I have the following data and table:
library(gt)
library(dplyr)
a <- rnorm(21, mean = 112, sd =12)
colour <- rep(c("Blue", "Red", "Green"), 7)
data <- data.frame(colour, a) %>%
group_by(colour) %>%
summarise(mean = mean(a), sd = sd(a), n = n()) %>%
mutate(grp = html("[H<sub>2</sub>O]")) %>%
rename(cat = colour)
b <- rnorm(21, mean = 60, sd =12)
day <- rep(c("2", "4", "6"), 7)
data2 <- data.frame(day, b) %>%
group_by(day) %>%
summarise(mean = mean(a), sd = sd(a), n = n()) %>%
mutate(grp = html("[H<sub>2</sub>O] Additition <br> (Days)")) %>%
rename(cat = day)
bind_rows(data, data2) %>%
group_by(grp) %>%
gt(rowname_col = "cat")
bind_rows(data, data2) %>%
group_by(grp) %>%
gt() %>%
tab_options(row_group.as_column = TRUE)
The row group labels appear literally as '[H<sub>2<\sub>O]', rather than [H2O] etc. It is likely that I am using HTML wrong and it needs to be used with another package/function. I have also tried using cols_label but doesn't recognise these as columns in the dataframe.
Is there also a way to have the row groups column vertically centered, rather than at the top where is currently is? How do you bold these row groups?
The html function won't work outside of a gt table, so you'll have to create the row groups using tab_row_group and add the html labels there.
data <- data.frame(colour, a) %>%
group_by(colour) %>%
summarise(mean = mean(a), sd = sd(a), n = n()) %>%
mutate(grp = "color") %>%
rename(cat = colour)
data2 <- data.frame(day, b) %>%
group_by(day) %>%
summarise(mean = mean(a), sd = sd(a), n = n()) %>%
mutate(grp = "day") %>%
rename(cat = day)
bind_rows(data, data2) %>%
gt() %>%
tab_row_group(
label = html("[H<sub>2</sub>O]"),
rows = grp == "color"
) %>%
tab_row_group(
label = html("[H<sub>2</sub>O] Additition <br> (Days)"),
rows = grp == "day"
) %>%
cols_hide(grp)
Are there any R packages that I use to replicate the table below -
I would like a table with conditional formatting for the table values but no conditional formatting on the row and column grand totals.
The code can be used to reproduce the values in the table along with the row and column grand totals -
library(tidyverse)
# vectors
dates <- rep(date_vec <- c(as.Date("2022-01-01"), as.Date("2022-02-01"), as.Date("2022-03-01")), 30)
row_groups <- c(rep("row_group1", 20), rep("row_group2", 30), rep("row_group3", 10), rep("row_group4", 30))
col_groups <- c(rep("col_group1", 10), rep("col_group2", 10), rep("col_group3", 30), rep("col_group4", 40))
# dataframe
df <- tibble(dates, row_groups, col_groups)
# column grand totals
col_group_total <- df %>%
group_by(dates, col_groups) %>%
count() %>%
group_by(col_groups) %>%
summarise(mean = mean(n)) %>%
mutate(pct = mean/sum(mean))
# row grand totals
row_group_total <- df %>%
group_by(dates, row_groups) %>%
count() %>%
group_by(row_groups) %>%
summarise(mean = mean(n)) %>%
mutate(pct = mean/sum(mean))%>%
ungroup()
# table values
group_total <- df %>%
group_by(dates, row_groups, col_groups) %>%
count() %>%
group_by(row_groups, col_groups) %>%
summarise(count = mean(n)) %>%
ungroup() %>%
mutate(pct = count/sum(count))%>%
ungroup()
red_color <- "#f4cccc"
yellow_color <- "#f3f0ce"
green_color <- "#d9ead3"
library(janitor); library(gt)
df %>%
tabyl(row_groups, col_groups) %>%
adorn_percentages("all") %>%
adorn_totals(c("col")) -> df_tabyl
gt(df_tabyl) %>%
data_color(columns = col_group1:col_group4,
colors = scales::col_numeric(
palette = c(red_color, yellow_color, green_color),
domain = range(df_tabyl[1:4,2:5])
)
) %>%
fmt_percent(columns = -row_groups,
rows = everything()) %>%
summary_rows(
columns = -row_groups,
fns = list("Total" = "sum"),
formatter = fmt_percent
)
The coloring varies with your example b/c the col_numeric function maps the colors linearly along the three provided colors, and 11% is only 1/3 of the way between 0% and 33%. Not sure what approach you expect.
Please find the input data and expected output as screenshot below:
However, the current plot with the below code:
I feel, I made it too complicated. But I shared input data and expected data along with struggled code along the way. Could you please help us
Mainly there are 2 issues.
1. If mutate is used, undesired stripes appear on the plot
Summarize used, then it is not adding to 100%
2. How can we extract the top contributors
Both have been tried by us but stuck somewhere
# Input data
df <- tibble(
country = c(rep(c("India","USA","Germany","Africa"), each = 8)),
type = c("sms","Other","whatsapp","web","online","shiny","whatsapp","whatsapp",
"sms","sms","sms","web","web","Other","online","whatsapp",
"sms","Other","whatsapp","shiny","online","shiny","whatsapp","whatsapp",
"sms","sms","sms","shiny","online","Other","online","Other"
),
cust = rep(c("google","Apple","wallmart","pg"),8),
quantity = c(10,20,30,40,50,60,70,80,
90,100,15,25,35,45,55,65,
75,85,95,105,10,15,20,25,
30,35,40,45,50,55,60,65)
)
# Without Customer
df %>%
group_by(country,type) %>%
summarise(kpi_wo_cust = sum(quantity)) %>%
ungroup() -> df_wo_cust
# With Customer
df %>%
group_by(country,type,cust) %>%
summarise(kpi_cust = sum(quantity)) %>%
ungroup() -> df_cust
df_combo <- left_join(df_cust, df_wo_cust, by = c("country","type"))
df_combo %>% glimpse()
# Aggregated data for certain KPIs for final plot
df_aggr <- df_combo %>%
group_by(country,type) %>%
mutate(kpi_cust_total = sum(kpi_cust),
per_kpi_cust = 100 * (kpi_cust/kpi_cust_total)) %>%
group_by(country) %>%
# In order to except from repeated counting, selecting unique()
mutate(kpi_cust_uniq_total = sum(kpi_cust) %>% unique(),
per_unq_kpi_cust = 100 * (kpi_cust/kpi_cust_uniq_total) %>% round(4))
#
plt = df_aggr %>% ungroup() %>%#glimpse()
# In order to obtain theTop 2 customers (Major contributor) within country and type
# However, if this code is used, there is an error
# group_by(country, type) %>%
# nest() %>%
# mutate(top_cust = purrr::map_chr(data, function(x){
# x %>% arrange(desc(per_kpi_cust)) %>%
# top_n(2,per_kpi_cust) %>%
# summarise(Cust = paste(cust,round(per_kpi_cust,2), collapse = "<br>")) %>%
# pull(cust)
# })#,data = NULL
# ) %>%
# unnest(cols = data) %>%
group_by(country, type) %>%
# If mutate is used, undesired stripes appear on the plot
# Summarize used, then it is not adding to 100%
mutate(avg_kpi_cust = per_unq_kpi_cust %>% mean()) %>%
#summarise(avg_kpi_cust = per_unq_kpi_cust %>% mean()) %>%
ggplot(aes(x = country,
y = avg_kpi_cust,
fill = type,
text = paste('<br>proportion: ', round(avg_kpi_cust,2), "%",
"<br>country:",country
))) +
geom_bar(stat = "identity"#, position=position_dodge()
) +
coord_flip() +
theme_bw()
ggplotly(plt)
The key was to use distinct() after mutate() instead of summarise()
Also, mean() was the wrong function used earlier instead of sum() which had resulted in incomplete barplot.
library(tidyverse)
library(plotly)
# Input data
df <- tibble(
country = c(rep(c("India","USA","Germany","Africa"), each = 8)),
type = c("sms","Other","whatsapp","web","online","shiny","whatsapp","whatsapp",
"sms","sms","sms","web","web","Other","online","whatsapp",
"sms","Other","whatsapp","shiny","online","shiny","whatsapp","whatsapp",
"sms","sms","sms","shiny","online","Other","online","Other"
),
cust = rep(c("google","Apple","wallmart","pg"),8),
quantity = c(10,20,30,40,50,60,70,80,
90,100,15,25,35,45,55,65,
75,85,95,105,10,15,20,25,
30,35,40,45,50,55,60,65)
)
# Without Customer
df %>%
group_by(country,type) %>%
summarise(kpi_wo_cust = sum(quantity)) %>%
ungroup() -> df_wo_cust
# With Customer
df %>%
group_by(country,type,cust) %>%
summarise(kpi_cust = sum(quantity)) %>%
ungroup() -> df_cust
df_combo <- left_join(df_cust, df_wo_cust, by = c("country","type"))
df_combo %>% glimpse()
# Aggregated data for certain KPIs for final plot
df_aggr <- df_combo %>%
group_by(country,type) %>%
mutate(kpi_cust_total = sum(kpi_cust),
per_kpi_cust = 100 * (kpi_cust/kpi_cust_total)) %>%
group_by(country) %>%
# In order to except from repeated counting, selecting unique()
mutate(kpi_cust_uniq_total = sum(kpi_cust) %>% unique(),
per_unq_kpi_cust = 100 * (kpi_cust/kpi_cust_uniq_total) %>% round(4))
plt = df_aggr %>% ungroup() %>%
# In order to diplay Top 2 customers (Major contributor) within country and type
group_by(country, type) %>%
nest() %>%
mutate(top_cust = purrr::map_chr(data, function(x){
x %>% arrange(desc(per_kpi_cust)) %>%
top_n(2,per_kpi_cust) %>%
summarise(Cust = paste(cust,round(per_kpi_cust,2), collapse = "<br>")) %>%
pull(Cust)
})) %>%
unnest(cols = data) %>%
group_by(country, type) %>%
# If mutate is used, undesired stripes appear on the plot
# Summarize used, then it is not adding to 100%.
# So distinct was used
mutate(avg_kpi_cust = per_unq_kpi_cust %>% sum()) %>%
ungroup() %>%
distinct(country, type, .keep_all = T) %>%
ggplot(aes(x = country,
y = avg_kpi_cust,
fill = type,
text = top_cust
)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_bw()
ggplotly(plt, tooltip = "text")
I have the following script. Option 1 uses a long format and group_by to identify the first step of many where the status equals 0.
Another option (2) is to use apply to calculate this value for each row, and then transform the data to a long format.
The firs option does not scale well. The second does, but I was unable to get it into a dplyr pipe. I tried to solve this with purrr but did not succeeed.
Questions:
Why does the first option not scale well?
How can I transform the second option in a dplyr pipe?
require(dplyr)
require(tidyr)
require(ggplot2)
set.seed(314)
# example data
dat <- as.data.frame(matrix(sample(c(0,1),
size = 9000000,
replace = TRUE,
prob = c(5,95)),
ncol = 9))
names(dat) <- paste("step",1:9, sep="_")
steps <- dat %>% select(starts_with("step_")) %>% names()
# option 1 is slow
dat.cum <- dat %>%
mutate(id = row_number()) %>%
gather(step, status,-id) %>%
group_by(id) %>%
mutate(drop = min(if_else(status==0,match(step, steps),99L))) %>%
mutate(status = if_else(match(step, steps)>=drop,0,1))
ggplot(dat.cum, aes(x = step, fill = factor(status))) +
geom_bar()
# option 2 is faster
dat$drop <- apply(dat,1,function(x) min(which(x==0),99))
dat.cum <- dat %>%
gather(step,status,-drop) %>%
mutate(status = if_else(match(step,steps)>=drop,0,1))
ggplot(dat.cum, aes(x = step, fill = factor(status))) +
geom_bar()
If you would like to map along rows you could do:
dat %>%
mutate(drop2 = map_int(seq_len(nrow(dat)), ~ min(which(dat[.x, ] == 0L), 99L)))
It could be that "gathering and grouping" is faster than Looping:
dat %>%
as_tibble() %>%
select(starts_with("step_")) %>%
mutate(row_nr = row_number()) %>%
gather(key = "col", value = "value", -row_nr) %>%
arrange(row_nr, col) %>%
group_by(row_nr) %>%
mutate(col_index = row_number()) %>%
filter(value == 0) %>%
summarise(drop3 = min(col_index)) %>%
ungroup() %>%
right_join(dat %>%
mutate(row_nr = row_number()),
by = "row_nr") %>%
mutate(drop3 = if_else(is.na(drop3), 99, drop3))
I am trying to produce a formatted html table which has columns for frequency, cumulative frequency, column percentage, and cumulative column percentage. The table should also have the data subsetted by a grouping variable, and including a group total.
I can almost achieve this using a combination of dplyr and tidyr, but the output is a dataframe which doesn't look so pretty. I wonder if there is an easier way using the tables::tabulate command?
# Sample data
dat <- data.frame(
id = 1:100,
group = factor(sample(c("A", "B"), 100, replace = TRUE)),
sessions = factor(sample(1:10, 100, replace = TRUE))
)
# dplyr/tidyr solution
library(dplyr)
library(tidyr)
dat %>%
group_by(group, sessions) %>%
tally() %>%
spread(key = group, value = n) %>%
mutate(All = rowSums(.[-1])) %>%
gather(key = group, value = n, -sessions) %>%
group_by(group) %>%
mutate(
cum_n = cumsum(n),
p = round(n / sum(n)*100,1),
cum_p = round(cum_n / sum(n)*100,1),
) %>%
data.frame() %>%
reshape(timevar = "group", idvar = "sessions", direction = "wide")
# As far as I get using tables::tabulate
library(tables)
tabular(
Factor(sessions, "Sessions") ~
(Heading()*group + 1) *
(
(n = 1) +
# (cum_n = ??) +
Heading("%")*Percent(denom = "col")*Format(digits = 2)
# + Heading("cum_%")*??*Format(digits = 2)
),
data = dat
)
I would recommend using knitr::kable and kableExtra, amazing packages for producing tables. You can also set it up for multiple format outputs, for example using the same code to produce html and latex for pdf.
library(dplyr)
library(tidyr)
library(knitr)
library(kableExtra)
dat %>%
group_by(group, sessions) %>%
tally() %>%
spread(key = group, value = n) %>%
mutate(All = rowSums(.[-1])) %>%
gather(key = group, value = n, -sessions) %>%
group_by(group) %>%
mutate(
cum_n = cumsum(n),
p = round(n / sum(n)*100,1),
cum_p = round(cum_n / sum(n)*100,1),
) %>%
data.frame() %>%
reshape(timevar = "group", idvar = "sessions", direction = "wide") %>%
kable("html") %>%
kable_styling(bootstrap_options = c("striped", "hover"))