Summarising data example code

Reading in the data

In this R Markdown file, the Excel file that is read in is called analytic_data.xlxs. The data frame is called EXAMPLE_DATA. Replace these with the names of the files you wish to use.

EXAMPLE_DATA <- read_excel("analytic_data.xlsx")
EXAMPLE_DATA <- EXAMPLE_DATA %>% 
  mutate_if(is.character,as.factor)

In all of the code below, you will need to replace EXAMPLE_DATA with the name of your data frame. You will need to use the appropriate variable names.

One numerical variable

EXAMPLE_DATA %>%
  summarise(Mean = mean(NUMERICAL_VARIABLE1),
            "Standard Deviation" = sd(NUMERICAL_VARIABLE1),
            n = n()) %>%
  gt() %>%
  fmt_number(c(Mean, "Standard Deviation"),
             decimals = 1) %>%
  tab_spanner(
    label = "Nice label for NUMERICAL_VARIABLE1",
    columns = c(1:3))

Nice label for NUMERICAL_VARIABLE1
Mean	Standard Deviation	n
4.3	2.3	20

One numerical variable and one categorical variable

Mean, SD and n

EXAMPLE_DATA %>%
  group_by(CATEGORICAL_VARIABLE1) %>%
  summarise(Mean = mean(NUMERICAL_VARIABLE1),
            "Standard Deviation" = sd(NUMERICAL_VARIABLE1),
            n = n()) %>%
  gt() %>%
  fmt_number(c(Mean, "Standard Deviation"),
             decimals = 1)

CATEGORICAL_VARIABLE1	Mean	Standard Deviation	n
A	4.0	1.4	10
B	4.7	2.9	10

Five number summary, including median and quartiles

EXAMPLE_DATA %>%
  group_by(CATEGORICAL_VARIABLE1) %>%
  summarise(n = n(),
            Minimum = min(NUMERICAL_VARIABLE1),
            "First quartile" = quantile(NUMERICAL_VARIABLE1, 0.25),
            Median = median(NUMERICAL_VARIABLE1),
            "Third quartile" = quantile(NUMERICAL_VARIABLE1, 0.75),
            Maximum = max(NUMERICAL_VARIABLE1))%>%
  gt() %>%
  fmt_number(c(Minimum, "First quartile", Median, "Third quartile", Maximum),
             decimals = 1)

CATEGORICAL_VARIABLE1	n	Minimum	First quartile	Median	Third quartile	Maximum
A	10	2.5	2.8	3.8	4.6	7.0
B	10	1.5	2.7	4.1	5.8	10.3

One categorical variable

EXAMPLE_DATA %>%
  tabyl(CATEGORICAL_VARIABLE1) %>%
  gt() %>%
  cols_label(percent = "proportion")

CATEGORICAL_VARIABLE1	n	proportion
A	10	0.5
B	10	0.5

There are additional adorn functions that can be used to improve the gt tables further.

EXAMPLE_DATA %>%
  tabyl(CATEGORICAL_VARIABLE1) %>%
  adorn_totals("row") %>%
  adorn_pct_formatting() %>%
  gt()

CATEGORICAL_VARIABLE1	n	percent
A	10	50.0%
B	10	50.0%
Total	20	100.0%

two categorical variables

EXAMPLE_DATA %>%
  tabyl(CATEGORICAL_VARIABLE1, CATEGORICAL_VARIABLE2) %>%
  gt()

CATEGORICAL_VARIABLE1	N	Y
A	2	8
B	7	3

An improvement adds the label of the second categorical variable so it is clear what the table is representing.

EXAMPLE_DATA %>%
  tabyl(CATEGORICAL_VARIABLE1, CATEGORICAL_VARIABLE2) %>%
  gt() %>%
  tab_spanner(
    label = "CATEGORICAL_VARIABLE2",
    columns = c(N,Y))

CATEGORICAL_VARIABLE1	CATEGORICAL_VARIABLE2
CATEGORICAL_VARIABLE1	N	Y
A	2	8
B	7	3

You can then use adorn to add in percentages.

EXAMPLE_DATA %>%
  tabyl(CATEGORICAL_VARIABLE1, CATEGORICAL_VARIABLE2) %>%
  adorn_percentages("row") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "front") %>%
  gt() %>%
  tab_spanner(
    label = "CATEGORICAL_VARIABLE2",
    columns = c(N,Y))

CATEGORICAL_VARIABLE1	CATEGORICAL_VARIABLE2
CATEGORICAL_VARIABLE1	N	Y
A	2 (20%)	8 (80%)
B	7 (70%)	3 (30%)