tutoring2
json
Jiaxiang Li 2018-11-23
library(data.table)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.2.1 --
## √ ggplot2 3.1.0 √ purrr 0.2.5
## √ tibble 1.4.2 √ dplyr 0.7.8
## √ tidyr 0.8.2 √ stringr 1.3.1
## √ readr 1.1.1 √ forcats 0.3.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::between() masks data.table::between()
## x dplyr::filter() masks stats::filter()
## x dplyr::first() masks data.table::first()
## x dplyr::lag() masks stats::lag()
## x dplyr::last() masks data.table::last()
## x purrr::transpose() masks data.table::transpose()
data <- fread('test.csv')
# profvis::profvis(
# for (i in 1:100) {
data %>%
# two cols
# json data is not std, clean it.
mutate(var = str_replace_all(var,'""','\"')) %>%
mutate(var = map(.x = var,.f = jsonlite::fromJSON)) %>%
unnest()
## # A tibble: 107 x 18
## cusnum 一般消费分期平台 银行个人业务 直销银行 财产保险 小额贷款公司
## <int> <chr> <chr> <chr> <chr> <chr>
## 1 1 3 1 1 1 1
## 2 2 3 <NA> 1 <NA> 1
## 3 3 5 <NA> <NA> 1 1
## 4 4 4 <NA> 1 <NA> 1
## 5 5 2 <NA> <NA> <NA> <NA>
## 6 6 4 <NA> <NA> <NA> <NA>
## 7 7 3 <NA> <NA> <NA> 4
## 8 8 5 <NA> <NA> <NA> 2
## 9 9 2 <NA> <NA> 1 1
## 10 10 3 <NA> <NA> <NA> <NA>
## # ... with 97 more rows, and 12 more variables: P2P网贷 <chr>,
## # 大型消费金融公司 <chr>, 大数据金融 <chr>, 银行消费金融公司 <chr>,
## # 信用卡中心 <chr>, 网上银行 <chr>, 综合类电商平台 <chr>,
## # 互联网金融门户 <chr>, 银行小微贷款 <chr>, O2O <chr>, 融资租赁 <chr>,
## # 第三方服务商 <chr>
# unnest, spread
# as_tibble() %>%
# print %>%
# write_excel_csv('tmp.csv')
# output
# }
# )
- json code 可以参考 7.30 json格式文件清理成dataframe
# profvis::profvis(
# for (i in 1:100){
data[, var := str_replace_all(var, '""', '\"')
][, {
I <- list()
for (i in 1:.N) {
I[[i]] <- c(jsonlite::fromJSON(var[i]), cusnum = cusnum[i])
}
rbindlist(I, fill = T)
}]
## # A tibble: 107 x 18
## 一般消费分期平台 银行个人业务 直销银行 财产保险 小额贷款公司 P2P网贷
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 3 1 1 1 1 2
## 2 3 <NA> 1 <NA> 1 3
## 3 5 <NA> <NA> 1 1 15
## 4 4 <NA> 1 <NA> 1 1
## 5 2 <NA> <NA> <NA> <NA> <NA>
## 6 4 <NA> <NA> <NA> <NA> 5
## 7 3 <NA> <NA> <NA> 4 12
## 8 5 <NA> <NA> <NA> 2 14
## 9 2 <NA> <NA> 1 1 3
## 10 3 <NA> <NA> <NA> <NA> 5
## # ... with 97 more rows, and 12 more variables: 大型消费金融公司 <chr>,
## # cusnum <int>, 大数据金融 <chr>, 银行消费金融公司 <chr>,
## # 信用卡中心 <chr>, 网上银行 <chr>, 综合类电商平台 <chr>,
## # 互联网金融门户 <chr>, 银行小微贷款 <chr>, O2O <chr>, 融资租赁 <chr>,
## # 第三方服务商 <chr>
# }
# )
dplyr and data.table
library(packagefinder)
library(dlstats)
library(cranly)
library(tidyverse)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:data.table':
##
## hour, isoweek, mday, minute, month, quarter, second, wday,
## week, yday, year
## The following object is masked from 'package:base':
##
## date
cran_stats(c('dplyr','data.table','tidyverse')) %>%
filter(year(end) >= 2017) %>%
ggplot(aes(x=end,y=downloads,col=package)) +
geom_line()
