04-datapasta-example - Githubissues

bensoltoff commented 3 years ago

Post your reproducible example here

deblnia commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
#> Warning: package 'ggplot2' was built under R version 3.6.2
#> Warning: package 'tibble' was built under R version 3.6.2
#> Warning: package 'tidyr' was built under R version 3.6.2
#> Warning: package 'purrr' was built under R version 3.6.2
#> Warning: package 'dplyr' was built under R version 3.6.2
library(here)
#> here() starts at /private/var/folders/5g/w1t31h252w31cy0ztgw0bw0c0000gn/T/RtmpBOWZh7/reprex167033eca065b

# import data file
# urban <- read_csv(here("data", "urbanization-state.csv"))

# datapasta::dpasta(head(urban, 10))

tibble::tribble(
                                                                       ~state, ~urbanindex,
                                                                    "Alabama",    9.605935,
                                                                     "Alaska",    8.735964,
                                                             "American Samoa",    11.08593,
                                                                    "Arizona",    11.29971,
                                                                   "Arkansas",    9.259444,
                                                                 "California",    12.19028,
                                                                   "Colorado",    11.15445,
                                                                "Connecticut",    11.40968,
                                                                   "Delaware",    11.00999,
                                                       "District of Columbia",    13.44057
                                                       ) %>% 
# how do I reorder the bars from largest to smallest?
ggplot(mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

vinsgromeo commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /home/romeov/uc-cfss-reproducible-examples-and-git-47116e5

# import data file
urban <- read_csv(here("data", "urbanization-state.csv"))
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   state = col_character(),
#>   urbanindex = col_double()
#> )

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

nearridge commented 3 years ago

library(tidyverse)
library(here)
#> here() starts at /tmp/RtmpEGBHKv/reprex18309438ea8787

# import data file
urban <- tibble::tribble(
                  ~state, ~urbanindex,
               "Alabama",    9.605935,
                "Alaska",    8.735964,
        "American Samoa",    11.08593,
               "Arizona",    11.29971,
              "Arkansas",    9.259444,
            "California",    12.19028,
              "Colorado",    11.15445,
           "Connecticut",    11.40968,
              "Delaware",    11.00999,
  "District of Columbia",    13.44057,
               "Florida",    11.46484,
               "Georgia",    10.55233,
                  "Guam",    11.08593,
                "Hawaii",    11.08621,
                 "Idaho",    9.593634,
              "Illinois",    11.62372,
               "Indiana",     10.4105,
                  "Iowa",    9.593525,
                "Kansas",    10.12044,
              "Kentucky",    9.789536,
             "Louisiana",    10.17518,
                 "Maine",    9.037091,
              "Maryland",    11.71105,
         "Massachusetts",    11.83973,
              "Michigan",    10.80559,
             "Minnesota",    10.45684,
           "Mississippi",    8.910859,
              "Missouri",    10.20212,
               "Montana",    8.470226,
              "Nebraska",    10.19912,
                "Nevada",    11.76972,
         "New Hampshire",    9.917139,
            "New Jersey",    12.23565,
            "New Mexico",    9.896993,
              "New York",    12.55857,
        "North Carolina",    10.32481,
          "North Dakota",    9.054678,
     "Northern Marianas",    11.08593,
                  "Ohio",    10.87687,
              "Oklahoma",     9.93928,
                "Oregon",    10.71233,
          "Pennsylvania",    11.14623,
           "Puerto Rico",    11.57168,
          "Rhode Island",    11.72124,
        "South Carolina",    10.11142,
          "South Dakota",    8.728642,
             "Tennessee",    10.19729,
                 "Texas",    11.17488,
                  "Utah",    10.96281,
               "Vermont",    8.843222,
        "Virgin Islands",    11.08593,
              "Virginia",    10.90625,
            "Washington",    11.11933,
         "West Virginia",    9.111112,
             "Wisconsin",    10.19131,
               "Wyoming",    8.256294
  )

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

lkataja commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /tmp/RtmpW1r3yi/reprex17f6f94ee57b32

# import data file
urban <- tibble::tribble(
                  ~state, ~urbanindex,
               "Alabama",    9.605935,
                "Alaska",    8.735964,
        "American Samoa",    11.08593,
               "Arizona",    11.29971,
              "Arkansas",    9.259444,
            "California",    12.19028,
              "Colorado",    11.15445,
           "Connecticut",    11.40968,
              "Delaware",    11.00999,
  "District of Columbia",    13.44057,
               "Florida",    11.46484,
               "Georgia",    10.55233,
                  "Guam",    11.08593,
                "Hawaii",    11.08621,
                 "Idaho",    9.593634,
              "Illinois",    11.62372,
               "Indiana",     10.4105,
                  "Iowa",    9.593525,
                "Kansas",    10.12044,
              "Kentucky",    9.789536,
             "Louisiana",    10.17518,
                 "Maine",    9.037091,
              "Maryland",    11.71105,
         "Massachusetts",    11.83973,
              "Michigan",    10.80559,
             "Minnesota",    10.45684,
           "Mississippi",    8.910859,
              "Missouri",    10.20212,
               "Montana",    8.470226,
              "Nebraska",    10.19912,
                "Nevada",    11.76972,
         "New Hampshire",    9.917139,
            "New Jersey",    12.23565,
            "New Mexico",    9.896993,
              "New York",    12.55857,
        "North Carolina",    10.32481,
          "North Dakota",    9.054678,
     "Northern Marianas",    11.08593,
                  "Ohio",    10.87687,
              "Oklahoma",     9.93928,
                "Oregon",    10.71233,
          "Pennsylvania",    11.14623,
           "Puerto Rico",    11.57168,
          "Rhode Island",    11.72124,
        "South Carolina",    10.11142,
          "South Dakota",    8.728642,
             "Tennessee",    10.19729,
                 "Texas",    11.17488,
                  "Utah",    10.96281,
               "Vermont",    8.843222,
        "Virgin Islands",    11.08593,
              "Virginia",    10.90625,
            "Washington",    11.11933,
         "West Virginia",    9.111112,
             "Wisconsin",    10.19131,
               "Wyoming",    8.256294
  )

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

bensoltoff commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /tmp/RtmpyRPUuS/reprex17fb00227e45d2

# import data file
urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa",    11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444,
  "California",    12.19028,
  "Colorado",    11.15445,
  "Connecticut",    11.40968,
  "Delaware",    11.00999,
  "District of Columbia",    13.44057,
  "Florida",    11.46484,
  "Georgia",    10.55233,
  "Guam",    11.08593,
  "Hawaii",    11.08621,
  "Idaho",    9.593634,
  "Illinois",    11.62372,
  "Indiana",     10.4105,
  "Iowa",    9.593525,
  "Kansas",    10.12044,
  "Kentucky",    9.789536,
  "Louisiana",    10.17518,
  "Maine",    9.037091,
  "Maryland",    11.71105,
  "Massachusetts",    11.83973,
  "Michigan",    10.80559,
  "Minnesota",    10.45684,
  "Mississippi",    8.910859,
  "Missouri",    10.20212,
  "Montana",    8.470226,
  "Nebraska",    10.19912,
  "Nevada",    11.76972,
  "New Hampshire",    9.917139,
  "New Jersey",    12.23565,
  "New Mexico",    9.896993,
  "New York",    12.55857,
  "North Carolina",    10.32481,
  "North Dakota",    9.054678,
  "Northern Marianas",    11.08593,
  "Ohio",    10.87687,
  "Oklahoma",     9.93928,
  "Oregon",    10.71233,
  "Pennsylvania",    11.14623,
  "Puerto Rico",    11.57168,
  "Rhode Island",    11.72124,
  "South Carolina",    10.11142,
  "South Dakota",    8.728642,
  "Tennessee",    10.19729,
  "Texas",    11.17488,
  "Utah",    10.96281,
  "Vermont",    8.843222,
  "Virgin Islands",    11.08593,
  "Virginia",    10.90625,
  "Washington",    11.11933,
  "West Virginia",    9.111112,
  "Wisconsin",    10.19131,
  "Wyoming",    8.256294
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

bensoltoff commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /tmp/RtmpyRPUuS/reprex17fb0070d358c6

# import data file
urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa",    11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444,
  "California",    12.19028
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

liu15611 commented 3 years ago

library(tidyverse)
library(here)
#> here() starts at /tmp/Rtmpc6RIB0/reprex17f99128181ce0

urban<-tibble::tribble(
                  ~state, ~urbanindex,
               "Alabama",    9.605935,
                "Alaska",    8.735964,
        "American Samoa",    11.08593,
               "Arizona",    11.29971,
              "Arkansas",    9.259444,
            "California",    12.19028,

  )

ggplot(data = urban, mapping=aes(x=state, y=urbanindex))+
  geom_col()+
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

jthedu commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /home/duj/uc-cfss-reproducible-examples-and-git-47116e5

# import data file
urban <- read_csv(here("data", "urbanization-state.csv"))
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   state = col_character(),
#>   urbanindex = col_double()
#> )

here::here("data", "urbanization-state.csv") %>%
  read_csv() %>%
  # only use the first six rows, keeps the data frame minimal
  head() %>%
  datapasta::dpasta()
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   state = col_character(),
#>   urbanindex = col_double()
#> )
#> tibble::tribble(
#>             ~state, ~urbanindex,
#>          "Alabama",    9.605935,
#>           "Alaska",    8.735964,
#>   "American Samoa",    11.08593,
#>          "Arizona",    11.29971,
#>         "Arkansas",    9.259444,
#>       "California",    12.19028
#>   )

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-02-18 by the reprex package (v0.3.0)}

bensoltoff commented 3 years ago

library(tidyverse)

# import data file
urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama", 9.605935,
  "Alaska", 8.735964,
  "American Samoa", 11.08593,
  "Arizona", 11.29971,
  "Arkansas", 9.259444,
  "California", 12.19028,
  "Colorado", 11.15445,
  "Connecticut", 11.40968,
  "Delaware", 11.00999,
  "District of Columbia", 13.44057,
  "Florida", 11.46484,
  "Georgia", 10.55233,
  "Guam", 11.08593,
  "Hawaii", 11.08621,
  "Idaho", 9.593634,
  "Illinois", 11.62372,
  "Indiana", 10.4105,
  "Iowa", 9.593525,
  "Kansas", 10.12044,
  "Kentucky", 9.789536,
  "Louisiana", 10.17518,
  "Maine", 9.037091,
  "Maryland", 11.71105,
  "Massachusetts", 11.83973,
  "Michigan", 10.80559,
  "Minnesota", 10.45684,
  "Mississippi", 8.910859,
  "Missouri", 10.20212,
  "Montana", 8.470226,
  "Nebraska", 10.19912,
  "Nevada", 11.76972,
  "New Hampshire", 9.917139,
  "New Jersey", 12.23565,
  "New Mexico", 9.896993,
  "New York", 12.55857,
  "North Carolina", 10.32481,
  "North Dakota", 9.054678,
  "Northern Marianas", 11.08593,
  "Ohio", 10.87687,
  "Oklahoma", 9.93928,
  "Oregon", 10.71233,
  "Pennsylvania", 11.14623,
  "Puerto Rico", 11.57168,
  "Rhode Island", 11.72124,
  "South Carolina", 10.11142,
  "South Dakota", 8.728642,
  "Tennessee", 10.19729,
  "Texas", 11.17488,
  "Utah", 10.96281,
  "Vermont", 8.843222,
  "Virgin Islands", 11.08593,
  "Virginia", 10.90625,
  "Washington", 11.11933,
  "West Virginia", 9.111112,
  "Wisconsin", 10.19131,
  "Wyoming", 8.256294
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.1 (2020-06-06) #> os Red Hat Enterprise Linux 8.4 (Ootpa) #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.1) #> backports 1.2.1 2020-12-09 [2] CRAN (R 4.0.1) #> broom 0.7.5 2021-02-19 [2] CRAN (R 4.0.1) #> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.1) #> cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.1) #> colorspace 2.0-0 2020-11-11 [2] CRAN (R 4.0.1) #> crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.1) #> curl 4.3.1 2021-04-30 [1] CRAN (R 4.0.1) #> DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.1) #> dbplyr 2.1.0 2021-02-03 [2] CRAN (R 4.0.1) #> digest 0.6.27 2020-10-24 [2] CRAN (R 4.0.1) #> dplyr * 1.0.7 2021-06-18 [1] CRAN (R 4.0.1) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.0.1) #> evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.1) #> fansi 0.5.0 2021-05-25 [1] CRAN (R 4.0.1) #> farver 2.1.0 2021-02-28 [2] CRAN (R 4.0.1) #> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.1) #> fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.1) #> generics 0.1.0 2020-10-31 [2] CRAN (R 4.0.1) #> ggplot2 * 3.3.3 2020-12-30 [2] CRAN (R 4.0.1) #> glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.1) #> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.1) #> haven 2.3.1 2020-06-01 [2] CRAN (R 4.0.1) #> highr 0.8 2019-03-20 [2] CRAN (R 4.0.1) #> hms 1.0.0 2021-01-13 [2] CRAN (R 4.0.1) #> htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [2] CRAN (R 4.0.1) #> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.1) #> knitr 1.31 2021-01-27 [2] CRAN (R 4.0.1) #> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.0.1) #> lifecycle 1.0.0 2021-02-15 [2] CRAN (R 4.0.1) #> lubridate 1.7.10 2021-02-26 [2] CRAN (R 4.0.1) #> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.0.1) #> mime 0.10 2021-02-13 [2] CRAN (R 4.0.1) #> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.0.1) #> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.1) #> pillar 1.6.1 2021-05-16 [1] CRAN (R 4.0.1) #> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.1) #> purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.1) #> R6 2.5.0 2020-10-28 [2] CRAN (R 4.0.1) #> Rcpp 1.0.6 2021-01-15 [2] CRAN (R 4.0.1) #> readr * 1.4.0 2020-10-05 [2] CRAN (R 4.0.1) #> readxl 1.3.1 2019-03-13 [2] CRAN (R 4.0.1) #> reprex 2.0.0 2021-04-02 [2] CRAN (R 4.0.1) #> rlang 0.4.11 2021-04-30 [1] CRAN (R 4.0.1) #> rmarkdown 2.9 2021-06-15 [1] CRAN (R 4.0.1) #> rstudioapi 0.13 2020-11-12 [2] CRAN (R 4.0.1) #> rvest 1.0.0 2021-03-09 [2] CRAN (R 4.0.1) #> scales 1.1.1 2020-05-11 [2] CRAN (R 4.0.1) #> sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 4.0.1) #> stringi 1.5.3 2020-09-09 [2] CRAN (R 4.0.1) #> stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.1) #> styler 1.4.1 2021-03-30 [2] CRAN (R 4.0.1) #> tibble * 3.1.2 2021-05-16 [1] CRAN (R 4.0.1) #> tidyr * 1.1.3 2021-03-03 [2] CRAN (R 4.0.1) #> tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.0.1) #> tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 4.0.1) #> utf8 1.2.1 2021-03-12 [2] CRAN (R 4.0.1) #> vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.0.1) #> withr 2.4.2 2021-04-18 [1] CRAN (R 4.0.1) #> xfun 0.22 2021-03-11 [2] CRAN (R 4.0.1) #> xml2 1.3.2 2020-04-23 [2] CRAN (R 4.0.1) #> yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.1) #> #> [1] /home/soltoffbc/R/x86_64-pc-linux-gnu-library/4.0 #> [2] /opt/R/4.0.1/lib/R/library ```

cslewis12 commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)

# import data file
urban <- tibble::tribble(
                  ~state, ~urbanindex,
               "Alabama",    9.605935,
                "Alaska",    8.735964,
        "American Samoa",    11.08593,
               "Arizona",    11.29971,
              "Arkansas",    9.259444,
            "California",    12.19028,
              "Colorado",    11.15445,
           "Connecticut",    11.40968,
              "Delaware",    11.00999,
  "District of Columbia",    13.44057,
               "Florida",    11.46484,
               "Georgia",    10.55233,
                  "Guam",    11.08593,
                "Hawaii",    11.08621,
                 "Idaho",    9.593634,
              "Illinois",    11.62372,
               "Indiana",     10.4105,
                  "Iowa",    9.593525,
                "Kansas",    10.12044,
              "Kentucky",    9.789536,
             "Louisiana",    10.17518,
                 "Maine",    9.037091,
              "Maryland",    11.71105,
         "Massachusetts",    11.83973,
              "Michigan",    10.80559,
             "Minnesota",    10.45684,
           "Mississippi",    8.910859,
              "Missouri",    10.20212,
               "Montana",    8.470226,
              "Nebraska",    10.19912,
                "Nevada",    11.76972,
         "New Hampshire",    9.917139,
            "New Jersey",    12.23565,
            "New Mexico",    9.896993,
              "New York",    12.55857,
        "North Carolina",    10.32481,
          "North Dakota",    9.054678,
     "Northern Marianas",    11.08593,
                  "Ohio",    10.87687,
              "Oklahoma",     9.93928,
                "Oregon",    10.71233,
          "Pennsylvania",    11.14623,
           "Puerto Rico",    11.57168,
          "Rhode Island",    11.72124,
        "South Carolina",    10.11142,
          "South Dakota",    8.728642,
             "Tennessee",    10.19729,
                 "Texas",    11.17488,
                  "Utah",    10.96281,
               "Vermont",    8.843222,
        "Virgin Islands",    11.08593,
              "Virginia",    10.90625,
            "Washington",    11.11933,
         "West Virginia",    9.111112,
             "Wisconsin",    10.19131,
               "Wyoming",    8.256294
  )

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.1 (2020-06-06) #> os Red Hat Enterprise Linux 8.4 (Ootpa) #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.1) #> backports 1.2.1 2020-12-09 [2] CRAN (R 4.0.1) #> broom 0.7.5 2021-02-19 [2] CRAN (R 4.0.1) #> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.1) #> cli 2.3.1 2021-02-23 [2] CRAN (R 4.0.1) #> colorspace 2.0-0 2020-11-11 [2] CRAN (R 4.0.1) #> crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.1) #> curl 4.3 2019-12-02 [2] CRAN (R 4.0.1) #> DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.1) #> dbplyr 2.1.0 2021-02-03 [2] CRAN (R 4.0.1) #> digest 0.6.27 2020-10-24 [2] CRAN (R 4.0.1) #> dplyr * 1.0.5 2021-03-05 [2] CRAN (R 4.0.1) #> ellipsis 0.3.1 2020-05-15 [2] CRAN (R 4.0.1) #> evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.1) #> fansi 0.4.2 2021-01-15 [2] CRAN (R 4.0.1) #> farver 2.1.0 2021-02-28 [2] CRAN (R 4.0.1) #> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.1) #> fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.1) #> generics 0.1.0 2020-10-31 [2] CRAN (R 4.0.1) #> ggplot2 * 3.3.3 2020-12-30 [2] CRAN (R 4.0.1) #> glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.1) #> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.1) #> haven 2.3.1 2020-06-01 [2] CRAN (R 4.0.1) #> highr 0.8 2019-03-20 [2] CRAN (R 4.0.1) #> hms 1.0.0 2021-01-13 [2] CRAN (R 4.0.1) #> htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [2] CRAN (R 4.0.1) #> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.1) #> knitr 1.31 2021-01-27 [2] CRAN (R 4.0.1) #> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.0.1) #> lifecycle 1.0.0 2021-02-15 [2] CRAN (R 4.0.1) #> lubridate 1.7.10 2021-02-26 [2] CRAN (R 4.0.1) #> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.0.1) #> mime 0.10 2021-02-13 [2] CRAN (R 4.0.1) #> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.0.1) #> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.1) #> pillar 1.5.1 2021-03-05 [2] CRAN (R 4.0.1) #> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.1) #> purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.1) #> R6 2.5.0 2020-10-28 [2] CRAN (R 4.0.1) #> Rcpp 1.0.6 2021-01-15 [2] CRAN (R 4.0.1) #> readr * 1.4.0 2020-10-05 [2] CRAN (R 4.0.1) #> readxl 1.3.1 2019-03-13 [2] CRAN (R 4.0.1) #> reprex 2.0.0 2021-04-02 [2] CRAN (R 4.0.1) #> rlang 0.4.10 2020-12-30 [2] CRAN (R 4.0.1) #> rmarkdown 2.7 2021-02-19 [2] CRAN (R 4.0.1) #> rvest 1.0.0 2021-03-09 [2] CRAN (R 4.0.1) #> scales 1.1.1 2020-05-11 [2] CRAN (R 4.0.1) #> sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 4.0.1) #> stringi 1.5.3 2020-09-09 [2] CRAN (R 4.0.1) #> stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.1) #> styler 1.4.1 2021-03-30 [2] CRAN (R 4.0.1) #> tibble * 3.1.0 2021-02-25 [2] CRAN (R 4.0.1) #> tidyr * 1.1.3 2021-03-03 [2] CRAN (R 4.0.1) #> tidyselect 1.1.0 2020-05-11 [2] CRAN (R 4.0.1) #> tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 4.0.1) #> utf8 1.2.1 2021-03-12 [2] CRAN (R 4.0.1) #> vctrs 0.3.7 2021-03-29 [2] CRAN (R 4.0.1) #> withr 2.4.1 2021-01-26 [2] CRAN (R 4.0.1) #> xfun 0.22 2021-03-11 [2] CRAN (R 4.0.1) #> xml2 1.3.2 2020-04-23 [2] CRAN (R 4.0.1) #> yaml 2.2.1.99 2021-07-06 [1] Github (viking/r-yaml@4788abe) #> #> [1] /home/cslewis/R/x86_64-pc-linux-gnu-library/4.0 #> [2] /opt/R/4.0.1/lib/R/library ```

bensoltoff commented 3 years ago

library(tidyverse)

# import data file
urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama", 9.605935,
  "Alaska", 8.735964,
  "American Samoa", 11.08593,
  "Arizona", 11.29971,
  "Arkansas", 9.259444
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.1 (2020-06-06) #> os Red Hat Enterprise Linux 8.4 (Ootpa) #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.1) #> backports 1.2.1 2020-12-09 [2] CRAN (R 4.0.1) #> broom 0.7.5 2021-02-19 [2] CRAN (R 4.0.1) #> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.1) #> cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.1) #> colorspace 2.0-0 2020-11-11 [2] CRAN (R 4.0.1) #> crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.1) #> curl 4.3.1 2021-04-30 [1] CRAN (R 4.0.1) #> DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.1) #> dbplyr 2.1.0 2021-02-03 [2] CRAN (R 4.0.1) #> digest 0.6.27 2020-10-24 [2] CRAN (R 4.0.1) #> dplyr * 1.0.7 2021-06-18 [1] CRAN (R 4.0.1) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.0.1) #> evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.1) #> fansi 0.5.0 2021-05-25 [1] CRAN (R 4.0.1) #> farver 2.1.0 2021-02-28 [2] CRAN (R 4.0.1) #> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.1) #> fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.1) #> generics 0.1.0 2020-10-31 [2] CRAN (R 4.0.1) #> ggplot2 * 3.3.3 2020-12-30 [2] CRAN (R 4.0.1) #> glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.1) #> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.1) #> haven 2.3.1 2020-06-01 [2] CRAN (R 4.0.1) #> highr 0.8 2019-03-20 [2] CRAN (R 4.0.1) #> hms 1.0.0 2021-01-13 [2] CRAN (R 4.0.1) #> htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [2] CRAN (R 4.0.1) #> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.1) #> knitr 1.31 2021-01-27 [2] CRAN (R 4.0.1) #> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.0.1) #> lifecycle 1.0.0 2021-02-15 [2] CRAN (R 4.0.1) #> lubridate 1.7.10 2021-02-26 [2] CRAN (R 4.0.1) #> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.0.1) #> mime 0.10 2021-02-13 [2] CRAN (R 4.0.1) #> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.0.1) #> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.1) #> pillar 1.6.1 2021-05-16 [1] CRAN (R 4.0.1) #> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.1) #> purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.1) #> R6 2.5.0 2020-10-28 [2] CRAN (R 4.0.1) #> Rcpp 1.0.6 2021-01-15 [2] CRAN (R 4.0.1) #> readr * 1.4.0 2020-10-05 [2] CRAN (R 4.0.1) #> readxl 1.3.1 2019-03-13 [2] CRAN (R 4.0.1) #> reprex 2.0.0 2021-04-02 [2] CRAN (R 4.0.1) #> rlang 0.4.11 2021-04-30 [1] CRAN (R 4.0.1) #> rmarkdown 2.9 2021-06-15 [1] CRAN (R 4.0.1) #> rstudioapi 0.13 2020-11-12 [2] CRAN (R 4.0.1) #> rvest 1.0.0 2021-03-09 [2] CRAN (R 4.0.1) #> scales 1.1.1 2020-05-11 [2] CRAN (R 4.0.1) #> sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 4.0.1) #> stringi 1.5.3 2020-09-09 [2] CRAN (R 4.0.1) #> stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.1) #> styler 1.4.1 2021-03-30 [2] CRAN (R 4.0.1) #> tibble * 3.1.2 2021-05-16 [1] CRAN (R 4.0.1) #> tidyr * 1.1.3 2021-03-03 [2] CRAN (R 4.0.1) #> tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.0.1) #> tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 4.0.1) #> utf8 1.2.1 2021-03-12 [2] CRAN (R 4.0.1) #> vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.0.1) #> withr 2.4.2 2021-04-18 [1] CRAN (R 4.0.1) #> xfun 0.22 2021-03-11 [2] CRAN (R 4.0.1) #> xml2 1.3.2 2020-04-23 [2] CRAN (R 4.0.1) #> yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.1) #> #> [1] /home/soltoffbc/R/x86_64-pc-linux-gnu-library/4.0 #> [2] /opt/R/4.0.1/lib/R/library ```

bethwang06 commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)

# import data file
ubran <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa",    11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444,)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()
#> Error in ggplot(data = urban, mapping = aes(x = state, y = urbanindex)): object 'urban' not found

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.1.0 (2021-05-18) #> os macOS Big Sur 10.16 #> system x86_64, darwin17.0 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0) #> backports 1.2.1 2020-12-09 [1] CRAN (R 4.1.0) #> broom 0.7.7 2021-06-13 [1] CRAN (R 4.1.0) #> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.1.0) #> cli 2.5.0 2021-04-26 [1] CRAN (R 4.1.0) #> colorspace 2.0-1 2021-05-04 [1] CRAN (R 4.1.0) #> crayon 1.4.1 2021-02-08 [1] CRAN (R 4.1.0) #> DBI 1.1.1 2021-01-15 [1] CRAN (R 4.1.0) #> dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.1.0) #> digest 0.6.27 2020-10-24 [1] CRAN (R 4.1.0) #> dplyr * 1.0.7 2021-06-18 [1] CRAN (R 4.1.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.1.0) #> evaluate 0.14 2019-05-28 [1] CRAN (R 4.1.0) #> fansi 0.5.0 2021-05-25 [1] CRAN (R 4.1.0) #> forcats * 0.5.1 2021-01-27 [1] CRAN (R 4.1.0) #> fs 1.5.0 2020-07-31 [1] CRAN (R 4.1.0) #> generics 0.1.0 2020-10-31 [1] CRAN (R 4.1.0) #> ggplot2 * 3.3.4 2021-06-16 [1] CRAN (R 4.1.0) #> glue 1.4.2 2020-08-27 [1] CRAN (R 4.1.0) #> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.0) #> haven 2.4.1 2021-04-23 [1] CRAN (R 4.1.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.1.0) #> hms 1.1.0 2021-05-17 [1] CRAN (R 4.1.0) #> htmltools 0.5.1.1 2021-01-22 [1] CRAN (R 4.1.0) #> httr 1.4.2 2020-07-20 [1] CRAN (R 4.1.0) #> jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.1.0) #> knitr 1.33 2021-04-24 [1] CRAN (R 4.1.0) #> lifecycle 1.0.0 2021-02-15 [1] CRAN (R 4.1.0) #> lubridate 1.7.10 2021-02-26 [1] CRAN (R 4.1.0) #> magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.1.0) #> modelr 0.1.8 2020-05-19 [1] CRAN (R 4.1.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0) #> pillar 1.6.1 2021-05-16 [1] CRAN (R 4.1.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0) #> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.1.0) #> R6 2.5.0 2020-10-28 [1] CRAN (R 4.1.0) #> Rcpp 1.0.6 2021-01-15 [1] CRAN (R 4.1.0) #> readr * 1.4.0 2020-10-05 [1] CRAN (R 4.1.0) #> readxl 1.3.1 2019-03-13 [1] CRAN (R 4.1.0) #> reprex 2.0.0 2021-04-02 [1] CRAN (R 4.1.0) #> rlang 0.4.11 2021-04-30 [1] CRAN (R 4.1.0) #> rmarkdown 2.9 2021-06-15 [1] CRAN (R 4.1.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0) #> rvest 1.0.0 2021-03-09 [1] CRAN (R 4.1.0) #> scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.0) #> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.1.0) #> stringi 1.6.2 2021-05-17 [1] CRAN (R 4.1.0) #> stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.1.0) #> styler 1.4.1 2021-03-30 [1] CRAN (R 4.1.0) #> tibble * 3.1.2 2021-05-16 [1] CRAN (R 4.1.0) #> tidyr * 1.1.3 2021-03-03 [1] CRAN (R 4.1.0) #> tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.1.0) #> tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.1.0) #> utf8 1.2.1 2021-03-12 [1] CRAN (R 4.1.0) #> vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0) #> withr 2.4.2 2021-04-18 [1] CRAN (R 4.1.0) #> xfun 0.24 2021-06-15 [1] CRAN (R 4.1.0) #> xml2 1.3.2 2020-04-23 [1] CRAN (R 4.1.0) #> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.1.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library ```

bethwang06 commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)

# import data file
urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa", 11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.1.0 (2021-05-18) #> os macOS Big Sur 10.16 #> system x86_64, darwin17.0 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0) #> backports 1.2.1 2020-12-09 [1] CRAN (R 4.1.0) #> broom 0.7.7 2021-06-13 [1] CRAN (R 4.1.0) #> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.1.0) #> cli 2.5.0 2021-04-26 [1] CRAN (R 4.1.0) #> colorspace 2.0-1 2021-05-04 [1] CRAN (R 4.1.0) #> crayon 1.4.1 2021-02-08 [1] CRAN (R 4.1.0) #> curl 4.3.1 2021-04-30 [1] CRAN (R 4.1.0) #> DBI 1.1.1 2021-01-15 [1] CRAN (R 4.1.0) #> dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.1.0) #> digest 0.6.27 2020-10-24 [1] CRAN (R 4.1.0) #> dplyr * 1.0.7 2021-06-18 [1] CRAN (R 4.1.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.1.0) #> evaluate 0.14 2019-05-28 [1] CRAN (R 4.1.0) #> fansi 0.5.0 2021-05-25 [1] CRAN (R 4.1.0) #> farver 2.1.0 2021-02-28 [1] CRAN (R 4.1.0) #> forcats * 0.5.1 2021-01-27 [1] CRAN (R 4.1.0) #> fs 1.5.0 2020-07-31 [1] CRAN (R 4.1.0) #> generics 0.1.0 2020-10-31 [1] CRAN (R 4.1.0) #> ggplot2 * 3.3.4 2021-06-16 [1] CRAN (R 4.1.0) #> glue 1.4.2 2020-08-27 [1] CRAN (R 4.1.0) #> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.0) #> haven 2.4.1 2021-04-23 [1] CRAN (R 4.1.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.1.0) #> hms 1.1.0 2021-05-17 [1] CRAN (R 4.1.0) #> htmltools 0.5.1.1 2021-01-22 [1] CRAN (R 4.1.0) #> httr 1.4.2 2020-07-20 [1] CRAN (R 4.1.0) #> jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.1.0) #> knitr 1.33 2021-04-24 [1] CRAN (R 4.1.0) #> labeling 0.4.2 2020-10-20 [1] CRAN (R 4.1.0) #> lifecycle 1.0.0 2021-02-15 [1] CRAN (R 4.1.0) #> lubridate 1.7.10 2021-02-26 [1] CRAN (R 4.1.0) #> magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.1.0) #> mime 0.10 2021-02-13 [1] CRAN (R 4.1.0) #> modelr 0.1.8 2020-05-19 [1] CRAN (R 4.1.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0) #> pillar 1.6.1 2021-05-16 [1] CRAN (R 4.1.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0) #> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.1.0) #> R6 2.5.0 2020-10-28 [1] CRAN (R 4.1.0) #> Rcpp 1.0.6 2021-01-15 [1] CRAN (R 4.1.0) #> readr * 1.4.0 2020-10-05 [1] CRAN (R 4.1.0) #> readxl 1.3.1 2019-03-13 [1] CRAN (R 4.1.0) #> reprex 2.0.0 2021-04-02 [1] CRAN (R 4.1.0) #> rlang 0.4.11 2021-04-30 [1] CRAN (R 4.1.0) #> rmarkdown 2.9 2021-06-15 [1] CRAN (R 4.1.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0) #> rvest 1.0.0 2021-03-09 [1] CRAN (R 4.1.0) #> scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.0) #> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.1.0) #> stringi 1.6.2 2021-05-17 [1] CRAN (R 4.1.0) #> stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.1.0) #> styler 1.4.1 2021-03-30 [1] CRAN (R 4.1.0) #> tibble * 3.1.2 2021-05-16 [1] CRAN (R 4.1.0) #> tidyr * 1.1.3 2021-03-03 [1] CRAN (R 4.1.0) #> tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.1.0) #> tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.1.0) #> utf8 1.2.1 2021-03-12 [1] CRAN (R 4.1.0) #> vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0) #> withr 2.4.2 2021-04-18 [1] CRAN (R 4.1.0) #> xfun 0.24 2021-06-15 [1] CRAN (R 4.1.0) #> xml2 1.3.2 2020-04-23 [1] CRAN (R 4.1.0) #> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.1.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library ```

annikaludwig commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /tmp/RtmpeQ9yyj/reprex-15f1ed1a290230-legal-mink

# import data file
urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa",    11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.1 (2020-06-06) #> os Red Hat Enterprise Linux 8.4 (Ootpa) #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.1) #> backports 1.2.1 2020-12-09 [2] CRAN (R 4.0.1) #> broom 0.7.5 2021-02-19 [2] CRAN (R 4.0.1) #> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.1) #> cli 2.3.1 2021-02-23 [2] CRAN (R 4.0.1) #> colorspace 2.0-0 2020-11-11 [2] CRAN (R 4.0.1) #> crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.1) #> curl 4.3 2019-12-02 [2] CRAN (R 4.0.1) #> DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.1) #> dbplyr 2.1.0 2021-02-03 [2] CRAN (R 4.0.1) #> digest 0.6.27 2020-10-24 [2] CRAN (R 4.0.1) #> dplyr * 1.0.5 2021-03-05 [2] CRAN (R 4.0.1) #> ellipsis 0.3.1 2020-05-15 [2] CRAN (R 4.0.1) #> evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.1) #> fansi 0.4.2 2021-01-15 [2] CRAN (R 4.0.1) #> farver 2.1.0 2021-02-28 [2] CRAN (R 4.0.1) #> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.1) #> fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.1) #> generics 0.1.0 2020-10-31 [2] CRAN (R 4.0.1) #> ggplot2 * 3.3.3 2020-12-30 [2] CRAN (R 4.0.1) #> glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.1) #> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.1) #> haven 2.3.1 2020-06-01 [2] CRAN (R 4.0.1) #> here * 1.0.1 2020-12-13 [2] CRAN (R 4.0.1) #> highr 0.8 2019-03-20 [2] CRAN (R 4.0.1) #> hms 1.0.0 2021-01-13 [2] CRAN (R 4.0.1) #> htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [2] CRAN (R 4.0.1) #> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.1) #> knitr 1.31 2021-01-27 [2] CRAN (R 4.0.1) #> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.0.1) #> lifecycle 1.0.0 2021-02-15 [2] CRAN (R 4.0.1) #> lubridate 1.7.10 2021-02-26 [2] CRAN (R 4.0.1) #> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.0.1) #> mime 0.10 2021-02-13 [2] CRAN (R 4.0.1) #> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.0.1) #> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.1) #> pillar 1.5.1 2021-03-05 [2] CRAN (R 4.0.1) #> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.1) #> purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.1) #> R6 2.5.0 2020-10-28 [2] CRAN (R 4.0.1) #> Rcpp 1.0.6 2021-01-15 [2] CRAN (R 4.0.1) #> readr * 1.4.0 2020-10-05 [2] CRAN (R 4.0.1) #> readxl 1.3.1 2019-03-13 [2] CRAN (R 4.0.1) #> reprex 2.0.0 2021-04-02 [2] CRAN (R 4.0.1) #> rlang 0.4.10 2020-12-30 [2] CRAN (R 4.0.1) #> rmarkdown 2.7 2021-02-19 [2] CRAN (R 4.0.1) #> rprojroot 2.0.2 2020-11-15 [2] CRAN (R 4.0.1) #> rvest 1.0.0 2021-03-09 [2] CRAN (R 4.0.1) #> scales 1.1.1 2020-05-11 [2] CRAN (R 4.0.1) #> sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 4.0.1) #> stringi 1.5.3 2020-09-09 [2] CRAN (R 4.0.1) #> stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.1) #> styler 1.4.1 2021-03-30 [2] CRAN (R 4.0.1) #> tibble * 3.1.0 2021-02-25 [2] CRAN (R 4.0.1) #> tidyr * 1.1.3 2021-03-03 [2] CRAN (R 4.0.1) #> tidyselect 1.1.0 2020-05-11 [2] CRAN (R 4.0.1) #> tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 4.0.1) #> utf8 1.2.1 2021-03-12 [2] CRAN (R 4.0.1) #> vctrs 0.3.7 2021-03-29 [2] CRAN (R 4.0.1) #> withr 2.4.1 2021-01-26 [2] CRAN (R 4.0.1) #> xfun 0.22 2021-03-11 [2] CRAN (R 4.0.1) #> xml2 1.3.2 2020-04-23 [2] CRAN (R 4.0.1) #> yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.1) #> #> [1] /home/aludwig/R/x86_64-pc-linux-gnu-library/4.0 #> [2] /opt/R/4.0.1/lib/R/library ```

ktakaira commented 3 years ago

library(tidyverse)

``` r
## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)

# import data file
urban <- tibble::tribble(
~state, ~urbanindex,
"Alabama",    9.605935,
"Alaska",    8.735964,
"American Samoa",    11.08593,
"Arizona",    11.29971,
"Arkansas",    9.259444)
# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

gdicera commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /home/giuseppedicera/uc-cfss-reproducible-examples-and-git-4cced8f

# import data file
urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa",    11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444,
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Maggie-Rivera commented 3 years ago

library(tidyverse)

# import data file
urban <- tibble::tribble(
                                                                       ~state, ~urbanindex,
                                                                    "Alabama",    9.605935,
                                                                     "Alaska",    8.735964,
                                                             "American Samoa",    11.08593,
                                                                    "Arizona",    11.29971,
                                                                   "Arkansas",    9.259444)
# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.1 (2020-06-06) #> os Red Hat Enterprise Linux 8.4 (Ootpa) #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.1) #> backports 1.2.1 2020-12-09 [2] CRAN (R 4.0.1) #> broom 0.7.5 2021-02-19 [2] CRAN (R 4.0.1) #> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.1) #> cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.1) #> colorspace 2.0-0 2020-11-11 [2] CRAN (R 4.0.1) #> crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.1) #> curl 4.3 2019-12-02 [2] CRAN (R 4.0.1) #> DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.1) #> dbplyr 2.1.0 2021-02-03 [2] CRAN (R 4.0.1) #> digest 0.6.27 2020-10-24 [2] CRAN (R 4.0.1) #> dplyr * 1.0.5 2021-03-05 [2] CRAN (R 4.0.1) #> ellipsis 0.3.1 2020-05-15 [2] CRAN (R 4.0.1) #> evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.1) #> fansi 0.4.2 2021-01-15 [2] CRAN (R 4.0.1) #> farver 2.1.0 2021-02-28 [2] CRAN (R 4.0.1) #> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.1) #> fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.1) #> generics 0.1.0 2020-10-31 [2] CRAN (R 4.0.1) #> ggplot2 * 3.3.3 2020-12-30 [2] CRAN (R 4.0.1) #> glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.1) #> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.1) #> haven 2.3.1 2020-06-01 [2] CRAN (R 4.0.1) #> here * 1.0.1 2020-12-13 [2] CRAN (R 4.0.1) #> highr 0.8 2019-03-20 [2] CRAN (R 4.0.1) #> hms 1.0.0 2021-01-13 [2] CRAN (R 4.0.1) #> htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [2] CRAN (R 4.0.1) #> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.1) #> knitr 1.31 2021-01-27 [2] CRAN (R 4.0.1) #> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.0.1) #> lifecycle 1.0.0 2021-02-15 [2] CRAN (R 4.0.1) #> lubridate 1.7.10 2021-02-26 [2] CRAN (R 4.0.1) #> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.0.1) #> mime 0.10 2021-02-13 [2] CRAN (R 4.0.1) #> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.0.1) #> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.1) #> pillar 1.5.1 2021-03-05 [2] CRAN (R 4.0.1) #> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.1) #> purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.1) #> R6 2.5.0 2020-10-28 [2] CRAN (R 4.0.1) #> Rcpp 1.0.6 2021-01-15 [2] CRAN (R 4.0.1) #> readr * 1.4.0 2020-10-05 [2] CRAN (R 4.0.1) #> readxl 1.3.1 2019-03-13 [2] CRAN (R 4.0.1) #> reprex 2.0.0 2021-04-02 [2] CRAN (R 4.0.1) #> rlang 0.4.11 2021-04-30 [1] CRAN (R 4.0.1) #> rmarkdown 2.7 2021-02-19 [2] CRAN (R 4.0.1) #> rprojroot 2.0.2 2020-11-15 [2] CRAN (R 4.0.1) #> rstudioapi 0.13 2020-11-12 [2] CRAN (R 4.0.1) #> rvest 1.0.0 2021-03-09 [2] CRAN (R 4.0.1) #> scales 1.1.1 2020-05-11 [2] CRAN (R 4.0.1) #> sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 4.0.1) #> stringi 1.5.3 2020-09-09 [2] CRAN (R 4.0.1) #> stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.1) #> styler 1.4.1 2021-03-30 [2] CRAN (R 4.0.1) #> tibble * 3.1.0 2021-02-25 [2] CRAN (R 4.0.1) #> tidyr * 1.1.3 2021-03-03 [2] CRAN (R 4.0.1) #> tidyselect 1.1.0 2020-05-11 [2] CRAN (R 4.0.1) #> tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 4.0.1) #> utf8 1.2.1 2021-03-12 [2] CRAN (R 4.0.1) #> vctrs 0.3.7 2021-03-29 [2] CRAN (R 4.0.1) #> withr 2.4.1 2021-01-26 [2] CRAN (R 4.0.1) #> xfun 0.22 2021-03-11 [2] CRAN (R 4.0.1) #> xml2 1.3.2 2020-04-23 [2] CRAN (R 4.0.1) #> yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.1) #> #> [1] /home/mmrivera/R/x86_64-pc-linux-gnu-library/4.0 #> [2] /opt/R/4.0.1/lib/R/library ```

mjparness commented 3 years ago

library(tidyverse)

tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa",    11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444,
  "California",    12.19028,
  "Colorado",    11.15445,
)
#> # A tibble: 7 x 2
#>   state          urbanindex
#>   <chr>               <dbl>
#> 1 Alabama              9.61
#> 2 Alaska               8.74
#> 3 American Samoa      11.1 
#> 4 Arizona             11.3 
#> 5 Arkansas             9.26
#> 6 California          12.2 
#> 7 Colorado            11.2

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()
#> Error in ggplot(data = urban, mapping = aes(x = state, y = urbanindex)): object 'urban' not found

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.1 (2020-06-06) #> os Red Hat Enterprise Linux 8.4 (Ootpa) #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.1) #> backports 1.2.1 2020-12-09 [2] CRAN (R 4.0.1) #> broom 0.7.5 2021-02-19 [2] CRAN (R 4.0.1) #> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.1) #> cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.1) #> colorspace 2.0-0 2020-11-11 [2] CRAN (R 4.0.1) #> crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.1) #> DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.1) #> dbplyr 2.1.0 2021-02-03 [2] CRAN (R 4.0.1) #> digest 0.6.27 2020-10-24 [2] CRAN (R 4.0.1) #> dplyr * 1.0.5 2021-03-05 [2] CRAN (R 4.0.1) #> ellipsis 0.3.1 2020-05-15 [2] CRAN (R 4.0.1) #> evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.1) #> fansi 0.4.2 2021-01-15 [2] CRAN (R 4.0.1) #> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.1) #> fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.1) #> generics 0.1.0 2020-10-31 [2] CRAN (R 4.0.1) #> ggplot2 * 3.3.3 2020-12-30 [2] CRAN (R 4.0.1) #> glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.1) #> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.1) #> haven 2.3.1 2020-06-01 [2] CRAN (R 4.0.1) #> highr 0.8 2019-03-20 [2] CRAN (R 4.0.1) #> hms 1.0.0 2021-01-13 [2] CRAN (R 4.0.1) #> htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [2] CRAN (R 4.0.1) #> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.1) #> knitr 1.31 2021-01-27 [2] CRAN (R 4.0.1) #> lifecycle 1.0.0 2021-02-15 [2] CRAN (R 4.0.1) #> lubridate 1.7.10 2021-02-26 [2] CRAN (R 4.0.1) #> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.0.1) #> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.0.1) #> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.1) #> pillar 1.5.1 2021-03-05 [2] CRAN (R 4.0.1) #> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.1) #> purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.1) #> R6 2.5.0 2020-10-28 [2] CRAN (R 4.0.1) #> Rcpp 1.0.6 2021-01-15 [2] CRAN (R 4.0.1) #> readr * 1.4.0 2020-10-05 [2] CRAN (R 4.0.1) #> readxl 1.3.1 2019-03-13 [2] CRAN (R 4.0.1) #> reprex 2.0.0 2021-04-02 [2] CRAN (R 4.0.1) #> rlang 0.4.11 2021-04-30 [1] CRAN (R 4.0.1) #> rmarkdown 2.7 2021-02-19 [2] CRAN (R 4.0.1) #> rstudioapi 0.13 2020-11-12 [2] CRAN (R 4.0.1) #> rvest 1.0.0 2021-03-09 [2] CRAN (R 4.0.1) #> scales 1.1.1 2020-05-11 [2] CRAN (R 4.0.1) #> sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 4.0.1) #> stringi 1.5.3 2020-09-09 [2] CRAN (R 4.0.1) #> stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.1) #> styler 1.4.1 2021-03-30 [2] CRAN (R 4.0.1) #> tibble * 3.1.0 2021-02-25 [2] CRAN (R 4.0.1) #> tidyr * 1.1.3 2021-03-03 [2] CRAN (R 4.0.1) #> tidyselect 1.1.0 2020-05-11 [2] CRAN (R 4.0.1) #> tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 4.0.1) #> utf8 1.2.1 2021-03-12 [2] CRAN (R 4.0.1) #> vctrs 0.3.7 2021-03-29 [2] CRAN (R 4.0.1) #> withr 2.4.1 2021-01-26 [2] CRAN (R 4.0.1) #> xfun 0.22 2021-03-11 [2] CRAN (R 4.0.1) #> xml2 1.3.2 2020-04-23 [2] CRAN (R 4.0.1) #> yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.1) #> #> [1] /home/mjparness/R/x86_64-pc-linux-gnu-library/4.0 #> [2] /opt/R/4.0.1/lib/R/library ```

dipro-ray commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /tmp/RtmpQWCSVw/reprex-15e6c76af68c31-ripe-human

urban <- tibble::tribble(
  ~state, ~urbanindex,
  "Alabama",    9.605935,
  "Alaska",    8.735964,
  "American Samoa",    11.08593,
  "Arizona",    11.29971,
  "Arkansas",    9.259444,
  "California",    12.19028
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

agodinez711 commented 3 years ago

## Use the script below to generate a reproducible example
## using the reprex package. Use datapasta::dpasta() to create
## `urban` in the script rather than relying on the source
## CSV file. Once you generate it, post it on
## https://github.com/uc-cfss/Discussion/issues/183

library(tidyverse)
library(here)
#> here() starts at /tmp/RtmpvYMBXG/reprex-15e7fa76ef9a42-ok-coqui

# import data file
urban <- tibble::tribble(
                                                                       ~state, ~urbanindex,
                                                                    "Alabama",    9.605935,
                                                                     "Alaska",    8.735964,
                                                             "American Samoa",    11.08593,
                                                                    "Arizona",    11.29971,
                                                                   "Arkansas",    9.259444
                                                       )
# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

mjparness commented 3 years ago

fixed,

library(tidyverse)

urban <- tibble::tribble(
                  ~state, ~urbanindex,
               "Alabama",    9.605935,
                "Alaska",    8.735964,
        "American Samoa",    11.08593,
               "Arizona",    11.29971,
              "Arkansas",    9.259444,
            "California",    12.19028,
              "Colorado",    11.15445,
)

# how do I reorder the bars from largest to smallest?
ggplot(data = urban, mapping = aes(x = state, y = urbanindex)) +
  geom_col() +
  coord_flip()

^{Created on 2021-07-13 by the reprex package (v2.0.0)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.1 (2020-06-06) #> os Red Hat Enterprise Linux 8.4 (Ootpa) #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Chicago #> date 2021-07-13 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.1) #> backports 1.2.1 2020-12-09 [2] CRAN (R 4.0.1) #> broom 0.7.5 2021-02-19 [2] CRAN (R 4.0.1) #> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.1) #> cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.1) #> colorspace 2.0-0 2020-11-11 [2] CRAN (R 4.0.1) #> crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.1) #> curl 4.3 2019-12-02 [2] CRAN (R 4.0.1) #> DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.1) #> dbplyr 2.1.0 2021-02-03 [2] CRAN (R 4.0.1) #> digest 0.6.27 2020-10-24 [2] CRAN (R 4.0.1) #> dplyr * 1.0.5 2021-03-05 [2] CRAN (R 4.0.1) #> ellipsis 0.3.1 2020-05-15 [2] CRAN (R 4.0.1) #> evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.1) #> fansi 0.4.2 2021-01-15 [2] CRAN (R 4.0.1) #> farver 2.1.0 2021-02-28 [2] CRAN (R 4.0.1) #> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.1) #> fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.1) #> generics 0.1.0 2020-10-31 [2] CRAN (R 4.0.1) #> ggplot2 * 3.3.3 2020-12-30 [2] CRAN (R 4.0.1) #> glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.1) #> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.1) #> haven 2.3.1 2020-06-01 [2] CRAN (R 4.0.1) #> highr 0.8 2019-03-20 [2] CRAN (R 4.0.1) #> hms 1.0.0 2021-01-13 [2] CRAN (R 4.0.1) #> htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [2] CRAN (R 4.0.1) #> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.1) #> knitr 1.31 2021-01-27 [2] CRAN (R 4.0.1) #> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.0.1) #> lifecycle 1.0.0 2021-02-15 [2] CRAN (R 4.0.1) #> lubridate 1.7.10 2021-02-26 [2] CRAN (R 4.0.1) #> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.0.1) #> mime 0.10 2021-02-13 [2] CRAN (R 4.0.1) #> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.0.1) #> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.1) #> pillar 1.5.1 2021-03-05 [2] CRAN (R 4.0.1) #> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.1) #> purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.1) #> R6 2.5.0 2020-10-28 [2] CRAN (R 4.0.1) #> Rcpp 1.0.6 2021-01-15 [2] CRAN (R 4.0.1) #> readr * 1.4.0 2020-10-05 [2] CRAN (R 4.0.1) #> readxl 1.3.1 2019-03-13 [2] CRAN (R 4.0.1) #> reprex 2.0.0 2021-04-02 [2] CRAN (R 4.0.1) #> rlang 0.4.11 2021-04-30 [1] CRAN (R 4.0.1) #> rmarkdown 2.7 2021-02-19 [2] CRAN (R 4.0.1) #> rstudioapi 0.13 2020-11-12 [2] CRAN (R 4.0.1) #> rvest 1.0.0 2021-03-09 [2] CRAN (R 4.0.1) #> scales 1.1.1 2020-05-11 [2] CRAN (R 4.0.1) #> sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 4.0.1) #> stringi 1.5.3 2020-09-09 [2] CRAN (R 4.0.1) #> stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.1) #> styler 1.4.1 2021-03-30 [2] CRAN (R 4.0.1) #> tibble * 3.1.0 2021-02-25 [2] CRAN (R 4.0.1) #> tidyr * 1.1.3 2021-03-03 [2] CRAN (R 4.0.1) #> tidyselect 1.1.0 2020-05-11 [2] CRAN (R 4.0.1) #> tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 4.0.1) #> utf8 1.2.1 2021-03-12 [2] CRAN (R 4.0.1) #> vctrs 0.3.7 2021-03-29 [2] CRAN (R 4.0.1) #> withr 2.4.1 2021-01-26 [2] CRAN (R 4.0.1) #> xfun 0.22 2021-03-11 [2] CRAN (R 4.0.1) #> xml2 1.3.2 2020-04-23 [2] CRAN (R 4.0.1) #> yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.1) #> #> [1] /home/mjparness/R/x86_64-pc-linux-gnu-library/4.0 #> [2] /opt/R/4.0.1/lib/R/library ```

cis-ds / Discussion

04-datapasta-example #183