目录
Importing data in R 学习笔记1
flat files:CSV
# Import swimming_pools.csv correctly: pools
pools<-read.csv("swimming_pools.csv",stringsAsFactors=FALSE)
txt文件
read.delim("name.txt",header=TRUE)
转化为table
# Path to the hotdogs.txt file: path
> path <- file.path("data", "hotdogs.txt")
>
> # Import the hotdogs.txt file: hotdogs
> hotdogs <- read.table(path,
sep = " ",
col.names = c("type", "calories", "sodium"))
>
> # Call head() on hotdogs
> head(hotdogs)
type calories sodium
1 Beef 186 495
2 Beef 181 477
3 Beef 176 425
4 Beef 149 322
5 Beef 184 482
6 Beef 190 587
tibble:简单数据框
read_对比read.
前者产生一个简单的数据框，并且会展示每一列的数据类型
packages：readr
read_csv()
读入csv格式
read_csv and read_tsv are special cases of the general read_delim. They're useful for reading the most common types of flat file data, comma separated values and tab separated values, respectively. read_csv2 uses ; for separators, instead of ,. This is common in European countries which use , as the decimal separator
read_tsv
读入txt格式
> # readr is already loaded
>
> # Column names
> properties <- c("area", "temp", "size", "storage", "method",
"texture", "flavor", "moistness")
>
> # Import potatoes.txt: potatoes
读入数据并指定行名
> potatoes<-read_tsv("potatoes.txt",col_names=properties)
Parsed with column specification:
cols(
area = col_integer(),
temp = col_integer(),
size = col_integer(),
storage = col_integer(),
method = col_integer(),
texture = col_double(),
flavor = col_double(),
moistness = col_double()
)
> col_names=properties
>
> # Call head() on potatoes
> head(potatoes)
# A tibble: 6 x 8
area temp size storage method texture flavor moistness
<int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
1 1 1 1 1 1 2.9 3.2 3
2 1 1 1 1 2 2.3 2.5 2.6
3 1 1 1 1 3 2.5 2.8 2.8
4 1 1 1 1 4 2.1 2.9 2.4
5 1 1 1 1 5 1.9 2.8 2.2
6 1 1 1 2 1 1.8 3 1.7
read_delim()
# Column names
> properties <- c("area", "temp", "size", "storage", "method",
"texture", "flavor", "moistness")
>
> # Import potatoes.txt using read_delim(): potatoes
> potatoes <- read_delim("potatoes.txt", delim = " ", col_names = properties)
Parsed with column specification:
cols(
area = col_integer(),
temp = col_integer(),
size = col_integer(),
storage = col_integer(),
method = col_integer(),
texture = col_double(),
flavor = col_double(),
moistness = col_double()
)
>
> # Print out potatoes
> potatoes
# A tibble: 160 x 8
area temp size storage method texture flavor moistness
<int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
1 1 1 1 1 1 2.9 3.2 3
2 1 1 1 1 2 2.3 2.5 2.6
3 1 1 1 1 3 2.5 2.8 2.8
4 1 1 1 1 4 2.1 2.9 2.4
5 1 1 1 1 5 1.9 2.8 2.2
6 1 1 1 2 1 1.8 3 1.7
7 1 1 1 2 2 2.6 3.1 2.4
8 1 1 1 2 3 3 3 2.9
9 1 1 1 2 4 2.2 3.2 2.5
10 1 1 1 2 5 2 2.8 1.9
# ... with 150 more rows
data.table()
fread
make up some column names itself
more convenience
# Import columns 6 and 8 of potatoes.csv: potatoes
> potatoes<-fread("potatoes.csv",select=c(6,8))
>
> # Plot texture (x) and moistness (y) of potatoes
> plot(potatoes$texture,potatoes$moistness)
readxl
excel_sheets()
library(readxl)
# Print the names of all worksheets
excel_sheets("urbanpop.xlsx")
# Read all Excel sheets with lapply(): pop_list
pop_list<- lapply(excel_sheets("urbanpop.xlsx"),
read_excel,
path = "urbanpop.xlsx")
# Display the structure of pop_list
str(pop_list)
read_excel()
# Import the second sheet of urbanpop.xlsx, skipping the first 21 rows: urbanpop_sel
urbanpop_sel <- read_excel("urbanpop.xlsx", sheet = 2, col_names = FALSE, skip = 21)
# Print out the first observation from urbanpop_sel
urbanpop_sel[1,]
gdata
read.xls()
读入xls格式的数据
# Column names for urban_pop
> columns <- c("country", paste0("year_", 1967:1974))
>
> # Finish the read.xls call
> urban_pop <- read.xls("urbanpop.xls", sheet = 2,
skip = 50, header = FALSE, stringsAsFactors = FALSE,
col.names = columns)
>
> # Print first 10 observation of urban_pop
> head(urban_pop,n=10)
country year_1967 year_1968 year_1969 year_1970
1 Cyprus 231929.74 237831.38 243983.34 250164.52
2 Czech Republic 6204409.91 6266304.50 6326368.97 6348794.89
3 Denmark 3777552.62 3826785.08 3874313.99 3930042.97
4 Djibouti 77788.04 84694.35 92045.77 99845.22
5 Dominica 27550.36 29527.32 31475.62 33328.25
6 Dominican Republic 1535485.43 1625455.76 1718315.40 1814060.00
7 Ecuador 2059355.12 2151395.14 2246890.79 2345864.41
8 Egypt 13798171.00 14248342.19 14703858.22 15162858.52
9 El Salvador 1345528.98 1387218.33 1429378.98 1472181.26
10 Equatorial Guinea 75364.50 77295.03 78445.74 78411.07
year_1971 year_1972 year_1973 year_1974
1 261213.21 272407.99 283774.90 295379.83
2 6437055.17 6572632.32 6718465.53 6873458.18
3 3981360.12 4028247.92 4076867.28 4120201.43
4 107799.69 116098.23 125391.58 136606.25
5 34761.52 36049.99 37260.05 38501.47
6 1915590.38 2020157.01 2127714.45 2238203.87
7 2453817.78 2565644.81 2681525.25 2801692.62
8 15603661.36 16047814.69 16498633.27 16960827.93
9 1527985.34 1584758.18 1642098.95 1699470.87
10 77055.29 74596.06 71438.96 68179.26
getSheets()
查看一个excel文件有多少的sheet，输出每个sheet的名字
XLConnect
loadWorkbook()
主要是加载excel文件
When working with XLConnect, the first step will be to load a workbook in your R session with loadWorkbook(); this function will build a "bridge" between your Excel file and your R session.
library("XLConnect")
>
> # Build connection to urbanpop.xlsx: my_book
> my_book<-loadWorkbook("urbanpop.xlsx")
>
> # Print out the class of my_book
> class(my_book)
[1] "workbook"
attr(,"package")
[1] "XLConnect"
readWorksheet（）
读取excel文件
所以顺序肯定是先加载再读取啊。
# Import columns 3, 4, and 5 from second sheet in my_book: urbanpop_sel
urbanpop_sel <- readWorksheet(my_book, sheet = 2,startCol=3,endCol=5)
# Import first column from second sheet in my_book: countries
countries<-readWorksheet(my_book, sheet = 2,startCol=1,endCol=1)
# cbind() urbanpop_sel and countries together: selection
selection<-cbind(countries,urbanpop_sel)
createSheet()
在已经有的excel中创建一个sheet，创建一个空的sheet
# Build connection to urbanpop.xlsx
> my_book <- loadWorkbook("urbanpop.xlsx")
>
> # Add a worksheet to my_book, named "data_summary"
> createSheet(my_book,"data_summary")
>
> # Use getSheets() on my_book
> getSheets(my_book)
[1] "1960-1966" "1967-1974" "1975-2011" "data_summary"
writeWorksheet()
Writes data to worksheets of a '>workbook.
saveWorkbook
保存工作表，就是存到磁盘上
# Build connection to urbanpop.xlsx
my_book <- loadWorkbook("urbanpop.xlsx")
# Add a worksheet to my_book, named "data_summary"
createSheet(my_book, "data_summary")
# Create data frame: summ
sheets <- getSheets(my_book)[1:3]
dims <- sapply(sheets, function(x) dim(readWorksheet(my_book, sheet = x)), USE.NAMES = FALSE)
summ <- data.frame(sheets = sheets,
nrows = dims[1, ],
ncols = dims[2, ])
# Add data in summ to "data_summary" sheet
writeWorksheet(my_book,summ,"data_summary")
# Save workbook as summary.xlsx
saveWorkbook(my_book,"summary.xlsx")
renameSheet()
给sheet表重命名
# Rename "data_summary" sheet to "summary"
renameSheet(my_book, "data_summary", "summary")
# Print out sheets of my_book
getSheets(my_book)
# Save workbook to "renamed.xlsx"
saveWorkbook(my_book, file = "renamed.xlsx")
我发现我自己真的很容易丢参数哦，然后死活调不出来。。。===。。。苦恼的人儿
removeSheet()
删除指定sheet
library(XLConnect)
# Build connection to renamed.xlsx: my_book
my_book<-loadWorkbook("renamed.xlsx")
# Remove the fourth sheet
removeSheet(my_book,sheet="summary")
# Save workbook to "clean.xlsx"
saveWorkbook(my_book,"clean.xlsx")