praktik 1 komstat dplyr

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options(repos = c(CRAN = "https://cloud.r-project.org"))

install.packages("tidyverse")

## Installing package into 'C:/Users/Naila Ramadhana/AppData/Local/R/win-library/4.5'
## (as 'lib' is unspecified)

## package 'tidyverse' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\Naila Ramadhana\AppData\Local\Temp\Rtmp67ffBH\downloaded_packages

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.5.3

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## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
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## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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# Menyimpan URL raw data GitHub ke dalam variabel
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.csv"

df_crop <- read_csv(url)

## Rows: 13075 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Entity, Code
## dbl (12): Year, Wheat (tonnes per hectare), Rice (tonnes per hectare), Maize...
## 
## 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.

Menampilkan kolom Entity, Year, Potatoes, dan Cassava saja.

data_select <- select(df_crop, Entity, Year, `Potatoes (tonnes per hectare)`, `Cassava (tonnes per hectare)`)
data_select

## # A tibble: 13,075 × 4
##    Entity       Year `Potatoes (tonnes per hectare)` Cassava (tonnes per hecta…¹
##    <chr>       <dbl>                           <dbl>                       <dbl>
##  1 Afghanistan  1961                            8.67                          NA
##  2 Afghanistan  1962                            7.67                          NA
##  3 Afghanistan  1963                            8.13                          NA
##  4 Afghanistan  1964                            8.6                           NA
##  5 Afghanistan  1965                            8.8                           NA
##  6 Afghanistan  1966                            9.07                          NA
##  7 Afghanistan  1967                            9.8                           NA
##  8 Afghanistan  1968                           10                             NA
##  9 Afghanistan  1969                           10.2                           NA
## 10 Afghanistan  1970                            9.54                          NA
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹`Cassava (tonnes per hectare)`

Mengeliminasi kolom Soybeans, Beans, dan Peas dari tabel.

select(df_crop, -c(`Soybeans (tonnes per hectare)`, `Beans (tonnes per hectare)`, `Peas (tonnes per hectare)`))

## # A tibble: 13,075 × 11
##    Entity      Code   Year `Wheat (tonnes per hectare)` Rice (tonnes per hecta…¹
##    <chr>       <chr> <dbl>                        <dbl>                    <dbl>
##  1 Afghanistan AFG    1961                        1.02                      1.52
##  2 Afghanistan AFG    1962                        0.974                     1.52
##  3 Afghanistan AFG    1963                        0.832                     1.52
##  4 Afghanistan AFG    1964                        0.951                     1.73
##  5 Afghanistan AFG    1965                        0.972                     1.73
##  6 Afghanistan AFG    1966                        0.867                     1.52
##  7 Afghanistan AFG    1967                        1.12                      1.92
##  8 Afghanistan AFG    1968                        1.16                      1.95
##  9 Afghanistan AFG    1969                        1.19                      1.98
## 10 Afghanistan AFG    1970                        0.956                     1.81
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹`Rice (tonnes per hectare)`
## # ℹ 6 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>,
## #   `Cassava (tonnes per hectare)` <dbl>, `Barley (tonnes per hectare)` <dbl>,
## #   `Cocoa beans (tonnes per hectare)` <dbl>,
## #   `Bananas (tonnes per hectare)` <dbl>

Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya di bawah 2 ton?

data_filter <- filter(df_crop, Entity=="Indonesia", `Rice (tonnes per hectare)` < 2) %>% 
  select(Entity, Year, `Rice (tonnes per hectare)`)
data_filter

## # A tibble: 7 × 3
##   Entity     Year `Rice (tonnes per hectare)`
##   <chr>     <dbl>                       <dbl>
## 1 Indonesia  1961                        1.76
## 2 Indonesia  1962                        1.79
## 3 Indonesia  1963                        1.72
## 4 Indonesia  1964                        1.76
## 5 Indonesia  1965                        1.77
## 6 Indonesia  1966                        1.77
## 7 Indonesia  1967                        1.76

Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada tahun 2000 ke atas?

filter(df_crop, Year > 2000, `Wheat (tonnes per hectare)` > 5)

## # A tibble: 406 × 14
##    Entity  Code   Year `Wheat (tonnes per hectare)` `Rice (tonnes per hectare)`
##    <chr>   <chr> <dbl>                        <dbl>                       <dbl>
##  1 Austria AUT    2001                         5.24                          NA
##  2 Austria AUT    2004                         5.92                          NA
##  3 Austria AUT    2005                         5.03                          NA
##  4 Austria AUT    2008                         5.69                          NA
##  5 Austria AUT    2010                         5.01                          NA
##  6 Austria AUT    2011                         5.85                          NA
##  7 Austria AUT    2013                         5.37                          NA
##  8 Austria AUT    2014                         5.92                          NA
##  9 Austria AUT    2015                         5.70                          NA
## 10 Austria AUT    2016                         6.25                          NA
## # ℹ 396 more rows
## # ℹ 9 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Soybeans (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>, `Beans (tonnes per hectare)` <dbl>,
## #   `Peas (tonnes per hectare)` <dbl>, `Cassava (tonnes per hectare)` <dbl>,
## #   `Barley (tonnes per hectare)` <dbl>,
## #   `Cocoa beans (tonnes per hectare)` <dbl>, …

Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja?

data_filter <- filter(df_crop,Entity %in% c("Indonesia", "Malaysia"), Year == 2015) %>% 
select(Entity, Year)

data_filter

## # A tibble: 2 × 2
##   Entity     Year
##   <chr>     <dbl>
## 1 Indonesia  2015
## 2 Malaysia   2015

Negara mana yang punya hasil jagung (Maize) paling rendah di tahun 2020?

data_filter <- df_crop %>% 
  filter(Year == 2020) %>% 
  arrange(`Maize (tonnes per hectare)`)

data_filter

## # A tibble: 0 × 14
## # ℹ 14 variables: Entity <chr>, Code <chr>, Year <dbl>,
## #   Wheat (tonnes per hectare) <dbl>, Rice (tonnes per hectare) <dbl>,
## #   Maize (tonnes per hectare) <dbl>, Soybeans (tonnes per hectare) <dbl>,
## #   Potatoes (tonnes per hectare) <dbl>, Beans (tonnes per hectare) <dbl>,
## #   Peas (tonnes per hectare) <dbl>, Cassava (tonnes per hectare) <dbl>,
## #   Barley (tonnes per hectare) <dbl>, Cocoa beans (tonnes per hectare) <dbl>,
## #   Bananas (tonnes per hectare) <dbl>

Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.

data_filter <- df_crop %>%
  filter(Entity == "Indonesia") %>%
  arrange(desc(`Potatoes (tonnes per hectare)`)) %>% 
  select(Entity,`Potatoes (tonnes per hectare)`)

data_filter

## # A tibble: 58 × 2
##    Entity    `Potatoes (tonnes per hectare)`
##    <chr>                               <dbl>
##  1 Indonesia                            18.7
##  2 Indonesia                            18.3
##  3 Indonesia                            18.2
##  4 Indonesia                            17.7
##  5 Indonesia                            16.9
##  6 Indonesia                            16.7
##  7 Indonesia                            16.6
##  8 Indonesia                            16.6
##  9 Indonesia                            16.5
## 10 Indonesia                            16.4
## # ℹ 48 more rows

Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya.

df_crop %>% 
  mutate(Rice_Status = ifelse(`Rice (tonnes per hectare)` > 4, "Tinggi", "Rendah"))

## # A tibble: 13,075 × 15
##    Entity      Code   Year `Wheat (tonnes per hectare)` Rice (tonnes per hecta…¹
##    <chr>       <chr> <dbl>                        <dbl>                    <dbl>
##  1 Afghanistan AFG    1961                        1.02                      1.52
##  2 Afghanistan AFG    1962                        0.974                     1.52
##  3 Afghanistan AFG    1963                        0.832                     1.52
##  4 Afghanistan AFG    1964                        0.951                     1.73
##  5 Afghanistan AFG    1965                        0.972                     1.73
##  6 Afghanistan AFG    1966                        0.867                     1.52
##  7 Afghanistan AFG    1967                        1.12                      1.92
##  8 Afghanistan AFG    1968                        1.16                      1.95
##  9 Afghanistan AFG    1969                        1.19                      1.98
## 10 Afghanistan AFG    1970                        0.956                     1.81
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹`Rice (tonnes per hectare)`
## # ℹ 10 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Soybeans (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>, `Beans (tonnes per hectare)` <dbl>,
## #   `Peas (tonnes per hectare)` <dbl>, `Cassava (tonnes per hectare)` <dbl>,
## #   `Barley (tonnes per hectare)` <dbl>, …

Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada?

mean_banana <- df_crop %>% 
  filter(Entity == "Indonesia") %>%
  summarise(rata_rata = mean(`Bananas (tonnes per hectare)`, na.rm = TRUE))

mean_banana

## # A tibble: 1 × 1
##   rata_rata
##       <dbl>
## 1      30.5

Tampilkan data jagung mulai tahun 2010, lalu menghitung simpangan baku per negara, dan mengurutkannya dari nilai yang paling besar

sd_maize <- df_crop %>%
  filter(Year >= 2010) %>% 
  group_by(Entity) %>% 
  summarise(simpangan_baku = sd(`Maize (tonnes per hectare)`, na.rm = TRUE)) %>%
  arrange(desc(simpangan_baku))

sd_maize

## # A tibble: 242 × 2
##    Entity                           simpangan_baku
##    <chr>                                     <dbl>
##  1 Kuwait                                     9.24
##  2 United Arab Emirates                       9.19
##  3 Jordan                                     7.03
##  4 Israel                                     4.80
##  5 Saint Vincent and the Grenadines           2.89
##  6 Qatar                                      2.74
##  7 French Guiana                              2.50
##  8 New Caledonia                              2.29
##  9 Slovakia                                   1.68
## 10 Oman                                       1.61
## # ℹ 232 more rows

praktik 1 komstat dplyr

Naila Ramadhana

2026-05-21

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