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Copy pathpyr_data_1120.R
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pyr_data_1120.R
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getfiles <- function(strpath) {
rfiles = dir(strpath,full.names = TRUE)
path.len = lapply(rfiles,nchar)
path.len = as.numeric(path.len)
path.df = data.frame(rfiles,path.len)
path.df = path.df %>% arrange(rfiles) %>% arrange(path.len)
path.df$rfiles = as.character(path.df$rfiles)
return (path.df$rfiles)
}
files15 <- getfiles('/mnt/e/pyr/data/y2015/2015x/')
df_fee = read_feather(files15[13])
df_veri = read_feather(files15[43])
df_fee = cbind(df_fee,df_veri)
df_fee = tbl_df(df_fee[,c(1,2,4)])
df_fee_seld = df_fee %>% filter(x262 == "False")
df_fee_seld$hosname = hos_dic[df_fee_seld$x5]
df_fee_seld = df_fee_seld %>% select(x5,hosname,x229)
names(df_fee_seld) = c('id','name','ttlfee')
df_fee_seld = df_fee_seld %>% filter(!is.na(name) & !is.na(ttlfee))
df_fee_seld$ttlfee = as.numeric(df_fee_seld$ttlfee)
df_fee_15 <- df_fee_seld %>% filter(ttlfee > 0)
df_fee_15 <- tbl_df(df_fee_15)
df_fee_15 <- df_fee_15 %>% filter(ttlfee < 1000000)
df_fee_15 <- tbl_df(df_fee_15)
str(df_fee_15)
sum_ttl <- df_fee_15 %>% group_by(id,name) %>%
summarise(n=n(),ttlfee_by_id = mean(ttlfee)) %>%
filter(n > 2000) %>%
arrange(ttlfee_by_id) %>%
ungroup %>%
View
df_fee_15 %>% filter(id == '410000000624')
quan999 <- quantile(df_fee_15$ttlfee,.9999)
quan001 <- quantile(df_fee_15$ttlfee,.0001)
df_fee_15 <-
df_fee_15 %>% filter(ttlfee < quan999 & ttlfee > quan001)
df_fee_15 %>% filter(ttlfee >= 200000)
hist_fee_15 <-
ggplot(df_fee_15,aes(x = ttlfee,
y = ..density..)) +
geom_histogram(fill = 'cornsilk',color = 'grey60',
binwidth = binsize) +
scale_x_continuous(breaks = breaks,
limits = c(0,200000))
geom_density()
binsize <- diff(range(df_fee_15$ttlfee)) / 2000
breaks = seq(0,max(df_fee_15$ttlfee),by = 50000)
ggplot(df_fee_15,aes(x = ttlfee)) +
geom_histogram(fill = 'cornsilk',color = 'purple',
binwidth = binsize) +
scale_x_continuous(breaks = breaks) +
coord_cartesian(xlim = c(0,150000))
df_fee_15_log <-
df_fee_15 %>% filter(ttlfee > 50) %>%
group_by(id,name) %>%
summarise(n=n())
smp_log <- sample(log(df_fee_15_log$ttlfee),1000)
ggplot(df_fee_15_log,aes(x = log(ttlfee))) +
geom_histogram(fill = 'cornsilk',color = 'purple')
pearson.test(smp_log)
shapiro.test(smp_log)
min(log(df_fee_15$ttlfee))
min(df_fee_15$ttlfee)
exp(9)
str(hist_fee_15)
summary(df_fee_seld$ttlfee)
################################fin_rep================================
fina_rep = read_csv('e:/pyr/data/procdata/fee_inp.csv',col_names = FALSE,
col_types = NULL,
locale(encoding = 'gbk'))
fina_rep_inpfee15 = fina_rep %>% select(X1,X12,X13,X30,X31)
fina_rep_inpfee15 = fina_rep_inpfee15[4:nrow(fina_rep_inpfee15),]
fina_rep_inpfee15[,2:5] <- lapply(fina_rep_inpfee15[,2:5] ,as.character)
fina_rep_inpfee15[,2:5] <- lapply(fina_rep_inpfee15[,2:5] ,as.numeric)
str(fina_rep_inpfee15)
#############################write part============================
write_feather(df_fee_seld,'./data/procdata/ttlfee_2015.pyr')
save(df_fee_seld,file='./data/procdata/ttlfee_2015.rdata')
save(hos_dic,file = 'e:/pyr/data/procdata/hos_dic.rdata')
write_feather(fina_rep_inpfee15,'./data/procdata/fina_rep_fee15.pyr')
hdf_fee <- H5Fcreate("./data/hdf5/R_fee_15.h5")
hdf_fee <- H5Fopen("./data/hdf5/R_fee_15.h5")
hos_dic <- data.frame(names(hos_dic),
hos_dic,stringsAsFactors = FALSE)
h5writeDataset.data.frame(hos_dic,hdf_fee, 'hos_dic',
level=7, DataFrameAsCompound = TRUE)
df_fee_seld <- lapply(df_fee_seld,as.character)
df_fee_seld <- data.frame(df_fee_seld,stringsAsFactors = FALSE)
h5writeDataset.data.frame(df_fee_seld,hdf_fee, 'fee_15',
level=7, DataFrameAsCompound = TRUE)
h5writeDataset.data.frame(fina_rep_inpfee15,hdf_fee, 'fin_fee_15',
level=7)
H5Fclose(hdf_fee)
h5ls(h5_perhos)
py_hdf_fee <- H5Fopen("./data/hdf5/py_fee_15.h5")
H5Fis_hdf5("./data/hdf5/py_fee_15.h5")
H5Dread(py_hdf_fee,'fin_rep15')
H5Fcreate("./data/hdf5/py_fee_15.h5")
H5Fclose(py_hdf_fee)
H5Fis_hdf5("./data/hdf5/py_fee_15.h5")
H5Fopen("./data/hdf5/py_fee_15.h5")
H5Fclose("./data/hdf5/py_fee_15.h5")
H5close()
fin_rep15 <- h5read("./data/hdf5/py_fee_15.h5",'fin_rep15')$table
fin_rep15$X1<-str_conv(fin_rep15$X1,'utf-8')
fin_rep15 <- tbl_df(fin_rep15)
##########################perhos======================
h5_perhos <- H5Fcreate("./data/hdf5/R_perhos.h5")
h5_perhos <- H5Fopen("./data/hdf5/R_perhos.h5")
nyzxyy <- read_feather(
'e:/pyr/data/y2015/2015perhos/410000002820_2015.pyr')
xmdyyy <- read_feather(
'e:/pyr/data/y2015/2015perhos/410000000884_2015.pyr')
out_ny <- nyzxyy %>% filter(x229 > 1000000)
out_xm <- xmdyyy %>% filter(x229 > 1000000)
out_xm %>% select(x229 : x260)
xmdyyy[,229:260] <- lapply(xmdyyy[,229:260],as.numeric)
h5writeDataset.data.frame(nyzxyy,h5_perhos,'410000002820',
level=7, DataFrameAsCompound = TRUE)
h5writeDataset.data.frame(xmdyyy,h5_perhos,'410000000884',
level=7)
H5Fclose(py_perhos_15)
py_perhos_15 = H5Fopen("./data/hdf5/py_perhos_15")
h5ls(py_perhos_15)
nyzxyy <- h5read(py_perhos_15,'fin_rep15')$table
nyzxyy <- tbl_df(nyzxyy)
nyzxyy
#############################load part==============================
write_feather(hos_dic,'./data/procdata/hos_dic.pyr')
load('./data/procdata/hos_dic.rdata')
load('./data/procdata/ttlfee_2015.rdata')
read_feather('./data/procdata/ttlfee_2015.pyr')