Hi , I am trying to create a function to create a summary for values in two columns.
sample data
df <- data.frame("Tier1 City"=c(NA,"","Ottawa","","buenes aries","New York","washington DC"),
"Tier2 City"= c(NA,"","Ottawa","","buenes aries","","washington DC"))
i have tried with tabyl(janitor), table function but not working as i want
i am looking for a function where i can give input parameters as data,variable list , and name of variable i want in summary output.
the output should be look like.
Hi,
Here is an example:
library(dplyr)
library(tidyr)
df <- data.frame("Tier1City"=c(NA,"","Ottawa","","buenes aries","New York","washington DC"),
"Tier2City"= c(NA,"","Ottawa","","buenes aries","","washington DC"))
myFunction = function(dataframe){
df = df %>% pivot_longer(everything()) %>%
group_by(value, name) %>% summarise(n = n(), .groups = "drop") %>%
pivot_wider(value, names_from = name, values_from = n, values_fill = 0) %>%
filter(value != "")
df[nrow(df) + 1,1] = "TOTAL"
df[nrow(df),-1] = as.list(colSums(df[,-1], na.rm = T))
df
}
myFunction(df)
#> # A tibble: 5 x 3
#> value Tier1City Tier2City
#> <chr> <int> <int>
#> 1 buenes aries 1 1
#> 2 New York 1 0
#> 3 Ottawa 1 1
#> 4 washington DC 1 1
#> 5 TOTAL 4 3
Created on 2021-02-09 by the reprex package (v1.0.0)
Hope this helps,
Thanks alot for help, but I have so many columns in my dataframe and i want to give input parameters as data, columns names as variable list, the variable names as header for summary.
for ex myfunc(data=df,var=c(colnames),var_names=c("Tier1_City","Tier2_City")
lars
February 9, 2021, 3:25pm
4
I had some time ago a similar need and came up with the following function. This function also accepts sorting on another output column than the default Total column..
library(tidyverse)
get_tabyl_2 <- function(df = df, by_row = by_row, by_col = by_col, sort_by = Total) {
library(janitor)
## embrace promised arguments
## see vignette("programming") section Indirection
df %>%
tabyl({{by_row}}, {{by_col}}) %>%
# adorn_totals(where = c("row", "col")) %>%
adorn_totals("col") %>%
adorn_percentages() %>%
adorn_pct_formatting() %>%
adorn_ns() %>%arrange(desc({{sort_by}})) %>%
as_tibble()
}
storms %>% get_tabyl_2(name, status)
#>
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#>
#> chisq.test, fisher.test
#> # A tibble: 198 x 5
#> name hurricane `tropical depression` `tropical storm` Total
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Emily 42.5% (88) 14.5% (30) 43.0% (89) 100.0% (207)
#> 2 Bonnie 38.4% (71) 23.8% (44) 37.8% (70) 100.0% (185)
#> 3 Claudette 15.6% (28) 34.4% (62) 50.0% (90) 100.0% (180)
#> 4 Felix 42.7% (76) 20.8% (37) 36.5% (65) 100.0% (178)
#> 5 Alberto 28.8% (49) 27.6% (47) 43.5% (74) 100.0% (170)
#> 6 Danielle 51.0% (80) 17.2% (27) 31.8% (50) 100.0% (157)
#> 7 Isidore 41.7% (65) 17.9% (28) 40.4% (63) 100.0% (156)
#> 8 Edouard 40.3% (60) 25.5% (38) 34.2% (51) 100.0% (149)
#> 9 Danny 19.2% (28) 34.9% (51) 45.9% (67) 100.0% (146)
#> 10 Ivan 61.8% (89) 14.6% (21) 23.6% (34) 100.0% (144)
#> # ... with 188 more rows
storms %>% get_tabyl_2(by_row = category, by_col = status, sort_by = category)
#> # A tibble: 7 x 5
#> category hurricane `tropical depression` `tropical storm` Total
#> <ord> <chr> <chr> <chr> <chr>
#> 1 5 100.0% (68) 0.0% (0) 0.0% (0) 100.0% (68)
#> 2 4 100.0% (348) 0.0% (0) 0.0% (0) 100.0% (348)
#> 3 3 100.0% (363) 0.0% (0) 0.0% (0) 100.0% (363)
#> 4 2 100.0% (628) 0.0% (0) 0.0% (0) 100.0% (628)
#> 5 1 99.9% (1684) 0.0% (0) 0.1% (1) 100.0% (1685)
#> 6 0 0.0% (0) 0.0% (0) 100.0% (4373) 100.0% (4373)
#> 7 -1 0.0% (0) 100.0% (2545) 0.0% (0) 100.0% (2545)
storms %>% get_tabyl_2(category, status)
#> # A tibble: 7 x 5
#> category hurricane `tropical depression` `tropical storm` Total
#> <ord> <chr> <chr> <chr> <chr>
#> 1 0 0.0% (0) 0.0% (0) 100.0% (4373) 100.0% (4373)
#> 2 -1 0.0% (0) 100.0% (2545) 0.0% (0) 100.0% (2545)
#> 3 1 99.9% (1684) 0.0% (0) 0.1% (1) 100.0% (1685)
#> 4 2 100.0% (628) 0.0% (0) 0.0% (0) 100.0% (628)
#> 5 3 100.0% (363) 0.0% (0) 0.0% (0) 100.0% (363)
#> 6 4 100.0% (348) 0.0% (0) 0.0% (0) 100.0% (348)
#> 7 5 100.0% (68) 0.0% (0) 0.0% (0) 100.0% (68)
mtcars %>% get_tabyl_2(cyl, am, cyl)
#> # A tibble: 3 x 4
#> cyl `0` `1` Total
#> <dbl> <chr> <chr> <chr>
#> 1 8 85.7% (12) 14.3% (2) 100.0% (14)
#> 2 6 57.1% (4) 42.9% (3) 100.0% (7)
#> 3 4 27.3% (3) 72.7% (8) 100.0% (11)
mtcars %>% get_tabyl_2(carb, cyl, carb)
#> # A tibble: 6 x 5
#> carb `4` `6` `8` Total
#> <dbl> <chr> <chr> <chr> <chr>
#> 1 8 0.0% (0) 0.0% (0) 100.0% (1) 100.0% (1)
#> 2 6 0.0% (0) 100.0% (1) 0.0% (0) 100.0% (1)
#> 3 4 0.0% (0) 40.0% (4) 60.0% (6) 100.0% (10)
#> 4 3 0.0% (0) 0.0% (0) 100.0% (3) 100.0% (3)
#> 5 2 60.0% (6) 0.0% (0) 40.0% (4) 100.0% (10)
#> 6 1 71.4% (5) 28.6% (2) 0.0% (0) 100.0% (7)
mtcars %>% get_tabyl_2(gear, cyl)
#> # A tibble: 3 x 5
#> gear `4` `6` `8` Total
#> <dbl> <chr> <chr> <chr> <chr>
#> 1 3 6.7% (1) 13.3% (2) 80.0% (12) 100.0% (15)
#> 2 4 66.7% (8) 33.3% (4) 0.0% (0) 100.0% (12)
#> 3 5 40.0% (2) 20.0% (1) 40.0% (2) 100.0% (5)
Created on 2021-02-09 by the reprex package (v1.0.0)
HTH
system
March 2, 2021, 3:25pm
5
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