Praktik 1

Nama: Rani Tri Hapsari NIM: 2511017220009

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.5.3
## Warning: package 'ggplot2' was built under R version 4.5.3
## Warning: package 'tidyr' was built under R version 4.5.3
## Warning: package 'purrr' was built under R version 4.5.3
## 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 ──
## ✔ dplyr     1.2.1     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.3     ✔ tibble    3.3.1
## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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.

1. 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)`

2. 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>

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

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

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

filter(df_crop, `Wheat (tonnes per hectare)` > 5, Year > 2000) %>% select(Entity, `Wheat (tonnes per hectare)`)
## # A tibble: 406 × 2
##    Entity  `Wheat (tonnes per hectare)`
##    <chr>                          <dbl>
##  1 Austria                         5.24
##  2 Austria                         5.92
##  3 Austria                         5.03
##  4 Austria                         5.69
##  5 Austria                         5.01
##  6 Austria                         5.85
##  7 Austria                         5.37
##  8 Austria                         5.92
##  9 Austria                         5.70
## 10 Austria                         6.25
## # ℹ 396 more rows

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

filter(df_crop, Entity %in% c("Indonesia", "Malaysia"), Year == 2015)
## # 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>

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

ket: pada dataset tidak terdapat hasil pada tahun 2020, makanya tidak muncul data yang sesuai hasil filter

df_crop %>% filter(Year == 2020) %>% arrange(`Maize (tonnes per hectare)`) %>%  head(1)
## # 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>
unique(df_crop$Year)
##  [1] 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975
## [16] 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990
## [31] 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [46] 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

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

df_crop %>% filter(Entity == "Indonesia") %>% 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

8. 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")) %>% select(Entity, Code, Year, `Rice (tonnes per hectare)`, Rice_Status)
## # A tibble: 13,075 × 5
##    Entity      Code   Year `Rice (tonnes per hectare)` Rice_Status
##    <chr>       <chr> <dbl>                       <dbl> <chr>      
##  1 Afghanistan AFG    1961                        1.52 Rendah     
##  2 Afghanistan AFG    1962                        1.52 Rendah     
##  3 Afghanistan AFG    1963                        1.52 Rendah     
##  4 Afghanistan AFG    1964                        1.73 Rendah     
##  5 Afghanistan AFG    1965                        1.73 Rendah     
##  6 Afghanistan AFG    1966                        1.52 Rendah     
##  7 Afghanistan AFG    1967                        1.92 Rendah     
##  8 Afghanistan AFG    1968                        1.95 Rendah     
##  9 Afghanistan AFG    1969                        1.98 Rendah     
## 10 Afghanistan AFG    1970                        1.81 Rendah     
## # ℹ 13,065 more rows

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

df_crop %>% 
  filter(Entity == "Indonesia") %>% 
  summarise(`Mean Bananas (tonnes per hectare)` = mean(`Bananas (tonnes per hectare)`))
## # A tibble: 1 × 1
##   `Mean Bananas (tonnes per hectare)`
##                                 <dbl>
## 1                                30.5

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

df_crop %>%
  filter(Year >= 2010) %>% group_by(Entity) %>% summarise(`Standar Deviasi Maize` = sd(`Maize (tonnes per hectare)`)) %>% filter(!is.na(`Standar Deviasi Maize`)) %>% arrange(desc(`Standar Deviasi Maize`))
## # A tibble: 202 × 2
##    Entity                           `Standar Deviasi Maize`
##    <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 192 more rows