Datacamp: Introduction to Importing Data in R
Chapter 1
1. Introduction & read.csv
Defaults: (1) header = TRUE
(2) sep = ","
eg. (1) Use read.csv() to import "swimming_pools.csv" as a data frame with the name pools.(2) Print the structure of pools using str().
# Import swimming_pools.csv: pools
pools <- read.csv("swimming_pools.csv")
# Print the structure of pools
str(pools)
stringsAsFactors: If seting stringsAsFactors to FALSE, the data frame columns corresponding to strings in your text file will be character. If seting stringsAsFactors to TRUE, you import strings as factors.
# Import swimming_pools.csv correctly: pools
pools <- read.csv("swimming_pools.csv", stringsAsFactors = FALSE)
# Check the structure of pools
str(pools)
2. read.delim & read.table
(1) read.delim (读取.txt文件)
Default: (1) header = TRUE (the first row contains the field names).
(2) sep = "\t" (fields in a record are delimited by tabs)
eg1. (1) Import the data in "hotdogs.txt" with read.delim(). Call the resulting data frame hotdogs and the variable names are not on the first line. (2) Call summary() on hotdogs. This will print out some summary statistics about all variables in the data frame.
# Import hotdogs.txt: hotdogs
hotdogs <- read.delim("hotdogs.txt", header = FALSE)
# Summarize hotdogs
summary(hotdogs)
(2) read.table (deal with more exotic flat file formats)
By default, the header argument defaults to FALSE and the sep argument is "" by default.
eg2. (1) The data is still hotdogs.txt. It has no column names in the first row, and the field separators are tabs. This time, though, the file is in the data folder inside your current working directory. A variable path with the location of this file is already coded for you. (2) Call head() on hotdogs; this will print the first 6 observations in the data frame.
# Path to the hotdogs.txt file: path
path <- file.path("data", "hotdogs.txt")
# Import the hotdogs.txt file: hotdogs
hotdogs <- read.table(path,
sep = "\t",
col.names = c("type", "calories", "sodium"))
# Call head() on hotdogs
head(hotdogs)
eg3. Lily and Tom decide that they will have one each hot dog. Lily wants to have the one with the fewest calories while Tom wants to have the one with the most sodium.(Next to calories and sodium, the hotdogs have one more variable: type. This can be one of three things: Beef, Meat or Poultry, so a categorical variable: a factor is fine.)
# Finish the read.delim() call
hotdogs <- read.delim("hotdogs.txt", header = FALSE, col.names = c("type", "calories", "sodium"))
# hotdogs(结果输出）
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
7 Beef 158 370
8 Beef 139 322
9 Beef 175 479
10 Beef 148 375
11 Beef 152 330
12 Beef 111 300
13 Beef 141 386
14 Beef 153 401
15 Beef 190 645
16 Beef 157 440
17 Beef 131 317
18 Beef 149 319
19 Beef 135 298
20 Beef 132 253
21 Meat 173 458
22 Meat 191 506
23 Meat 182 473
24 Meat 190 545
25 Meat 172 496
26 Meat 147 360
27 Meat 146 387
28 Meat 139 386
29 Meat 175 507
30 Meat 136 393
31 Meat 179 405
32 Meat 153 372
33 Meat 107 144
34 Meat 195 511
35 Meat 135 405
36 Meat 140 428
37 Meat 138 339
38 Poultry 129 430
39 Poultry 132 375
40 Poultry 102 396
41 Poultry 106 383
42 Poultry 94 387
43 Poultry 102 542
44 Poultry 87 359
45 Poultry 99 357
46 Poultry 107 528
47 Poultry 113 513
48 Poultry 135 426
49 Poultry 142 513
50 Poultry 86 358
51 Poultry 143 581
52 Poultry 152 588
53 Poultry 146 522
54 Poultry 144 545
# Select the hot dog with the least calories: lily
lily <- hotdogs[which.min(hotdogs$calories), ]
# Select the observation with the most sodium: tom
tom <- hotdogs[which.max(hotdogs$sodium), ]
3. Column classes
colClasses: specify the column types or column classes of the resulting data frame.
(1) useful if have some columns that should be factors and others that should be characters and do not have to use stringsAsFactors
(2) if a column is set to "NULL" in the colClasses vector, this column will be skipped and will not be loaded into the data frame
read.delim("my_file.txt",
colClasses = c("character",
"numeric",
"logical"))
Chapter 2
1. readr: read_csv & read_ts
install.packages("readr")
library(readr)
(1) read_csv
# Load the readr package
library(readr)
# Import potatoes.csv with read_csv(): potatoes
potatoes <- read_csv("potatoes.csv")
(2) read_tsv
# readr is already loaded
# Column names
properties <- c("area", "temp", "size", "storage", "method",
"texture", "flavor", "moistness")
# Import potatoes.txt: potatoes
potatoes <- read_tsv("potatoes.txt", col_names = properties)
2. readr: read_delim
read_delim() takes two mandatory arguments:
(1) file: the file that contains the data
(2) delim: the character that separates the values in the data file
# readr is already loaded
# 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 = "\t", col_names = properties)
3. skip and n_max
例. You have a CSV file with 20 lines, and set skip = 2 and n_max = 3, you're only reading in lines 3, 4 and 5 of the file.
(PS: Once you skip some lines, you also skip the first line that can contain column names.)
4. col_types
col_types: 指定导入的数据框架中的列应具有哪些类型（ _ skips the column as a whole）
(1) c: character
(2) d: double
(3) i: integer
(4) l: logical
5. col_types with collectors
(1) col_integer(): the column should be interpreted as an integer
(2) col_factor(levels, ordered = FALSE): the column should be interpreted as a factor with levels
# Edit the col_types argument to import the data correctly: hotdogs_factor
hotdogs_factor <- read_tsv("hotdogs.txt",
col_names = c("type", "calories", "sodium"),
col_types = list(fac, int, int))
6. data.table: fread
install.packages("data.table")
library(data.table)
# Import potatoes.csv with fread(): potatoes
potatoes <- fread("potatoes.csv")
Two arguments of the function: drop and select
例. You have a dataset that contains 5 variables and you want to keep the first and fifth variable, named "a" and "e". The following options will all do the trick:
fread("path/to/file.txt", drop = 2:4)
fread("path/to/file.txt", select = c(1, 5))
fread("path/to/file.txt", drop = c("b", "c", "d"))
fread("path/to/file.txt", select = c("a", "e"))
eg1.
# fread is already loaded
# 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)
eg2. The class of the result of fread() is both data.table and data.frame. read_csv() creates an object with three classes: tbl_df, tb and data.frame.
Chapter 3
1. readxl(1)
excel_sheets(): list different sheets
read_excel(): actually import data into R
# Load the readxl package
install.packages("readxl")
library(readxl)
# Print the names of all worksheets
excel_sheets("urbanpop.xlsx")
# 输出结果
[1] "1960-1966" "1967-1974" "1975-2011"
# Read the sheets, one by one
pop_1 <- read_excel("urbanpop.xlsx", sheet = 1)
pop_2 <- read_excel("urbanpop.xlsx", sheet = 2)
pop_3 <- read_excel("urbanpop.xlsx", sheet = 3)
# 方法一
# Put pop_1, pop_2 and pop_3 in a list: pop_list
pop_list <- list(pop_1, pop_2, pop_3)
# 方法二
# Read all Excel sheets with lapply(): pop_list
pop_list <- lapply(excel_sheets("urbanpop.xlsx"), read_excel, path = "urbanpop.xlsx")
2. Reading a workbook
my_workbook <- lapply(excel_sheets("data.xlsx"),
read_excel,
path = "data.xlsx")
3. readxl(2)
read_excel(path, sheet = 1,
col_names = TRUE,
col_types = NULL,
skip = 0)
col_names:
(1) col_names = FALSE: R assigns names itself
(2) col_names = character vector: manually specify
(3) 默认为TRUE，表示 Excel 工作表中的第一行是否包含列名
skip:
# the first 15 rows in the first sheet of "data.xlsx" are ignored
read_excel("data.xlsx", skip = 15)
4. gdata
(1) Entire suite of tools for data manipulation
(2) Supercharges basic R
(3) read.xls()
(4) Support for XLS
(5) Support for XLSX with additional driver
(6) No readxl::excel_sheets() equivalent
# Load the gdata package
library(gdata)
# Import the second sheet of urbanpop.xls: urban_pop
urban_pop <- read.xls("urbanpop.xls", sheet = "1967-1974")
# Print the first 11 observations using head()
head(urban_pop, n = 11)
# Add code to import data from all three sheets in urbanpop.xls
path <- "urbanpop.xls"
urban_sheet1 <- read.xls(path, sheet = 1, stringsAsFactors = FALSE)
urban_sheet2 <- read.xls(path, sheet = 2, stringsAsFactors = FALSE)
urban_sheet3 <- read.xls(path, sheet = 3, stringsAsFactors = FALSE)
# Extend the cbind() call to include urban_sheet3: urban
urban <- cbind(urban_sheet1, urban_sheet2[-1], urban_sheet3[-1])
# Remove all rows with NAs from urban: urban_clean
urban_clean <- na.omit(urban)
Chapter 4
1. Reading sheets
install.packages("XLConnect")
(1) loadWorkbook()
library("XLConnect")
book <- loadWorkbook("cities.xlsx")
(2) getSheets(): list the sheets in an Excel file
getSheets(book)
library(readxl)
excel_sheets("cities.xlsx")
(3) readWorksheet(): import data from a sheet
Customize readWorksheet:
my_book <- loadWorkbook("urbanpop.xlsx")
sheets <- getSheets(my_book)
all <- lapply(sheets, readWorksheet, object = my_book)
str(all)
2. Adapting sheets
pop_2010 <- ... # truncated
library(XLConnect)
book <- LoadWorkbook("cities.xlsx")
createSheet(book, name = "year_2010")
writeWorksheet(book, pop_2010, sheet = "year_2010")
saveWorkbook(book, file = "cities2.xlsx")
removeSheet(book, sheet = "Y2010")
saveWorkbook(book, file = "cities4.xlsx")
3. Renaming sheets
eg. Use renameSheet() to rename the fourth sheet to "summary".
my_book
# 输出结果
[1] "1960-1966" "1967-1974" "1975-2011" "data_summary"
# Rename "data_summary" sheet to "summary"
renameSheet(my_book, "data_summary", "summary")