Module 11: Apply it to your data 10
Taylor Nelson
Import your data
data <- read_excel("../01_module4/data/myData.xlsx")
data## # A tibble: 1,104 × 31
## town11cd town11nm population_2011 size_flag rgn11nm coastal coastal_detailed
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 E34000007 Carlton… 5456 Small To… East M… Non-co… Smaller non-coa…
## 2 E34000016 Dorches… 19060 Small To… South … Non-co… Smaller non-coa…
## 3 E34000020 Ely BUA 19090 Small To… East o… Non-co… Smaller non-coa…
## 4 E34000026 Market … 6429 Small To… Yorksh… Non-co… Smaller non-coa…
## 5 E34000027 Downham… 10884 Small To… East o… Non-co… Smaller non-coa…
## 6 E34000039 Penrith… 15181 Small To… North … Non-co… Smaller non-coa…
## 7 E34000048 Bolsove… 11754 Small To… East M… Non-co… Smaller non-coa…
## 8 E34000055 March B… 21051 Medium T… East o… Non-co… Large non-coast…
## 9 E34000056 Southam… 6567 Small To… West M… Non-co… Smaller non-coa…
## 10 E34000067 Royston… 15781 Small To… East o… Non-co… Smaller non-coa…
## # ℹ 1,094 more rows
## # ℹ 24 more variables: ttwa11cd <chr>, ttwa11nm <chr>,
## # ttwa_classification <chr>, job_density_flag <chr>, income_flag <chr>,
## # university_flag <chr>, level4qual_residents35_64_2011 <chr>,
## # ks4_2012_2013_counts <dbl>,
## # key_stage_2_attainment_school_year_2007_to_2008 <dbl>,
## # key_stage_4_attainment_school_year_2012_to_2013 <dbl>, …Chapter 15
Create a factor
unique(data$size_flag)## [1] "Small Towns" "Medium Towns" "Large Towns" "City"
## [5] "Inner London BUA" "Outer london BUA" "Other Small BUAs" "Not BUA"size_levels <- c("Other Small BUAs", "Small Towns", "Medium Towns", "Large Towns", "City", "Not BUA", "Outer london BUA", "Inner London BUA")
data$size_flag <- factor(data$size_flag, levels = size_levels)
data$size_flag## [1] Small Towns Small Towns Small Towns Small Towns
## [5] Small Towns Small Towns Small Towns Medium Towns
## [9] Small Towns Small Towns Small Towns Small Towns
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## [1101] Inner London BUA Outer london BUA Other Small BUAs Not BUA
## 8 Levels: Other Small BUAs Small Towns Medium Towns Large Towns ... Inner London BUAlevels(data$size_flag)## [1] "Other Small BUAs" "Small Towns" "Medium Towns" "Large Towns"
## [5] "City" "Not BUA" "Outer london BUA" "Inner London BUA"Modify factor order
Make two charts here - one before ordering another after
# Before ordering
size_summary <- data %>%
group_by(size_flag) %>%
summarise(population = mean(population_2011, na.rm = TRUE), n = n())
ggplot(size_summary, aes(population, size_flag)) +
geom_point()## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
# After ordering
size_summary %>%
mutate(size_flag = fct_reorder(size_flag, population)) %>%
ggplot(aes(population, size_flag)) +
geom_point()## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `size_flag = fct_reorder(size_flag, population)`.
## Caused by warning:
## ! `fct_reorder()` removing 4 missing values.
## ℹ Use `.na_rm = TRUE` to silence this message.
## ℹ Use `.na_rm = FALSE` to preserve NAs.
## Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
Modify factor levels
Show examples of three functions:
- fct_recode
data %>%
mutate(size_flag = fct_recode(size_flag, "Small Towns" = "Small Towns", "Medium Towns" = "Medium Towns", "Large Towns" = "Large Towns", "Not BUA" = "Not BUA")) %>%
count(size_flag)## # A tibble: 8 × 2
## size_flag n
## <fct> <int>
## 1 Other Small BUAs 1
## 2 Small Towns 662
## 3 Medium Towns 331
## 4 Large Towns 89
## 5 City 18
## 6 Not BUA 1
## 7 Outer london BUA 1
## 8 Inner London BUA 1- fct_collapse
data %>%
mutate(size_flag = fct_collapse(size_flag, towns = c("Small Towns", "Medium Towns", "Large Towns"), london = c("Inner London BUA", "Outer london BUA"), other = c("Other Small BUAs", "Not BUA", "City"))) %>%
count(size_flag)## # A tibble: 3 × 2
## size_flag n
## <fct> <int>
## 1 other 20
## 2 towns 1082
## 3 london 2- fct_lump
data %>%
mutate(size_flag = fct_lump(size_flag, n = 3)) %>%
count(size_flag)## # A tibble: 4 × 2
## size_flag n
## <fct> <int>
## 1 Small Towns 662
## 2 Medium Towns 331
## 3 Large Towns 89
## 4 Other 22Chapter 16
No need to do anything here.