means, aes(x = school_year, y = percent, group = subgroup, color = subgroup)) + - geom_point(data = all_average, aes(x = school_year, y = percent), - inherit.aes = FALSE, color = "darkgrey") + - geom_smooth(data = all_average, aes(x = school_year, y = percent, group = "average"), - color = "darkgrey", size = 2, se=FALSE) + - geom_point(size = 1) + - geom_path(size = 1) + - geom_label(data = demo_labels, aes(label = subgroup), hjust = -.1, size = 2.5) + - geom_label(data = avg_labels, aes(x = school_year, y = percent, label = label, group = "average"), - hjust = -.1, size = 2.5, color = "darkgrey") + - facet_wrap(~division) + - scale_y_continuous(labels = function(x) paste0(x, "%")) + - labs(title = "Student Demographics", - x = "School Year", - y = "Rate of Chronic Absenteeism") + - theme_bw() + - theme(axis.text.x = element_text(angle = 25, vjust = .7), legend.position = "none") + - scale_x_discrete(expand = expansion(add = c(0.5, 2.5))) - -``` - ------------------------------------------------------------------------- ## Table diff --git a/absenteeism.html b/absenteeism.html index 20a4973..08dff69 100644 --- a/absenteeism.html +++ b/absenteeism.html @@ -3365,12 +3365,83 @@

Samantha Toet, -

Charlottesville City Schools

+
+

Charlottesville & Albemarle Public Schools

# Data downloaded in the absenteeism.R script
 chronic_absenteeism <- read_csv("/Users/sct2td/Desktop/absenteeism/chronic_absenteeism.csv")
 
-ccs <- chronic_absenteeism %>%
+# Set Community Lab School / Murray as high school
+chronic_absenteeism <- chronic_absenteeism %>%
+  mutate(school_level = case_when(
+    school == "Murray School" ~ "High",
+    TRUE ~ school_level
+  ))
+
+all <- chronic_absenteeism %>%
+  filter(subgroup == "All Students") %>%
+  group_by(division, school_year, school_level) %>%
+  summarise(percent = mean(percent))
+
+ggplot(all, aes(x = school_year, y = percent, colour = school_level, group = school_level)) +
+  geom_point() +
+  geom_path(size = 1) +
+  facet_wrap(~division) +
+  scale_y_continuous(labels = function(x) paste0(x, "%")) +
+  scale_color_manual(values = ec_colors, breaks = c("Elementary", "Middle", "High")) +
+  labs(y = "Rate of Chronic Absenteeism",
+       x = "School Year") +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 25, vjust = .7))
+

+
+
+

Student Demographics

+
all_average <- chronic_absenteeism %>%
+  filter(subgroup == "All Students") %>%
+  group_by(school_year, division) %>%
+  summarise(percent = mean(percent))
+
+demo_means <- chronic_absenteeism %>% 
+  filter(subgroup != "All Students") %>%
+  group_by(division, subgroup, school_year) %>%
+  summarise(percent = mean(percent))
+
+demo_labels <- demo_means %>%
+  filter(school_year == "2023-2024") %>%
+  mutate(subgroup = case_when(
+    subgroup == "Economically Disadvantaged" ~ "Economically\nDisadvantaged",
+    subgroup == "Students with Disabilities" ~ "Students with\nDisabilities",
+    TRUE ~ subgroup))
+
+avg_labels <- all_average %>%
+  filter(school_year == "2023-2024")
+
+avg_labels$label <- c("District Average", "District Average")
+
+ggplot(demo_means, aes(x = school_year, y = percent, group = subgroup, color = subgroup)) +
+  geom_point(data = all_average, aes(x = school_year, y = percent), 
+             inherit.aes = FALSE, color = "darkgrey") +
+  geom_smooth(data = all_average, aes(x = school_year, y = percent, group = "average"), 
+              color =  "darkgrey", size = 2, se=FALSE) +
+  geom_point(size = 1) +
+  geom_path(size = 1) +
+  geom_label(data = demo_labels, aes(label = subgroup), hjust = -.1, size = 2.5) +
+  geom_label(data = avg_labels, aes(x = school_year, y = percent, label = label, group = "average"),
+             hjust = -.1, size = 2.5, color = "darkgrey") +
+  facet_wrap(~division) +
+  scale_y_continuous(labels = function(x) paste0(x, "%")) +
+  labs(title = "Student Demographics",
+       x = "School Year",
+       y = "Rate of Chronic Absenteeism") +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 25, vjust = .7), legend.position = "none") +
+  scale_x_discrete(expand = expansion(add = c(0.5, 2.5)))
+

+
+
+
+

Charlottesville City Schools

+
ccs <- chronic_absenteeism %>%
   filter(subgroup == "All Students",
          division == "CCS")
 
@@ -3468,51 +3539,6 @@ 
ACPS Elementary Schools Deep Dive
-
-

Student Demographics

-
all_average <- chronic_absenteeism %>%
-  filter(subgroup == "All Students") %>%
-  group_by(school_year, division) %>%
-  summarise(percent = mean(percent))
-
-demo_means <- chronic_absenteeism %>% 
-  filter(subgroup != "All Students") %>%
-  group_by(division, subgroup, school_year) %>%
-  summarise(percent = mean(percent))
-
-demo_labels <- demo_means %>%
-  filter(school_year == "2023-2024") %>%
-  mutate(subgroup = case_when(
-    subgroup == "Economically Disadvantaged" ~ "Economically\nDisadvantaged",
-    subgroup == "Students with Disabilities" ~ "Students with\nDisabilities",
-    TRUE ~ subgroup))
-
-avg_labels <- all_average %>%
-  filter(school_year == "2023-2024")
-
-avg_labels$label <- c("District Average", "District Average")
-
-ggplot(demo_means, aes(x = school_year, y = percent, group = subgroup, color = subgroup)) +
-  geom_point(data = all_average, aes(x = school_year, y = percent), 
-             inherit.aes = FALSE, color = "darkgrey") +
-  geom_smooth(data = all_average, aes(x = school_year, y = percent, group = "average"), 
-              color =  "darkgrey", size = 2, se=FALSE) +
-  geom_point(size = 1) +
-  geom_path(size = 1) +
-  geom_label(data = demo_labels, aes(label = subgroup), hjust = -.1, size = 2.5) +
-  geom_label(data = avg_labels, aes(x = school_year, y = percent, label = label, group = "average"),
-             hjust = -.1, size = 2.5, color = "darkgrey") +
-  facet_wrap(~division) +
-  scale_y_continuous(labels = function(x) paste0(x, "%")) +
-  labs(title = "Student Demographics",
-       x = "School Year",
-       y = "Rate of Chronic Absenteeism") +
-  theme_bw() +
-  theme(axis.text.x = element_text(angle = 25, vjust = .7), legend.position = "none") +
-  scale_x_discrete(expand = expansion(add = c(0.5, 2.5)))
-

-
-

Table

chronic_absenteeism %>%
@@ -3538,8 +3564,8 @@ 
Table

     filterable = TRUE,
     searchable = TRUE
   )
-
- +
+