Zoom Notes 06: Tidying Data

Associated Material

Module: Module 06 - Tidying data

Readings

• R for Data Science Chapter 10
• R for Data Science Chapter 11
• R for Data Science Chapter 12
• R for Data Science Chapter 14

Cleaning and Tidying

Cleaning data values

• Checking data types on data import
• Creating consistent missing data
• Converting characters to numeric with as.numeric

Cleaning dataframe column names

• Using the janitor package to create cleaned dataframe column names

Tidying

Tidy data principles:

1. Each column is a variable or property that is being measured
2. Each row is an observation
3. A single cell should contain a single piece of information

Reshaping data

Wide to long

• Use pivot_longer from the tidyr package

Long to wide

• Use pivot_wider from the tidyr package

Excercises

1. Using the rodents_untidy.csv, read the data in and then:
   1. use janitor to change the column names to the “small_camel” case
   2. clean the data “weight” column
      • convert “?” and -999 to NA
      • convert the column to numeric
   3. split the plotLocation column into two columns: “plot” and “location”
   4. make the “plot” column into a numeric
   5. challenge: fix the data in the species column for plot 3. The genus and species columns had accidentally been combined

2. Using the gapminder_yearly_population_total.csv data create “long” a format dataset for
   1. New Zealand’s population from 1900 to 2000
   2. a country of your choice for the same time range

Example solutions

library(tidyverse)
rodents <- read_csv("data/rodents_untidy.csv")
#> Rows: 41 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (6): Plot location, Date collected, Family, Genus, Species, Weight
#> 
#> 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.
# a)
rodents <- rodents %>% janitor::clean_names(case = "small_camel")

# b)
rodents$weight[rodents$weight %in% c("-999", "?")] <- NA
rodents$weight <- as.numeric(rodents$weight)

# c)
rodents <- rodents %>% separate(plotLocation, into = c("plot", "location"), sep = "_")
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 7 rows [35, 36, 37, 38,
#> 39, 40, 41].

# d)
rodents$plot <- as.numeric(rodents$plot)

# e)
plot1_2 <- rodents %>% 
  filter(plot ==1 | plot == 2)

plot_3 <- rodents %>% 
  filter(plot == 3) %>% 
  select(-genus) %>% 
  separate(species, into = c("genus", "species"), sep=" ")

rodents_clean <- rbind(plot1_2, plot_3)
rodents_clean
#> # A tibble: 41 × 7
#>     plot location dateCollected family       genus     species     weight
#>    <dbl> <chr>    <chr>         <chr>        <chr>     <chr>        <dbl>
#>  1     1 slope    01/09/14      Heteromyidae Dipodomys merriami        40
#>  2     1 slope    01/09/14      Heteromyidae Dipodomys merriami        36
#>  3     1 slope    01/09/14      Heteromyidae Dipodomys spectabilis    135
#>  4     1 rocks    01/09/14      Heteromyidae Dipodomys merriami        39
#>  5     1 grass    01/20/14      Heteromyidae Dipodomys merriami        43
#>  6     1 rocks    01/20/14      Heteromyidae Dipodomys spectabilis    144
#>  7     1 rocks    03/13/14      Heteromyidae Dipodomys merriami        51
#>  8     1 fence    03/13/14      Heteromyidae Dipodomys merriami        44
#>  9     1 fence    03/13/14      Heteromyidae Dipodomys spectabilis    146
#> 10     2 rocks    01/08/14      Cricetidae   Neotoma   albigula        NA
#> # ℹ 31 more rows

---

library(tidyverse)
gapminder_yearly_pop <- read_csv("data/gapminder_yearly_population_millions_total.csv") %>% mutate(across(-country, ))

gapminder_yearly_pop_long <- gapminder_yearly_pop %>% pivot_longer(-country, names_to = "year", values_to = "population_millions") %>% mutate(year = as.numeric(year))

# a)
nz_1900_2000 <- gapminder_yearly_pop_long %>% 
  filter(between(year, 1900, 2000)) %>% 
  filter(country == "New Zealand")

# b)
jamaica_1900_2000 <- gapminder_yearly_pop_long %>% 
  filter(between(year, 1900, 2000)) %>% 
  filter(country == "Jamaica")

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