Dear all,
Is there anyone who can help me? please 
I have made tons of dataset in R studio like you could see in the right part of the image below and I would like to make a simple table like a summary which contains only "name, number of observation and number of variables" of all of each dataset. (I want the table to be made just like the RIGHT PART OF THE IMAGE below)
Hopefully the table should be in excel or csv form.
PLEASE HELP!
Thank you all!
Here is an example with one approach to this, using 2 standard datasets
library(tidyverse)
library(purrr)
cars <- mtcars
chicks <- chickwts
list <- list(cars, chicks)
map_dfc(list, dim) %>%
set_names('rows', 'columns')
#> New names:
#> * NA -> ...1
#> * NA -> ...2
#> # A tibble: 2 x 2
#> rows columns
#> <int> <int>
#> 1 32 71
#> 2 11 2
Created on 2020-05-16 by the reprex package (v0.3.0)
Here is another approach. The lines where I load the data sets mtcars, iris and iris3 are only there to provide some objects for the reproducible example. You can write the object TBL as a csv or an Excel file.
library(tibble)
library(purrr)
M <- mtcars
I <- iris
I3 <- iris3 #not a data frame
DataNames <- ls()
GetInfo <- function(x) {
Obj <- get(x)
if (grepl("data.frame", class(Obj))) {
Name <- x
Obs <- nrow(Obj)
Vars <- ncol(Obj)
} else {
Name <- x
Obs <- NA
Vars <- NA
}
tibble(Name = Name, Observations = Obs, Variables = Vars)
}
TBL <- map_dfr(DataNames, GetInfo )
TBL
#> # A tibble: 3 x 3
#> Name Observations Variables
#> <chr> <int> <int>
#> 1 I 150 5
#> 2 I3 NA NA
#> 3 M 32 11
Created on 2020-05-16 by the reprex package (v0.3.0)
thank you for your help. it runs well but the thing is it shows me all the values as NA (
I tell you this hopefully it could be a hint to you. All of the dataset I made in R studio, they are made with a package dplyr(tbl_df).
Please don't use images to communicate code and console outputs. It's all text in RStudio so copy and paste the text into here. Use three backticks on their own line to format code as code
So its nice to read
Ok. With that out if the way, I think it's a mistake to grepl the result of class. Rather use is.dataframe function to test for that more directly. Tibbles have 3 classes after all.
You are right. I should have used copy and paste to ask and share with people here. Thank you for let me know.
I have made all of the datasets by these codes below.
original_data <- read.csv("00000.csv",header = TRUE, sep = ",", na.strings = "NA")
str(original_data)
library(dplyr)
data <- tbl_df(original_data)
data
TV_Yes <- filter(data, A10>="2")
TV_Yes <- TV_Yes[,-1:-496]
> is.data.frame(TV_Yes)
[1] TRUE
and as all of the dataset were made in the same way,
they are all dataframe.
I don't know why it isn't work even they all are dataframe like you could see below.
> library(tibble)
> library(purrr)
> DataNames <- ls()
> GetInfo <- function(x) {
+ Obj <- get(x)
+ if (grepl("data.frame", class(Obj))) {
+ Name <- x
+ Obs <- nrow(Obj)
+ Vars <- ncol(Obj)
+ } else {
+ Name <- x
+ Obs <- NA
+ Vars <- NA
+ }
+ tibble(Name = Name, Observations = Obs, Variables = Vars)
+
+ }
> TBL <- map_dfr(DataNames, GetInfo )
> TBL
# A tibble: 160 x 3
Name Observations Variables
<chr> <lgl> <lgl>
1 art_No NA NA
2 art_Yes NA NA
3 Before_sleep_music NA NA
4 Before_sleep_nothing NA NA
5 Before_sleep_PC NA NA
6 Before_sleep_smartphone NA NA
7 Before_sleep_TV NA NA
8 CableTV_No NA NA
9 CableTV_Yes NA NA
10 cafe_No NA NA
# ... with 150 more rows
original_data <- read.csv("00000.csv",header = TRUE, sep = ",", na.strings = "NA")
str(original_data)
library(dplyr)
data <- tbl_df(original_data)
data
TV_Yes <- filter(data, A10>="2")
TV_Yes <- TV_Yes[,-1:-496]
> is.data.frame(TV_Yes)
[1] TRUE
change this to
if (is.data.frame(Obj)){wow it works!
This is just the thing what I have wanted!!
Thank you so much for all of your help!
> library(tibble)
> library(purrr)
>
> DataNames <- ls()
> GetInfo <- function(x) {
+ Obj <- get(x)
+ if (is.data.frame(Obj)){
+ Name <- x
+ Obs <- nrow(Obj)
+ Vars <- ncol(Obj)
+ } else {
+ Name <- x
+ Obs <- NA
+ Vars <- NA
+ }
+ tibble(Name = Name, Observations = Obs, Variables = Vars)
+
+ }
> TBL <- map_dfr(DataNames, GetInfo )
> TBL
# A tibble: 160 x 3
Name Observations Variables
<chr> <int> <int>
1 art_No 946 18
2 art_Yes 254 18
3 Before_sleep_music 32 18
4 Before_sleep_nothing 44 18
5 Before_sleep_PC 99 18
6 Before_sleep_smartphone 715 18
7 Before_sleep_TV 235 18
8 CableTV_No 135 18
9 CableTV_Yes 968 18
10 cafe_No 794 18
# ... with 150 more rows
This topic was automatically closed 7 days after the last reply. New replies are no longer allowed.