Import your data

stats <- read_csv("../00_data/myData.csv")
## New names:
## Rows: 39 Columns: 8
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Country, ...8 dbl (5): Kill/Death Ratio, Player Rating, Headshot
## Percentage, Kills Per Rou... lgl (1): ...7
## 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.
## • `` -> `...7`
## • `` -> `...8`
stats <- stats %>%
    janitor::clean_names()

stats_small <- stats %>%
    select(country, player_impact, player_rating)

Pivoting

stats_wide <- stats_small %>% pivot_wider(names_from = player_impact, values_from = player_rating)

long to wide form

wide to long form

stats_long <- stats_wide %>% pivot_longer(`1.07` : `1.11`, names_to = "player_impact", values_to = "player_rating", values_drop_na = TRUE)

Separating and Uniting

Unite two columns

stats_united <- stats %>% 
    
    unite(col = "combine", player_impact,player_rating, sep = "/", remove = FALSE)

Separate a column

stats_united %>% separate(col = combine, into = c("player_impact","player_rating", sep = "/"))
## Warning: Expected 3 pieces. Additional pieces discarded in 28 rows [1, 2, 3, 4, 5, 6, 8,
## 9, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, ...].
## # A tibble: 39 × 9
##    country        kill_death…¹ playe…² playe…³ `/`   heads…⁴ kills…⁵ x7    x8   
##    <chr>                 <dbl> <chr>   <chr>   <chr>   <dbl>   <dbl> <lgl> <chr>
##  1 Argentina              1.03 1       07      1        46.2    0.69 NA    Filt…
##  2 Australia              1.05 1       08      1        44.7    0.7  NA    <NA> 
##  3 Belarus                0.99 1       01      0        48.2    0.67 NA    <NA> 
##  4 Belgium                1.03 1       05      1        46.9    0.68 NA    <NA> 
##  5 Brazil                 1.07 1       07      1        44.0    0.7  NA    <NA> 
##  6 Bulgaria               1.03 1       05      1        45.6    0.69 NA    <NA> 
##  7 Canada                 1.01 1       04      1        47.1    0.68 NA    <NA> 
##  8 China                  1.05 1       09      1        48.0    0.71 NA    <NA> 
##  9 Czech Republic         1.06 1       08      1        46.2    0.7  NA    <NA> 
## 10 Denmark                1.03 1       04      1        45.8    0.68 NA    <NA> 
## # … with 29 more rows, and abbreviated variable names ¹​kill_death_ratio,
## #   ²​player_impact, ³​player_rating, ⁴​headshot_percentage, ⁵​kills_per_round

Missing Values