我正在尝试读取此链接上的档案： COVID CSV
我正在使用read.csv，但它似乎不起作用：
read.table(file = "https://data.brasil.io/dataset/covid19/caso.csv.gz")
Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
line 1 did not have 3 elements
我正在尝试构建一个代码，以使用COVID信息从该网站提取数据，因此我不必每次都不想使用它时就下载它。
答案 0 :(得分：0)
我们可以使用fread
library(data.table)
fread("https://data.brasil.io/dataset/covid19/caso.csv.gz")
# date state city place_type confirmed deaths order_for_place is_last estimated_population_2019
# 1: 2020-07-17 AP state 33436 499 119 TRUE 845731
# 2: 2020-07-16 AP state 33004 493 118 FALSE 845731
# 3: 2020-07-15 AP state 32408 488 117 FALSE 845731
# 4: 2020-07-14 AP state 31885 483 116 FALSE 845731
# 5: 2020-07-13 AP state 31552 478 115 FALSE 845731
# ---
#372166: 2020-06-23 SP Óleo city 1 0 5 FALSE 2496
#372167: 2020-06-22 SP Óleo city 1 0 4 FALSE 2496
#372168: 2020-06-21 SP Óleo city 1 0 3 FALSE 2496
#372169: 2020-06-20 SP Óleo city 1 0 2 FALSE 2496
#372170: 2020-06-19 SP Óleo city 1 0 1 FALSE 2496
# city_ibge_code confirmed_per_100k_inhabitants death_rate
# 1: 16 3953.5030 0.0149
# 2: 16 3902.4229 0.0149
# 3: 16 3831.9513 0.0151
# 4: 16 3770.1113 0.0151
# 5: 16 3730.7371 0.0151
# ---
#372166: 3533809 40.0641 0.0000
#372167: 3533809 40.0641 0.0000
#372168: 3533809 40.0641 0.0000
#372169: 3533809 40.0641 0.0000
#372170: 3533809 40.0641 0.0000
答案 1 :(得分：0)
似乎可以与readr::read_csv
readr::read_csv("https://data.brasil.io/dataset/covid19/caso.csv.gz")
# A tibble: 376,064 x 12
# date state city place_type confirmed deaths order_for_place is_last
# <date> <chr> <chr> <chr> <dbl> <dbl> <dbl> <lgl>
# 1 2020-07-18 AC NA state 17202 457 124 TRUE
# 2 2020-07-17 AC NA state 16965 452 123 FALSE
# 3 2020-07-16 AC NA state 16865 447 122 FALSE
# 4 2020-07-15 AC NA state 16672 446 121 FALSE
# 5 2020-07-14 AC NA state 16479 436 120 FALSE
# 6 2020-07-13 AC NA state 16260 430 119 FALSE
# 7 2020-07-12 AC NA state 16190 426 118 FALSE
# 8 2020-07-11 AC NA state 16080 419 117 FALSE
# 9 2020-07-10 AC NA state 15768 417 116 FALSE
#10 2020-07-09 AC NA state 15465 411 115 FALSE
# … with 376,054 more rows, and 4 more variables:
# estimated_population_2019 <dbl>, city_ibge_code <dbl>,
# confirmed_per_100k_inhabitants <dbl>, death_rate <dbl>