Kateo

.gitignore

@@ -12,3 +12,6 @@
 *.zip
 *.dat
 *.dta
+kate_notebook.R
+first_clean_pisa2000r.dta
+filtered_pisa2000r.dta

ko_first_clean_data_no_eth.R

@@ -0,0 +1,55 @@
+library(haven)  # Importing Stata datasets
+library(dplyr)
+library(labelled)
+
+# Load in the reading scores dataset
+datapath <- "DATA/PISA2000"
+pisa <- read_dta(file.path(datapath, "pisa2000r.dta")) |> as_factor()
+
+school_pisa <- read_dta("/Users/kateohara/Library/CloudStorage/OneDrive-UniversityofStirling/0_courses_seminars/lss_hackathon_2026/PISA2000/PISA2000_school_questionnaire.dta")
+
+school_pisa_usa <- school_pisa |>
+  filter(cnt == "USA") |>
+  distinct(schoolid, .keep_all = TRUE)
+
+uspisa <- pisa |>
+  filter(cnt == "USA") |>  # Filter country early for performance
+  left_join(
+    school_pisa_usa |> select(schoolid, sc03q01, wnrschbw),
+    by = "schoolid"
+  ) |> 
+  rename(
+    grade = st02q01,
+    female = st03q01,
+    english = st17q01,
+    schooltype = sc03q01,
+    studentweight = w_fstuwt,
+    schoolweight = wnrschbw
+  ) |> 
+  filter(grade %in% c(9, 10)) |> 
+  group_by(schoolid) |>
+  mutate(school_hisei = mean(hisei, na.rm = TRUE)) |>
+  ungroup()
+
+# Keep only selected variables
+uspisa_short <- uspisa |> select(
+  pv1read, pv2read, pv3read, pv4read, pv5read, 
+  effper, belong, 
+  grade, female, hisei, school_hisei,english, schooltype,
+  studentweight, schoolweight)
+
+
+# Drop if missing on reading score, effper, and emote:
+uspisa_short_nona <- uspisa_short |> 
+  filter(
+    if_all(
+      c(pv1read, pv2read, pv3read, pv4read, pv5read), 
+      \(x) x != "N/A" & !is.na(x)
+    ),
+    belong != "N/A" & !is.na(belong),
+    effper != "N/A" & !is.na(effper)
+  )
+
+final <- uspisa_short_nona
+head(final)
+write_dta(final, "first_clean_pisa2000r.dta")