## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()social <- read_csv("data/data_process/clean_social.csv")## Rows: 39 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): gender, education, profession, workDuration, typeSocial, useSocial,...
## dbl (3): id, age, socialDuration
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.view(social)We need to split data into separate variable in every observation. We will need to make composite variable to get mean score.
split <- social %>%
separate(typeSocial, into = c("useFacebok", "useTwitter", "useYoutube", "uselinkedin", "useTiktok", "useSnapchat", "useInstagram"), sep = ",", extra = "drop", fill = "right")social <- split %>%
select(gender, useFacebok, useTwitter, useYoutube, uselinkedin, useTiktok, useSnapchat, useInstagram)head(social)## # A tibble: 6 × 8
## gender useFacebok useTwitter useYoutube uselinkedin useTiktok useSnapchat
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 male facebook twitter youtube <NA> <NA> <NA>
## 2 male facebook youtube tiktok snapchat <NA> <NA>
## 3 female facebook twitter linkedin tiktok snapchat youtube
## 4 male facebook twitter youtube <NA> <NA> <NA>
## 5 male facebook youtube tiktok snapchat <NA> <NA>
## 6 female facebook twitter linkedin tiktok snapchat youtube
## # … with 1 more variable: useInstagram <chr>