Last updated: 2020-05-23
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| html | e1a5784 | KaranSShakya | 2020-05-19 | link on all pages |
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| Rmd | 6c2aa83 | KaranSShakya | 2020-05-17 | data manipulation updates |
| html | 6c2aa83 | KaranSShakya | 2020-05-17 | data manipulation updates |
| Rmd | 5adafbc | KaranSShakya | 2020-05-16 | panel regression + numeric/character |
| html | 5adafbc | KaranSShakya | 2020-05-16 | panel regression + numeric/character |
| Rmd | d927485 | KaranSShakya | 2020-05-16 | GIT to R-Studio commits |
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To use data readily online. You need the readr package which reads csv files.
url <- "https://raw.githubusercontent.com/rafalab/dslabs/master/inst/extdata/murders.csv"
dat <- read_csv(url)To download the file we can use: (or an alternative is to simply write.excel or write.csv).
download.file(url, tmp_filename)If x is character and y is numeric, and you want to reverse both these:
y <- as.numeric(x)
x <- as.character(y)This is mainly used to change WorldBank datasets. Example table:
# A tibble: 3 x 4
Country `2001` `2002` `2003`
<chr> <dbl> <dbl> <dbl>
1 Nepal 12 435 5524
2 India 31 355 2424
3 China 64 353 2244key The columns to turn into rows. value what to call the data.
long <- wide %>%
gather(key="Year", value="Population", 2:4)
long# A tibble: 9 x 3
Country Year Population
<chr> <chr> <dbl>
1 Nepal 2001 12
2 India 2001 31
3 China 2001 64
4 Nepal 2002 435
5 India 2002 355
6 China 2002 353
7 Nepal 2003 5524
8 India 2003 2424
9 China 2003 2244names(data)[1] <- "New_name"
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.3.1 forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[5] purrr_0.3.4 readr_1.3.1 tidyr_1.0.3 tibble_3.0.1
[9] ggplot2_3.3.0 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.13 haven_2.2.0 lattice_0.20-41
[5] colorspace_1.4-1 vctrs_0.3.0 generics_0.0.2 htmltools_0.4.0
[9] yaml_2.2.1 utf8_1.1.4 rlang_0.4.6 later_1.0.0
[13] pillar_1.4.4 withr_2.2.0 glue_1.4.1 DBI_1.1.0
[17] dbplyr_1.4.3 modelr_0.1.7 lifecycle_0.2.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.5 evaluate_0.14
[25] knitr_1.28 httpuv_1.5.2 curl_4.3 fansi_0.4.1
[29] broom_0.5.6 Rcpp_1.0.4.6 promises_1.1.0 backports_1.1.6
[33] scales_1.1.1 jsonlite_1.6.1 fs_1.4.1 hms_0.5.3
[37] digest_0.6.25 stringi_1.4.6 grid_4.0.0 rprojroot_1.3-2
[41] cli_2.0.2 tools_4.0.0 magrittr_1.5 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.0 xml2_1.3.2
[49] reprex_0.3.0 lubridate_1.7.8 rstudioapi_0.11 assertthat_0.2.1
[53] rmarkdown_2.1 httr_1.4.1 R6_2.4.1 nlme_3.1-147
[57] git2r_0.27.1 compiler_4.0.0