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R导入和导出带有邮政编码标题的CSV

read.csv r

0

Bluebird · 技术社区 · 7 年前

问题2:导出数据帧并保持邮政编码完整的正确方法是什么?

源DF: FDIC

library(readr)
library(dplyr)
library(tidyr)

FDIC_1_source <- read_csv("OFFICES2_ALL.CSV")

NEState<-c("CT", "DC", "DE", "MA", "MD", "ME", "NH", "NJ", "NY", "PA", "RI", "VA", "VT")

FDIC_2_filtered<-FDIC_1_source[FDIC_1_source$STALP %in% NEState,]

write.csv(FDIC_2_filtered,file="FDIC_2_filtered.CSV")

With Dates and Numbers 然而,我想不出一种方法来使用邮政编码的文档。

2 回复 | 直到 7 年前

1

Matt Jewett 7 年前

如果源文件中缺少前导零。尝试一下,因为我不知道邮政编码的列名,在这个示例中,我只使用 zip.col 作为列名。

FDIC_1_source <- read_csv("OFFICES2_ALL.CSV")

FDIC_1_source$zip.col <- sapply(FDIC_1_source$zip.col, function(x) paste0(paste0(rep("0",5 - nchar(x)), collapse = ""), x))

2

0

Bluebird 7 年前

Here 。将用代码更新以反映新发现的库。

library(readr)
library(dplyr)
library(tidyr)
library(zipcode)

FDIC_1_source <- read_csv("OFFICES2_ALL.CSV")

NEState<-c("CT", "DC", "DE", "MA", "MD", "ME", "NH", "NJ", "NY", "PA", "RI", "VA", "VT")

FDIC_2_filtered<-FDIC_1_source[FDIC_1_source$STALP %in% NEState,]

FDIC_2_filtered$ZIP = clean.zipcodes(FDIC_2_filtered$ZIP) #this will fix any zip code errors, esecially working with North East States

write.csv(FDIC_2_filtered,file="FDIC_2_filtered.CSV")