presentations/ica_hackathon_2022/.Rhistory

   1 ls()
   2 weight
   3 weight
   4 lablr
   5 labelr
   6 nrow(labelr)
   7 names(labelr)
   8 names(labelr$data)
   9 labelr$data
  10 labelr
  11 names(labelr)
  12 labelr$labelr
  13 labelr$toxic
  14 setwd("..")
  15 q()
  16 n
  17 summary(toxicity_calibrated)
  18 qplot(labelr$toxic,type='hist')
  19 names(labelr)
  20 labelr$n
  21 labelr
  22 names(labelr)
  23 fbyg
  24 gghist(fbyg$weight)
  25 hist(fbyg$weight)
  26 hist(log(fbyg$weight))
  27 fbyg$weight==1
  28 all(fbyg$weight==1)
  29 fbyg$weight[fbyg$weight != 1]
  30 fbyg[fbyg$weight != 1]
  31 fbyg[,fbyg$weight != 1]
  32 fbyg[[fbyg$weight != 1]]
  33 fbyg[fbyg$weight != 1,]
  34 names(labelr)
  35 summary(toxicity_calibrated)
  36 toxicity_calibrated
  37 val.data
  38 names(labelr)
  39 labelr$data
  40 labelr
  41 labelr[data]
  42 labelr[data=='yg']
  43 labelr[,data=='yg']
  44 labelr[data=='yg',]
  45 labelr[labelr$data=='yg']
  46 labelr[,labelr$data=='yg']
  47 labelr[labelr$data=='yg',]
  48 toxicity_calibrated
  49 summary(toxicity_calibrated)
  50 yg3
  51 yg3[,['toxic','toxic_pred']]
  52 yg3 %>% select('toxic','toxic_pred')
  53 yg3 |> select('toxic','toxic_pred')
  54 names(yg3)
  55 yg3[,c('toxic_pred','toxic')]
  56 corr(yg3[,c('toxic_pred','toxic')])
  57 cor(yg3[,c('toxic_pred','toxic')])
  58 cor(yg3[,c('toxic_pred','toxic')],na.rm=T)
  59 cor(yg3[,c('toxic_pred','toxic')],rm.na=T)
  60 ?cor(yg3[,c('toxic_pred','toxic')],use=
  61 ?cor
  62 cor(yg3[,c('toxic_pred','toxic')],use='all.obs')
  63 ?cor
  64 cor(yg3[,c('toxic_pred','toxic')],use='complete.obs')
  65 cor(yg3[,c('toxic_pred','toxic')],use='complete.obs',method='spearman')
  66 ?predict
  67 yg3$toxic_pred
  68 names(preds)
  69 preds
  70 preds
  71 preds$error
  72 preds
  73 preds
  74 summary(errormod)
  75 summary(errormod)
  76 summary(preds)
  77 names(preds)
  78 preds
  79 resids
  80 qplot(resids)
  81 resids
  82 ?predict.lm
  83 dnorm(1)
  84 dnorm(2)
  85 dnorm(1)
  86 pnorm(1)
  87 preds
  88 p1 + p2
  89 p1 + p2
  90 p1
  91 p2
  92 preds
  93 preds1 <- preds
  94 preds1$diff - preds$diff
  95 preds1$diff
  96 preds1$diff - preds1$diff
  97 preds1$diff - preds$diff
  98 preds1$diff - preds$diff
  99 preds1$diff - preds$diff
 100 preds1$diff - preds$diff
 101 preds1
 102 preds
 103 dnorm(-1)
 104 dnorm(1)
 105 pnorm(1)
 106 pnorm(-1)
 107 pnorm(2)
 108 pnorm(9)
 109 pnorm(6)
 110 pnorm(2)
 111 dnorm(0.95)
 112 qnorm(0.95)
 113 qnorm(0.841)
 114 fulldata_preds
 115 names(yg3)
 116 yg3$toxic_feature_1
 117 yg3$toxic_feature_2
 118 yg3
 119 yg3[,.(toxic_pred,toxic_var)]
 120 yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma)]
 121 yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,cov(toxicity_2_pred,toxicity_1_pred))]
 122 cov(1,2)
 123 cov(c(1),c(3))
 124 cov(c(1),c(3,2))
 125 cov(c(1,1),c(3,2))
 126 cov(c(1,2),c(3,2))
 127 covterm
 128 covterm
 129 ?cov
 130 covterm
 131 yg3
 132 yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,cov(toxicity_2_pred,toxicity_1_pred))]
 133 yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,toxic_var)]
 134 yg3[,.(toxic_pred,toxicity_2_pred_sigma,toxicity_1_pred_sigma,toxic_var,toxic_sd)]
 135 yg3
 136 names(yg3)
 137 print(sg)
 138 print(sg)
 139 1+1
 140 library(stargazer)
 141
 142 stargazer(w1,w2,w3,w4,w5,t1,t2,t3,t4,t5, type="text",
 143           keep = c("cond1","meantox","cond1:meantox","Constant"),
 144           keep.stat=c("n","adj.rsq"),
 145           model.numbers = F,
 146           dep.var.labels = c("DV = Willingness to comment","DV = Toxicity of YG respondent comments"),
 147           covariate.labels = c("Treatment (top comments shown)",
 148                                "Average toxicity of top comments",
 149                                "Treatment $\times$ top comments toxicity",
 150                                "Constant"),
 151           add.lines = list(c("Article fixed effects","No","No","No","Yes","Yes","No","No","No","Yes","Yes")),
 152           star.cutoffs = c(0.05,0.01,0.005),
 153           notes = "Standard errors are clustered at the respondent level.",
 154           column.labels = c("(1)","(2)","(3)","(4)","(5)","(6)","(7)","(8)","(9)","(10)"),
 155           style = "apsr")
 156
 157 q()
 158 n
 159 yglabels
 160 labelr
 161 names(labelr)
 162 fb
 163 names(fb
 164 )
 165 fb.comment_id
 166 fb['comment_id']
 167 fb[,'comment_id']
 168 labelr[,'comment_id']
 169 names(fb)
 170 fb.labeled
 171 names(fb.labeled)
 172 names(yg)
 173 ?amelia
 174 yg
 175 names(yg)
 176 names(yg3)
 177 ?rbind
 178 nrow(yg3)
 179 nrow(yg)
 180 yg3[,.(.N),by=.(toxic,fb)]
 181 yg3.toimpute
 182 names(yg3.toimpute)
 183 yg3.toimpute
 184 names(yg3.toimpute)
 185 names(labelr)
 186 nrow(yg3)
 187 nrow(labelr)
 188 ?merge.data.table
 189 labelr
 190 is.data.table(labelr)
 191 yg3.toimpute
 192 overimp.grid
 193 overimp.grid
 194 ?amelia
 195 q()
 196 n
 197 setwd("presentations/ica_hackathon_2022/")
 198 ls()
 199 attach(r)
 200 example_2_B.plot.df
 201 library(ggplot2)
 202 example_2_B.plot.df[(variable=='x') && (m < 1000)]
 203 example_2_B.plot.df[(variable=='x') && (m < 1000)]
 204 theme_set(theme_default())
 205 theme_set(theme_minimal())
 206 theme_set(theme_classic())
 207 example_2_B.plot.df[(variable=='x') && (m < 1000)]
 208 example_2_B.plot.df[(variable=='x') && (m < 1000),unique(method)]
 209 as.factor
 210 update.packages()
 211 update.packages()
 212 update.packages()
 213 cancel
 214 plot.df
 215 example_2_B.plot.df
 216 plot.df
 217 example_2_B.plot.df
 218 example_2_B.plot.df$method %>% unique
 219 example_2_B.plot.df$method |> unique
 220 example_2_B.plot.df$method  |> uniq
 221 unique(example_2_B.plot.df$method)
 222 example_2_B.plot.df$method
 223 example_2_B.plot.df$method
 224 example_2_B.plot.df$method
 225 example_2_B.plot.df$method
 226 example_2_B.plot.df <- r$example_2_B.plot.df
 227 q()
 228 n
 229 setwd("presentations/ica_hackathon_2022/')
 230 setwd("presentations/ica_hackathon_2022/")
 231 example_2_B.plot.df$method
 232 example_2_B.plot.df$method
 233 q()
 234 n
 235 example_2_B.plot.df$method
 236 example_2_B.plot.df$method
 237 q()
 238 n
 239 example_2_B.plot.df$method
 240 example_2_B.plot.df$method
 241 q()
 242 n
 243 q()
 244 n
 245 plot.df
 246 plot.df
 247 plot.df[,.N,by=.(N,m)]
 248 plot.df[,.N,by=.(N,m,method)]
 249 plot.df[variable=='x',.N,by=.(N,m,method)]
 250 plot.df
 251 plot.df[(variable=='x') & (m < 1000) & (!is.na(p.true.in.ci))]
 252 plot.df[(variable=='x') & (m != 1000) & (!is.na(p.true.in.ci))]
 253 plot.df
 254 ?label_wrap_gen
 255 install.packages("ggplot2")
 256 devtools::install_github("tidyverse/ggplot2")
 257 2
 258 library(ggplot2)
 259 ggplot2::version
 260 sessioninfo()
 261 sessionInfo()
 262 q()
 263 n
 264 sessionInfo()
 265 ?scale_x_discrete
 266 ?facet_grid
 267 plot.df
 268 plot.df
 269 plot.df[method="2SLS+gmm"]
 270 plot.df[method=="2SLS+gmm"]
 271 df <- example_2_B.plot.df
 272 df
 273 q()
 274 n
 275 plot.df
 276 plot.df[m=50]
 277 plot.df[m==50]
 278 plot.df.example.2[m==50][method=2SLS+gmm]
 279 plot.df.example.2[m==50][method==2SLS+gmm]
 280 plot.df.example.2[(m==50) & (method==2SLS+gmm)]
 281 plot.df.example.2[(m==50) & (method=="2SLS+gmm")]
 282 plot.df[m==50]
 283 plot.df.example.3
 284 plot.df.example.3
 285 plot.df.example.3[N=25000]
 286 plot.df.example.3[N==25000]
 287 plot.df
 288 plot.df
 289 plot.df
 290 q()
 291 n