library(gt)
library(tidyverse)
#setwd("C:/Users/AlexThomson/OneDrive - Statistic For Sustainable Development/June Seminar - tables from R/gt/")
load("imdb.Rdata")
CC3_farm <- readRDS("CC3_farm.rds")You can create a very basic table by simply calling the
gt() function and specifying your data frame. You can also
pipe the function directly from using dplyr, tidyr etc. to manipulate
your data just like you would any other tidyverese package. In the
example below the function has even automatically detected that the
first column can be used as a group name for rows column in the
stub.
Remember that in gt terminology the stub is the row names and the spanner is a name for a group of columns.
imdb_animation_avgs <- imdb %>%
filter(type != "videoGame")%>%
group_by(type, animation)%>%
summarise(Mean.Votes = mean(numVotes, na.rm = TRUE),
Mean.Rating = mean(averageRating, na.rm = TRUE),
Number.Of.Entries = n())
gt(imdb_animation_avgs) # automatically detected row groupings| animation | Mean.Votes | Mean.Rating | Number.Of.Entries |
|---|---|---|---|
| movie | |||
| FALSE | 17337.709 | 6.254202 | 41085 |
| TRUE | 36393.630 | 6.656642 | 1197 |
| short | |||
| FALSE | 1576.763 | 6.823500 | 1183 |
| TRUE | 1822.737 | 7.363987 | 933 |
| tvMiniSeries | |||
| FALSE | 5779.093 | 7.452980 | 906 |
| TRUE | 2829.885 | 7.411458 | 96 |
| tvMovie | |||
| FALSE | 1857.997 | 6.093077 | 3019 |
| TRUE | 2162.584 | 7.100000 | 125 |
| tvSeries | |||
| FALSE | 12953.661 | 7.208266 | 4682 |
| TRUE | 7238.361 | 7.269250 | 1174 |
| tvShort | |||
| FALSE | 1929.167 | 7.422917 | 48 |
| TRUE | 2902.484 | 7.179121 | 91 |
| tvSpecial | |||
| FALSE | 1830.817 | 7.427378 | 431 |
| TRUE | 5492.600 | 7.820000 | 5 |
| video | |||
| FALSE | 2556.080 | 5.559368 | 1425 |
| TRUE | 5036.685 | 6.431738 | 397 |
You can easily specify a title and subtitle directly unlike when
using Flextable. This is done using the tab_header()
function and then specifying a couple strings for your titles.
top_20_movies <- imdb %>%
filter(type == "movie" & numVotes > 99999)%>%
arrange(desc(averageRating))%>%
select(title, year, averageRating, numVotes)%>%
slice(1:20)
tab1<-top_20_movies%>%
gt()%>%
tab_header(title = "Top 20 Movies of all time", # add a title
subtitle = "IMDB Entries with at least 100,000 votes") # add a subtitle
tab1| Top 20 Movies of all time | |||
| IMDB Entries with at least 100,000 votes | |||
| title | year | averageRating | numVotes |
|---|---|---|---|
| The Shawshank Redemption | 1994 | 9.3 | 2138866 |
| The Godfather | 1972 | 9.2 | 1468315 |
| The Godfather: Part II | 1974 | 9.0 | 1021652 |
| The Dark Knight | 2008 | 9.0 | 2102907 |
| The Mountain II | 2016 | 9.0 | 101445 |
| 12 Angry Men | 1957 | 8.9 | 610262 |
| Schindler's List | 1993 | 8.9 | 1109973 |
| Pulp Fiction | 1994 | 8.9 | 1678715 |
| The Lord of the Rings: The Return of the King | 2003 | 8.9 | 1520719 |
| The Good, the Bad and the Ugly | 1966 | 8.8 | 635197 |
| Forrest Gump | 1994 | 8.8 | 1646150 |
| The Lord of the Rings: The Fellowship of the Ring | 2001 | 8.8 | 1536315 |
| Fight Club | 1999 | 8.8 | 1709679 |
| Inception | 2010 | 8.8 | 1875664 |
| One Flew Over the Cuckoo's Nest | 1975 | 8.7 | 845858 |
| Star Wars: Episode V - The Empire Strikes Back | 1980 | 8.7 | 1070168 |
| Goodfellas | 1990 | 8.7 | 924279 |
| The Matrix | 1999 | 8.7 | 1540221 |
| The Lord of the Rings: The Two Towers | 2002 | 8.7 | 1375568 |
| It's a Wonderful Life | 1946 | 8.6 | 363682 |
Changing column names works basically in the same way as flextable by
writing variable.name = "new column name" within the
function cols_label().
There are many formatting functions provided by gt including ones for currencies, dates, times, date-times, etc.
The fmt_number() function allows you to edit numeric
values. This allows you to set many features including; decimal places,
trailing zeros, scaling, suffixing (K(thousand),M(million),B(billion)),
separation markers.
`random_movies` <- imdb %>%
filter(type == "movie" & numVotes > 99999)
random_movies_5 <- `random_movies`[sample(nrow(`random_movies`), 5),]
random_movies_5 <- random_movies_5%>%
arrange(title)%>%
select(title,year,length, numVotes, averageRating, director)
gt(random_movies_5,
rowname_col = "title")%>%
tab_header(title = md("**5 Movies**"),
subtitle = md("IMDB Entries with *at least* 100,000 votes"))%>%
cols_label(year = "Year",
length = "Length (Minutes)",
numVotes = "Total Votes",
averageRating = "Rating",
director = "Director")%>% # set new column names
fmt_number(columns = vars(numVotes), # choose column to format
suffixing = TRUE) # add suffixing K = thousand, M = Million etc.Using the tab_options() function allows you to set many,
many aesthetic characteristics including borders, font, headings, column
labels, footnotes, colours etc. If you see the help page for this
function you will find a very long list of all the changes you can make
using this function. For demonstration I have just showed you that i can
set the background colour for the entire table.
You can add footnotes using the tab_footnote() function
which requires a few arguments including the location of the footnote
(i.e. in cells, the stub or the header) and to which column/row should
it be applied to. You can set these conditionally such as setting the
footnote to apply to the row in a column where the column is at its
minimum. You can use the tab_options() to also set the
appearance of the footnote itself including font size, padding,
separation markers and what marks should be used for the footnotes
(numbers, alphabet etc.)
Similarly you can also add a source for your data using
source_note().
tab1 <- imdb %>%
filter(type == "movie" & numVotes > 99999)%>%
arrange(desc(averageRating))%>%
select(title, year, averageRating, numVotes)%>%
slice(1:20)%>%
gt()%>% # can directly pipe on from other functions
cols_label(title = "Movie",
year = "Year of Release",
averageRating = "Average Rating",
numVotes = "Number of Votes")%>%
tab_header(title = md("**Top 20 Movies of all time**"), # can use markdown notation to make text bold or italic
subtitle = md("IMDB Entries with *at least* 100,000 votes"))%>%
tab_source_note( #create a source note for data
source_note = "Source : https://www.imdb.com/search/title/?count=100&groups=top_1000&sort=user_rating (2020)" # add source
)%>%
tab_footnote(
footnote = "Christopher Nolan Films",
locations = cells_body( # set where this footnote applies (see help for where as can go basically anywhere in the table)
columns = vars(title), #column it applies to
rows = c(4,14) # row it applies to
) # create a footnote
)%>%
tab_footnote(
footnote = "Peter Jackson Films",
locations = cells_body(
columns = vars(title),
rows = c(9,12,19)
)
)%>%
fmt_number(columns = vars(numVotes),
suffixing = TRUE)%>% # format numeric columns
tab_footnote(
footnote = "Least Voted film",
locations = cells_body(
columns = vars(numVotes),
rows = numVotes == min(numVotes)
)
)%>%
tab_options(table.background.color = "blue") #change many many options about the appearance of the table including colours
tab1| Top 20 Movies of all time | |||
| IMDB Entries with at least 100,000 votes | |||
| Movie | Year of Release | Average Rating | Number of Votes |
|---|---|---|---|
| The Shawshank Redemption | 1994 | 9.3 | 2.14M |
| The Godfather | 1972 | 9.2 | 1.47M |
| The Godfather: Part II | 1974 | 9.0 | 1.02M |
| The Dark Knight1 | 2008 | 9.0 | 2.10M |
| The Mountain II | 2016 | 9.0 | 2 101.45K |
| 12 Angry Men | 1957 | 8.9 | 610.26K |
| Schindler's List | 1993 | 8.9 | 1.11M |
| Pulp Fiction | 1994 | 8.9 | 1.68M |
| The Lord of the Rings: The Return of the King3 | 2003 | 8.9 | 1.52M |
| The Good, the Bad and the Ugly | 1966 | 8.8 | 635.20K |
| Forrest Gump | 1994 | 8.8 | 1.65M |
| The Lord of the Rings: The Fellowship of the Ring3 | 2001 | 8.8 | 1.54M |
| Fight Club | 1999 | 8.8 | 1.71M |
| Inception1 | 2010 | 8.8 | 1.88M |
| One Flew Over the Cuckoo's Nest | 1975 | 8.7 | 845.86K |
| Star Wars: Episode V - The Empire Strikes Back | 1980 | 8.7 | 1.07M |
| Goodfellas | 1990 | 8.7 | 924.28K |
| The Matrix | 1999 | 8.7 | 1.54M |
| The Lord of the Rings: The Two Towers3 | 2002 | 8.7 | 1.38M |
| It's a Wonderful Life | 1946 | 8.6 | 363.68K |
| Source : https://www.imdb.com/search/title/?count=100&groups=top_1000&sort=user_rating (2020) | |||
| 1 Christopher Nolan Films | |||
| 2 Least Voted film | |||
| 3 Peter Jackson Films | |||
The cotton farming example shown in the seminar works similarly to
the flextable example in that a column of group names was required on
which the data could be grouped by. However rather than needing a
separate function to do the grouping, instead you can specify the
gropname_col = x within the call to gt().
tab1 <- gt(A, groupname_col = "Group")%>% # create table object and set groupnames for rows
cols_label(`Sub-group` = "Sub-group",
`Mean (Farm)` = "Farm Area (Mean)",
`Median (Farm)` = "Farm Area (Median)",
`Mean (Cottton)` = "Cotton Area (Mean)",
`Median (Cotton)` = "Cotton Area (Median)",
`Mean (%Cotton)` = "Cotton Area as % of Farm Area (Mean)",
`Median (%Cotton)` = "Cotton Area as % of Farm Area (Median)",
N = "N")%>% #set column names
tab_header("Farm Area (Acre), Cotton Area (Acre), Cotton Area as % of Farm Area") #create a table header
tab1| Farm Area (Acre), Cotton Area (Acre), Cotton Area as % of Farm Area | |||||||
| Sub-group | Farm Area (Mean) | Farm Area (Median) | Cotton Area (Mean) | Cotton Area (Median) | Cotton Area as % of Farm Area (Mean) | Cotton Area as % of Farm Area (Median) | N |
|---|---|---|---|---|---|---|---|
| State | |||||||
| Gujarat | 4.97 | 5.00 | 4.72 | 4.50 | 95.3 | 100.0 | 100 |
| Madhya Pradesh | 4.24 | 4.00 | 3.32 | 3.00 | 77.9 | 83.8 | 100 |
| Maharashtra | 2.12 | 1.00 | 2.04 | 1.00 | 98.5 | 100.0 | 100 |
| Rajasthan | 4.80 | 4.30 | 2.01 | 1.50 | 43.1 | 48.2 | 50 |
| Partner | |||||||
| 1 | 5.31 | 5.05 | 4.87 | 5.00 | 91.6 | 100.0 | 50 |
| 2 | 4.64 | 4.05 | 4.57 | 4.00 | 99.0 | 100.0 | 50 |
| 3 | 1.00 | 1.00 | 1.00 | 1.00 | 100.0 | 100.0 | 50 |
| 4 | 3.24 | 3.00 | 3.08 | 2.25 | 97.1 | 100.0 | 50 |
| 5 | 4.69 | 5.05 | 3.91 | 3.00 | 82.9 | 100.0 | 50 |
| 6 | 3.79 | 4.00 | 2.72 | 3.00 | 72.9 | 75.0 | 50 |
| 7 | 4.80 | 4.30 | 2.01 | 1.50 | 43.1 | 48.2 | 50 |
| Overall | |||||||
| Total | 3.92 | 4.00 | 3.16 | 3.00 | 83.8 | 100.0 | 350 |
movies <- imdb%>%
filter(type == "movie")%>%
mutate(num_Votes_10000 = numVotes/10000)
model1 <- aov(averageRating ~ length + num_Votes_10000 + animation, movies)
m1 <- broom::tidy(model1)
gt(m1)%>%
fmt_number(columns = c(3:4),
suffixing = T)%>%
fmt_number(columns = 5,
decimals = 1,
)%>%
fmt_scientific(columns = 6,
decimals = 2)%>%
fmt_missing(columns = 5:6,
missing_text = "")| term | df | sumsq | meansq | statistic | p.value |
|---|---|---|---|---|---|
| length | 1 | 3.71K | 3.71K | 2,834.6 | 0.00 |
| num_Votes_10000 | 1 | 1.17K | 1.17K | 893.8 | 2.34 × 10−194 |
| animation | 1 | 339.00 | 339.00 | 259.0 | 4.16 × 10−58 |
| Residuals | 42278 | 55.33K | 1.31 |
lm(averageRating ~ length + num_Votes_10000 + animation, movies) %>%
broom::tidy()%>%
gt()%>%
fmt_number(columns = 2:3,
decimals = 3)%>%
fmt_number(columns = 4,
decimals = 1)%>%
fmt_scientific(columns = 5,
decimals = 2)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.925 | 0.026 | 187.8 | 0.00 |
| length | 0.012 | 0.000 | 50.0 | 0.00 |
| num_Votes_10000 | 0.023 | 0.001 | 28.9 | 2.42 × 10−181 |
| animationTRUE | 0.544 | 0.034 | 16.1 | 4.16 × 10−58 |