My first Data Analysis project - need help

[vpaliuka]

Hello All,

I am working on my first data analysis project and not familiar with everything yet, I hope you would be able to help with my question. I am working with BellaBeat Dataset from Kaggle and wanted to do some data cleaning.
I run install.packages and library functions for all packages needed. Currently I am trying to run basic pipe function and I receive a message: Error in clean_names(.) : could not find function "clean_names". If I remove this function from the pipe, it gives me error for different line with the same error message. I researched online and in R Community and it looks like it could be issue with not reading libraries properly. I tried to re-install everything, however I am receiving the same message. I tried to run basic function without pipe, however it didn't recognize the column names which I copy pasted from the table.

my query:

install.packages("tidyverse")
library(tidyverse)
head(daily_activity)
str(daily_activity)
install.packages("here")
library(here)
install.packages("skimr")
library(skimr)
install.packages("janitor")
library(janitor)
install.packages("dplyr")
library(dplyr)
glimpse(daily_activity)
str(daily_activity)
install.packages("lubridate")
library(lubridate)
install.packages("tidyr")
library(tidyr)
head(daily_activity)


Daily <- daily_activity %>%
  clean_names() %>%
  mutate(ActivityDate=mdy(ActivityDate)) %>%
  rename(ActivityDate=Date, TotalSteps=Steps) %>%
  select(-c(5:10))

project link: RStudio Cloud
Any advise would be appreciated.

Thank you,
Valeryia

[JackDavison]

In your project, I have managed to get a pipeline working using this code:

library(tidyverse)
library(janitor)
library(lubridate)

daily_activity <- read_csv("/cloud/BellaBeat/dailyActivity_merged.csv")

daily <- daily_activity %>%
  clean_names() %>%
  mutate(activity_date=mdy(activity_date)) %>%
  rename(date = activity_date, 
         steps = total_steps) %>%
  select(-c(5:10))

head(daily)

To unpack what is happening here:

1. janitor::clean_names makes the column names of your data frame "snake_case" (easier to use!)
2. dplyr::mutate is converting the activity_date column to a date using the function lubridate::mdy
3. dplyr::rename is renaming the column headers, using the syntax new_name = old_name
4. dplyr::select is dropping the fifth through the tenth columns

When writing a script, it is not typical to include install.packages as you will not want to re-do that with every re-run of a script. You install once, you load (using library) every time.

[vpaliuka]

@JackDavison

library(tidyverse)
library(janitor)
library(lubridate)

daily_activity <- read_csv("/cloud/BellaBeat/dailyActivity_merged.csv")

daily <- daily_activity %>%
  clean_names() %>%
  mutate(activity_date=mdy(activity_date)) %>%
  rename(date = activity_date, 
         steps = total_steps) %>%
  select(-c(5:10))

head(daily)

Thank you very much Jack! Appreciate your help.