From b1c0f98cc901fcf9f758d2dbcfd48eca0c4e9239 Mon Sep 17 00:00:00 2001 From: Ben Anderson <dataknut@icloud.com> Date: Thu, 31 Mar 2016 15:57:49 +0100 Subject: [PATCH] updated notes and added analysis of autocorrelation coefficient --- Census2022.Rproj | 13 +++++++ Census2022_CER_CEUS_paper_analysis.R | 58 ++++++++++++++++++++++------ 2 files changed, 60 insertions(+), 11 deletions(-) create mode 100644 Census2022.Rproj diff --git a/Census2022.Rproj b/Census2022.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/Census2022.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX diff --git a/Census2022_CER_CEUS_paper_analysis.R b/Census2022_CER_CEUS_paper_analysis.R index 8077c7e..8aa525c 100644 --- a/Census2022_CER_CEUS_paper_analysis.R +++ b/Census2022_CER_CEUS_paper_analysis.R @@ -13,10 +13,10 @@ # In addition the paper uses: -# Multilevel regression results calculated using: +# Multilevel regression results (Table 7) calculated using: # https://github.com/dataknut/CER/blob/master/Census2022-CER-mixed_model_0910.R -# Logistic regression results calculated using: +# Logistic regression results (Table 8 & 9) calculated using: # https://github.com/dataknut/CER/blob/master/Census2022-CER_regP_48_CLUSTER_std-1-5-15.R # This work was funded by RCUK through the ESRC's Transformative Social Science Programme via the @@ -318,7 +318,7 @@ print(paste0("Oct 09 IDs who both answered pre trial survey and recorded data: " # Linkage and analysis ---- -# Descriptives for Table 1 (Table 1) ---- +# Descriptives for Table 1 ---- # Pre-trial survey completions: uniqueN(cerResPreSurveyDTred$ID) # Number of households in residential data: @@ -329,7 +329,7 @@ uniqueN(cerResPostSurveyDTred$ID) cerSurveysDT <- cerResPreSurveyDTred[cerResPostSurveyDTred] table(cerSurveysDT$baCompletedPreSurvey,cerSurveysDT$baCompletedPostSurvey, useNA = "always") -# Descriptive statistics for mid-week (Table 2) +# Descriptive statistics for mid-week (Table 2) ---- # half hour level - all describe(cerOct09DT[mid_week == 1, kWh]) # baseload 02:00 - 05:00 @@ -343,7 +343,7 @@ describe(cerOct09DT[mid_week == 1 & r_hour >= 16 & r_hour <= 20, ] ) -# daily summaries +# daily summaries for use in tables octSummarybyDateDT <- cerOct09DT[mid_week == 1, .( N = length(kWh), # n half hour records @@ -359,15 +359,13 @@ octSummarybyDateDT[, Mean_daily_total = mean(Sum) ), by = baHeat -][order(baHeat)] +][order(baHeat)] # for use in discussion of affect of heat types -# test heat differences -boxplot(octSummarybyDateDT$Sum~octSummarybyDateDT$baHeat) # remember skew! #diff_heat <- kruskal.test(Sum~baHeat, data = octSummarybyDateDT, na.action = na.omit ) #summary(diff_heat) -# Descriptive statistics for mid-week (Table 3 - new) +# Descriptive statistics for mid-week (Table 3 - new) ---- # by number of people cerOct09DT[mid_week == 1, .( @@ -437,7 +435,44 @@ boxHotWater <- ggplot(data = octSummarybyDateDT, boxHotWater + geom_boxplot() ggsave(paste0(rPath,"boxHotWater.png"), width = 10, height = 10) -# Analysis of autocorrelation coefficients #### +# kwh by time of day for Fig 1 ---- +print("Paid work") +cerOct09DT[mid_week == 1 & ba_empl == "paid_work", + .( + Mean = mean(kWh, na.rm = TRUE), + sd = sd(kWh, na.rm = TRUE) + ), + by = r_hour, + ][order(r_hour)] # order results + +print("Unemployed") +cerOct09DT[mid_week == 1 & ba_empl == "unemployed", + .( + Mean = mean(kWh, na.rm = TRUE), + sd = sd(kWh, na.rm = TRUE) + ), + by = r_hour, + ][order(r_hour)] # order results + +print("Retired") +cerOct09DT[mid_week == 1 & ba_empl == "retired", + .( + Mean = mean(kWh, na.rm = TRUE), + sd = sd(kWh, na.rm = TRUE) + ), + by = r_hour, + ][order(r_hour)] # order results + +print("Carer") +cerOct09DT[mid_week == 1 & ba_empl == "carer", + .( + Mean = mean(kWh, na.rm = TRUE), + sd = sd(kWh, na.rm = TRUE) + ), + by = r_hour, + ][order(r_hour)] # order results + +# Analysis of autocorrelation coefficients - for Model 2.3 ---- # These were calculated using STATA and then aggregated, see # https://github.com/dataknut/CER/blob/master/Census2022-CER-calculate-AR.do @@ -474,6 +509,7 @@ archByEmpl boxArchByEmpl <- ggplot(data = cerArchrDT[lag_id == "mid-week" & lag == 36], aes(ba_empl, archr) -) + ) # for discussion of model 2.3 + boxArchByEmpl + geom_boxplot() ggsave(paste0(rPath,"boxArchByEmpl.png"), width = 10, height = 10) -- GitLab