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

first things first, we need to load tidyverse

library(tidyverse)

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

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))

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")

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

lotr <- read_csv("lotr_tidy.csv")

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

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

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

#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"))

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")

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