Day 11 of NTRESS 6000

Written on October 1, 2024

today we were working on data import, export, and conversion between data types!

first things first, we need to load tidyverse

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

coronavirus <- read_csv('https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv')

## Rows: 919308 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (8): province, country, type, iso2, iso3, combined_key, continent_name,...
## dbl  (6): lat, long, cases, uid, code3, population
## date (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

we have been working with this coronavirus dataset from here

coronavirus |> 
  filter(cases > 0) |> 
  group_by(date,type) |> 
  summarise(cases=sum(cases)) |> 
  ggplot() + 
  geom_col(aes(date,cases,fill =type))

## `summarise()` has grouped output by 'date'. You can override using the
## `.groups` argument.

this is what our data looks like

head(coronavirus)

## # A tibble: 6 × 15
##   date       province country   lat  long type     cases   uid iso2  iso3  code3
##   <date>     <chr>    <chr>   <dbl> <dbl> <chr>    <dbl> <dbl> <chr> <chr> <dbl>
## 1 2020-01-22 Alberta  Canada   53.9 -117. confirm…     0 12401 CA    CAN     124
## 2 2020-01-23 Alberta  Canada   53.9 -117. confirm…     0 12401 CA    CAN     124
## 3 2020-01-24 Alberta  Canada   53.9 -117. confirm…     0 12401 CA    CAN     124
## 4 2020-01-25 Alberta  Canada   53.9 -117. confirm…     0 12401 CA    CAN     124
## 5 2020-01-26 Alberta  Canada   53.9 -117. confirm…     0 12401 CA    CAN     124
## 6 2020-01-27 Alberta  Canada   53.9 -117. confirm…     0 12401 CA    CAN     124
## # ℹ 4 more variables: combined_key <chr>, population <dbl>,
## #   continent_name <chr>, continent_code <chr>

**lets filter this raw dataset to keep only the date, country,province,type,and cases
we then are piping that dataframe into filter to only keep cases on january 3rd, 2021

coronavirus |> 
  select(date,country,province,type,cases) |> 
  filter(date == "2021-01-03")

## # A tibble: 852 × 5
##    date       country        province                         type      cases
##    <date>     <chr>          <chr>                            <chr>     <dbl>
##  1 2021-01-03 Canada         Alberta                          confirmed  3800
##  2 2021-01-03 United Kingdom Anguilla                         confirmed     2
##  3 2021-01-03 China          Anhui                            confirmed     0
##  4 2021-01-03 Netherlands    Aruba                            confirmed    32
##  5 2021-01-03 Australia      Australian Capital Territory     confirmed     0
##  6 2021-01-03 China          Beijing                          confirmed     2
##  7 2021-01-03 United Kingdom Bermuda                          confirmed     0
##  8 2021-01-03 Netherlands    Bonaire, Sint Eustatius and Saba confirmed     0
##  9 2021-01-03 Canada         British Columbia                 confirmed     0
## 10 2021-01-03 United Kingdom British Virgin Islands           confirmed     0
## # ℹ 842 more rows

okay i got too lazy with marking up this file so deal with some dirty code below!!

lotr <- read_csv("https://raw.githubusercontent.com/jennybc/lotr-tidy/master/data/lotr_tidy.csv")

## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Film, Race, Gender
## dbl (1): Words
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

write_csv(lotr, file = "lotr_tidy.csv")

lotr <- read_csv("lotr_tidy.csv")

## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Film, Race, Gender
## dbl (1): Words
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

lotr <- read_csv("lotr_tidy.csv", skip = 2)

## Rows: 16 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): The Fellowship Of The Ring, Hobbit, Female
## dbl (1): 14
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

library(googlesheets4) #install.packages("googlesheets4")

gs4_deauth()
lotr_gs <- read_sheet("https://docs.google.com/spreadsheets/d/1X98JobRtA3JGBFacs_JSjiX-4DPQ0vZYtNl_ozqF6IE/edit#gid=754443596")

## ✔ Reading from "LOTR".

## ✔ Range 'tidy'.

#will help turn whatever you have into a number - helpful if your data isnt read as numeric
parse_double("1,23", locale = locale(decimal_mark = ","))

## [1] 1.23

students <- read_csv("https://pos.it/r4ds-students-csv", na = c("","N/A"))

## Rows: 6 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Full Name, favourite.food, mealPlan, AGE
## dbl (1): Student ID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

students |> 
  rename(
    student_id = `Student ID`,
    full_name = `Full Name`)

## # A tibble: 6 × 5
##   student_id full_name        favourite.food     mealPlan            AGE  
##        <dbl> <chr>            <chr>              <chr>               <chr>
## 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only          4    
## 2          2 Barclay Lynn     French fries       Lunch only          5    
## 3          3 Jayendra Lyne    <NA>               Breakfast and lunch 7    
## 4          4 Leon Rossini     Anchovies          Lunch only          <NA> 
## 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch five 
## 6          6 Güvenç Attila    Ice cream          Lunch only          6

library(janitor) #install.packages("janitor")

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

students |> 
  clean_names(case="lower_upper") |> 
  mutate(
  age = parse_number(ifelse(age == "five", 5, age)))

## # A tibble: 6 × 5
##   studentID fullNAME         favouriteFOOD      mealPLAN              age
##       <dbl> <chr>            <chr>              <chr>               <dbl>
## 1         1 Sunil Huffmann   Strawberry yoghurt Lunch only              4
## 2         2 Barclay Lynn     French fries       Lunch only              5
## 3         3 Jayendra Lyne    <NA>               Breakfast and lunch     7
## 4         4 Leon Rossini     Anchovies          Lunch only             NA
## 5         5 Chidiegwu Dunkel Pizza              Breakfast and lunch     5
## 6         6 Güvenç Attila    Ice cream          Lunch only              6