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Menampilkan kolom Entity, Year, Potatoes, dan Cassava saja.
Mengeliminasi kolom Soybeans, Beans, dan Peas dari tabel.
Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya di bawah 2 ton?
Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada tahun 2000 ke atas?
Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja?
Negara mana yang punya hasil jagung (Maize) paling rendah di tahun 2020?
Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.
Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya.
Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada? 10.Tampilkan data jagung mulai tahun 2010, lalu menghitung simpangan baku per negara, dan mengurutkannya dari nilai yang paling besar

library(tidyverse)

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

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

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

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

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

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

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats   1.0.1     ✔ stringr   1.6.0
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## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.2     
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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"

Membaca data read_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.

Melihat data

glimpse(df_crop)

## Rows: 13,075
## Columns: 14
## $ Entity                             <chr> "Afghanistan", "Afghanistan", "Afgh…
## $ Code                               <chr> "AFG", "AFG", "AFG", "AFG", "AFG", …
## $ Year                               <dbl> 1961, 1962, 1963, 1964, 1965, 1966,…
## $ `Wheat (tonnes per hectare)`       <dbl> 1.0220, 0.9735, 0.8317, 0.9510, 0.9…
## $ `Rice (tonnes per hectare)`        <dbl> 1.5190, 1.5190, 1.5190, 1.7273, 1.7…
## $ `Maize (tonnes per hectare)`       <dbl> 1.4000, 1.4000, 1.4260, 1.4257, 1.4…
## $ `Soybeans (tonnes per hectare)`    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Potatoes (tonnes per hectare)`    <dbl> 8.6667, 7.6667, 8.1333, 8.6000, 8.8…
## $ `Beans (tonnes per hectare)`       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Peas (tonnes per hectare)`        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Cassava (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Barley (tonnes per hectare)`      <dbl> 1.0800, 1.0800, 1.0800, 1.0857, 1.0…
## $ `Cocoa beans (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Bananas (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…

Nomor 1

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)`

Nomor 2

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

## # 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>

Nomor 3

filter(df_crop,Code=="IDN" , `Rice (tonnes per hectare)` < 2.0 )

## # A tibble: 7 × 14
##   Entity    Code   Year `Wheat (tonnes per hectare)` `Rice (tonnes per hectare)`
##   <chr>     <chr> <dbl>                        <dbl>                       <dbl>
## 1 Indonesia IDN    1961                           NA                        1.76
## 2 Indonesia IDN    1962                           NA                        1.79
## 3 Indonesia IDN    1963                           NA                        1.72
## 4 Indonesia IDN    1964                           NA                        1.76
## 5 Indonesia IDN    1965                           NA                        1.77
## 6 Indonesia IDN    1966                           NA                        1.77
## 7 Indonesia IDN    1967                           NA                        1.76
## # 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>,
## #   `Bananas (tonnes per hectare)` <dbl>

Nomor 4

filter(df_crop, Year > 2000, `Wheat (tonnes per hectare)` > 5.0)

## # 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>, …

Nomor 5

data_filter2 <- filter(df_crop, Code %in% c("IDN", "MYS"), Year == 2015)
data_filter2

## # A tibble: 2 × 14
##   Entity    Code   Year `Wheat (tonnes per hectare)` `Rice (tonnes per hectare)`
##   <chr>     <chr> <dbl>                        <dbl>                       <dbl>
## 1 Indonesia IDN    2015                           NA                        5.34
## 2 Malaysia  MYS    2015                           NA                        4.02
## # 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>,
## #   `Bananas (tonnes per hectare)` <dbl>

Nomor 6 (ERROR KRN TAHUN 2020 TDK ADA DI KOLOM, MENTOK SAMPAI 2018 SAJA DATA NYA YG TERSEDIA)

df_crop %>%
  filter(
    Year == 2020,
    !is.na(`Maize (tonnes per hectare)`)
  ) %>%
  arrange(`Maize (tonnes per hectare)`) %>%
  select(Entity, Code, `Maize (tonnes per hectare)`) %>%
  head(1)

## # A tibble: 0 × 3
## # ℹ 3 variables: Entity <chr>, Code <chr>, Maize (tonnes per hectare) <dbl>

Nomor 7

df_crop %>%
  filter(Code == "IDN") %>%
  arrange(desc(`Potatoes (tonnes per hectare)`)) %>%
  select(Entity, Year, `Potatoes (tonnes per hectare)`)

## # A tibble: 58 × 3
##    Entity     Year `Potatoes (tonnes per hectare)`
##    <chr>     <dbl>                           <dbl>
##  1 Indonesia  2018                            18.7
##  2 Indonesia  2016                            18.3
##  3 Indonesia  2015                            18.2
##  4 Indonesia  2014                            17.7
##  5 Indonesia  2006                            16.9
##  6 Indonesia  2008                            16.7
##  7 Indonesia  1995                            16.6
##  8 Indonesia  2012                            16.6
##  9 Indonesia  2009                            16.5
## 10 Indonesia  2005                            16.4
## # ℹ 48 more rows

Nomor 8

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

## # A tibble: 6 × 15
##   Entity      Code   Year `Wheat (tonnes per hectare)` Rice (tonnes per hectar…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
## # 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>,
## #   `Cocoa beans (tonnes per hectare)` <dbl>, …

Nomor 9

df_crop %>%
  filter(Code == "IDN") %>%
  summarise(`Rata-rata Bananas` =
              mean(`Bananas (tonnes per hectare)`,
                   na.rm = TRUE))

## # A tibble: 1 × 1
##   `Rata-rata Bananas`
##                 <dbl>
## 1                30.5

Nomor 10

df_crop %>%
  filter(Year >= 2010,
         !is.na(`Maize (tonnes per hectare)`)) %>%
  group_by(Entity, Code) %>%
  summarise(`Simpangan Baku Maize` =
              sd(`Maize (tonnes per hectare)`,
                 na.rm = TRUE)) %>%
  arrange(desc(`Simpangan Baku Maize`))

## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by Entity and Code.
## ℹ Output is grouped by Entity.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(Entity, Code))` for per-operation grouping
##   (`?dplyr::dplyr_by`) instead.

## # A tibble: 202 × 3
## # Groups:   Entity [202]
##    Entity                           Code  `Simpangan Baku Maize`
##    <chr>                            <chr>                  <dbl>
##  1 Kuwait                           KWT                     9.24
##  2 United Arab Emirates             ARE                     9.19
##  3 Jordan                           JOR                     7.03
##  4 Israel                           ISR                     4.80
##  5 Saint Vincent and the Grenadines VCT                     2.89
##  6 Qatar                            QAT                     2.74
##  7 French Guiana                    GUF                     2.50
##  8 New Caledonia                    NCL                     2.29
##  9 Slovakia                         SVK                     1.68
## 10 Oman                             OMN                     1.61
## # ℹ 192 more rows

Praktik 1 Komputasi Statistika

2026-05-22

Menyimpan URL raw data GitHub ke dalam variabel

Membaca data read_csv

Melihat data