Module 11: Apply it to your data 10

Import your data

data <- read_excel("../00_data/MyData-Charts.xlsx")
data

## # A tibble: 1,222 × 11
##     year months    state colon…¹ colon…² colon…³ colon…⁴ colon…⁵ colon…⁶ colon…⁷
##    <dbl> <chr>     <chr>   <dbl> <chr>     <dbl>   <dbl> <chr>   <chr>   <chr>  
##  1  2015 January-… Alab…    7000 7000       1800      26 2800    250     4      
##  2  2015 January-… Ariz…   35000 35000      4600      13 3400    2100    6      
##  3  2015 January-… Arka…   13000 14000      1500      11 1200    90      1      
##  4  2015 January-… Cali… 1440000 1690000  255000      15 250000  124000  7      
##  5  2015 January-… Colo…    3500 12500      1500      12 200     140     1      
##  6  2015 January-… Conn…    3900 3900        870      22 290     NA      NA     
##  7  2015 January-… Flor…  305000 315000    42000      13 54000   25000   8      
##  8  2015 January-… Geor…  104000 105000    14500      14 47000   9500    9      
##  9  2015 January-… Hawa…   10500 10500       380       4 3400    760     7      
## 10  2015 January-… Idaho   81000 88000      3700       4 2600    8000    9      
## # … with 1,212 more rows, 1 more variable: `Growth of colonies` <dbl>, and
## #   abbreviated variable names ¹​colony_n, ²​colony_max, ³​colony_lost,
## #   ⁴​colony_lost_pct, ⁵​colony_added, ⁶​colony_reno, ⁷​colony_reno_pct

data_small <- read_excel("../00_data/Datasimpler.xlsx")

Chapter 15

Create a factor

Modify factor order

Make two bar charts here - one before ordering another after

data %>%
    count(months) %>%

    ggplot(aes(n, months)) + 
    geom_col()

data %>%
    count(months) %>%

    ggplot(aes(n, fct_reorder(months, n))) + 
    geom_col()

data %>%
  mutate(months = months %>% fct_infreq() %>% fct_rev()) %>%
  ggplot(aes(months)) +
   geom_bar()

Modify factor levels

Show examples of three functions:

• fct_recode

data %>%
  mutate(months = fct_recode(months,
    "Jan-Mar"    = "January-March",
    "Apr-Jun"      = "April-June",
    "Jul-Sep" = "July-September",
    "Oct-Dec" = "October-December",
  )) %>%
  count(months)

## # A tibble: 4 × 2
##   months      n
##   <fct>   <int>
## 1 Apr-Jun   329
## 2 Jan-Mar   329
## 3 Jul-Sep   282
## 4 Oct-Dec   282

• fct_collapse

data %>%
  mutate(state = fct_collapse(state,
    East_coast = c("Maine", "Connecticut", "Massachusetts"),
    West_coast = c("California", "Arizona", "Colorado", "Idaho", "Montana"),
  )) %>%
  count(state)

## # A tibble: 41 × 2
##    state          n
##    <fct>      <int>
##  1 Alabama       26
##  2 West_coast   130
##  3 Arkansas      26
##  4 East_coast    78
##  5 Florida       26
##  6 Georgia       26
##  7 Hawaii        26
##  8 Illinois      26
##  9 Indiana       26
## 10 Iowa          26
## # … with 31 more rows

• fct_lump

data %>%
  mutate(months = fct_lump(months,n=1)) %>%
  count(months)

## # A tibble: 3 × 2
##   months            n
##   <fct>         <int>
## 1 April-June      329
## 2 January-March   329
## 3 Other           564

Chapter 16

No need to do anything here.