Clean up code for R CMD check --as-cran

NAMESPACE

@@ -26,7 +26,7 @@ importFrom(HDInterval, hdi)
 importFrom(Hmisc, hdquantile)
 importFrom(MASS, rlm)
 importFrom(SGP, getStateAbbreviation, is.SGP)
-importFrom(stats, cor, lm, na.omit, pbeta, pf, predict, quantile, qt, reshape, rnorm, sd, setNames, var, weighted.mean)
+importFrom(stats, cor, lm, median, na.omit, pbeta, pf, predict, quantile, qt, reshape, rnorm, sd, setNames, var, weighted.mean)
 importFrom(toOrdinal, toOrdinalDate)
 importFrom(tidyr, fill)
 importFrom(tidyselect, all_of)

R/academicImpactSummary.R

@@ -18,6 +18,11 @@
  MSGP_BASELINE_DIFFERENCE_ADJUSTED <- MSGP_BASELINE_DIFFERENCE_UNCORRECTED <- MSSS_DIFFERENCE_ADJUSTED <- NULL
  MSSS_DIFFERENCE_UNCORRECTED <- PRIOR_MSGP_CENTERED_2YEAR <- PRIOR_MSSS_CENTERED_2YEAR <- SCALE_SCORE <- NULL
  SCALE_SCORE_PRIOR_STANDARDIZED <- SCALE_SCORE_PRIOR_STANDARDIZED_2YEAR <- SCALE_SCORE_STANDARDIZED <- SGP <- SGP_BASELINE <- VALID_CASE <- YEAR <- NULL
+ COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSP <- COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSP_ADJ <- COVID_ACADEMIC_IMPACT_SSP_DIFF <- NULL
+ COVID_ACADEMIC_IMPACT_SSP_DIFF_ADJ <- GES_MEDIAN_SSP <- GES_MEDIAN_SSP_ADJUSTED <- MEAN_SCALE_SCORE_PERCENTILE <- NULL
+ MEAN_SCALE_SCORE_PRIOR_2YEAR_PERCENTILE <- MEAN_SCALE_SCORE_PRIOR_2YEAR_STANDARDIZED <- MEAN_SCALE_SCORE_PRIOR_PERCENTILE <- NULL
+ MEDIAN_SGP_BASELINE_PRIOR_2YEAR <- MEDIAN_SGP_PRIOR_1YEAR <- MSSP_DIFFERENCE_ADJUSTED <- MSSP_DIFFERENCE_UNCORRECTED <- NULL
+ PERCENT_PROFICIENT <- PERCENT_PROFICIENT_PRIOR <- PERCENT_PROFICIENT_PRIOR_2YEAR <- PRIOR_MSSP_CENTERED_2YEAR <- SCALE_SCORE_PERCENTILE <- NULL
 
  ### Create state (if NULL) from sgp_object (if possible)
  if (is.null(state)) {
@@ -60,7 +65,7 @@
  tmp.percentile.cuts <- quantile(data_table[YEAR==reference.year][[var.to.percentile]], probs=seq(0.005, 0.995, length=100), na.rm = rm.na)
  }
  if (!all(is.na(tmp.percentile.cuts))) {
- findInterval(data_table[[var.to.percentile]], tmp.percentile.cuts, rightmost.close=TRUE)
+ findInterval(data_table[[var.to.percentile]], tmp.percentile.cuts, rightmost.closed=TRUE)
  } else rep(as.integer(NA), length(data_table[[var.to.percentile]]))
  }
 
@@ -87,7 +92,7 @@
 
  ### Create `aggregation_group` Level Summary Table(s)
  grp_status <- sgp_data[
- VALID_CASE=="VALID_CASE" & GRADE %in% all_grades, .(
+ VALID_CASE=="VALID_CASE" & GRADE %in% all_grades, list(
  MEAN_SCALE_SCORE_STANDARDIZED = mean(SCALE_SCORE_STANDARDIZED, na.rm=TRUE),
  MEAN_SCALE_SCORE_PERCENTILE = mean(SCALE_SCORE_PERCENTILE, na.rm=TRUE),
  MEDIAN_SCALE_SCORE_PERCENTILE = median(as.numeric(SCALE_SCORE_PERCENTILE), na.rm=TRUE),
@@ -96,7 +101,7 @@
  keyby=c("YEAR", aggregation_group, "CONTENT_AREA")]
 
  grp_growth <- sgp_data[
- VALID_CASE=="VALID_CASE" & GRADE %in% sgp_grades, .(
+ VALID_CASE=="VALID_CASE" & GRADE %in% sgp_grades, list(
  MEAN_SGP=mean(SGP, na.rm=TRUE),
  MEDIAN_SGP=hdmedian(as.numeric(SGP), na.rm=TRUE),
  MEAN_SGP_BASELINE=mean(SGP_BASELINE, na.rm=TRUE),