Using Rstudio Cloud and when importing CSV into R it is adding an X at the beginning of the date.
Tried to change the encoding to different versions and change the file type but none of those worked.
Do I need to change the class? How do I change the class for the top row from 5 to 81?
'''R
covid19 <- read.csv("timeseries_covid19_global.CSV")
View(covid19)
'''
Hi, and welcome!
Please see the FAQ: What's a reproducible example (`reprex`) and how do I do one? Using a reprex, complete with representative data will attract quicker and more answers. Screenshots are only useful as a shortcut to highlight something like an RStudio menu choice question. For data, they are seldom useful.
I can just barely make out the column names in the screenshot so I may be wrong. It appears that the source csv file column names were numeric. That's a no-no in R, so read.csv corrected it by prepending X1 and converting the result to character class.
One way to deal with this is to rename the columns in bulk with character names describing the type of data.
colnames(covid19) <- c("loc","nat_reg","lat","lng","pop", "gdp" ...)
( I'm a big fan of all lowercase and as short as possible to still show the data, but that's me.)
Thanks for the Reply and that worked like a charm.
Is there a way to loop so I can label column 5 as a predefined start date "1/22/20" then add a day to the next column "1/23/20" until there are no columns left to label?
You are getting the X in front of those column names because they are non-syntactical column names and bad practice but if you want to keep them anyways simply use check.names = FALSE while reading the file.
covid19 <- read.csv("https://data.humdata.org/hxlproxy/api/data-preview.csv?url=https%3A%2F%2Fraw.githubusercontent.com%2FCSSEGISandData%2FCOVID-19%2Fmaster%2Fcsse_covid_19_data%2Fcsse_covid_19_time_series%2Ftime_series_covid19_confirmed_global.csv&filename=time_series_covid19_confirmed_global.csv",
check.names = FALSE)
head(covid19)
#> Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20
#> 1 Afghanistan 33.0000 65.0000 0 0 0
#> 2 Albania 41.1533 20.1683 0 0 0
#> 3 Algeria 28.0339 1.6596 0 0 0
#> 4 Andorra 42.5063 1.5218 0 0 0
#> 5 Angola -11.2027 17.8739 0 0 0
#> 6 Antigua and Barbuda 17.0608 -61.7964 0 0 0
#> 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 2/1/20 2/2/20 2/3/20
#> 1 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0
#> 2/4/20 2/5/20 2/6/20 2/7/20 2/8/20 2/9/20 2/10/20 2/11/20 2/12/20 2/13/20
#> 1 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0
#> 2/14/20 2/15/20 2/16/20 2/17/20 2/18/20 2/19/20 2/20/20 2/21/20 2/22/20
#> 1 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0
#> 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 3/2/20 3/3/20
#> 1 0 1 1 1 1 1 1 1 1 1
#> 2 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 1 1 1 1 1 1 3 5
#> 4 0 0 0 0 0 0 0 0 1 1
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0
#> 3/4/20 3/5/20 3/6/20 3/7/20 3/8/20 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20
#> 1 1 1 1 1 4 4 5 7 7 7
#> 2 0 0 0 0 0 2 10 12 23 33
#> 3 12 12 17 17 19 20 20 20 24 26
#> 4 1 1 1 1 1 1 1 1 1 1
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 1
#> 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
#> 1 11 16 21 22 22 22 24 24 40
#> 2 38 42 51 55 59 64 70 76 89
#> 3 37 48 54 60 74 87 90 139 201
#> 4 1 1 2 39 39 53 75 88 113
#> 5 0 0 0 0 0 0 1 2 2
#> 6 1 1 1 1 1 1 1 1 1
#> 3/23/20 3/24/20 3/25/20 3/26/20 3/27/20 3/28/20 3/29/20 3/30/20 3/31/20
#> 1 40 74 84 94 110 110 120 170 174
#> 2 104 123 146 174 186 197 212 223 243
#> 3 230 264 302 367 409 454 511 584 716
#> 4 133 164 188 224 267 308 334 370 376
#> 5 3 3 3 4 4 5 7 7 7
#> 6 3 3 3 7 7 7 7 7 7
#> 4/1/20 4/2/20 4/3/20 4/4/20 4/5/20 4/6/20 4/7/20 4/8/20
#> 1 237 273 281 299 349 367 423 444
#> 2 259 277 304 333 361 377 383 400
#> 3 847 986 1171 1251 1320 1423 1468 1572
#> 4 390 428 439 466 501 525 545 564
#> 5 8 8 8 10 14 16 17 19
#> 6 7 9 15 15 15 15 19 19
Created on 2020-04-09 by the reprex package (v0.3.0)
Yes, but I'd suggest better column names, since the column rows can be created as date objects showing the date.
To create a sequence of start-0x
l <- length(your_data) - [number of non-start columns]
``` r
l <- 10
left_side <- c("fee")
paste0("start_",seq(1:10)) -> right_side
col_labels <- c(left_side,right_side)
colnames(mtcars) <- col_labels
head(mtcars)
#> fee start_1 start_2 start_3 start_4 start_5 start_6 start_7
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1
#> start_8 start_9 start_10
#> Mazda RX4 1 4 4
#> Mazda RX4 Wag 1 4 4
#> Datsun 710 1 4 1
#> Hornet 4 Drive 0 3 1
#> Hornet Sportabout 0 3 2
#> Valiant 0 3 1
Created on 2020-04-09 by the reprex package (v0.3.0)
I'd go further than recommending better names. I'd recommend tidy data. Dates are values not column names. They should be in a column with a meaningful name describing the role of the dates and the table should be more vertical than it presently is. Students will benefit greatly from understanding the benefits of tidydata
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