Merge remote-tracking branch 'upstream/initial_actions_kmn' into init…

…ial_actions

Updating initial actions branch

R/configTests.R

@@ -1,4 +1,4 @@
-
+require(tidyverse)
 ###### Dimension Tests ######
 #' test_loadData
 #'
@@ -129,7 +129,7 @@ general_config_test <- function(
     configuredData = NULL, ### List of configured data
     # reshapedFile   = "." %>% file.path("data_tests", "reshapedData_testResults.csv"), ### File name of reshaped data test
     outPath   = ".",
-    xlsxName  = "testResults_general_config.xlsx",
+    xlsxName  = "generalConfig_testResults.xlsx",
     save      = TRUE,
     return    = TRUE,
     overwrite = TRUE, ### Whether to overwrite an existing file,
@@ -363,11 +363,11 @@ general_config_test <- function(
 #'
 #' @examples
 newSectors_config_test <- function(
-    newData = NULL,
+    newData   = NULL,
     refDataFile = "." %>% file.path("data", "sysdata.rda"),
     # sector_id = "",
     outPath   = ".",
-    xslxName  = "testResults_newSectors_config.xlsx",
+    xslxName  = "newSectorsConfig_testResults.xlsx",
     save      = T,
     return    = T,
     overwrite = T

R/createSystemData.R

@@ -128,17 +128,17 @@ createSystemData <- function(
 
   ### Calculate national population and add to data list
   df_national <- pop_default %>% 
-    group_by_at(.vars=c(all_of(group0))) %>% 
-    summarize_at(.vars=c(all_of(sum0)), sum, na.rm=T) %>% ungroup
-  df_national <- df_national %>% rename_at(.vars=c(all_of(sum0)), ~c("national_pop"))
+    group_by_at(.vars=c(group0)) %>% 
+    summarize_at(.vars=c(sum0), sum, na.rm=T) %>% ungroup
+  df_national <- df_national %>% rename_at(.vars=c(sum0), ~c("national_pop"))
   rDataList[["national_pop_default"]] <- df_national
   # df_national %>% names %>% print
 
   ### Default scenario: Join national GDP with national population by year
   ### Default scenario: Join national values with regional population by year
   ### Calculate GDP per capita and add to list
-  df_national <- gdp_default %>% left_join(df_national, by=c(all_of(group0)))
-  df_national <- df_national %>% left_join(pop_default, by=c(all_of(group0)))
+  df_national <- gdp_default %>% left_join(df_national, by=c(group0))
+  df_national <- df_national %>% left_join(pop_default, by=c(group0))
   df_national <- df_national %>% mutate(gdp_percap = gdp_usd / national_pop)
   rDataList[["df_defaultScenario"]] <- df_national
   # df_defaultScenario %>% names %>% print
@@ -152,11 +152,11 @@ createSystemData <- function(
   mutate0     <- c("model_type")
   string0     <- c("slr")
   ### Replace NA values
-  slr_cm      <- slr_cm     %>% mutate_at(.vars = c(all_of(mutate0)), replace_na, string0)
-  slrImpacts  <- slrImpacts %>% mutate_at(.vars = c(all_of(mutate0)), replace_na, string0)
+  slr_cm      <- slr_cm     %>% mutate_at(.vars = c(mutate0), replace_na, string0)
+  slrImpacts  <- slrImpacts %>% mutate_at(.vars = c(mutate0), replace_na, string0)
   ### Convert to character
-  slr_cm      <- slr_cm     %>% mutate_at(.vars = c(all_of(mutate0)), as.character)
-  slrImpacts  <- slrImpacts %>% mutate_at(.vars = c(all_of(mutate0)), as.character)
+  slr_cm      <- slr_cm     %>% mutate_at(.vars = c(mutate0), as.character)
+  slrImpacts  <- slrImpacts %>% mutate_at(.vars = c(mutate0), as.character)
   ### Create data for extreme values above 250cm
   slrExtremes <- fun_slrConfigExtremes(
     slr_x = slr_cm,    ### rDataList$slr_cm
@@ -195,7 +195,7 @@ createSystemData <- function(
   join0          <- c("joinCol")
   df_sectorsInfo <- df_sectorsInfo %>% mutate(joinCol=1)
   df_national    <- df_national    %>% mutate(joinCol=1)
-  df_results0    <- df_sectorsInfo %>% left_join(df_national, by=c(all_of(join0)))
+  df_results0    <- df_sectorsInfo %>% left_join(df_national, by=c(join0), relationship = "many-to-many")
   df_results0    <- df_results0    %>% select(-c(all_of(join0)))
   rm("join0"); rm("df_sectorsInfo", "df_national")
 

R/loadData.R

@@ -28,7 +28,7 @@ loadData <- function(
   ### Filter to those for importing and replace NA values in some columns
   mutate0 <- c("excludeCol_ids", "Notes")
   df_tableNames <- df_tableNames %>% filter(Import == 1)
-  df_tableNames <- df_tableNames %>% mutate_at(.vars = c(all_of(mutate0)), replace_na, "")
+  df_tableNames <- df_tableNames %>% mutate_at(.vars = c(mutate0), replace_na, "")
   rm("mutate0")
 
   ### Number of data tables

R/reshapeData.R

@@ -55,7 +55,7 @@ reshapeData <- function(
   co_impactYears <- co_impactYears %>% gather(key = "impactYear_label", value = "impactYear2", -c(all_of(drop0)))
   co_impactYears <- co_impactYears %>% filter(!is.na(impactYear2))
   co_impactYears <- co_impactYears %>% select(-c(all_of(drop1)))
-  co_impactYears <- co_impactYears %>% left_join(co_impactYearLevels, by = c(all_of(join0)))
+  co_impactYears <- co_impactYears %>% left_join(co_impactYearLevels, by = c(join0))
   # co_impactYears %>% glimpse
   ### Update/add tables in list
   dataList[["co_impactYears"     ]] <- co_impactYears
@@ -78,7 +78,7 @@ reshapeData <- function(
   ### Remove intermediate variables
   join0          <- c("modelType")
   co_modelTypes2 <- co_modelTypes %>% rename(modelType = modelType_id)
-  co_models      <- co_models     %>% left_join(co_modelTypes2, by = c(all_of(join0))) # ; co_models %>% glimpse
+  co_models      <- co_models     %>% left_join(co_modelTypes2, by = c(join0)) # ; co_models %>% glimpse
   dataList[["co_models"]] <- co_models
   rm("join0", "co_modelTypes2")
 
@@ -96,13 +96,13 @@ reshapeData <- function(
   drop2   <- c("impactType_id_excel", "impactType_label", "impactType_description")
   join0   <- c("sector_id")
   rename0 <- c("sector", "variant", "impactYear", "impactType") %>% paste("id", sep = "_")
-  rename1 <- gsub("_id", "", c(all_of(rename0)))
+  rename1 <- gsub("_id", "", c(rename0))
   ### Join with co_impactYears, co_impactTypes
-  df_sectorsInfo <- co_sectors     %>% left_join(co_variants    %>% select(-c(all_of(drop0))), by = c(all_of(join0)))
-  df_sectorsInfo <- df_sectorsInfo %>% left_join(co_impactYears %>% select(-c(all_of(drop1))), by = c(all_of(join0)))
-  df_sectorsInfo <- df_sectorsInfo %>% left_join(co_impactTypes %>% select(-c(all_of(drop2))), by = c(all_of(join0)))
+  df_sectorsInfo <- co_sectors     %>% left_join(co_variants    %>% select(-c(all_of(drop0))), by = c(join0))
+  df_sectorsInfo <- df_sectorsInfo %>% left_join(co_impactYears %>% select(-c(all_of(drop1))), by = c(join0), relationship = "many-to-many")
+  df_sectorsInfo <- df_sectorsInfo %>% left_join(co_impactTypes %>% select(-c(all_of(drop2))), by = c(join0), relationship = "many-to-many")
   ### Rename
-  df_sectorsInfo <- df_sectorsInfo %>% rename_at(.vars = c(all_of(rename0)), ~ all_of(rename1)) # ; df_sectorsInfo %>% glimpse
+  df_sectorsInfo <- df_sectorsInfo %>% rename_at(.vars = c(rename0), ~ rename1) # ; df_sectorsInfo %>% glimpse
   ### Update in list
   dataList[["df_sectorsInfo"]] <- df_sectorsInfo
   ### Remove intermediate variables
@@ -177,7 +177,7 @@ reshapeData <- function(
   ### Check impacts
   "nrow(c_regSlr) == nrow(slrImpacts)" %>% paste0(": ", nrow(c_regSlr) == nrow(slrImpacts)) %>% print()
   ### Join and drop join columns
-  slrImpacts <- slrImpacts %>% left_join(c_regSlr, by = c(all_of(join0)))
+  slrImpacts <- slrImpacts %>% left_join(c_regSlr, by = c(join0))
   slrImpacts <- slrImpacts %>% select(-c(all_of(join0), all_of(select0)))
   ### Drop intermediate values (update in list further down)
   rm("idCols0", "select0", "names0", "join0", "c_regSlr")
@@ -233,7 +233,7 @@ reshapeData <- function(
       df_i <- df_i %>% mutate(model_type = model_dot %>% factor(levels1, labels1))
       ### Convert to character
       mutate0 <- c("variant", "impactYear", "impactType", "model_dot")
-      df_i    <- df_i %>% mutate_at(.vars = c(all_of(mutate0)), as.character)
+      df_i    <- df_i %>% mutate_at(.vars = c(mutate0), as.character)
       # rm("levels0", "labels0", "doDot0", "mutate0")
       return(df_i)
     }) %>%